Determinants Of Modern Contraceptive (mCP) Use Mong Married Rural Women In 21 African Countries: Multi-Level Modeling (MLM) using recent Demographic and Health Surveys (DHS)

Determinants Of Modern Contraceptive (mCP) Use Mong Married Rural Women In 21 African Countries: Multi-Level Modeling (MLM) using recent Demographic and Health Surveys (DHS) | 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 Determinants Of Modern Contraceptive (mCP) Use Mong Married Rural Women In 21 African Countries: Multi-Level Modeling (MLM) using recent Demographic and Health Surveys (DHS) Filmon G. Mebrahtu, Yonatan T. Gebreamlak, Habtemichael M. Teklemariam, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6695350/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 Family planning is a cost-effective and high-yield investment with benefits beyond controlling birth. Previously stunted efforts have been revived following the FP2020 initiative that aimed to provide contraceptives to women in the poorest countries. Since its start, the number of contraceptive users has grown globally yet, progress has been uneven, with women in rural areas and other vulnerable groups often being neglected. Data from 21 Sub-Saharan African countries was compiled to create the dataset for this study. This study focused on rural married women residing in those countries. Variables conceptualized to affect contraceptive use were categorized as individual and community-level variables. Tables and graphs were used for descriptive statistics while two-level multilevel regression was done to find out factors associated with contraceptive use. Prevalence of modern contraceptive use was found to be 34.6% with injectable and implants being the most common. Contraceptive use varied across countries with Southern Africa countries recording higher proportion. Individual factors that affected contraceptive use include age, wealth quintile index, educational level and fertility desires. Community variables that were found to be associated with contraceptive use include mean age of debut (sex, birth and marriage), community wealth quintile index, community domestic violence (DV) score and decision autonomy scores and so on. Modern contraceptive use among married, rural residing women in 21 countries was low compared to other studies. Generally, contraceptives have been effectively utilized in a community where women are typically empowered, have at least primary education, and have higher decision autonomy. Identifying individual and community factors dictating contraceptive use would help governmental and non-governmental organizations to scale up their effort to provide contraceptives for poor and marginalized societies in Sub-Saharan Africa. Obstetrics & Gynecology Maternal & Fetal Medicine Sexual & Reproductive Medicine Epidemiology Obstetrics & Gynecology Maternal & Fetal Medicine Sexual & Reproductive Medicine Epidemiology Obstetrics & Gynecology Maternal & Fetal Medicine Sexual & Reproductive Medicine Epidemiology Obstetrics & Gynecology Maternal & Fetal Medicine Sexual & Reproductive Medicine Epidemiology Contraceptives FP2020 Sub-Saharan Africa education empowerment community factors Figures Figure 1 Introduction Family planning (FP) is a cost-effective and high-yield investment with benefits beyond controlling birth [ 1 – 5 ]. It is hailed as one of the most successful public health initiatives of the 20th century. The interest in adoption of contraceptives is growing as their health and economic benefits become evident [ 4 , 6 ]. A plethora of evidence indicates the health benefits of contraceptives [ 7 ]. Modern contraceptives help couples space their pregnancies, control their family size, and thus reduce child and maternal death by preventing unsafe abortions, birth injuries, and pregnancy-related complications [ 2 , 8 , 9 ]. Contraceptives also protect against cancers (ovarian and uterine), benign cysts (of breasts and ovaries), pelvic inflammatory disease, and sexually transmitted infections, including HIV/AIDS [ 10 ]. Contraceptives are key components of poverty reduction and economic progress strategies through larger wealth accumulation and reducing governmental and familial expenditures. [ 2 , 4 , 11 ]. A single dollar invested in contraceptives saves 31 dollars that would have been spent on essential services[ 2 , 3 ]. A reduction in Total Fertility Rate (TFR) of 0.5 children per woman results in an increase of 11.9% Gross Domestic product (GDP) per capita in 50 years [ 12 ]. Africa is on the verge of a "demographic transition"-a decrease in the young dependents to the working-age population [ 13 , 14 ] and economic progress in return. However, fast population growth could threaten such scenario and as such contraceptive are an important tool to structure population to facilitate economic progress. African population is projected to exceed 2 billion by 2040 [ 15 ]. Africa’s vulnerability to destructive events like droughts, famines, and global warming which are expected to be worse with population rise- making contraceptives an exigency that cannot be overstated [ 10 ]. At the turn of the century, contraceptives were placed at the back-seat of global health issues as the world shifted towards other topics [ 2 ]. Decoupling family planning from economic causes has further dwarfed attention to contraceptives [ 9 ]. Nowadays, however, contraceptives are regarded as a remedy to global problems such as rapid population growth, climate change, and developmental difficulties [ 10 ]. Family Planning 2020 (FP2020) was the latest attempt to revitalize the global family planning agenda [ 16 ]. The initiative aimed to enable 120 million women in 69 of the poorest countries in the world to be contraceptive users [ 17 ]. Contraceptive use has grown since then, yet progress has been uneven and non-inclusive, leaving behind poorer women and rural residents [ 18 ]. Globally there were 190 million women who had had un-met needs of contraception in 2019 [ 19 ]. The highest burden of this disparity was in Sub-Saharan African (SSA) countries (24%) [ 20 ]. So, many women in SSA are suffering from problems that could have easily been reduced if not eliminated had universal access to modern contraceptives been adopted [ 1 , 3 , 21 – 23 ]. Rural communities have limited, if not non-existent access, to quality health care and have poorer health status as a result [ 24 ]. Consequently, rural women have low access to health services, including contraceptives, which translates into a greater unmet need of contraception and its sequels [ 7 , 8 , 24 ]. There is a shortage of data on contraceptive use among rural-based women and its determinants [ 25 ]. This creates a gap in service provisions, undercutting many vulnerable and marginalized women from reproductive health care services [ 21 ]. Moreover, the role of community factors in contraceptive use among rural residing women is poorly studied. Identifying those factors will be vital to improve the provision of modern contraceptives to women and the outcomes of maternal and child health interventions. In this paper, we measured the prevalence of contraceptive use among married rural women aged 15 to 49 in 21 countries and investigated individual and community factors associated with contraceptive use. This study aims to provide insight into current contraceptive use among rural-dwelling married women and thus help policy makers and planners design effective strategies to address discrepancies in modern contraceptive use and the unmet need of contraceptives. Methods Data source We utilized data from the latest DHS surveys conducted between 2013 and 2020 across 21 SSA countries to conduct this study. The countries included were Angola, Benin, Burundi, Gambia, Ghana, Guinea, Lesotho, Liberia, Malawi, Mali, Senegal, Sierra Leon, Tanzania, Uganda, Ethiopia, Kenya, Namibia, Rwanda, Zimbabwe, Zambia, and South Africa. The datasets for the DHS are available at http://dhsprogram.com/data/available-datasets.cfm . Study population In this study, non-pregnant and sexually active married women who live in rural areas between the ages of 15 and 49 were considered. Variable Definition Outcome variable The outcome variable was "current modern contraceptive use." Responses were recoded as "Yes" for respondents who were currently using a modern contraceptive and "No" for respondents who were not using any modern contraceptive methods. Predictor Variables The predictor variables were grouped into individual and community level factors. The variables were selected based on their theoretical relevance and practical significance with modern contraceptives use. Individual-level factors The individual-level variables included in this study are, age in years (15–19, 20–29,30–39,40–49), education level (no education, primary, secondary, higher), wealth index (poorest, poorer, middle, richer and richest), current employment (employed vs unemployed), number of living children (0, 1–2, 3–4, 5–6, 7+), age difference with partner (wife older or same age, wife younger by 10 years), mass media exposure (not exposed, exposed to at least one media, exposed to 2 media and exposed to all media), domestic violence (0 = none justified, 1 = at least one justified), decision making autonomy in the house (0, 1–2, 3–4), husband’s desire for children (both want the same, husband wants more, husband wants fewer and did not know), characteristics of partner (educational level, age), obstetric variables including total number of living children, history of miscarriage and abortion (no/yes), age at first sex, age at first birth, age at first marriage, fertility variables (the desire for more children, ideal number of children, and knowledge of contraceptive services). Community-level variables Contraceptive utilization is a complex scenario that involves the interaction between individual and community variables [ 26 ]. DHS does not collect community level data, and as such community variables were drawn from individual-level data. In the spirit of K. Mariam Elfstrom et al. [ 27 ], factors associated with modern contraceptive use in Africa were grouped into four domains: community demographics and fertility norms; community economic prosperity; community gender norms and inequalities; and community health knowledge and media exposure (Table 1 ). Community Demographics and Fertility Norms Community attitudes towards fertility, marriage, sexual intercourse, and perceived attitudes towards ideal family size and fertility patterns set the expected script for women. Such attitudes shape their attitude toward using contraceptives and fertility preferences [ 27 , 28 ]. Five variables were chosen to represent community demographics and fertility norms. They are Age at debut (sex, marriage, and first birth) in the community, the mean ideal number of children each woman desire to have in the community, and the community children's gender composition. Community Economic prosperity In a typical African setting, women depend on their spouse's income to fulfill their family needs, and contraceptives represent a significant health cost in family expenditures [ 29 ]. Various studies found wealth to be associated with contraceptive use as wealthier families had a higher potential to allocate scarce resources for contraceptives [ 18 , 27 ]. Community-level wealth was measured by taking each cluster's mean household index factor score. Community Gender Norms and Inequalities Traditional script on gender roles and sex-based relationships limits women's ability to access resources and use modern contraceptives [ 30 ]. These limits may translate into violence against women, deprivation of the rights and opportunities enjoyed by women in communities, and an imbalance in marital relationships [ 30 , 31 ]. To measure this domain five variables were selected. They are-The mean community violence justification index score, the mean community decision making autonomy score, the proportion of women in the community with at least a primary education, the proportion of men in the community with at least a primary education, and the ratio of men to women employed in the community. Community Health knowledge and Media exposure There is consistent evidence that higher health knowledge and exposure to health messages through media are associated with positive health outcomes [ 8 , 27 , 32 ]. Three variables were chosen to measure this domain: mean community HIV knowledge index score, mean community reproductive index score, and mean community media exposure score. Table 1 Domains of community variables Community-level variables Definition Community demographics and fertility norms Mean age at marriage in the community Mean age at marriage for women ages 15–49 Mean age at first intercourse in the community Mean age at first intercourse for women ages 15–49 Mean age at first birth in the community Mean age at first birth for women ages 15–49 Mean ideal number of children in the community Mean ideal number of children in the community Gender composition of children in the community Ratio of boys to girls in the community, calculated as divided by the number of living girls Community economic prosperity Mean community wealth index factor score Mean wealth index factor score reflects ownership of durable goods and housing characteristics Community gender norms and inequalities Mean community violence justification index score A 5-point scale of attitude towards domestic violence. Variables included in this index are going out without telling the husband, neglecting children, arguing with the husband, refusing sex with the husband, and burning food. The score in all the variables was coded as justified and unjustified. A lower score indicates violence is not justified. Mean community decision making autonomy score A 4-point scale for decision-making autonomy. A higher score shows higher decision-making control. The variables included in this index were, final say on own health care, final say on making large household purchases, and final say on visits to family or relatives. Women in the community with at least a primary education Proportion of women in the community with at least a primary level education men in the community with at least a primary education Proportion of men in the community with at least a primary level education Ratio of men to women employed in the community Ratio of men employed in the community to women (coded: 0 = no, 1 = yes) Community health knowledge & media exposure Mean community HIV knowledge index score A 6-point scale for knowledge of HIV where higher scores state a greater knowledge of HIV. Variables included were the following: whether the respondent had heard of HIV/AIDS, two questions about reducing the risk of infection (using condoms, and having just one uninfected partner who has not had other partners), and two questions about transmission (can people get AIDS virus from mosquitoes, can people get AIDS virus by sharing food with a person who has AIDS). Mean community reproductive knowledge index score A 5-points scale for reproductive health knowledge where higher scores show greater knowledge of reproductive health. Variables included are knowledge of the ovulatory cycle, knowledge of a contraceptive method, heard of AIDS or other STDs, and heard of other STDs. Some country-specific variations regarding questions included Mean community media exposure index score A 4-point scale for exposure to reproductive health messages in the media in the past month (radio, TV, and newspaper). A higher score indicates exposure to more media. Statistical analysis Modeling approach We utilized multivariable multilevel logistic regression models to analyze the association of individual and community factors with contraceptive use. A two-level model was specified for binary response reporting contraceptive use or not for women (at level 1) living in a community (level 2). A total of four models were constructed. The first null or unconditional model did not contain a predictor variable to decompose the amount of variance between clusters levels. The second model consisted only of individual-level factors, whereas the third model had only community-level variables. The final model controlled both individual and community factors (full model). DHS studies have a hierarchical nature that violates the independence of observation and equal variance assumption. Hence, multilevel modeling is the preferred method of analysis. We used Stata v14 for windows for extracting and cleaning the data. The final data was analyzed using MLwiN version 2.36 software. We utilized a methodology suggested by Rasbash et al. [ 33 ] (i.e., Marginal quasi-likelihood (MQL) was used to generate starting values followed by 2nd order Predictive/Penalized quasi-likelihood (PQL) to get the final estimate). We have then run a Malkov-chain Monte-Carlo Estimation to extract the Deviance Information Criterion for model comparison. Fixed effects (measures of association) Results of fixed effects were reported as odds ratios (ORs) with 95% confidence intervals (Cls). Contextual effects were measured using the median odds ratio (MOR). MOR measures higher-level variance as an odds ratio and estimates the probability of contraceptive use that can be attributed to community context. MOR equal to one indicates no community effect on contraceptive use. The higher the MOR, the more important the contextual effects of understanding the probability of contraceptive use. Sensitivity analysis The countries included in this study were heterogenous in terms of the outcome variables and sensitivity analysis was warranted to check the robustness of the results. For such purpose two consecutive sensitivity analysis were done. Firstly, the data was divided by year into studies done between 2013 and 2016 and those done from 2017 to 2020. Cumulative prevalence of contraceptives was 41.9% in the first study and 26.7% in the latter. Then, the dataset was then divided into two by taking the median contraceptive use in the included countries as a decision rule. Countries with prevalence below 28.7% were grouped in one category, while countries above the cut point were grouped in second dataset. Contraceptive use was 53.04% in the high prevalence country dataset, while it was 20.08% in the low prevalence countries. An identical set of analysis was done in all the generated datasets. The results were then tabulated and compared with the final dataset that contained all the countries. The final results showed that the similar variables dictated the use of contraceptive in all the datasets and, the direction of association was in similar direction in majority of the variables included in the final model of this study. we can safely conclude that the final results of the study are robust enough to not be affected by extreme values (supplementary material 1). Ethical considerations A request to download the survey dataset for each country of interest was made to the DHS program and granted following processing. Procedures and questionnaires for standard DHS surveys were reviewed and approved by the ICF Institutional Review Board (IRB) and the IRBs of the host countries. Before starting interviewing and/or conducting biomarker tests, written and oral informed consent were obtained from the study participants. For child or adolescent partcipants, permission to conduct the interview was sought from a parent or guardian. Participation was voluntary, and the data were protected and de-identified following the Health Insurance Portability and Accountability Act (HIPAA) stipulations. Results A total of 94,836 women from 21 countries were included in the study. Modern contraceptive use varies significantly across the study countries, from as low as 1.8% in Angola to 70.9% in Zimbabwe. Countries in southern Africa generally had a higher mCP prevalence rate when compared to countries from other regions of Africa (Table 2 ). Table 2 countries included, years of study and the prevalence rate of contraceptives Countries DHS Year Users Total Prevalence Lower CI Upper CI 1 Angola 2015/16 47 2667 1.8% 1.3% 2.3% 2 Benin 2017/18 710 5364 13.2% 12.3% 14.2% 3 Burundi 2016/17 1608 6638 24.2% 23.2% 25.3% 4 Ethiopia 2016 1804 6280 28.7% 27.6% 29.9% 5 Ghana 2014 672 2530 26.6% 24.9% 28.3% 6 Gambia 2019/20 584 3486 16.8% 15.5% 18.0% 7 Guinea 2018 375 4445 8.4% 7.6% 9.3% 8 Kenya 2014 2330 4909 47.5% 46.1% 48.9% 9 Liberia 2019/20 856 2623 32.6% 30.9% 34.4% 10 Lesotho 2014 1440 2354 61.2% 59.2% 63.1% 11 Mali 2018 791 4757 16.6% 15.6% 17.7% 12 Malawi 2015/16 6086 10068 60.4% 59.5% 61.4% 13 Namibia 2013 710 1348 52.7% 50.0% 55.3% 14 Rwanda 2019/20 3221 4905 65.7% 64.3% 67.0% 15 Serra Leon 2019 1119 5749 19.5% 18.5% 20.5% 16 Senegal 2019 792 3407 23.2% 21.9% 24.7% 17 Tanzania 2015/16 2153 7751 27.8% 26.8% 28.8% 18 Uganda 2016 2658 7285 36.5% 35.4% 37.6% 19 South Africa 2016 511 940 54.4% 51.2% 57.5% 20 Zambia 2018 2110 4188 50.4% 48.9% 51.9% 21 Zimbabwe 2015 2227 3142 70.9% 69.3% 72.4% Total 32804 94,836 34.6% 34.3% 34.9% Types of contraceptive methods Injectables were the most common contraceptive method (17.1%) utilized by women in the study. Implants were the second most common (8.9%), followed by pills (4.9%), traditional methods (2.8%), male condoms (2.0%), and withdrawal (1.3%) (Figure-1). Demographic characteristics of the participants Women tend to use mCP more consistently in the age range of 20–39 than women aged between 15–19 and 40–49. In all wealth quintile index groups, mCP users surpassed the non-users except in the poorest and poorer sub-groups. Among the mCP users, those who wanted no more children were 43.5%, followed by those who wanted to have children after two years (41.4%). A third of non-users (32%) did not want any children, while 28.3% wanted to have children within two years. Groups categorized as undecided (6.0%) and want but uncertain timing (4.0%) comprised the lowest percentage of non-users. Half of the women who use mCP had primary education (52.1%), while the most common educational category among those who don't use contraceptives was illiterate (51.1%). Women who used mCP exceeded non-users in all educational groups except the illiterate, which were predominantly mCP non-users. There were similar employment statistics as approximately 67% of the women in both groups were employed. Only 3.84% of mCP users and 2.35% of non-users were exposed to family planning messages from Radio, Newspapers, and TV. More women who use mCP had higher decision-making autonomy, i.e., 53.9% of users can take three decisions independently or with the consultation of a partner versus 42.9% of the non-users. Comparing those who had no decision autonomy, more non-users had limited freedom compared with users (26% versus 14.3%). In the educational background of spouses, more non-user’s partners than mCP users' were illiterate (44.2% vs. 20.0%). But there were more users whose partners had primary, secondary, and higher education than non-users. More mCP users were noted in couples of the same age as well as couples with 10 years difference than non-users (17.3%). More modern CP users had concordance in desired fertility with their partner than those who are not users (48.3% versus 33.4%). Educational background of partner showed that women whose partner had a primary education level were the predominant subgroup among mCP users, while among those who don't use mCP, partners with no education were the dominant group (44.3%) (Table 3 ). Table 3 Demographic Characteristics of Respondents Respondent Classification n(%) Variables Categories Non-Users Users Women's wealth index Poorest 20912(33.7) 7774(23.7) Poorer 16313(26.3) 8339(25.4) Middle 12790(20.6) 7913(24.1) Richer 8264(13.3) 5956(18.2) Richest 3753(6.1) 2822(8.6) Total 62032(100.0) 32804(100.0) Desire for more children wants within two years 17555(28.3) 3146(9.6) wants after 2 + years 18437(29.7) 13575(41.4) wants, unsure timing 2454(4.0) 623(1.9) Undecided 3732(6.0) 1201(3.7) wants no more 19851(32.0) 14259(43.5) Total 62029(100.0) 32804(100.0) Spouse education level no education 27417(44.2) 6571(20.0) Primary 20264(32.7) 15159(46.2) Secondary 10869(17.5) 9019(27.5) Higher 1885(3.0) 1493(4.6) don't know 1560(2.5) 543(1.7) Total 61995(100.0) 32785(100.0) Fertility Concordance both want same 20740(33.4) 15836(48.3) husband wants more 19834(32.0) 7947(24.2) husband wants fewer 3919(6.3) 3330(10.2) don't know 17528(28.3) 5681(17.3) Total 62021(100.0) 32794(100.0) Women Age 15–19 4585(7.4) 1472(4.5) 20–29 22430(36.2) 13579(41.4) 30–39 20756(33.5) 12680(38.7) 40–49 14261(23.0) 5073(15.5) Total 62032(100.0) 32804(100.0) Total Individual FP Message Exposure 0 39436(63.57) 17475(53.27) 1 16490(26.58) 10792(32.90) 2 4650(7.50) 3277(9.99) 3 1456(2.35) 1260(3.84) Total 62032(100) 32804(100) Women Education no Education 31729(51.1) 7547(23.0) Primary 21664(34.9) 17096(52.1) Secondary 7987(12.9) 7459(22.7) Higher 652(1.1) 702(2.1) Total 62032(100.0) 32804(100.0) Decision Taking Autonomy 0 16157(26.0) 4693(14.3) 1–2 19275(31.1) 10426(31.8) 3 26600(42.9) 17685(53.9) Total 62032(100.0) 32804(100.0) Couples Age difference Same age or older 4614(7.4) 2988(9.1) Men older by ≤ 10 40063(64.6) 24118(73.6) Men older by >10 17305(27.9) 5672(17.3) Total 61982(100.0) 32778(100.0) Factors Associated with contraceptive use After checking the DIC value of each constructed model, model III was selected as it had the lowest value of DIC when compared to other models. The following variables were identified as strong predictors of contraceptive use among women residing in rural settings in African countries. Individual-level variables that were significant include women's age, employment status, educational level, decision-making autonomy, desired fertility concordance with a partner, desire for more children, age at first birth, history of abortion, and stillbirth, and total family planning methods known by the women. Women aged between 20 to 29 and 30 to 39 had 1.40 and 1.52 more odds of using contraceptives when compared with the reference (age group 15–19). Women in the poorer and middle categories were at 1.07 and 1.16 more odds of using contraceptives, whereas the richer had 1.11 more odds than the reference (poorest). Compared with the reference (no education), women with primary, secondary, and higher education levels were at 1.31, 1.40, and 1.32 more odds of using a contraceptive. Women with higher decision autonomy were at increased odds of using contraceptives. Those who can take 1 or 2 decisions had 1.17 higher odds, and those who can take three decisions had 1.15 odds of using contraceptives than those who had no decision autonomy (those who do not decide by themselves or in consultation with a partner). Compared to women who want to have children within two years, women who want to have children after two years and those who wish to have no more had similar odds of using contraceptives (3.24). Those who were undecided and those who wanted but were unsure of timing had 1.97 and 1.49 odds of using a contraceptive. Fertility concordance was a significant predictor of contraceptive use, as women whose partners want more children were 21% less likely to use contraceptives when compared to couples who want the same number of children. Those women who didn't know their partner's desire were 29% less likely to use contraceptives. Women who had a history of abortion or stillbirth were 21% less likely to use contraception. A unit increase in the total number of contraceptive methods known increased the odds of using contraceptives by 1.16. The number of children was a significant predictor of mCP use. Compared to women with no children, women with at least one child had three times the odds of using contraceptives. Those insured were at 1.10 more odds of using mCP than those uninsured. Partner educational level was significantly associated with using mCP as the odds of using mCP increased with partners' academic level. In the community-level variables, for every unit increase in mean age at first intercourse in the community, the odds of using mCP increased by 3%. Those communities categorized as poorer and middle were at similar odds of using contraceptives, i.e., 1.20 Vs. 1.22. In the community gender norms and inequalities, a general upward trend of odds of using contraceptives was found significant in all the items except one. In a community where men were employed more than women, the odds of using mCP was 1.74 compared to communities where more women were employed than men (Table 4). Model I Model II Model III Model IV AOR(95% CI) AOR(95% CI) AOR(95% CI) AOR(95% CI) Respondent age 15–19 Ref Ref 20–29 1.44(1.18–1.76)*** 1.40(1.27–1.54)*** 30–39 1.77(1.39–2.27)*** 1.52(1.35–1.72)*** 40–49 0.95(0.70–1.27) 0.90(0.78–1.04) Wealth Quantile index poorest Ref Ref poorer 0.99(0.89–1.10) 1.07(1.02–1.13)* middle 1.12(1.00-1.26) 1.16(1.09–1.23)*** richer 1.12(0.97–1.29) 1.11(1.03–1.19)** richest 0.81(0.67–0.99)* 1.03(0.93–1.14) Respondent employment status Unemployed Ref Ref Employed 0.95(0.87–1.04) 1.04(0.99–1.09) Respondent educational level no Education Ref Ref Primary 1.98(1.72–2.28)*** 1.31(1.23–1.40)*** Secondary 2.67(2.23–3.19)*** 1.40(1.29–1.54)*** Higher 3.06(2.05–4.56)*** 1.32(1.10–1.58)** Health insurance coverage no Ref Ref yes 0.91(0.80–1.04) 1.10(1.02–1.18)* Decision autonomy 0 Ref Ref 1–2 1.52(1.34–1.72)*** 1.17(1.09–1.24)*** 3 1.84(1.63–2.08)*** 1.15(1.08–1.23)*** Domestic violence justification None justified Ref Ref At least one justified 1.07(0.98–1.16) 1.02(0.98–1.06) husband/partner's education level no education Ref Ref primary 1.47(1.26–1.73)*** 1.31(1.20–1.42)*** secondary 1.77(1.45–2.16)*** 1.39(1.26–1.54)*** higher 1.48(1.09-2.00)* 1.35(1.17–1.56)*** don't know 1.85(1.01–3.39)* 0.88(0.69–1.11) Fertility concordance both want same Ref Ref husband wants more 0.68(0.61–0.76)*** 0.79(0.76–0.83)*** husband wants fewer 1.10(0.96–1.26) 1.01(0.94–1.08) don't know 0.58(0.51–0.65)*** 0.71(0.67–0.75)*** Spousal education difference equally educated Ref Ref husband better educated 0.96(0.82–1.12) 0.95(0.89–1.01) Wife better educated 0.89(0.76–1.04) 1.08(1.00-1.16)* neither educated 1.14(0.88–1.47) 1.08(0.95–1.22) DK/missing 0.76(0.44–1.33) 1.20(0.98–1.47) Spousal age difference Wife older or Same age Ref Ref Men older 10 1.13(0.92–1.40) 1.08(0.97–1.19) Family planning message exposure Exposed to three Ref Ref Exposed to two 0.89(0.67–1.19) 1.03(0.99–1.08) Exposed to at least one 0.94(0.71–1.25) 0.98(0.90–1.07) Exposed to none 0.93(0.68–1.26) 1.13(0.99–1.30) Number of Living Children Zero Ref Ref 1 or 2 6.01(2.83–12.78)*** 3.43(2.44–4.83)*** 3 or 4 6.06(2.85–12.90)*** 3.67(2.60–5.17)*** 5 or 6 5.86(2.74–12.54)*** 3.54(2.50–5.01)*** More than 7 4.72(2.18–10.21)*** 3.19(2.24–4.54)*** desire for more children wants within 2 years Ref Ref wants after 2 + years 3.25(2.85–3.72)*** 3.248(2.843–3.71)*** wants, unsure timing 1.20(0.89–1.62) 1.496(1.16–1.929)** undecided 1.81(1.43–2.30)*** 1.974(1.567–2.489)*** wants no more 3.40(2.94–3.93)*** 3.241(2.824–3.721)*** History of abortion, miscarriage, stillbirths no Ref Ref yes 0.82(0.75–0.9)*** 0.794(0.715–0.88)*** Partner Age 15–19 Ref Ref 20–29 0.70(0.31–1.58) 1.16(0.86–1.57) 30–39 0.70(0.31–1.58) 1.12(0.83–1.53) 40–49 0.61(0.26–1.40) 1.03(0.76–1.41) 50–59 0.50(0.21–1.17) 0.76(0.55–1.05) 60+ 0.36(0.15–0.86) 0.67(0.47–0.94)* Age at first marriage 1.01(0.99–1.02) 1.00(1.00-1.01) Age at First Sex 1.03(1.01–1.05)** 1.00(0.99–1.01) Age at first Birth 0.94(0.92–0.96)*** 0.96(0.96–0.97)*** Total Knowledge of Family planning methods 1.34(1.29–1.39)*** 1.16(1.11–1.21)*** Community Variables Mean age at marriage in the community 1.01(0.97–1.06) 1.06(1.03–1.08)*** Mean age at first intercourse in the community 1.28(1.21–1.34)*** 1.03(1.00-1.06)** Mean age at first birth in the community 0.75(0.71–0.80)*** 0.91(0.88–0.94)*** Mean ideal number of children in the community 0.97(0.96–0.98)*** 0.99(0.98–0.99)*** Gender composition of children in the community More female Ref Ref More male 0.96(0.86–1.08) 0.95(0.90–1.01) Mean community wealth index factor score Poorest Ref Ref Poorer 1.38(1.22–1.57)*** 1.20(1.12–1.29)*** Middle 1.38(1.19–1.60)*** 1.22(1.11–1.34)*** Richer 1.17(0.90–1.51) 1.18(1.01–1.38)* Richest 1.29(0.71–2.33) 1.35(0.74–2.47) Community gender norms and inequalities Mean community violence justification index score 1.10(1.05–1.16)*** 0.97(0.94–1.01) Mean community decision making autonomy score 1.66(1.53–1.80)*** 1.31(1.25–1.39)*** Women in the community with at least a primary education 1.09(1.07–1.12)*** 1.08(1.07–1.10)*** men in the community with at least a primary education 0.99(0.97–1.02) 0.96(0.95–0.98)*** Ratio of men to women employed in the community More women Ref Ref More men 1.23(1.06–1.42)** 1.74(1.57–1.93)*** Community health knowledge and media exposure Mean community HIV knowledge index score 0.76(0.68–0.83)*** 0.76(0.72–0.81)*** Mean community reproductive knowledge index score 1.26(1.22–1.30)*** 0.92(0.87–0.96)** Mean community media exposure index score Three Ref Ref Two 0.28(0.06–1.36) 0.88(0.27–2.91) One 0.24(0.05–1.17) 0.88(0.27–2.90) Zero 0.39(0.08–1.94) 1.12(0.33–3.75) Random Effect coef(SE) coef(SE) coef(SE) Model Comparisons DIC 103302.60 73909.026 76356.23 68590.12 MOR (95% Crl) 3.70(3.64–3.76) 4.79(4.51–5.08) 2.46(2.43–2.49) 2.46(2.42–2.50) Log Likelihood Ratio 96174.209 69520.243 70806.654 69280.394 Discussion This study analyzed the modern contraceptive use and associated factors in rural communities in 21 African countries. Our findings of factors affecting modern contraceptive use were consistent with current literature [ 8 , 27 , 34 – 36 ]. In the multilevel logistic regression, respondent age, educational level, decision-making autonomy, fertility concordance, desire for more children, age at first marriage, and total knowledge of family planning methods were significant individual determinants of modern contraceptives. Community variables that were significant determinants of contraceptives were mean age at first sex in the community, mean age at first birth in the community, mean ideal number of children in the community, mean community DV justification score, mean community decision autonomy score, ratio of women and men employed in the community, and HIV knowledge index score, and reproductive knowledge index score. The pooled prevalence of modern contraceptive use in this study was 34.6%. The prevalence in the current study is higher than a study conducted by Tessema et al. that found 20.68% [ 35 ] and a study done by Mutumba et al., which found 17.8% modern contraceptive utilization [ 34 ], and Apanga's study, which identified an mCP prevalence rate of 26% [ 7 ]. But this result was lower than that of a study conducted using meta-analysis by Cahil et al. [ 37 ]. Modern contraceptive use significantly varies among countries, from as low as 1.8% in Angola to a high of 70.9% in Zimbabwe. Women's age significantly affects modern contraceptives use. Compared with respondents aged 15–19, women aged 20–29 and 30–39 had 1.39 and 1.43 odds of using a contraceptive respectively. This finding is contrasted with a study done by Tessema et al., which found lower odds of using contraceptives as age progresses [ 35 , 38 ]. In a study done among 20 African countries, women aged 25–34 were more likely to use contraceptives when compared with women aged 15–24 [ 7 ]. A possible reason for this could be the low knowledge of young females about the benefits of contraceptives and consequences of unprotected sex [ 23 , 32 ]. Societal stigma on premarital sex and contraceptives may have contributed to it [ 23 ]. There was an insignificant association between women aged 40–49 using contraceptives, and a similar finding was reported in a Zambian study [ 8 ]. One plausible reason for this is the lower contraception need with decreased sexual activity in older women [ 8 ]. There is strong evidence that educated women are more likely to use contraceptives than uneducated women [ 29 , 38 – 40 ]. The odds of using contraceptives increases with the increment of educational level. Similar findings were reported in Uganda, Nigeria, Tanzania, and East Africa [ 8 , 14 , 29 , 36 ]. Additionally, an educated woman knows the benefit of modern contraceptives through reading newspapers, social, and mass media. Additionally, educated woman is more likely to have good health-seeking behavior and health services, including family planning [ 38 ]. As a result, an educated woman is empowered to delay marriage and child-bearing and thus is more informed to use modern contraceptives than an uneducated woman [ 40 ]. In some studies, education was not a significant predictor of contraceptives [ 26 , 36 ]. The possible explanation could be the diffusion of contraceptive service to everyone and the amplified role of tradition, which plays a massive role in contraceptive decisions than education in those settings [ 26 , 36 ] Employed women have higher income, autonomy, and freedom than unemployed women, and therefore are more empowered to use contraceptives [ 27 ]. In this study, employment was not a significant predictor. But, a study among 11 African countries, found a higher odds of contraceptive use [AoR = 2.43] among employed women [ 35 ]. In a study done among rural Zambian women, occupation wasn't a significant predictor of contraceptive use [ 8 ]. The lower odd of association between occupation and contraceptive use could be due to the nature of the women's work. In rural settings, the available job for women are those that provide little monetary relief, such as farming and logging; thus, they contribute little to empowering women to use contraceptives [ 8 ]. In this study, women who make one to two decisions in their households had 1.16 more odds of using contraceptives when compared to women who make zero decisions in their homes. In a study done by Yaya et al., women with high decision-making power were 23% more likely to use contraceptives than women with low decision-making power [ 41 ]. Decision-making power in a household reflects the family power dynamics, which influences women's ability to decide their health, including the use of contraceptives. Olakunde et al. found that women are 68% more likely to use contraceptives if they have an equal say in decisions about their household [ 42 ]. Concordance in family size reflects such dynamics as found in our study, as women with husbands who wanted more children were 19% less likely to use contraception when compared to couples who wanted the same number of children. Women who didn't know their partners’ desire had a 29% less likelihood of using contraceptives than women who had the same desire as their partners. Fertility preferences of couples were the most significant predictor of contraceptive use in this study. Couples who wanted to delay having children or stop altogether had approximately three times the odds of using contraceptives than couples who wanted to have children within two years. In a study done in a peri-urban communities of three African countries, those who didn’t want children were at higher odds of using contraceptives than those who wanted more in all study settings [ 43 ]. In this study, mCP use was highly associated with the number of living children a woman has. Compared to women with no children, women with at least one child have more than three times the odds of using a contraceptive. Similarly, a Ugandan study found two times the odds of using contraceptive for women with children[ 38 ]. Contraceptive use is a scenario influenced by individual and community factors [ 27 , 42 ]. Women's characteristics interact with their community and mold the norms and customs of such a community. Inversely, the community influences the characteristics of the individual inside it [ 26 ]. For a unit increase in the mean age of marriage and sex, the likelihood of using contraceptives increased by 5%. In a community where women are expected to be married and give birth at a young age, contraceptive use is discouraged [ 28 ]. Mutumba et al. reported similar results, i.e: women living in communities with a higher age at first marriage were 8% more likely to use contraceptives. Such association was more pronounced among African and European communities, where higher mean age at first marriage was associated with AoR of 1.12 and 1.31, respectively [ 34 ]. In many African and Asian countries parental preference for their children's gender (sons over daughters) has driven their contraceptive behavior [ 44 , 45 ]. In a study done by Hoq et al., contraceptive use among women with only daughters was comparatively lower [ 45 ]. But, this was not a significant predictor of contraceptive use in our study. Reasons could be the type of contraceptives commonly cited in our research. Preference for sons is a significant predictor of permanent contraceptive use but bears no relation to short term contraceptives [ 44 ]. Attitude towards violence may impact women's autonomy and ability to seek health services and reflect greater gender inequality [ 31 ]. Besides, in communities where domestic violence is the rule not exception, women may find it hard to communicate their desire of fertility and contraception. The mean DV justification index wasn't a significant predictor of contraceptive use in our study. However, with a unit increase in the mean community decision autonomy score, the odds of using contraceptives increased by 1.38. Other studies also reported similar findings[ 27 , 34 ]. Higher educational attainment of women in the community was significantly associated with contraceptive use, like results of other studies [ 27 , 34 , 42 ]. Generally, contraceptives have been effectively utilized in a community where women are typically empowered, have at least primary-level education, and have higher decision autonomy. Programs specifically designed for rural women have increased use of modern contraceptive use through empowering women, providing access to modern contraceptive, educating mother on family planning. Accredited social health activist (ASHA) program was instrumental in reducing maternal mortality through promoting antenatal care (ANC) visits, institutional delivery and modern contraceptives use [ 46 ] Evaluating the impact of similar approach in Pakistan showed that, having Lady health worker increases the probability of women utilizing short term contraceptive by 9.9 percentage points [ 47 ]. An Ethiopian study have noted that presence of more women development army in a district have resulted in more ANC visits, contraceptive use, and institutional deliveries [ 48 ]. Those programs have empowered women, improved women knowledge, and access of contraceptive to women. Lessons from such programs should be evaluated and used to create diffused programs in LMIC to facilitate faster adoption of modern contraceptive use. This study is the first of its kind as it targeted married women residing in rural areas. The study has also included community variables during analysis which would further enhance the study's findings. However, this study is not without limitations as it doesn't establish a temporal relationship between the outcome and predictor variable. Additionally, this study didn't account for structural factors (distance to a health facility, quality of FP services), peer-related factors, and socio-cultural factors like religion. As DHS studies are affected by social desirability bias and recall bias, such problems could have affected the study results. In conclusion, the modern contraceptive use among married, rural residing women in 21 countries was low compared to other studies. In this study, individual variables that were significant predictors of contraceptive use were women's age, education, fertility preference, concordance with a partner, partner's educational level, women's decision autonomy, being knowledgeable about contraceptives, and the number of living children. Among community variables, mean age at first sex, birth, mean ideal number of children, mean community decision-making autonomy, and higher educational attainment in a community were significant determinants of contraceptive use. Identifying individual and community factors would help governmental and non-governmental organizations to scale up their efforts to provide contraceptives for poor and marginalized societies in Sub-Saharan Africa. Empowering women through education, employment, and family planning promotion would enhance the uptake of contraception by women. Additionally, strategies targeting disadvantaged communities would benefit not only the women but also the families and the country as well. Declarations Acknowledgment The authors would like to thank MEASURE DHS for granting access to the data for this analysis. Our gratitude goes to Mrs. Yirgalem Solomon and Mr. Bipin Jha from UNICEF, and Mr. Zenawi Zeremariam Araya, Dr. Araia Berhane Mesfin, Staff of Communicable Disease Controls Division of Eritrea for their unwavering support. We would also like to thank Mr. Meron M. Kiflu, Dr. Amanuel K. Andegorgish for their encouragement and support. Availability of materials and data Data for this study were sourced from the Demographic and Health Surveys (DHS) and available here: https://dhsprogram.com/data/available-datasets.cfm Competing Interests The authors declare that they have no competing interests(financial or non-financial). Consent for publication Not applicable Authors contribution FGM and HMT have conceptualized the study and wrote the first draft. FGM and HGW designed the methodology and FGM curated the data and analyzed it. YTG, HGW and KTS supervised and validated the analysis and its results. All authors reviewed and edited the final version of the manuscript. References Shukla A et al (2020) Association between modern contraceptive use and child mortality in India: A calendar data analysis of the National Family Health Survey (2015-16). SSM-population health, 11: p. 100588 Smith R et al (2009) Family planning saves lives. Population Reference Bureau, Washington DC, p 5 Singh S, Darroch JE (2012) Adding it up: Costs and benefits of contraceptive services. Guttmacher Institute and UNFPA, pp 1269–1286 Orbeta AC Jr (2005) Poverty, vulnerability and family size: evidence from the Philippines. Poverty strategies in Asia, : p. 171 Fotso JC et al (2013) Birth spacing and child mortality: an analysis of prospective data from the Nairobi urban health and demographic surveillance system. J Biosoc Sci 45(6):779–798 Control CfD (1999) Ten Great Public Health Achievements — United States, 1900–1999. Morbidity and Mortality weekly report, April 2, 48(12) Apanga PA et al (2020) Prevalence and factors associated with modern contraceptive use among women of reproductive age in 20 African countries: a large population-based study. BMJ open 10(9):e041103 Lasong J et al (2020) Determinants of modern contraceptive use among married women of reproductive age: a cross-sectional study in rural Zambia. BMJ open 10(3):e030980 Cleland J et al (2006) Family planning: the unfinished agenda. lancet 368(9549):1810–1827 Aliyu AA (2018) Family planning services in Africa: The successes and challenges. Family Plann, 69 Eliason S et al (2014) Determinants of unintended pregnancies in rural Ghana. BMC Pregnancy Childbirth 14(1):1–9 Ashraf QH (2013) Weil, and Joshua Wilde The effect of fertility reduction on economic growth . Popul Dev Rev 39:97–130 Bloom DE, Kuhn M, Prettner K (2017) Africa's prospects for enjoying a demographic dividend. J Demographic Econ 83(1):63–76 Towriss CA, Timæus IM (2018) Contraceptive use and lengthening birth intervals in rural and urban Eastern Africa. Demographic Res 38:2027–2052 Department SR (2022) Forcast of the total population of Africa 2020–2050. Ahmed S et al (2019) Trends in contraceptive prevalence rates in sub-Saharan Africa since the 2012 London Summit on Family Planning: results from repeated cross-sectional surveys. Lancet Global Health 7(7):e904–e911 Brown W et al (2014) Developing the 120 by 20 goal for the Global FP2020 Initiative. Stud Fam Plann 45(1):73–84 Creanga AA et al (2011) Low use of contraception among poor women in Africa: an equity issue. Bull World Health Organ 89:258–266 United Nations Department of Economic and Social, Affairs PD (2019) Contraceptive Use by Method 2019: Data Booklet. ( ST/ESA/SER.A/435 ) Division UP (2017) World population prospects: The 2017 revision New York: United Nations, Department of Economic and Social Affairs. Ahinkorah BO (2020) Predictors of modern contraceptive use among adolescent girls and young women in sub-Saharan Africa: a mixed effects multilevel analysis of data from 29 demographic and health surveys. Contracept reproductive Med 5(1):1–12 Ahmed S et al (2012) Maternal deaths averted by contraceptive use: an analysis of 172 countries. Lancet 380:111–125 Kassa GM et al (2018) Prevalence and determinants of adolescent pregnancy in Africa: a systematic review and meta-analysis. Reproductive health 15(1):1–17 Versteeg M, Du Toit L, Couper I (2013) Building consensus on key priorities for rural health care in South Africa using the Delphi technique. Global health action 6(1):19522 United Nations Department of Economic and Social, Affairs PD (2020) World Family Planning 2020 Highlights: Accelerating action to ensure universal access to family planning. ( ST/ESA/SER.A/450 ) Abraha TH et al (2018) Predictors of postpartum contraceptive use in rural Tigray region, northern Ethiopia: a multilevel analysis. BMC Public Health 18(1):1–10 Elfstrom KM, Stephenson R (2012) The role of place in shaping contraceptive use among women in Africa. PLoS ONE 7(7):e40670 Ahmed WAM et al (2015) Factors affecting utilization of family planning services in a post-conflict setting, South Sudan: a qualitative study. AIMS public health 2(4):655 Ogboghodo E, Adam V, Wagbatsoma V (2017) Prevalence and determinants of contraceptive use among women of child-bearing age in a rural community in southern Nigeria. J Community Med Prim Health Care 29(2):97–107 Okigbo CC et al (2018) Gender norms and modern contraceptive use in urban Nigeria: a multilevel longitudinal study. BMC Womens Health 18(1):178 Diop-Sidibé N, Campbell JC, Becker S (2006) Domestic violence against women in Egypt—wife beating and health outcomes, vol 62. Social science & medicine, pp 1260–1277. 5 Mandiwa C et al (2018) Factors associated with contraceptive use among young women in Malawi: analysis of the 2015–16 Malawi demographic and health survey data. Contracept Reproductive Med 3(1):1–8 Rasbash J et al (2016) A user’s guide to MLwiN, v2. 36. Centre for Multilevel Modelling, University of Bristol, Bristol Mutumba M, Wekesa E, Stephenson R (2018) Community influences on modern contraceptive use among young women in low and middle-income countries: a cross-sectional multi-country analysis. BMC Public Health 18(1):1–9 Tessema ZT et al (2021) Pooled prevalence and determinants of modern contraceptive utilization in East Africa: A Multi-country Analysis of recent Demographic and Health Surveys. PLoS ONE 16(3):e0247992 Yussuf MH et al (2020) Trends and predictors of changes in modern contraceptive use among women aged 15–49 years in Tanzania from 2004–2016: evidence from Tanzania demographic and health surveys. PLoS ONE 15(6):e0234980 Cahill N et al (2018) Modern contraceptive use, unmet need, and demand satisfied among women of reproductive age who are married or in a union in the focus countries of the Family Planning 2020 initiative: a systematic analysis using the Family Planning Estimation Tool. Lancet 391(10123):870–882 Namasivayam A et al (2020) Predictors of modern contraceptive use among women and men in Uganda: a population-level analysis. BMJ open 10(2):e034675 Rutaremwa G et al (2015) Predictors of modern contraceptive use during the postpartum period among women in Uganda: a population-based cross sectional study. BMC Public Health 15(1):1–9 Tekelab T, Melka AS, Wirtu D (2015) Predictors of modern contraceptive methods use among married women of reproductive age groups in Western Ethiopia: a community based cross-sectional study. BMC Womens Health 15(1):1–8 Yaya S et al (2018) Women empowerment as an enabling factor of contraceptive use in sub-Saharan Africa: a multilevel analysis of cross-sectional surveys of 32 countries. Reproductive health 15(1):1–12 Olakunde BO et al (2020) Individual-and country-level correlates of female permanent contraception use in sub-Saharan Africa. PLoS ONE 15(12):e0243316 OlaOlorun F et al (2016) Women's fertility desires and contraceptive behavior in three peri-urban communities in sub Saharan Africa. Reproductive health 13(1):1–6 Channon MD (2015) Son preference, parity progression and contraceptive use in South Asia. Popul Horizons 12(1):24–36 Hoq MN (2020) Influence of the preference for sons on contraceptive use in Bangladesh: A multivariate analysis. Heliyon 6(10):e05120 Smisha Agarwal SLC, Gustavo Angeles IS, Speizer K, Singh, Thomas JC (2019) The impact of India’s accredited social health activist (ASHA) program on the utilization of maternity services: a nationally representative longitudinal modelling study. BMC Hum Resour Health 17(68):13 management OP (2019) Lady Health Worker Programme, Pakistan Performance Evaluation Damtew ZA et al (2018) Z.A.D.,., Correlates of the Women's Development Army strategy implementation strength with household reproductive, maternal, newborn and child healthcare practices: a cross-sectional study in four regions of Ethiopia. Additional Declarations The authors declare no competing interests. Supplementary Files Sensitivityanalysis.docx 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. 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Mebrahtu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBAC9mbmBjDDgL35AJCSkCGohecwI1QLz7EEkBYewloOwLRI5BiABQhrYWds3fBxR62cOc+Zz69u1FjwMLAfProBrxZmxrabM88cN7Zs791mnXMM6DCetLQb+LTYA7Xc5m07lrjhzNltxjlsQC0SPGZ4tfBAtdRvuJHzzDjnH/FaahIMbuQwP85tI1LLzZltBwx39hwzY87tk+BhI+QXHv7Dx258bKuTN2dvfvw551udHD87UASfFig4DCLYJMAkEcpBoA5EMH8gUvUoGAWjYBSMMAAA8MRKrl00XoYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5306-2465","institution":"Ministry of Health, Asmara, Eritrea","correspondingAuthor":true,"prefix":"","firstName":"Filmon","middleName":"G.","lastName":"Mebrahtu","suffix":""},{"id":458467677,"identity":"935b1daa-2ea0-44e5-8996-ce2668a2dd87","order_by":1,"name":"Yonatan T. Gebreamlak","email":"","orcid":"","institution":"Ministry of Health, Ghindae Regional Referral Hospital, Ghindae, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Yonatan","middleName":"T.","lastName":"Gebreamlak","suffix":""},{"id":458467678,"identity":"22944102-d14e-4d86-b12c-9e7590d21ed9","order_by":2,"name":"Habtemichael M. Teklemariam","email":"","orcid":"","institution":"Ministry of Health, Tessenay Hospital, Tessenay, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Habtemichael","middleName":"M.","lastName":"Teklemariam","suffix":""},{"id":458467679,"identity":"b43fce58-da60-45a3-aeb2-3bff301c9c28","order_by":3,"name":"Kiflu T. Sengal","email":"","orcid":"","institution":"Orotta College of Medicine and Health Sciences, Asmara, Eritrea","correspondingAuthor":false,"prefix":"","firstName":"Kiflu","middleName":"T.","lastName":"Sengal","suffix":""},{"id":458467680,"identity":"0f714fb8-7e8d-4e9e-ae19-9cd9ac1f005d","order_by":4,"name":"Henok G. Woldu","email":"","orcid":"https://orcid.org/0000-0002-6334-0024","institution":"Center for Health Analytics for National and Global Equity (Change), USA","correspondingAuthor":false,"prefix":"","firstName":"Henok","middleName":"G.","lastName":"Woldu","suffix":""}],"badges":[],"createdAt":"2025-05-19 05:55:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6695350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6695350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83137114,"identity":"d0ac1389-e10a-4e3a-9049-2bb150657fe8","added_by":"auto","created_at":"2025-05-20 11:36:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74294,"visible":true,"origin":"","legend":"\u003cp\u003eContraceptive use by Method type\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6695350/v1/a4cc54bac7fa3ad7909feb5a.jpg"},{"id":83138582,"identity":"3acafc12-5607-4cb7-8f06-c395d2e91943","added_by":"auto","created_at":"2025-05-20 11:52:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1829018,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6695350/v1/1a2b63b7-eab6-4233-874d-0f7e9776576b.pdf"},{"id":83137113,"identity":"6ecc90bf-414a-4ba0-8215-1dfcdef9aabd","added_by":"auto","created_at":"2025-05-20 11:36:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39640,"visible":true,"origin":"","legend":"","description":"","filename":"Sensitivityanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-6695350/v1/328eb128acc5156efbbcbf9d.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeterminants Of Modern Contraceptive (mCP) Use Mong Married Rural Women In 21 African Countries: Multi-Level Modeling (MLM) using recent Demographic and Health Surveys (DHS)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFamily planning (FP) is a cost-effective and high-yield investment with benefits beyond controlling birth [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is hailed as one of the most successful public health initiatives of the 20th century. The interest in adoption of contraceptives is growing as their health and economic benefits become evident [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A plethora of evidence indicates the health benefits of contraceptives [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Modern contraceptives help couples space their pregnancies, control their family size, and thus reduce child and maternal death by preventing unsafe abortions, birth injuries, and pregnancy-related complications [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Contraceptives also protect against cancers (ovarian and uterine), benign cysts (of breasts and ovaries), pelvic inflammatory disease, and sexually transmitted infections, including HIV/AIDS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContraceptives are key components of poverty reduction and economic progress strategies through larger wealth accumulation and reducing governmental and familial expenditures. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A single dollar invested in contraceptives saves 31 dollars that would have been spent on essential services[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A reduction in Total Fertility Rate (TFR) of 0.5 children per woman results in an increase of 11.9% Gross Domestic product (GDP) per capita in 50 years [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Africa is on the verge of a \"demographic transition\"-a decrease in the young dependents to the working-age population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and economic progress in return. However, fast population growth could threaten such scenario and as such contraceptive are an important tool to structure population to facilitate economic progress. African population is projected to exceed 2\u0026nbsp;billion by 2040 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Africa\u0026rsquo;s vulnerability to destructive events like droughts, famines, and global warming which are expected to be worse with population rise- making contraceptives an exigency that cannot be overstated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the turn of the century, contraceptives were placed at the back-seat of global health issues as the world shifted towards other topics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Decoupling family planning from economic causes has further dwarfed attention to contraceptives [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nowadays, however, contraceptives are regarded as a remedy to global problems such as rapid population growth, climate change, and developmental difficulties [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Family Planning 2020 (FP2020) was the latest attempt to revitalize the global family planning agenda [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The initiative aimed to enable 120\u0026nbsp;million women in 69 of the poorest countries in the world to be contraceptive users [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Contraceptive use has grown since then, yet progress has been uneven and non-inclusive, leaving behind poorer women and rural residents [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Globally there were 190\u0026nbsp;million women who had had un-met needs of contraception in 2019 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The highest burden of this disparity was in Sub-Saharan African (SSA) countries (24%) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. So, many women in SSA are suffering from problems that could have easily been reduced if not eliminated had universal access to modern contraceptives been adopted [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRural communities have limited, if not non-existent access, to quality health care and have poorer health status as a result [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Consequently, rural women have low access to health services, including contraceptives, which translates into a greater unmet need of contraception and its sequels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. There is a shortage of data on contraceptive use among rural-based women and its determinants [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This creates a gap in service provisions, undercutting many vulnerable and marginalized women from reproductive health care services [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, the role of community factors in contraceptive use among rural residing women is poorly studied. Identifying those factors will be vital to improve the provision of modern contraceptives to women and the outcomes of maternal and child health interventions.\u003c/p\u003e \u003cp\u003eIn this paper, we measured the prevalence of contraceptive use among married rural women aged 15 to 49 in 21 countries and investigated individual and community factors associated with contraceptive use. This study aims to provide insight into current contraceptive use among rural-dwelling married women and thus help policy makers and planners design effective strategies to address discrepancies in modern contraceptive use and the unmet need of contraceptives.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eWe utilized data from the latest DHS surveys conducted between 2013 and 2020 across 21 SSA countries to conduct this study. The countries included were Angola, Benin, Burundi, Gambia, Ghana, Guinea, Lesotho, Liberia, Malawi, Mali, Senegal, Sierra Leon, Tanzania, Uganda, Ethiopia, Kenya, Namibia, Rwanda, Zimbabwe, Zambia, and South Africa. The datasets for the DHS are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dhsprogram.com/data/available-datasets.cfm\u003c/span\u003e\u003cspan address=\"http://dhsprogram.com/data/available-datasets.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eIn this study, non-pregnant and sexually active married women who live in rural areas between the ages of 15 and 49 were considered.\u003c/p\u003e\n\u003ch3\u003eVariable Definition\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eThe outcome variable was \"current modern contraceptive use.\" Responses were recoded as \"Yes\" for respondents who were currently using a modern contraceptive and \"No\" for respondents who were not using any modern contraceptive methods.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictor Variables\u003c/h3\u003e\n\u003cp\u003eThe predictor variables were grouped into individual and community level factors. The variables were selected based on their theoretical relevance and practical significance with modern contraceptives use.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndividual-level factors\u003c/h2\u003e \u003cp\u003eThe individual-level variables included in this study are, age in years (15\u0026ndash;19, 20\u0026ndash;29,30\u0026ndash;39,40\u0026ndash;49), education level (no education, primary, secondary, higher), wealth index (poorest, poorer, middle, richer and richest), current employment (employed vs unemployed), number of living children (0, 1\u0026ndash;2, 3\u0026ndash;4, 5\u0026ndash;6, 7+), age difference with partner (wife older or same age, wife younger by \u0026lt;\u0026thinsp;10 years, wife younger by \u0026gt;\u0026thinsp;10 years), mass media exposure (not exposed, exposed to at least one media, exposed to 2 media and exposed to all media), domestic violence (0\u0026thinsp;=\u0026thinsp;none justified, 1\u0026thinsp;=\u0026thinsp;at least one justified), decision making autonomy in the house (0, 1\u0026ndash;2, 3\u0026ndash;4), husband\u0026rsquo;s desire for children (both want the same, husband wants more, husband wants fewer and did not know), characteristics of partner (educational level, age), obstetric variables including total number of living children, history of miscarriage and abortion (no/yes), age at first sex, age at first birth, age at first marriage, fertility variables (the desire for more children, ideal number of children, and knowledge of contraceptive services).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCommunity-level variables\u003c/h3\u003e\n\u003cp\u003eContraceptive utilization is a complex scenario that involves the interaction between individual and community variables [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. DHS does not collect community level data, and as such community variables were drawn from individual-level data. In the spirit of K. Mariam Elfstrom et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], factors associated with modern contraceptive use in Africa were grouped into four domains: community demographics and fertility norms; community economic prosperity; community gender norms and inequalities; and community health knowledge and media exposure (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCommunity Demographics and Fertility Norms\u003c/h3\u003e\n\u003cp\u003eCommunity attitudes towards fertility, marriage, sexual intercourse, and perceived attitudes towards ideal family size and fertility patterns set the expected script for women. Such attitudes shape their attitude toward using contraceptives and fertility preferences [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Five variables were chosen to represent community demographics and fertility norms. They are Age at debut (sex, marriage, and first birth) in the community, the mean ideal number of children each woman desire to have in the community, and the community children's gender composition.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Economic prosperity\u003c/h2\u003e \u003cp\u003eIn a typical African setting, women depend on their spouse's income to fulfill their family needs, and contraceptives represent a significant health cost in family expenditures [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Various studies found wealth to be associated with contraceptive use as wealthier families had a higher potential to allocate scarce resources for contraceptives [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Community-level wealth was measured by taking each cluster's mean household index factor score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Gender Norms and Inequalities\u003c/h2\u003e \u003cp\u003eTraditional script on gender roles and sex-based relationships limits women's ability to access resources and use modern contraceptives [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These limits may translate into violence against women, deprivation of the rights and opportunities enjoyed by women in communities, and an imbalance in marital relationships [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To measure this domain five variables were selected. They are-The mean community violence justification index score, the mean community decision making autonomy score, the proportion of women in the community with at least a primary education, the proportion of men in the community with at least a primary education, and the ratio of men to women employed in the community.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Health knowledge and Media exposure\u003c/h2\u003e \u003cp\u003eThere is consistent evidence that higher health knowledge and exposure to health messages through media are associated with positive health outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Three variables were chosen to measure this domain: mean community HIV knowledge index score, mean community reproductive index score, and mean community media exposure score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDomains of community variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-level variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity demographics and fertility norms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age at marriage in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean age at marriage for women ages 15\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age at first intercourse in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean age at first intercourse for women ages 15\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age at first birth in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean age at first birth for women ages 15\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean ideal number of children in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean ideal number of children in the community\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender composition of children in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio of boys to girls in the community, calculated as divided by the number of living girls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity economic prosperity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean community wealth index factor score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean wealth index factor score reflects ownership of durable goods and housing characteristics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity gender norms and inequalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean community violence justification index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 5-point scale of attitude towards domestic violence. Variables included in this index are going out without telling the husband, neglecting children, arguing with the husband, refusing sex with the husband, and burning food. The score in all the variables was coded as justified and unjustified. A lower score indicates violence is not justified.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean community decision making autonomy score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 4-point scale for decision-making autonomy. A higher score shows higher decision-making control. The variables included in this index were, final say on own health care, final say on making large household purchases, and final say on visits to family or relatives.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen in the community with at least a primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of women in the community with at least a primary level education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emen in the community with at least a primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of men in the community with at least a primary level education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of men to women employed in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio of men employed in the community to women (coded: 0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity health knowledge \u0026amp; media exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean community HIV knowledge index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 6-point scale for knowledge of HIV where higher scores state a greater knowledge of HIV. Variables included were the following: whether the respondent had heard of HIV/AIDS, two questions about reducing the risk of infection (using condoms, and having just one uninfected partner who has not had other partners), and two questions about transmission (can people get AIDS virus from mosquitoes, can people get AIDS virus by sharing food with a person who has AIDS).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean community reproductive knowledge index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 5-points scale for reproductive health knowledge where higher scores show greater knowledge of reproductive health. Variables included are knowledge of the ovulatory cycle, knowledge of a contraceptive method, heard of AIDS or other STDs, and heard of other STDs. Some country-specific variations regarding questions included\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean community media exposure index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 4-point scale for exposure to reproductive health messages in the media in the past month (radio, TV, and newspaper). A higher score indicates exposure to more media.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eModeling approach\u003c/h2\u003e \u003cp\u003eWe utilized multivariable multilevel logistic regression models to analyze the association of individual and community factors with contraceptive use. A two-level model was specified for binary response reporting contraceptive use or not for women (at level 1) living in a community (level 2). A total of four models were constructed. The first null or unconditional model did not contain a predictor variable to decompose the amount of variance between clusters levels. The second model consisted only of individual-level factors, whereas the third model had only community-level variables. The final model controlled both individual and community factors (full model).\u003c/p\u003e \u003cp\u003eDHS studies have a hierarchical nature that violates the independence of observation and equal variance assumption. Hence, multilevel modeling is the preferred method of analysis. We used Stata v14 for windows for extracting and cleaning the data. The final data was analyzed using MLwiN version 2.36 software. We utilized a methodology suggested by Rasbash et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (i.e., Marginal quasi-likelihood (MQL) was used to generate starting values followed by 2nd order Predictive/Penalized quasi-likelihood (PQL) to get the final estimate). We have then run a Malkov-chain Monte-Carlo Estimation to extract the Deviance Information Criterion for model comparison.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFixed effects (measures of association)\u003c/h2\u003e \u003cp\u003eResults of fixed effects were reported as odds ratios (ORs) with 95% confidence intervals (Cls). Contextual effects were measured using the median odds ratio (MOR). MOR measures higher-level variance as an odds ratio and estimates the probability of contraceptive use that can be attributed to community context. MOR equal to one indicates no community effect on contraceptive use. The higher the MOR, the more important the contextual effects of understanding the probability of contraceptive use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThe countries included in this study were heterogenous in terms of the outcome variables and sensitivity analysis was warranted to check the robustness of the results. For such purpose two consecutive sensitivity analysis were done. Firstly, the data was divided by year into studies done between 2013 and 2016 and those done from 2017 to 2020. Cumulative prevalence of contraceptives was 41.9% in the first study and 26.7% in the latter. Then, the dataset was then divided into two by taking the median contraceptive use in the included countries as a decision rule. Countries with prevalence below 28.7% were grouped in one category, while countries above the cut point were grouped in second dataset. Contraceptive use was 53.04% in the high prevalence country dataset, while it was 20.08% in the low prevalence countries. An identical set of analysis was done in all the generated datasets. The results were then tabulated and compared with the final dataset that contained all the countries. The final results showed that the similar variables dictated the use of contraceptive in all the datasets and, the direction of association was in similar direction in majority of the variables included in the final model of this study. we can safely conclude that the final results of the study are robust enough to not be affected by extreme values (supplementary material 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eA request to download the survey dataset for each country of interest was made to the DHS program and granted following processing. Procedures and questionnaires for standard DHS surveys were reviewed and approved by the ICF Institutional Review Board (IRB) and the IRBs of the host countries. Before starting interviewing and/or conducting biomarker tests, written and oral informed consent were obtained from the study participants. For child or adolescent partcipants, permission to conduct the interview was sought from a parent or guardian. Participation was voluntary, and the data were protected and de-identified following the Health Insurance Portability and Accountability Act (HIPAA) stipulations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 94,836 women from 21 countries were included in the study. Modern contraceptive use varies significantly across the study countries, from as low as 1.8% in Angola to 70.9% in Zimbabwe. Countries in southern Africa generally had a higher mCP prevalence rate when compared to countries from other regions of Africa (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ecountries included, years of study and the prevalence rate of contraceptives\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDHS Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAngola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurundi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGambia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuinea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e48.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiberia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLesotho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalawi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNamibia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRwanda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerra Leon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZambia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32804\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e94,836\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e34.6%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e34.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e34.9%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTypes of contraceptive methods\u003c/h2\u003e \u003cp\u003eInjectables were the most common contraceptive method (17.1%) utilized by women in the study. Implants were the second most common (8.9%), followed by pills (4.9%), traditional methods (2.8%), male condoms (2.0%), and withdrawal (1.3%) (Figure-1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics of the participants\u003c/h2\u003e \u003cp\u003eWomen tend to use mCP more consistently in the age range of 20\u0026ndash;39 than women aged between 15\u0026ndash;19 and 40\u0026ndash;49. In all wealth quintile index groups, mCP users surpassed the non-users except in the poorest and poorer sub-groups. Among the mCP users, those who wanted no more children were 43.5%, followed by those who wanted to have children after two years (41.4%). A third of non-users (32%) did not want any children, while 28.3% wanted to have children within two years. Groups categorized as undecided (6.0%) and want but uncertain timing (4.0%) comprised the lowest percentage of non-users. Half of the women who use mCP had primary education (52.1%), while the most common educational category among those who don't use contraceptives was illiterate (51.1%). Women who used mCP exceeded non-users in all educational groups except the illiterate, which were predominantly mCP non-users. There were similar employment statistics as approximately 67% of the women in both groups were employed. Only 3.84% of mCP users and 2.35% of non-users were exposed to family planning messages from Radio, Newspapers, and TV. More women who use mCP had higher decision-making autonomy, i.e., 53.9% of users can take three decisions independently or with the consultation of a partner versus 42.9% of the non-users. Comparing those who had no decision autonomy, more non-users had limited freedom compared with users (26% versus 14.3%). In the educational background of spouses, more non-user\u0026rsquo;s partners than mCP users' were illiterate (44.2% vs. 20.0%). But there were more users whose partners had primary, secondary, and higher education than non-users. More mCP users were noted in couples of the same age as well as couples with \u0026lt;\u0026thinsp;10 years difference, whereas non-users (27.9%) dominated in the category\u0026thinsp;\u0026gt;\u0026thinsp;10 years difference than non-users (17.3%). More modern CP users had concordance in desired fertility with their partner than those who are not users (48.3% versus 33.4%). Educational background of partner showed that women whose partner had a primary education level were the predominant subgroup among mCP users, while among those who don't use mCP, partners with no education were the dominant group (44.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRespondent Classification n(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCategories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNon-Users\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eUsers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen's wealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20912(33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7774(23.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16313(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8339(25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12790(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7913(24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8264(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5956(18.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3753(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2822(8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62032(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32804(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesire for more children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants within two years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17555(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3146(9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants after 2\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18437(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13575(41.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants, unsure timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2454(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623(1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndecided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3732(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1201(3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants no more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19851(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14259(43.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62029(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32804(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27417(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6571(20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20264(32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15159(46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10869(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9019(27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1885(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1493(4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1560(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e543(1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61995(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32785(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility Concordance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eboth want same\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20740(33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15836(48.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehusband wants more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19834(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7947(24.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehusband wants fewer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3919(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3330(10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17528(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5681(17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62021(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32794(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4585(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1472(4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22430(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13579(41.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20756(33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12680(38.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14261(23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5073(15.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62032(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32804(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Individual FP Message Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39436(63.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17475(53.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16490(26.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10792(32.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4650(7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3277(9.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1456(2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1260(3.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62032(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32804(100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31729(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7547(23.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21664(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17096(52.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7987(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7459(22.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e652(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e702(2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62032(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32804(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Taking Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16157(26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4693(14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19275(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10426(31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26600(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17685(53.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62032(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32804(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouples Age difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSame age or older\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4614(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2988(9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen older by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40063(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24118(73.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen older by \u0026gt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17305(27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5672(17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61982(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32778(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with contraceptive use\u003c/h2\u003e \u003cp\u003eAfter checking the DIC value of each constructed model, model III was selected as it had the lowest value of DIC when compared to other models. The following variables were identified as strong predictors of contraceptive use among women residing in rural settings in African countries. Individual-level variables that were significant include women's age, employment status, educational level, decision-making autonomy, desired fertility concordance with a partner, desire for more children, age at first birth, history of abortion, and stillbirth, and total family planning methods known by the women.\u003c/p\u003e \u003cp\u003eWomen aged between 20 to 29 and 30 to 39 had 1.40 and 1.52 more odds of using contraceptives when compared with the reference (age group 15\u0026ndash;19). Women in the poorer and middle categories were at 1.07 and 1.16 more odds of using contraceptives, whereas the richer had 1.11 more odds than the reference (poorest). Compared with the reference (no education), women with primary, secondary, and higher education levels were at 1.31, 1.40, and 1.32 more odds of using a contraceptive. Women with higher decision autonomy were at increased odds of using contraceptives. Those who can take 1 or 2 decisions had 1.17 higher odds, and those who can take three decisions had 1.15 odds of using contraceptives than those who had no decision autonomy (those who do not decide by themselves or in consultation with a partner).\u003c/p\u003e \u003cp\u003eCompared to women who want to have children within two years, women who want to have children after two years and those who wish to have no more had similar odds of using contraceptives (3.24). Those who were undecided and those who wanted but were unsure of timing had 1.97 and 1.49 odds of using a contraceptive. Fertility concordance was a significant predictor of contraceptive use, as women whose partners want more children were 21% less likely to use contraceptives when compared to couples who want the same number of children. Those women who didn't know their partner's desire were 29% less likely to use contraceptives. Women who had a history of abortion or stillbirth were 21% less likely to use contraception. A unit increase in the total number of contraceptive methods known increased the odds of using contraceptives by 1.16.\u003c/p\u003e \u003cp\u003eThe number of children was a significant predictor of mCP use. Compared to women with no children, women with at least one child had three times the odds of using contraceptives. Those insured were at 1.10 more odds of using mCP than those uninsured. Partner educational level was significantly associated with using mCP as the odds of using mCP increased with partners' academic level.\u003c/p\u003e \u003cp\u003eIn the community-level variables, for every unit increase in mean age at first intercourse in the community, the odds of using mCP increased by 3%. Those communities categorized as poorer and middle were at similar odds of using contraceptives, i.e., 1.20 Vs. 1.22. In the community gender norms and inequalities, a general upward trend of odds of using contraceptives was found significant in all the items except one. In a community where men were employed more than women, the odds of using mCP was 1.74 compared to communities where more women were employed than men (Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel IV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44(1.18\u0026ndash;1.76)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40(1.27\u0026ndash;1.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77(1.39\u0026ndash;2.27)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52(1.35\u0026ndash;1.72)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.70\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90(0.78\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth Quantile index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99(0.89\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07(1.02\u0026ndash;1.13)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12(1.00-1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16(1.09\u0026ndash;1.23)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ericher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12(0.97\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11(1.03\u0026ndash;1.19)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81(0.67\u0026ndash;0.99)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03(0.93\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent employment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.87\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.99\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent educational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98(1.72\u0026ndash;2.28)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31(1.23\u0026ndash;1.40)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67(2.23\u0026ndash;3.19)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40(1.29\u0026ndash;1.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06(2.05\u0026ndash;4.56)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(1.10\u0026ndash;1.58)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91(0.80\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10(1.02\u0026ndash;1.18)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52(1.34\u0026ndash;1.72)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17(1.09\u0026ndash;1.24)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84(1.63\u0026ndash;2.08)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15(1.08\u0026ndash;1.23)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic violence justification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone justified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least one justified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.98\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02(0.98\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehusband/partner's education level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.26\u0026ndash;1.73)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31(1.20\u0026ndash;1.42)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77(1.45\u0026ndash;2.16)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39(1.26\u0026ndash;1.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48(1.09-2.00)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35(1.17\u0026ndash;1.56)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85(1.01\u0026ndash;3.39)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88(0.69\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertility concordance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eboth want same\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehusband wants more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68(0.61\u0026ndash;0.76)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79(0.76\u0026ndash;0.83)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehusband wants fewer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10(0.96\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01(0.94\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58(0.51\u0026ndash;0.65)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71(0.67\u0026ndash;0.75)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpousal education difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eequally educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ehusband better educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96(0.82\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95(0.89\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWife better educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.76\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(1.00-1.16)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eneither educated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14(0.88\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(0.95\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDK/missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76(0.44\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20(0.98\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpousal age difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWife older or Same age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen older\u0026thinsp;\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09(0.94\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(0.97\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen older\u0026thinsp;\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13(0.92\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(0.97\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily planning message exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed to three\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed to two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.67\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03(0.99\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed to at least one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94(0.71\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98(0.90\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed to none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.68\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13(0.99\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Living Children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 or 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.01(2.83\u0026ndash;12.78)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.43(2.44\u0026ndash;4.83)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 or 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.06(2.85\u0026ndash;12.90)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.67(2.60\u0026ndash;5.17)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 or 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.86(2.74\u0026ndash;12.54)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54(2.50\u0026ndash;5.01)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72(2.18\u0026ndash;10.21)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.19(2.24\u0026ndash;4.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edesire for more children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants within 2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants after 2\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25(2.85\u0026ndash;3.72)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.248(2.843\u0026ndash;3.71)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants, unsure timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20(0.89\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.496(1.16\u0026ndash;1.929)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eundecided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81(1.43\u0026ndash;2.30)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.974(1.567\u0026ndash;2.489)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewants no more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.40(2.94\u0026ndash;3.93)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.241(2.824\u0026ndash;3.721)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of abortion, miscarriage, stillbirths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.75\u0026ndash;0.9)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.794(0.715\u0026ndash;0.88)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70(0.31\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16(0.86\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70(0.31\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12(0.83\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61(0.26\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03(0.76\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50(0.21\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76(0.55\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36(0.15\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67(0.47\u0026ndash;0.94)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first marriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01(0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at First Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03(1.01\u0026ndash;1.05)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00(0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first Birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94(0.92\u0026ndash;0.96)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96(0.96\u0026ndash;0.97)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal Knowledge of Family planning methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34(1.29\u0026ndash;1.39)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16(1.11\u0026ndash;1.21)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age at marriage in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01(0.97\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(1.03\u0026ndash;1.08)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean age at first intercourse in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28(1.21\u0026ndash;1.34)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03(1.00-1.06)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age at first birth in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75(0.71\u0026ndash;0.80)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91(0.88\u0026ndash;0.94)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean ideal number of children in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97(0.96\u0026ndash;0.98)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99(0.98\u0026ndash;0.99)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender composition of children in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.86\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95(0.90\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean community wealth index factor score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38(1.22\u0026ndash;1.57)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20(1.12\u0026ndash;1.29)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38(1.19\u0026ndash;1.60)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22(1.11\u0026ndash;1.34)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17(0.90\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18(1.01\u0026ndash;1.38)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29(0.71\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35(0.74\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity gender norms and inequalities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean community violence justification index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(1.05\u0026ndash;1.16)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97(0.94\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean community decision making autonomy score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66(1.53\u0026ndash;1.80)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31(1.25\u0026ndash;1.39)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWomen in the community with at least a primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09(1.07\u0026ndash;1.12)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08(1.07\u0026ndash;1.10)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003emen in the community with at least a primary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99(0.97\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96(0.95\u0026ndash;0.98)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRatio of men to women employed in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore men\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23(1.06\u0026ndash;1.42)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74(1.57\u0026ndash;1.93)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity health knowledge and media exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean community HIV knowledge index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76(0.68\u0026ndash;0.83)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76(0.72\u0026ndash;0.81)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean community reproductive knowledge index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26(1.22\u0026ndash;1.30)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92(0.87\u0026ndash;0.96)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean community media exposure index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28(0.06\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88(0.27\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24(0.05\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88(0.27\u0026ndash;2.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39(0.08\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12(0.33\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecoef(SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecoef(SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecoef(SE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Comparisons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103302.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73909.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76356.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68590.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOR (95% Crl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70(3.64\u0026ndash;3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.79(4.51\u0026ndash;5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.46(2.43\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46(2.42\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Likelihood Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96174.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69520.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70806.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69280.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the modern contraceptive use and associated factors in rural communities in 21 African countries. Our findings of factors affecting modern contraceptive use were consistent with current literature [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the multilevel logistic regression, respondent age, educational level, decision-making autonomy, fertility concordance, desire for more children, age at first marriage, and total knowledge of family planning methods were significant individual determinants of modern contraceptives. Community variables that were significant determinants of contraceptives were mean age at first sex in the community, mean age at first birth in the community, mean ideal number of children in the community, mean community DV justification score, mean community decision autonomy score, ratio of women and men employed in the community, and HIV knowledge index score, and reproductive knowledge index score.\u003c/p\u003e \u003cp\u003eThe pooled prevalence of modern contraceptive use in this study was 34.6%. The prevalence in the current study is higher than a study conducted by Tessema et al. that found 20.68% [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and a study done by Mutumba et al., which found 17.8% modern contraceptive utilization [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and Apanga's study, which identified an mCP prevalence rate of 26% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. But this result was lower than that of a study conducted using meta-analysis by Cahil et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Modern contraceptive use significantly varies among countries, from as low as 1.8% in Angola to a high of 70.9% in Zimbabwe.\u003c/p\u003e \u003cp\u003eWomen's age significantly affects modern contraceptives use. Compared with respondents aged 15\u0026ndash;19, women aged 20\u0026ndash;29 and 30\u0026ndash;39 had 1.39 and 1.43 odds of using a contraceptive respectively. This finding is contrasted with a study done by Tessema et al., which found lower odds of using contraceptives as age progresses [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In a study done among 20 African countries, women aged 25\u0026ndash;34 were more likely to use contraceptives when compared with women aged 15\u0026ndash;24 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A possible reason for this could be the low knowledge of young females about the benefits of contraceptives and consequences of unprotected sex [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Societal stigma on premarital sex and contraceptives may have contributed to it [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There was an insignificant association between women aged 40\u0026ndash;49 using contraceptives, and a similar finding was reported in a Zambian study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. One plausible reason for this is the lower contraception need with decreased sexual activity in older women [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is strong evidence that educated women are more likely to use contraceptives than uneducated women [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The odds of using contraceptives increases with the increment of educational level. Similar findings were reported in Uganda, Nigeria, Tanzania, and East Africa [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, an educated woman knows the benefit of modern contraceptives through reading newspapers, social, and mass media. Additionally, educated woman is more likely to have good health-seeking behavior and health services, including family planning [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As a result, an educated woman is empowered to delay marriage and child-bearing and thus is more informed to use modern contraceptives than an uneducated woman [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In some studies, education was not a significant predictor of contraceptives [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The possible explanation could be the diffusion of contraceptive service to everyone and the amplified role of tradition, which plays a massive role in contraceptive decisions than education in those settings [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eEmployed women have higher income, autonomy, and freedom than unemployed women, and therefore are more empowered to use contraceptives [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, employment was not a significant predictor. But, a study among 11 African countries, found a higher odds of contraceptive use [AoR\u0026thinsp;=\u0026thinsp;2.43] among employed women [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In a study done among rural Zambian women, occupation wasn't a significant predictor of contraceptive use [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The lower odd of association between occupation and contraceptive use could be due to the nature of the women's work. In rural settings, the available job for women are those that provide little monetary relief, such as farming and logging; thus, they contribute little to empowering women to use contraceptives [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, women who make one to two decisions in their households had 1.16 more odds of using contraceptives when compared to women who make zero decisions in their homes. In a study done by Yaya et al., women with high decision-making power were 23% more likely to use contraceptives than women with low decision-making power [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Decision-making power in a household reflects the family power dynamics, which influences women's ability to decide their health, including the use of contraceptives. Olakunde et al. found that women are 68% more likely to use contraceptives if they have an equal say in decisions about their household [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Concordance in family size reflects such dynamics as found in our study, as women with husbands who wanted more children were 19% less likely to use contraception when compared to couples who wanted the same number of children. Women who didn't know their partners\u0026rsquo; desire had a 29% less likelihood of using contraceptives than women who had the same desire as their partners.\u003c/p\u003e \u003cp\u003eFertility preferences of couples were the most significant predictor of contraceptive use in this study. Couples who wanted to delay having children or stop altogether had approximately three times the odds of using contraceptives than couples who wanted to have children within two years. In a study done in a peri-urban communities of three African countries, those who didn\u0026rsquo;t want children were at higher odds of using contraceptives than those who wanted more in all study settings [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this study, mCP use was highly associated with the number of living children a woman has. Compared to women with no children, women with at least one child have more than three times the odds of using a contraceptive. Similarly, a Ugandan study found two times the odds of using contraceptive for women with children[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContraceptive use is a scenario influenced by individual and community factors [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Women's characteristics interact with their community and mold the norms and customs of such a community. Inversely, the community influences the characteristics of the individual inside it [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For a unit increase in the mean age of marriage and sex, the likelihood of using contraceptives increased by 5%. In a community where women are expected to be married and give birth at a young age, contraceptive use is discouraged [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mutumba et al. reported similar results, i.e: women living in communities with a higher age at first marriage were 8% more likely to use contraceptives. Such association was more pronounced among African and European communities, where higher mean age at first marriage was associated with AoR of 1.12 and 1.31, respectively [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn many African and Asian countries parental preference for their children's gender (sons over daughters) has driven their contraceptive behavior [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In a study done by Hoq et al., contraceptive use among women with only daughters was comparatively lower [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. But, this was not a significant predictor of contraceptive use in our study. Reasons could be the type of contraceptives commonly cited in our research. Preference for sons is a significant predictor of permanent contraceptive use but bears no relation to short term contraceptives [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAttitude towards violence may impact women's autonomy and ability to seek health services and reflect greater gender inequality [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Besides, in communities where domestic violence is the rule not exception, women may find it hard to communicate their desire of fertility and contraception. The mean DV justification index wasn't a significant predictor of contraceptive use in our study. However, with a unit increase in the mean community decision autonomy score, the odds of using contraceptives increased by 1.38. Other studies also reported similar findings[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Higher educational attainment of women in the community was significantly associated with contraceptive use, like results of other studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Generally, contraceptives have been effectively utilized in a community where women are typically empowered, have at least primary-level education, and have higher decision autonomy.\u003c/p\u003e \u003cp\u003ePrograms specifically designed for rural women have increased use of modern contraceptive use through empowering women, providing access to modern contraceptive, educating mother on family planning. Accredited social health activist (ASHA) program was instrumental in reducing maternal mortality through promoting antenatal care (ANC) visits, institutional delivery and modern contraceptives use [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Evaluating the impact of similar approach in Pakistan showed that, having Lady health worker increases the probability of women utilizing short term contraceptive by 9.9 percentage points [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. An Ethiopian study have noted that presence of more women development army in a district have resulted in more ANC visits, contraceptive use, and institutional deliveries [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Those programs have empowered women, improved women knowledge, and access of contraceptive to women. Lessons from such programs should be evaluated and used to create diffused programs in LMIC to facilitate faster adoption of modern contraceptive use.\u003c/p\u003e \u003cp\u003eThis study is the first of its kind as it targeted married women residing in rural areas. The study has also included community variables during analysis which would further enhance the study's findings. However, this study is not without limitations as it doesn't establish a temporal relationship between the outcome and predictor variable. Additionally, this study didn't account for structural factors (distance to a health facility, quality of FP services), peer-related factors, and socio-cultural factors like religion. As DHS studies are affected by social desirability bias and recall bias, such problems could have affected the study results.\u003c/p\u003e \u003cp\u003eIn conclusion, the modern contraceptive use among married, rural residing women in 21 countries was low compared to other studies. In this study, individual variables that were significant predictors of contraceptive use were women's age, education, fertility preference, concordance with a partner, partner's educational level, women's decision autonomy, being knowledgeable about contraceptives, and the number of living children. Among community variables, mean age at first sex, birth, mean ideal number of children, mean community decision-making autonomy, and higher educational attainment in a community were significant determinants of contraceptive use. Identifying individual and community factors would help governmental and non-governmental organizations to scale up their efforts to provide contraceptives for poor and marginalized societies in Sub-Saharan Africa. Empowering women through education, employment, and family planning promotion would enhance the uptake of contraception by women. Additionally, strategies targeting disadvantaged communities would benefit not only the women but also the families and the country as well.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank MEASURE DHS for granting access to the data for this analysis. \u0026nbsp;Our gratitude goes to Mrs. Yirgalem Solomon and Mr. Bipin Jha from UNICEF, and Mr. Zenawi Zeremariam Araya, Dr. Araia Berhane Mesfin, Staff of Communicable Disease Controls Division of Eritrea for their unwavering support. We would also like to thank Mr. Meron M. Kiflu, Dr. Amanuel K. Andegorgish for their encouragement and support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of materials and data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were sourced from the Demographic and Health Surveys (DHS) and available here: https://dhsprogram.com/data/available-datasets.cfm\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(financial or non-financial).\u0026nbsp;\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\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFGM and HMT have conceptualized the study and wrote the first draft. FGM and HGW designed the methodology and FGM curated the data and analyzed it. YTG, HGW and KTS supervised and validated the analysis and its results. All authors reviewed and edited the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShukla A et al (2020) \u003cem\u003eAssociation between modern contraceptive use and child mortality in India: A calendar data analysis of the National Family Health Survey (2015-16).\u003c/em\u003e SSM-population health, 11: p. 100588\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith R et al (2009) Family planning saves lives. Population Reference Bureau, Washington DC, p 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Darroch JE (2012) Adding it up: Costs and benefits of contraceptive services. Guttmacher Institute and UNFPA, pp 1269\u0026ndash;1286\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrbeta AC Jr (2005) \u003cem\u003ePoverty, vulnerability and family size: evidence from the Philippines.\u003c/em\u003e Poverty strategies in Asia, : p. 171\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFotso JC et al (2013) Birth spacing and child mortality: an analysis of prospective data from the Nairobi urban health and demographic surveillance system. J Biosoc Sci 45(6):779\u0026ndash;798\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eControl CfD (1999) \u003cem\u003eTen Great Public Health Achievements \u0026mdash; United States, 1900\u0026ndash;1999.\u003c/em\u003e Morbidity and Mortality weekly report, April 2, 48(12)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApanga PA et al (2020) Prevalence and factors associated with modern contraceptive use among women of reproductive age in 20 African countries: a large population-based study. BMJ open 10(9):e041103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLasong J et al (2020) Determinants of modern contraceptive use among married women of reproductive age: a cross-sectional study in rural Zambia. BMJ open 10(3):e030980\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCleland J et al (2006) Family planning: the unfinished agenda. lancet 368(9549):1810\u0026ndash;1827\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAliyu AA (2018) Family planning services in Africa: The successes and challenges. Family Plann, 69\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEliason S et al (2014) Determinants of unintended pregnancies in rural Ghana. BMC Pregnancy Childbirth 14(1):1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshraf QH (2013) Weil, and Joshua Wilde \u003cem\u003eThe effect of fertility reduction on economic growth\u003c/em\u003e. Popul Dev Rev 39:97\u0026ndash;130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloom DE, Kuhn M, Prettner K (2017) Africa's prospects for enjoying a demographic dividend. J Demographic Econ 83(1):63\u0026ndash;76\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTowriss CA, Tim\u0026aelig;us IM (2018) Contraceptive use and lengthening birth intervals in rural and urban Eastern Africa. Demographic Res 38:2027\u0026ndash;2052\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment SR (2022) \u003cem\u003eForcast of the total population of Africa 2020\u0026ndash;2050.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed S et al (2019) Trends in contraceptive prevalence rates in sub-Saharan Africa since the 2012 London Summit on Family Planning: results from repeated cross-sectional surveys. Lancet Global Health 7(7):e904\u0026ndash;e911\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown W et al (2014) Developing the 120 by 20 goal for the Global FP2020 Initiative. Stud Fam Plann 45(1):73\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreanga AA et al (2011) Low use of contraception among poor women in Africa: an equity issue. Bull World Health Organ 89:258\u0026ndash;266\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Department of Economic and Social, Affairs PD (2019) \u003cem\u003eContraceptive Use by Method 2019: Data Booklet.\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eST/ESA/SER.A/435\u003c/span\u003e\u003cspan address=\"http://ST/ESA/SER.A/435\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDivision UP (2017) \u003cem\u003eWorld population prospects: The 2017 revision New York: United Nations, Department of Economic and Social Affairs.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhinkorah BO (2020) Predictors of modern contraceptive use among adolescent girls and young women in sub-Saharan Africa: a mixed effects multilevel analysis of data from 29 demographic and health surveys. Contracept reproductive Med 5(1):1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed S et al (2012) Maternal deaths averted by contraceptive use: an analysis of 172 countries. Lancet 380:111\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassa GM et al (2018) Prevalence and determinants of adolescent pregnancy in Africa: a systematic review and meta-analysis. Reproductive health 15(1):1\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVersteeg M, Du Toit L, Couper I (2013) Building consensus on key priorities for rural health care in South Africa using the Delphi technique. Global health action 6(1):19522\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Department of Economic and Social, Affairs PD (2020) \u003cem\u003eWorld Family Planning 2020 Highlights: Accelerating action to ensure universal access to family planning.\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eST/ESA/SER.A/450\u003c/span\u003e\u003cspan address=\"http://ST/ESA/SER.A/450\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraha TH et al (2018) Predictors of postpartum contraceptive use in rural Tigray region, northern Ethiopia: a multilevel analysis. BMC Public Health 18(1):1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElfstrom KM, Stephenson R (2012) The role of place in shaping contraceptive use among women in Africa. PLoS ONE 7(7):e40670\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed WAM et al (2015) Factors affecting utilization of family planning services in a post-conflict setting, South Sudan: a qualitative study. AIMS public health 2(4):655\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgboghodo E, Adam V, Wagbatsoma V (2017) Prevalence and determinants of contraceptive use among women of child-bearing age in a rural community in southern Nigeria. J Community Med Prim Health Care 29(2):97\u0026ndash;107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkigbo CC et al (2018) Gender norms and modern contraceptive use in urban Nigeria: a multilevel longitudinal study. BMC Womens Health 18(1):178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiop-Sidib\u0026eacute; N, Campbell JC, Becker S (2006) Domestic violence against women in Egypt\u0026mdash;wife beating and health outcomes, vol 62. Social science \u0026amp; medicine, pp 1260\u0026ndash;1277. 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandiwa C et al (2018) Factors associated with contraceptive use among young women in Malawi: analysis of the 2015\u0026ndash;16 Malawi demographic and health survey data. Contracept Reproductive Med 3(1):1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasbash J et al (2016) A user\u0026rsquo;s guide to MLwiN, v2. 36. Centre for Multilevel Modelling, University of Bristol, Bristol\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutumba M, Wekesa E, Stephenson R (2018) Community influences on modern contraceptive use among young women in low and middle-income countries: a cross-sectional multi-country analysis. BMC Public Health 18(1):1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTessema ZT et al (2021) Pooled prevalence and determinants of modern contraceptive utilization in East Africa: A Multi-country Analysis of recent Demographic and Health Surveys. PLoS ONE 16(3):e0247992\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYussuf MH et al (2020) Trends and predictors of changes in modern contraceptive use among women aged 15\u0026ndash;49 years in Tanzania from 2004\u0026ndash;2016: evidence from Tanzania demographic and health surveys. PLoS ONE 15(6):e0234980\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCahill N et al (2018) Modern contraceptive use, unmet need, and demand satisfied among women of reproductive age who are married or in a union in the focus countries of the Family Planning 2020 initiative: a systematic analysis using the Family Planning Estimation Tool. Lancet 391(10123):870\u0026ndash;882\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNamasivayam A et al (2020) Predictors of modern contraceptive use among women and men in Uganda: a population-level analysis. BMJ open 10(2):e034675\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutaremwa G et al (2015) Predictors of modern contraceptive use during the postpartum period among women in Uganda: a population-based cross sectional study. BMC Public Health 15(1):1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTekelab T, Melka AS, Wirtu D (2015) Predictors of modern contraceptive methods use among married women of reproductive age groups in Western Ethiopia: a community based cross-sectional study. BMC Womens Health 15(1):1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaya S et al (2018) Women empowerment as an enabling factor of contraceptive use in sub-Saharan Africa: a multilevel analysis of cross-sectional surveys of 32 countries. Reproductive health 15(1):1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlakunde BO et al (2020) Individual-and country-level correlates of female permanent contraception use in sub-Saharan Africa. PLoS ONE 15(12):e0243316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlaOlorun F et al (2016) Women's fertility desires and contraceptive behavior in three peri-urban communities in sub Saharan Africa. Reproductive health 13(1):1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChannon MD (2015) Son preference, parity progression and contraceptive use in South Asia. Popul Horizons 12(1):24\u0026ndash;36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoq MN (2020) Influence of the preference for sons on contraceptive use in Bangladesh: A multivariate analysis. Heliyon 6(10):e05120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmisha Agarwal SLC, Gustavo Angeles IS, Speizer K, Singh, Thomas JC (2019) The impact of India\u0026rsquo;s accredited social health activist (ASHA) program on the utilization of maternity services: a nationally representative longitudinal modelling study. BMC Hum Resour Health 17(68):13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003emanagement OP (2019) \u003cem\u003eLady Health Worker Programme, Pakistan Performance Evaluation\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamtew ZA et al (2018) Z.A.D.,., \u003cem\u003eCorrelates of the Women's Development Army strategy implementation strength with household reproductive, maternal, newborn and child healthcare practices: a cross-sectional study in four regions of Ethiopia.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Contraceptives, FP2020, Sub-Saharan Africa, education, empowerment, community factors","lastPublishedDoi":"10.21203/rs.3.rs-6695350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6695350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFamily planning is a cost-effective and high-yield investment with benefits beyond controlling birth. Previously stunted efforts have been revived following the FP2020 initiative that aimed to provide contraceptives to women in the poorest countries. Since its start, the number of contraceptive users has grown globally yet, progress has been uneven, with women in rural areas and other vulnerable groups often being neglected. Data from 21 Sub-Saharan African countries was compiled to create the dataset for this study. This study focused on rural married women residing in those countries. Variables conceptualized to affect contraceptive use were categorized as individual and community-level variables. Tables and graphs were used for descriptive statistics while two-level multilevel regression was done to find out factors associated with contraceptive use. Prevalence of modern contraceptive use was found to be 34.6% with injectable and implants being the most common. Contraceptive use varied across countries with Southern Africa countries recording higher proportion. Individual factors that affected contraceptive use include age, wealth quintile index, educational level and fertility desires. Community variables that were found to be associated with contraceptive use include mean age of debut (sex, birth and marriage), community wealth quintile index, community domestic violence (DV) score and decision autonomy scores and so on. Modern contraceptive use among married, rural residing women in 21 countries was low compared to other studies. Generally, contraceptives have been effectively utilized in a community where women are typically empowered, have at least primary education, and have higher decision autonomy. Identifying individual and community factors dictating contraceptive use would help governmental and non-governmental organizations to scale up their effort to provide contraceptives for poor and marginalized societies in Sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Determinants Of Modern Contraceptive (mCP) Use Mong Married Rural Women In 21 African Countries: Multi-Level Modeling (MLM) using recent Demographic and Health Surveys (DHS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 11:36:49","doi":"10.21203/rs.3.rs-6695350/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":"5b22ee41-b192-40e1-a159-3e9135d6b8df","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48703618,"name":"Obstetrics \u0026 Gynecology"},{"id":48703619,"name":"Maternal \u0026 Fetal Medicine"},{"id":48703620,"name":"Sexual \u0026 Reproductive Medicine"},{"id":48703621,"name":"Epidemiology"},{"id":48703622,"name":"Obstetrics \u0026 Gynecology"},{"id":48703623,"name":"Maternal \u0026 Fetal Medicine"},{"id":48703624,"name":"Sexual \u0026 Reproductive Medicine"},{"id":48703625,"name":"Epidemiology"},{"id":48703626,"name":"Obstetrics \u0026 Gynecology"},{"id":48703627,"name":"Maternal \u0026 Fetal Medicine"},{"id":48703628,"name":"Sexual \u0026 Reproductive Medicine"},{"id":48703629,"name":"Epidemiology"},{"id":48703630,"name":"Obstetrics \u0026 Gynecology"},{"id":48703631,"name":"Maternal \u0026 Fetal Medicine"},{"id":48703632,"name":"Sexual \u0026 Reproductive Medicine"},{"id":48703633,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-05-20T11:36:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 11:36:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6695350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6695350","identity":"rs-6695350","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}