Equity analysis: To understand the equity gap regarding the menstrual regulation service in Bangladesh

Equity analysis: To understand the equity gap regarding the menstrual regulation service in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Equity analysis: To understand the equity gap regarding the menstrual regulation service in Bangladesh Fariya Rahman, Tasmiah Afrin Emma, Farhia Azrin, Mohammad Hridoy Patwary, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5275379/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Despite menstrual regulation (MR) being recognised as a vital component of reproductive health and rights of women by the Government of Bangladesh, its utilisation remains limited. This paper aims to examine trends and associated factors of MR utilisation as well as the extent of socioeconomic inequalities in MR utilisation and factors contributing to the inequality over time. Methods Data for this study was extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007, 2011, 2014, and 2017-18 datasets. After adjusting for sampling weight, data from a total of 65,552 ever-married women aged 15–49 years were included. Descriptive statistics and bivariate analysis using Pearson’s Chi-squared tests were employed to explore associations between outcome and explanatory variables across different wealth quintiles. Simple and multiple logistic regression models were fitted to identify significant predictors of MR utilisation. Socio-economic inequalities in MR utilisation were examined using Lorenz curves and Erreygers normalised concentration indices. Finally, a decomposition analysis of the concentration index was conducted to assess the contribution of various factors to the observed inequality. Results MR service utilisation in Bangladesh remained consistent over the years, peaking slightly in 2011 (6.4%) and reaching its lowest rate in 2017-18 (5.4%). Utilisation was higher in urban areas, with the highest rates in Rajshahi and Barishal divisions and the lowest in Sylhet, and an overall upward trend by wealth quintile over time. Women’s age, education, husband’s education, wealth index, division, place of residence, employment status, exposure to media, number of living children, contraceptive use, and survey year were significant factors associated with MR utilisation. The weighted Erreygers normalised concentration index (ECI) revealed a pro-rich concentration of MR utilisation, although the equity gap narrowed from 2007 (ECI = 0.310) to 2017-18 (ECI = 0.157). Wealth index and exposure to media emerged as the leading contributors to the overall socio-economic inequality in MR utilisation. Conclusion This study highlights trends and factors contributing to inequalities in MR usage, which can guide the government and relevant stakeholders to place greater efforts in reducing socioeconomic and geographical disparities in MR utilisation by enhancing awareness through mass-media, training healthcare providers, and ensuring availability of MR services, particularly among less affluent women. Menstrual regulation Bangladesh Demographic and Health Survey Trends Equity Wealth quintile Concentration index Decomposition analysis Reproductive health Figures Figure 1 Figure 2 Figure 3 Figure 4 Plain English summary Menstrual regulation (MR) is a significant aspect of reproductive health and rights of women in Bangladesh, supported by the government through its Family Planning Programme. Despite its potential in reducing complications from unsafe abortions, utilisation of MR services remain limited, particularly among the less affluent. This paper investigates trends and factors influencing MR utilisation among ever-married women aged 15-49 years as well as the extent of socioeconomic inequalities in MR utilisation and factors contributing to the inequality over time, using data from nationally representative surveys conducted in Bangladesh in 2007, 2011, 2014, and 2017-18. The findings indicate that utilisation of MR services has remained fairly steady over time. Utilisation was higher in urban areas, with the highest rates in Rajshahi and Barishal divisions and the lowest in Sylhet, and an overall upward trend by wealth quintile over time. Factors such as age, education, husband's education, wealth index, division, place of residence, employment status, media exposure, number of living children, and contraceptive use were found to significantly influence MR utilisation. Moreover, wealthier women were found to access these services more, although the gap between wealthy and poor women has decreased gradually over time. Wealth index and media exposure contributed the most to the overall socio-economic inequality in MR utilisation. This study emphasises the need to improve access to MR services, particularly among less affluent women. Enhancing awareness through mass-media and training healthcare providers can help reduce disparities in MR usage and ensure equitable access to reproductive health services for all women. Introduction Abortion is a medical or surgical procedure used to terminate pregnancy (1) whereas menstrual regulation (MR) is defined as “…Uterine evacuation without laboratory or ultrasound confirmation of pregnancy for women who report recent delayed menses” (2). The definition provided by World Health Organization (WHO) is open to interpretation and does not clearly specify whether a medical or surgical procedure performed on a woman with confirmed pregnancy to establish non-pregnancy will be classified as an abortion or not. As of 2021, abortion is not permissible at any point or under any condition in 24 countries, and only 37 countries worldwide allow abortion on the condition that it will be conducted to save the mother’s life (3). However, nearly for all the countries where abortion is legal, it is permitted to be performed only up to a certain gestational week and this limit is typically set at 12 weeks of gestation (3). According to the Guttmacher Institute, the legality of abortion does not have any effect on abortion rates whatsoever, since a report published by them states that countries with higher levels of restrictions experience similar frequency of abortion as countries with lower levels of restrictions (4). It has been evident that countries that deny fundamental reproductive rights of women including MR and abortion have higher unsafe abortion rates (5). Under the Penal Code, 1860 (Act No. XLV of 1860), Bangladesh criminalises abortion unless it is to save the mother’s life (6). However, since 1979, MR services have been made available in Bangladesh through consistent efforts of the government (7). In Bangladesh, the use of MR is permitted through the application of either manual vacuum aspiration (MVA) or medication including misoprostol (with or without mifepristone) without definite confirmation of pregnancy (8). Though the Government of Bangladesh (GoB) has invested a lot of effort in terms of time, manpower, and money, MR services in Bangladesh is not efficient enough yet. Additionally, women of all social and financial strata do not have equal access to MR services (9, 10). A few studies have been conducted in Bangladesh which explored the systematic barriers that induce inequity amongst women of different socio-economic and cultural background and the findings are centered around a few common topics including trained provider and equipment shortage at periphery levels such as, Union Health & Family Welfare Centres (UH&FWCs) and Upazila Health Complexes (UHCs), and social and cultural norms not being reflective of the Bangladesh National Comprehensive Menstrual Regulation (MR) and Post-Abortion Care (PAC) Services Guidelines (8, 11-13). However, they fail to report the equity gap in MR service utilisation or the contribution of different factors influencing that inequity through rigorous statistical analyses. An illustrative example of inequity in receiving PAC services was well-evident through a study which found that while almost 85% of non-poor and urban women can access PAC services after an unsafe abortion, only 47% poor and rural women have the same opportunity (8). In spite of having numerous studies done exploring factors associated with the inequity stemming from various socio-cultural and economic reasons in accessing abortion-related services, there remains a significant gap in evaluating and understanding the trends of inequity in terms of MR service usage over time among women of different socio-economic backgrounds, and therefore highlighting the necessity for more comprehensive research. Therefore, this paper aims to investigate the prevalence and trends of MR service usage by ever-married women of different wealth quintiles over time, the factors associated with MR service utilisation, the extent and direction of existing inequality in MR service utilisation as well as decompose the factors contributing to the observed socio-economic disparities. Methodology Data source and study design This study utilised data from the nationally representative Bangladesh Demographic and Health Survey (BDHS) for the years 2007, 2011, 2014, and 2017-18. These nationwide surveys were carried out by the National Institute of Population Research and Training (NIPORT), Bangladesh (14-17) and all employed a cross-sectional study design. The sampling followed a two-stage stratified cluster sampling technique to select households from a list of enumeration areas (EAs), which served as primary sampling units (PSU) for the BDHS. Data on socio-demographic characteristics, reproductive health, family planning, utilisation of maternal and child health services, etc. were gathered by interviewing women of reproductive age (15-49 years) using standard questionnaires. Detailed information on research design, sampling technique, survey questionnaire, data validity, reliability, and quality control has been documented elsewhere (14-17). Data from the most recent BDHS 2022 was not included in this study, since data pertaining to menstrual regulation (MR) was unavailable in it (18). Study sample The study population consisted of ever-married women aged 15-49 years. A total of 10,400, 17,141, 17,300, and 19,457 households were interviewed during BDHS 2007, 2011, 2014 and 2017-18, respectively. During this time, a total of 66,828 women aged 15-49 years were interviewed. After pre-processing the dataset by excluding missing and inconsistent observations (approximately 2%), the final dataset included 10,990, 17,749, 17,831, and 18,893 observations from 2007, 2011, 2014 and 2017-18, respectively. The total combined sample size for this study was 65,463 women aged 15-49 years, which after adjusting for survey weights yielded a weighted sample size of 65,552 women ( Supplementary Figure 1 ). Variable selection To construct the outcome variable ‘Ever used MR’, we used the variables ‘Ever heard of MR’ and ‘Ever used MR’ from the BDHS datasets. For this study, the ‘Ever used MR’ variable was dichotomised, with responses coded as Yes=1 for those who responded ‘Yes’ and No=0 otherwise.Additionally, if response to the ‘Ever heard of MR’ variable was ‘No’ then ‘Ever used MR’ was recoded as ‘No’. After a thorough literature review, age, respondent’s education, husband’s education, wealth index, division, place of residence, working status, exposure to at least one form of media (television, radio, newspaper/magazine), number of living children, contraceptive use, and visit by family planning (FP) workers were included in the study as exposure variables to evaluate the socio-economic inequalities in MR service utilisation. Rangpur, established in 2010, was previously part of Rajshahi division, and Mymensingh, formed in 2015, was previously part of Dhaka division. Thus, data from Rangpur was included with Rajshahi in 2007, and Mymensingh with Dhaka for 2007, 2011, and 2014. To maintain consistency, we combined Rangpur with Rajshahi to create ‘Rajshahi division’ for BDHS 2011, 2014, and 2017-18, and Mymensingh with Dhaka to create ‘Dhaka division’ for BDHS 2017-18 (19). Statistical analyses All statistical analyses were executed using STATA 15 (StataCorp, College Station, TX, USA). The datasets of 2007 to 2017-18 were merged and weighted using the Stata command ‘ svy ’ to adjust for the complex sampling design of BDHS. Descriptive analyses were performed to describe the prevalence and trends in MR service utilisation over time. Pearson’s Chi-squared tests were conducted to determine association between the outcome of interest and various factors across different wealth quintiles. Simple and multiple logistic regression analyses were conducted using the pooled data to determine the factors associated with MR service utilisation. The p-value was considered to be significant at 5% level of significance (two-tailed). Lorenz curves and Erreygers normalised concentration indices (ECIs) were equipped to illustrate and quantify the degree of inequality in MR service utilisation across various socio-demographic and economic characteristics over time. Finally, a regression-based decomposition analysis was conducted to assess the contribution of different factors to the inequality in the utilisation of MR services. Concentration index and Lorenz curve The concentration index, proposed by the World Health Organization (WHO), is a valuable tool for evaluating the extent of inequity in health outcomes or health-service utilisation such as MR service utilisation, across various socio-economic contexts (20). It uses the concentration curve to illustrate inequity by plotting the cumulative percentages of MR service utilisation against the cumulative percentages of wealth score. When the curve aligns completely with the 45° equity line (or Justice line), it signifies ‘perfect equity’. It means that there is no wealth-related disparity in MR service utilisation. A curve above this line indicates that utilisation is more concentrated among the poor (pro-poor scenario), while a curve below it points to more concentration among the wealthy (pro-rich scenario). The concentration index quantifies this inequity from -1 to +1, where 0 represents perfect equity. An index closer to -1 reflects higher utilisation among less affluent households (pro-poor), while values near +1 suggest concentration among wealthier households (pro-rich). For binary outcome variables, such as the utilisation of MR services, where the values of the standard concentration index do not fall within the range of -1 to 1, the Erreygers normalised concentration index (ECI) has been recommended in literature (21). For this study, the ECIs were calculated using the ‘ conindex ’ command (22) and the Lorenz curves were constructed using the ‘glcurve’ command (23) in STATA. Decomposition of concentration index Merely computing the ECI and constructing Lorenz curves are not sufficient to assess the impact of different socio-demographic and economic factors on the inequality observed in MR service utilisation. To address this, a separate regression-based decomposition analysis technique proposed by O’Donnell et al. was employed (24). Specifically, a generalised linear model with the binomial family and probit link was fitted, given the binary nature of the outcome variable (25). The results from the decomposition of concentration index were broken down into concentration index values (ECI) and percentage contribution of each explanatory factor to the ECI. The percentage contribution reveals how much each factor in the model contributes to the overall disparity in MR services utilisation. A positive percentage contribution signifies that a particular factor contributes to the increase in observed inequality, whereas a negative percentage contribution indicates that the factor aids in reducing the observed inequality (25). That is, a positive percentage contribution of a factor implies that if the factor were equally distributed across the respondents in all wealth quintiles, then inequalities in MR service utilisation would decrease by that percentage. Conversely, a negative percentage contribution indicates that equal distribution of the factor would lead to an increase in inequalities in MR service utilisation by the corresponding percentage (26). Ethical considerations The results of this study have been computed from the nationally representative BDHS 2007, 2011, 2014, and 2017-18 datasets. BDHS maintains the privacy and confidentiality of all respondents quite strictly. All 65,436 respondents used in this study had provided informed and verbal consent before participating in the survey. The final datasets were made publicly available in the DHS website (https://dhsprogram.com/data/) after removing all identifiable information. Results Background characteristics of the respondents Table 1 displays the weighted percentage distribution of using menstrual regulation (MR) services among the pooled sample of women aged 15-49 years belonging to different wealth quintiles (poorest, poorer, middle, richer, and richest) by their background characteristics. Table 1 shows that the percentage of women using MR services increases as their wealth quintile increases, with 9.72% women in the richest quintile receiving MR services compared to 2.91% women in the poorest quintile. In 2017-18, the highest usage of MR services was observed among the poorest (3.31%), poorer (4.82%), and middle (5.35%) quintiles compared to previous years, while the richer (5.20%) and richest (7.77%) quintiles exhibited the least usage during the same period. Women aged 30-39 years had the highest MR service usage across the poorest (4.07%), poorer (5.87%), middle (7.66%), and richest (13.25%) quintiles whereas women aged greater than 40 years had the highest usage in the richer (8.94%) quintile. Higher educated women showed greater MR service usage in the poorer (5.72%), middle (5.94%), and richest (10.36%) quintiles while higher educated husbands showed greater usage in the poorer (5.27%), middle (6.42%), richer (7.29%), and richest (10.84%) quintiles. MR service usage was the highest in Rajshahi for the poorest (4.11%), and poorer (5.67%) quintiles, and in Barisal for the middle (7.77%), richer (10.67%), and richest (14.05%) quintiles, while it was lowest in Sylhet for the poorer (2.25%), middle (3.16%), richer (2.67%), and richest (4.90%) quintiles. Rural women from the poorer (4.10%) quintile and urban women from the poorest (3.53%), middle (5.83%), richer (6.57%), and richest (10.99%) quintiles had greater MR service usage. Traditional contraceptive usage as well as having three children led to higher MR service usage across all wealth quintiles. On the contrary, unemployed women, women not having exposure to at least one form of mass-media, women with no children, women who did not use any contraceptive, and women who were not visited by FP workers had the least MR service usage across all wealth quintiles. Table 1: Weighted percentage distribution of MR utilisation by respondents’ background characteristics across different wealth quintiles (n=65,552) Background Characteristics Poorest (n = 12189) Poorer (n = 12776) Middle (n = 13151) Richer (n = 13656) Richest (n = 13780) Used MR Did not use MR p-value Used MR Did not use MR p-value Used MR Did not use MR p-value Used MR Did not use MR p-value Used MR Did not use MR p-value Year 0.588 0.006 0.582 0.027 <0.001 2007 2.78 97.22 2.77 97.23 4.29 95.71 7.25 92.75 10.14 89.86 2011 2.81 97.19 4.47 95.53 5.30 94.70 7.05 92.95 11.48 88.52 2014 2.66 97.34 3.63 96.37 4.97 95.03 6.02 93.80 9.74 90.26 2017-18 3.31 96.69 4.82 95.18 5.35 94.94 5.20 94.80 7.77 92.23 Age of respondent <0.001 <0.001 <0.001 <0.001 <0.001 =40 2.55 97.45 3.68 96.32 5.63 94.37 8.94 91.06 12.63 87.37 Education of respondent 0.001 0.114 0.158 0.298 0.011 No education 2.20 97.80 3.43 96.57 4.39 95.61 6.42 93.58 6.73 93.27 Primary 3.30 96.70 4.47 95.53 5.63 94.37 6.40 93.60 9.78 90.22 Secondary 3.76 96.24 4.10 95.90 4.87 95.13 6.52 93.48 9.94 90.06 Higher 3.69 96.31 5.72 94.28 5.94 94.06 4.82 95.18 10.36 89.64 Husband’s education 0.065 0.233 0.035 0.019 0.001 No education 2.53 97.47 3.79 96.21 4.15 95.85 5.03 94.97 6.41 93.59 Primary 3.25 96.75 3.89 96.11 4.97 95.03 5.87 94.13 8.60 91.40 Secondary 3.72 96.28 4.59 95.41 5.54 94.46 6.82 93.18 9.81 90.19 Higher 2.58 97.42 5.27 94.73 6.42 93.58 7.29 92.71 10.84 89.16 Division <0.001 <0.001 <0.001 <0.001 <0.001 Barisal 3.29 96.71 5.22 94.78 7.77 92.23 10.67 89.33 14.05 85.95 Chittagong 1.40 98.60 2.56 97.44 4.26 95.74 5.55 94.45 7.60 92.40 Dhaka 2.61 97.39 3.61 96.39 3.89 96.11 5.03 94.97 10.49 89.51 Khulna 1.94 98.06 3.07 96.93 3.63 96.37 6.41 93.59 8.42 91.58 Rajshahi 4.11 95.89 5.67 94.33 7.23 92.77 9.20 90.80 12.53 87.47 Sylhet 1.58 98.42 2.24 97.76 3.23 96.77 2.57 97.43 4.87 95.13 Place of residence 0.194 0.338 0.129 0.462 <0.001 Urban 3.53 96.47 3.58 96.42 5.83 94.17 6.57 93.43 10.99 89.01 Rural 2.85 97.15 4.10 95.90 4.93 95.07 6.18 93.82 7.27 92.73 Employment status <0.001 0.002 0.003 0.003 0.153 Not employed 2.30 97.70 3.56 96.44 4.58 95.42 5.80 94.20 9.48 90.52 Employed 3.71 96.29 4.86 95.15 6.03 93.97 7.41 92.59 10.45 89.55 Exposure to media 0.001 0.009 0.044 0.009 <0.001 No 2.55 97.45 3.57 96.43 4.42 95.58 4.92 95.08 5.38 94.62 Yes 3.88 96.12 4.66 95.34 5.37 94.63 6.58 93.42 9.99 90.01 Number of living children <0.001 <0.001 <0.001 <0.001 <0.001 0 1.23 98.77 0.87 99.13 1.20 98.80 2.26 97.74 3.00 97.00 1 1.86 98.14 3.07 96.93 3.11 96.89 3.68 96.32 6.06 93.94 2 3.28 96.72 5.03 94.97 6.63 93.37 7.39 92.61 12.44 87.56 3 4.11 95.89 5.31 94.69 6.74 93.26 8.66 91.34 14.50 85.50 >=4 2.78 97.22 3.94 96.06 5.39 94.61 8.56 91.44 10.72 89.28 Contraceptive use <0.001 <0.001 <0.001 <0.001 <0.001 No contraceptive 2.15 97.85 2.88 97.12 3.36 96.64 4.55 95.45 7.90 92.10 Traditional method 3.73 96.27 5.53 94.47 8.61 91.39 12.37 87.63 13.55 86.45 Modern method 3.40 96.60 4.76 95.24 5.91 94.09 6.80 93.20 10.50 89.50 Visited by FP † worker 0.074 0.035 0.001 0.205 0.976 No 2.77 97.23 3.85 96.15 4.72 95.28 6.17 93.83 9.72 90.28 Yes 3.51 96.49 4.94 95.06 6.43 93.57 7.00 93.00 9.69 90.31 Overall 2.91 97.09 4.06 95.94 5.06 94.94 6.31 93.69 9.72 90.28 † FP = Family planning Prevalence of MR awareness and utilisation Figure 1(a) shows that, initially, although a declining trend was observed in awareness regarding MR, reaching its lowest point in 2014 (45.24%), it rose back again in 2017-18 (71.68%). Similarly, Figure 1(b) shows the trends in awareness regarding MR services within each wealth quintile, with the lowest rates consistently observed in the poorest quintile and the highest rates in the richest quintile throughout all survey years. Figure 2(a) shows that MR service usage remained consistent across Bangladesh over the years, with a slight increase in 2011 (6.4%) and the lowest rate in 2017-18 (5.4%) compared to previous years. Figure 2(b) highlights higher MR usage in urban areas compared to rural areas, though its usage began to decline gradually in urban areas in 2014 (8.2%) and 2017-18 (7.1%). Figure 2(c) represents MR service usage across different divisions of Bangladesh from 2007 to 2017-18, with consistently higher rates in Rajshahi and Barishal, and Barishal peaking in 2011 (7.8%). In contrast, Sylhet had the lowest usage rate, with the exception of 2017-18 (3.9%). Figure 2(d) shows an upward trend in MR usage by wealth quintile across the years, except for a slight dip in the richer quintile (5.2%) in 2017-18. Factors associated with MR utilisation Women’s age, education, husband’s education, wealth index, division, place of residence, employment status, exposure to media, number of living children, contraceptive use, and survey year were significantly associated with MR service utilisation (Table 2). Since, ‘Visit by FP worker’ was not significant at 5% level of significance in the unadjusted logistic regression, it was not further included in the adjusted analysis. Women aged 30-39 years had 4.17 times more odds of MR usage (AOR=4.17, 95% CI=3.18-5.46) compared to women aged 19 years or younger. Similarly, women and their husbands having up to secondary level of education had 61% (AOR=1.61, 95% CI=1.40-1.86) and 46% (AOR=1.46, 95% CI=1.27-1.69) higher odds of MR utilisation compared to their counterparts having no education. Women from the richest wealth quintile had 2.1 times greater odds (AOR=2.10, 95% CI=1.74-2.52) of utilising MR services compared to those from the poorest quintile. While Rajshahi and Barishal residents had 50% (AOR=1.50, 95% CI=1.32-1.70) and 49% (AOR=1.49, 95% CI=1.26-1.75) higher odds, residents of Sylhet had 39% lower odds (AOR=0.61, 95% CI=0.50-0.75) of MR usage compared to Dhaka residents. Urban women had 31% (AOR=1.31, 95% CI=1.19-1.45) and employed women had 20% (AOR=1.20, 95% CI=1.10-1.32) higher odds of MR utilisation compared to rural and unemployed women. Women exposed to media had 36% (AOR=1.36, 95% CI=1.23-1.51) and those using traditional contraceptive methods had 63% (AOR=1.63, 95% CI=1.42-1.87) greater odds of MR usage compared to women without media exposure and those not using contraceptive. Lastly, women with 3 children had 2.73 times more odds (AOR=2.73, 95% CI=2.13-3.51) of MR utilisation compared to those with no children. Table 2: Factors associated with MR utilisation among ever-married women aged 15-49 years Variables UOR (95% CI) p-value AOR (95% CI) p-value Age of respondent (years) <=19 (ref) - - - - 20-29 4.04 (3.18-5.14) * <0.001 2.67 (2.06-3.46) * <0.001 30-39 7.28 (5.73-9.27) * <0.001 4.17 (3.18-5.46) * =40 6.28 (4.91-8.03) * <0.001 3.97 (3.00-5.25) * <0.001 Education of respondent No education (ref) - - - - Primary 1.43 (1.27-1.60) * <0.001 1.38 (1.22-1.56) * <0.001 Secondary 1.69 (1.51-1.90) * <0.001 1.61 (1.40-1.86) * <0.001 Higher 2.23 (1.92-2.58) * <0.001 1.56 (1.29-1.90) * <0.001 Husband’s education No education (ref) - - - - Primary 1.34 (1.19-1.52) * <0.001 1.23 (1.08-1.40) * 0.002 Secondary 1.86 (1.64-2.11) * <0.001 1.46 (1.27-1.69) * <0.001 Higher 2.54 (2.24-2.89) * <0.001 1.43 (1.21-1.68) * <0.001 Wealth index Poorest (ref) - - - - Poorer 1.41 (1.21-1.65) * <0.001 1.25 (1.07-1.46) * 0.006 Middle 1.78 (1.49-2.12) * <0.001 1.39 (1.16-1.66) * <0.001 Richer 2.25 (1.91-2.64) * <0.001 1.58 (1.32-1.89) * <0.001 Richest 3.60 (3.10-4.17) * <0.001 2.10 (1.74-2.52) * <0.001 Division Dhaka (ref) - - - - Barisal 1.23 (1.06-1.43) * 0.010 1.49 (1.26-1.75) * <0.001 Chittagong 0.79 (0.68-0.93) * 0.004 0.84 (0.73-0.98) * 0.029 Khulna 0.81 (0.69-0.94) * 0.006 0.86 (0.74-1.01) * 0.058 Rajshahi 1.20 (1.06-1.35) * 0.004 1.50 (1.32-1.70) * <0.001 Sylhet 0.47 (0.38-0.58) * <0.001 0.61 (0.50-0.75) * <0.001 Place of residence Rural (ref) - - - - Urban 1.86 (1.69-2.04) * <0.001 1.31 (1.19-1.45) * <0.001 Employment status Not employed (ref) - - - - Employed 1.13 (1.04-1.23) * 0.003 1.20 (1.10-1.32) * <0.001 Exposure to media No (ref) - - - - Yes 2.03 (1.85-2.23) * <0.001 1.36 (1.23-1.51) * <0.001 Number of living children 0 (ref) - - - - 1 2.09 (1.68-2.59) * <0.001 1.49 (1.18-1.88) * 0.001 2 4.15 (3.33-5.16) * <0.001 2.37 (1.86-3.02) * <0.001 3 4.49 (3.62-5.57) * <0.001 2.73 (2.13-3.51) * =4 3.19 (2.54-4.00) * <0.001 2.41 (1.85-3.13) * <0.001 Contraceptive use No contraceptive (ref) - - - - Traditional method 2.27 (1.99-2.58) * <0.001 1.63 (1.42-1.87) * <0.001 Modern method 1.52 (1.40-1.66) * <0.001 1.22 (1.10-1.34) * <0.001 Visited by FP † worker No (ref) - - - - Yes 1.09 (0.99-1.19) 0.081 - - Year 2007 (ref) - - - - 2011 1.16 (1.01-1.35) * 0.045 1.12 (1.02-1.28) * 0.010 2014 1.10 (1.01-1.16) * 0.040 1.09 (1.01-1.15) * 0.022 2017-18 0.96 (0.84-0.99) * 0.041 0.79 (0.69-0.90) * 0.001 *p-value<0.05; † FP = Family planning Trends of socio-economic inequalities in MR utilisation The richest-to-poorest ratio with regard to MR service utilisation was 3.65:1 in 2007 (Table 3). While the ratio widened in the year 2011 to 4.09:1, it gradually decreased and became 2.35:1 in 2017-18. The weighted Erreygers normalised concentration index (ECI) had significant positive values (p-value<0.001) throughout the survey years revealing that MR service utilisation was disproportionately concentrated among the wealthier population, that is, a pro-rich situation. Moreover, the ECIs progressively declined from 0.310 (SE=0.030, p-value<0.001) in 2007 to 0.157 (SE=0.025, p-value<0.001) in 2017-18, illustrating a decline in equity gap over time. Table 3: Richest-to-poorest ratio and Erreygers normalised concentration index (ECI) from 2007 to 2017-18 Year Richest: Poorest ECI Standard Error p-value 2007 3.65: 1 0.310 0.030 <0.001 2011 4.09: 1 0.289 0.025 <0.001 2014 3.66: 1 0.270 0.025 <0.001 2017-18 2.35: 1 0.157 0.025 <0.001 Figures 3 (a), (b), and (c) demonstrates the Lorenz curves for MR service utilisation, depicting the existing equity gap among different wealth quintiles in terms of MR service utilisation over time, by place of residence, and by different divisions of Bangladesh from 2007 to 2017-18. From Figure 3(a) it can be observed that, the Lorenz curve is maintaining a distance from the equity line and is below the equity line in each of the year, indicating that the concentration of MR usage is more among the wealthier groups compared to the poorer groups (pro-rich scenario). However, the narrowing gap between the Lorenz curve and equity line in 2017-18 compared to 2007 indicates a gradual decline in inequity over the years. Figure 3(b) shows that throughout the years, the Lorenz curve remained below the equity line in both rural and urban areas indicating a pro-rich situation. In urban areas, the inequity levels remained fairly same over the years. On the contrary, a shrink is visible towards the equity line in 2017-18 compared to 2007 in rural areas, indicating a decrease in inequity. Figure 3(c) reflects the equity gap across the six divisions where a similar pro-rich pattern was observed. Notably, Chittagong shows a reduced equity gap in 2017-18 compared to 2007. Rajshahi, on the other hand, experienced a decline in inequity in 2011, which became stable thereafter. Dec omposition of concentration index To understand the degree to which different factors contribute to the persisting socio-economic inequality in MR service utilisation, a decomposition analysis was conducted based on the Erreygers normalised concentration index (ECI) where the concentration index and percentage contribution to concentration index were computed (Table 4). ECI describes the degree and direction of the socio-economic inequality in MR service utilisation pertaining to each factor. Percentage contribution is the contribution of each factor to the overall concentration index. Since, ‘Visit by FP worker’ was not a significant predictor of MR utilisation, it was excluded from the decomposition analysis. Table 4: Decomposition analysis to measure contribution(%) of factors to MR utilisation inequity from 2007 to 2017-18 Variables 2007 2011 2014 2017-18 ECI Contribution (%) Total Contribution (%) ECI Contribution (%) Total Contribution (%) ECI Contribution (%) Total Contribution (%) ECI Contribution (%) Total Contribution (%) Age of respondent (years) =40 0.027 2.267 0.023 1.807 0.011 1.056 0.013 2.227 Education of respondent No education (ref) - - - - - - - - Primary -0.087 -2.467 13.269 -0.15 -3.993 11.625 -0.162 -5.241 11.803 -0.219 -12.539 7.527 Secondary 0.278 14.019 0.289 13.692 0.254 14.608 0.143 14.635 Higher 0.158 1.717 0.189 1.926 0.196 2.436 0.246 5.431 Husband’s education No education (ref) - - - - - - - - Primary -0.085 -1.398 10.446 -0.109 -1.695 10.229 -0.129 -2.426 11.352 -0.206 -6.901 14.555 Secondary 0.225 7.449 0.233 7.268 0.219 8.307 0.17 11.414 Higher 0.272 4.395 0.307 4.656 0.297 5.471 0.307 10.042 Wealth Index Poorest (ref) - - - - - - - - Poorest -0.329 -4.31 37.861 -0.344 -4.229 35.558 -0.33 -4.942 43.346 -0.344 -9.137 76.219 Middle -0.019 -0.379 -0.032 -0.599 -0.034 -0.777 -0.031 -1.261 Richer 0.313 9.341 0.303 8.512 0.308 10.51 0.314 19.002 Richest 0.657 33.209 0.671 31.874 0.667 38.555 0.659 67.615 Division Dhaka (ref) - - - - - - - - Barisal -0.043 -0.329 -6.302 -0.032 -0.229 -5.948 -0.035 -0.306 -8.181 -0.058 -0.904 -17.358 Chittagong 0.086 -0.878 0.039 -0.373 0.082 -0.958 0.074 -1.533 Khulna 0.014 -0.072 0.007 -0.031 -0.007 0.043 0.009 -0.097 Rajshahi -0.146 -4.987 -0.168 -5.377 -0.188 -7.306 -0.218 -15.092 Sylhet 0.004 -0.036 -0.007 0.062 -0.034 0.346 -0.015 0.268 Place of residence Rural (ref) - - - - - - - - Urban -0.445 10.244 10.244 0.502 10.864 10.864 0.484 12.726 12.726 0.464 21.661 21.661 Working status Not working (ref) - - - - - - - - Working -0.185 -2.104 -2.104 0.002 0.019 0.019 -0.143 -1.858 -1.858 -0.277 -6.409 -6.409 Exposure to media No (ref) - - - - - - - - Yes 0.515 31.205 31.205 0.562 31.989 31.989 0.651 45.048 45.048 0.533 65.525 65.525 Number of living children 0 (ref) - - - - - - - - 1 0.062 1.595 -3.151 0.066 1.594 -2.037 0.075 2.233 -3.145 0.073 3.845 -7.005 2 0.015 1.076 0.045 3.099 0.044 3.705 0.023 3.372 3 -0.018 -1.055 -0.021 -1.188 -0.035 -2.393 -0.033 -3.957 >=4 -0.097 -4.767 -0.12 -5.542 -0.119 -6.69 -0.103 -10.265 Contraceptive use No contraceptive (ref) - - - - - - - - Traditional method 0.018 0.244 1.063 0.007 0.093 -0.033 0.021 0.326 0.01 0.008 0.225 -3.209 Modern method 0.028 0.819 -0.005 -0.126 -0.009 -0.316 -0.057 -3.434 The positive values of ECIs presented in Table 4 indicate that, between 2007 and 2017-18, MR service utilisation was predominantly concentrated among wealthier women (pro-rich) with somewhat similar patterns observed across the years. In both 2007 and 2011, MR services were mainly accessed by wealthy women aged 30 years and above, those with secondary or higher education, and similarly educated husbands. These women typically belonged to the richer and richest wealth quintiles, resided in Chittagong or Khulna, had exposure to at least one form of media, had 1-2 children, and used some form of contraceptive. However, in 2011, wealthier working women (ECI: 0.002) from urban areas (ECI: 0.502), and those using only traditional contraceptive methods (ECI: 0.021) began utilising MR services with greater frequency, marking a shift from 2007. By 2014 and 2017-18, wealthier women aged 20-29 years also began accessing MR services at higher rates, including the characteristics noted in 2007 and 2014, except working women. Moreover, MR services were exclusively accessed by wealthy women from Chittagong (ECI: 0.082) in 2014. In contrast, the negative ECI values (pro-poor scenario) suggest that, across all survey years, MR service utilisation was more concentrated among less affluent women with education limited to primary level and similarly educated husbands, women from the poorer and middle wealth quintiles, women residing in Barishal, Rajshahi or Sylhet divisions, and women having 3 or more children. The percentage contribution of different factors in Table 4 shows that wealth index, having exposure to media, being educated, having educated husband, residing in urban areas, and having age>19 years contributed towards increasing the observed inequality from 2007 to 2017-18, as indicated by the positive values of total percentage contribution. Additionally, in 2007 and 2014, using any form of contraceptive also contributed towards the observed socio-economic disparity in MR usage (1.06% and 0.01%, respectively). On the contrary, the negative total percentage contributions of residing outside the Dhaka division, being employed and having children indicate that these factors contributed towards reducing the observed inequality from 2007 to 2017-18. The negative percentage contribution of division implies that, if division was equally distributed across all wealth quintiles, then inequalities in MR service utilisation would increase by 6.30%, 5.95%, 8.18%, and 17.36% in 2007, 2011, 2014, and 2017-18, respectively. Wealth index and exposure to media emerged as the two most leading factors contributing towards increasing the overall socio-economic inequality in MR service utilisation throughout all the survey years. Wealth index, using poorest quintile as the reference group, was the major contributing factor in 2007 (37.86%), 2011 (35.56%), and 2017-18 (76.22%), with having media exposure being the second most major contributing factor (31.21%, 31.99%, and 65.53%, respectively). However, in 2014, exposure to media turned out to be the dominant contributing factor (45.05%) pushing wealth index to the second position (43.35%). This implies that if wealth index was evenly distributed across respondents of all wealth quintiles, inequalities in MR service utilisation would drop by 37.86%, 35.56%, 43.35%, and 76.22% in 2007, 2011, 2014, and 2017-18, respectively. Similarly, equal distribution of exposure to media would reduce inequality by 31.21%, 31.99%, 45.05%, and 65.53%, respectively, over the same periods. These findings reveal that women in higher wealth quintiles and those having media exposure are more at an advantage to use MR services compared to those in the poorest quintiles and those not having any media exposure, ultimately contributing to the overall inequality in MR service usage. The percentage contributions of different factors to the ECI have been presented visually in Supplementary Figure 2 . Figure 4 illustrates the change in percentage contribution over the years. It reveals that the contribution of age, husband’s education, wealth index, exposure to media, and place of residence on the observed inequality in MR service usage gradually rose from 2007 to 2017-18. In contrast, the contribution of respondent’s education, division, working status, number of living children, and contraceptive usage gradually diminished over the same period. Discussion Unsafe abortions continue to be a major contributor to maternal morbidity and mortality worldwide, despite the availability of effective preventative measures ( 27 ). Efforts to reduce maternal deaths in Bangladesh resulting from unsafe abortions have shown progress, largely due to improved access to menstrual regulation (MR) services as well as emergency obstetric care facilities ( 28 ). This study aimed to explore the prevalence, trends, and factors influencing MR service utilisation by ever-married women aged 15–49 years in Bangladesh from 2007 to 2017-18. Moreover, to the best of our knowledge, this study is the first to measure and compare the extent of socio-economic inequality in MR service utilisation in Bangladesh over different time periods and to use a decomposition analysis to identify and quantify the contribution of various factors responsible for the observed inequality during each period. Although the MR program has been promoted by the GoB since 1979, a large number of women still remain unaware of the availability of such services ( 8 ). Our findings reveal a declining trend in awareness of MR services since 2007, particularly among the lower wealth quintiles, with nearly 55% of ever-married women in Bangladesh reporting that they had never heard of such services in 2014. However, data from 2017-18 shows a significant improvement in awareness regarding MR services (71.68%). Despite this improvement in knowledge, a declining trend in MR service utilisation was observed over time, with the lowest usage recorded in 2017-18 (5.4%). This decline may be attributed to the increase in contraceptive use from 62–68% between 2014 to 2018, potentially influencing the reduced demand for MR services ( 29 – 31 ). Additionally, various studies have documented the discontinuation of MR services after 2014, primarily due to a shortage of qualified MR service providers since many of them may have reached retirement age and insufficient training of new staff. This was compounded by limited availability of essential medical equipment and inadequate facilities, particularly in the UH&FWCs and UHCs, which are primarily responsible for delivering MR services in peripheral areas where majority of the population resides ( 8 , 11 – 13 ). Furthermore, a number of religious and cultural factors, conservative health attitudes and practices, fears of judgement or shame by service providers and actually being disrespected or turned away by them on many occasions, and fears of negative consequences arising from the procedure such as pain, infections, infertility, uterine damage, cancer, etc. further deterred women from seeking MR services, despite being aware of them ( 32 , 33 ). Significant geographical variations were observed in MR service utilisation across divisions, with women from Sylhet and Chittagong showing lower usage and those from Barishal and Rajshahi exhibiting higher usage, compared to women from Dhaka. This disparity could be due to limited access to MR services, structural poverty existing in certain remote areas, and greater social and religious stigma faced by women from Sylhet and Chittagong ( 30 , 34 , 35 ). On the contrary, higher awareness of MR services among women in Barishal and Rajshahi likely contributed to their increased utilisation ( 36 ). Moreover, our results show that women in urban areas were more likely to utilise MR services compared to their rural counterparts. This disparity may stem from differences in access to information, healthcare facilities, and media exposure. Although UH&FWCs and NGOs offer MR services, particularly in suburban and rural areas, very few are actually properly equipped to provide MR services ( 12 ). Moreover, living in remote rural areas with poor transportation facilities and longer distance to healthcare facilities are likely to contribute to the utilisation inequity between rural and urban women ( 33 , 37 ). Women in the higher wealth quintiles were more likely to use MR than their counterparts in all the survey years. Previous studies conducted in developing countries have consistently shown a significant positive association between socio-economic status and MR utilisation ( 30 , 38 , 39 ). This may be because women with higher socio-economic status tend have better quality of life, better education, educated husbands, greater autonomy over their reproductive choices, and improved access to both public and private healthcare services ( 38 ). Additionally, women from lower socio-economic backgrounds often experience limited access to family planning services ( 40 ) and tend to have lower educational attainment as well as lower educated husband resulting in lower rates of MR use, which is also consistent with the findings in our study. Interestingly, in 2017-18, while the rates of MR utilisation increased among women in lower wealth quintiles, it showed a declining trend among those in upper wealth quintiles compared to previous years. This shift may be attributed to heightened awareness among poorer women over time leading to increased utilisation of MR services by them ( 41 ). This study found that women aged ≥ 30 years, irrespective of wealth quintile, demonstrated greater usage of MR services compared to women aged < 30 years. This aligns with previous findings, where it was reported that older women are more likely to be familiar about safer pregnancy termination methods and are more conscious of the risks associated with unsafe abortions compared to their younger counterparts ( 42 ). Moreover, it can be argued that older women may consider MR services a safer alternative to end their pregnancies ( 43 ), since pregnancies in older women are often frowned upon in society ( 44 ). Additionally, it is likely that older women might have greater number of children and may be more inclined to limit further births in order to reduce family size ( 39 ). This is consistent with our findings which show that women with three or more children have greater utilisation of MR services across all wealth quintiles. Women using traditional contraceptive methods, having employment, and being exposed to at least one form of mass-media were also found to have greater utilisation of MR services across all wealth quintiles in this study. A possible explanation could be that working women are more empowered, leading them to act on their fertility choices, decide upon family size, avoid unintended pregnancies and have better access to MR services ( 12 , 30 , 39 ). Moreover, exposure to mass media has the ability to influence healthy behavioral changes by providing healthcare related information, promoting healthy healthcare practice through public service content, and fostering social relationships within communities ( 45 ). Furthermore, women who rely on traditional contraceptive methods such as withdrawal, rhythm, or periodic abstinence, may experience higher rates of contraceptive failure ( 46 ). This can lead to an increased need MR services to manage unintended pregnancies. Interestingly enough, women who do not use any contraceptive were found to have the least usage of MR services across wealth quintiles. This could be due to factors like limited reproductive health awareness, cultural or religious beliefs, fear of being labelled as promiscuous, and a higher unmet need for family planning ( 47 – 49 ). Based on the Erreygers concentration index (ECI) values and Lorenz curves obtained in this study, MR utilisation was found to be disproportionately concentrated among wealthier women, which aligns with the findings obtained in previous studies ( 11 , 50 ). By 2017-18, MR services usage was highly concentrated among wealthy women over 19 years old, with secondary or higher education, similarly educated husbands, residing in urban areas, belonging from the richer and richest wealth quintiles, hailing from Chittagong or Khulna, exposed to at least one form of mass-media, having 1–2 children, and using traditional contraceptive methods ( 51 , 52 ). However, both richest-to-poorest ratio as well as socio-economic disparity in MR utlisation across time, place of residence and divisions has shown a steady decline from 2007 to 2017-18. This might be due to the initiation of various community-based programs throughout Bangladesh aimed at improving access to maternal and reproductive healthcare for poor and vulnerable women, such as UH&FWCs, UHCs, community clinics, rural dispensaries, and numerous outreach services ( 41 ). The percentage contributions in the decomposition analysis revealed that wealth index, media exposure, education, having an educated husband, urban residence, and being over 19 years old increased the observed inequality in MR service utilisation from 2007 to 2017-18. Conversely, living outside Dhaka, having employment, and having one or more children was found to reduce inequality during the same period. Wealth index and media exposure were the most dominant contributors to socio-economic inequality in MR usage across all survey years. Wealthier women, in general, tend to have better access to healthcare resources ( 53 , 54 ). Wealth frequently correlates with higher levels of education and having more educated spouses, rendering a deeper understanding of reproductive healthcare services and facilities ( 38 , 55 ). Educated individuals, typically residing in urban areas, are more inclined to seek out and utilise available healthcare services than their less affluent counterparts ( 56 ). Wealthy women, typically receiving higher education, possess enhanced skills to navigate and engage with various media platforms effectively. Their superior media literacy allows them to critically evaluate and decide upon credible sources of healthcare information ( 57 , 58 ). By enhancing awareness and availability of MR services among disadvantaged populations, the GoB must strive to reduce the existing socio-economic disparities, ensuring that all women, regardless of their socio-economic status, can access safe and effective reproductive healthcare service options. Furthermore, sexual and reproductive health services, including safe abortion practices, are integral components of the United Nations Sustainable Development Goals (SDGs), particularly Goal 1 (Reduce Poverty), Goal 3 (Good Health and Well-Being), Goal 5 (Achieve Gender Equality and Empower all Women and Girls), and Goal 10 (Reduced Inequalities) ( 59 ). The promotion of safe MR services aligns directly with these global objectives, supporting efforts to improve reproductive health outcomes and eventually reduce inequalities. Strengths and limitations The strength of this study lies in its ability to generalise the obtained findings for Bangladesh, as the surveys were all nationally representative and comprising of data from all administrative divisions of Bangladesh, collected following a rigorous sampling technique. All statistical analyses were conducted employing appropriate statistical methods and sampling weight was used to account for the complex hierarchical nature of the BDHS, ensuring both reliability and validity of the obtained findings. Moreover, this is the first study to examine the extent and make a comparison of the socio-economic inequality in MR service utilisation across Bangladesh over time and at the same time decompose the contribution of various factors driving that inequality during each period. However, despite its strengths, the study is not devoid of limitations. Due to the cross-sectional nature of the study, causal inferences could not be made. Moreover, data on MR service utilisation may be at the risk of recall bias, since they were self-reported. Another important limitation might be that the latest BDHS 2022 did not collect data pertaining to MR, preventing an up-to-date analysis of the current situation in Bangladesh. Furthermore, since the BDHS does not collect important facility-level data on MR such as the availability of MR services on the respondent’s locality, its impact on MR utilisation could not be assessed. Conclusion and recommendations Safe utilisation of menstrual regulation services has the potential to reduce complications arising from unsafe abortions ( 60 ). The findings from this study shed light on the trends prevailing in MR service utilisation as well as determines the extent of and contributors to the inequality existing in MR service utilisation over time. These can facilitate the Directorate General of Family Planning (DGFP) under the Ministry of Health and Family Welfare (MoHFW) in shaping crucial area- and region-specific family planning strategies, policies, and programmes and in implementing the current national guideline on MR. The lower rates of MR utilisation, particularly among less affluent women, highlights the necessity for enhanced initiatives in training healthcare providers in proper administration of MR, ensuring availability of MR providing health facilities in localities, and ensuring availability of necessary equipment and medications in these health facilities. Moreover, the government should put forward greater efforts to utilise mass-media platforms to launch comprehensive awareness campaigns on MR, while remaining mindful of cultural sensitivities, so that people can learn about this comparatively safer and legal alternative to unsafe abortions, where to avail MR services, and when they are legally eligible to avail these services. Furthermore, the government should work towards promoting and increasing provisions for high quality contraceptives which may enable them in their efforts to reduce unintended pregnancies. By ensuring the accessibility and availability of MR services, health policymakers can effectively address the socio-economic inequalities in MR usage. Despite a decline in inequity over time, the government still has considerable work ahead of them to eliminate the persisting equity gap between the wealthy and the poor. Failure to do so may jeopardise their progress towards achieving key sexual and reproductive health-related SDGs by 2030. Abbreviations AOR: Adjusted odds ratio BDHS: Bangladesh Demographic and Health Survey CI: Confidence interval DGFP: Directorate General of Family Planning EA: Enumeration area ECI: Erreygers normalised concentration index FP: Family planning GoB: Government of Bangladesh MR: Menstrual regulation MoHFW: Ministry of Health and Family Welfare NGO: Non-governmental organisation NIPORT: National Institute of Population Research and Training PAC: Post-abortion care PSU: Primary sampling unit SDG: Sustainable Development Goals SE: Standard error UHC: Upazila Health Complex UH&FWC: Union Health & Family Welfare Centre UOR: Unadjusted odds ratio WHO: World Health Organization Declarations Ethics approval and consent to participate Ethical approval was not required for the study as secondary data were used. Consent for publication Not applicable. Availability of data and materials All data are publicly available upon registration in USAID’s DHS program at https://dhsprogram.com/data/available-datasets.cfm?ctryid=1 Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors’ contributions FR and AA were responsible for the idea of manuscript composition. FA and TAE were responsible for data curation. TAE and FA performed the statistical analyses and TAE constructed the graphs. TAE contributed to the interpretation of results. FR and TAE drafted the study for publication. FR, TAE, FA, MHP, and AA substantively revised the manuscript. All authors have approved the submitted version of the manuscript. Acknowledgements icddr,b is grateful to the Government of Bangladesh, and Global Affairs Canada for providing core/unrestricted support. Authors’ information Authors and Affiliations: Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh Fariya Rahman, Tasmiah Afrin Emma, Farhia Azrin, Mohammad Hridoy Patwary, Anisuddin Ahmed Department of Women's and Children's Health, Uppsala University, 75205 Uppsala, Sweden Anisuddin Ahmed References Grimes DA, Benson J, Singh S, Romero M, Ganatra B, Okonofua FE, Shah IH. Unsafe abortion: the preventable pandemic. Lancet. 2006;368(9550):1908-19. WHO Guidelines Approved by the Guidelines Review Committee. Safe Abortion: Technical and Policy Guidance for Health Systems. Geneva: World Health Organization Copyright © 2012, World Health Organization.; 2012. Review WP. Countries Where Abortion Is Illegal 2024 2024 [Available from: https://worldpopulationreview.com/country-rankings/countries-where-abortion-is-illegal. Susheela Singh LR, Gilda Sedgh, Lorraine Kwok and Tsuyoshi Onda. Abortion Worldwide 2017: Uneven Progress and Unequal Access. 2018. Latt SM, Milner A, Kavanagh A. Abortion laws reform may reduce maternal mortality: an ecological study in 162 countries. BMC Womens Health. 2019;19(1):1. Bangladesh Go. The Penal Code, 1860. 1860. (MOHFW) MoHaFW. Bangladesh National Comprehensive Menstrual Regulation (MR) and Post-Abortion Care (PAC) Services Guidelines 2021. Menstrual Regulation and Unsafe Abortion in Bangladesh. 2017. Crouthamel B, Pearson E, Tilford S, Hurst S, Paul D, Aqtar F, et al. Out-of-clinic and self-managed abortion in Bangladesh: menstrual regulation provider perspectives. Reproductive Health. 2021;18:1-12. Access to safe abortion is a fundamental human right. eClinicalMedicine. 2022. Kundu S. Factors influencing menstrual regulation and its socioeconomic inequalities among ever-married women in Bangladesh: findings from a nationwide cross-sectional survey. Journal of Biosocial Science. 2023;55(4):755-66. Altaf Hossain IM-Z, Meghan Ingerick, Hadayeat Ullah Bhuiyan, Michael Vlassoff and Susheela Singh. Access to and Quality of Menstrual Regulation and Postabortion Care in Bangladesh: Evidence from a Survey of Health Facilities, 2014. 2017. Michael Vlassoff AH, Isaac Maddow-Zimet, Susheela Singh and Hadayeat Ullah Bhuiyan. Menstrual Regulation and Postabortion Care in Bangladesh: Factors Associated with Access to and Quality of Services. 2012. Bangladesh Demographic and Health Survey 2007. Dhaka, Bangladesh; 2009. Bangladesh Demographic and Health Survey 2011. Dhaka, Bangladesh; 2013. Bangladesh Demographic and Health Survey 2014. Dhaka, Bangladesh and Rockville, Maryland, USA; 2016. Bangladesh Demographic and Health Survey 2017-18. Dhaka, Bangladesh, and Rockville, Maryland, USA; 2020. Bangladesh Demographic and Health Survey 2022. Dhaka, Bangladesh, and Rockville, Maryland, USA; 2023. Ahmed A, Sayeed A, Tanwi TS, Saha N, Hanson M, Protyai DA, et al. Trends and inequity in improved sanitation facility utilisation in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys. BMC Res Notes. 2023;16(1):303. Erreygers G. Correcting the concentration index. Journal of health economics. 2009;28(2):504-15. Kjellsson G, Gerdtham UG. On correcting the concentration index for binary variables. J Health Econ. 2013;32(3):659-70. O'Donnell O, O'Neill S, Van Ourti T, Walsh B. conindex: Estimation of concentration indices. Stata J. 2016;16(1):112-38. Van Kerm P, Jenkins SP. Generalized Lorenz curves and related graphs: an update for Stata 7. The Stata Journal. 2001;1(1):107-12. Van Doorslaer E, Wagstaff A, Lindelow M. Analyzing health equity using household survey data. Washington, DC: The Word Bank. 2007:2008. Mosquera PA, San Sebastian M, Waenerlund A-K, Ivarsson A, Weinehall L, Gustafsson PE. Income-related inequalities in cardiovascular disease from mid-life to old age in a Northern Swedish cohort: A decomposition analysis. Social Science & Medicine. 2016;149:135-44. Shiferie F, Gebremedhin S, Andargie G, Tsegaye DA, Alemayehu WA, Fenta TG. Decomposition Analysis of Socioeconomic Inequalities in Vaccination Dropout in Remote and Underserved Settings in Ethiopia. Am J Trop Med Hyg. 2024;111(1):196-204. Willis B, Church K, Perttu E, Thompson H, Weerasinghe S, Macias-Konstantopoulos W. The preventable burden of mortality from unsafe abortion among female sex workers: a Community Knowledge Approach survey among peer networks in eight countries. Sex Reprod Health Matters. 2023;31(1):2250618. Chowdhury ME, Ahmed A, Kalim N, Koblinsky M. Causes of maternal mortality decline in Matlab, Bangladesh. J Health Popul Nutr. 2009;27(2):108-23. Hossain M, Khan M, Ababneh F, Shaw JEH. Identifying factors influencing contraceptive use in Bangladesh: evidence from BDHS 2014 data. BMC public health. 2018;18:1-14. Rana J, Sen KK, Sultana T, Hossain MB, Islam RM. Prevalence and determinants of menstrual regulation among ever-married women in Bangladesh: evidence from a national survey. Reproductive health. 2019;16:1-9. Khan MN, Islam MM, Islam RM. Pattern of contraceptive use among reproductive-aged women with diabetes and/or hypertension: findings from Bangladesh Demographic and Health Survey. BMC women's health. 2022;22(1):230. Ramirez AM, Tabassum T, Filippa S, Katz A, Chowdhury R, Bercu C, Baum SE. Clients' expectations and experiences with providers of menstrual regulation: a qualitative study in Bangladesh. BMC Womens Health. 2024;24(1):291. Singh S, Hossain A, Maddow-Zimet I, Vlassoff M, Bhuiyan HU, Ingerick M. The Incidence of Menstrual Regulation Procedures and Abortion in Bangladesh, 2014. Int Perspect Sex Reprod Health. 2017;43(1):1-11. Kant S, Srivastava R, Rai SK, Misra P, Charlette L, Pandav CS. Induced abortion in villages of Ballabgarh HDSS: rates, trends, causes and determinants. Reprod Health. 2015;12:51. Kapil Ahmed M, van Ginneken J, Razzaque A. Factors associated with adolescent abortion in a rural area of Bangladesh. Trop Med Int Health. 2005;10(2):198-205. Alam MZ, Sultan S. Knowledge and practice of menstrual regulation (MR) in Bangladesh: Patterns and determinants. Journal of Population and Social Studies [JPSS]. 2019;27(3):220-31. Sarker BK, Rahman M, Rahman T, Hossain J, Reichenbach L, Mitra DK. Reasons for Preference of Home Delivery with Traditional Birth Attendants (TBAs) in Rural Bangladesh: A Qualitative Exploration. PLoS One. 2016;11(1):e0146161. Bose S, Trent K. Socio-demographic determinants of abortion in India: a north-South comparison. J Biosoc Sci. 2006;38(2):261-82. Ankara HG. SOCIOECONOMIC VARIATIONS IN INDUCED ABORTION IN TURKEY. J Biosoc Sci. 2017;49(1):99-122. Adato M, Roopnaraine T, Becker E. Understanding use of health services in conditional cash transfer programs: insights from qualitative research in Latin America and Turkey. Soc Sci Med. 2011;72(12):1921-9. Huq NL, Ahmed A, Haque NA, Hossaine M, Uddin J, Ahmed F, Quaiyum MA. Effect of an integrated maternal health intervention on skilled provider's care for maternal health in remote rural areas of Bangladesh: a pre and post study. BMC Pregnancy Childbirth. 2015;15:104. DaVanzo J, Rahman M, Ahmed S, Razzaque A. Influences on pregnancy-termination decisions in Matlab, Bangladesh. Demography. 2013;50(5):1739-64. Fauzia Akhter Huda SA, Bidhan Krishna Sarker, Hassan Rushekh Mahmood, Anadil Alam Introduction and approval of menstrual regulation with medication in Bangladesh: A stakeholder analysis. Dhaka: icddr,b.; 2017. Tan J-P. Marital fertility at older ages in Nepal, Bangladesh and Sri Lanka. Population Studies. 1983;37(3):433-44. Viswanath K. RS, Kontos E.Z. . Macrosocial determinants of population health. New York, NY: Springer; 2007. Bawah AA, Sato R, Asuming P, Henry EG, Agula C, Agyei-Asabere C, et al. Contraceptive method use, discontinuation and failure rates among women aged 15-49 years: evidence from selected low income settings in Kumasi, Ghana. Contracept Reprod Med. 2021;6(1):9. Najafi-Sharjabad F, Zainiyah Syed Yahya S, Abdul Rahman H, Hanafiah Juni M, Abdul Manaf R. Barriers of modern contraceptive practices among Asian women: a mini literature review. Glob J Health Sci. 2013;5(5):181-92. Dal NA, Beydağ KD. Attitudes of Married Muslim Women Regarding Family Planning Methods During the COVID-19 Pandemic in Western Turkey. J Relig Health. 2021;60(5):3394-405. Mir AS, Malik R. Emergency contraceptive pills: Exploring the knowledge and attitudes of community health workers in a developing Muslim country. N Am J Med Sci. 2010;2(8):359-64. Pulok MH, Uddin J, Enemark U, Hossin MZ. Socioeconomic inequality in maternal healthcare: An analysis of regional variation in Bangladesh. Health Place. 2018;52:205-14. Solar O IA. A conceptual framework for action on the social determinants of health. Policy and Practice. 2010. Ogundele OJ, Pavlova M, Groot W. Examining trends in inequality in the use of reproductive health care services in Ghana and Nigeria. BMC Pregnancy Childbirth. 2018;18(1):492. Liang J, Dai L, Zhu J, Li X, Zeng W, Wang H, et al. Preventable maternal mortality: geographic/rural-urban differences and associated factors from the population-based Maternal Mortality Surveillance System in China. BMC Public Health. 2011;11:243. Sanneving L, Trygg N, Saxena D, Mavalankar D, Thomsen S. Inequity in India: the case of maternal and reproductive health. Glob Health Action. 2013;6:19145. Misu F, Alam K. Comparison of inequality in utilization of postnatal care services between Bangladesh and Pakistan: Evidence from the Demographic and Health Survey 2017-2018. BMC Pregnancy Childbirth. 2023;23(1):461. Kamal N, Curtis S, Hasan MS, Jamil K. Trends in equity in use of maternal health services in urban and rural Bangladesh. Int J Equity Health. 2016;15:27. Oh JH. Educational expansion and health disparities in Ethiopia, 2005-2016. Soc Sci Med. 2019;235:112316. R. M. . Social Determinants Affecting Utilization of Maternal Health Services in Africa: A Narrative Review of the Evidence. Int J Health Serv Res Policy. 2020. The Sustainable Development Goals Report 2024. 2024. Altaf Hossain IM-Z, Susheela Singh and Lisa Remez. Menstrual Regulation, Unsafe Abortion And Maternal Health in Bangladesh. 2012. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.png Supplementary Figure 1: Flowchart for distribution of study sample SupplementaryFigure2.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5275379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367663005,"identity":"970db6c8-a89e-4dc9-bdfc-a224ebfc0459","order_by":0,"name":"Fariya Rahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBAC9gYGhgNglgSIqACSBxLY8GrhOYCi5QyRWhjgWhjbGIjQwn724WGeGoZ8/tndiZ8L51nI8x3PffaAoeKeXQMuLTzpBod5jjFYzrhzdrP0zG0ShjPPPDc3YDhTnIxLiz1DGsNhHjYGA4YbuRukebdJJBjcSGOTYGxLSMbpMP5nQC3/GAzkb+Ru/s07hxgtEkBbeNsYDAxu5G6T5m1AaLHDreUZw8G5fRIGhkAt1jzHQH55xm6QcCYhAbfD0pg/vPlmYyAHdNhtnpo6YIilsT34UJFgj0sLCDDxQOIRCQCtSGzAo4XxBzZRvLaMglEwCkbBiAIAkQlVf/nkgqcAAAAASUVORK5CYII=","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":true,"prefix":"","firstName":"Fariya","middleName":"","lastName":"Rahman","suffix":""},{"id":367663006,"identity":"014c8b66-ad58-42c2-a509-d04dcecd6305","order_by":1,"name":"Tasmiah Afrin Emma","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"prefix":"","firstName":"Tasmiah","middleName":"Afrin","lastName":"Emma","suffix":""},{"id":367663007,"identity":"5d309f6a-78d9-4506-a4f3-9b4aa0e3ff6c","order_by":2,"name":"Farhia Azrin","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"prefix":"","firstName":"Farhia","middleName":"","lastName":"Azrin","suffix":""},{"id":367663008,"identity":"a9b78b6e-fc4e-4c41-a87d-6afb2909b14a","order_by":3,"name":"Mohammad Hridoy Patwary","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hridoy","lastName":"Patwary","suffix":""},{"id":367663009,"identity":"ded79a84-f74a-4d80-a0fb-d5b318703bbd","order_by":4,"name":"Anisuddin Ahmed","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"prefix":"","firstName":"Anisuddin","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2024-10-16 11:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5275379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5275379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67294775,"identity":"b8e14cbf-6b3b-4f8c-881c-1a18d5e22fe9","added_by":"auto","created_at":"2024-10-23 10:47:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84562,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in MR awareness across (a) years and (b) wealth quintiles from 2007 to 2017-18\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/ab9cd6cf083a9aa13329e7a0.png"},{"id":67295443,"identity":"f67bfb30-5878-454e-b42a-d950b2a9a706","added_by":"auto","created_at":"2024-10-23 10:55:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87194,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in MR service utilisation from 2007 to 2017-18\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/f3287a39fa78337a0d75d512.png"},{"id":67295444,"identity":"9d0794c1-35c2-46d9-9ff4-e9a631473761","added_by":"auto","created_at":"2024-10-23 10:55:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":317220,"visible":true,"origin":"","legend":"\u003cp\u003eLorenz curves for MR utilisation across (a)years, (b)residence, and (c)division from 2007 to 2017-18\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/f285a5d15de5d93b60b32ac1.png"},{"id":67294780,"identity":"07350c34-fc39-4684-8d78-a573ce5fd124","added_by":"auto","created_at":"2024-10-23 10:47:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":252084,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in contribution (%) of different factors to ECI from 2007 to 2017-18\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/da118fed2dcb193f2c00b9fa.png"},{"id":67296142,"identity":"d3074651-507a-4a14-87f1-7e56a0e8acef","added_by":"auto","created_at":"2024-10-23 11:11:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2368921,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/71014c8a-a9f6-446a-a927-94903b580a1e.pdf"},{"id":67294777,"identity":"df0dc69a-395a-4309-9c91-369b551f5abf","added_by":"auto","created_at":"2024-10-23 10:47:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1:\u003c/strong\u003e Flowchart for distribution of study sample\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/2c48131d711fd29408565567.png"},{"id":67294779,"identity":"01a5db15-adab-4518-b15c-41e7be6c7c2b","added_by":"auto","created_at":"2024-10-23 10:47:18","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":56633,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5275379/v1/86d3e518f0b1ce72d318c067.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Equity analysis: To understand the equity gap regarding the menstrual regulation service in Bangladesh","fulltext":[{"header":"Plain English summary","content":"\u003cp\u003eMenstrual regulation (MR) is a significant aspect of reproductive health and rights of women in Bangladesh, supported by the government through its Family Planning Programme. Despite its potential in reducing complications from unsafe abortions, utilisation of MR services remain limited, particularly among the less affluent. This paper investigates trends and factors influencing MR utilisation among ever-married women aged 15-49 years as well as the extent of socioeconomic inequalities in MR utilisation and factors contributing to the inequality over time, using data from nationally representative surveys conducted in Bangladesh in 2007, 2011, 2014, and 2017-18. The findings indicate that utilisation of MR services has remained fairly steady over time. Utilisation was higher in urban areas, with the highest rates in Rajshahi and Barishal divisions and the lowest in Sylhet, and an overall upward trend by wealth quintile over time. Factors such as age, education, husband\u0026apos;s education, wealth index, division, place of residence, employment status, media exposure, number of living children, and contraceptive use were found to significantly influence MR utilisation. Moreover, wealthier women were found to access these services more, although the gap between wealthy and poor women has decreased gradually over time. Wealth index and media exposure contributed the most to the overall socio-economic inequality in MR utilisation. This study emphasises the need to improve access to MR services, particularly among less affluent women. Enhancing awareness through mass-media and training healthcare providers can help reduce disparities in MR usage and ensure equitable access to reproductive health services for all women.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAbortion is a medical or surgical procedure used to terminate pregnancy\u0026nbsp;(1)\u0026nbsp;whereas menstrual regulation (MR) is defined as “…Uterine evacuation without laboratory or ultrasound confirmation of pregnancy for women who report recent delayed menses”\u0026nbsp;(2). The definition provided by World Health Organization (WHO) is open to interpretation and does not clearly specify whether a medical or surgical procedure performed on a woman with confirmed pregnancy to establish non-pregnancy will be classified as an abortion or not. As of 2021, abortion is not permissible at any point or under any condition in 24 countries, and only 37 countries worldwide allow abortion on the condition that it will be conducted to save the mother’s life\u0026nbsp;(3). However, nearly for all the countries where abortion is legal, it is permitted to be performed only up to a certain gestational week and this limit is typically set at 12 weeks of gestation\u0026nbsp;(3).\u003c/p\u003e\n\u003cp\u003eAccording to the Guttmacher Institute, the legality of abortion does not have any effect on abortion rates whatsoever, since a report published by them states that countries with higher levels of restrictions experience similar frequency of abortion as countries with lower levels of restrictions\u0026nbsp;(4). It has been evident that countries that deny fundamental reproductive rights of women including MR and abortion have higher unsafe abortion rates\u0026nbsp;(5). Under the Penal Code, 1860 (Act No. XLV of 1860), Bangladesh criminalises abortion unless it is to save the mother’s life\u0026nbsp;(6). However, since 1979, MR services have been made available in Bangladesh through consistent efforts of the government\u0026nbsp;(7). In Bangladesh, the use of MR is permitted through the application of either manual vacuum aspiration (MVA) or medication including misoprostol (with or without mifepristone) without definite confirmation of pregnancy\u0026nbsp;(8). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThough the Government of Bangladesh (GoB) has invested a lot of effort in terms of time, manpower, and money, MR services in Bangladesh is not efficient enough yet. Additionally, women of all social and financial strata do not have equal access to MR services\u0026nbsp;(9, 10). A few studies have been conducted in Bangladesh which explored the systematic barriers that induce inequity amongst women of different socio-economic and cultural background and the findings are centered around a few common topics including trained provider and equipment shortage at periphery levels such as, Union Health \u0026amp; Family Welfare Centres (UH\u0026amp;FWCs) and Upazila Health Complexes (UHCs), and social and cultural norms not being reflective of the Bangladesh National Comprehensive Menstrual Regulation (MR) and Post-Abortion Care (PAC) Services Guidelines\u0026nbsp;(8, 11-13). However, they fail to report the equity gap in MR service utilisation or the contribution of different factors influencing that inequity through rigorous statistical analyses. An illustrative example of inequity in receiving PAC services was well-evident through a study which found that while almost 85% of non-poor and urban women can access PAC services after an unsafe abortion, only 47% poor and rural women have the same opportunity\u0026nbsp;(8).\u003c/p\u003e\n\u003cp\u003eIn spite of having numerous studies done exploring factors associated with the inequity stemming from various socio-cultural and economic reasons in accessing abortion-related services, there remains a significant gap in evaluating and understanding the trends of inequity in terms of MR service usage over time among women of different socio-economic backgrounds, and therefore highlighting the necessity for more comprehensive research. Therefore, this paper aims to investigate the prevalence and trends of MR service usage by ever-married women of different wealth quintiles over time, the factors associated with MR service utilisation, the extent and direction of existing inequality in MR service utilisation as well as decompose the factors contributing to the observed socio-economic disparities.\u0026nbsp;\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eData source and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilised data from the nationally representative Bangladesh Demographic and Health Survey (BDHS) for the years 2007, 2011, 2014, and 2017-18. These nationwide surveys were carried out by the National Institute of Population Research and Training (NIPORT), Bangladesh\u0026nbsp;(14-17)\u0026nbsp;and all employed a cross-sectional study design. The sampling followed a two-stage stratified cluster sampling technique to select households from a list of enumeration areas (EAs), which served as primary sampling units (PSU) for the BDHS. Data on\u0026nbsp;socio-demographic characteristics, reproductive health, family planning,\u0026nbsp;utilisation of maternal and child health services, etc. were gathered by interviewing women of reproductive age (15-49 years) using standard questionnaires. Detailed information on research design, sampling technique, survey questionnaire, data validity, reliability, and quality control has been documented elsewhere\u0026nbsp;(14-17). Data from the most recent BDHS 2022 was not included in this study, since data pertaining to menstrual regulation (MR) was unavailable in it\u0026nbsp;(18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population consisted of ever-married women aged 15-49 years. A total of 10,400, 17,141, 17,300, and 19,457 households were interviewed during BDHS 2007, 2011, 2014 and 2017-18, respectively. During this time, a total of 66,828 women aged 15-49 years were interviewed. After pre-processing the dataset by excluding missing and inconsistent observations (approximately 2%), the final dataset included 10,990, 17,749, 17,831, and 18,893 observations from 2007, 2011, 2014 and 2017-18, respectively. The total combined sample size for this study was 65,463 women aged 15-49 years, which after adjusting for survey weights yielded a weighted sample size of 65,552 women (\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eVariable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo construct the outcome variable \u0026lsquo;Ever used MR\u0026rsquo;, we used the variables \u0026lsquo;Ever heard of MR\u0026rsquo; and \u0026lsquo;Ever used MR\u0026rsquo; from the BDHS datasets. For this study, the \u0026lsquo;Ever used MR\u0026rsquo; variable was dichotomised, with responses coded as Yes=1 for those who responded \u0026lsquo;Yes\u0026rsquo; and No=0 otherwise.Additionally, if response to the \u0026lsquo;Ever heard of MR\u0026rsquo; variable was \u0026lsquo;No\u0026rsquo; then \u0026lsquo;Ever used MR\u0026rsquo; was recoded as \u0026lsquo;No\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eAfter a thorough literature review, age, respondent\u0026rsquo;s education, husband\u0026rsquo;s education, wealth index, division, place of residence, working status, exposure to at least one form of media (television, radio, newspaper/magazine), number of living children, contraceptive use, and visit by family planning (FP) workers were included in the study as exposure variables to evaluate the socio-economic inequalities in MR service utilisation. Rangpur, established in 2010, was previously part of Rajshahi division, and Mymensingh, formed in 2015, was previously part of Dhaka division. Thus, data from Rangpur was included with Rajshahi in 2007, and Mymensingh with Dhaka for 2007, 2011, and 2014. To maintain consistency, we combined Rangpur with Rajshahi to create \u0026lsquo;Rajshahi division\u0026rsquo; for BDHS 2011, 2014, and 2017-18, and Mymensingh with Dhaka to create \u0026lsquo;Dhaka division\u0026rsquo; for BDHS 2017-18\u0026nbsp;(19).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were executed using STATA 15 (StataCorp, College Station, TX, USA). The datasets of 2007 to 2017-18 were merged and weighted using the Stata command \u0026lsquo;\u003cem\u003esvy\u003c/em\u003e\u0026rsquo; to adjust for the complex sampling design of BDHS. Descriptive analyses were performed to describe the prevalence and trends in MR service utilisation over time. Pearson\u0026rsquo;s Chi-squared tests were conducted to determine association between the outcome of interest and various factors across different wealth quintiles. Simple and multiple logistic regression analyses were conducted using the pooled data to determine the factors associated with MR service utilisation. The p-value was considered to be significant at 5% level of significance (two-tailed). Lorenz curves and Erreygers normalised concentration indices (ECIs) were equipped to illustrate and quantify the degree of inequality in MR service utilisation across various socio-demographic and economic characteristics over time. Finally,\u0026nbsp;a regression-based decomposition analysis was conducted to assess the contribution of different factors to the inequality in the utilisation of MR services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConcentration index and Lorenz curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concentration index, proposed by the World Health Organization (WHO), is a valuable tool for evaluating the extent of inequity in health outcomes or health-service utilisation such as MR service utilisation, across various socio-economic contexts\u0026nbsp;(20). It uses the concentration curve to illustrate inequity by plotting the cumulative percentages of MR service utilisation against the cumulative percentages of wealth score. When the curve aligns completely with the 45\u0026deg; equity line (or Justice line), it signifies \u0026lsquo;perfect equity\u0026rsquo;. It means that there is no wealth-related disparity in MR service utilisation. A curve above this line indicates that utilisation is more concentrated among the poor (pro-poor scenario), while a curve below it points to more concentration among the wealthy (pro-rich scenario). The concentration index quantifies this inequity from -1 to +1, where 0 represents perfect equity. An index closer to -1 reflects higher utilisation among less affluent households (pro-poor), while values near +1 suggest concentration among wealthier households (pro-rich). For binary outcome variables, such as the utilisation of MR services, where the values of the standard concentration index do not fall within the range of -1 to 1, the Erreygers normalised concentration index (ECI) has been recommended in literature\u0026nbsp;(21). For this study, the ECIs were calculated using the \u0026lsquo;\u003cem\u003econindex\u003c/em\u003e\u0026rsquo; command\u0026nbsp;(22)\u0026nbsp;and the Lorenz curves were constructed using the \u003cem\u003e\u0026lsquo;glcurve\u0026rsquo;\u003c/em\u003e command\u0026nbsp;(23)\u0026nbsp;in STATA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecomposition of concentration index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMerely computing the ECI and constructing Lorenz curves are not sufficient to assess the impact of different socio-demographic and economic factors on the inequality observed in MR service utilisation. To address this, a separate regression-based decomposition analysis technique proposed by O\u0026rsquo;Donnell et al. was employed\u0026nbsp;(24). \u0026nbsp;Specifically, a generalised linear model with the binomial family and probit link was fitted, given the binary nature of the outcome variable\u0026nbsp;(25).\u0026nbsp;The results from the decomposition of concentration index were broken down into concentration index values (ECI) and percentage contribution of each explanatory factor to the ECI.\u0026nbsp;The percentage contribution reveals how much each factor in the model contributes to the overall disparity in MR services utilisation.\u0026nbsp;A positive percentage contribution signifies that a particular factor contributes to the increase in observed inequality, whereas a negative percentage contribution indicates that the factor aids in reducing the observed inequality\u0026nbsp;(25). That is, a positive percentage contribution of a factor implies that if the factor were equally distributed across the respondents in all wealth quintiles, then inequalities in MR service utilisation would decrease by that percentage. Conversely, a negative percentage contribution indicates that equal distribution of the factor would lead to an increase in inequalities in MR service utilisation by the corresponding percentage\u0026nbsp;(26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study have been computed from the nationally representative BDHS 2007, 2011, 2014, and 2017-18 datasets. BDHS maintains the privacy and confidentiality of all respondents quite strictly. All 65,436 respondents used in this study had provided informed and verbal consent before participating in the survey. The final datasets were made publicly available in the DHS website (https://dhsprogram.com/data/) after removing all identifiable information.\u003c/p\u003e"},{"header":" Results","content":"\u003cp\u003e\u003cstrong\u003eBackground characteristics of the respondents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 displays the weighted percentage distribution of using menstrual regulation (MR) services among the pooled sample of women aged 15-49 years belonging to different wealth quintiles (poorest, poorer, middle, richer, and richest) by their background characteristics. Table 1 shows that the percentage of women using MR services increases as their wealth quintile increases, with 9.72% women in the richest quintile receiving MR services compared to 2.91% women in the poorest quintile.\u003c/p\u003e\n\u003cp\u003eIn 2017-18, the highest usage of MR services was observed among the poorest (3.31%), poorer (4.82%), and middle (5.35%) quintiles compared to previous years, while the richer (5.20%) and richest (7.77%) quintiles exhibited the least usage during the same period. Women aged 30-39 years had the highest MR service usage across the poorest (4.07%), poorer (5.87%), middle (7.66%), and richest (13.25%) quintiles whereas women aged greater than 40 years had the highest usage in the richer (8.94%) quintile. Higher educated women showed greater MR service usage in the poorer (5.72%), middle (5.94%), and richest (10.36%) quintiles while higher educated husbands showed greater usage in the poorer (5.27%), middle (6.42%), richer (7.29%), and richest (10.84%) quintiles. MR service usage was the highest in Rajshahi for the poorest (4.11%), and poorer (5.67%) quintiles, and in Barisal for the middle (7.77%), richer (10.67%), and richest (14.05%) quintiles, while it was lowest in Sylhet for the poorer (2.25%), middle (3.16%), richer (2.67%), and richest (4.90%) quintiles. Rural women from the poorer (4.10%) quintile and urban women from the poorest (3.53%), middle (5.83%), richer (6.57%), and richest (10.99%) quintiles had greater MR service usage. Traditional contraceptive usage as well as having three children led to higher MR service usage across all wealth quintiles. On the contrary, unemployed women, women not having exposure to at least one form of mass-media, women with no children, women who did not use any contraceptive, and women who were not visited by FP workers had the least MR service usage across all wealth quintiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Weighted percentage distribution of MR utilisation by respondents\u0026rsquo; background characteristics across different wealth quintiles (n=65,552)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1002\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackground Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorest (n = 12189)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorer (n = 12776)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle (n = 13151)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRicher (n = 13656)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRichest (n = 13780)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed MR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid not use MR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed MR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid not use MR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed MR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid not use MR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed MR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid not use MR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed MR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid not use MR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e11.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e88.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2017-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;=19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e99.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e98.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e98.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e86.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e87.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation of respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHusband\u0026rsquo;s education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDivision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Barisal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e85.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Chittagong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Dhaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Khulna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rajshahi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e87.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Sylhet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e97.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to media\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of living children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e99.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e98.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e97.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e97.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e98.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e12.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e87.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e14.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e85.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;=4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContraceptive use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No contraceptive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e96.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e92.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Traditional method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e94.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e12.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e87.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e86.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Modern method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e94.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisited by FP\u003c/strong\u003e\u003cem\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/em\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eworker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e97.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e95.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e95.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e93.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026dagger; FP = Family planning\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of MR awareness and utilisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1(a) shows that, initially, although a declining trend was observed in awareness regarding MR, reaching its lowest point in 2014 (45.24%), it rose back again in 2017-18 (71.68%). Similarly, Figure 1(b) shows the trends in awareness regarding MR services within each wealth quintile, with the lowest rates consistently observed in the poorest quintile and the highest rates in the richest quintile throughout all survey years.\u003c/p\u003e\n\u003cp\u003eFigure 2(a) shows that MR service usage remained consistent across Bangladesh over the years, with a slight increase in 2011 (6.4%) and the lowest rate in 2017-18 (5.4%) compared to previous years. Figure 2(b) highlights higher MR usage in urban areas compared to rural areas, though its usage began to decline gradually in urban areas in 2014 (8.2%) and 2017-18 (7.1%). Figure 2(c) represents MR service usage across different divisions of Bangladesh from 2007 to 2017-18, with consistently higher rates in Rajshahi and Barishal, and Barishal peaking in 2011 (7.8%). In contrast, Sylhet had the lowest usage rate, with the exception of 2017-18 (3.9%). Figure 2(d) shows an upward trend in MR usage by wealth quintile across the years, except for a slight dip in the richer quintile (5.2%) in 2017-18.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with MR utilisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWomen\u0026rsquo;s age, education, husband\u0026rsquo;s education, wealth index, division, place of residence, employment status, exposure to media, number of living children, contraceptive use, and survey year were significantly associated with MR service utilisation (Table 2). Since, \u0026lsquo;Visit by FP worker\u0026rsquo; was not significant at 5% level of significance in the unadjusted logistic regression, it was not further included in the adjusted analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWomen aged 30-39 years had 4.17 times more odds of MR usage (AOR=4.17, 95% CI=3.18-5.46) compared to women aged 19 years or younger. Similarly, women and their husbands having up to secondary level of education had 61% (AOR=1.61, 95% CI=1.40-1.86) and 46% (AOR=1.46, 95% CI=1.27-1.69) higher odds of MR utilisation compared to their counterparts having no education. Women from the richest wealth quintile had 2.1 times greater odds (AOR=2.10, 95% CI=1.74-2.52) of utilising MR services compared to those from the poorest quintile. While Rajshahi and Barishal residents had 50% (AOR=1.50, 95% CI=1.32-1.70) and 49% (AOR=1.49, 95% CI=1.26-1.75) higher odds, residents of Sylhet had 39% lower odds (AOR=0.61, 95% CI=0.50-0.75) of MR usage compared to Dhaka residents. Urban women had 31% (AOR=1.31, 95% CI=1.19-1.45) and employed women had 20% (AOR=1.20, 95% CI=1.10-1.32) higher odds of MR utilisation compared to rural and unemployed women. Women exposed to media had 36% (AOR=1.36, 95% CI=1.23-1.51) and those using traditional contraceptive methods had 63% (AOR=1.63, 95% CI=1.42-1.87) greater odds of MR usage compared to women without media exposure and those not using contraceptive. Lastly, women with 3 children had 2.73 times more odds (AOR=2.73, 95% CI=2.13-3.51) of MR utilisation compared to those with no children.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Factors associated with MR utilisation among ever-married women aged 15-49 years\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of respondent (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;=19 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e4.04 (3.18-5.14)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e2.67 (2.06-3.46)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e7.28 (5.73-9.27)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e4.17 (3.18-5.46)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e6.28 (4.91-8.03)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e3.97 (3.00-5.25)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation of respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No education (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.43 (1.27-1.60)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.38 (1.22-1.56)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.69 (1.51-1.90)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.61 (1.40-1.86)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e2.23 (1.92-2.58)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.56 (1.29-1.90)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHusband\u0026rsquo;s education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No education (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.34 (1.19-1.52)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.23 (1.08-1.40)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.86 (1.64-2.11)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.46 (1.27-1.69)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e2.54 (2.24-2.89)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.43 (1.21-1.68)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Poorest (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.41 (1.21-1.65)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.25 (1.07-1.46)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.78 (1.49-2.12)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.39 (1.16-1.66)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e2.25 (1.91-2.64)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.58 (1.32-1.89)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e3.60 (3.10-4.17)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e2.10 (1.74-2.52)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDivision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Dhaka (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Barisal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.23 (1.06-1.43)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.49 (1.26-1.75)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Chittagong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e0.79 (0.68-0.93)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.84 (0.73-0.98)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Khulna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e0.81 (0.69-0.94)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.86 (0.74-1.01)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rajshahi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.20 (1.06-1.35)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.50 (1.32-1.70)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Sylhet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e0.47 (0.38-0.58)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.61 (0.50-0.75)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rural (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.86 (1.69-2.04)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.31 (1.19-1.45)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not employed (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.13 (1.04-1.23)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.20 (1.10-1.32)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to media\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e2.03 (1.85-2.23)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.36 (1.23-1.51)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of living children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e2.09 (1.68-2.59)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.49 (1.18-1.88)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e4.15 (3.33-5.16)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e2.37 (1.86-3.02)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e4.49 (3.62-5.57)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e2.73 (2.13-3.51)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;=4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e3.19 (2.54-4.00)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e2.41 (1.85-3.13)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContraceptive use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No contraceptive (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Traditional method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e2.27 (1.99-2.58)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.63 (1.42-1.87)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Modern method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.52 (1.40-1.66)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.22 (1.10-1.34)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisited by FP\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eworker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.09 (0.99-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2007 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.16 (1.01-1.35)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.12 (1.02-1.28)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e1.10 (1.01-1.16)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e1.09 (1.01-1.15)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.9588%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 2017-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5258%;\"\u003e\n \u003cp\u003e0.96 (0.84-0.99)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.79 (0.69-0.90)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*p-value\u0026lt;0.05; \u0026dagger; FP = Family planning\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends of socio-economic inequalities in MR utilisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe richest-to-poorest ratio with regard to MR service utilisation was 3.65:1 in 2007 (Table 3). While the ratio widened in the year 2011 to 4.09:1, it gradually decreased and became 2.35:1 in 2017-18. The weighted Erreygers normalised concentration index (ECI) had significant positive values (p-value\u0026lt;0.001) throughout the survey years revealing that MR service utilisation was disproportionately concentrated among the wealthier population, that is, a pro-rich situation. Moreover, the ECIs progressively declined from 0.310 (SE=0.030, p-value\u0026lt;0.001) in 2007 to 0.157 (SE=0.025, p-value\u0026lt;0.001) in 2017-18, illustrating a decline in equity gap over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Richest-to-poorest ratio and Erreygers normalised concentration index (ECI) from 2007 to 2017-18\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRichest: Poorest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.65: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.09: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.66: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2017-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.35: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigures 3 (a), (b), and (c) demonstrates the Lorenz curves for MR service utilisation, depicting the existing equity gap among different wealth quintiles in terms of MR service utilisation over time, by place of residence, and by different divisions of Bangladesh from 2007 to 2017-18. From Figure 3(a) it can be observed that, the Lorenz curve is maintaining a distance from the equity line and is below the equity line in each of the year, indicating that the concentration of MR usage is more among the wealthier groups compared to the poorer groups (pro-rich scenario). However, the narrowing gap between the Lorenz curve and equity line in 2017-18 compared to 2007 indicates a gradual decline in inequity over the years. Figure 3(b) shows that throughout the years, the Lorenz curve remained below the equity line in both rural and urban areas indicating a pro-rich situation. In urban areas, the inequity levels remained fairly same over the years. On the contrary, a shrink is visible towards the equity line in 2017-18 compared to 2007 in rural areas, indicating a decrease in inequity. Figure 3(c) reflects the equity gap across the six divisions where a similar pro-rich pattern was observed. Notably, Chittagong shows a reduced equity gap in 2017-18 compared to 2007. Rajshahi, on the other hand, experienced a decline in inequity in 2011, which became stable thereafter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDec\u003c/strong\u003e\u003cstrong\u003eomposition of concentration index\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the degree to which different factors contribute to the persisting socio-economic inequality in MR service utilisation, a decomposition analysis was conducted based on the Erreygers normalised concentration index (ECI) where the concentration index and percentage contribution to concentration index were computed (Table 4). ECI describes the degree and direction of the socio-economic inequality in MR service utilisation pertaining to each factor. Percentage contribution is the contribution of each factor to the overall concentration index. Since, \u0026lsquo;Visit by FP worker\u0026rsquo; was not a significant predictor of MR utilisation, it was excluded from the decomposition analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Decomposition analysis to measure contribution(%) of factors to MR utilisation inequity from 2007 to 2017-18\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1038\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2017-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Contribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Contribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Contribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Contribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of respondent (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;=19 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation of respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No education (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-2.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003e13.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e11.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-5.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e11.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-12.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e7.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e14.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e13.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e14.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e14.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHusband\u0026rsquo;s education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No education (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003e10.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e10.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-2.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e11.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-6.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e14.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e7.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e7.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e8.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e11.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e4.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e10.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Poorest (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 89px;\"\u003e\n \u003cp\u003e37.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-4.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 90px;\"\u003e\n \u003cp\u003e35.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-4.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 90px;\"\u003e\n \u003cp\u003e43.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-9.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 90px;\"\u003e\n \u003cp\u003e76.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e9.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e8.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e19.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e33.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e31.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e38.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e67.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDivision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Dhaka (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Barisal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-6.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-5.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-8.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-17.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Chittagong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Khulna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rajshahi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-4.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-5.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-7.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-15.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Sylhet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Rural (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e10.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e10.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e12.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e12.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e21.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e21.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not working (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-2.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-2.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-6.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-6.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure to media\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e31.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e31.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e31.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e31.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e45.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e45.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e65.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e65.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of living children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-3.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-2.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-7.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-2.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-4.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-5.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-10.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContraceptive use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No contraceptive (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Traditional method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Modern method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe positive values of ECIs presented in Table 4 indicate that, between 2007 and 2017-18, MR service utilisation was predominantly concentrated among wealthier women (pro-rich) with somewhat similar patterns observed across the years. In both 2007 and 2011, MR services were mainly accessed by wealthy women aged 30 years and above, those with secondary or higher education, and similarly educated husbands. These women typically belonged to the richer and richest wealth quintiles, resided in Chittagong or Khulna, had exposure to at least one form of media, had 1-2 children, and used some form of contraceptive. However, in 2011, wealthier working women (ECI: 0.002) from urban areas (ECI: 0.502), and those using only traditional contraceptive methods (ECI: 0.021) began utilising MR services with greater frequency, marking a shift from 2007. By 2014 and 2017-18, wealthier women aged 20-29 years also began accessing MR services at higher rates, including the characteristics noted in 2007 and 2014, except working women. Moreover, MR services were exclusively accessed by wealthy women from Chittagong (ECI: 0.082) in 2014. In contrast, the negative ECI values (pro-poor scenario) suggest that, across all survey years, MR service utilisation was more concentrated among less affluent women with education limited to primary level and similarly educated husbands, women from the poorer and middle wealth quintiles, women residing in Barishal, Rajshahi or Sylhet divisions, and women having 3 or more children.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe percentage contribution of different factors in Table 4 shows that wealth index, having exposure to media, being educated, having educated husband, residing in urban areas, and having age\u0026gt;19 years contributed towards increasing the observed inequality from 2007 to 2017-18, as indicated by the positive values of total percentage contribution. Additionally, in 2007 and 2014, using any form of contraceptive also contributed towards the observed socio-economic disparity in MR usage (1.06% and 0.01%, respectively). On the contrary, the negative total percentage contributions of residing outside the Dhaka division, being employed and having children indicate that these factors contributed towards reducing the observed inequality from 2007 to 2017-18. The negative percentage contribution of division implies that, if division was equally distributed across all wealth quintiles, then inequalities in MR service utilisation would increase by 6.30%, 5.95%, 8.18%, and 17.36% in 2007, 2011, 2014, and 2017-18, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWealth index and exposure to media emerged as the two most leading factors contributing towards increasing the overall socio-economic inequality in MR service utilisation throughout all the survey years. Wealth index, using poorest quintile as the reference group, was the major contributing factor in 2007 (37.86%), 2011 (35.56%), and 2017-18 (76.22%), with having media exposure being the second most major contributing factor (31.21%, 31.99%, and 65.53%, respectively). However, in 2014, exposure to media turned out to be the dominant contributing factor (45.05%) pushing wealth index to the second position (43.35%). This implies that if wealth index was evenly distributed across respondents of all wealth quintiles, inequalities in MR service utilisation would drop by 37.86%, 35.56%, 43.35%, and 76.22% in 2007, 2011, 2014, and 2017-18, respectively. Similarly, equal distribution of exposure to media would reduce inequality by 31.21%, 31.99%, 45.05%, and 65.53%, respectively, over the same periods. These findings reveal that women in higher wealth quintiles and those having media exposure are more at an advantage to use MR services compared to those in the poorest quintiles and those not having any media exposure, ultimately contributing to the overall inequality in MR service usage. The percentage contributions of different factors to the ECI have been presented visually in \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFigure 4 illustrates the change in percentage contribution over the years. It reveals that the contribution of age, husband\u0026rsquo;s education, wealth index, exposure to media, and place of residence on the observed inequality in MR service usage gradually rose from 2007 to 2017-18. In contrast, the contribution of respondent\u0026rsquo;s education, division, working status, number of living children, and contraceptive usage gradually diminished over the same period.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnsafe abortions continue to be a major contributor to maternal morbidity and mortality worldwide, despite the availability of effective preventative measures (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Efforts to reduce maternal deaths in Bangladesh resulting from unsafe abortions have shown progress, largely due to improved access to menstrual regulation (MR) services as well as emergency obstetric care facilities (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This study aimed to explore the prevalence, trends, and factors influencing MR service utilisation by ever-married women aged 15–49 years in Bangladesh from 2007 to 2017-18. Moreover, to the best of our knowledge, this study is the first to measure and compare the extent of socio-economic inequality in MR service utilisation in Bangladesh over different time periods and to use a decomposition analysis to identify and quantify the contribution of various factors responsible for the observed inequality during each period.\u003c/p\u003e \u003cp\u003eAlthough the MR program has been promoted by the GoB since 1979, a large number of women still remain unaware of the availability of such services (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Our findings reveal a declining trend in awareness of MR services since 2007, particularly among the lower wealth quintiles, with nearly 55% of ever-married women in Bangladesh reporting that they had never heard of such services in 2014. However, data from 2017-18 shows a significant improvement in awareness regarding MR services (71.68%). Despite this improvement in knowledge, a declining trend in MR service utilisation was observed over time, with the lowest usage recorded in 2017-18 (5.4%). This decline may be attributed to the increase in contraceptive use from 62–68% between 2014 to 2018, potentially influencing the reduced demand for MR services (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e–\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Additionally, various studies have documented the discontinuation of MR services after 2014, primarily due to a shortage of qualified MR service providers since many of them may have reached retirement age and insufficient training of new staff. This was compounded by limited availability of essential medical equipment and inadequate facilities, particularly in the UH\u0026amp;FWCs and UHCs, which are primarily responsible for delivering MR services in peripheral areas where majority of the population resides (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Furthermore, a number of religious and cultural factors, conservative health attitudes and practices, fears of judgement or shame by service providers and actually being disrespected or turned away by them on many occasions, and fears of negative consequences arising from the procedure such as pain, infections, infertility, uterine damage, cancer, etc. further deterred women from seeking MR services, despite being aware of them (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant geographical variations were observed in MR service utilisation across divisions, with women from Sylhet and Chittagong showing lower usage and those from Barishal and Rajshahi exhibiting higher usage, compared to women from Dhaka. This disparity could be due to limited access to MR services, structural poverty existing in certain remote areas, and greater social and religious stigma faced by women from Sylhet and Chittagong (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). On the contrary, higher awareness of MR services among women in Barishal and Rajshahi likely contributed to their increased utilisation (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Moreover, our results show that women in urban areas were more likely to utilise MR services compared to their rural counterparts. This disparity may stem from differences in access to information, healthcare facilities, and media exposure. Although UH\u0026amp;FWCs and NGOs offer MR services, particularly in suburban and rural areas, very few are actually properly equipped to provide MR services (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Moreover, living in remote rural areas with poor transportation facilities and longer distance to healthcare facilities are likely to contribute to the utilisation inequity between rural and urban women (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWomen in the higher wealth quintiles were more likely to use MR than their counterparts in all the survey years. Previous studies conducted in developing countries have consistently shown a significant positive association between socio-economic status and MR utilisation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This may be because women with higher socio-economic status tend have better quality of life, better education, educated husbands, greater autonomy over their reproductive choices, and improved access to both public and private healthcare services (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Additionally, women from lower socio-economic backgrounds often experience limited access to family planning services (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and tend to have lower educational attainment as well as lower educated husband resulting in lower rates of MR use, which is also consistent with the findings in our study. Interestingly, in 2017-18, while the rates of MR utilisation increased among women in lower wealth quintiles, it showed a declining trend among those in upper wealth quintiles compared to previous years. This shift may be attributed to heightened awareness among poorer women over time leading to increased utilisation of MR services by them (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study found that women aged ≥ 30 years, irrespective of wealth quintile, demonstrated greater usage of MR services compared to women aged \u0026lt; 30 years. This aligns with previous findings, where it was reported that older women are more likely to be familiar about safer pregnancy termination methods and are more conscious of the risks associated with unsafe abortions compared to their younger counterparts (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Moreover, it can be argued that older women may consider MR services a safer alternative to end their pregnancies (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), since pregnancies in older women are often frowned upon in society (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Additionally, it is likely that older women might have greater number of children and may be more inclined to limit further births in order to reduce family size (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This is consistent with our findings which show that women with three or more children have greater utilisation of MR services across all wealth quintiles. Women using traditional contraceptive methods, having employment, and being exposed to at least one form of mass-media were also found to have greater utilisation of MR services across all wealth quintiles in this study. A possible explanation could be that working women are more empowered, leading them to act on their fertility choices, decide upon family size, avoid unintended pregnancies and have better access to MR services (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Moreover, exposure to mass media has the ability to influence healthy behavioral changes by providing healthcare related information, promoting healthy healthcare practice through public service content, and fostering social relationships within communities (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Furthermore, women who rely on traditional contraceptive methods such as withdrawal, rhythm, or periodic abstinence, may experience higher rates of contraceptive failure (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). This can lead to an increased need MR services to manage unintended pregnancies. Interestingly enough, women who do not use any contraceptive were found to have the least usage of MR services across wealth quintiles. This could be due to factors like limited reproductive health awareness, cultural or religious beliefs, fear of being labelled as promiscuous, and a higher unmet need for family planning (\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the Erreygers concentration index (ECI) values and Lorenz curves obtained in this study, MR utilisation was found to be disproportionately concentrated among wealthier women, which aligns with the findings obtained in previous studies (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). By 2017-18, MR services usage was highly concentrated among wealthy women over 19 years old, with secondary or higher education, similarly educated husbands, residing in urban areas, belonging from the richer and richest wealth quintiles, hailing from Chittagong or Khulna, exposed to at least one form of mass-media, having 1–2 children, and using traditional contraceptive methods (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). However, both richest-to-poorest ratio as well as socio-economic disparity in MR utlisation across time, place of residence and divisions has shown a steady decline from 2007 to 2017-18. This might be due to the initiation of various community-based programs throughout Bangladesh aimed at improving access to maternal and reproductive healthcare for poor and vulnerable women, such as UH\u0026amp;FWCs, UHCs, community clinics, rural dispensaries, and numerous outreach services (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe percentage contributions in the decomposition analysis revealed that wealth index, media exposure, education, having an educated husband, urban residence, and being over 19 years old increased the observed inequality in MR service utilisation from 2007 to 2017-18. Conversely, living outside Dhaka, having employment, and having one or more children was found to reduce inequality during the same period. Wealth index and media exposure were the most dominant contributors to socio-economic inequality in MR usage across all survey years. Wealthier women, in general, tend to have better access to healthcare resources (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Wealth frequently correlates with higher levels of education and having more educated spouses, rendering a deeper understanding of reproductive healthcare services and facilities (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Educated individuals, typically residing in urban areas, are more inclined to seek out and utilise available healthcare services than their less affluent counterparts (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Wealthy women, typically receiving higher education, possess enhanced skills to navigate and engage with various media platforms effectively. Their superior media literacy allows them to critically evaluate and decide upon credible sources of healthcare information (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy enhancing awareness and availability of MR services among disadvantaged populations, the GoB must strive to reduce the existing socio-economic disparities, ensuring that all women, regardless of their socio-economic status, can access safe and effective reproductive healthcare service options. Furthermore, sexual and reproductive health services, including safe abortion practices, are integral components of the United Nations Sustainable Development Goals (SDGs), particularly Goal 1 (Reduce Poverty), Goal 3 (Good Health and Well-Being), Goal 5 (Achieve Gender Equality and Empower all Women and Girls), and Goal 10 (Reduced Inequalities) (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). The promotion of safe MR services aligns directly with these global objectives, supporting efforts to improve reproductive health outcomes and eventually reduce inequalities.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations\u003c/h3\u003e\n\u003cp\u003eThe strength of this study lies in its ability to generalise the obtained findings for Bangladesh, as the surveys were all nationally representative and comprising of data from all administrative divisions of Bangladesh, collected following a rigorous sampling technique. All statistical analyses were conducted employing appropriate statistical methods and sampling weight was used to account for the complex hierarchical nature of the BDHS, ensuring both reliability and validity of the obtained findings. Moreover, this is the first study to examine the extent and make a comparison of the socio-economic inequality in MR service utilisation across Bangladesh over time and at the same time decompose the contribution of various factors driving that inequality during each period. However, despite its strengths, the study is not devoid of limitations. Due to the cross-sectional nature of the study, causal inferences could not be made. Moreover, data on MR service utilisation may be at the risk of recall bias, since they were self-reported. Another important limitation might be that the latest BDHS 2022 did not collect data pertaining to MR, preventing an up-to-date analysis of the current situation in Bangladesh. Furthermore, since the BDHS does not collect important facility-level data on MR such as the availability of MR services on the respondent’s locality, its impact on MR utilisation could not be assessed.\u003c/p\u003e "},{"header":"Conclusion and recommendations","content":"\u003cp\u003eSafe utilisation of menstrual regulation services has the potential to reduce complications arising from unsafe abortions (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). The findings from this study shed light on the trends prevailing in MR service utilisation as well as determines the extent of and contributors to the inequality existing in MR service utilisation over time. These can facilitate the Directorate General of Family Planning (DGFP) under the Ministry of Health and Family Welfare (MoHFW) in shaping crucial area- and region-specific family planning strategies, policies, and programmes and in implementing the current national guideline on MR. The lower rates of MR utilisation, particularly among less affluent women, highlights the necessity for enhanced initiatives in training healthcare providers in proper administration of MR, ensuring availability of MR providing health facilities in localities, and ensuring availability of necessary equipment and medications in these health facilities. Moreover, the government should put forward greater efforts to utilise mass-media platforms to launch comprehensive awareness campaigns on MR, while remaining mindful of cultural sensitivities, so that people can learn about this comparatively safer and legal alternative to unsafe abortions, where to avail MR services, and when they are legally eligible to avail these services. Furthermore, the government should work towards promoting and increasing provisions for high quality contraceptives which may enable them in their efforts to reduce unintended pregnancies. By ensuring the accessibility and availability of MR services, health policymakers can effectively address the socio-economic inequalities in MR usage. Despite a decline in inequity over time, the government still has considerable work ahead of them to eliminate the persisting equity gap between the wealthy and the poor. Failure to do so may jeopardise their progress towards achieving key sexual and reproductive health-related SDGs by 2030.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAOR:\u0026nbsp;\u003c/strong\u003eAdjusted odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBDHS:\u003c/strong\u003e Bangladesh Demographic and Health Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u003c/strong\u003e Confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDGFP:\u003c/strong\u003e Directorate General of Family Planning\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEA:\u003c/strong\u003e Enumeration area\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECI:\u003c/strong\u003e Erreygers normalised concentration index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFP:\u003c/strong\u003e Family planning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGoB:\u003c/strong\u003e Government of Bangladesh\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR:\u003c/strong\u003e Menstrual regulation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMoHFW:\u003c/strong\u003e Ministry of Health and Family Welfare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNGO:\u003c/strong\u003e Non-governmental organisation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNIPORT:\u003c/strong\u003e National Institute of Population Research and Training\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePAC:\u003c/strong\u003e Post-abortion care\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSU:\u003c/strong\u003e Primary sampling unit\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSDG:\u003c/strong\u003e Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSE:\u003c/strong\u003e Standard error\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUHC:\u003c/strong\u003e Upazila Health Complex\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUH\u0026amp;FWC:\u003c/strong\u003e Union Health \u0026amp; Family Welfare Centre\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUOR:\u003c/strong\u003e Unadjusted odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO:\u003c/strong\u003e World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for the study as secondary data were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are publicly available upon registration in USAID’s DHS program at https://dhsprogram.com/data/available-datasets.cfm?ctryid=1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFR and AA were responsible for the idea of manuscript composition. FA and TAE were responsible for data curation. TAE and FA performed the statistical analyses and TAE constructed the graphs. TAE contributed to the interpretation of results. FR and TAE drafted the study for publication. FR, TAE, FA, MHP, and AA substantively revised the manuscript. All authors have approved the submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eicddr,b is grateful to the Government of Bangladesh, and Global Affairs Canada for providing core/unrestricted support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations:\u003c/p\u003e\n\u003cp\u003eMaternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh\u003c/p\u003e\n\u003cp\u003eFariya Rahman, Tasmiah Afrin Emma, Farhia Azrin, Mohammad Hridoy Patwary, Anisuddin Ahmed\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Women's and Children's Health, Uppsala University, 75205 Uppsala, Sweden Anisuddin Ahmed\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrimes DA, Benson J, Singh S, Romero M, Ganatra B, Okonofua FE, Shah IH. Unsafe abortion: the preventable pandemic. Lancet. 2006;368(9550):1908-19.\u003c/li\u003e\n\u003cli\u003eWHO Guidelines Approved by the Guidelines Review Committee. Safe Abortion: Technical and Policy Guidance for Health Systems. Geneva: World Health Organization Copyright \u0026copy; 2012, World Health Organization.; 2012.\u003c/li\u003e\n\u003cli\u003eReview WP. Countries Where Abortion Is Illegal 2024 2024 [Available from: https://worldpopulationreview.com/country-rankings/countries-where-abortion-is-illegal.\u003c/li\u003e\n\u003cli\u003eSusheela Singh LR, Gilda Sedgh, Lorraine Kwok and Tsuyoshi Onda. Abortion Worldwide 2017: Uneven Progress and Unequal Access. 2018.\u003c/li\u003e\n\u003cli\u003eLatt SM, Milner A, Kavanagh A. Abortion laws reform may reduce maternal mortality: an ecological study in 162 countries. BMC Womens Health. 2019;19(1):1.\u003c/li\u003e\n\u003cli\u003eBangladesh Go. The Penal Code, 1860. 1860.\u003c/li\u003e\n\u003cli\u003e(MOHFW) MoHaFW. Bangladesh National Comprehensive Menstrual Regulation (MR) and Post-Abortion Care (PAC) Services Guidelines 2021.\u003c/li\u003e\n\u003cli\u003eMenstrual Regulation and Unsafe Abortion in Bangladesh. 2017.\u003c/li\u003e\n\u003cli\u003eCrouthamel B, Pearson E, Tilford S, Hurst S, Paul D, Aqtar F, et al. Out-of-clinic and self-managed abortion in Bangladesh: menstrual regulation provider perspectives. Reproductive Health. 2021;18:1-12.\u003c/li\u003e\n\u003cli\u003eAccess to safe abortion is a fundamental human right. eClinicalMedicine. 2022.\u003c/li\u003e\n\u003cli\u003eKundu S. Factors influencing menstrual regulation and its socioeconomic inequalities among ever-married women in Bangladesh: findings from a nationwide cross-sectional survey. Journal of Biosocial Science. 2023;55(4):755-66.\u003c/li\u003e\n\u003cli\u003eAltaf Hossain IM-Z, Meghan Ingerick, Hadayeat Ullah Bhuiyan, Michael Vlassoff and Susheela Singh. Access to and Quality of Menstrual Regulation and Postabortion Care in Bangladesh: Evidence from a Survey of Health Facilities, 2014. 2017.\u003c/li\u003e\n\u003cli\u003eMichael Vlassoff AH, Isaac Maddow-Zimet, Susheela Singh and Hadayeat Ullah Bhuiyan. Menstrual Regulation and Postabortion Care in Bangladesh: Factors Associated with Access to and Quality of Services. 2012.\u003c/li\u003e\n\u003cli\u003eBangladesh Demographic and Health Survey 2007. Dhaka, Bangladesh; 2009.\u003c/li\u003e\n\u003cli\u003eBangladesh Demographic and Health Survey 2011. Dhaka, Bangladesh; 2013.\u003c/li\u003e\n\u003cli\u003eBangladesh Demographic and Health Survey 2014. Dhaka, Bangladesh and Rockville, Maryland, USA; 2016.\u003c/li\u003e\n\u003cli\u003eBangladesh Demographic and Health Survey 2017-18. Dhaka, Bangladesh, and Rockville, Maryland, USA; 2020.\u003c/li\u003e\n\u003cli\u003eBangladesh Demographic and Health Survey 2022. Dhaka, Bangladesh, and Rockville, Maryland, USA; 2023.\u003c/li\u003e\n\u003cli\u003eAhmed A, Sayeed A, Tanwi TS, Saha N, Hanson M, Protyai DA, et al. Trends and inequity in improved sanitation facility utilisation in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys. BMC Res Notes. 2023;16(1):303.\u003c/li\u003e\n\u003cli\u003eErreygers G. Correcting the concentration index. Journal of health economics. 2009;28(2):504-15.\u003c/li\u003e\n\u003cli\u003eKjellsson G, Gerdtham UG. On correcting the concentration index for binary variables. J Health Econ. 2013;32(3):659-70.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Donnell O, O\u0026apos;Neill S, Van Ourti T, Walsh B. conindex: Estimation of concentration indices. Stata J. 2016;16(1):112-38.\u003c/li\u003e\n\u003cli\u003eVan Kerm P, Jenkins SP. Generalized Lorenz curves and related graphs: an update for Stata 7. The Stata Journal. 2001;1(1):107-12.\u003c/li\u003e\n\u003cli\u003eVan Doorslaer E, Wagstaff A, Lindelow M. Analyzing health equity using household survey data. Washington, DC: The Word Bank. 2007:2008.\u003c/li\u003e\n\u003cli\u003eMosquera PA, San Sebastian M, Waenerlund A-K, Ivarsson A, Weinehall L, Gustafsson PE. Income-related inequalities in cardiovascular disease from mid-life to old age in a Northern Swedish cohort: A decomposition analysis. Social Science \u0026amp; Medicine. 2016;149:135-44.\u003c/li\u003e\n\u003cli\u003eShiferie F, Gebremedhin S, Andargie G, Tsegaye DA, Alemayehu WA, Fenta TG. Decomposition Analysis of Socioeconomic Inequalities in Vaccination Dropout in Remote and Underserved Settings in Ethiopia. Am J Trop Med Hyg. 2024;111(1):196-204.\u003c/li\u003e\n\u003cli\u003eWillis B, Church K, Perttu E, Thompson H, Weerasinghe S, Macias-Konstantopoulos W. The preventable burden of mortality from unsafe abortion among female sex workers: a Community Knowledge Approach survey among peer networks in eight countries. Sex Reprod Health Matters. 2023;31(1):2250618.\u003c/li\u003e\n\u003cli\u003eChowdhury ME, Ahmed A, Kalim N, Koblinsky M. Causes of maternal mortality decline in Matlab, Bangladesh. J Health Popul Nutr. 2009;27(2):108-23.\u003c/li\u003e\n\u003cli\u003eHossain M, Khan M, Ababneh F, Shaw JEH. Identifying factors influencing contraceptive use in Bangladesh: evidence from BDHS 2014 data. BMC public health. 2018;18:1-14.\u003c/li\u003e\n\u003cli\u003eRana J, Sen KK, Sultana T, Hossain MB, Islam RM. Prevalence and determinants of menstrual regulation among ever-married women in Bangladesh: evidence from a national survey. Reproductive health. 2019;16:1-9.\u003c/li\u003e\n\u003cli\u003eKhan MN, Islam MM, Islam RM. Pattern of contraceptive use among reproductive-aged women with diabetes and/or hypertension: findings from Bangladesh Demographic and Health Survey. BMC women\u0026apos;s health. 2022;22(1):230.\u003c/li\u003e\n\u003cli\u003eRamirez AM, Tabassum T, Filippa S, Katz A, Chowdhury R, Bercu C, Baum SE. Clients\u0026apos; expectations and experiences with providers of menstrual regulation: a qualitative study in Bangladesh. BMC Womens Health. 2024;24(1):291.\u003c/li\u003e\n\u003cli\u003eSingh S, Hossain A, Maddow-Zimet I, Vlassoff M, Bhuiyan HU, Ingerick M. The Incidence of Menstrual Regulation Procedures and Abortion in Bangladesh, 2014. Int Perspect Sex Reprod Health. 2017;43(1):1-11.\u003c/li\u003e\n\u003cli\u003eKant S, Srivastava R, Rai SK, Misra P, Charlette L, Pandav CS. Induced abortion in villages of Ballabgarh HDSS: rates, trends, causes and determinants. Reprod Health. 2015;12:51.\u003c/li\u003e\n\u003cli\u003eKapil Ahmed M, van Ginneken J, Razzaque A. Factors associated with adolescent abortion in a rural area of Bangladesh. Trop Med Int Health. 2005;10(2):198-205.\u003c/li\u003e\n\u003cli\u003eAlam MZ, Sultan S. Knowledge and practice of menstrual regulation (MR) in Bangladesh: Patterns and determinants. Journal of Population and Social Studies [JPSS]. 2019;27(3):220-31.\u003c/li\u003e\n\u003cli\u003eSarker BK, Rahman M, Rahman T, Hossain J, Reichenbach L, Mitra DK. Reasons for Preference of Home Delivery with Traditional Birth Attendants (TBAs) in Rural Bangladesh: A Qualitative Exploration. PLoS One. 2016;11(1):e0146161.\u003c/li\u003e\n\u003cli\u003eBose S, Trent K. Socio-demographic determinants of abortion in India: a north-South comparison. J Biosoc Sci. 2006;38(2):261-82.\u003c/li\u003e\n\u003cli\u003eAnkara HG. SOCIOECONOMIC VARIATIONS IN INDUCED ABORTION IN TURKEY. J Biosoc Sci. 2017;49(1):99-122.\u003c/li\u003e\n\u003cli\u003eAdato M, Roopnaraine T, Becker E. Understanding use of health services in conditional cash transfer programs: insights from qualitative research in Latin America and Turkey. Soc Sci Med. 2011;72(12):1921-9.\u003c/li\u003e\n\u003cli\u003eHuq NL, Ahmed A, Haque NA, Hossaine M, Uddin J, Ahmed F, Quaiyum MA. Effect of an integrated maternal health intervention on skilled provider\u0026apos;s care for maternal health in remote rural areas of Bangladesh: a pre and post study. BMC Pregnancy Childbirth. 2015;15:104.\u003c/li\u003e\n\u003cli\u003eDaVanzo J, Rahman M, Ahmed S, Razzaque A. Influences on pregnancy-termination decisions in Matlab, Bangladesh. Demography. 2013;50(5):1739-64.\u003c/li\u003e\n\u003cli\u003eFauzia Akhter Huda SA, Bidhan Krishna Sarker, Hassan Rushekh Mahmood, Anadil Alam Introduction and approval of menstrual regulation with medication in Bangladesh: A stakeholder analysis. Dhaka: icddr,b.; 2017.\u003c/li\u003e\n\u003cli\u003eTan J-P. Marital fertility at older ages in Nepal, Bangladesh and Sri Lanka. Population Studies. 1983;37(3):433-44.\u003c/li\u003e\n\u003cli\u003eViswanath K. RS, Kontos E.Z. . Macrosocial determinants of population health. New York, NY: Springer; 2007.\u003c/li\u003e\n\u003cli\u003eBawah AA, Sato R, Asuming P, Henry EG, Agula C, Agyei-Asabere C, et al. Contraceptive method use, discontinuation and failure rates among women aged 15-49 years: evidence from selected low income settings in Kumasi, Ghana. Contracept Reprod Med. 2021;6(1):9.\u003c/li\u003e\n\u003cli\u003eNajafi-Sharjabad F, Zainiyah Syed Yahya S, Abdul Rahman H, Hanafiah Juni M, Abdul Manaf R. Barriers of modern contraceptive practices among Asian women: a mini literature review. Glob J Health Sci. 2013;5(5):181-92.\u003c/li\u003e\n\u003cli\u003eDal NA, Beydağ KD. Attitudes of Married Muslim Women Regarding Family Planning Methods During the COVID-19 Pandemic in Western Turkey. J Relig Health. 2021;60(5):3394-405.\u003c/li\u003e\n\u003cli\u003eMir AS, Malik R. Emergency contraceptive pills: Exploring the knowledge and attitudes of community health workers in a developing Muslim country. N Am J Med Sci. 2010;2(8):359-64.\u003c/li\u003e\n\u003cli\u003ePulok MH, Uddin J, Enemark U, Hossin MZ. Socioeconomic inequality in maternal healthcare: An analysis of regional variation in Bangladesh. Health Place. 2018;52:205-14.\u003c/li\u003e\n\u003cli\u003eSolar O IA. A conceptual framework for action on the social determinants of health. Policy and Practice. 2010.\u003c/li\u003e\n\u003cli\u003eOgundele OJ, Pavlova M, Groot W. Examining trends in inequality in the use of reproductive health care services in Ghana and Nigeria. BMC Pregnancy Childbirth. 2018;18(1):492.\u003c/li\u003e\n\u003cli\u003eLiang J, Dai L, Zhu J, Li X, Zeng W, Wang H, et al. Preventable maternal mortality: geographic/rural-urban differences and associated factors from the population-based Maternal Mortality Surveillance System in China. BMC Public Health. 2011;11:243.\u003c/li\u003e\n\u003cli\u003eSanneving L, Trygg N, Saxena D, Mavalankar D, Thomsen S. Inequity in India: the case of maternal and reproductive health. Glob Health Action. 2013;6:19145.\u003c/li\u003e\n\u003cli\u003eMisu F, Alam K. Comparison of inequality in utilization of postnatal care services between Bangladesh and Pakistan: Evidence from the Demographic and Health Survey 2017-2018. BMC Pregnancy Childbirth. 2023;23(1):461.\u003c/li\u003e\n\u003cli\u003eKamal N, Curtis S, Hasan MS, Jamil K. Trends in equity in use of maternal health services in urban and rural Bangladesh. Int J Equity Health. 2016;15:27.\u003c/li\u003e\n\u003cli\u003eOh JH. Educational expansion and health disparities in Ethiopia, 2005-2016. Soc Sci Med. 2019;235:112316.\u003c/li\u003e\n\u003cli\u003eR. M. . Social Determinants Affecting Utilization of Maternal Health Services in Africa: A Narrative Review of the Evidence. Int J Health Serv Res Policy. 2020.\u003c/li\u003e\n\u003cli\u003eThe Sustainable Development Goals Report 2024. 2024.\u003c/li\u003e\n\u003cli\u003eAltaf Hossain IM-Z, Susheela Singh and Lisa Remez. Menstrual Regulation, Unsafe Abortion And Maternal Health in Bangladesh. 2012.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Menstrual regulation, Bangladesh Demographic and Health Survey, Trends, Equity, Wealth quintile, Concentration index, Decomposition analysis, Reproductive health","lastPublishedDoi":"10.21203/rs.3.rs-5275379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5275379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eDespite menstrual regulation (MR) being recognised as a vital component of reproductive health and rights of women by the Government of Bangladesh, its utilisation remains limited. This paper aims to examine trends and associated factors of MR utilisation as well as the extent of socioeconomic inequalities in MR utilisation and factors contributing to the inequality over time.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData for this study was extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007, 2011, 2014, and 2017-18 datasets. After adjusting for sampling weight, data from a total of 65,552 ever-married women aged 15\u0026ndash;49 years were included. Descriptive statistics and bivariate analysis using Pearson\u0026rsquo;s Chi-squared tests were employed to explore associations between outcome and explanatory variables across different wealth quintiles. Simple and multiple logistic regression models were fitted to identify significant predictors of MR utilisation. Socio-economic inequalities in MR utilisation were examined using Lorenz curves and Erreygers normalised concentration indices. Finally, a decomposition analysis of the concentration index was conducted to assess the contribution of various factors to the observed inequality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR service utilisation in Bangladesh remained consistent over the years, peaking slightly in 2011 (6.4%) and reaching its lowest rate in 2017-18 (5.4%). Utilisation was higher in urban areas, with the highest rates in Rajshahi and Barishal divisions and the lowest in Sylhet, and an overall upward trend by wealth quintile over time. Women\u0026rsquo;s age, education, husband\u0026rsquo;s education, wealth index, division, place of residence, employment status, exposure to media, number of living children, contraceptive use, and survey year were significant factors associated with MR utilisation. The weighted Erreygers normalised concentration index (ECI) revealed a pro-rich concentration of MR utilisation, although the equity gap narrowed from 2007 (ECI\u0026thinsp;=\u0026thinsp;0.310) to 2017-18 (ECI\u0026thinsp;=\u0026thinsp;0.157). Wealth index and exposure to media emerged as the leading contributors to the overall socio-economic inequality in MR utilisation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights trends and factors contributing to inequalities in MR usage, which can guide the government and relevant stakeholders to place greater efforts in reducing socioeconomic and geographical disparities in MR utilisation by enhancing awareness through mass-media, training healthcare providers, and ensuring availability of MR services, particularly among less affluent women.\u003c/p\u003e","manuscriptTitle":"Equity analysis: To understand the equity gap regarding the menstrual regulation service in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 10:47:13","doi":"10.21203/rs.3.rs-5275379/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db090ef0-adde-4a3f-bcfd-c415f61bb39c","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-05T13:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 10:47:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5275379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5275379","identity":"rs-5275379","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}