{"paper_id":"1b9c5ce5-0d5b-4ed3-ab76-b014fb297934","body_text":"A Zero-Truncated Generalized Poisson Approach to Under-Dispersed Count Data: Fertility Patterns in Bangladesh, 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Zero-Truncated Generalized Poisson Approach to Under-Dispersed Count Data: Fertility Patterns in Bangladesh, 2022 Sanjoy Kumar Roy, Ajoy Rjbongshi, Nandita Scholastica Costa, Md. Asrafur Rahman Ashiq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7312541/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background The number of children ever born (CEB) to women of reproductive age is a key factor of fertility and a critical factor in shaping population dynamics, particularly in developing countries like Bangladesh. It reflects cumulative reproductive behavior and is influenced by a range of demographic, socioeconomic, and cultural factors, making it a vital metric for understanding fertility trends. Based on data from Bangladesh Demographic and Health Survey 2022 (BDHS), this study focuses on women who have given birth to at least one child during the reproductive age. Methods To fit an appropriate model to the zero-truncated count and under-dispersed data effectively, both Zero-Truncated Poisson (ZTP) and Zero-Truncated Generalized Poisson (ZTGP) regression models were used. The ZTGP model provided the best fit and outperformed ZTP by addressing the under-dispersion of CEB, as resulted in narrower confidence intervals for the Incidence Rate Ratios (IRRs) and lower AIC and BIC values. Results Findings of this study indicate that maternal age, education, religion, place of residence, wealth index, and fertility preferences significantly influence the number of children. Specifically, higher education and economic status were associated with lower fertility, while rural residence and Muslim affiliation correlated with higher fertility rates. However, paternal occupation showed no significant impact on the number of children ever born in both ZTP and ZTGP models. Conclusion The Zero-Truncated Generalized Poisson (ZTGP) model effectively addressed the under-dispersion in the fertility data, yielding more accurate estimates of children ever born. The findings reveal significant socio-demographic determinants of fertility, reflecting the complex interplay of social, cultural, and economic factors and underscoring the importance of targeted, evidence-based family planning policies in Bangladesh. Fertility Children Ever Born Under-dispersion Fertility modeling Figures Figure 1 Figure 2 1. Background The number of children ever born (CEB) is a fundamental indicator of the population dynamics, influencing a country's population size and structure. It represents the total number of live births experienced by married women aged 15 years or older, encompassing both living and deceased children at the time of data collection [ 1 ]. According to the report of Worldometer in 2025, the global population is estimated at approximately 8.23 billion, with a median age of 30.9 years and a total fertility rate (TFR) of 2.2. In Bangladesh, the population has reached at 175.7 million with 42.6% residing in urban areas and a median age of 26 years, according to the same source. Based on the successive surveys of Bangladesh Demographic and Health Survey, the total fertility rate (TFR) declined from approximately 6.3 in 1971–75 to about 2.7 by 2007, and further to 2.3 by 2011 [ 2 , 3 ]. According to the BDHS 2014 and 2022 reports, the TFR in Bangladesh has remained at 2.3 children per woman, a level that has not changed since 2011 [ 4 , 5 ]. Women's education plays a crucial role in reducing fertility preferences, outweighing the influence of other factors. The rise in female educational attainment and the spread of preferences for smaller families appear to be key drivers behind the notable fertility decline in Bangladesh [ 6 ]. Globally, fertility research has consistently emphasized the variable Children Ever Born (CEB), with cross-national studies identifying distinct social and economic determinants shaping reproductive behavior [ 7 , 8 ]. These forces often contribute to postponed family formation, exacerbating fertility decline. Replacement fertility is the TFR at which a population replaces itself without migration, typically estimated TFR 2.1 in developed countries [ 9 ]. A sustained TFR below this level leads to population decline unless offset by immigration [ 10 ]. In Bangladesh, although several studies have explored fertility patterns using BDHS data, including those by a significant gap remains in the application of appropriate statistical models to handle the nature of the Children Ever Born (CEB) variable. These studies primarily examined associations between fertility and factors such as education, age at first marriage, religion, and wealth index. A study conducted using BDHS 2014 data found that fertility was significantly higher among Muslim, illiterate, and rural women, particularly those with early age at first birth and limited media exposure. To investigate this, the researchers used Poisson regression for analyzing count-based outcomes like number of children [ 11 ]. Similarly, another study highlights the influence of women's education on cumulative fertility, revealing a strong inverse relationship between education level and number of children ever born [ 12 ]. More recently, multilevel regression across seven BDHS survey rounds demonstrated that female education had the most substantial effect on fertility decline even more influential than family planning initiatives [ 6 ]. A study models fertility behavior in India using Poisson and multinomial regression, revealing that religion, caste, wealth, female education, and occupation significantly influence birth counts, with a strong preference for two children [ 13 ]. In a study weighted Bayesian Poisson regression model was applied using MICS-2019 data to estimates of fertility determinants in Bangladesh [ 14 ]. Using NDHS 2013 data, in a study modeled total children ever born among Nigerian women using Negative Binomial and Generalized Poisson regression, identifying age at first birth, age of household head, religion, and urban-rural status as significant factors, with the Generalized Poisson model best addressing dispersion issues [ 15 ]. A generalized Poisson regression (GPR) model also used to BDHS 2017-18 data and demonstrated that standard Poisson models underestimate variance when dealing with under-dispersed fertility data [ 16 ]. In contrast, a study analyzing fertility determinants in Western Tanzania applied a negative binomial regression model to account for over-dispersion in count data, yielding more robust estimates than the standard Poisson approach [ 17 ]. However, since CEB data typically excludes women who have not given birth (i.e., zero-truncation), this assumption leads to biased estimates and incorrect inferences. These findings emphasize the need for more sophisticated statistical approaches, such as zero-truncated Poisson or Bayesian multi-level Poisson to accurately capture fertility dynamics [ 18 ]. Without accounting for the structural properties of CEB data, policy recommendations based on flawed models may misguide interventions aimed at reducing fertility disparities. This study employing Zero-Truncated Poisson (ZTP) and Zero-Truncated Generalized Poisson (ZTGP) models to more accurately analyze Children Ever Born (CEB) data. These models are specifically chosen because ZTP is suitable for count data where zero values are structurally absent, such as CEB, which only includes women who have given birth at least once [ 19 ]. In a study ZTP and ZTGP models were used to capture fertility determinants in Indian states, especially among women aged 15–50, where the variance of CEB was significantly lower than the mean (mean = 2.203, variance = 0.601) and ZTP model performed better than ZTGP [ 20 ]. Moreover, another study demonstrated that Generalized Poisson regression outperforms standard Poisson and Negative Binomial models in fertility studies due to its flexibility in handling dispersion [ 15 ]. By comparing these models, we aim to identify the most suitable approach for modeling CEB in Bangladesh, thus improving the robustness of the analysis. This study focusses on its methodological rigor and comprehensive scope. Unlike prior work focusing on descriptive statistics or standard models, this research employs advanced technique that suited to the data's structural properties. Since fertility indicators like CEB exhibit zero truncation and under-dispersion, this study uses ZTGP model, that are proven by literature to handle under-dispersed data. It also analyzes a wide array of predictors, including parental education, religion, wealth, fertility preferences, and providing new empirical insights into fertility trends in Bangladesh. Ultimately, the study’s findings aim to inform data-driven interventions for fertility management and population planning, enabling policymakers and public health professionals to design context-specific strategies that promote reproductive equity and demographic sustainability. 2. Data and Variable Description 2.1. Data Source This study utilized data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2022 dataset. The survey was conducted by the National Institute of Population Research and Training (NIPORT), under the Ministry of Health and Family Welfare's Medical Education and Family Welfare Division. Mitra and Associates, a private research firm, carried out the fieldwork between June and December 2022. Financial support for the survey was provided by the Government of Bangladesh and the United States Agency for International Development (USAID). Technical assistance was facilitated by ICF through The DHS Program, a USAID-funded initiative that supports the implementation of population and health surveys globally. 2.2. Sampling Method Figure 1 shows that the survey employed a two-stage stratified sampling method. A total of 20,217 women were eligible for the full questionnaire, and 19,987 were successfully interviewed. After handling missing values, 16,987 women remained for analysis in this study. 2.3. Variable Description In this study, children ever born (CEB) considered as the response variable, among the ever-married women at their reproductive age (15–49 years) in Bangladesh. Response variable CEB is a count variable having no zeros (1, 2, …, 11), so it is a zero truncated count data. The explanatory variables are Women’s age (in 5-year age group, 15–19, …, 44–49), Residence type (Urban, Rural), Religion (Muslim, Hinduism, Others), Division (Barisa, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, Sylhet), Women’s education (No, Primary, Secondary, Higher), Women’s occupation (Unemployed, Physical labor, Service or Business, Household or others), Contraceptive Method (No, Traditional, Modern), Fertility preference (1, 2, 3 or more), Pregnancy losses (0, 1, 2 or more), Husband’s Age (18–27,…, 48–57, 58+), Husband’s education (No, Primary, Secondary, Higher), Husband’s occupation (Unemployed, Physical labor, Service or Business, Household or others), Household Head (Male, Female), Wealth index combined (Poor, Middle, Rich). 3. Methodology The Poisson regression model is the most suitable for modeling count data, as it effectively captures the non-linear interaction with explanatory variables. It is useful when events occur independently and the variance equals the mean. However, if zero values are absent in the count data and the mean and variance are not equal, Poisson regression is not suitable. For zero-truncated under dispersed count data, the Zero-Truncated Generalized Poisson model is commonly used, while for over dispersed count data, the Zero-Truncated Negative Binomial regression model is widely preferred. 3.1. Zero Truncated Poisson (ZTP) Regression Model Let, \\(\\:{Y}_{i}\\) represents the number of children ever born to the ever-married women of their reproductive age in Bangladesh. At least one child is born among each of the women, so CEB follows a Zero Truncated Poisson distribution with the vector of explanatory variables \\(\\:{x}_{i}\\) . The probability mass function (pmf) for ZTP distribution with mean \\(\\:{\\mu\\:}_{i}\\) is given by $$\\:P\\left({Y}_{i}={y}_{i}\\right|\\:{y}_{i}>0)=\\frac{\\text{e}\\text{x}\\text{p}(-{\\mu\\:}_{i}){\\mu\\:}_{i}^{{y}_{i}}}{\\left[1-\\text{exp}\\left(-{\\mu\\:}_{i}\\right)\\right]{y}_{i}!}\\:;\\:{y}_{i}=\\text{1,2},3,\\dots\\:$$ 1 In ZTP regression, $$\\:{\\mu\\:}_{i}=\\text{e}\\text{x}\\text{p}\\left({x}_{i}^{T}\\beta\\:\\right)$$ 2 where, \\(\\:{x}_{i}=[{x}_{1i,\\:}\\:{x}_{2i},\\:\\dots\\:,{x}_{ki}]\\) is the covariate vector for the observation and \\(\\:\\beta\\:\\) is the coefficient vector. 3.2. Zero Truncated Generalized Poisson (ZTGP) Regression Model For parameter µ and θ and y be the number of children ever born, the probability mass function for generalized Poisson distribution with mean µ and variance µ(1 + θµ) 2 , $$\\:f\\left(y;{\\mu\\:}\\right)=\\:\\frac{1}{y!}{\\left(\\frac{{\\mu\\:}}{1+{\\theta\\:}{\\mu\\:}}\\right)}^{y}{\\left(1+{\\theta\\:}y\\right)}^{y-1}\\:exp\\left\\{-\\frac{{\\mu\\:}\\left(1+{\\theta\\:}y\\right)}{1+{\\theta\\:}{\\mu\\:}}\\right\\}$$ 3 Here, y = 0, 1, 2, …, and θ is interpreted as a dispersion parameter. The outcome variable of the study is CEB which simply means that the variable can never assume zero values [ 21 ]. To model zero-truncated and under-dispersed data, we find the formula for calculating zero counts, subtract 1 from it, and then divide the count distribution probability function by what is left over. Then the zero-truncated generalized Poisson distribution ZTGP(µ, θ) can be expressed as, $$\\:f\\left(y;{\\mu\\:}|\\text{y}>0\\right)=\\:\\frac{1}{y!\\left\\{\\text{exp}\\left(\\frac{{\\mu\\:}}{1+{\\theta\\:}{\\mu\\:}}\\right)-1\\right\\}}{\\left(\\frac{{\\mu\\:}}{1+{\\theta\\:}{\\mu\\:}}\\right)}^{y}{\\left(1+{\\theta\\:}y\\right)}^{y-1}\\:exp\\left\\{-\\frac{{\\mu\\:}\\left({\\theta\\:}y\\right)}{1+{\\theta\\:}{\\mu\\:}}\\right\\}$$ 4 with y = 1, 2, 3, 4, …, and θ = 0 for zero-truncated Poisson model [ 22 ]. Assume that each observation follows zero-truncated generalized Poisson model, i.e. y i ~ ZTGP(µ i , θ i ), i = 1, 2, 3, …, n. Following the log-link function as, $$\\:\\text{log}{{\\mu\\:}}_{i}=\\:{x}_{i}^{T}\\beta\\:$$ 5 where, \\(\\:{x}_{i}^{T}\\:\\) is the vector of covariates as \\(\\:{x}_{i}^{T}=\\left({x}_{i1},{x}_{i2},\\dots\\:,{x}_{ip}\\right),\\:\\:i=1,\\:2,\\:\\dots\\:,\\:n\\:\\) and \\(\\:\\beta\\:\\) being a p-dimensional vector of regression coefficients. 4. Results 4.1. Descriptive Statistics Table 1 Descriptive statistics for the explanatory variables Variables Category n % \\(\\:{\\varvec{\\chi\\:}}^{2}\\) \\(\\:\\varvec{p}-\\varvec{v}\\varvec{a}\\varvec{l}\\varvec{u}\\varvec{e}\\) Women’s age 15–19 739 4.35 7619.6 \\(\\:<0.001\\) 20–24 2,546 14.99 25–29 3,192 18.79 30–34 3,178 18.71 35–39 3,099 18.24 40–44 2,307 13.58 45–49 1,926 11.34 Residence type Urban 5,906 34.77 183.79 \\(\\:<0.001\\) Rural 11,081 65.23 Religion Muslim 15,192 89.43 140.90 \\(\\:<0.001\\) Hinduism 1,590 9.36 Others 205 1.21 Division Barisal 1,816 10.69 697.22 \\(\\:<0.001\\) Chattogram 2,535 14.92 Dhaka 2,552 15.02 Khulna 2,225 13.10 Mymensingh 1,835 10.80 Rajshahi 2,169 12.77 Rangpur 2,076 12.22 Sylhet 1,779 10.47 Women’s Education No 2,347 13.82 3264.40 \\(\\:<0.001\\) Primary 4,672 27.50 Secondary 7,662 45.11 Higher 2,306 13.58 Women’s Occupation Unemployed 10,722 63.12 379.03 \\(\\:<0.001\\) Physical labor 323 1.90 Service or Business 4,777 28.12 Household or others 1,165 6.86 Contraceptive Method No 5,311 31.27 372.77 \\(\\:<0.001\\) Traditional 1,702 10.02 Modern 9,974 58.72 Fertility Preference 1 4,525 26.64 7143.6 \\(\\:<0.001\\) 2 995 5.86 3 or more 11,467 67.50 Pregnancy Losses 0 12,800 75.35 130.49 \\(\\:<0.001\\) 1 3,325 19.57 2 or more 862 5.07 Husband’s Age 18–27 1,504 8.85 5786.6 \\(\\:<0.001\\) 28–37 5,340 31.44 38–47 5,563 32.75 48–57 3,461 20.37 58+ 1,119 6.59 Husband’s Education No 3,848 22.65 1977.5 \\(\\:<0.001\\) Primary 4,927 29.00 Secondary 5,329 31.37 Higher 2,883 16.97 Husband’s Occupation Unemployed 573 3.37 456.97 \\(\\:<0.001\\) Physical labor 51 0.30 Service or Business 8,623 50.76 Household or others 7,740 45.56 Household Head Male 14,966 88.10 20.63 0.023 Female 2,021 11.90 Wealth Index Poor 6,435 37.88 418.74 \\(\\:<0.001\\) Middle 3,381 19.90 Rich 7,171 42.21 Total - 16,987 100 - - Table 1 presents the percentage distribution of demographic and socio-economic variables used in this study. It reveals that the highest proportion of women was in the 25–29 age group (18.79%), whereas the 15–19 age group had the smallest proportion (4.35%). Additionally, more than half of the women lived in rural areas, with the majority following the Muslim religion. The study population was evenly distributed across all divisions, with the highest proportion in Dhaka (15.02%) and the lowest in Sylhet (10.47%). Moreover, approximately half of the women had a secondary education, while only 13.82% had no formal education. However, the majority of the women were unemployed, and there was a varied preference for contraceptive methods, with 58.72% using modern methods, followed by 31.27% using no method and 10.02% using traditional methods. Additionally, two-thirds of the women preferred three or more children, and approximately 75% of the women experienced zero pregnancy losses. Furthermore, more than four-fifths of the women’s husbands were in the 28–37, 38–47, and 48–57 age groups combined, with about one-third of them having secondary education, followed by primary education. Additionally, the majority of household heads were male, with occupations in either service/business or homemaker/other categories, and about 42% of households belonged to the rich wealth index, followed by poor (37.88%). To investigate the bivariate association between CEB and the variables, Chi-square measures of association were used, and it was found that all predictor variables had a significant effect on CEB at a 5% level of significance. Figure 2 illustrates the frequency distribution of the number of children ever born (CEB) to ever-married women aged 15–49 years. The highest number of mothers, 6,201 (36.50%), had two children. This is followed by 4,241 mothers with one child and 3,823 mothers with three children. Additionally, the number of mothers who had four or more children was consistently below 1,700 for each category. Table 2 Descriptive statistics of children ever born (CEB) Children ever born (CEB) Mean 2.39 Variance 1.58 Dispersion ratio 0.505 Table 2 shows that the mean number of children ever born (CEB) in the study population was 2.39, with a variance of 1.58. The variance of CEB is lower than its mean, suggesting under-dispersion. The Pearson residuals dispersion test yielded a dispersion ratio of 0.505, which is significantly less than 1, confirming under-dispersion. 4.2. Model Estimates Table 3 Estimates of the parameters of the Zero Truncated Poisson and Zero Truncated Generalized Poisson regression models Variables Zero Truncated Poisson Zero Truncated Generalized Poisson \\(\\:\\varvec{\\beta\\:}\\) Sig. IRR CI \\(\\:\\varvec{\\beta\\:}\\) Sig. IRR CI Intercept 1 -1.876 < 0.001 0.153 (0.119, 0.196) -0.041 0.246 0.95 (0.89, 1.02) Intercept 2 - - - - -0.351 < 0.001 0.70 (0.69, 0.71) Women’s age 15–19* - - - - - - - - 20–24 1.144 < 0.001 3.14 (2.46, 3.99) 0.459 < 0.001 1.58 (1.53, 1.63) 25–29 1.603 < 0.001 4.96 (3.89, 6.33) 0.635 < 0.001 1.88 (1.81, 1.96) 30–34 1.773 < 0.001 5.88 (4.61, 7.51) 0.783 < 0.001 2.18 (2.09, 2.28) 35–39 1.817 < 0.001 6.15 (481, 7.87) 0.846 < 0.001 2.33 (2.22, 2.44) 40–44 1.922 < 0.001 6.83 (5.34, 8.75) 0.931 < 0.001 2.53 (2.41, 2.67) 45–49 2.015 < 0.001 7.50 (5.85, 9.61) 1.007 < 0.001 2.73 (2.59, 2.89) Residence type Urban* - - - - - - - - Rural 0.059 < 0.001 1.06 (1.03, 1.09) 0.033 < 0.001 1.03 (1.01, 1.05) Religion Muslim* - - - - - - - - Hinduism -0.279 < 0.001 0.75 (0.72, 0.79) -0.081 < 0.001 0.92 (0.90, 0.94) Others -0.343 < 0.001 0.70 (0.63, 0.79) -0.279 < 0.001 0.75 (0.70, 0.81) Division Barisal* - - - - - - - - Chattogram 0.171 < 0.001 1.18 (1.13, 1.24) 0.144 < 0.001 1.15 (1.12, 1.18) Dhaka -0.019 0.421 0.98 (0.93, 1.02) -0.015 0.281 0.98 (0.95, 1.01) Khulna -0.175 < 0.001 0.83 (0.79, 0.88) -0.154 < 0.001 0.85 (0.83, 0.88) Mymensingh 0.078 0.001 1.08 (1.02, 1.13) 0.136 < 0.001 1.14 (1.11, 1.17) Rajshahi -0.187 < 0.001 0.82 (0.78, 0.87) -0.108 < 0.001 0.89 (0.87, 0.92) Rangpur -0.066 0.009 0.93 (0.89, 0.98) -0.070 < 0.001 0.93 (0.90, 0.96) Sylhet 0.202 < 0.001 1.22 (1.16, 1.28) 0.177 < 0.001 1.19 (1.15, 123) Women’s Education No* - - - - - - - - Primary -0.045 0.007 0.95 (0.92, 0.98) -0.042 < 0.001 0.95 (0.93, 0.97) Secondary -0.143 < 0.001 0.86 (0.83, 0.89) -0.154 < 0.001 0.85 (0.83, 0.87) Higher -0.394 < 0.001 0.67 (0.62, 0.72) -0.347 < 0.001 0.70 (0.68, 0.73) Mother’s Occupation Unemployed* - - - - - - - - Physical labor -0.005 0.886 0.99 (0.92, 1.07) -0.002 0.928 0.99 (0.94, 1.04) Service or Business 0.025 0.065 1.02 (0.99, 1.05) 0.043 < 0.001 1.04 (1.02, 1.06) Household or others -0.074 0.006 0.92 (0.87, 0.97) -0.077 < 0.001 0.92 (0.89, 0.95) Contraceptive Method No* - - - - - - - - Traditional 0.070 < 0.001 1.07 (1.02, 1.11) 0.102 < 0.001 1.10 (1.08, 1.13) Modern 0.112 < 0.001 1.11 (1.08, 1.15) 0.076 < 0.001 1.07 (1.06, 1.09) Fertility Preference 1* - - - - - - - - 2 0.465 < 0.001 1.59 (1.47, 1.71) 0.161 < 0.001 1.17 (1.12, 1.22) 3 or more 0.731 < 0.001 2.07 (1.97, 2.18) 0.392 < 0.001 1.48 (1.44, 1.51) Pregnancy Losses 0* - - - - - - - - 1 -0.020 0.159 0.97 (0.95, 1.00) -0.028 0.001 0.97 (0.95, 0.98) 2 or more -0.038 0.137 0.96 (0.91, 1.01) -0.031 0.056 0.96 (0.93, 1.00) Husband’s Age 18–27* - - - - - - - - 28–37 0.318 < 0.001 1.37 (1.23, 1.53) 0.145 < 0.001 1.15 (1.12, 1.19) 38–47 0.385 < 0.001 1.47 (1.31, 1.64) 0.195 < 0.001 1.21 (1.16, 1.26) 48–57 0.398 < 0.001 1.49 (1.32, 1.67) 0.223 < 0.001 1.25 (1.19, 1.30) 58+ 0.439 < 0.001 1.55 (1.37, 1.75) 0.266 < 0.001 1.30 (1.23, 1.37) Husband’s Education No* - - - - - - - - Primary -0.026 0.096 0.97 (0.94, 1.00) -0.024 0.013 0.97 (0.95, 0.99) Secondary -0.095 < 0.001 0.90 (0.87, 0.94) -0.071 < 0.001 0.93 (0.91, 0.95) Higher -0.137 < 0.001 0.87 (0.82, 0.92) -0.133 < 0.001 0.87 (0.87, 0.90) Husband’s Occupation Unemployed* - - - - - - - - Physical labor 0.052 0.602 1.05 (0.86, 1.28) 0.031 0.637 1.03 (0.90, 1.17) Service or Business 0.030 0.301 1.03 (0.97. 1.09) 0.027 0.165 1.02 (0.98, 1.06) Household or others 0.027 0.364 1.02 (0.93, 1.01) 0.004 0.837 1.00 (0.96, 1.04) Household Head Male* - - - - - - - - Female -0.023 0.224 0.97 (0.93, 1.01) -0.033 0.007 0.96 (0.94, 0.99) Wealth index Poor* - - - - - - - - Middle -0.064 < 0.001 0.93 (0.90, 0.96) -0.048 < 0.001 0.95 (0.93, 0.97) Rich -0.090 < 0.001 0.91 (0.88, 0.94) -0.077 < 0.001 0.92 (0.90, 0.94) *Reference category Intercept 1: Baseline log count when all predictors are at reference levels Intercept 2: Intercept for dispersion/shape parameter in ZTGP model Table 3 presents the regression coefficients, along with their incidence rate ratios (IRRs), and corresponding confidence intervals from ZTP and ZTGP models for the response variable \"children ever born\" (CEB). The results from the zero-truncated Poisson model revealed that women's age, place of residence, contraceptive use, fertility preference, husband's age, and husband's occupation had a statistically significant positive effect on CEB. Conversely, religion, women's education, women's occupation (except for service or business), pregnancy losses, husband's education, and wealth index had a negative effect on CEB. Additionally, it was found that women aged 20–24 had approximately three times as many children as women aged 15–19, and this number increased to nearly five times for the 25–29 age group compared to the reference category. This increasing trend of childbearing continued for subsequent age groups. Rural women had 6% more children compared to those in urban areas. Regarding the number of children, the religion of mothers played a significant role. Non-Muslim women had fewer children than Muslim women, with Hindu women having an IRR of 0.75 (CI: 0.72–0.79) and women of other religions having an IRR of 0.70 (CI: 0.63–0.79). Moreover, women from Chittagong, Mymensingh, and Sylhet had more children than those from Barisal, while women from Dhaka, Khulna, Rajshahi, and Rangpur had fewer children than the reference category. The level of education had a significant effect on the number of children ever born. An increase in mothers' educational attainment significantly reduced the rate of children ever born. Specifically, women with primary education had 5% fewer children than women with no formal education, while those with secondary education had 14% fewer children. Women with higher education had 33% fewer children compared to those with no formal education. Furthermore, the husband's education also had a similar effect on the number of children ever born. Women whose husbands had the highest level of education had 13% fewer children compared to those whose husbands had no formal education, followed by husbands with secondary and primary education, who had 10% and 3% fewer children, respectively, compared to those with no formal education. Additionally, women engaged in household or other types of work had fewer children compared to unemployed women, while the other two groups showed no significant difference in the expected number of children. Women whose husbands were engaged in household or other types of work had 2% more children, and those whose husbands were physical laborers had 5% more children, compared to women with unemployed husbands. For the wealth index, it was found that women from the richest families tended to have 9% fewer children compared to those from the poorest families, and women from middle-class families had 7% fewer children than those from poor families. The results obtained from the zero-truncated generalized Poisson (ZTGP) model were mostly consistent with the zero-truncated Poisson (ZTP) model output, except for the variables women’s age, fertility preferences and women’s husband’s age. Notably, the incidence rate ratio (IRRs) and confidence intervals (CIs) for women’s age, fertility preferences and women’s husband’s age were lower in the ZTGP model than in the ZTP model, likely because the response variable was under-dispersed. For women’s age categories, the IRRs and corresponding 95% Confidence Intervals (CIs) were as follows: 1.58 (CI: 1.53–1.63) for ages 20–24, 1.88 (CI: 1.81–1.96) for ages 25–29, 2.18 (CI: 2.09–2.28) for ages 30–34, 2.33 (CI: 2.22–2.44) for ages 35–39, 2.53 (CI: 2.41–2.67) for ages 40–44, and 2.73 (CI: 2.59–2.89) for ages 45–49. Indicating that women in the 20–24 age group had 58% higher children than the 15–19 age group, and it was 88% for the 25–29 age group compared to the reference category. This trend continued to increase across older age groups. With respect to fertility preferences, the Incidence IRRs and CIs were 1.17 (CI: 1.12–1.22) for women preferring two children and 1.48 (CI: 1.44–1.51) for those preferring three or more children. For the husband's age categories, the IRRs and corresponding CIs were as follows: 1.15 (CI: 1.12–1.19) for ages 28–37, 1.21 (CI: 1.16–1.26) for ages 38–47, 1.25 (CI: 1.19–1.30) for ages 48–57, and 1.30 (CI: 1.23–1.37) for ages 58 and above. Women whose husbands were aged 28–37 had, on average, 15% more children compared to those whose husbands were aged 18–27. This increasing trend in fertility persisted across the subsequent age groups, indicating a positive association between paternal age and the number of children. Table 4 Model selection criteria for children ever born Models Log Likelihood AIC BIC Zero Truncated Poisson -21584.21 43252.42 43577.51 Zero Truncated Generalized Poisson -20300.34 40686.68 41019.51 To evaluate the best-fitted statistical model between the ZTGP and ZTP for an under-dispersed dataset, the well-known model selection criteria-log likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used. The output of the selection criterion values, presented in Table 4 , where the log-likelihood value (-20300.34) for the ZTGP model was higher than that of the ZTP model, suggests that the ZTGP performed better compared to the ZTP model, a conclusion supported by the principle that higher log-likelihood values indicate better model fitting. Additionally, this finding was also consistent with the AIC and BIC model selection criteria. The ZTGP model had lower AIC (40686.68) and BIC (41019.51) values than the ZTP model’s AIC (43252.42) and BIC (43577.51) values, which confirms that the ZTGP performed better than the ZTP model based on the lower AIC and BIC values. Therefore, based on the model selection metrics comparing the two fitted models, the Zero-Truncated Generalized Poisson (ZTGP) model was considered the best-fitting model for the under-dispersion dataset in this study. 5. Discussion The study identifies significant factors which influence the number of children ever born to women of reproductive age, providing important insights into the role of demographic variables. Maternal age has a positive association with the number of children, with older mothers tending to have more children compared to the younger mothers. This trend may reflect cumulative reproductive opportunities over time, as older women have had more years to bear children, aligning with life course theories of fertility. Geographic and religious determinants also play a significant role in the number of children, with rural women having slightly higher fertility rates than urban women, possibly due to differences in access to resources, education, cultural norms, or economic pressures that encourage larger families in rural settings. These findings are consistent with those reported in other studies [ 23 , 24 ]. Muslim women also reported higher fertility rates compared to women of other religious affiliations, which could be attributed to cultural or religious values that emphasize larger family sizes, consistent with prior studies on religion and fertility [ 25 ]. In addition, maternal education emerged as a critical determinant, with higher maternal education levels associated with lower fertility intentions and outcomes, which supports the hypothesis that maternal education level increases awareness of family planning, child-rearing costs, and access to resources, leading to more deliberate fertility decisions [ 26 , 27 ]. Educated mothers tend to prioritize quality over quantity in child bearing, focusing on providing better care and opportunities for fewer children [ 28 – 30 ]. Similarly, paternal education was inversely related to fertility [ 31 ], likely due to increased awareness of family planning and the economic demands of modern lifestyles, which may discourage larger families [ 32 ]. The study also highlighted the influence of maternal occupation, where unemployed mothers and those engaged in business or service sectors exhibited almost similar fertility patterns, suggesting that employment type may not significantly differentiate fertility outcomes in this context. However, a study conducted in Bangladesh revealed that working status, specifically employment in the formal sector was related to lower fertility rates because employed women faced higher opportunity costs of parenting and had more access to family planning tools [ 33 ]. Wealth index showed a significant effect on the number of children, with poorer families having more children compared to wealthier families [ 34 ]. This aligns with economic theories of fertility, where resource constraints in poorer households may reduce the perceived costs of additional children, while wealthier families prioritize investments in fewer children [ 35 ]. 6. Conclusion Leveraging nationally representative demographic and health survey data, this study demonstrates that the zero-truncated generalized Poisson (ZTGP) model is more appropriate than the traditional zero-truncated Poisson (ZTP) model for analyzing under-dispersed count data. The ZTGP model yielded narrower confidence intervals, suggesting improved estimation precision, likely due to its capacity to accommodate under-dispersion. Furthermore, the ZTGP model yielded lower AIC and BIC values, further confirming its superior model fit. According to the ZTGP model, mothers’ age, place of residence, religion, educational attainment, and wealth index were the most influential factors determining number of children ever born. Moreover, use of contraceptive, husbands’ age, and husbands’ education also played significant role on the outcome variable. Targeted intervention, focusing on improving maternal education, family planning decisions, and lifestyles of mothers, particularly in rural areas, could help reduce the fertility rates in overpopulated countries like Bangladesh. Given these findings, we recommend the implementation of the ZTGP model in future demographic and fertility studies involving under-dispersed count data in Bangladesh, particularly for policy formulation and targeted reproductive health interventions. Abbreviations CEB: Children Ever Born ZTP: Zero-Truncated Poisson ZTGP: Zero-Truncated Generalized Poisson IRR: Incidence Rate Ratio CI: Confidence Interval AIC: Akaike Information Criterion BIC: Bayesian Information Criterion Declarations Ethics approval and consent to participate This study used publicly available, anonymized data from the BDHS 2022. Ethical approval and informed consent were obtained by the original survey organizers. Clinical trial number Not applicable Consent for publication Not applicable Availability of data and materials The dataset used in this study is publicly available from the Demographic and Health Surveys (DHS) Program website: The DHS Program - Bangladesh: Standard DHS, 2022 Dataset Competing interests The authors declare that they have no competing interests. Funding This research was conducted without any external funding or financial support. Authors' contributions Sanjoy Kumar Roy conceptualized the study, developed the methodology, conducted data curation and formal analysis, and supervised the project. Ajoy Rjbongshi contributed to conceptualization, performed data analysis, contributed to interpretation, and conducted formal analysis. Nandita Scholastica Costa contributed to writing the original draft, review and editing, and interpretation. Md. Asrafur Rahman Ashiq contributed to the original draft, review and editing, and methodology. All authors read and approved the final manuscript. Acknowledgements Not applicable References DESA, U., Population Division (2013b). World Population Prospects: The 2012 Revision, 2012. 1 . Research, N.I.o.P., et al., Bangladesh Demographic and Health Survey 2011 . 2013, NIPORT, Mitra and Associates, and ICF International: Dhaka, Bangladesh. Research, N.I.o.P., et al., Bangladesh Demographic and Health Survey 2007 . 2009, NIPORT, Mitra and Associates, and Macro International: Dhaka, Bangladesh. Roy, S. and S.M.I. Hossain, Fertility differential of women in Bangladesh demographic and health survey 2014. Fertility research and practice, 2017. 3 (1): p. 16. Research, N.I.o.P., et al., Bangladesh Demographic and Health Survey 2014 . 2016, NIPORT, Mitra and Associates, and ICF International: Dhaka, Bangladesh. Bora, J.K., et al., Revisiting the causes of fertility decline in Bangladesh: the relative importance of female education and family planning programs. Asian Population Studies, 2023. 19 (1): p. 81-104. Khan, M.M., et al., Effect of socio-economic, cultural and demographic factors on woman reproductive health. 2009. Breschi, M., et al., Social and economic determinants of reproductive behavior before the fertility decline. The case of six Italian communities during the nineteenth century. European Journal of Population, 2014. 30 (3): p. 291-315. Smallwood, S. and J. Chamberlain, Replacement fertility, what has it been and what does it mean? Population trends, 2005. 119 (Spring): p. 16-27. Parr, N., An alternative perspective on the changing relationships between fertility and replacement level in european countries. Population and Development Review, 2023. 49 (2): p. 255-278. Nahar, M.Z. and M.S. Zahangir, Determinants of fertility in Bangladesh: Evidence from the 2014 demographic and health survey. International quarterly of community health education, 2019. 40 (1): p. 29-38. Hwang, J. and J. Ha Lee, Women's education and the timing and level of fertility. International Journal of Social Economics, 2014. 41 (9): p. 862-874. Pandey, R. and C. Kaur, Modelling fertility: an application of count regression models. Chinese Journal of Population Resources and Environment, 2015. 13 (4): p. 349-357. Tomal, J.H., J.R. Khan, and A.S. Wahed, Weighted Bayesian Poisson regression for the number of children ever born per woman in Bangladesh. Journal of Statistical Theory and Applications, 2022. 21 (3): p. 79-105. Ibeji, J.U., et al., Modelling fertility levels in Nigeria using Generalized Poisson regression-based approach. Scientific African, 2020. 9 : p. e00494. Akib, M.M.H. and B. Pal, Analyzing Number of Children Ever Born in Bangladesh Using Generalized Poisson Regression Model. Dhaka University Journal of Science, 2024. 72 (2): p. 1-6. Masanja, G.F., Modeling Fertility Determinants among Women of the Reproductive Age in Western Tanzania Using Negative Binomial Regression. Journal of Economics, 2020. 8 (1): p. 89-103. Adesina, O.S., Model for Bayesian zero truncated count data. 2019. Haque, M.E., T.S. Mallick, and W. Bari, Zero truncated Poisson model: an alternative approach for analyzing count data with excess zeros. Journal of Statistical Computation and Simulation, 2022. 92 (3): p. 476-487. Muniswamy, B. and M. Lavanya, ZERO TRUNCATED POISSON REGRESSION MODEL FOR REPRODUCTIVE PATTERNS ON COUNT DATA. Reliability: Theory & Applications, 2025. 20 (1 (82)): p. 1070-1088. Gabreyohannes, E., Correlates of the number of children ever born to women in rural Ethiopia: Application of truncated generalized Poisson model with exposure. 2024. Wei-hua, Z., F. Yu, and L. Ze-an, Zero-truncated generalized Poisson regression model and its score tests. Journal of East China Normal University (Natural Science), 2010. 2010 (1): p. 17. Rahman, A., et al., Determinants of children ever born among ever-married women in Bangladesh: evidence from the Demographic and Health Survey 2017–2018. BMJ open, 2022. 12 (6): p. e055223. Karmakar, S., Patterns of climate change and its impacts in northwestern Bangladesh. J. Eng. Sci, 2019. 10 : p. 33-48. Bein, C., A.H. Gauthier, and M. Mynarska, Religiosity and fertility intentions: can the gender regime explain cross-country differences? European Journal of Population, 2021. 37 (2): p. 443-472. Akinyoade, A. and K.S. Dickson, Women’s education and higher fertility outcomes in Ghana: an exploratory study. Afriche e Orienti, 2023. 26 (2): p. 28-51. aisal Ahmmed, F. and M. Nasser, Modeling and Predicting of Children Ever Born in Bangladesh. 2012. Amara, M., Multilevel modelling of individual fertility decisions in Tunisia: Household and regional contextual effects. Social Indicators Research, 2015. 124 (2): p. 477-499. Arokiasamy, P., K. McNay, and R.H. Cassen, Female education and fertility decline: recent developments in the relationship. Economic and Political Weekly, 2004: p. 4503-4507. Chaudhury, R.H., The influence of female education, labor force participation, and age at marriage on fertility behavior in Bangladesh. Social Biology, 1984. 31 (1-2): p. 59-74. Breierova, L. and E. Duflo, The impact of education on fertility and child mortality: Do fathers really matter less than mothers? 2004, National bureau of economic research Cambridge, Mass., USA. Kebede, E., A. Goujon, and W. Lutz, Stalls in Africa’s fertility decline partly result from disruptions in female education. Proceedings of the National Academy of Sciences, 2019. 116 (8): p. 2891-2896. Chowdhury, S., M.M. Rahman, and M.A. Haque, Role of women's empowerment in determining fertility and reproductive health in Bangladesh: a systematic literature review. AJOG global reports, 2023. 3 (3): p. 100239. Dribe, M., et al., Socio-economic status and fertility decline: Insights from historical transitions in Europe and North America. Population studies, 2017. 71 (1): p. 3-21. Willis, R.J., A new approach to the economic theory of fertility behavior. Journal of political Economy, 1973. 81 (2, Part 2): p. S14-S64. Additional Declarations No competing interests reported. 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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-7312541\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":512570296,\"identity\":\"ed55b71d-2781-48ae-b471-671baaf474f5\",\"order_by\":0,\"name\":\"Sanjoy Kumar Roy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Dhaka\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sanjoy\",\"middleName\":\"Kumar\",\"lastName\":\"Roy\",\"suffix\":\"\"},{\"id\":512570297,\"identity\":\"d20ea4d6-182d-43a0-aa94-a89464cc7dc3\",\"order_by\":1,\"name\":\"Ajoy Rjbongshi\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYHCCNBAhx8AOF0ggTosxAzMJWthARGID0Vr4Zx949uDjjtr0/mbmoxt/VNxh4GfPMcCrReJcQrrhzDPHc2ccZku7zXPmGYNkzxv8WhjOMKRJ87Ydy93AzGN2m7HtMIPBDQK2yEO1pBsw83+7+ROoxZ6QFgOIlpoEA2Yethu8IFskCGgxPMMA9EvbAUOgX8yAfjnMI3HmWQFeLXJneNIefGyrk+dvb35280fFYTn+9uQNeLUwMPAkAInDCC4B5SDAfgBI1BGhcBSMglEwCkYsAABnXEfwH9PsfAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"University of Dhaka\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ajoy\",\"middleName\":\"\",\"lastName\":\"Rjbongshi\",\"suffix\":\"\"},{\"id\":512570298,\"identity\":\"5e57966d-ef0d-40e4-b46e-bf60923640e1\",\"order_by\":2,\"name\":\"Nandita Scholastica Costa\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Dhaka\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nandita\",\"middleName\":\"Scholastica\",\"lastName\":\"Costa\",\"suffix\":\"\"},{\"id\":512570299,\"identity\":\"676a8f9b-bd56-45c5-a7bf-275a7ca9f35c\",\"order_by\":3,\"name\":\"Md. Asrafur Rahman Ashiq\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Southeast University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Md.\",\"middleName\":\"Asrafur Rahman\",\"lastName\":\"Ashiq\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-06 19:08:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7312541/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7312541/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":91078348,\"identity\":\"26de62ad-cd6c-49e6-802c-61717218428d\",\"added_by\":\"auto\",\"created_at\":\"2025-09-11 11:20:02\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":96432,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHousehold selection process in BDHS 2022\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7312541/v1/37613fd135541e5288b133e6.png\"},{\"id\":91076670,\"identity\":\"caca1a9f-a95d-476a-a1cb-fe31e46d783b\",\"added_by\":\"auto\",\"created_at\":\"2025-09-11 11:12:02\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":26811,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of number of children ever born.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7312541/v1/341fb74e120eaa46c983534b.png\"},{\"id\":91080353,\"identity\":\"7e6f0160-4a92-4063-91c1-4e5a57871a7c\",\"added_by\":\"auto\",\"created_at\":\"2025-09-11 11:36:04\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1704786,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7312541/v1/3fec041a-c909-4525-bc4b-a3710dbc3d38.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Zero-Truncated Generalized Poisson Approach to Under-Dispersed Count Data: Fertility Patterns in Bangladesh, 2022\",\"fulltext\":[{\"header\":\"1. Background\",\"content\":\"\\u003cp\\u003eThe number of children ever born (CEB) is a fundamental indicator of the population dynamics, influencing a country's population size and structure. It represents the total number of live births experienced by married women aged 15 years or older, encompassing both living and deceased children at the time of data collection [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. According to the report of Worldometer in 2025, the global population is estimated at approximately 8.23\\u0026nbsp;billion, with a median age of 30.9 years and a total fertility rate (TFR) of 2.2. In Bangladesh, the population has reached at 175.7\\u0026nbsp;million with 42.6% residing in urban areas and a median age of 26 years, according to the same source. Based on the successive surveys of Bangladesh Demographic and Health Survey, the total fertility rate (TFR) declined from approximately 6.3 in 1971\\u0026ndash;75 to about 2.7 by 2007, and further to 2.3 by 2011 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. According to the BDHS 2014 and 2022 reports, the TFR in Bangladesh has remained at 2.3 children per woman, a level that has not changed since 2011 [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Women's education plays a crucial role in reducing fertility preferences, outweighing the influence of other factors. The rise in female educational attainment and the spread of preferences for smaller families appear to be key drivers behind the notable fertility decline in Bangladesh [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eGlobally, fertility research has consistently emphasized the variable Children Ever Born (CEB), with cross-national studies identifying distinct social and economic determinants shaping reproductive behavior [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. These forces often contribute to postponed family formation, exacerbating fertility decline. Replacement fertility is the TFR at which a population replaces itself without migration, typically estimated TFR 2.1 in developed countries [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. A sustained TFR below this level leads to population decline unless offset by immigration [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eIn Bangladesh, although several studies have explored fertility patterns using BDHS data, including those by a significant gap remains in the application of appropriate statistical models to handle the nature of the Children Ever Born (CEB) variable. These studies primarily examined associations between fertility and factors such as education, age at first marriage, religion, and wealth index. A study conducted using BDHS 2014 data found that fertility was significantly higher among Muslim, illiterate, and rural women, particularly those with early age at first birth and limited media exposure. To investigate this, the researchers used Poisson regression for analyzing count-based outcomes like number of children [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Similarly, another study highlights the influence of women's education on cumulative fertility, revealing a strong inverse relationship between education level and number of children ever born [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. More recently, multilevel regression across seven BDHS survey rounds demonstrated that female education had the most substantial effect on fertility decline even more influential than family planning initiatives [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eA study models fertility behavior in India using Poisson and multinomial regression, revealing that religion, caste, wealth, female education, and occupation significantly influence birth counts, with a strong preference for two children [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. In a study weighted Bayesian Poisson regression model was applied using MICS-2019 data to estimates of fertility determinants in Bangladesh [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Using NDHS 2013 data, in a study modeled total children ever born among Nigerian women using Negative Binomial and Generalized Poisson regression, identifying age at first birth, age of household head, religion, and urban-rural status as significant factors, with the Generalized Poisson model best addressing dispersion issues [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. A generalized Poisson regression (GPR) model also used to BDHS 2017-18 data and demonstrated that standard Poisson models underestimate variance when dealing with under-dispersed fertility data [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. In contrast, a study analyzing fertility determinants in Western Tanzania applied a negative binomial regression model to account for over-dispersion in count data, yielding more robust estimates than the standard Poisson approach [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. However, since CEB data typically excludes women who have not given birth (i.e., zero-truncation), this assumption leads to biased estimates and incorrect inferences. These findings emphasize the need for more sophisticated statistical approaches, such as zero-truncated Poisson or Bayesian multi-level Poisson to accurately capture fertility dynamics [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Without accounting for the structural properties of CEB data, policy recommendations based on flawed models may misguide interventions aimed at reducing fertility disparities.\\u003c/p\\u003e\\u003cp\\u003eThis study employing Zero-Truncated Poisson (ZTP) and Zero-Truncated Generalized Poisson (ZTGP) models to more accurately analyze Children Ever Born (CEB) data. These models are specifically chosen because ZTP is suitable for count data where zero values are structurally absent, such as CEB, which only includes women who have given birth at least once [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. In a study ZTP and ZTGP models were used to capture fertility determinants in Indian states, especially among women aged 15\\u0026ndash;50, where the variance of CEB was significantly lower than the mean (mean\\u0026thinsp;=\\u0026thinsp;2.203, variance\\u0026thinsp;=\\u0026thinsp;0.601) and ZTP model performed better than ZTGP [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Moreover, another study demonstrated that Generalized Poisson regression outperforms standard Poisson and Negative Binomial models in fertility studies due to its flexibility in handling dispersion [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. By comparing these models, we aim to identify the most suitable approach for modeling CEB in Bangladesh, thus improving the robustness of the analysis.\\u003c/p\\u003e\\u003cp\\u003eThis study focusses on its methodological rigor and comprehensive scope. Unlike prior work focusing on descriptive statistics or standard models, this research employs advanced technique that suited to the data's structural properties. Since fertility indicators like CEB exhibit zero truncation and under-dispersion, this study uses ZTGP model, that are proven by literature to handle under-dispersed data. It also analyzes a wide array of predictors, including parental education, religion, wealth, fertility preferences, and providing new empirical insights into fertility trends in Bangladesh. Ultimately, the study\\u0026rsquo;s findings aim to inform data-driven interventions for fertility management and population planning, enabling policymakers and public health professionals to design context-specific strategies that promote reproductive equity and demographic sustainability.\\u003c/p\\u003e\"},{\"header\":\"2. Data and Variable Description\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1. Data Source\\u003c/h2\\u003e\\u003cp\\u003eThis study utilized data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2022 dataset. The survey was conducted by the National Institute of Population Research and Training (NIPORT), under the Ministry of Health and Family Welfare's Medical Education and Family Welfare Division. Mitra and Associates, a private research firm, carried out the fieldwork between June and December 2022. Financial support for the survey was provided by the Government of Bangladesh and the United States Agency for International Development (USAID). Technical assistance was facilitated by ICF through The DHS Program, a USAID-funded initiative that supports the implementation of population and health surveys globally.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2. Sampling Method\\u003c/h2\\u003e\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows that the survey employed a two-stage stratified sampling method. A total of 20,217 women were eligible for the full questionnaire, and 19,987 were successfully interviewed. After handling missing values, 16,987 women remained for analysis in this study.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Variable Description\\u003c/h2\\u003e\\u003cp\\u003eIn this study, children ever born (CEB) considered as the response variable, among the ever-married women at their reproductive age (15\\u0026ndash;49 years) in Bangladesh. Response variable CEB is a count variable having no zeros (1, 2, \\u0026hellip;, 11), so it is a zero truncated count data. The explanatory variables are Women\\u0026rsquo;s age (in 5-year age group, 15\\u0026ndash;19, \\u0026hellip;, 44\\u0026ndash;49), Residence type (Urban, Rural), Religion (Muslim, Hinduism, Others), Division (Barisa, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, Sylhet), Women\\u0026rsquo;s education (No, Primary, Secondary, Higher), Women\\u0026rsquo;s occupation (Unemployed, Physical labor, Service or Business, Household or others), Contraceptive Method (No, Traditional, Modern), Fertility preference (1, 2, 3 or more), Pregnancy losses (0, 1, 2 or more), Husband\\u0026rsquo;s Age (18\\u0026ndash;27,\\u0026hellip;, 48\\u0026ndash;57, 58+), Husband\\u0026rsquo;s education (No, Primary, Secondary, Higher), Husband\\u0026rsquo;s occupation (Unemployed, Physical labor, Service or Business, Household or others), Household Head (Male, Female), Wealth index combined (Poor, Middle, Rich).\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Methodology\",\"content\":\"\\u003cp\\u003eThe Poisson regression model is the most suitable for modeling count data, as it effectively captures the non-linear interaction with explanatory variables. It is useful when events occur independently and the variance equals the mean. However, if zero values are absent in the count data and the mean and variance are not equal, Poisson regression is not suitable. For zero-truncated under dispersed count data, the Zero-Truncated Generalized Poisson model is commonly used, while for over dispersed count data, the Zero-Truncated Negative Binomial regression model is widely preferred.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1. Zero Truncated Poisson (ZTP) Regression Model\\u003c/h2\\u003e\\u003cp\\u003eLet, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{Y}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the number of children ever born to the ever-married women of their reproductive age in Bangladesh. At least one child is born among each of the women, so CEB follows a Zero Truncated Poisson distribution with the vector of explanatory variables \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{x}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003eThe probability mass function (pmf) for ZTP distribution with mean \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\mu\\\\:}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is given by\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:P\\\\left({Y}_{i}={y}_{i}\\\\right|\\\\:{y}_{i}\\u0026gt;0)=\\\\frac{\\\\text{e}\\\\text{x}\\\\text{p}(-{\\\\mu\\\\:}_{i}){\\\\mu\\\\:}_{i}^{{y}_{i}}}{\\\\left[1-\\\\text{exp}\\\\left(-{\\\\mu\\\\:}_{i}\\\\right)\\\\right]{y}_{i}!}\\\\:;\\\\:{y}_{i}=\\\\text{1,2},3,\\\\dots\\\\:$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn ZTP regression,\\u003cdiv id=\\\"Equ2\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ2\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{\\\\mu\\\\:}_{i}=\\\\text{e}\\\\text{x}\\\\text{p}\\\\left({x}_{i}^{T}\\\\beta\\\\:\\\\right)$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e2\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{x}_{i}=[{x}_{1i,\\\\:}\\\\:{x}_{2i},\\\\:\\\\dots\\\\:,{x}_{ki}]\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the covariate vector for the observation and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\beta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the coefficient vector.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2. Zero Truncated Generalized Poisson (ZTGP) Regression Model\\u003c/h2\\u003e\\u003cp\\u003eFor parameter \\u0026micro; and θ and y be the number of children ever born, the probability mass function for generalized Poisson distribution with mean \\u0026micro; and variance \\u0026micro;(1\\u0026thinsp;+\\u0026thinsp;θ\\u0026micro;)\\u003csup\\u003e2\\u003c/sup\\u003e,\\u003cdiv id=\\\"Equ3\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ3\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:f\\\\left(y;{\\\\mu\\\\:}\\\\right)=\\\\:\\\\frac{1}{y!}{\\\\left(\\\\frac{{\\\\mu\\\\:}}{1+{\\\\theta\\\\:}{\\\\mu\\\\:}}\\\\right)}^{y}{\\\\left(1+{\\\\theta\\\\:}y\\\\right)}^{y-1}\\\\:exp\\\\left\\\\{-\\\\frac{{\\\\mu\\\\:}\\\\left(1+{\\\\theta\\\\:}y\\\\right)}{1+{\\\\theta\\\\:}{\\\\mu\\\\:}}\\\\right\\\\}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e3\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eHere, y\\u0026thinsp;=\\u0026thinsp;0, 1, 2, \\u0026hellip;, and θ is interpreted as a dispersion parameter. The outcome variable of the study is CEB which simply means that the variable can never assume zero values [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. To model zero-truncated and under-dispersed data, we find the formula for calculating zero counts, subtract 1 from it, and then divide the count distribution probability function by what is left over. Then the zero-truncated generalized Poisson distribution ZTGP(\\u0026micro;, θ) can be expressed as,\\u003cdiv id=\\\"Equ4\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ4\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:f\\\\left(y;{\\\\mu\\\\:}|\\\\text{y}\\u0026gt;0\\\\right)=\\\\:\\\\frac{1}{y!\\\\left\\\\{\\\\text{exp}\\\\left(\\\\frac{{\\\\mu\\\\:}}{1+{\\\\theta\\\\:}{\\\\mu\\\\:}}\\\\right)-1\\\\right\\\\}}{\\\\left(\\\\frac{{\\\\mu\\\\:}}{1+{\\\\theta\\\\:}{\\\\mu\\\\:}}\\\\right)}^{y}{\\\\left(1+{\\\\theta\\\\:}y\\\\right)}^{y-1}\\\\:exp\\\\left\\\\{-\\\\frac{{\\\\mu\\\\:}\\\\left({\\\\theta\\\\:}y\\\\right)}{1+{\\\\theta\\\\:}{\\\\mu\\\\:}}\\\\right\\\\}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e4\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ewith y\\u0026thinsp;=\\u0026thinsp;1, 2, 3, 4, \\u0026hellip;, and θ\\u0026thinsp;=\\u0026thinsp;0 for zero-truncated Poisson model [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Assume that each observation follows zero-truncated generalized Poisson model, i.e. y\\u003csub\\u003ei\\u003c/sub\\u003e ~ ZTGP(\\u0026micro;\\u003csub\\u003ei\\u003c/sub\\u003e, θ\\u003csub\\u003ei\\u003c/sub\\u003e), i\\u0026thinsp;=\\u0026thinsp;1, 2, 3, \\u0026hellip;, n. Following the log-link function as,\\u003cdiv id=\\\"Equ5\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ5\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\text{log}{{\\\\mu\\\\:}}_{i}=\\\\:{x}_{i}^{T}\\\\beta\\\\:$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e5\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{x}_{i}^{T}\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003eis the vector of covariates as \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{x}_{i}^{T}=\\\\left({x}_{i1},{x}_{i2},\\\\dots\\\\:,{x}_{ip}\\\\right),\\\\:\\\\:i=1,\\\\:2,\\\\:\\\\dots\\\\:,\\\\:n\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003eand \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\beta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e being a p-dimensional vector of regression coefficients.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1. Descriptive Statistics\\u003c/h2\\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\\u003eDescriptive statistics for the explanatory variables\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"9\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\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\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{\\\\chi\\\\:}}^{2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\varvec{p}-\\\\varvec{v}\\\\varvec{a}\\\\varvec{l}\\\\varvec{u}\\\\varvec{e}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWomen\\u0026rsquo;s age\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e15\\u0026ndash;19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e739\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e\\u003cp\\u003e7619.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"6\\\" rowspan=\\\"7\\\"\\u003e\\u003cp\\u003e\\u003cspan 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colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,192\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30\\u0026ndash;34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,178\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35\\u0026ndash;39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,099\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40\\u0026ndash;44\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,307\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e13.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e45\\u0026ndash;49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,926\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e11.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eResidence type\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5,906\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e34.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e183.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11,081\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e65.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eReligion\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMuslim\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15,192\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e89.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e140.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHinduism\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,590\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOthers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e205\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,816\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e\\u003cp\\u003e697.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eChattogram\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,535\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.92\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,552\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,225\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e13.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,835\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10.80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,169\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e12.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,076\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e12.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,779\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWomen\\u0026rsquo;s Education\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,347\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e13.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e3264.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4,672\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e27.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7,662\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e45.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,306\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e13.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWomen\\u0026rsquo;s Occupation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnemployed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10,722\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e63.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e379.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePhysical labor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e323\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eService or Business\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4,777\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e28.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHousehold or others\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,165\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.86\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eContraceptive Method\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5,311\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e31.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e372.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTraditional\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,702\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eModern\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9,974\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e58.72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFertility Preference\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4,525\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e26.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e7143.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e995\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.86\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 or more\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11,467\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e67.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePregnancy Losses\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e12,800\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e75.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e130.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,325\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e19.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 or more\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e862\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHusband\\u0026rsquo;s Age\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18\\u0026ndash;27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,504\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e5786.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e28\\u0026ndash;37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5,340\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e31.44\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e38\\u0026ndash;47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5,563\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e32.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e48\\u0026ndash;57\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,461\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e20.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e58+\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1,119\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHusband\\u0026rsquo;s Education\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,848\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e22.65\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e1977.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4,927\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e29.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5,329\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e31.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,883\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e16.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHusband\\u0026rsquo;s Occupation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUnemployed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e573\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e456.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePhysical labor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e51\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eService or Business\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8,623\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e50.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHousehold or others\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7,740\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e45.56\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHousehold Head\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14,966\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e88.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e20.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2,021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e11.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWealth Index\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePoor\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6,435\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e37.88\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e418.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\u0026lt;0.001\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3,381\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e19.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRich\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7,171\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e42.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e16,987\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e100\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the percentage distribution of demographic and socio-economic variables used in this study. It reveals that the highest proportion of women was in the 25\\u0026ndash;29 age group (18.79%), whereas the 15\\u0026ndash;19 age group had the smallest proportion (4.35%). Additionally, more than half of the women lived in rural areas, with the majority following the Muslim religion. The study population was evenly distributed across all divisions, with the highest proportion in Dhaka (15.02%) and the lowest in Sylhet (10.47%). Moreover, approximately half of the women had a secondary education, while only 13.82% had no formal education. However, the majority of the women were unemployed, and there was a varied preference for contraceptive methods, with 58.72% using modern methods, followed by 31.27% using no method and 10.02% using traditional methods. Additionally, two-thirds of the women preferred three or more children, and approximately 75% of the women experienced zero pregnancy losses. Furthermore, more than four-fifths of the women\\u0026rsquo;s husbands were in the 28\\u0026ndash;37, 38\\u0026ndash;47, and 48\\u0026ndash;57 age groups combined, with about one-third of them having secondary education, followed by primary education. Additionally, the majority of household heads were male, with occupations in either service/business or homemaker/other categories, and about 42% of households belonged to the rich wealth index, followed by poor (37.88%). To investigate the bivariate association between CEB and the variables, Chi-square measures of association were used, and it was found that all predictor variables had a significant effect on CEB at a 5% level of significance.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e illustrates the frequency distribution of the number of children ever born (CEB) to ever-married women aged 15\\u0026ndash;49 years. The highest number of mothers, 6,201 (36.50%), had two children. This is followed by 4,241 mothers with one child and 3,823 mothers with three children. Additionally, the number of mothers who had four or more children was consistently below 1,700 for each category.\\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\\u003eDescriptive statistics of children ever born (CEB)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eChildren ever born (CEB)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariance\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDispersion ratio\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.505\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows that the mean number of children ever born (CEB) in the study population was 2.39, with a variance of 1.58. The variance of CEB is lower than its mean, suggesting under-dispersion. The Pearson residuals dispersion test yielded a dispersion ratio of 0.505, which is significantly less than 1, confirming under-dispersion.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.2. Model Estimates\\u003c/h2\\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\\u003eEstimates of the parameters of the Zero Truncated Poisson and Zero Truncated Generalized Poisson regression models\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"10\\\"\\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\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\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\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eZero Truncated Poisson\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c10\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003eZero Truncated Generalized Poisson\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\varvec{\\\\beta\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSig.\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIRR\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCI\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\varvec{\\\\beta\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSig.\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIRR\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eCI\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIntercept 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1.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.837\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e(0.96, 1.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHousehold Head\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.224\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(0.93, 1.01)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e(0.94, 0.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWealth index\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePoor*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-\\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\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.064\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(0.90, 0.96)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-0.048\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.95\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e(0.93, 0.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRich\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.090\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.91\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e(0.88, 0.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e-0.077\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.92\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e(0.90, 0.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"10\\\"\\u003e\\u003cem\\u003e*Reference category\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eIntercept 1: Baseline log count when all predictors are at reference levels\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eIntercept 2: Intercept for dispersion/shape parameter in ZTGP model\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents the regression coefficients, along with their incidence rate ratios (IRRs), and corresponding confidence intervals from ZTP and ZTGP models for the response variable \\\"children ever born\\\" (CEB). The results from the zero-truncated Poisson model revealed that women's age, place of residence, contraceptive use, fertility preference, husband's age, and husband's occupation had a statistically significant positive effect on CEB. Conversely, religion, women's education, women's occupation (except for service or business), pregnancy losses, husband's education, and wealth index had a negative effect on CEB. Additionally, it was found that women aged 20\\u0026ndash;24 had approximately three times as many children as women aged 15\\u0026ndash;19, and this number increased to nearly five times for the 25\\u0026ndash;29 age group compared to the reference category. This increasing trend of childbearing continued for subsequent age groups. Rural women had 6% more children compared to those in urban areas. Regarding the number of children, the religion of mothers played a significant role. Non-Muslim women had fewer children than Muslim women, with Hindu women having an IRR of 0.75 (CI: 0.72\\u0026ndash;0.79) and women of other religions having an IRR of 0.70 (CI: 0.63\\u0026ndash;0.79). Moreover, women from Chittagong, Mymensingh, and Sylhet had more children than those from Barisal, while women from Dhaka, Khulna, Rajshahi, and Rangpur had fewer children than the reference category. The level of education had a significant effect on the number of children ever born. An increase in mothers' educational attainment significantly reduced the rate of children ever born. Specifically, women with primary education had 5% fewer children than women with no formal education, while those with secondary education had 14% fewer children. Women with higher education had 33% fewer children compared to those with no formal education. Furthermore, the husband's education also had a similar effect on the number of children ever born. Women whose husbands had the highest level of education had 13% fewer children compared to those whose husbands had no formal education, followed by husbands with secondary and primary education, who had 10% and 3% fewer children, respectively, compared to those with no formal education. Additionally, women engaged in household or other types of work had fewer children compared to unemployed women, while the other two groups showed no significant difference in the expected number of children. Women whose husbands were engaged in household or other types of work had 2% more children, and those whose husbands were physical laborers had 5% more children, compared to women with unemployed husbands. For the wealth index, it was found that women from the richest families tended to have 9% fewer children compared to those from the poorest families, and women from middle-class families had 7% fewer children than those from poor families.\\u003c/p\\u003e\\u003cp\\u003eThe results obtained from the zero-truncated generalized Poisson (ZTGP) model were mostly consistent with the zero-truncated Poisson (ZTP) model output, except for the variables women\\u0026rsquo;s age, fertility preferences and women\\u0026rsquo;s husband\\u0026rsquo;s age. Notably, the incidence rate ratio (IRRs) and confidence intervals (CIs) for women\\u0026rsquo;s age, fertility preferences and women\\u0026rsquo;s husband\\u0026rsquo;s age were lower in the ZTGP model than in the ZTP model, likely because the response variable was under-dispersed. For women\\u0026rsquo;s age categories, the IRRs and corresponding 95% Confidence Intervals (CIs) were as follows: 1.58 (CI: 1.53\\u0026ndash;1.63) for ages 20\\u0026ndash;24, 1.88 (CI: 1.81\\u0026ndash;1.96) for ages 25\\u0026ndash;29, 2.18 (CI: 2.09\\u0026ndash;2.28) for ages 30\\u0026ndash;34, 2.33 (CI: 2.22\\u0026ndash;2.44) for ages 35\\u0026ndash;39, 2.53 (CI: 2.41\\u0026ndash;2.67) for ages 40\\u0026ndash;44, and 2.73 (CI: 2.59\\u0026ndash;2.89) for ages 45\\u0026ndash;49. Indicating that women in the 20\\u0026ndash;24 age group had 58% higher children than the 15\\u0026ndash;19 age group, and it was 88% for the 25\\u0026ndash;29 age group compared to the reference category. This trend continued to increase across older age groups. With respect to fertility preferences, the Incidence IRRs and CIs were 1.17 (CI: 1.12\\u0026ndash;1.22) for women preferring two children and 1.48 (CI: 1.44\\u0026ndash;1.51) for those preferring three or more children. For the husband's age categories, the IRRs and corresponding CIs were as follows: 1.15 (CI: 1.12\\u0026ndash;1.19) for ages 28\\u0026ndash;37, 1.21 (CI: 1.16\\u0026ndash;1.26) for ages 38\\u0026ndash;47, 1.25 (CI: 1.19\\u0026ndash;1.30) for ages 48\\u0026ndash;57, and 1.30 (CI: 1.23\\u0026ndash;1.37) for ages 58 and above. Women whose husbands were aged 28\\u0026ndash;37 had, on average, 15% more children compared to those whose husbands were aged 18\\u0026ndash;27. This increasing trend in fertility persisted across the subsequent age groups, indicating a positive association between paternal age and the number of children.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eModel selection criteria for children ever born\\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=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModels\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLog Likelihood\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAIC\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eBIC\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eZero Truncated Poisson\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-21584.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43252.42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e43577.51\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eZero Truncated Generalized Poisson\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-20300.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e40686.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e41019.51\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo evaluate the best-fitted statistical model between the ZTGP and ZTP for an under-dispersed dataset, the well-known model selection criteria-log likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used. The output of the selection criterion values, presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, where the log-likelihood value (-20300.34) for the ZTGP model was higher than that of the ZTP model, suggests that the ZTGP performed better compared to the ZTP model, a conclusion supported by the principle that higher log-likelihood values indicate better model fitting. Additionally, this finding was also consistent with the AIC and BIC model selection criteria. The ZTGP model had lower AIC (40686.68) and BIC (41019.51) values than the ZTP model\\u0026rsquo;s AIC (43252.42) and BIC (43577.51) values, which confirms that the ZTGP performed better than the ZTP model based on the lower AIC and BIC values. Therefore, based on the model selection metrics comparing the two fitted models, the Zero-Truncated Generalized Poisson (ZTGP) model was considered the best-fitting model for the under-dispersion dataset in this study.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"5. Discussion\",\"content\":\"\\u003cp\\u003eThe study identifies significant factors which influence the number of children ever born to women of reproductive age, providing important insights into the role of demographic variables. Maternal age has a positive association with the number of children, with older mothers tending to have more children compared to the younger mothers. This trend may reflect cumulative reproductive opportunities over time, as older women have had more years to bear children, aligning with life course theories of fertility. Geographic and religious determinants also play a significant role in the number of children, with rural women having slightly higher fertility rates than urban women, possibly due to differences in access to resources, education, cultural norms, or economic pressures that encourage larger families in rural settings. These findings are consistent with those reported in other studies [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Muslim women also reported higher fertility rates compared to women of other religious affiliations, which could be attributed to cultural or religious values that emphasize larger family sizes, consistent with prior studies on religion and fertility [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. In addition, maternal education emerged as a critical determinant, with higher maternal education levels associated with lower fertility intentions and outcomes, which supports the hypothesis that maternal education level increases awareness of family planning, child-rearing costs, and access to resources, leading to more deliberate fertility decisions [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Educated mothers tend to prioritize quality over quantity in child bearing, focusing on providing better care and opportunities for fewer children [\\u003cspan additionalcitationids=\\\"CR29\\\" citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Similarly, paternal education was inversely related to fertility [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e], likely due to increased awareness of family planning and the economic demands of modern lifestyles, which may discourage larger families [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. The study also highlighted the influence of maternal occupation, where unemployed mothers and those engaged in business or service sectors exhibited almost similar fertility patterns, suggesting that employment type may not significantly differentiate fertility outcomes in this context. However, a study conducted in Bangladesh revealed that working status, specifically employment in the formal sector was related to lower fertility rates because employed women faced higher opportunity costs of parenting and had more access to family planning tools [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Wealth index showed a significant effect on the number of children, with poorer families having more children compared to wealthier families [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. This aligns with economic theories of fertility, where resource constraints in poorer households may reduce the perceived costs of additional children, while wealthier families prioritize investments in fewer children [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"6. Conclusion\",\"content\":\"\\u003cp\\u003eLeveraging nationally representative demographic and health survey data, this study demonstrates that the zero-truncated generalized Poisson (ZTGP) model is more appropriate than the traditional zero-truncated Poisson (ZTP) model for analyzing under-dispersed count data. The ZTGP model yielded narrower confidence intervals, suggesting improved estimation precision, likely due to its capacity to accommodate under-dispersion. Furthermore, the ZTGP model yielded lower AIC and BIC values, further confirming its superior model fit. According to the ZTGP model, mothers\\u0026rsquo; age, place of residence, religion, educational attainment, and wealth index were the most influential factors determining number of children ever born. Moreover, use of contraceptive, husbands\\u0026rsquo; age, and husbands\\u0026rsquo; education also played significant role on the outcome variable. Targeted intervention, focusing on improving maternal education, family planning decisions, and lifestyles of mothers, particularly in rural areas, could help reduce the fertility rates in overpopulated countries like Bangladesh. Given these findings, we recommend the implementation of the ZTGP model in future demographic and fertility studies involving under-dispersed count data in Bangladesh, particularly for policy formulation and targeted reproductive health interventions.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCEB:\\u0026nbsp;\\u003c/strong\\u003eChildren Ever Born\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eZTP:\\u0026nbsp;\\u003c/strong\\u003eZero-Truncated Poisson\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eZTGP:\\u003c/strong\\u003e Zero-Truncated Generalized Poisson\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIRR:\\u003c/strong\\u003e Incidence Rate Ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCI:\\u003c/strong\\u003e Confidence Interval\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAIC:\\u0026nbsp;\\u003c/strong\\u003eAkaike Information Criterion\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBIC:\\u003c/strong\\u003e Bayesian Information Criterion\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study used publicly available, anonymized data from the BDHS 2022. Ethical approval and informed consent were obtained by the original survey organizers.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe dataset used in this study is publicly available from the Demographic and Health Surveys (DHS) Program website: The DHS Program - Bangladesh: Standard DHS, 2022 Dataset\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was conducted without any external funding or financial support.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSanjoy Kumar Roy conceptualized the study, developed the methodology, conducted data curation and formal analysis, and supervised the project. Ajoy Rjbongshi contributed to conceptualization, performed data analysis, contributed to interpretation, and conducted formal analysis. Nandita Scholastica Costa contributed to writing the original draft, review and editing, and interpretation. Md. Asrafur Rahman Ashiq contributed to the original draft, review and editing, and methodology. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eDESA, U., \\u003cem\\u003ePopulation Division (2013b).\\u003c/em\\u003e World Population Prospects: The 2012 Revision, 2012. \\u003cstrong\\u003e1\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eResearch, N.I.o.P., et al., \\u003cem\\u003eBangladesh Demographic and Health Survey 2011\\u003c/em\\u003e. 2013, NIPORT, Mitra and Associates, and ICF International: Dhaka, Bangladesh.\\u003c/li\\u003e\\n\\u003cli\\u003eResearch, N.I.o.P., et al., \\u003cem\\u003eBangladesh Demographic and Health Survey 2007\\u003c/em\\u003e. 2009, NIPORT, Mitra and Associates, and Macro International: Dhaka, Bangladesh.\\u003c/li\\u003e\\n\\u003cli\\u003eRoy, S. and S.M.I. 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Nasser, \\u003cem\\u003eModeling and Predicting of Children Ever Born in Bangladesh.\\u003c/em\\u003e 2012.\\u003c/li\\u003e\\n\\u003cli\\u003eAmara, M., \\u003cem\\u003eMultilevel modelling of individual fertility decisions in Tunisia: Household and regional contextual effects.\\u003c/em\\u003e Social Indicators Research, 2015. \\u003cstrong\\u003e124\\u003c/strong\\u003e(2): p. 477-499.\\u003c/li\\u003e\\n\\u003cli\\u003eArokiasamy, P., K. McNay, and R.H. Cassen, \\u003cem\\u003eFemale education and fertility decline: recent developments in the relationship.\\u003c/em\\u003e Economic and Political Weekly, 2004: p. 4503-4507.\\u003c/li\\u003e\\n\\u003cli\\u003eChaudhury, R.H., \\u003cem\\u003eThe influence of female education, labor force participation, and age at marriage on fertility behavior in Bangladesh.\\u003c/em\\u003e Social Biology, 1984. \\u003cstrong\\u003e31\\u003c/strong\\u003e(1-2): p. 59-74.\\u003c/li\\u003e\\n\\u003cli\\u003eBreierova, L. and E. Duflo, \\u003cem\\u003eThe impact of education on fertility and child mortality: Do fathers really matter less than mothers?\\u003c/em\\u003e 2004, National bureau of economic research Cambridge, Mass., USA.\\u003c/li\\u003e\\n\\u003cli\\u003eKebede, E., A. Goujon, and W. Lutz, \\u003cem\\u003eStalls in Africa\\u0026rsquo;s fertility decline partly result from disruptions in female education.\\u003c/em\\u003e Proceedings of the National Academy of Sciences, 2019. \\u003cstrong\\u003e116\\u003c/strong\\u003e(8): p. 2891-2896.\\u003c/li\\u003e\\n\\u003cli\\u003eChowdhury, S., M.M. Rahman, and M.A. Haque, \\u003cem\\u003eRole of women\\u0026apos;s empowerment in determining fertility and reproductive health in Bangladesh: a systematic literature review.\\u003c/em\\u003e AJOG global reports, 2023. \\u003cstrong\\u003e3\\u003c/strong\\u003e(3): p. 100239.\\u003c/li\\u003e\\n\\u003cli\\u003eDribe, M., et al., \\u003cem\\u003eSocio-economic status and fertility decline: Insights from historical transitions in Europe and North America.\\u003c/em\\u003e Population studies, 2017. \\u003cstrong\\u003e71\\u003c/strong\\u003e(1): p. 3-21.\\u003c/li\\u003e\\n\\u003cli\\u003eWillis, R.J., \\u003cem\\u003eA new approach to the economic theory of fertility behavior.\\u003c/em\\u003e Journal of political Economy, 1973. \\u003cstrong\\u003e81\\u003c/strong\\u003e(2, Part 2): p. S14-S64.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Fertility, Children Ever Born, Under-dispersion, Fertility modeling\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7312541/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7312541/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eThe number of children ever born (CEB) to women of reproductive age is a key factor of fertility and a critical factor in shaping population dynamics, particularly in developing countries like Bangladesh. It reflects cumulative reproductive behavior and is influenced by a range of demographic, socioeconomic, and cultural factors, making it a vital metric for understanding fertility trends. Based on data from Bangladesh Demographic and Health Survey 2022 (BDHS), this study focuses on women who have given birth to at least one child during the reproductive age.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eTo fit an appropriate model to the zero-truncated count and under-dispersed data effectively, both Zero-Truncated Poisson (ZTP) and Zero-Truncated Generalized Poisson (ZTGP) regression models were used. The ZTGP model provided the best fit and outperformed ZTP by addressing the under-dispersion of CEB, as resulted in narrower confidence intervals for the Incidence Rate Ratios (IRRs) and lower AIC and BIC values.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eFindings of this study indicate that maternal age, education, religion, place of residence, wealth index, and fertility preferences significantly influence the number of children. Specifically, higher education and economic status were associated with lower fertility, while rural residence and Muslim affiliation correlated with higher fertility rates. However, paternal occupation showed no significant impact on the number of children ever born in both ZTP and ZTGP models.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThe Zero-Truncated Generalized Poisson (ZTGP) model effectively addressed the under-dispersion in the fertility data, yielding more accurate estimates of children ever born. The findings reveal significant socio-demographic determinants of fertility, reflecting the complex interplay of social, cultural, and economic factors and underscoring the importance of targeted, evidence-based family planning policies in Bangladesh.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Zero-Truncated Generalized Poisson Approach to Under-Dispersed Count Data: Fertility Patterns in Bangladesh, 2022\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-11 11:11:58\",\"doi\":\"10.21203/rs.3.rs-7312541/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-10-03T12:21:54+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-15T17:22:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-12T23:43:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"59055117398187678428637485304494472999\",\"date\":\"2025-09-11T17:53:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"3565237569158762297977456070759172918\",\"date\":\"2025-09-08T17:46:29+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-07T02:33:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"239226200323524082041759779292652887989\",\"date\":\"2025-09-07T02:06:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"168974270044191336122057171284200820021\",\"date\":\"2025-09-04T12:02:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-04T10:30:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-08-11T10:16:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-08-08T03:54:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-08-08T03:54:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Public Health\",\"date\":\"2025-08-06T18:58:37+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pubh\",\"sideBox\":\"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/pubh/default.aspx\",\"title\":\"BMC Public Health\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"40c650ed-6890-43da-a778-4071bb839eca\",\"owner\":[],\"postedDate\":\"September 11th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-16T16:08:15+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-11 11:11:58\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7312541\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7312541\",\"identity\":\"rs-7312541\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}