Analyzing the Determinants of Women’s First Birth Interval in Bangladesh: An Application of the Log-normal AFT Model

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Abu Hasnat Abdullah, Rowshan Ara Afrin, Gowranga Kumar Paul This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7831578/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 The waiting time to first birth is considered to be an economic empowerment indicator of women in any country. It plays a crucial role in determining population growth, demographic changes, and age structure. It is also linked up with the economic empowerment of women of a country. The main focus of this study is to identify the factors that influence women’s waiting time to first birth after marriage, based on the 2017-18 Bangladesh Demographic and Health Survey data. This research utilizes the log-rank test and log-normal accelerated failure time survival regression model to uncover the association of socio-demographic, cultural, and maternal health-related determinants with the first birth interval among women in Bangladesh. The statistical analysis indicates that region, respondent’s educational level, media exposure, terminated pregnancy, partner’s education, age at first marriage, and spousal age difference exhibit significant association with the women’s first birth interval in Bangladesh. This study is expected to provide guidelines for policymakers and healthcare professionals to address issues concerning family planning, maternal health, population growth, and reproductive health challenges in any country. AFT model family planning first birth interval log-rank test population growth Background Fertility plays a key role for population growth, and it influences the size and age structure of population in any country (Lutz et al. 2002). Fertility rate has direct impact on demographic transitions (Tadesse et al. 2010) where the first birth interval (FBI) serves as an indicator for assessing the fertility patterns (National Research Council et al. 2005). The FBI refers to the duration between a woman's initial marriage or commencement of sexual activity and the birth of her first child. The waiting time to first birth holds significance as motherhood entails a substantial investment of time and resources (Ali et al. 2020). Again, the first childbirth interval holds economic significance for enhancing a woman's participation in the labor market and promotes her wage growth (Bratti et al. 2014; Herr et al. 2007) and women's ability to make decisions within the household is positively impacted by delaying the first pregnancy (Ashraf et al., 2014 ). Moreover, giving birth to a first child before the age of twenty is linked to an elevated risk of various diseases such as heart disease, lung disease, cancer, and mortality (Henretta, 2007 ). So, the interval of first child birth (FBI) plays a vital role in a woman’s life as well as in many aspects of a country. The FBI among women showed significant associations with their age at marriage, educational attainment, and occupation (Patil et al., 2023 ). Typically, women with higher levels of education tend to become mothers later in life, as extended education often correlates with postponed marriage (Alderman et al. 2017; Rendall et al. 2003; Berg et al. 2014). Additionally, educated spouse usually delays the timing of the first childbirth (Ali et al. 2020). Ahammed et al. ( 2019 ), report that a mother's employment status serves as a protective factor for the first birth interval. The initial childbearing age of women varies by location and religion. Women from higher socio-economic classes typically delay their first birth longer than those from lower socio-economic groups (Alemu et al. 2022; S et al. 2022). Along with other demographic factors, the first birth interval is impacted by income, and economic difficulties such as employment instability, inadequate housing, and infrastructural challenges. These factors may directly or indirectly lower the chance of pregnancy (Miri et al. 2018). Faruk ( 2018 ) suggests that the first birth interval in Indonesia is primarily affected by the educational attainment of women and their partners, contraceptive awareness, media exposure, wealth index, and employment status. In Nepal, women belonging to higher castes and of older age, possessing knowledge about family planning techniques, tend to have shorter intervals between their first births (Karkee and Lee, 2016 ). However, in Kenya, shorter first birth intervals are observed among women who are employed, reside in rural areas, and have higher levels of education (Mulwa, 2009 ). Furthermore, increased availability of childcare facilities in Norway contributes to a higher likelihood of transitioning to motherhood at various ages, thereby shortening the first birth interval (Rindfuss et al. 2007 ). In case of Bangladesh, more than half of the women had their first child before reaching the age 18 (Sobhan et al. 2024 ; Bhowmik et al. 2021 ; Nahar et al. 2013) which is one of the main reasons of child and maternal mortality (Trommlerová et al. 2020), in this regard the causes of shorter interval should be pointed out to assess the risks factors and help to take initiatives for the betterment of child and maternal health. Again, although the rate of fertility is declining, the estimated replacement-level fertility for Bangladesh is not yet achieved (NIPORT et al., 2017–2018). Since Bangladesh is a densely populated country and first birth interval has a direct relationship with fertility pattern, it is important to find out the determinants behind short first birth interval. But only a few studies focused on the factors influencing the waiting to first birth interval (FBI) of women in Bangladesh. The main purpose of this study is to provide a comprehensive examination on various factors concerning the timing of the first birth among women in Bangladesh, employing sophisticated regression modeling techniques, which will help the policy makers to develop effective family planning programs. Methods Data Source The study utilizes secondary data from the 2017–2018 Bangladesh Demographic and Health Survey (BDHS). A sample of about 20,250 randomly selected households were included in this nationally representative survey. Individual interviews were conducted with all married women aged 15 to 49 residing in the selected households or staying overnight prior to the study. The survey employed a two-stage stratified sampling approach. Initially, 675 enumeration areas were chosen using probability proportionate to size, with 250 allocated to urban areas and 425 to rural areas. Then a systematic sample of approximately 30 households per enumeration area was chosen in the second phase of sampling. Finally, a total of 20,100 married women aged 15 to 49 were included in the survey for interview. Detail sampling designs are available on the BDHS 2017-18 report (NIPORT et al., 2020). Study Population This study utilizes the Individual Recode (IR) file from the BDHS-2017-18 dataset, focusing exclusively on women whose marital duration is 60 months (five years) or less. Marital duration was computed by subtracting the date of marriage from the date of the interview. After excluding missing cases, a total of 2,708 women were included in the analysis. Response Variable The response variable in this study is the first child birth interval among ever married women in Bangladesh. A censoring indicator is created for each respondent based on whether the event has occurred or not. This indicator is binary, taking the value 1 if a woman experiences first child before or within 60 months of marriage (event), and 0 otherwise (censored). Here the event of interest is the birth of first child and the time variable (months) is defined as Predictor Variables This study considers demographic, socio-economic, fertility, and maternal health related characteristics as predictor variables. The predictor variables include residence (rural, urban); region (Dhaka, Khulna, Chattogram, Rangpur, Sylhet, Barisal, Rajshahi); parents education level (illiterate, primary, secondary, higher-level); age at first marriage (15 years or less, 16–19, More than 20 years); media exposure (no, yes); spousal age difference (3–5 years, 6–9 years, 10 or more years); residing with partner (no, yes); employment status before marriage (yes, no); terminated pregnancy (no, yes); knowledge about ovulatory cycle (no, yes); body mass index (BMI) (underweight: BMI 25). Statistical Analysis The dataset comprises respondents with both censored and uncensored observations, along with their corresponding event times. Given the presence of censored observations, survival analysis techniques are more suitable to handle this kind of data. This study utilizes log-rank test in bi-variate analysis to determine the significant association between predictors and first birth interval among women in Bangladesh. Then the parametric log-normal accelerated failure time (AFT) survival regression model is used to find the net effect of each predictor on women's waiting time to first birth in Bangladesh. Log-rank Test In Survival analysis, a key objective is to test whether sub-populations behave in the same way. Log-Rank test is employed to compare the survival probabilities between two or more group of individuals. By considering j factor group, the log rank test is used to test the hypothesis; \(\:{H}_{0}\) \(\:{S}_{1}\) (t) = \(\:{S}_{2}\) (t) = \(\:\:{S}_{3}\) (t) = \(\:\:\dots\:={S}_{k}\) (t) \(\:{\:H}_{1}\) Not all \(\:{S}_{j}\) (t) are equal; \(\:j=1,\:\text{2,3},\:\dots\:,k.\) Where \(\:{S}_{j}\) (t) is the estimated survival function for the \(\:{j}^{th}\) group. In general, the Logrank test is a chi-square statistic that compares the observed \(\:{(O}_{j})\) and expected \(\:\:{(E}_{j})\) numbers of first births under the hypothesis. The chi-square test statistic with \(\:(k-1)\:\) degrees of freedom is given by; $$\:{\chi\:}^{2}=\sum\:_{j=1}^{k}\frac{{({O}_{j}-{E}_{j})}^{2}}{{E}_{j}}$$ Log-normal AFT Model A statistical model is known as a survival regression model if the result variable in the regression analysis is the survival time or time. Survival time is the time to occur an event of interest. However, the log of the survival time T and the covariates are assumed to be a linear function in the AFT model, which is given as, $$\:Y=lnT=(\mu\:+{x}^{{\prime\:}}\beta\:)+\sigma\:\epsilon\:$$ Where, \(\:T\) be the lifetime in the survival regression with \(\:p\) covariates \(\:x={({x}_{1},{x}_{2},\dots\:{x}_{p})}^{{\prime\:}}\) , \(\:\beta\:={({\beta\:}_{1},{\beta\:}_{2}{,\dots\:,\beta\:}_{p})}^{{\prime\:}}\) be the \(\:(p\times\:1)\:\) vector of regression coefficients corresponding to \(\:x\) and \(\:\sigma\:\) .be the unknown parameter. For a Lognormal distribution, \(\:\epsilon\:\) is a standard normal random variable with survival, density and hazard functions $$\:S\left(t\right)=1-\:\phi\:\left(\frac{\text{l}\text{n}\text{t}-{x}^{{\prime\:}}\beta\:-\mu\:}{\sigma\:}\right)\:$$ Where, \(\:\phi\:(.)\) is the cumulative distribution function of a standard normal variable. $$\:f\left(t\right)=\frac{1}{\sigma\:t\sqrt{2\pi\:}}{e}^{-{\frac{1}{2}\left(\right(\text{l}\text{n}\text{t}-{x}^{{\prime\:}}\beta\:-\mu\:)/\sigma\:)}^{2}}$$ Then probability density function for an individual with covariate vector X is given by, $$\:h\left(t\right)=f\left(t\right)/s\left(t\right)$$ Results Univariate Analysis The background characteristics of the respondents are presented in Table 1 . The majority of respondents (16.1%) were from the Dhaka division, and most resided in rural (63%) areas. Nearly half of the respondents (47.3%) and their partners (36.2%) had attained secondary education. A significant portion of the respondents (61.4%) had no knowledge about the ovulatory cycle and only a small portion (10%) had experienced terminated pregnancies. The majority of women (88.7%) had not worked outside before marriage. More than half of the women (54.3%) were married between the ages of 16 to 19, most of them (77.4%) currently residing with their partner. Table 1 Percentage Distribution on Women’s Background Characteristics. Variables Categories Frequency Percentage Socio-demographic charateristics Division Barisal 261 9.6% Chittagong 419 15.5% Dhaka 437 16.1% Khulna 300 11.1% Mymensingh 305 11.3% Rajshahi 305 11.3% Rangpur 321 11.9% Sylhet 360 13.3% Place of Residence Urban 1002 37% Rural 1706 63% Respondent’s educational qualifications No education 56 2.1% Primary education 478 17.7% Secondary 1281 47.3% Higher 893 33% Partner’s educational qualifications No education 184 6.8% Primary education 766 28.3% Secondary 980 36.2% Higher 778 28.7% Working outside before marriage No 2403 88.7% Yes 305 11.2% Residing with partner No 611 22.6% Yes 2097 77.4% Age at first marriage 10–15 653 24.1% 16–19 1473 54.3% 20–49 582 21.4% Spousal age difference 0–2 253 9.3% 3–5 662 24.4% 6–9 961 35.4% 10 or More 832 30.7% Socio-cultural variables No 1077 39.8% Yes 1631 60.2% Health -related variables Terminated pregnancy No 2438 90% Yes 270 10% Knowledge of ovulatory cycle No 1046 38.6% Yes 1662 61.4% Knowledge of ovulatory cycle Underweight 495 18.3% Normal weight 1727 63.8% Overweight 424 15.7% Obesity 62 2.3% Bivariate Analysis: Log-rank Test This study uses log-rank test to determine the significant predictors of women’s first birth interval in Bangladesh. The null hypothesis assumes the waiting time to first birth is same across all categories of the predictors. The log-rank test statistic asymptotically follows chi-square distribution with k-1 degrees of freedom, where k is the number of categories of each covariate. The results of the log-rank test for the selected variables, along with their corresponding p-values , are presented in Table 2 . Considering a significance level of 5%, the statistical test indicates that the first birth interval had significant association with division, place of residence, respondent's educational level, media exposure, terminated pregnancy, partner's education level, age at first marriage, spousal age difference, cohabitation with partner, employment outside before marriage, and BMI. The significant covariates will be used as predictors to the Log-normal AFT survival regression model. Table 2 Association between First Birth Interval and Predictor Variables (Log-rank Test). Variables Categories Chi-square p-value Socio-demographic Region Barisal 89.301 < 0.001 Chittagong Dhaka Khulna Mymensingh Rajshahi Rangpur Sylhet Place of residence Urban 11.112 0.001 Rural Respondent’s education No education 70.038 < 0.001 Primary education Secondary Higher Partner’s education No education 61.088 < 0.001 Primary Secondary Higher Working outside before marriage No 9.439 0.002 Yes Residing with partner No 20.514 < 0.001 Yes Socio-demographic Age at first marriage 10–15 13.122 0.001 16–19 20–49 Spousal age difference 0–2 13.49 0.004 3–5 6–9 10 or More Socio-cultural Media exposure No 52.283 < 0.001 Yes Health -related Terminated pregnancy No 57.569 < 0.001 Yes Knowledge of ovulatory cycle No 0.728 0.393 Yes BMI Underweight 19.857 < 0.001 Normal Weight Overweight Obesity Log-normal AFT Survival Regression Model The findings reveal that in comparison to the Barisal division, the waiting time to first birth in the Chittagong, Dhaka, Rajshahi, Rangpur, and Sylhet divisions were, respectively, 28.8%, 15.4%, 14.0%, 19.0%, and 35.68% shorter, indicating earlier childbirth in these regions (QR = 0.711, 95% CI: 0.618–0.817, p-value < 0.001; QR = 0.845, 95% CI: 0.739–0.997, P = 0.020, QR = 0.859, 95% CI: 0.698–0.938, P = 0.005, and QR = 0.641, 95% CI: 0.555–0.740, p-value < 0.001). Additionally, the first-birth spacing is 41.2% longer for respondents with higher levels of education compared to the illiterate group (QR = 1.412, 95% CI: 1.103–1.807, p-value = 0.006). For respondents with media access, the first birth interval was 14.9% longer compared to those with no media exposure (QR = 1.149, 95% CI: 1.068–1.237, p-value < 0.001) and women who experienced pregnancy termination had a first birth interval 56.2% longer than those without such experience (QR = 1.562, 95% CI: 1.397–1.747, p-value < 0.001). The findings also indicate that women whose partners had attained secondary and higher education had 17.9% and 27.2% longer time to first birth, respectively, compared to those married to illiterate husbands (QR = 1.179, 95% CI: 1.024–1.358, p-value = 0.022 and QR = 1.272, 95% CI: 1.087–1.488, p-value = 0.003). Women whose age at first marriage falls between 16 to 19 and 20 to 49, respectively, had a 16.7% and 23.0% higher likelihood of having their first child, indicating a shorter first birth interval compared to respondents who married before the age of 16 (QR = 0.832, 95% CI: 0.766–0.905, P < 0.001 and QR = 0.769, 95% CI: 0.684–0.865, p-value < 0.001). Women with an age gap of more than 5 years with their partners experienced a shorter waiting time to first birth (QR = 0.876, 95% CI: 0.770–0.996, p-value = 0.044 and QR = 0.823, 95% CI: 0.721–0.939, p-value = 0.004). Moreover, women who were residing with their husbands had a shorter first interval before having their first child (QR = 0.841, 95% CI: 0.773–0.914, P < 0.001). Table 3 Parameter estimates, p-value and quantile ratio (QR) of log-normal AFT model. Variable Category Coefficient (β) SE (Coefficient) QR (95% CI) p-value Socio-demographic Region Barisal (RC*) - - - - Chittagong -0.341 0.071 0.711(0.618,0.817) < 0.001 Dhaka -0.167 0.072 0.845(0.734,0.973) 0.02 Khulna -0.139 0.077 0.870(0.748,1.012) 0.072 Mymensingh -0.145 0.076 0.864(0.745,1.003) 0.055 Rajshahi -0.152 0.076 0.859(0.739,0.997) 0.047 Rangpur -0.211 0.075 0.809(0.698,0.938) 0.005 Sylhet -0.444 0.073 0.641(0.555,0.740) < 0.001 Place of residence Urban - - - - Rural -0.029 0.038 0.971(0.901,1.046) 0.442 Respondents’ education No Education (RC*) - - - - Primary Education -0.053 0.121 0.948(0.748,1.201) 0.659 Secondary 0.162 0.12 1.175(0.930,1.486) 0.176 Higher 0.345 0.126 1.412(1.103,1.807) 0.006 Partner’s education No Education (RC*) - - - - Primary 0.061 0.071 1.063(0.926,1.221) 0.384 Secondary 0.165 0.072 1.179(1.024,1.358) 0.022 Higher 0.241 0.08 1.272(1.087,1.488) 0.003 Working outside before marriage No (RC*) - - - - Yes 0.044 0.059 1.045(0.931,1.173) 0.451 Residing with partner No (RC*) - - - - Yes -0.173 0.043 0.841(0.773,0.914) < 0.001 Age at first marriage 10–15 (RC*) - - - - 16–19 -0.183 0.043 0.832(0.766,0.905) < 0.001 20–49 -0.262 0.06 0.769(0.684,0.865) < 0.001 Spousal age difference 0–2(RC*) - - - - 3–5 -0.036 0.068 0.964(0.844,1.102) 0.595 6–9 -0.132 0.066 0.876(0.770,0.996) 0.044 10 or More -0.194 0.067 0.823(0.721,0.939) 0.004 Socio-cultural Media Exposure No (RC*) - - - - Yes 0.139 0.038 1.149(1.068,1.237) < 0.001 Health –related Terminated Pregnancy No (RC*) - - - - Yes 0.446 0.057 1.562(1.397,1.747) < 0.001 BMI Underweight (RC*) - - - - Normal Weight 0.014 0.045 1.013(0.927,1.107) 0.762 Overweight 0.093 0.06 1.097(0.976,1.233) 0.119 Obesity 0.139 0.119 1.149(0.909,1.451) 0.243 RC*: Reference category Discussion This study examined the current factors influencing the women’s first birth interval (FBI) and identified that a number of socio-demographic, socio-cultural, and health-related variables significantly impact on the women’s waiting time to first birth in Bangladesh. Regional variations in the FBI were observed, consistent with previous studies by Mustafi et al. (2013) and Chowdhury et al. ( 2018 ). The study found that a longer first birth interval was associated with higher education levels among both women and their partners, consistent with the findings of Hossain et al. (2019) and Rabbi et al. ( 2013 ). Women and their husbands with higher education levels tend to delay their first childbearing, likely due to their awareness of the risks associated with early pregnancy. Consistent with findings from Alam et al. (2015), this study did not find a significant outcome associated with the place of residence. The results on age at first marriage are supported by Gibson et al. (2002), who found that women who married later in life had a higher likelihood of shorter FBI. Similar to the findings of Negash et al. (2022), this study revealed that women who are older than their husbands by more than five years are more likely to give birth to their children early. This outcome may be attributed to the lack of reproductive health knowledge among women younger than men by more than two years, as highlighted in the study by Sharma et al. ( 2021 ). In the present study, the previously working status of women before marriage is insignificant on conceiving first live birth, consistent with the result of Santow et al. (2001). Moreover, similar to the findings of Ahammed et al. ( 2019 ), this study concluded that mass media exposure acts as a protective factor for the waiting time to first birth in Bangladesh. According to this study, women who have experienced terminated pregnancies are less likely to become mothers soon. This result is similar to the findings of Iddrisu et al. ( 2020 ). This conclusion could be explained by the fact that women who have had pregnancies aborted would need more time to mentally and physically recuperate before attempting another pregnancy. Again, this study investigated the timing of the first birth for women currently residing with their husbands, which is similar to the study of Choe et al. ( 2005 ) where it is investigated that the women who are not living with their partners for a year or two, tend to delay the birth of their first child. Strengths and Limitations This study deals with the latest Bangladesh Demographic and Health Survey Data, which enhances the relevance and utility of this research findings in relation to the ongoing discussions and regulations. The AFT model provides deeper knowledge for understanding the factors influencing the FBI and analyzing the FBI will contribute greatly on population studies and other related fields. However, as secondary data is used here for time restrictions, it limits the control over data quality and potential biases and the generalizability of the study findings might be limited based on the secondary data sources. Again, there might be some other factors that influence the FBI, which are not included in this study. Conclusions Bangladesh is a developing nation and population growth is a pressing concern, it is critical to take the necessary efforts to manage population growth. In this regard, understanding the socio-demographic, cultural and maternal health related characteristics is crucial. This study has shown that couples with higher levels of education tend to have longer first birth intervals. Therefore, action must be taken to ensure that all citizens have access to at least a minimum level of education. Again, first births intervals vary across different regions of Bangladesh; therefore, in those areas where first births occur more quickly, awareness should be raised. Strict regulations should be enforced to prevent early pregnancy and marriage. All things considered, couples in Bangladesh should prioritize increasing their knowledge of family planning programs, reducing the negative socio-economic standards associated with early pregnancy, and accessing reproductive health information from health-facilitated places. Declarations Ethics Approval and consent to participate Not applicable Consent for publication All the authors have agreed to publish this study. Consent for publication Availability of data and materials Competing interest The authors declared no conflicts of interest regarding this work. Funding Not applicable. Author Contribution TT conceptualized the study, curated, and analyzed the data for this study. TT and RAA developed 1st draft of the article. 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Higher qualifications, first-birth timing and further childbearing in England and Wales. POPULATION TRENDS-LONDON- , 18–26. Rindfuss, R. R., Guilkey, D., Morgan, S. P., Kravdal, Ø., & Guzzo, K. B. (2007). Child care availability and first-birth timing in Norway. Demography , 44 (2), 345–372. S Oyamakin, O., & Emenike, I. C. (2022). An Investigation into Social and Cultural Pressures of Vulnerable Women using Survival Analysis with Covariates. Asian Journal of Probability and Statistics , 18 (3), 38–57. Santow, G., & Bracher. (2001). Deferment of the first birth and fluctuating fertility in Sweden. European Journal of Population/Revue européenne de Démographie , 17 , 343–363. Sharma, S., Akhtar, F., Singh, R. K., & Mehra, S. (2021). Early marriage and spousal age difference: predictors of preconception health of young married women in Delhi, India. Journal of Health Research , 36 (6), 1118–1130. Singh, N. S., Singh, W. T., & Singh, T. M. (2013). Retrospective reporting on the determinants of post-partum amenorrhea in rural Manipur. International Journal of Mathematical Sciences and Engineering Application , 7 (1), 445–457. Sobhan, A., Moinuddin, M., & Hossain, M. M. (2024). Investigating time to first birth among women of reproductive age in Bangladesh: a survival analysis of nationwide cross-sectional survey data. Journal of Health, Population and Nutrition , 43 (1), 2. Trommlerová, S. K. (2020). When children have children: The effects of child marriages and teenage pregnancies on early childhood mortality in Bangladesh. Economics & Human Biology , 39 , 100904. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7831578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529946523,"identity":"f30e4eb6-192d-4172-bb61-e5cc9b626e0d","order_by":0,"name":"Tasmiah Tamanna","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Tasmiah","middleName":"","lastName":"Tamanna","suffix":""},{"id":529946524,"identity":"85a750f1-4f3f-458d-9f63-56eb4ee8ae8c","order_by":1,"name":"Md. Abu Hasnat Abdullah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACAwglIcfYwHwAxJAhUkuCjTFjA1sCSAsPsVrSEhsYeMBswlrM2Y8/e/Dzx2HG5hk5n1/dqLHgYWA/fHQDPi2WPTnmhj0Jh5kZZ+Rus845BnQYT1raDbwOO5DDJsGTcJgNpMUYxAZ6xwy/lvPPn0n+STjMwzgj55lxzj9itNxIMJPmSUiTAGphfpzbRpSWN2bSMmk2Bow9z8yYc/skeNgI+uV8+jPJNzYS9Rvbkx9/zvlWJ8fPfvgYXi1wYNjAwCYBYrARpRwE5BkYmD8QrXoUjIJRMApGFAAAL+xH2/Sn5GwAAAAASUVORK5CYII=","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Abu Hasnat","lastName":"Abdullah","suffix":""},{"id":529946525,"identity":"3b87562d-b74d-4dd6-9b42-b6de0d5eee3d","order_by":2,"name":"Rowshan Ara Afrin","email":"","orcid":"","institution":"German University Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Rowshan","middleName":"Ara","lastName":"Afrin","suffix":""},{"id":529946526,"identity":"e3003701-2b0d-43f4-a944-68fa64556f74","order_by":3,"name":"Gowranga Kumar Paul","email":"","orcid":"","institution":"Mawlana Bhashani Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Gowranga","middleName":"Kumar","lastName":"Paul","suffix":""}],"badges":[],"createdAt":"2025-10-11 04:23:21","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-7831578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7831578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93653659,"identity":"d5fa37db-a330-49c9-ac88-c4555830fc45","added_by":"auto","created_at":"2025-10-16 06:37:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7831578/v1/6f46e2ea-052c-4cdd-829b-d8be8245aac2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the Determinants of Women’s First Birth Interval in Bangladesh: An Application of the Log-normal AFT Model","fulltext":[{"header":"Background","content":"\u003cp\u003eFertility plays a key role for population growth, and it influences the size and age structure of population in any country (Lutz et al. 2002). Fertility rate has direct impact on demographic transitions (Tadesse et al. 2010) where the first birth interval (FBI) serves as an indicator for assessing the fertility patterns (National Research Council et al. 2005). The FBI refers to the duration between a woman's initial marriage or commencement of sexual activity and the birth of her first child. The waiting time to first birth holds significance as motherhood entails a substantial investment of time and resources (Ali et al. 2020). Again, the first childbirth interval holds economic significance for enhancing a woman's participation in the labor market and promotes her wage growth (Bratti et al. 2014; Herr et al. 2007) and women's ability to make decisions within the household is positively impacted by delaying the first pregnancy (Ashraf et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, giving birth to a first child before the age of twenty is linked to an elevated risk of various diseases such as heart disease, lung disease, cancer, and mortality (Henretta, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). So, the interval of first child birth (FBI) plays a vital role in a woman\u0026rsquo;s life as well as in many aspects of a country.\u003c/p\u003e\u003cp\u003eThe FBI among women showed significant associations with their age at marriage, educational attainment, and occupation (Patil et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Typically, women with higher levels of education tend to become mothers later in life, as extended education often correlates with postponed marriage (Alderman et al. 2017; Rendall et al. 2003; Berg et al. 2014). Additionally, educated spouse usually delays the timing of the first childbirth (Ali et al. 2020). Ahammed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), report that a mother's employment status serves as a protective factor for the first birth interval. The initial childbearing age of women varies by location and religion. Women from higher socio-economic classes typically delay their first birth longer than those from lower socio-economic groups (Alemu et al. 2022; S et al. 2022). Along with other demographic factors, the first birth interval is impacted by income, and economic difficulties such as employment instability, inadequate housing, and infrastructural challenges. These factors may directly or indirectly lower the chance of pregnancy (Miri et al. 2018).\u003c/p\u003e\u003cp\u003eFaruk (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggests that the first birth interval in Indonesia is primarily affected by the educational attainment of women and their partners, contraceptive awareness, media exposure, wealth index, and employment status. In Nepal, women belonging to higher castes and of older age, possessing knowledge about family planning techniques, tend to have shorter intervals between their first births (Karkee and Lee, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, in Kenya, shorter first birth intervals are observed among women who are employed, reside in rural areas, and have higher levels of education (Mulwa, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, increased availability of childcare facilities in Norway contributes to a higher likelihood of transitioning to motherhood at various ages, thereby shortening the first birth interval (Rindfuss et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn case of Bangladesh, more than half of the women had their first child before reaching the age 18 (Sobhan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bhowmik et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nahar et al. 2013) which is one of the main reasons of child and maternal mortality (Trommlerov\u0026aacute; et al. 2020), in this regard the causes of shorter interval should be pointed out to assess the risks factors and help to take initiatives for the betterment of child and maternal health. Again, although the rate of fertility is declining, the estimated replacement-level fertility for Bangladesh is not yet achieved \u003cb\u003e(NIPORT et al., 2017\u0026ndash;2018).\u003c/b\u003e Since Bangladesh is a densely populated country and first birth interval has a direct relationship with fertility pattern, it is important to find out the determinants behind short first birth interval. But only a few studies focused on the factors influencing the waiting to first birth interval (FBI) of women in Bangladesh. The main purpose of this study is to provide a comprehensive examination on various factors concerning the timing of the first birth among women in Bangladesh, employing sophisticated regression modeling techniques, which will help the policy makers to develop effective family planning programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eThe study utilizes secondary data from the 2017\u0026ndash;2018 Bangladesh Demographic and Health Survey (BDHS). A sample of about 20,250 randomly selected households were included in this nationally representative survey. Individual interviews were conducted with all married women aged 15 to 49 residing in the selected households or staying overnight prior to the study. The survey employed a two-stage stratified sampling approach. Initially, 675 enumeration areas were chosen using probability proportionate to size, with 250 allocated to urban areas and 425 to rural areas. Then a systematic sample of approximately 30 households per enumeration area was chosen in the second phase of sampling. Finally, a total of 20,100 married women aged 15 to 49 were included in the survey for interview. Detail sampling designs are available on the BDHS 2017-18 report (NIPORT et al., 2020).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThis study utilizes the Individual Recode (IR) file from the BDHS-2017-18 dataset, focusing exclusively on women whose marital duration is 60 months (five years) or less. Marital duration was computed by subtracting the date of marriage from the date of the interview. After excluding missing cases, a total of 2,708 women were included in the analysis.\u003c/p\u003e\n\u003ch3\u003eResponse Variable\u003c/h3\u003e\n\u003cp\u003eThe response variable in this study is the first child birth interval among ever married women in Bangladesh. A censoring indicator is created for each respondent based on whether the event has occurred or not. This indicator is binary, taking the value 1 if a woman experiences first child before or within 60 months of marriage (event), and 0 otherwise (censored). Here the event of interest is the birth of first child and the time variable (months) is defined as \u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1760595965.png\" style=\"width: 532px;\"\u003e\u003c/p\u003e\n\u003ch3\u003ePredictor Variables\u003c/h3\u003e\n\u003cp\u003eThis study considers demographic, socio-economic, fertility, and maternal health related characteristics as predictor variables. The predictor variables include residence (rural, urban); region (Dhaka, Khulna, Chattogram, Rangpur, Sylhet, Barisal, Rajshahi); parents education level (illiterate, primary, secondary, higher-level); age at first marriage (15 years or less, 16\u0026ndash;19, More than 20 years); media exposure (no, yes); spousal age difference (3\u0026ndash;5 years, 6\u0026ndash;9 years, 10 or more years); residing with partner (no, yes); employment status before marriage (yes, no); terminated pregnancy (no, yes); knowledge about ovulatory cycle (no, yes); body mass index (BMI) (underweight: BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5, normal BMI: 18.5\u0026ndash;24.9, overweight: BMI\u0026thinsp;\u0026gt;\u0026thinsp;25).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe dataset comprises respondents with both censored and uncensored observations, along with their corresponding event times. Given the presence of censored observations, survival analysis techniques are more suitable to handle this kind of data. This study utilizes log-rank test in bi-variate analysis to determine the significant association between predictors and first birth interval among women in Bangladesh. Then the parametric log-normal accelerated failure time (AFT) survival regression model is used to find the net effect of each predictor on women's waiting time to first birth in Bangladesh.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLog-rank Test\u003c/strong\u003e\u003cp\u003eIn Survival analysis, a key objective is to test whether sub-populations behave in the same way. Log-Rank test is employed to compare the survival probabilities between two or more group of individuals. By considering \u003cem\u003ej\u003c/em\u003e factor group, the log rank test is used to test the hypothesis;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{0}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(t) =\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(t) =\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{S}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(t) =\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\dots\\:={S}_{k}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(t)\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:H}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/strong\u003e\u003cp\u003e\u003cem\u003eNot all\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{j}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(t) are equal;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j=1,\\:\\text{2,3},\\:\\dots\\:,k.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{j}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e(t)\u003c/em\u003e is the estimated survival function for the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{j}^{th}\\)\u003c/span\u003e\u003c/span\u003e group. In general, the Logrank test is a chi-square statistic that compares the observed \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{(O}_{j})\\)\u003c/span\u003e\u003c/span\u003e and expected \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{(E}_{j})\\)\u003c/span\u003e\u003c/span\u003e numbers of first births under the hypothesis. The chi-square test statistic with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(k-1)\\:\\)\u003c/span\u003e\u003c/span\u003edegrees of freedom is given by;\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\chi\\:}^{2}=\\sum\\:_{j=1}^{k}\\frac{{({O}_{j}-{E}_{j})}^{2}}{{E}_{j}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLog-normal AFT Model\u003c/strong\u003e\u003cp\u003eA statistical model is known as a survival regression model if the result variable in the regression analysis is the survival time or time. Survival time is the time to occur an event of interest. However, the log of the survival time T and the covariates are assumed to be a linear function in the AFT model, which is given as,\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Y=lnT=(\\mu\\:+{x}^{{\\prime\\:}}\\beta\\:)+\\sigma\\:\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T\\)\u003c/span\u003e\u003c/span\u003e be the lifetime in the survival regression with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e covariates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x={({x}_{1},{x}_{2},\\dots\\:{x}_{p})}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:={({\\beta\\:}_{1},{\\beta\\:}_{2}{,\\dots\\:,\\beta\\:}_{p})}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e be the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(p\\times\\:1)\\:\\)\u003c/span\u003e\u003c/span\u003evector of regression coefficients corresponding to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e.be the unknown parameter. For a Lognormal distribution, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e is a standard normal random variable with survival, density and hazard functions\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:S\\left(t\\right)=1-\\:\\phi\\:\\left(\\frac{\\text{l}\\text{n}\\text{t}-{x}^{{\\prime\\:}}\\beta\\:-\\mu\\:}{\\sigma\\:}\\right)\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\phi\\:(.)\\)\u003c/span\u003e\u003c/span\u003e is the cumulative distribution function of a standard normal variable.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:f\\left(t\\right)=\\frac{1}{\\sigma\\:t\\sqrt{2\\pi\\:}}{e}^{-{\\frac{1}{2}\\left(\\right(\\text{l}\\text{n}\\text{t}-{x}^{{\\prime\\:}}\\beta\\:-\\mu\\:)/\\sigma\\:)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThen probability density function for an individual with covariate vector \u003cem\u003eX\u003c/em\u003e is given by,\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:h\\left(t\\right)=f\\left(t\\right)/s\\left(t\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate Analysis\u003c/h2\u003e\u003cp\u003eThe background characteristics of the respondents are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The majority of respondents (16.1%) were from the Dhaka division, and most resided in rural (63%) areas. Nearly half of the respondents (47.3%) and their partners (36.2%) had attained secondary education. A significant portion of the respondents (61.4%) had no knowledge about the ovulatory cycle and only a small portion (10%) had experienced terminated pregnancies. The majority of women (88.7%) had not worked outside before marriage. More than half of the women (54.3%) were married between the ages of 16 to 19, most of them (77.4%) currently residing with their partner.\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\u003ePercentage Distribution on Women\u0026rsquo;s Background Characteristics.\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\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSocio-demographic charateristics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eDivision\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBarisal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChittagong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDhaka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhulna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMymensingh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRajshahi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRangpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSylhet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eRespondent\u0026rsquo;s educational qualifications\u003c/b\u003e\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\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003ePartner\u0026rsquo;s educational qualifications\u003c/b\u003e\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\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eWorking outside before marriage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eResiding with partner\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAge at first marriage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eSpousal age difference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 or More\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-cultural variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\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\u003cp\u003e1077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth -related variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eTerminated pregnancy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eKnowledge of ovulatory cycle\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eKnowledge of ovulatory cycle\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3%\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\n\u003ch3\u003eBivariate Analysis: Log-rank Test\u003c/h3\u003e\n\u003cp\u003eThis study uses log-rank test to determine the significant predictors of women\u0026rsquo;s first birth interval in Bangladesh. The null hypothesis assumes the waiting time to first birth is same across all categories of the predictors. The log-rank test statistic asymptotically follows chi-square distribution with \u003cem\u003ek-1\u003c/em\u003edegrees of freedom, where \u003cem\u003ek\u003c/em\u003e is the number of categories of each covariate. The results of the log-rank test for the selected variables, along with their corresponding \u003cem\u003ep-values\u003c/em\u003e, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Considering a significance level of 5%, the statistical test indicates that the first birth interval had significant association with division, place of residence, respondent's educational level, media exposure, terminated pregnancy, partner's education level, age at first marriage, spousal age difference, cohabitation with partner, employment outside before marriage, and BMI. The significant covariates will be used as predictors to the Log-normal AFT survival regression model.\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\u003eAssociation between First Birth Interval and Predictor Variables (Log-rank Test).\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=\"char\" char=\".\" 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\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChi-square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSocio-demographic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBarisal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e89.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChittagong\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDhaka\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhulna\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMymensingh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRajshahi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRangpur\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSylhet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e11.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eRespondent\u0026rsquo;s education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e70.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePartner\u0026rsquo;s education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e61.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWorking outside before marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e9.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResiding with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e20.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-demographic\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge at first marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e13.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSpousal age difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e13.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 or More\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-cultural\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMedia exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e52.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth -related\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTerminated pregnancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e57.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKnowledge of ovulatory cycle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e19.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal Weight\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObesity\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=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLog-normal AFT Survival Regression Model\u003c/h2\u003e\u003cp\u003eThe findings reveal that in comparison to the Barisal division, the waiting time to first birth in the Chittagong, Dhaka, Rajshahi, Rangpur, and Sylhet divisions were, respectively, 28.8%, 15.4%, 14.0%, 19.0%, and 35.68% shorter, indicating earlier childbirth in these regions (QR\u0026thinsp;=\u0026thinsp;0.711, 95% CI: 0.618\u0026ndash;0.817, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; QR\u0026thinsp;=\u0026thinsp;0.845, 95% CI: 0.739\u0026ndash;0.997, P\u0026thinsp;=\u0026thinsp;0.020, QR\u0026thinsp;=\u0026thinsp;0.859, 95% CI: 0.698\u0026ndash;0.938, P\u0026thinsp;=\u0026thinsp;0.005, and QR\u0026thinsp;=\u0026thinsp;0.641, 95% CI: 0.555\u0026ndash;0.740, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the first-birth spacing is 41.2% longer for respondents with higher levels of education compared to the illiterate group (QR\u0026thinsp;=\u0026thinsp;1.412, 95% CI: 1.103\u0026ndash;1.807, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). For respondents with media access, the first birth interval was 14.9% longer compared to those with no media exposure (QR\u0026thinsp;=\u0026thinsp;1.149, 95% CI: 1.068\u0026ndash;1.237, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and women who experienced pregnancy termination had a first birth interval 56.2% longer than those without such experience (QR\u0026thinsp;=\u0026thinsp;1.562, 95% CI: 1.397\u0026ndash;1.747, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThe findings also indicate that women whose partners had attained secondary and higher education had 17.9% and 27.2% longer time to first birth, respectively, compared to those married to illiterate husbands (QR\u0026thinsp;=\u0026thinsp;1.179, 95% CI: 1.024\u0026ndash;1.358, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022 and QR\u0026thinsp;=\u0026thinsp;1.272, 95% CI: 1.087\u0026ndash;1.488, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Women whose age at first marriage falls between 16 to 19 and 20 to 49, respectively, had a 16.7% and 23.0% higher likelihood of having their first child, indicating a shorter first birth interval compared to respondents who married before the age of 16 (QR\u0026thinsp;=\u0026thinsp;0.832, 95% CI: 0.766\u0026ndash;0.905, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and QR\u0026thinsp;=\u0026thinsp;0.769, 95% CI: 0.684\u0026ndash;0.865, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eWomen with an age gap of more than 5 years with their partners experienced a shorter waiting time to first birth (QR\u0026thinsp;=\u0026thinsp;0.876, 95% CI: 0.770\u0026ndash;0.996, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044 and QR\u0026thinsp;=\u0026thinsp;0.823, 95% CI: 0.721\u0026ndash;0.939, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Moreover, women who were residing with their husbands had a shorter first interval before having their first child (QR\u0026thinsp;=\u0026thinsp;0.841, 95% CI: 0.773\u0026ndash;0.914, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eParameter estimates, \u003cem\u003ep-value\u003c/em\u003e and quantile ratio (QR) of log-normal AFT model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\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\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE (Coefficient)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eSocio-demographic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBarisal (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChittagong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.711(0.618,0.817)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDhaka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.845(0.734,0.973)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhulna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.870(0.748,1.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMymensingh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.864(0.745,1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRajshahi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.859(0.739,0.997)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRangpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.809(0.698,0.938)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSylhet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.641(0.555,0.740)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePlace of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.971(0.901,1.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eRespondents\u0026rsquo; education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Education (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.948(0.748,1.201)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.175(0.930,1.486)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.412(1.103,1.807)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePartner\u0026rsquo;s education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Education (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.063(0.926,1.221)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.179(1.024,1.358)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.272(1.087,1.488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWorking outside before marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.045(0.931,1.173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResiding with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.841(0.773,0.914)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge at first marriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;15 (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.832(0.766,0.905)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.769(0.684,0.865)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSpousal age difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;2(RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.964(0.844,1.102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.876(0.770,0.996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 or More\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.823(0.721,0.939)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-cultural\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMedia Exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.149(1.068,1.237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth \u0026ndash;related\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTerminated Pregnancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.562(1.397,1.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight (RC*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.013(0.927,1.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.097(0.976,1.233)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.149(0.909,1.451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.243\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=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRC*: Reference category\u003c/h2\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the current factors influencing the women\u0026rsquo;s first birth interval (FBI) and identified that a number of socio-demographic, socio-cultural, and health-related variables significantly impact on the women\u0026rsquo;s waiting time to first birth in Bangladesh. Regional variations in the FBI were observed, consistent with previous studies by Mustafi et al. (2013) and Chowdhury et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The study found that a longer first birth interval was associated with higher education levels among both women and their partners, consistent with the findings of Hossain et al. (2019) and Rabbi et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Women and their husbands with higher education levels tend to delay their first childbearing, likely due to their awareness of the risks associated with early pregnancy. Consistent with findings from Alam et al. (2015), this study did not find a significant outcome associated with the place of residence. The results on age at first marriage are supported by Gibson et al. (2002), who found that women who married later in life had a higher likelihood of shorter FBI. Similar to the findings of Negash et al. (2022), this study revealed that women who are older than their husbands by more than five years are more likely to give birth to their children early. This outcome may be attributed to the lack of reproductive health knowledge among women younger than men by more than two years, as highlighted in the study by Sharma et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the present study, the previously working status of women before marriage is insignificant on conceiving first live birth, consistent with the result of Santow et al. (2001). Moreover, similar to the findings of Ahammed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), this study concluded that mass media exposure acts as a protective factor for the waiting time to first birth in Bangladesh.\u003c/p\u003e\u003cp\u003eAccording to this study, women who have experienced terminated pregnancies are less likely to become mothers soon. This result is similar to the findings of Iddrisu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This conclusion could be explained by the fact that women who have had pregnancies aborted would need more time to mentally and physically recuperate before attempting another pregnancy. Again, this study investigated the timing of the first birth for women currently residing with their husbands, which is similar to the study of Choe et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) where it is investigated that the women who are not living with their partners for a year or two, tend to delay the birth of their first child.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis study deals with the latest Bangladesh Demographic and Health Survey Data, which enhances the relevance and utility of this research findings in relation to the ongoing discussions and regulations. The AFT model provides deeper knowledge for understanding the factors influencing the FBI and analyzing the FBI will contribute greatly on population studies and other related fields. However, as secondary data is used here for time restrictions, it limits the control over data quality and potential biases and the generalizability of the study findings might be limited based on the secondary data sources. Again, there might be some other factors that influence the FBI, which are not included in this study.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBangladesh is a developing nation and population growth is a pressing concern, it is critical to take the necessary efforts to manage population growth. In this regard, understanding the socio-demographic, cultural and maternal health related characteristics is crucial. This study has shown that couples with higher levels of education tend to have longer first birth intervals. Therefore, action must be taken to ensure that all citizens have access to at least a minimum level of education. Again, first births intervals vary across different regions of Bangladesh; therefore, in those areas where first births occur more quickly, awareness should be raised. Strict regulations should be enforced to prevent early pregnancy and marriage. All things considered, couples in Bangladesh should prioritize increasing their knowledge of family planning programs, reducing the negative socio-economic standards associated with early pregnancy, and accessing reproductive health information from health-facilitated places.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eAll the authors have agreed to publish this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eAvailability of data and materials\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eCompeting interest\u003c/h2\u003e\u003cp\u003eThe authors declared no conflicts of interest regarding this work.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTT conceptualized the study, curated, and analyzed the data for this study. TT and RAA developed 1st draft of the article. TT and AHA interpreted the results, discussion, and substantively revised the manuscript. GKP supervised the study including conceptualization, methodology selection. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhammed, B., Kabir, M. R., Abedin, M. M., Ali, M., \u0026amp; Islam, M. A. (2019). Determinants of different birth intervals of ever married women: Evidence from Bangladesh. \u003cem\u003eClinical Epidemiology and Global Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 450\u0026ndash;456.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlam, M. M. 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When children have children: The effects of child marriages and teenage pregnancies on early childhood mortality in Bangladesh. \u003cem\u003eEconomics \u0026amp; Human Biology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 100904.\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":true,"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":"AFT model, family planning, first birth interval, log-rank test, population growth","lastPublishedDoi":"10.21203/rs.3.rs-7831578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7831578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe waiting time to first birth is considered to be an economic empowerment indicator of women in any country. It plays a crucial role in determining population growth, demographic changes, and age structure. It is also linked up with the economic empowerment of women of a country. The main focus of this study is to identify the factors that influence women\u0026rsquo;s waiting time to first birth after marriage, based on the 2017-18 Bangladesh Demographic and Health Survey data. This research utilizes the log-rank test and log-normal accelerated failure time survival regression model to uncover the association of socio-demographic, cultural, and maternal health-related determinants with the first birth interval among women in Bangladesh. The statistical analysis indicates that region, respondent\u0026rsquo;s educational level, media exposure, terminated pregnancy, partner\u0026rsquo;s education, age at first marriage, and spousal age difference exhibit significant association with the women\u0026rsquo;s first birth interval in Bangladesh. This study is expected to provide guidelines for policymakers and healthcare professionals to address issues concerning family planning, maternal health, population growth, and reproductive health challenges in any country.\u003c/p\u003e","manuscriptTitle":"Analyzing the Determinants of Women’s First Birth Interval in Bangladesh: An Application of the Log-normal AFT Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 06:29:32","doi":"10.21203/rs.3.rs-7831578/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":"d94c8fc9-2933-48f2-92f3-dcd41a93eee0","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-16T06:29:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 06:29:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7831578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7831578","identity":"rs-7831578","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}