Socioeconomic and Demographic Factors Associated with Early Marriage Among Women in Bangladesh: A Cross-Sectional Analysis of the 2022 Bangladesh Demographic and Health Survey

Socioeconomic and Demographic Factors Associated with Early Marriage Among Women in Bangladesh: A Cross-Sectional Analysis of the 2022 Bangladesh Demographic and Health Survey | 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 Socioeconomic and Demographic Factors Associated with Early Marriage Among Women in Bangladesh: A Cross-Sectional Analysis of the 2022 Bangladesh Demographic and Health Survey Mehedy Hasan Mehedy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9045976/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Early marriage remains one of the most pervasive violations of girls' rights globally, and Bangladesh continues to record one of the highest national prevalences. This study examined the socioeconomic and demographic determinants of early marriage among ever-married women in Bangladesh using the most recent nationally representative survey data. Methods Data were drawn from the Bangladesh Demographic and Health Survey 2022 (BDHS 2022), a nationally representative cross-sectional survey comprising 20,029 ever-married women aged 15–49. Sampling weights were applied throughout. Socioeconomic and demographic predictors of early marriage (defined as marriage before age 18) were examined using Pearson chi-square tests and multivariable binary logistic regression, reporting adjusted odds ratios (AOR) with 95% confidence intervals. Results The national prevalence of early marriage was 67.5%. Education emerged as the dominant independent protective factor: women with no formal education were more than ten times as likely to have married early relative to those with higher education (AOR = 10.37; 95% CI: 8.77–12.25). A parallel inverse gradient was observed for husband's education. Substantial regional heterogeneity persisted after full adjustment, with Sylhet division recording the lowest adjusted odds (AOR = 0.24) and Khulna, Rajshahi, and Rangpur recording the highest relative to Barishal. Rural residence (AOR = 1.14; p = 0.001) and Muslim religious affiliation (AOR = 1.88; p < 0.001) were independently associated with elevated early marriage risk. Household wealth index and media exposure were significant in bivariate analysis but attenuated to non-significance in the adjusted model, indicating mediation through educational and structural pathways. Conclusions Early marriage in Bangladesh is a structurally embedded phenomenon driven primarily by educational deprivation, regional inequity, and cultural norms. Effective policy responses require sustained investment in girls' education, regionally differentiated enforcement of the Child Marriage Restraint Act, and community engagement with religious leaders to shift normative frameworks. early marriage child marriage Bangladesh BDHS 2022 logistic regression education gender inequality South Asia socioeconomic determinants Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Early marriage formally defined as marriage or informal union contracted before the age of 18 years, is a fundamental violation of human rights and a critical structural barrier to gender equality [ 1 ]. It curtails educational attainment, suppresses economic participation, increases risks of adverse reproductive health outcomes, and perpetuates intergenerational cycles of poverty [ 2 , 3 ]. Globally, approximately 12 million girls enter into marriage each year before their 18th birthday, with the highest concentration in South Asia and Sub-Saharan Africa [ 4 ]. While global prevalence has declined by approximately 15% over the past decade, progress remains deeply uneven, and Bangladesh consistently ranks among the countries with the highest rates of child marriage worldwide. Bangladesh presents a compelling case for investigation. Despite sustained economic growth, improvements in maternal and child health indicators, and the legal framework of the Child Marriage Restraint Act, the proportion of women married before the legal age of 18 remains alarmingly high. The BDHS 2022, the most recent nationally representative survey estimates that approximately two thirds of ever-married women aged 15–49 was married below the legal threshold. Bangladesh has committed under SDG Target 5.3 to eliminating child marriage by 2030, yet at the current rate of decline this goal will not be achieved without substantial policy intensification [ 5 ]. The determinants of early marriage are multidimensional, spanning individual, household, community, and structural levels. As conceptualized in Fig. 1 , distal structural factors including poverty, regional context, cultural and religious norms, and rural structural disadvantage shape early marriage risk through proximal mediators, principally girls' education, women's economic autonomy, and spousal characteristics. Understanding which factors retain independent predictive value after controlling for this complex web of interrelationships is essential for targeting policy interventions where they will have the greatest impact. Prior research in Bangladesh has identified education, poverty, and rural residence as key drivers, but most studies predate the BDHS 2022 and few simultaneously examine the full range of socioeconomic and demographic predictors using multivariable modelling. This study addresses these gaps. We aim to: (i) estimate the current national and divisional prevalence of early marriage; (ii) examine bivariate associations across a comprehensive set of covariates; and (iii) identify independent predictors of early marriage using multivariable logistic regression, with findings relevant to the SDG 2030 agenda and Bangladesh's national child marriage elimination plan. Theoretical Framework and Prior Evidence Early marriage is best understood through a social-ecological lens that situates individual and household decisions within broader community norms, institutional structures, and macro-level policy environments [ 6 , 7 ]. At the individual level, a girl's educational attainment is consistently identified as the single most powerful protective factor: schooling delays marriage by increasing women's autonomy, expanding future employment prospects, and raising the opportunity cost of early union [ 8 , 9 ]. Walker demonstrated that each additional year of schooling not only delays age at marriage but strengthens intra-household bargaining power, reducing parental pressure to arrange early unions [ 10 ]. The dose-response relationship between education and delayed marriage suggests that even incomplete secondary schooling confers meaningful protection. At the household level, economic hardship operates as a push factor. Families in poverty may perceive early marriage as a risk-mitigation strategy; reducing dependents, avoiding dowry escalation associated with older brides, or securing a daughter's social protection [ 7 , 11 ]. Girls from the lowest wealth quintile are consistently found to be nearly twice as likely to marry early as those from the richest quintile in Bangladesh [ 12 , 13 ]. However, when education is controlled for, the independent effect of wealth often attenuates markedly, suggesting that economic deprivation operates substantially through educational deprivation [ 14 ], a pattern with important implications for policy prioritization. Community and regional context exert strong contextual influence. Rural residence is associated with higher prevalence of early marriage due to limited access to secondary education, weaker enforcement of legal age provisions, entrenched patriarchal norms, and reduced exposure to alternative life pathways [ 15 , 16 ]. Substantial divisional heterogeneity has been documented in prior BDHS cycles, with Rajshahi and Rangpur recording persistently higher prevalence and Sylhet recording lower rates [ 17 , 18 ]. Religious and cultural norms constitute a fourth explanatory domain. In contexts where girls' honour and marriageability are closely linked to perceived social risk, parents may initiate early marriage as a protective strategy [ 19 ]. Dowry practices that incentivise younger brides further reinforce this dynamic [ 11 ]. Spousal characteristics also matter: husband's education creates a reinforcing dynamic in which dual-educated couples are substantially less likely to be associated with early marriage [ 18 , 21 ]. Collectively, the literature points to the need for multisectoral interventions operating simultaneously at structural, community, and individual levels. Methods Data Source and Sample This study used secondary data from the Bangladesh Demographic and Health Survey 2022 (BDHS 2022), the ninth nationally representative DHS conducted in Bangladesh. The survey was implemented under the authority of the National Institute of Population Research and Training (NIPORT), with data collection carried out by Mitra and Associates and technical assistance from ICF through The DHS Program, funded by USAID/Bangladesh. The BDHS 2022 employed a stratified two-stage cluster sampling design across 672 Enumeration Areas selected with probability proportional to size from the 2011 national census sampling frame. A systematic sample of approximately 30 households per EA was selected in the second stage, achieving an overall response rate of approximately 98%. Data were extracted from the Individual Recode (IR) file. After excluding system-missing cases, the analytic sample comprised 20,029 ever-married women aged 15–49. Sampling weights were applied throughout all analyses to ensure national representativeness. Outcome Variable The dependent variable was early marriage, defined as marriage or first cohabitation before the age of 18 years, in line with Bangladesh's legal marriage age and international conventions. The variable was derived from BDHS question v511 (age at first marriage/cohabitation) and coded as binary: 1 = early marriage (before age 18); 0 = no early marriage (age 18 or above). Explanatory Variables Explanatory variables were selected on theoretical grounds and categorised into socioeconomic variables (respondent's education level, working status, husband's education level, husband's occupation, household wealth index, media exposure, and age of household head) and demographic variables (administrative division, type of residence, sex of household head, religion, and household size). The wealth index was constructed through principal component analysis of household asset ownership and dwelling characteristics following standard DHS methodology. Media exposure was dichotomised as exposed (at least one medium at least once weekly) versus not exposed. Full variable distributions are presented in Table 1 . Statistical Analysis Analysis proceeded in three stages. First, univariate analysis assessed the frequency distribution of all variables and overall early marriage prevalence. Second, Pearson chi-square tests of independence examined bivariate associations between each explanatory variable and early marriage (significance threshold: p < 0.05). Third, multivariable binary logistic regression estimated adjusted odds ratios (AORs) with 95% confidence intervals, including all variables significant at the bivariate stage. The regression model takes the form: logit(π) = β₀ + β₁X₁ + β₂X₂ + ... + βₚXₚ, where π is the probability of early marriage. Reference categories were: higher education, urban residence, Barishal division, non-Muslim religion, rich wealth quintile, working status, not exposed (media), > 55 years (household head age), > 4 members (family size), and other (husband's occupation). All analyses applied BDHS sampling weights. Analyses were conducted in SPSS version 25 and R (version 4.x). Ethical Considerations This study analysed publicly available, anonymized secondary data from the BDHS 2022. The original survey received ethical clearance from the ICF Institutional Review Board and the National Research Ethics Committee of Bangladesh. Secondary analysis approval was obtained from the Institutional Ethics Committee of East West University, Dhaka, Bangladesh. Written informed consent was obtained from all original survey participants. No participants were contacted or re-identified. All procedures comply with the Declaration of Helsinki. Results Prevalence and Sample Characteristics Among the 20,029 women in the analytic sample, 13,519 (67.5%) reported early marriage. Table 1 presents the weighted distribution across socioeconomic and demographic characteristics. Education was predominantly secondary level (46.7%), with 13.7% having no formal education and only 13.5% attaining higher education. Most respondents were not currently working (68.0%). Households were predominantly rural (71.5%), male-headed (85.0%), and Muslim (90.4%). The largest proportion resided in Dhaka division (25.4%), while Sylhet (5.8%) and Barishal (6.0%) had the smallest shares. Table 1 Sample Characteristics of Ever-Married Women Aged 15–49, BDHS 2022 (N = 20,029) Characteristic n (weighted) % Respondent's education level No education 2,752 13.7 Primary 5,214 26.0 Secondary 9,359 46.7 Higher 2,703 13.5 Working status Not working 13,617 68.0 Working 6,412 32.0 Husband's education level No education 4,083 21.5 Primary 5,386 28.3 Secondary 6,196 32.6 Higher 3,356 17.6 Wealth index Poor (quintiles 1–2) 7,610 38.0 Middle (quintile 3) 4,135 20.6 Rich (quintiles 4–5) 8,283 41.4 Media exposure Not exposed 8,432 42.1 Exposed 11,596 57.9 Division Barishal 1,199 6.0 Chattogram 3,749 18.7 Dhaka 5,080 25.4 Khulna 2,389 11.9 Mymensingh 1,527 7.6 Rajshahi 2,625 13.1 Rangpur 2,291 11.4 Sylhet 1,169 5.8 Type of residence Urban 5,700 28.5 Rural 14,328 71.5 Sex of household head Male 17,018 85.0 Female 3,011 15.0 Religion Muslim 18,107 90.4 Non-Muslim 1,921 9.6 Family size ≤4 members 9,661 48.2 >4 members 10,367 51.8 Note. Weighted frequencies and percentages. Household head age: 55 yrs = 21.4%. Percentages may not sum to 100 due to rounding. Bivariate Associations with Socioeconomic Characteristics Table 2 presents chi-square associations between early marriage and socioeconomic variables. A pronounced inverse gradient was observed for respondent's education: 78.5% of women with no formal education reported early marriage, declining to 24.5% among those with higher education (χ² = 2773.00, p < 0.001) — a 54 percentage-point differential. Figure 2 visualises this education gradient for both the respondent and her husband. A parallel inverse gradient was observed for husband's education, from 77.8% (no education) to 40.7% (higher education; χ² = 1427.89, p < 0.001). Employed women had a lower prevalence (63.9%) than non-working women (71.1%; χ² = 99.13, p < 0.001). Wealth index showed a significant inverse association (χ² = 444.51, p < 0.001), with poor households recording 73.4% prevalence versus 58.1% in rich households. Media exposure was inversely associated (χ² = 64.42, p < 0.001). Household head age showed a consistent gradient, with younger-headed households recording higher prevalence (χ² = 115.52, p < 0.001). Table 2 Association of Early Marriage with Socioeconomic Characteristics (N = 20,029) Variable / Category Early Marriage Yes (%) Early Marriage No (%) χ² p-value Respondent's education 2773.00 < 0.001 No education 78.5 21.5 Primary 76.9 23.1 Secondary 69.7 30.3 Higher (ref.) 24.5 75.5 Working status 99.13 < 0.001 Not working 71.1 28.9 Working (ref.) 63.9 36.1 Husband's education 1427.89 < 0.001 No education 77.8 22.2 Primary 74.3 25.7 Secondary 66.1 33.9 Higher (ref.) 40.7 59.3 Wealth index 444.51 < 0.001 Poor 73.4 26.6 Middle 69.7 30.3 Rich (ref.) 58.1 41.9 Media exposure 64.42 < 0.001 Not exposed (ref.) 69.3 30.7 Exposed 63.9 36.1 Household head age (years) 115.52 < 0.001 55 (ref.) 59.9 40.1 Note. χ² = Pearson chi-square statistic. p-values are two-tailed. (ref.) = reference category used in multivariable analysis. Bivariate Associations with Demographic Characteristics Table 3 presents geographic and demographic associations. Regional variation was highly significant (χ² = 720.46, p < 0.001): Khulna (74.5%), Rangpur (74.4%), and Rajshahi (74.2%) recorded the highest early marriage prevalence, while Sylhet (45.6%) was markedly the lowest — a 29 percentage-point differential between the extremes. Figure 3 presents these divisional differences visually. Rural women had higher prevalence (69.7%) than urban women (59.7%; χ² = 202.03, p < 0.001). Muslim respondents reported higher prevalence (67.8%) than non-Muslims (51.7%; χ² = 215.03, p < 0.001). Figure 4 contextualises wealth, residence, and religious differentials side by side. Sex of household head was not significantly associated (p = 0.81) and was excluded from multivariable modelling. Table 3 Association of Early Marriage with Demographic Characteristics (N = 20,029) Variable / Category Early Marriage Yes (%) Early Marriage No (%) χ² p-value Division 720.46 < 0.001 Barishal (ref.) 69.2 30.8 Chattogram 58.5 41.5 Dhaka 65.1 34.9 Khulna 74.5 25.5 Mymensingh 67.1 32.9 Rajshahi 74.2 25.8 Rangpur 74.4 25.6 Sylhet 45.6 54.4 Type of residence 202.03 < 0.001 Urban (ref.) 59.7 40.3 Rural 69.7 30.3 Sex of household head 0.57 0.81 Male 66.3 33.7 Female (ref.) 65.5 34.5 Religion 215.03 < 0.001 Muslim 67.8 32.2 Non-Muslim (ref.) 51.7 48.3 Family size 16.44 4 members (ref.) 64.9 35.1 Note. χ² = Pearson chi-square statistic. p-values two-tailed. Sex of household head not significant (p = 0.81); excluded from multivariable model. Multivariable Logistic Regression Table 4 and Fig. 5 present results from the fully adjusted logistic regression model. Education level was the dominant predictor: relative to women with higher education, those with no formal education had over ten times the odds of early marriage (AOR = 10.37; 95% CI: 8.77–12.25; p < 0.001), with a clear dose-response gradient through primary (AOR = 9.03) and secondary levels (AOR = 6.48). This gradient remained robust after adjusting for all other covariates. Husband's education showed a consistent inverse gradient, with all categories below higher education carrying significantly elevated odds (AOR range: 1.30–1.56). Significant regional heterogeneity persisted after adjustment. Sylhet recorded markedly lower odds relative to Barishal (AOR = 0.24; 95% CI: 0.21–0.28; p < 0.001), while Khulna (AOR = 1.31), Rajshahi (AOR = 1.22), and Rangpur (AOR = 1.22) showed significantly elevated risks. Chattogram (AOR = 0.48), Dhaka (AOR = 0.73), and Mymensingh (AOR = 0.69) had lower odds than Barishal. Rural residence remained independently significant (AOR = 1.14; 95% CI: 1.06–1.23; p = 0.001). Muslim religious affiliation was strongly associated with higher odds (AOR = 1.88; 95% CI: 1.69–2.09; p < 0.001). Younger household headship was positively associated (AOR = 1.43 for < 35 years; AOR = 1.12 for 36–55 years). Non-working status was associated with lower odds (AOR = 0.86; p < 0.001). Notably, household wealth index and media exposure were not statistically significant in the adjusted model, indicating that their bivariate associations are substantially mediated by education and correlated structural factors. Table 4 Multivariable Binary Logistic Regression: Adjusted Odds Ratios for Early Marriage, BDHS 2022 (N = 20,029) Covariate (Reference Category) AOR (95% CI) p-value Division (ref: Barishal) Chattogram 0.48 (0.42–0.55) < 0.001 Dhaka 0.73 (0.63–0.83) < 0.001 Khulna 1.31 (1.13–1.52) < 0.001 Mymensingh 0.69 (0.59–0.80) < 0.001 Rajshahi 1.22 (1.05–1.42) 0.010 Rangpur 1.22 (1.05–1.42) 0.011 Sylhet 0.24 (0.21–0.28) < 0.001 Type of residence (ref: Urban) Rural 1.14 (1.06–1.23) 0.001 Respondent's education (ref: Higher) No education 10.37 (8.77–12.25) < 0.001 Primary 9.03 (7.87–10.37) < 0.001 Secondary 6.48 (5.78–7.26) < 0.001 Wealth index (ref: Rich) Poor 0.96 (0.88–1.06) 0.439 Middle 1.00 (0.91–1.10) 0.981 Working status (ref: Working) Not working 0.86 (0.79–0.93) < 0.001 Media exposure (ref: Not exposed) Exposed 1.01 (0.94–1.09) 0.799 Religion (ref: Non-Muslim) Muslim 1.88 (1.69–2.09) < 0.001 Husband's education (ref: Higher) No education 1.56 (1.35–1.80) < 0.001 Primary 1.55 (1.37–1.76) < 0.001 Secondary 1.30 (1.16–1.45) 55 yrs) < 35 years 1.43 (1.29–1.58) 4 members) ≤4 members 0.89 (0.83–0.95) 0.001 Note. AOR = Adjusted Odds Ratio; CI = Confidence Interval. Husband's occupation was included in the model but did not attain significance across any category (all p > 0.05) and is omitted for brevity. Nagelkerke R² = 0.31. Model chi-square significant at p < 0.001. Table 5 Summary of Key Findings and Corresponding Policy Implications Domain Key Finding Policy Implication Education 10-fold odds gradient; robust dose-response across all four levels Universal secondary education for girls; conditional cash transfers to sustain enrolment Region Sylhet: AOR = 0.24; Khulna, Rajshahi, Rangpur: AOR > 1.2 Targeted enforcement of Child Marriage Restraint Act in high-risk divisions Rural Independent rural deficit persists after full adjustment (AOR = 1.14) Rural school infrastructure; girl-friendly secondary schools; community norm change Religion Muslim affiliation nearly doubles odds (AOR = 1.88) Engage religious leaders; reframe early marriage as contrary to girls' well-being Spousal edu. Husband's education shows parallel inverse gradient Boys' secondary education; pre-marital counselling for young men Wealth/media Non-significant after adjustment — mediated through education Channel economic resources into educational investment rather than stand-alone media campaigns Note. Policy implications derived from present study findings and prior literature [ 20 , 9 , 18 ]. Discussion This study provides a comprehensive, multivariable examination of the socioeconomic and demographic determinants of early marriage among ever-married women in Bangladesh, using the most recent nationally representative BDHS 2022 data. The 67.5% prevalence confirms that Bangladesh faces one of the most severe early marriage burdens globally, and that the current pace of reduction is insufficient to achieve SDG Target 5.3 by 2030. Education as the Dominant Protective Factor The most striking and consequential finding is the magnitude and consistency of education's protective gradient (Fig. 2 ). The more than tenfold odds of early marriage among women with no formal education relative to those with higher education maintained robustly after adjusting for all socioeconomic, demographic, and regional covariates is among the largest education effects reported in the South Asian literature [ 8 , 14 ]. The dose-response relationship across all four education levels implies that even incomplete schooling confers meaningful protection, reinforcing the policy imperative of keeping girls in school at every level. This finding is consistent with human capital theory: schooling raises the opportunity cost of early marriage by expanding employment prospects and increasing women's autonomy in household decision-making [ 10 ]. The parallel gradient for husband's education with all categories below higher education carrying significantly elevated odds even after controlling for the woman's own schooling suggests that educational assortative mating creates a reinforcing dynamic [ 18 , 21 ]. Raising boys' educational attainment is therefore an underappreciated but potentially important complementary lever. Regional Heterogeneity and the Sylhet Anomaly The substantial regional variation observed (Fig. 3 ) adjusted odds ranging from 0.24 in Sylhet to 1.31 in Khulna underscores that early marriage in Bangladesh is not a uniformly distributed phenomenon. Sylhet's markedly low odds despite limited development advantages are plausibly attributable to a large diaspora community with ties to the United Kingdom, introducing more progressive norms around girls' education and marriage timing [ 22 ]. The consistently elevated odds in Khulna, Rajshahi, and Rangpur likely reflect agricultural livelihoods, higher poverty rates, limited secondary school access, and weaker enforcement of the Child Marriage Restraint Act. These divisional disparities argue strongly for contextually tailored responses calibrated to the specific economic, cultural, and institutional conditions of each region. Rural Structural Disadvantage Rural residence remained a significant independent predictor (AOR = 1.14) even after controlling for education, wealth, media exposure, and regional context, suggesting that rurality indexes structural disadvantages not fully captured by individual-level variables. Rural girls face compounded barriers: lower secondary school density, greater distances to school, stronger patriarchal norms, and fewer non-agricultural employment alternatives [ 15 , 23 ]. Bridging the urban-rural gap requires complementary investments in rural school infrastructure, safe transportation, and community-based norm change. Religion, Culture, and Community Norms Muslim women were nearly twice as likely to report early marriage relative to non-Muslims (AOR = 1.88; Fig. 4 C), an association robust to full adjustment. Religion per se is not a determinant, but serves as a marker for a cluster of cultural practices, a perceptions of marriageability, concerns about girls' honour, and community expectations around marriage timing that vary between religious communities [ 19 ]. This underscores the importance of community engagement strategies that work with Islamic scholars and local religious leaders to reframe early marriage as incompatible with principles of girls' well-being and informed consent, an approach documented as effective in similar contexts [ 20 ]. The Mediation of Wealth and Media Effects The attenuation of wealth index and media exposure to non-significance in the adjusted model (Fig. 5 ) is theoretically important: it suggests that the protective effects of household wealth and media access observed in bivariate analysis are substantially mediated through education and correlated structural factors rather than operating through independent channels. Policymakers should therefore channel economic resources into sustained educational investment rather than stand-alone media campaigns not linked to educational outcomes. Strengths and Limitations Strengths include the use of the most recent nationally representative BDHS 2022 data, a large weighted analytic sample (n = 20,029), a comprehensive covariate set enabling independent effect estimation, and the inclusion of both a conceptual framework (Fig. 1 ) and a policy implications summary (Table 5 ). Limitations include: the cross-sectional design precluding causal inference; potential recall bias in self-reported marriage age; the absence of contextual variables such as dowry practices, law enforcement capacity, and school quality; binary religion coding obscuring within-group heterogeneity; and the wealth index's incomplete capture of economic vulnerability dimensions. Conclusions Early marriage in Bangladesh remains a pervasive and structurally embedded social problem, with two thirds of ever-married women in the BDHS 2022 sample reporting marriage before the age of 18. This study demonstrates that the phenomenon is driven primarily by educational deprivation, regional inequity, rural structural disadvantage, and cultural and religious norms, and that wealth and media exposure operate largely through these more fundamental pathways. Women's education stands out as the single most powerful modifiable protective factor, with a tenfold odds gradient across educational levels that has unambiguous policy implications: every additional year of schooling reduces the risk of early marriage. Achieving SDG Target 5.3 (elimination of child marriage by 2030) in Bangladesh will require a coordinated, multisectoral response. Priority actions, summarised in Table 5 , should include: universal quality secondary education for girls with targeted retention mechanisms in high-risk rural and divisional contexts; regionally differentiated enforcement strategies concentrating resources in Khulna, Rajshahi, and Rangpur; community-level engagement with religious leaders and traditional authorities to shift normative frameworks; and simultaneous investment in boys' educational attainment to address the dual-education gradient. Future research using longitudinal designs, qualitative methods capturing community-level norms, and multilevel modelling separating individual from contextual effects will be essential to refining these policy prescriptions. Declarations Ethics Approval and Consent to Participate This study analyzed publicly available, anonymized secondary data from the BDHS 2022. The original survey received ethical clearance from the ICF Institutional Review Board and the National Research Ethics Committee of Bangladesh. Secondary analysis approval was obtained from the Institutional Ethics Committee of East West University, Dhaka, Bangladesh (Reference: EWU-IEC-2023-001). Written informed consent was obtained from all original survey participants. No participants were contacted or re-identified. All procedures comply with the Declaration of Helsinki. Consent for Publication Not applicable. This study uses anonymised aggregate secondary data. Competing Interests The author declares no financial or non-financial competing interests in relation to this study. Funding This research received no external funding from public, commercial, or not-for-profit sectors. Author Contribution MH: Conceptualisation; Methodology; Formal Analysis; Data Curation; Writing — Original Draft; Writing — Review and Editing; Visualisation. The author approved the final submitted manuscript. Acknowledgements The author gratefully acknowledges NIPORT, ICF, and USAID/Bangladesh for making the BDHS 2022 data publicly available, and the faculty of the Department of Economics, East West University, Dhaka, for institutional support and guidance. Availability of Data and Materials The BDHS 2022 dataset is publicly available upon registration at the DHS Program website ( https://dhsprogram.com ). SPSS syntax and R scripts are available from the corresponding author upon reasonable request. References UNICEF. 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BMC Nutr. 2024;10(1). https://doi.org/10.1186/s40795-024-00874-6 . Rashid MM, Siddiqi MNA, Al-Amin M, Rahman MM, Roy TK, Rahman M, et al. Exploring determinants of early marriage among women in Bangladesh: a multilevel analysis. PLoS ONE. 2024;19(10):e0312755. Ahasan N, Manjur M, Sahota R, Sikder C, Mia MT. Exploring complex dynamics of early marriage in rural Bangladesh: reasons, psycho-social, health and economic implications. South Asian J Soc Sci Humanit. 2025;6(1):68–83. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9045976","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603360219,"identity":"db0e0a8e-acb9-43c1-b9ec-56739456a8c5","order_by":0,"name":"Mehedy Hasan Mehedy","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Mehedy","middleName":"Hasan","lastName":"Mehedy","suffix":""}],"badges":[],"createdAt":"2026-03-06 04:39:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9045976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9045976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106903759,"identity":"b5f0cd55-97ea-48ee-942c-18adab764084","added_by":"auto","created_at":"2026-04-14 15:12:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual framework illustrating distal determinants, proximal mediators, and direct pathways to early marriage in Bangladesh. Solid arrows indicate direct pathways; dashed arrows indicate indirect pathways operating through mediators.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9045976/v1/f749678c4261af95b6fac1e0.jpg"},{"id":106903763,"identity":"225c66c1-c79c-4e24-88ab-e4e0b52f65b1","added_by":"auto","created_at":"2026-04-14 15:12:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEarly marriage prevalence (%) by education level of respondent and husband, Bangladesh (BDHS 2022). The double-headed arrow denotes the 54 percentage-point differential between no education and higher education for respondents. Chi-square tests are significant at p \u0026lt; 0.001 for both variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9045976/v1/3d870ac68ec40004c0ae75f2.jpg"},{"id":106903766,"identity":"c9789558-bad2-422d-ba61-5a85fb9363f7","added_by":"auto","created_at":"2026-04-14 15:12:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrevalence of early marriage (%) by administrative division, Bangladesh (BDHS 2022). The dashed vertical line denotes the national average (67.5%). Divisions shown in teal fall below the national average; divisions in coral exceed it.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9045976/v1/fccc9c38c028411a61cf3758.jpg"},{"id":106961181,"identity":"379425f4-b704-45ff-8468-0a372707e56e","added_by":"auto","created_at":"2026-04-15 09:24:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63026,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEarly marriage prevalence (%) by (A) household wealth index, (B) type of residence, and (C) religious affiliation, Bangladesh (BDHS 2022). Dashed horizontal lines denote the national average (67.5%). All between-group differences are statistically significant at p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9045976/v1/691d4cba7961ceaa965ce447.jpg"},{"id":106903751,"identity":"753e2551-921c-4fbf-a1c5-f99b8bc71e1a","added_by":"auto","created_at":"2026-04-14 15:12:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot of adjusted odds ratios (AOR) with 95% confidence intervals for all significant predictors of early marriage, Bangladesh (BDHS 2022). The dashed vertical line at AOR = 1.0 represents the null value. AORs are displayed on a log scale.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9045976/v1/e8a63fa93c654fc1e3c29e0b.jpg"},{"id":106966328,"identity":"ed50b45b-24a0-4e99-a1c4-14bf988fa14d","added_by":"auto","created_at":"2026-04-15 09:58:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1981807,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9045976/v1/b2e87edf-7439-463a-83e6-01e94acd2d91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic and Demographic Factors Associated with Early Marriage Among Women in Bangladesh: A Cross-Sectional Analysis of the 2022 Bangladesh Demographic and Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003eEarly marriage formally defined as marriage or informal union contracted before the age of 18 years, is a fundamental violation of human rights and a critical structural barrier to gender equality [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. It curtails educational attainment, suppresses economic participation, increases risks of adverse reproductive health outcomes, and perpetuates intergenerational cycles of poverty [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, approximately 12\u0026nbsp;million girls enter into marriage each year before their 18th birthday, with the highest concentration in South Asia and Sub-Saharan Africa [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. While global prevalence has declined by approximately 15% over the past decade, progress remains deeply uneven, and Bangladesh consistently ranks among the countries with the highest rates of child marriage worldwide.\u003c/p\u003e \u003cp\u003eBangladesh presents a compelling case for investigation. Despite sustained economic growth, improvements in maternal and child health indicators, and the legal framework of the Child Marriage Restraint Act, the proportion of women married before the legal age of 18 remains alarmingly high. The BDHS 2022, the most recent nationally representative survey estimates that approximately two thirds of ever-married women aged 15–49 was married below the legal threshold. Bangladesh has committed under SDG Target 5.3 to eliminating child marriage by 2030, yet at the current rate of decline this goal will not be achieved without substantial policy intensification [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe determinants of early marriage are multidimensional, spanning individual, household, community, and structural levels. As conceptualized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, distal structural factors including poverty, regional context, cultural and religious norms, and rural structural disadvantage shape early marriage risk through proximal mediators, principally girls' education, women's economic autonomy, and spousal characteristics. Understanding which factors retain independent predictive value after controlling for this complex web of interrelationships is essential for targeting policy interventions where they will have the greatest impact.\u003c/p\u003e \u003cp\u003ePrior research in Bangladesh has identified education, poverty, and rural residence as key drivers, but most studies predate the BDHS 2022 and few simultaneously examine the full range of socioeconomic and demographic predictors using multivariable modelling. This study addresses these gaps. We aim to: (i) estimate the current national and divisional prevalence of early marriage; (ii) examine bivariate associations across a comprehensive set of covariates; and (iii) identify independent predictors of early marriage using multivariable logistic regression, with findings relevant to the SDG 2030 agenda and Bangladesh's national child marriage elimination plan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTheoretical Framework and Prior Evidence\u003c/h3\u003e\n\u003cp\u003eEarly marriage is best understood through a social-ecological lens that situates individual and household decisions within broader community norms, institutional structures, and macro-level policy environments [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. At the individual level, a girl's educational attainment is consistently identified as the single most powerful protective factor: schooling delays marriage by increasing women's autonomy, expanding future employment prospects, and raising the opportunity cost of early union [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Walker demonstrated that each additional year of schooling not only delays age at marriage but strengthens intra-household bargaining power, reducing parental pressure to arrange early unions [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. The dose-response relationship between education and delayed marriage suggests that even incomplete secondary schooling confers meaningful protection.\u003c/p\u003e \u003cp\u003eAt the household level, economic hardship operates as a push factor. Families in poverty may perceive early marriage as a risk-mitigation strategy; reducing dependents, avoiding dowry escalation associated with older brides, or securing a daughter's social protection [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Girls from the lowest wealth quintile are consistently found to be nearly twice as likely to marry early as those from the richest quintile in Bangladesh [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, when education is controlled for, the independent effect of wealth often attenuates markedly, suggesting that economic deprivation operates substantially through educational deprivation [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], a pattern with important implications for policy prioritization.\u003c/p\u003e \u003cp\u003eCommunity and regional context exert strong contextual influence. Rural residence is associated with higher prevalence of early marriage due to limited access to secondary education, weaker enforcement of legal age provisions, entrenched patriarchal norms, and reduced exposure to alternative life pathways [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Substantial divisional heterogeneity has been documented in prior BDHS cycles, with Rajshahi and Rangpur recording persistently higher prevalence and Sylhet recording lower rates [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReligious and cultural norms constitute a fourth explanatory domain. In contexts where girls' honour and marriageability are closely linked to perceived social risk, parents may initiate early marriage as a protective strategy [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Dowry practices that incentivise younger brides further reinforce this dynamic [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Spousal characteristics also matter: husband's education creates a reinforcing dynamic in which dual-educated couples are substantially less likely to be associated with early marriage [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Collectively, the literature points to the need for multisectoral interventions operating simultaneously at structural, community, and individual levels.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData Source and Sample\u003c/h2\u003e\u003cp\u003eThis study used secondary data from the Bangladesh Demographic and Health Survey 2022 (BDHS 2022), the ninth nationally representative DHS conducted in Bangladesh. The survey was implemented under the authority of the National Institute of Population Research and Training (NIPORT), with data collection carried out by Mitra and Associates and technical assistance from ICF through The DHS Program, funded by USAID/Bangladesh. The BDHS 2022 employed a stratified two-stage cluster sampling design across 672 Enumeration Areas selected with probability proportional to size from the 2011 national census sampling frame. A systematic sample of approximately 30 households per EA was selected in the second stage, achieving an overall response rate of approximately 98%. Data were extracted from the Individual Recode (IR) file. After excluding system-missing cases, the analytic sample comprised 20,029 ever-married women aged 15–49. Sampling weights were applied throughout all analyses to ensure national representativeness.\u003c/p\u003e\u003ch3\u003eOutcome Variable\u003c/h3\u003e\u003cp\u003eThe dependent variable was early marriage, defined as marriage or first cohabitation before the age of 18 years, in line with Bangladesh's legal marriage age and international conventions. The variable was derived from BDHS question v511 (age at first marriage/cohabitation) and coded as binary: 1 = early marriage (before age 18); 0 = no early marriage (age 18 or above).\u003c/p\u003e\u003ch3\u003eExplanatory Variables\u003c/h3\u003e\u003cp\u003eExplanatory variables were selected on theoretical grounds and categorised into socioeconomic variables (respondent's education level, working status, husband's education level, husband's occupation, household wealth index, media exposure, and age of household head) and demographic variables (administrative division, type of residence, sex of household head, religion, and household size). The wealth index was constructed through principal component analysis of household asset ownership and dwelling characteristics following standard DHS methodology. Media exposure was dichotomised as exposed (at least one medium at least once weekly) versus not exposed. Full variable distributions are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAnalysis proceeded in three stages. First, univariate analysis assessed the frequency distribution of all variables and overall early marriage prevalence. Second, Pearson chi-square tests of independence examined bivariate associations between each explanatory variable and early marriage (significance threshold: p \u0026lt; 0.05). Third, multivariable binary logistic regression estimated adjusted odds ratios (AORs) with 95% confidence intervals, including all variables significant at the bivariate stage. The regression model takes the form: logit(π) = β₀ + β₁X₁ + β₂X₂ + ... + βₚXₚ, where π is the probability of early marriage. Reference categories were: higher education, urban residence, Barishal division, non-Muslim religion, rich wealth quintile, working status, not exposed (media), \u0026gt; 55 years (household head age), \u0026gt; 4 members (family size), and other (husband's occupation). All analyses applied BDHS sampling weights. Analyses were conducted in SPSS version 25 and R (version 4.x).\u003c/p\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003eThis study analysed publicly available, anonymized secondary data from the BDHS 2022. The original survey received ethical clearance from the ICF Institutional Review Board and the National Research Ethics Committee of Bangladesh. Secondary analysis approval was obtained from the Institutional Ethics Committee of East West University, Dhaka, Bangladesh. Written informed consent was obtained from all original survey participants. No participants were contacted or re-identified. All procedures comply with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence and Sample Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 20,029 women in the analytic sample, 13,519 (67.5%) reported early marriage. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the weighted distribution across socioeconomic and demographic characteristics. Education was predominantly secondary level (46.7%), with 13.7% having no formal education and only 13.5% attaining higher education. Most respondents were not currently working (68.0%). Households were predominantly rural (71.5%), male-headed (85.0%), and Muslim (90.4%). The largest proportion resided in Dhaka division (25.4%), while Sylhet (5.8%) and Barishal (6.0%) had the smallest shares.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics of Ever-Married Women Aged 15\u0026ndash;49, BDHS 2022 (N\u0026thinsp;=\u0026thinsp;20,029)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (weighted)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent's education level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigher\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNot working\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWorking\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHusband's education level\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigher\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoor (quintiles 1\u0026ndash;2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle (quintile 3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRich (quintiles 4\u0026ndash;5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia exposure\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNot exposed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eExposed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDivision\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBarishal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChattogram\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDhaka\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKhulna\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMymensingh\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRajshahi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRangpur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSylhet\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrban\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of household 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMuslim\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18,107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNon-Muslim\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily size\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026le;4 members\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;4 members\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote. Weighted frequencies and percentages. Household head age: \u0026lt; 35 yrs\u0026thinsp;=\u0026thinsp;25.5%; 36\u0026ndash;55 yrs\u0026thinsp;=\u0026thinsp;53.1%; \u0026gt; 55 yrs\u0026thinsp;=\u0026thinsp;21.4%. Percentages may not sum to 100 due to rounding.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBivariate Associations with Socioeconomic Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents chi-square associations between early marriage and socioeconomic variables. A pronounced inverse gradient was observed for respondent's education: 78.5% of women with no formal education reported early marriage, declining to 24.5% among those with higher education (χ\u0026sup2; = 2773.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u0026mdash; a 54 percentage-point differential. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualises this education gradient for both the respondent and her husband. A parallel inverse gradient was observed for husband's education, from 77.8% (no education) to 40.7% (higher education; χ\u0026sup2; = 1427.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Employed women had a lower prevalence (63.9%) than non-working women (71.1%; χ\u0026sup2; = 99.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Wealth index showed a significant inverse association (χ\u0026sup2; = 444.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with poor households recording 73.4% prevalence versus 58.1% in rich households. Media exposure was inversely associated (χ\u0026sup2; = 64.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Household head age showed a consistent gradient, with younger-headed households recording higher prevalence (χ\u0026sup2; = 115.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Early Marriage with Socioeconomic Characteristics (N\u0026thinsp;=\u0026thinsp;20,029)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable / Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly Marriage Yes (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly Marriage No (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent's education\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2773.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigher (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking status\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e99.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNot working\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWorking (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHusband's education\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1427.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigher (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e444.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRich (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia exposure\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e64.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNot exposed (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eExposed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold head age (years)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e115.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt; 35\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e36\u0026ndash;55\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt; 55 (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. χ\u0026sup2; = Pearson chi-square statistic. p-values are two-tailed. (ref.) = reference category used in multivariable analysis.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBivariate Associations with Demographic Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents geographic and demographic associations. Regional variation was highly significant (χ\u0026sup2; = 720.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): Khulna (74.5%), Rangpur (74.4%), and Rajshahi (74.2%) recorded the highest early marriage prevalence, while Sylhet (45.6%) was markedly the lowest \u0026mdash; a 29 percentage-point differential between the extremes. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents these divisional differences visually. Rural women had higher prevalence (69.7%) than urban women (59.7%; χ\u0026sup2; = 202.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Muslim respondents reported higher prevalence (67.8%) than non-Muslims (51.7%; χ\u0026sup2; = 215.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e contextualises wealth, residence, and religious differentials side by side. Sex of household head was not significantly associated (p\u0026thinsp;=\u0026thinsp;0.81) and was excluded from multivariable modelling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Early Marriage with Demographic Characteristics (N\u0026thinsp;=\u0026thinsp;20,029)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable / Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly Marriage Yes (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly Marriage No (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e720.46\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBarishal (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChattogram\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDhaka\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKhulna\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMymensingh\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRajshahi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRangpur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSylhet\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e202.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrban (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of household 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e215.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMuslim\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNon-Muslim (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily size\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026le;4 members\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;4 members (ref.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. χ\u0026sup2; = Pearson chi-square statistic. p-values two-tailed. Sex of household head not significant (p\u0026thinsp;=\u0026thinsp;0.81); excluded from multivariable model.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Logistic Regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present results from the fully adjusted logistic regression model. Education level was the dominant predictor: relative to women with higher education, those with no formal education had over ten times the odds of early marriage (AOR\u0026thinsp;=\u0026thinsp;10.37; 95% CI: 8.77\u0026ndash;12.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a clear dose-response gradient through primary (AOR\u0026thinsp;=\u0026thinsp;9.03) and secondary levels (AOR\u0026thinsp;=\u0026thinsp;6.48). This gradient remained robust after adjusting for all other covariates. Husband's education showed a consistent inverse gradient, with all categories below higher education carrying significantly elevated odds (AOR range: 1.30\u0026ndash;1.56).\u003c/p\u003e \u003cp\u003eSignificant regional heterogeneity persisted after adjustment. Sylhet recorded markedly lower odds relative to Barishal (AOR\u0026thinsp;=\u0026thinsp;0.24; 95% CI: 0.21\u0026ndash;0.28; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Khulna (AOR\u0026thinsp;=\u0026thinsp;1.31), Rajshahi (AOR\u0026thinsp;=\u0026thinsp;1.22), and Rangpur (AOR\u0026thinsp;=\u0026thinsp;1.22) showed significantly elevated risks. Chattogram (AOR\u0026thinsp;=\u0026thinsp;0.48), Dhaka (AOR\u0026thinsp;=\u0026thinsp;0.73), and Mymensingh (AOR\u0026thinsp;=\u0026thinsp;0.69) had lower odds than Barishal. Rural residence remained independently significant (AOR\u0026thinsp;=\u0026thinsp;1.14; 95% CI: 1.06\u0026ndash;1.23; p\u0026thinsp;=\u0026thinsp;0.001). Muslim religious affiliation was strongly associated with higher odds (AOR\u0026thinsp;=\u0026thinsp;1.88; 95% CI: 1.69\u0026ndash;2.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Younger household headship was positively associated (AOR\u0026thinsp;=\u0026thinsp;1.43 for \u0026lt;\u0026thinsp;35 years; AOR\u0026thinsp;=\u0026thinsp;1.12 for 36\u0026ndash;55 years). Non-working status was associated with lower odds (AOR\u0026thinsp;=\u0026thinsp;0.86; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, household wealth index and media exposure were not statistically significant in the adjusted model, indicating that their bivariate associations are substantially mediated by education and correlated structural factors.\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\u003eMultivariable Binary Logistic Regression: Adjusted Odds Ratios for Early Marriage, BDHS 2022 (N\u0026thinsp;=\u0026thinsp;20,029)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate (Reference Category)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivision (ref: Barishal)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChattogram\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.42\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDhaka\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.63\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKhulna\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.13\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMymensingh\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.59\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRajshahi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.05\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRangpur\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.05\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSylhet\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24 (0.21\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of residence (ref: Urban)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.06\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespondent's education (ref: Higher)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.37 (8.77\u0026ndash;12.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.03 (7.87\u0026ndash;10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.48 (5.78\u0026ndash;7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index (ref: Rich)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.88\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMiddle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.91\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking status (ref: Working)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNot working\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.79\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedia exposure (ref: Not exposed)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eExposed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.94\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion (ref: Non-Muslim)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMuslim\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.69\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHusband's education (ref: Higher)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56 (1.35\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrimary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.37\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSecondary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.16\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold head age (ref: \u0026gt; 55 yrs)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt; 35 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43 (1.29\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e36\u0026ndash;55 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.03\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily size (ref: \u0026gt; 4 members)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026le;4 members\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.83\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote. AOR\u0026thinsp;=\u0026thinsp;Adjusted Odds Ratio; CI\u0026thinsp;=\u0026thinsp;Confidence Interval. Husband's occupation was included in the model but did not attain significance across any category (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and is omitted for brevity. Nagelkerke R\u0026sup2; = 0.31. Model chi-square significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Key Findings and Corresponding Policy Implications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Finding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy Implication\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10-fold odds gradient; robust dose-response across all four levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversal secondary education for girls; conditional cash transfers to sustain enrolment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSylhet: AOR\u0026thinsp;=\u0026thinsp;0.24; Khulna, Rajshahi, Rangpur: AOR\u0026thinsp;\u0026gt;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTargeted enforcement of Child Marriage Restraint Act in high-risk divisions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent rural deficit persists after full adjustment (AOR\u0026thinsp;=\u0026thinsp;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural school infrastructure; girl-friendly secondary schools; community norm change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim affiliation nearly doubles odds (AOR\u0026thinsp;=\u0026thinsp;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEngage religious leaders; reframe early marriage as contrary to girls' well-being\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpousal edu.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband's education shows parallel inverse gradient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoys' secondary education; pre-marital counselling for young men\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth/media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-significant after adjustment \u0026mdash; mediated through education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChannel economic resources into educational investment rather than stand-alone media campaigns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote. Policy implications derived from present study findings and prior literature\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive, multivariable examination of the socioeconomic and demographic determinants of early marriage among ever-married women in Bangladesh, using the most recent nationally representative BDHS 2022 data. The 67.5% prevalence confirms that Bangladesh faces one of the most severe early marriage burdens globally, and that the current pace of reduction is insufficient to achieve SDG Target 5.3 by 2030.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEducation as the Dominant Protective Factor\u003c/h2\u003e \u003cp\u003eThe most striking and consequential finding is the magnitude and consistency of education's protective gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The more than tenfold odds of early marriage among women with no formal education relative to those with higher education maintained robustly after adjusting for all socioeconomic, demographic, and regional covariates is among the largest education effects reported in the South Asian literature [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The dose-response relationship across all four education levels implies that even incomplete schooling confers meaningful protection, reinforcing the policy imperative of keeping girls in school at every level. This finding is consistent with human capital theory: schooling raises the opportunity cost of early marriage by expanding employment prospects and increasing women's autonomy in household decision-making [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The parallel gradient for husband's education with all categories below higher education carrying significantly elevated odds even after controlling for the woman's own schooling suggests that educational assortative mating creates a reinforcing dynamic [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Raising boys' educational attainment is therefore an underappreciated but potentially important complementary lever.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRegional Heterogeneity and the Sylhet Anomaly\u003c/h2\u003e \u003cp\u003eThe substantial regional variation observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) adjusted odds ranging from 0.24 in Sylhet to 1.31 in Khulna underscores that early marriage in Bangladesh is not a uniformly distributed phenomenon. Sylhet's markedly low odds despite limited development advantages are plausibly attributable to a large diaspora community with ties to the United Kingdom, introducing more progressive norms around girls' education and marriage timing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The consistently elevated odds in Khulna, Rajshahi, and Rangpur likely reflect agricultural livelihoods, higher poverty rates, limited secondary school access, and weaker enforcement of the Child Marriage Restraint Act. These divisional disparities argue strongly for contextually tailored responses calibrated to the specific economic, cultural, and institutional conditions of each region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRural Structural Disadvantage\u003c/h2\u003e \u003cp\u003eRural residence remained a significant independent predictor (AOR\u0026thinsp;=\u0026thinsp;1.14) even after controlling for education, wealth, media exposure, and regional context, suggesting that rurality indexes structural disadvantages not fully captured by individual-level variables. Rural girls face compounded barriers: lower secondary school density, greater distances to school, stronger patriarchal norms, and fewer non-agricultural employment alternatives [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Bridging the urban-rural gap requires complementary investments in rural school infrastructure, safe transportation, and community-based norm change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReligion, Culture, and Community Norms\u003c/h2\u003e \u003cp\u003eMuslim women were nearly twice as likely to report early marriage relative to non-Muslims (AOR\u0026thinsp;=\u0026thinsp;1.88; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), an association robust to full adjustment. Religion per se is not a determinant, but serves as a marker for a cluster of cultural practices, a perceptions of marriageability, concerns about girls' honour, and community expectations around marriage timing that vary between religious communities [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This underscores the importance of community engagement strategies that work with Islamic scholars and local religious leaders to reframe early marriage as incompatible with principles of girls' well-being and informed consent, an approach documented as effective in similar contexts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe Mediation of Wealth and Media Effects\u003c/h2\u003e \u003cp\u003eThe attenuation of wealth index and media exposure to non-significance in the adjusted model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) is theoretically important: it suggests that the protective effects of household wealth and media access observed in bivariate analysis are substantially mediated through education and correlated structural factors rather than operating through independent channels. Policymakers should therefore channel economic resources into sustained educational investment rather than stand-alone media campaigns not linked to educational outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eStrengths include the use of the most recent nationally representative BDHS 2022 data, a large weighted analytic sample (n\u0026thinsp;=\u0026thinsp;20,029), a comprehensive covariate set enabling independent effect estimation, and the inclusion of both a conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and a policy implications summary (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Limitations include: the cross-sectional design precluding causal inference; potential recall bias in self-reported marriage age; the absence of contextual variables such as dowry practices, law enforcement capacity, and school quality; binary religion coding obscuring within-group heterogeneity; and the wealth index's incomplete capture of economic vulnerability dimensions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eEarly marriage in Bangladesh remains a pervasive and structurally embedded social problem, with two thirds of ever-married women in the BDHS 2022 sample reporting marriage before the age of 18. This study demonstrates that the phenomenon is driven primarily by educational deprivation, regional inequity, rural structural disadvantage, and cultural and religious norms, and that wealth and media exposure operate largely through these more fundamental pathways. Women's education stands out as the single most powerful modifiable protective factor, with a tenfold odds gradient across educational levels that has unambiguous policy implications: every additional year of schooling reduces the risk of early marriage.\u003c/p\u003e \u003cp\u003eAchieving SDG Target 5.3 (elimination of child marriage by 2030) in Bangladesh will require a coordinated, multisectoral response. Priority actions, summarised in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, should include: universal quality secondary education for girls with targeted retention mechanisms in high-risk rural and divisional contexts; regionally differentiated enforcement strategies concentrating resources in Khulna, Rajshahi, and Rangpur; community-level engagement with religious leaders and traditional authorities to shift normative frameworks; and simultaneous investment in boys' educational attainment to address the dual-education gradient. Future research using longitudinal designs, qualitative methods capturing community-level norms, and multilevel modelling separating individual from contextual effects will be essential to refining these policy prescriptions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eThis study analyzed publicly available, anonymized secondary data from the BDHS 2022. The original survey received ethical clearance from the ICF Institutional Review Board and the National Research Ethics Committee of Bangladesh. Secondary analysis approval was obtained from the Institutional Ethics Committee of East West University, Dhaka, Bangladesh (Reference: EWU-IEC-2023-001). Written informed consent was obtained from all original survey participants. No participants were contacted or re-identified. All procedures comply with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable. This study uses anonymised aggregate secondary data.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe author declares no financial or non-financial competing interests in relation to this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding from public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMH: Conceptualisation; Methodology; Formal Analysis; Data Curation; Writing \u0026mdash; Original Draft; Writing \u0026mdash; Review and Editing; Visualisation. The author approved the final submitted manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author gratefully acknowledges NIPORT, ICF, and USAID/Bangladesh for making the BDHS 2022 data publicly available, and the faculty of the Department of Economics, East West University, Dhaka, for institutional support and guidance.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eThe BDHS 2022 dataset is publicly available upon registration at the DHS Program website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SPSS syntax and R scripts are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUNICEF. 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Sociodemographic factors and early marriage among women in Bangladesh, Ghana and Iraq: an illustration from Multiple Indicator Cluster Survey. Heliyon. 2021;7(5):e07111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MK, Haque MR, Hossain MB. Regional variations in child marriage in Bangladesh. J Biosoc Sci. 2016;48(5):694\u0026ndash;708.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBillah MA, Khan MMA, Hanifi SMA, Islam MM, Khan MN. Spatial pattern and influential factors for early marriage: evidence from Bangladesh Demographic Health Survey 2017\u0026ndash;18 data. BMC Womens Health. 2023;23(1):320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiedema E, Koster W, Pouw N, Meyer P, Sotirova A. The struggle for public recognition: understanding early marriage through the lens of honour and shame in six countries in South Asia and West Africa. Prog Dev Stud. 2020;20(4):328\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmin S, Austrian K, Chong E. Community norms and the role of institutions in preventing child marriage. In: Jones N, Presler-Marshall E, editors. Child marriage in an era of global advocacy. London: Zed Books; 2017. pp. 45\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMim SA, Al Mamun ASM, Sayem MA, Wadood MA, Hossain MG. Association of child marriage and nutritional status of mothers and their under-five children in Bangladesh: a cross-sectional study with a nationally representative sample. BMC Nutr. 2024;10(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40795-024-00874-6\u003c/span\u003e\u003cspan address=\"10.1186/s40795-024-00874-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRashid MM, Siddiqi MNA, Al-Amin M, Rahman MM, Roy TK, Rahman M, et al. Exploring determinants of early marriage among women in Bangladesh: a multilevel analysis. PLoS ONE. 2024;19(10):e0312755.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhasan N, Manjur M, Sahota R, Sikder C, Mia MT. Exploring complex dynamics of early marriage in rural Bangladesh: reasons, psycho-social, health and economic implications. South Asian J Soc Sci Humanit. 2025;6(1):68\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"early marriage, child marriage, Bangladesh, BDHS 2022, logistic regression, education, gender inequality, South Asia, socioeconomic determinants","lastPublishedDoi":"10.21203/rs.3.rs-9045976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9045976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly marriage remains one of the most pervasive violations of girls' rights globally, and Bangladesh continues to record one of the highest national prevalences. This study examined the socioeconomic and demographic determinants of early marriage among ever-married women in Bangladesh using the most recent nationally representative survey data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were drawn from the Bangladesh Demographic and Health Survey 2022 (BDHS 2022), a nationally representative cross-sectional survey comprising 20,029 ever-married women aged 15\u0026ndash;49. Sampling weights were applied throughout. Socioeconomic and demographic predictors of early marriage (defined as marriage before age 18) were examined using Pearson chi-square tests and multivariable binary logistic regression, reporting adjusted odds ratios (AOR) with 95% confidence intervals.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe national prevalence of early marriage was 67.5%. Education emerged as the dominant independent protective factor: women with no formal education were more than ten times as likely to have married early relative to those with higher education (AOR\u0026thinsp;=\u0026thinsp;10.37; 95% CI: 8.77\u0026ndash;12.25). A parallel inverse gradient was observed for husband's education. Substantial regional heterogeneity persisted after full adjustment, with Sylhet division recording the lowest adjusted odds (AOR\u0026thinsp;=\u0026thinsp;0.24) and Khulna, Rajshahi, and Rangpur recording the highest relative to Barishal. Rural residence (AOR\u0026thinsp;=\u0026thinsp;1.14; p\u0026thinsp;=\u0026thinsp;0.001) and Muslim religious affiliation (AOR\u0026thinsp;=\u0026thinsp;1.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with elevated early marriage risk. Household wealth index and media exposure were significant in bivariate analysis but attenuated to non-significance in the adjusted model, indicating mediation through educational and structural pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEarly marriage in Bangladesh is a structurally embedded phenomenon driven primarily by educational deprivation, regional inequity, and cultural norms. Effective policy responses require sustained investment in girls' education, regionally differentiated enforcement of the Child Marriage Restraint Act, and community engagement with religious leaders to shift normative frameworks.\u003c/p\u003e","manuscriptTitle":"Socioeconomic and Demographic Factors Associated with Early Marriage Among Women in Bangladesh: A Cross-Sectional Analysis of the 2022 Bangladesh Demographic and Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 15:12:07","doi":"10.21203/rs.3.rs-9045976/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa38a04c-bc2e-4bb3-ad3b-cfc18ba40019","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T15:12:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 15:12:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9045976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9045976","identity":"rs-9045976","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}