Socioeconomic Inequality and Postpartum Depression in Bangladesh: Evidence from BDHS 2022

Socioeconomic Inequality and Postpartum Depression in Bangladesh: Evidence from BDHS 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Socioeconomic Inequality and Postpartum Depression in Bangladesh: Evidence from BDHS 2022 Tamanna Islam, Ashiqur Rahman Rony, Kazi Sabbir Ahmad Nahin, Abida S Asha, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9100287/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Postpartum depression (PPD) is a common maternal mental health disorder with significant implications for mothers and children, particularly in low- and middle-income countries (LMICs). Nationally representative evidence on postpartum depressive symptoms in Bangladesh has been limited. This study aimed to estimate the national prevalence of postpartum depressive symptoms and examine individual- and community-level determinants using data from the 2022 Bangladesh Demographic and Health Survey (BDHS). Methods We used data from the 2022 Bangladesh Demographic and Health Survey (BDHS), including 3,117 women aged 15–49 years who had a live birth within the 12 months preceding the survey. Postpartum depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), with a score of ≥ 5 indicating the presence of depressive symptoms. Weighted descriptive analyses were conducted, and multilevel mixed-effects logistic regression models with random intercepts for clusters were applied to examine associated factors. Results are presented as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). Results The weighted prevalence of postpartum depressive symptoms was 16.17%. Prevalence varied across administrative divisions, ranging from 13.4% in Mymensingh to 18.1% in Rajshahi. In the fully adjusted multilevel model, household wealth index was significantly associated with postpartum depressive symptoms. Compared with women in the poorest quintile, those in the poorer (AOR 0.59; 95% CI 0.39–0.88), middle (AOR 0.62; 95% CI 0.41–0.95), richer (AOR 0.56; 95% CI 0.34–0.90), and richest (AOR 0.48; 95% CI 0.27–0.86) quintiles had progressively lower odds of depressive symptoms. Community education was associated with lower odds in the community-level model but was not statistically significant in the fully adjusted model. No significant associations were observed for maternal age, education, parity, pregnancy intention, antenatal care visits, delivery characteristics, media exposure, place of residence, or community wealth. Conclusions PPD affects about one in six postpartum women in Bangladesh and is strongly patterned by socioeconomic disadvantage, geographic and community context. Integrating mental health screening into routine maternal health services and addressing socioeconomic disparities may help reduce the burden of postpartum depressive symptoms in Bangladesh. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Postpartum depression Maternal mental health Socioeconomic inequality Multilevel analysis BDHS 2022 Figures Figure 1 Figure 2 Introduction The transition to motherhood is widely recognized as a critical period in a woman’s life course, marked by profound biological, psychological, and social changes. While this transition is often celebrated, it also carries a considerable risk of mental health disorders ( 1 ). Among these, postpartum depression (PPD) stands out as one of the most prevalent global maternal health challenges, characterized by non-psychotic depressive episodes occurring within the first twelve months after childbirth ( 2 – 4 ). According to the World Health Organization (WHO), approximately 10% of pregnant women and 13% of women who have just given birth experience a mental disorder, primarily depression, contributing significantly to the global burden of disease. ( 5 ). Common symptoms of PPD, such as persistent low mood, fatigue, sleep disturbance, and feelings of guilt or worthlessness can severely impair maternal functioning ( 6 ). Global prevalence estimates of postpartum depression vary widely, ranging from 0.5% to 60.8%, largely depending on the measurement tools, timing of assessment, and sociocultural context ( 7 ). Evidence consistently shows higher prevalence in low- and middle-income countries (LMICs), which collectively possess less than 20% of global mental health resources despite hosting the majority of the world’s population ( 8 , 9 ). A comprehensive meta-analysis of more than 80 LMIC studies estimated a pooled prevalence of 17.7%, with South Asia reporting some of the highest burdens—often exceeding 25% ( 6 ). Country-specific findings indicate prevalence rates of 33.7% in Nepal, 20–23% in India, and over 30% in parts of Myanmar ( 10 – 12 ). In Bangladesh, subnational and community-based studies have documented rates from 18% in rural settings to as high as 39–46% among women in urban slums and socioeconomically disadvantaged environments ( 12 , 13 ). While these studies underscore the severity of postpartum depressive symptoms, their limited geographic scope restricts their generalizability and leaves national prevalence unknown. The consequences of postpartum depression extend beyond maternal wellbeing. PPD is associated with heightened risks of child undernutrition, reduced exclusive breastfeeding, incomplete immunization, impaired mother–infant bonding, and delayed cognitive development ( 12 , 14 ). Longitudinal research indicates that 25–50% of women with untreated PPD continue to experience symptoms beyond six months postpartum ( 15 ). Globally, suicide accounts for 7–10% of maternal deaths, underscoring the serious consequences of untreated perinatal mental health conditions ( 5 ). Addressing postpartum mental health thus aligns directly with Sustainable Development Goal (SDG) 3; promoting good health and well-being and SDG 5; advancing gender equality ( 16 , 17 ). Bangladesh has made significant progress in maternal health over the past two decades. The maternal mortality ratio dropped from 322 deaths per 100,000 live births in 2001 to 173 in 2017 ( 18 ). The 2022 Bangladesh Demographic and Health Survey (BDHS) further reports that 67.5% of births occurred in health facilities and 61.5% of women received at least four antenatal care visits ( 19 ). However, mental health screening remains largely absent from routine maternal health services. National mental health expenditure is less than 0.5% of total health spending, and the country has fewer than 0.2 psychiatrists per 100,000 population—far below the global median (~ 1.3 per 100,000) ( 20 ). These structural gaps highlight the urgency of integrating maternal mental health into national health strategies. Socioeconomic inequality is a well-established determinant of depressive disorders. Individuals from the lowest income groups are 1.5–2 times more likely to experience depression than those from higher-income groups ( 21 ). In South Asia, poverty, unemployment, unintended pregnancy, and limited social support are repeatedly identified as key predictors of postpartum depressive symptoms ( 10 , 13 ). Bangladesh continues to experience substantial income inequality, with a recent Gini coefficient above 0.48 ( 22 ). Whether such macro-level disparities translate into measurable socioeconomic gradients in postpartum depressive symptoms at the national level remains unclear. The 2022 Bangladesh Demographic and Health Survey (BDHS) represent a significant advancement in the study of maternal mental health in Bangladesh, as it incorporated, for the first time, the validated Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms among women with a recent live birth ( 23 ). Leveraging a weighted sample of 3,173 women who had delivered within the preceding 12 months, this study fills a critical gap in the national evidence base by estimating the prevalence of postpartum depressive symptoms across the country and its administrative regions, assessing socioeconomic disparities with a focus on household wealth, and examining community-level contextual influences using multilevel mixed-effects logistic regression models. By integrating both individual and structural determinants, this study conceptualizes postpartum depressive symptoms as socially patterned outcomes embedded within broader socioeconomic and community environments, rather than isolated psychological experiences. Producing nationally representative evidence is therefore an essential step toward ensuring that maternal mental health is recognized, prioritized, and integrated into Bangladesh’s evolving health system and its broader commitments to achieving the Sustainable Development Goals. Methods Study design and data source This study was a secondary analysis of data from the 2022 Bangladesh Demographic and Health Survey (BDHS 2022), a nationally representative cross-sectional household survey (24). The BDHS employed a two-stage stratified cluster sampling design. In the first stage, primary sampling units (clusters) were selected from the national sampling frame using probability proportional to size. In the second stage, a fixed number of households were selected systematically within each cluster. All women aged 15–49 years residing in selected households were eligible for interview. Among the 30,078 women interviewed in BDHS 2022, those who had a live birth within the 12 months preceding the survey were identified using the reported age of the most recent child (≤12 months). A total of 3,117 women met this criterion and constituted the final analytic sample. No additional exclusions were required, as complete PHQ-9 data were available for all eligible women. The sample selection process is presented in Figure 1. Outcome measure The outcome of interest was postpartum depressive symptoms, assessed using the Patient Health Questionnaire-9 (PHQ-9) included in BDHS 2022. The PHQ-9 comprises nine items assessing the frequency of depressive symptoms over the preceding two weeks, each scored from 0 (“not at all”) to 3 (“nearly every day”). A total score ranging from 0 to 27 was calculated by summing responses across the nine items. In accordance with established practice, postpartum depressive symptoms were defined as a PHQ-9 score ≥5, indicating at least mild depressive symptomatology. The outcome variable was dichotomized as 0 (no depressive symptoms; score 0–4) and 1 (postpartum depressive symptoms; score ≥5) (25). Independent variables Individual-level factors We included socio-demographic and reproductive characteristics identified in prior literature as potential determinants of maternal mental health. These variables were categorized as follows: maternal age (15–19, 20–24, 25–29, 30–34, 35–39, 40–49 years); educational attainment (no education, primary, secondary, higher); marital status (currently married; formerly married [divorced, widowed, or separated]); employment status (working; not working); parity (≤1 child, 2–3 children, ≥4 children); pregnancy intention (wanted at the time; unintended); number of antenatal care (ANC) visits (<4; ≥4 visits); place of delivery (home; health facility); mode of delivery (vaginal; caesarean section); wealth quintile (poorest, poorer, middle, richer, richest); and place of residence (urban; rural). Media exposure was defined as exposure to at least one of the following: newspaper, radio, or television (yes; no). All covariates were treated as categorical variables. Community-level factors To account for contextual influences, two community-level variables were constructed at the cluster level. Community education was defined as the proportion of women within a cluster who had attained at least secondary education. Community wealth was defined as the proportion of women within a cluster belonging to the middle, richer, or richest wealth quintiles (wealth quintile ≥3). Clusters were classified as having “high” or “low” community education and wealth based on the median values of these proportions. These measures captured broader socio-economic context beyond individual characteristics. Statistical analysis All analyses accounted for the complex survey design of BDHS 2022. Sampling weights were applied in descriptive analyses to obtain nationally representative estimates. To examine factors associated with postpartum depressive symptoms, multilevel mixed-effects logistic regression models with a random intercept for clusters were fitted to account for intra-cluster correlation. For multilevel modelling, sampling weights were rescaled by dividing each weight by its mean to stabilize model estimation. Four sequential models were estimated: (1) a null model without explanatory variables; (2) a model including individual-level covariates; (3) a model including community-level variables; and (4) a full model incorporating both individual- and community-level factors. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Model fit was evaluated using log-likelihood, deviance, Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC). Cluster-level variation was quantified using the intra-class correlation coefficient (ICC) and the median odds ratio (MOR). Statistical significance was defined as p<0.05. Statistical analyses were performed using Stata version 18 (StataCorp LLC, College Station, TX, USA), while the flow diagram and geographical distribution map were created using R (version 4.5.2). Results Descriptive characteristics of respondents A total of 3,117 women who had a live birth within the 12 months preceding the BDHS 2022 survey were included in the analysis. The largest age group was 20–24 years (34.2%), followed by 25–29 years (24.4%) and 15–19 years (19.7%). More than half of the respondents (55.4%) completed secondary education, whereas 20.7% had primary education, 19.2% had higher education, and 4.6% had no education. Nearly all participants were currently married (99.5%), and 81.8% were not engaged in paid employment. Regarding reproductive characteristics, 51.6% of women had two to three children, while 41.8% had one or fewer children. Most pregnancies were wanted at the time (80.1%), and 61.5% of women attended at least four antenatal care visits. A majority of deliveries occurred in health facilities (67.5%), and 47% were by caesarean section. Most women lived in rural areas (73.4%), and the wealth distribution across quintiles was relatively even, ranging from 17.8% in the richest to 21.0% in the poorest households. Community characteristics were also balanced, with 51.8% of respondents residing in areas of high community education and 50.5% in areas of high community wealth (Table 1). Table 1: Descriptive characteristics of the study participants from the Bangladesh DHS data of 2022 Variable Category Weighted frequency (n = 3173) Percentage (%) Age group 15–19 626 19.7 20–24 1086 34.2 25–29 773 24.4 30–34 482 15.2 35–39 173 5.4 40–49 34 1.1 Mother’s education No education 147 4.6 Primary 657 20.7 Secondary 1758 55.4 Higher 610 19.2 Marital status Married 3158 99.5 Formerly married 15 0.5 Employment status Not working 2596 81.8 Working 577 18.2 Parity ≤1 child 1326 41.8 2–3 children 1639 51.6 4+ children 209 6.6 Pregnancy intention Wanted then 2542 80.1 Unintended pregnancy 632 19.9 Antenatal care visits <4 ANC visits 1222 38.5 ≥4 ANC visits 1951 61.5 Place of delivery Home 1030 32.5 Health facility 2143 67.5 Mode of delivery Vaginal delivery 1682 53 Cesarean section 1491 47 Media exposure No media exposure 1404 44.2 Any media exposure 1769 55.8 Wealth index Poorest 667 21 Poorer 627 19.8 Middle 690 21.7 Richer 625 19.7 Richest 565 17.8 Place of residence Urban 844 26.6 Rural 2330 73.4 Community education Low community education 1529 48.2 High community education 1645 51.8 Community wealth Low community wealth 1571 49.5 High community wealth 1602 50.5 Prevalence of postpartum depressive symptoms A total of 512 women reported postpartum depressive symptoms (PHQ-9 ≥5), representing a weighted prevalence of 16.17% nationally. As shown in Figure 2, Geographic variation was observed across administrative divisions: prevalence ranged from 13.42% in Mymensingh to 18.10% in Rajshahi. Higher levels of depressive symptoms were found in Rajshahi (18.1%) and Chattogram (17.9%), whereas Rangpur (13.9%) and Mymensingh (13.4%) exhibited lower prevalence. Model fitness Table 2 summarizes the model fit statistics for the multilevel logistic regression models. In the null model, the intra-class correlation coefficient (ICC) was 0.157, indicating that 15.7% of the total variation in postpartum depressive symptoms was attributable to differences between clusters. The median odds ratio (MOR) was 2.11, demonstrating substantial between-cluster heterogeneity. This indicates that, for two otherwise similar women selected from two randomly chosen clusters, the woman in the higher-risk cluster had 2.11 times higher odds of experiencing postpartum depressive symptoms compared with a woman in a lower-risk cluster. Model fit improved progressively with the addition of individual-level and community-level variables. The deviance decreased from 2747.54 in the null model to 2163.47 in the full model, and the Akaike Information Criterion (AIC) similarly declined from 2751.54 to 2215.47, reflecting better model performance. Based on these improvements, the full model was identified as the best-fitting model and was retained for interpretation. Table 2: Model fitness and statistical analysis of the postpartum depression in Bangladesh. Statistical summary Model 0 Model 1 Model 2 Model 3 Likelihood ratio −1373.768 −1083.521 −1341.223 −1081.735 ICC 0.1572286 0.226861 Deviance 2747.537 2167.042 2682.446 2163.47 AIC 2751.537 2215.042 2690.446 2215.47 BIC 2763.626 2349.998 2714.624 2361.672 MOR 2.1112994 *ICC: Intra-class correlation; MOR: Median odds ratio; AIC: Akaike information criterion; BIC: Bayesian information criterion Factors associated with postpartum depressive symptoms The results from the multilevel logistic regression models are shown in Table 3. One of the clearest patterns was the role of household wealth. Compared with women in the poorest wealth group, women in the poorer group had lower odds of postpartum depressive symptoms (AOR = 0.59; 95% CI: 0.39–0.88). The same trend continued across the other groups: the middle (AOR = 0.62; 95% CI: 0.41–0.95), richer (AOR = 0.56; 95% CI: 0.34–0.90), and richest (AOR = 0.48; 95% CI: 0.27–0.86) quintiles all showed significantly lower odds. In simple terms, the wealthier the household, the less likely women were to experience postpartum depressive symptoms. Community-level education also showed an interesting pattern. In the community-only model, women living in areas with higher education levels had lower odds of postpartum depression (AOR = 0.74; 95% CI: 0.57–0.97). But once individual-level factors were added into the full model, this effect weakened and was no longer statistically significant (AOR = 0.79; 95% CI: 0.57–1.09). For the other variables—such as age, education, marital status, employment, number of children, whether the pregnancy was intended, antenatal care visits, where or how the baby was delivered, media exposure, place of residence, and community wealth—the fully adjusted model did not show any significant associations. Since the confidence intervals for these variables all crossed 1, we could not conclude that they were genuinely associated with postpartum depressive symptoms. Table 3: Multilevel multivariable logistic regression analysis from Bangladesh's recent DHS data. Variable Model I/Null model Model II Model III Model IV Age (years) 15–19 1.00 1.00 20–24 1.03 [0.67,1.58] 1.03 [0.67,1.57] 25–29 1.19 [0.72,1.98] 1.18 [0.71,1.97] 30–34 1.41 [0.79,2.51] 1.41 [0.79,2.51] 35–39 1.55 [0.75,3.20] 1.58 [0.76,3.27] 40–49 1.86 [0.54,6.34] 1.94 [0.57,6.65] Maternal education No education 1.00 1.00 Primary 1.00 [0.54,1.87] 1.02 [0.54,1.91] Secondary 0.85 [0.45,1.63] 0.94 [0.48,1.83] Higher 0.57 [0.27,1.19] 0.64 [0.30,1.34] Marital status Married 1.00 1.00 Formerly married 2.47 [0.39,15.71] 2.60 [0.42,16.23] Employment status Not working 1.00 1.00 Working 1.02 [0.73,1.43] 1.03 [0.73,1.45] Parity ≤1 child 1.00 1.00 2–3 children 0.92 [0.64,1.33] 0.92 [0.64,1.32] 4+ children 0.94 [0.51,1.76] 0.95 [0.51,1.77] Pregnancy intention Wanted then 1.00 1.00 Unintended pregnancy 1.20 [0.86,1.68] 1.20 [0.86,1.67] ANC visits <4 ANC visits 1.00 1.00 ≥4 ANC visits 0.92 [0.68,1.24] 0.92 [0.68,1.25] Place of delivery Home 1.00 1.00 Health facility 1.03 [0.70,1.51] 1.04 [0.71,1.52] Mode of delivery Vaginal delivery 1.00 1.00 Cesarean section 0.93 [0.66,1.31] 0.93 [0.66,1.32] Wealth index Poorest 1.00 1.00 Poorer 0.59* [0.39,0.89] 0.59* [0.39,0.88] Middle 0.68 [0.45,1.02] 0.62* [0.41,0.95] Richer 0.62* [0.39,0.97] 0.56* [0.34,0.90] Richest 0.54* [0.32,0.93] 0.48* [0.27,0.86] Media exposure No media exposure 1.00 1.00 Any media exposure 1.21 [0.92,1.59] 1.21 [0.92,1.59] Place of residence Urban 1.00 1.00 Rural 0.75 [0.53,1.04] 0.79 [0.57,1.10] Community education Low community education 1.00 1.00 High community education 0.74* [0.57,0.97] 0.79 [0.57,1.09] Community wealth Low community wealth 1.00 1.00 High community wealth 1.13 [0.86,1.47] 1.29 [0.90,1.84] Exponentiated coefficients; 95% confidence intervals in brackets * p<0.05, ** p<0.01, *** p<0.001 Discussion This study offers the first nationwide picture of postpartum depressive symptoms in Bangladesh using data from the 2022 BDHS. We found that 16.17% of women who had given birth in the previous year showed signs of depression. In simple terms, one in six new mothers may be struggling with their mental well-being. This level is similar to what has been reported in many LMICs, where about 17–20% of women experience these symptoms ( 26 , 27 ). However, it is lower than earlier findings from smaller studies in very poor or overcrowded areas of Bangladesh, where rates have been more than 30% ( 28 , 29 ). Because the BDHS survey includes women from many backgrounds, it provides a more complete nationwide estimate. One of the most important findings of this study is the strong link between household wealth and postpartum mental health. Women from wealthier families were consistently less likely to experience depressive symptoms than those from the poorest families. This pattern remained even after considering other factors such as age, education, number of children, pregnancy intention, and use of antenatal care. Similar relationships between financial hardship and depression have been found in studies from many other countries ( 30 , 31 ). In Bangladesh, where income inequality remains high, this means that economic circumstances continue to shape women’s mental health during the postpartum period. Beyond individual characteristics, the findings show that the community context also matters for postpartum depressive symptoms. A significant portion of the variation was attributable to community-level differences, underscoring the influence of local context on women’s mental health. In the community-only model, women in areas with higher community education had lower odds of depressive symptoms, but this association became non-significant after individual factors were included (AOR = 0.79; 95% CI: 0.57–1.09). Even so, the cluster-level variation remained in the fully adjusted model, showing that community differences continued to contribute to the pattern of depressive symptoms. This aligns with evidence from other LMIC settings suggesting that broader community environments—including social norms and local support networks—can influence maternal wellbeing ( 21 ). Importantly, this study reveals clear geographic variation in postpartum depressive symptoms. Prevalence ranged from 13% to 18%, with higher levels in Rajshahi and Chattogram and lower levels in Mymensingh and Rangpur. Although the difference is only about 5%, it still represents thousands of women at the population level. The multilevel results further support this pattern: 15.7% of the variation in symptoms stemmed from differences between communities, and a median odds ratio above 2.0 showed that two similar women could face more than double the risk simply based on where they live. These findings show that where a woman lives plays an important role in her postpartum mental health, aligning with earlier evidence linking maternal wellbeing to geographic inequality ( 32 , 33 ). In simple terms, women living in poorer areas may have fewer services and less support available to them, which can increase their risk of experiencing postpartum depressive symptoms. Notably, several factors often highlighted in smaller or facility-based studies, such as maternal age, parity, pregnancy intention, and mode of delivery were not independently associated with postpartum depressive symptoms in this nationally representative analysis. Previous research has reported mixed findings for these variables ( 34 , 35 ). In our study, the strong and consistent association with household wealth suggests that broader socioeconomic conditions may play a more central role at the population level. Although Bangladesh has achieved substantial improvements in maternal health service utilization, these gains have not translated into lower levels of postpartum depressive symptoms. This indicates that expansion of physical maternal health services alone is insufficient and that mental health screening and support have not yet been systematically integrated into routine maternal care. Strengths and limitations This study has several notable strengths. It draws on nationally representative data from the 2022 Bangladesh Demographic and Health Survey, allowing the findings to reflect postpartum women across the country. The use of sampling weights ensured that estimates accurately represented the national population. Additionally, the multilevel mixed-effects modeling approach made it possible to examine both individual and community influences while accounting for cluster-level differences, strengthening the robustness of the results. The inclusion of the PHQ-9, a widely validated screening tool, also enhances comparability with studies from other settings and contributes to alignment with global maternal mental health research. At the same time, a few limitations should be noted. Because the study is based on cross-sectional data, it cannot determine causal relationships or the direction of the associations observed. Depressive symptoms were measured using a screening instrument rather than a clinical diagnostic assessment, and a cutoff of ≥ 5 primarily identifies mild symptoms rather than clinically confirmed depression. The dataset also lacks important psychosocial variables, such as intimate partner violence, past mental health conditions, and detailed measures of social support, which may influence postpartum depressive symptoms but could not be examined here. Finally, as with all self-reported survey data, responses may be affected by recall or social desirability bias, potentially leading to underreporting of symptoms. Policy recommendations The findings point to several practical steps for strengthening maternal mental health care in Bangladesh. Because PPD symptoms were patterned by socioeconomic status and varied across communities, routine mental health screening should be incorporated into antenatal and postnatal visits. Using brief tools such as the PHQ-9 within existing maternal health services would allow providers to identify women who may need additional support without creating new parallel systems. Given the clear socioeconomic gradient observed in this study, targeted support for women from economically disadvantaged households is especially important. This could include counselling, follow-up through community health workers, or connection to existing social protection programs. At the community level, simple measures can also help. Awareness activities may reduce stigma and encourage women to seek help when needed, while strengthening referral pathways between primary facilities and mental health professionals can improve access to care, particularly in low-resource settings where specialist services are limited. Overall, the results suggest that improving postpartum mental health will require a combination of strengthened screening, timely referral, and focused attention to women facing socioeconomic disadvantage. These actions would support progress toward Bangladesh’s broader maternal health and development goals. Conclusion This study provides the first nationally representative estimate of postpartum depressive symptoms in Bangladesh and shows that about one in six women experience symptoms within the first year after childbirth. Household wealth was the only factor consistently associated with postpartum depressive symptoms, while other maternal and service-related variables showed no independent effects. A clear socioeconomic gradient was observed, with women from poorer households facing a substantially higher burden. We also identified meaningful geographic and community-level variation, indicating that residing area contributes to women’s risk of experiencing postpartum depressive symptoms. All these results underscore the influence of broader socioeconomic conditions and highlight a gap in current maternal healthcare, where mental health support is still largely missing. Practical steps, such as adding routine mental health screening to antenatal and postnatal care and prioritizing support for women facing economic disadvantage, will be essential to reduce the burden of postpartum depressive symptoms in Bangladesh. Declarations Acknowledgements The authors thank the Demographic and Health Surveys (DHS) Program for granting access to the Bangladesh Demographic and Health Survey data. Ethics approval and consent to participate This study used publicly available, anonymized secondary data from the Bangladesh Demographic and Health Surveys (BDHS). Ethical approval for the original surveys was obtained by the National Research Ethics Committee of Bangladesh and the relevant international institutional review boards. Written informed consent was obtained from all participants during the original data collection. No additional ethical approval was required for this secondary analysis. Clinical trial number: not applicable. Data availability statement The data used in this study are publicly available from the Demographic and Health Surveys (DHS) Program upon reasonable request and approval. Access to the data can be obtained from https://dhsprogram.com Replication materials, including Stata code and supporting files, are available at: https://drive.google.com/file/d/1_4Uda1Emz9x1lyJNfT7S2u1CpJCILMui/view?usp=sharing Due to DHS data use restrictions, the raw dataset cannot be shared publicly. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing interests The authors declare that they have no competing interests. Patient and public involvement Patients and the public were not involved in the design, conduct, reporting, or dissemination plans of this research. Author Contribution TI came up with the original idea of this study. She led the overall research. She oversaw the study design and coordinated the work over the project. TI and ARR prepared and handled the dataset. The statistical analysis and multilevel modelling approach were undertaken by ARR. The methodology refinement and result interpretation were done by TI, ARR, and KSAN. The manuscript was drafted by TI, ARR and KSAN helped to revise and strengthen the analytical interpretation.The manuscript was critically reviewed and provided intellectual inputs by ASA, AH and MGH to improve the final version. All contributors examined the manuscript and approved it. References Liu X, Wang S, Wang G. Prevalence and Risk Factors of Postpartum Depression in Women: A Systematic Review and Meta-analysis. Journal of Clinical Nursing. 2022;31(19–20):2665–77. doi:10.1111/jocn.16121 Khamidullina Z, Marat A, Muratbekova S, Mustapayeva NM, Chingayeva GN, Shepetov AM, et al. Postpartum Depression Epidemiology, Risk Factors, Diagnosis, and Management: An Appraisal of the Current Knowledge and Future Perspectives. Journal of Clinical Medicine. 2025 Jan;14(7):2418. doi:10.3390/jcm14072418 O’Hara MW, McCabe JE. Postpartum Depression: Current Status and Future Directions. Annual Review of Clinical Psychology. 2013 Mar 28;9(Volume 9, 2013):379–407. doi:10.1146/annurev-clinpsy-050212-185612 Tosto V, Ceccobelli M, Lucarini E, Tortorella A, Gerli S, Parazzini F, et al. Maternity Blues: A Narrative Review. Journal of Personalized Medicine. 2023 Jan;13(1):154. doi:10.3390/jpm13010154 Depression and Other Common Mental Disorders [Internet]. [cited 2026 Feb 17]. Available from: https://www.who.int/publications/i/item/depression-global-health-estimates Dadi AF, Miller ER, Mwanri L. Postnatal depression and its association with adverse infant health outcomes in low- and middle-income countries: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2020 Jul 22;20(1):416. doi:10.1186/s12884-020-03092-7 Halbreich U, Karkun S. Cross-cultural and social diversity of prevalence of postpartum depression and depressive symptoms. Journal of Affective Disorders. 2006 Apr 1;91(2):97–111. doi:10.1016/j.jad.2005.12.051 Tronick E, Reck C. Infants of Depressed Mothers. Harvard Review of Psychiatry. 2009 Jan 1;17(2):147–56. doi:10.1080/10673220902899714 PubMed PMID: 19373622. Patel V, Prince M. Global Mental Health: A New Global Health Field Comes of Age. JAMA. 2010 May 19;303(19):1976–7. doi:10.1001/jama.2010.616 Singh DR, Sunuwar DR, Adhikari S, Singh S, Karki K. Determining factors for the prevalence of depressive symptoms among postpartum mothers in lowland region in southern Nepal. PLOS ONE. 2021 Jan 22;16(1):e0245199. doi:10.1371/journal.pone.0245199 Modi VP, Parikh MN, Valipay SK. A Study on Prevalence of Postpartum Depression and Correlation with Risk Factors. Annals of Indian Psychiatry. 2018 Jun;2(1):27. doi:10.4103/aip.aip_48_17 Myo T, Hong SA, Thepthien B on, Hongkrailert N. Prevalence and Factors Associated with Postpartum Depression in Primary Healthcare Centres in Yangon, Myanmar. Malays J Med Sci. 2021 Aug;28(4):71–86. doi:10.21315/mjms2021.28.4.8 PubMed PMID: 34512132; PubMed Central PMCID: PMC8407790. Azad R, Fahmi R, Shrestha S, Joshi H, Hasan M, Khan ANS, et al. Prevalence and risk factors of postpartum depression within one year after birth in urban slums of Dhaka, Bangladesh. PLoS One. 2019 May 2;14(5):e0215735. doi:10.1371/journal.pone.0215735 PubMed PMID: 31048832; PubMed Central PMCID: PMC6497249. Dadi A. DadiThesis2020 Mastercopy. 2020. doi:10.13140/RG.2.2.26752.46086 Stewart DE, Vigod S. Postpartum Depression. New England Journal of Medicine. 2016 Dec 1;375(22):2177–86. doi:10.1056/NEJMcp1607649 Targets of Sustainable Development Goal 3 [Internet]. [cited 2026 Feb 17]. Available from: https://www.who.int/europe/about-us/our-work/sustainable-development-goals/targets-of-sustainable-development-goal-3 UNDP [Internet]. [cited 2026 Feb 17]. United Nations Development Programme. Available from: https://www.undp.org/sustainable-development-goals health F under: M, Monitoring, Mortality M. Bangladesh Maternal Mortality and Health Care Survey 2016: Preliminary Report — MEASURE Evaluation [Publication] [Internet]. [cited 2026 Feb 17]. Available from: https://www.measureevaluation.org/resources/publications/tr-17-218.html National Institute of Population Research and Training, Medical Education and Family Welfare Division, Ministry of Health and Family Welfare, ICF. Bangladesh Demographic and Health Survey 2022: Final Report [Internet]. Dhaka, Bangladesh, and Rockville, Maryland, USA: NIPORT and ICF; 2024. Available from: https://www.dhsprogram.com/pubs/pdf/FR386/FR386.pdf Mental Health Atlas [Internet]. [cited 2026 Feb 17]. Available from: https://www.who.int/teams/mental-health-and-substance-use/data-research/mental-health-atlas Lund C, Breen A, Flisher AJ, Kakuma R, Corrigall J, Joska JA, et al. Poverty and common mental disorders in low and middle income countries: A systematic review. Social Science & Medicine. 2010 Aug 1;71(3):517–28. doi:10.1016/j.socscimed.2010.04.027 Poverty and Inequality Platform [Internet]. [cited 2026 Feb 17]. Available from: https://pip.worldbank.org/country-profiles/BGD Cumbe VFJ, Muanido A, Manaca MN, Fumo H, Chiruca P, Hicks L, et al. Validity and item response theory properties of the Patient Health Questionnaire-9 for primary care depression screening in Mozambique (PHQ-9-MZ). BMC Psychiatry. 2020 Jul 22;20(1):382. doi:10.1186/s12888-020-02772-0 The DHS Program - Bangladesh: Standard DHS, 2022 Dataset [Internet]. [cited 2026 Feb 23]. Available from: https://www.dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0 Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. Journal of General Internal Medicine. 2001;16(9):606–13. doi:10.1046/j.1525-1497.2001.016009606.x Fisher J, Mello MC de, Patel V, Rahman A, Tran T, Holton S, et al. Prevalence and determinants of common perinatal mental disorders in women in low-and lower-middle-income countries: a systematic review. Bulletin of the World Health Organization. 2012;90:139–49. Shorey S, Chee CYI, Ng ED, Chan YH, Tam WWS, Chong YS. Prevalence and incidence of postpartum depression among healthy mothers: A systematic review and meta-analysis. Journal of Psychiatric Research. 2018 Sep 1;104:235–48. doi:10.1016/j.jpsychires.2018.08.001 Gausia K, Fisher C, Ali M, Oosthuizen J. Antenatal depression and suicidal ideation among rural Bangladeshi women: a community-based study. Archives of women’s mental health. 2009;12(5):351–8. Nasreen HE, Kabir ZN, Forsell Y, Edhborg M. Prevalence and associated factors of depressive and anxiety symptoms during pregnancy: a population based study in rural Bangladesh. BMC women’s health. 2011;11(1):22. Lorant V, Deliège D, Eaton W, Robert A, Philippot P, Ansseau M. Socioeconomic Inequalities in Depression: A Meta-Analysis. Am J Epidemiol. 2003 Jan 15;157(2):98–112. doi:10.1093/aje/kwf182 Patel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, et al. The Lancet Commission on global mental health and sustainable development. The Lancet. 2018 Oct 27;392(10157):1553–98. doi:10.1016/S0140-6736(18)31612-X PubMed PMID: 30314863. Raza S, Banik R, Noor STA, Sayeed A, Saha A, Jahan E, et al. Anxiety and depression among reproductive-aged women in Bangladesh: burden, determinants, and care-seeking practices based on a nationally representative demographic and health survey. Arch Womens Ment Health. 2025 Oct 1;28(5):1125–41. doi:10.1007/s00737-025-01564-3 Villegas L, McKay K, Dennis CL, Ross LE. Postpartum Depression Among Rural Women From Developed and Developing Countries: A Systematic Review. The Journal of Rural Health. 2011;27(3):278–88. doi:10.1111/j.1748-0361.2010.00339.x Upadhyay UD, Karasek D. Women’s empowerment and achievement of desired fertility in Sub-Saharan Africa. DHS Working Papers No. 80 [Internet]. Calverton, Maryland, USA: ICF Macro; 2010. Available from: http://dhsprogram.com/pubs/pdf/WP80/WP80.pdf Gelaye B, Rondon MB, Araya R, Williams MA. Epidemiology of maternal depression, risk factors, and child outcomes in low-income and middle-income countries. The Lancet Psychiatry. 2016 Oct 1;3(10):973–82. doi:10.1016/S2215-0366(16)30284-X PubMed PMID: 27650773. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9100287","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":632835015,"identity":"8de80400-266b-4e4b-8336-80686ca6c69b","order_by":0,"name":"Tamanna Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBADHgZ2EMVmAyQYGw/gVwqjmcFa0kBaGojSwgDVchjMxqvFnv104ufCHXYy8s3MTzf+KDtvt7b9MNCWGptonLbw5G6WnnkmmcfgMJvZbZ5zt5O3nUkEajmWltuA02G5G6R525h5DJgZzG4ztt1ONjsA1MLYcBi3Fv63m3/zttXzyDezf7v5s+1cstn5hwS0SORuA9pymIfhMI/ZDd62A3ZmNwjZcuPtNmvetuNAv/CUAf2SnGB2A2hLAh6/sPfnbr7N21ZtL9/evu3mjzI7e7Pz6Q8ffKixwakFAySCVSYQqxwE7ElRPApGwSgYBSMDAADXsl8FzzRi9gAAAABJRU5ErkJggg==","orcid":"","institution":"Eden Mohila College","correspondingAuthor":true,"prefix":"","firstName":"Tamanna","middleName":"","lastName":"Islam","suffix":""},{"id":632835016,"identity":"185a9a05-11cd-4bbb-aca7-521530c3246c","order_by":1,"name":"Ashiqur Rahman Rony","email":"","orcid":"","institution":"Development Research Initiative","correspondingAuthor":false,"prefix":"","firstName":"Ashiqur","middleName":"Rahman","lastName":"Rony","suffix":""},{"id":632835017,"identity":"ebd93ed1-9033-4c49-b88b-4a315e7c58bf","order_by":2,"name":"Kazi Sabbir Ahmad Nahin","email":"","orcid":"","institution":"University of Kentucky College of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kazi","middleName":"Sabbir Ahmad","lastName":"Nahin","suffix":""},{"id":632835018,"identity":"a46a2bf2-85a3-4e39-ae52-5dd9fd5f23bb","order_by":3,"name":"Abida S Asha","email":"","orcid":"","institution":"University of Kentucky College of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abida","middleName":"S","lastName":"Asha","suffix":""},{"id":632835019,"identity":"0def8b1f-7558-47f3-9a54-d0120662c32c","order_by":4,"name":"Arman Hossen","email":"","orcid":"","institution":"Miami University","correspondingAuthor":false,"prefix":"","firstName":"Arman","middleName":"","lastName":"Hossen","suffix":""},{"id":632835020,"identity":"d4714e0b-63e2-4c5b-bb88-5d13e75545eb","order_by":5,"name":"Md Galib Hossain","email":"","orcid":"","institution":"Government Titumir College","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Galib","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2026-03-12 05:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9100287/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9100287/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108946036,"identity":"1c09dfa9-f013-4a1d-9947-e5bcc2158fec","added_by":"auto","created_at":"2026-05-11 06:20:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of participant selection for the analysis of postpartum depressive symptoms, BDHS 2022\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9100287/v1/07eb4fbd3d15375782f8531e.png"},{"id":108946037,"identity":"b6a153ba-747d-4925-97aa-70ea3f0747f4","added_by":"auto","created_at":"2026-05-11 06:20:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":368258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical distribution of postpartum depressive symptoms in Bangladesh, BDHS 2022\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9100287/v1/53da04c77ea49d3fe549f693.png"},{"id":109067859,"identity":"6dfbaa09-6968-4437-929d-e0d2756c353f","added_by":"auto","created_at":"2026-05-12 10:02:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":714395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9100287/v1/24b2dab6-3e42-4ecb-8cb5-e48d4a999a0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic Inequality and Postpartum Depression in Bangladesh: Evidence from BDHS 2022","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe transition to motherhood is widely recognized as a critical period in a woman\u0026rsquo;s life course, marked by profound biological, psychological, and social changes. While this transition is often celebrated, it also carries a considerable risk of mental health disorders (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Among these, postpartum depression (PPD) stands out as one of the most prevalent global maternal health challenges, characterized by non-psychotic depressive episodes occurring within the first twelve months after childbirth (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). According to the World Health Organization (WHO), approximately 10% of pregnant women and 13% of women who have just given birth experience a mental disorder, primarily depression, contributing significantly to the global burden of disease. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Common symptoms of PPD, such as persistent low mood, fatigue, sleep disturbance, and feelings of guilt or worthlessness can severely impair maternal functioning (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobal prevalence estimates of postpartum depression vary widely, ranging from 0.5% to 60.8%, largely depending on the measurement tools, timing of assessment, and sociocultural context (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Evidence consistently shows higher prevalence in low- and middle-income countries (LMICs), which collectively possess less than 20% of global mental health resources despite hosting the majority of the world\u0026rsquo;s population (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A comprehensive meta-analysis of more than 80 LMIC studies estimated a pooled prevalence of 17.7%, with South Asia reporting some of the highest burdens\u0026mdash;often exceeding 25% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Country-specific findings indicate prevalence rates of 33.7% in Nepal, 20\u0026ndash;23% in India, and over 30% in parts of Myanmar (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In Bangladesh, subnational and community-based studies have documented rates from 18% in rural settings to as high as 39\u0026ndash;46% among women in urban slums and socioeconomically disadvantaged environments (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). While these studies underscore the severity of postpartum depressive symptoms, their limited geographic scope restricts their generalizability and leaves national prevalence unknown.\u003c/p\u003e \u003cp\u003eThe consequences of postpartum depression extend beyond maternal wellbeing. PPD is associated with heightened risks of child undernutrition, reduced exclusive breastfeeding, incomplete immunization, impaired mother\u0026ndash;infant bonding, and delayed cognitive development (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Longitudinal research indicates that 25\u0026ndash;50% of women with untreated PPD continue to experience symptoms beyond six months postpartum (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Globally, suicide accounts for 7\u0026ndash;10% of maternal deaths, underscoring the serious consequences of untreated perinatal mental health conditions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Addressing postpartum mental health thus aligns directly with Sustainable Development Goal (SDG) 3; promoting good health and well-being and SDG 5; advancing gender equality (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBangladesh has made significant progress in maternal health over the past two decades. The maternal mortality ratio dropped from 322 deaths per 100,000 live births in 2001 to 173 in 2017 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The 2022 Bangladesh Demographic and Health Survey (BDHS) further reports that 67.5% of births occurred in health facilities and 61.5% of women received at least four antenatal care visits (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, mental health screening remains largely absent from routine maternal health services. National mental health expenditure is less than 0.5% of total health spending, and the country has fewer than 0.2 psychiatrists per 100,000 population\u0026mdash;far below the global median (~\u0026thinsp;1.3 per 100,000) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These structural gaps highlight the urgency of integrating maternal mental health into national health strategies.\u003c/p\u003e \u003cp\u003eSocioeconomic inequality is a well-established determinant of depressive disorders. Individuals from the lowest income groups are 1.5\u0026ndash;2 times more likely to experience depression than those from higher-income groups (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In South Asia, poverty, unemployment, unintended pregnancy, and limited social support are repeatedly identified as key predictors of postpartum depressive symptoms (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Bangladesh continues to experience substantial income inequality, with a recent Gini coefficient above 0.48 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Whether such macro-level disparities translate into measurable socioeconomic gradients in postpartum depressive symptoms at the national level remains unclear.\u003c/p\u003e \u003cp\u003eThe 2022 Bangladesh Demographic and Health Survey (BDHS) represent a significant advancement in the study of maternal mental health in Bangladesh, as it incorporated, for the first time, the validated Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms among women with a recent live birth (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Leveraging a weighted sample of 3,173 women who had delivered within the preceding 12 months, this study fills a critical gap in the national evidence base by estimating the prevalence of postpartum depressive symptoms across the country and its administrative regions, assessing socioeconomic disparities with a focus on household wealth, and examining community-level contextual influences using multilevel mixed-effects logistic regression models.\u003c/p\u003e \u003cp\u003eBy integrating both individual and structural determinants, this study conceptualizes postpartum depressive symptoms as socially patterned outcomes embedded within broader socioeconomic and community environments, rather than isolated psychological experiences. Producing nationally representative evidence is therefore an essential step toward ensuring that maternal mental health is recognized, prioritized, and integrated into Bangladesh\u0026rsquo;s evolving health system and its broader commitments to achieving the Sustainable Development Goals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a secondary analysis of data from the 2022 Bangladesh Demographic and Health Survey (BDHS 2022), a nationally representative cross-sectional household survey (24). The BDHS employed a two-stage stratified cluster sampling design. In the first stage, primary sampling units (clusters) were selected from the national sampling frame using probability proportional to size. In the second stage, a fixed number of households were selected systematically within each cluster. All women aged 15\u0026ndash;49 years residing in selected households were eligible for interview.\u003c/p\u003e\n\u003cp\u003eAmong the 30,078 women interviewed in BDHS 2022, those who had a live birth within the 12 months preceding the survey were identified using the reported age of the most recent child (\u0026le;12 months). A total of 3,117 women met this criterion and constituted the final analytic sample. No additional exclusions were required, as complete PHQ-9 data were available for all eligible women. The sample selection process is presented in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome of interest was postpartum depressive symptoms, assessed using the Patient Health Questionnaire-9 (PHQ-9) included in BDHS 2022. The PHQ-9 comprises nine items assessing the frequency of depressive symptoms over the preceding two weeks, each scored from 0 (\u0026ldquo;not at all\u0026rdquo;) to 3 (\u0026ldquo;nearly every day\u0026rdquo;). A total score ranging from 0 to 27 was calculated by summing responses across the nine items. In accordance with established practice, postpartum depressive symptoms were defined as a PHQ-9 score \u0026ge;5, indicating at least mild depressive symptomatology. The outcome variable was dichotomized as 0 (no depressive symptoms; score 0\u0026ndash;4) and 1 (postpartum depressive symptoms; score \u0026ge;5) (25).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual-level factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included socio-demographic and reproductive characteristics identified in prior literature as potential determinants of maternal mental health. These variables were categorized as follows: maternal age (15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;49 years); educational attainment (no education, primary, secondary, higher); marital status (currently married; formerly married [divorced, widowed, or separated]); employment status (working; not working); parity (\u0026le;1 child, 2\u0026ndash;3 children, \u0026ge;4 children); pregnancy intention (wanted at the time; unintended); number of antenatal care (ANC) visits (\u0026lt;4; \u0026ge;4 visits); place of delivery (home; health facility); mode of delivery (vaginal; caesarean section); wealth quintile (poorest, poorer, middle, richer, richest); and place of residence (urban; rural). Media exposure was defined as exposure to at least one of the following: newspaper, radio, or television (yes; no). All covariates were treated as categorical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity-level factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo account for contextual influences, two community-level variables were constructed at the cluster level. Community education was defined as the proportion of women within a cluster who had attained at least secondary education. Community wealth was defined as the proportion of women within a cluster belonging to the middle, richer, or richest wealth quintiles (wealth quintile \u0026ge;3). Clusters were classified as having \u0026ldquo;high\u0026rdquo; or \u0026ldquo;low\u0026rdquo; community education and wealth based on the median values of these proportions. These measures captured broader socio-economic context beyond individual characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses accounted for the complex survey design of BDHS 2022. Sampling weights were applied in descriptive analyses to obtain nationally representative estimates.\u003c/p\u003e\n\u003cp\u003eTo examine factors associated with postpartum depressive symptoms, multilevel mixed-effects logistic regression models with a random intercept for clusters were fitted to account for intra-cluster correlation. For multilevel modelling, sampling weights were rescaled by dividing each weight by its mean to stabilize model estimation. Four sequential models were estimated: (1) a null model without explanatory variables; (2) a model including individual-level covariates; (3) a model including community-level variables; and (4) a full model incorporating both individual- and community-level factors. Adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported.\u003c/p\u003e\n\u003cp\u003eModel fit was evaluated using log-likelihood, deviance, Akaike\u0026rsquo;s Information Criterion (AIC), and Bayesian Information Criterion (BIC). Cluster-level variation was quantified using the intra-class correlation coefficient (ICC) and the median odds ratio (MOR). Statistical significance was defined as p\u0026lt;0.05. Statistical analyses were performed using Stata version 18 (StataCorp LLC, College Station, TX, USA), while the flow diagram and geographical distribution map were created using R (version 4.5.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive characteristics of respondents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3,117 women who had a live birth within the 12 months preceding the BDHS 2022 survey were included in the analysis. The largest age group was 20\u0026ndash;24 years (34.2%), followed by 25\u0026ndash;29 years (24.4%) and 15\u0026ndash;19 years (19.7%). More than half of the respondents (55.4%) completed secondary education, whereas 20.7% had primary education, 19.2% had higher education, and 4.6% had no education. Nearly all participants were currently married (99.5%), and 81.8% were not engaged in paid employment. Regarding reproductive characteristics, 51.6% of women had two to three children, while 41.8% had one or fewer children. Most pregnancies were wanted at the time (80.1%), and 61.5% of women attended at least four antenatal care visits. A majority of deliveries occurred in health facilities (67.5%), and 47% were by caesarean section. Most women lived in rural areas (73.4%), and the wealth distribution across quintiles was relatively even, ranging from 17.8% in the richest to 21.0% in the poorest households. Community characteristics were also balanced, with 51.8% of respondents residing in areas of high community education and 50.5% in areas of high community wealth (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Descriptive characteristics of the study participants from the Bangladesh DHS data of 2022\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 100%;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted frequency (n = 3173)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"6\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"4\"\u003e\n \u003cp\u003eMother\u0026rsquo;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e99.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFormerly married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026le;1 child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2\u0026ndash;3 children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4+ children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003ePregnancy intention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWanted then\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e80.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUnintended pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eAntenatal care visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026lt;4 ANC visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026ge;4 ANC visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003ePlace of delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHealth facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eMode of delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCesarean section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eMedia exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNo media exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAny media exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"5\"\u003e\n \u003cp\u003eWealth index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eCommunity education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLow community education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigh community education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\"\u003e\n \u003cp\u003eCommunity wealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLow community wealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigh community wealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of postpartum depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 512 women reported postpartum depressive symptoms (PHQ-9 \u0026ge;5), representing a weighted prevalence of 16.17% nationally. As shown in Figure 2, Geographic variation was observed across administrative divisions: prevalence ranged from 13.42% in Mymensingh to 18.10% in Rajshahi. Higher levels of depressive symptoms were found in Rajshahi (18.1%) and Chattogram (17.9%), whereas Rangpur (13.9%) and Mymensingh (13.4%) exhibited lower prevalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel fitness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes the model fit statistics for the multilevel logistic regression models. In the null model, the intra-class correlation coefficient (ICC) was 0.157, indicating that 15.7% of the total variation in postpartum depressive symptoms was attributable to differences between clusters. The median odds ratio (MOR) was 2.11, demonstrating substantial between-cluster heterogeneity. This indicates that, for two otherwise similar women selected from two randomly chosen clusters, the woman in the higher-risk cluster had 2.11 times higher odds of experiencing postpartum depressive symptoms compared with a woman in a lower-risk cluster.\u003c/p\u003e\n\u003cp\u003eModel fit improved progressively with the addition of individual-level and community-level variables. The deviance decreased from 2747.54 in the null model to 2163.47 in the full model, and the Akaike Information Criterion (AIC) similarly declined from 2751.54 to 2215.47, reflecting better model performance. Based on these improvements, the full model was identified as the best-fitting model and was retained for interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Model fitness and statistical analysis of the postpartum depression in Bangladesh.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 100%;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLikelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026minus;1373.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026minus;1083.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026minus;1341.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026minus;1081.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.1572286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.226861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2747.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2167.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2682.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2163.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2751.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2215.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2690.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2215.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2763.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2349.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2714.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2361.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.1112994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*ICC: Intra-class correlation; MOR: Median odds ratio; AIC: Akaike information criterion; BIC: Bayesian information criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with postpartum depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from the multilevel logistic regression models are shown in Table 3. One of the clearest patterns was the role of household wealth. Compared with women in the poorest wealth group, women in the poorer group had lower odds of postpartum depressive symptoms (AOR = 0.59; 95% CI: 0.39\u0026ndash;0.88). The same trend continued across the other groups: the middle (AOR = 0.62; 95% CI: 0.41\u0026ndash;0.95), richer (AOR = 0.56; 95% CI: 0.34\u0026ndash;0.90), and richest (AOR = 0.48; 95% CI: 0.27\u0026ndash;0.86) quintiles all showed significantly lower odds. In simple terms, the wealthier the household, the less likely women were to experience postpartum depressive symptoms.\u003c/p\u003e\n\u003cp\u003eCommunity-level education also showed an interesting pattern. In the community-only model, women living in areas with higher education levels had lower odds of postpartum depression (AOR = 0.74; 95% CI: 0.57\u0026ndash;0.97). But once individual-level factors were added into the full model, this effect weakened and was no longer statistically significant (AOR = 0.79; 95% CI: 0.57\u0026ndash;1.09).\u003c/p\u003e\n\u003cp\u003eFor the other variables\u0026mdash;such as age, education, marital status, employment, number of children, whether the pregnancy was intended, antenatal care visits, where or how the baby was delivered, media exposure, place of residence, and community wealth\u0026mdash;the fully adjusted model did not show any significant associations. Since the confidence intervals for these variables all crossed 1, we could not conclude that they were genuinely associated with postpartum depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Multilevel multivariable logistic regression analysis from Bangladesh\u0026apos;s recent DHS data.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 100%;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel I/Null model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel IV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.03 [0.67,1.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.03 [0.67,1.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.19 [0.72,1.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.18 [0.71,1.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.41 [0.79,2.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.41 [0.79,2.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.55 [0.75,3.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.58 [0.76,3.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.86 [0.54,6.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.94 [0.57,6.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00 [0.54,1.87]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.02 [0.54,1.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.85 [0.45,1.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.94 [0.48,1.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.57 [0.27,1.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.64 [0.30,1.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFormerly married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.47 [0.39,15.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.60 [0.42,16.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.02 [0.73,1.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.03 [0.73,1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026le;1 child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2\u0026ndash;3 children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.92 [0.64,1.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.92 [0.64,1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4+ children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.94 [0.51,1.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.95 [0.51,1.77]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePregnancy intention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWanted then\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUnintended pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.20 [0.86,1.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.20 [0.86,1.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eANC visits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026lt;4 ANC visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026ge;4 ANC visits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.92 [0.68,1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.92 [0.68,1.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHealth facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.03 [0.70,1.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.04 [0.71,1.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMode of delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCesarean section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.93 [0.66,1.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.93 [0.66,1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59* [0.39,0.89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.59* [0.39,0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.68 [0.45,1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.62* [0.41,0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.62* [0.39,0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.56* [0.34,0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.54* [0.32,0.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.48* [0.27,0.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedia exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNo media exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAny media exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.21 [0.92,1.59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.21 [0.92,1.59]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.75 [0.53,1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.79 [0.57,1.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLow community education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigh community education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.74* [0.57,0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.79 [0.57,1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity wealth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLow community wealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigh community wealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.13 [0.86,1.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.29 [0.90,1.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eExponentiated coefficients; 95% confidence intervals in brackets * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers the first nationwide picture of postpartum depressive symptoms in Bangladesh using data from the 2022 BDHS. We found that 16.17% of women who had given birth in the previous year showed signs of depression. In simple terms, one in six new mothers may be struggling with their mental well-being. This level is similar to what has been reported in many LMICs, where about 17\u0026ndash;20% of women experience these symptoms (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, it is lower than earlier findings from smaller studies in very poor or overcrowded areas of Bangladesh, where rates have been more than 30% (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Because the BDHS survey includes women from many backgrounds, it provides a more complete nationwide estimate.\u003c/p\u003e \u003cp\u003eOne of the most important findings of this study is the strong link between household wealth and postpartum mental health. Women from wealthier families were consistently less likely to experience depressive symptoms than those from the poorest families. This pattern remained even after considering other factors such as age, education, number of children, pregnancy intention, and use of antenatal care. Similar relationships between financial hardship and depression have been found in studies from many other countries (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In Bangladesh, where income inequality remains high, this means that economic circumstances continue to shape women\u0026rsquo;s mental health during the postpartum period.\u003c/p\u003e \u003cp\u003eBeyond individual characteristics, the findings show that the community context also matters for postpartum depressive symptoms. A significant portion of the variation was attributable to community-level differences, underscoring the influence of local context on women\u0026rsquo;s mental health. In the community-only model, women in areas with higher community education had lower odds of depressive symptoms, but this association became non-significant after individual factors were included (AOR\u0026thinsp;=\u0026thinsp;0.79; 95% CI: 0.57\u0026ndash;1.09). Even so, the cluster-level variation remained in the fully adjusted model, showing that community differences continued to contribute to the pattern of depressive symptoms. This aligns with evidence from other LMIC settings suggesting that broader community environments\u0026mdash;including social norms and local support networks\u0026mdash;can influence maternal wellbeing (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, this study reveals clear geographic variation in postpartum depressive symptoms. Prevalence ranged from 13% to 18%, with higher levels in Rajshahi and Chattogram and lower levels in Mymensingh and Rangpur. Although the difference is only about 5%, it still represents thousands of women at the population level. The multilevel results further support this pattern: 15.7% of the variation in symptoms stemmed from differences between communities, and a median odds ratio above 2.0 showed that two similar women could face more than double the risk simply based on where they live. These findings show that where a woman lives plays an important role in her postpartum mental health, aligning with earlier evidence linking maternal wellbeing to geographic inequality (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In simple terms, women living in poorer areas may have fewer services and less support available to them, which can increase their risk of experiencing postpartum depressive symptoms.\u003c/p\u003e \u003cp\u003eNotably, several factors often highlighted in smaller or facility-based studies, such as maternal age, parity, pregnancy intention, and mode of delivery were not independently associated with postpartum depressive symptoms in this nationally representative analysis. Previous research has reported mixed findings for these variables (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In our study, the strong and consistent association with household wealth suggests that broader socioeconomic conditions may play a more central role at the population level. Although Bangladesh has achieved substantial improvements in maternal health service utilization, these gains have not translated into lower levels of postpartum depressive symptoms. This indicates that expansion of physical maternal health services alone is insufficient and that mental health screening and support have not yet been systematically integrated into routine maternal care.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several notable strengths. It draws on nationally representative data from the 2022 Bangladesh Demographic and Health Survey, allowing the findings to reflect postpartum women across the country. The use of sampling weights ensured that estimates accurately represented the national population. Additionally, the multilevel mixed-effects modeling approach made it possible to examine both individual and community influences while accounting for cluster-level differences, strengthening the robustness of the results. The inclusion of the PHQ-9, a widely validated screening tool, also enhances comparability with studies from other settings and contributes to alignment with global maternal mental health research.\u003c/p\u003e \u003cp\u003eAt the same time, a few limitations should be noted. Because the study is based on cross-sectional data, it cannot determine causal relationships or the direction of the associations observed. Depressive symptoms were measured using a screening instrument rather than a clinical diagnostic assessment, and a cutoff of \u0026ge;\u0026thinsp;5 primarily identifies mild symptoms rather than clinically confirmed depression. The dataset also lacks important psychosocial variables, such as intimate partner violence, past mental health conditions, and detailed measures of social support, which may influence postpartum depressive symptoms but could not be examined here. Finally, as with all self-reported survey data, responses may be affected by recall or social desirability bias, potentially leading to underreporting of symptoms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePolicy recommendations\u003c/h2\u003e \u003cp\u003eThe findings point to several practical steps for strengthening maternal mental health care in Bangladesh. Because PPD symptoms were patterned by socioeconomic status and varied across communities, routine mental health screening should be incorporated into antenatal and postnatal visits. Using brief tools such as the PHQ-9 within existing maternal health services would allow providers to identify women who may need additional support without creating new parallel systems.\u003c/p\u003e \u003cp\u003eGiven the clear socioeconomic gradient observed in this study, targeted support for women from economically disadvantaged households is especially important. This could include counselling, follow-up through community health workers, or connection to existing social protection programs.\u003c/p\u003e \u003cp\u003eAt the community level, simple measures can also help. Awareness activities may reduce stigma and encourage women to seek help when needed, while strengthening referral pathways between primary facilities and mental health professionals can improve access to care, particularly in low-resource settings where specialist services are limited.\u003c/p\u003e \u003cp\u003eOverall, the results suggest that improving postpartum mental health will require a combination of strengthened screening, timely referral, and focused attention to women facing socioeconomic disadvantage. These actions would support progress toward Bangladesh\u0026rsquo;s broader maternal health and development goals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first nationally representative estimate of postpartum depressive symptoms in Bangladesh and shows that about one in six women experience symptoms within the first year after childbirth. Household wealth was the only factor consistently associated with postpartum depressive symptoms, while other maternal and service-related variables showed no independent effects. A clear socioeconomic gradient was observed, with women from poorer households facing a substantially higher burden. We also identified meaningful geographic and community-level variation, indicating that residing area contributes to women\u0026rsquo;s risk of experiencing postpartum depressive symptoms. All these results underscore the influence of broader socioeconomic conditions and highlight a gap in current maternal healthcare, where mental health support is still largely missing. Practical steps, such as adding routine mental health screening to antenatal and postnatal care and prioritizing support for women facing economic disadvantage, will be essential to reduce the burden of postpartum depressive symptoms in Bangladesh.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Demographic and Health Surveys (DHS) Program for granting access to the Bangladesh Demographic and Health Survey data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available, anonymized secondary data from the Bangladesh Demographic and Health Surveys (BDHS). Ethical approval for the original surveys was obtained by the National Research Ethics Committee of Bangladesh and the relevant international institutional review boards. Written informed consent was obtained from all participants during the original data collection. No additional ethical approval was required for this secondary analysis.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available from the Demographic and Health Surveys (DHS) Program upon reasonable request and approval. Access to the data can be obtained from\u003ca href=\"https://dhsprogram.com/\"\u003e\u0026nbsp;\u003c/a\u003ehttps://dhsprogram.com\u003c/p\u003e\n\u003cp\u003eReplication materials, including Stata code and supporting files, are available at: https://drive.google.com/file/d/1_4Uda1Emz9x1lyJNfT7S2u1CpJCILMui/view?usp=sharing\u003c/p\u003e\n\u003cp\u003eDue to DHS data use restrictions, the raw dataset cannot be shared publicly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients and the public were not involved in the design, conduct, reporting, or dissemination plans of this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTI came up with the original idea of this study. She led the overall research. She oversaw the study design and coordinated the work over the project. TI and ARR prepared and handled the dataset. The statistical analysis and multilevel modelling approach were undertaken by ARR. The methodology refinement and result interpretation were done by TI, ARR, and KSAN. The manuscript was drafted by TI, ARR and KSAN helped to revise and strengthen the analytical interpretation.The manuscript was critically reviewed and provided intellectual inputs by ASA, AH and MGH to improve the final version. All contributors examined the manuscript and approved it.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu X, Wang S, Wang G. Prevalence and Risk Factors of Postpartum Depression in Women: A Systematic Review and Meta-analysis. Journal of Clinical Nursing. 2022;31(19\u0026ndash;20):2665\u0026ndash;77. doi:10.1111/jocn.16121 \u003c/li\u003e\n\u003cli\u003eKhamidullina Z, Marat A, Muratbekova S, Mustapayeva NM, Chingayeva GN, Shepetov AM, et al. Postpartum Depression Epidemiology, Risk Factors, Diagnosis, and Management: An Appraisal of the Current Knowledge and Future Perspectives. Journal of Clinical Medicine. 2025 Jan;14(7):2418. doi:10.3390/jcm14072418 \u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Hara MW, McCabe JE. Postpartum Depression: Current Status and Future Directions. Annual Review of Clinical Psychology. 2013 Mar 28;9(Volume 9, 2013):379\u0026ndash;407. doi:10.1146/annurev-clinpsy-050212-185612 \u003c/li\u003e\n\u003cli\u003eTosto V, Ceccobelli M, Lucarini E, Tortorella A, Gerli S, Parazzini F, et al. Maternity Blues: A Narrative Review. Journal of Personalized Medicine. 2023 Jan;13(1):154. doi:10.3390/jpm13010154 \u003c/li\u003e\n\u003cli\u003eDepression and Other Common Mental Disorders [Internet]. [cited 2026 Feb 17]. Available from: https://www.who.int/publications/i/item/depression-global-health-estimates \u003c/li\u003e\n\u003cli\u003eDadi AF, Miller ER, Mwanri L. Postnatal depression and its association with adverse infant health outcomes in low- and middle-income countries: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2020 Jul 22;20(1):416. doi:10.1186/s12884-020-03092-7 \u003c/li\u003e\n\u003cli\u003eHalbreich U, Karkun S. Cross-cultural and social diversity of prevalence of postpartum depression and depressive symptoms. Journal of Affective Disorders. 2006 Apr 1;91(2):97\u0026ndash;111. doi:10.1016/j.jad.2005.12.051 \u003c/li\u003e\n\u003cli\u003eTronick E, Reck C. Infants of Depressed Mothers. Harvard Review of Psychiatry. 2009 Jan 1;17(2):147\u0026ndash;56. doi:10.1080/10673220902899714 PubMed PMID: 19373622. \u003c/li\u003e\n\u003cli\u003ePatel V, Prince M. Global Mental Health: A New Global Health Field Comes of Age. JAMA. 2010 May 19;303(19):1976\u0026ndash;7. doi:10.1001/jama.2010.616 \u003c/li\u003e\n\u003cli\u003eSingh DR, Sunuwar DR, Adhikari S, Singh S, Karki K. Determining factors for the prevalence of depressive symptoms among postpartum mothers in lowland region in southern Nepal. PLOS ONE. 2021 Jan 22;16(1):e0245199. doi:10.1371/journal.pone.0245199 \u003c/li\u003e\n\u003cli\u003eModi VP, Parikh MN, Valipay SK. A Study on Prevalence of Postpartum Depression and Correlation with Risk Factors. Annals of Indian Psychiatry. 2018 Jun;2(1):27. doi:10.4103/aip.aip_48_17 \u003c/li\u003e\n\u003cli\u003eMyo T, Hong SA, Thepthien B on, Hongkrailert N. Prevalence and Factors Associated with Postpartum Depression in Primary Healthcare Centres in Yangon, Myanmar. Malays J Med Sci. 2021 Aug;28(4):71\u0026ndash;86. doi:10.21315/mjms2021.28.4.8 PubMed PMID: 34512132; PubMed Central PMCID: PMC8407790. \u003c/li\u003e\n\u003cli\u003eAzad R, Fahmi R, Shrestha S, Joshi H, Hasan M, Khan ANS, et al. Prevalence and risk factors of postpartum depression within one year after birth in urban slums of Dhaka, Bangladesh. PLoS One. 2019 May 2;14(5):e0215735. doi:10.1371/journal.pone.0215735 PubMed PMID: 31048832; PubMed Central PMCID: PMC6497249. \u003c/li\u003e\n\u003cli\u003eDadi A. DadiThesis2020 Mastercopy. 2020. doi:10.13140/RG.2.2.26752.46086 \u003c/li\u003e\n\u003cli\u003eStewart DE, Vigod S. Postpartum Depression. New England Journal of Medicine. 2016 Dec 1;375(22):2177\u0026ndash;86. doi:10.1056/NEJMcp1607649 \u003c/li\u003e\n\u003cli\u003eTargets of Sustainable Development Goal 3 [Internet]. [cited 2026 Feb 17]. Available from: https://www.who.int/europe/about-us/our-work/sustainable-development-goals/targets-of-sustainable-development-goal-3 \u003c/li\u003e\n\u003cli\u003eUNDP [Internet]. [cited 2026 Feb 17]. United Nations Development Programme. Available from: https://www.undp.org/sustainable-development-goals \u003c/li\u003e\n\u003cli\u003ehealth F under: M, Monitoring, Mortality M. Bangladesh Maternal Mortality and Health Care Survey 2016: Preliminary Report \u0026mdash; MEASURE Evaluation [Publication] [Internet]. [cited 2026 Feb 17]. Available from: https://www.measureevaluation.org/resources/publications/tr-17-218.html \u003c/li\u003e\n\u003cli\u003eNational Institute of Population Research and Training, Medical Education and Family Welfare Division, Ministry of Health and Family Welfare, ICF. Bangladesh Demographic and Health Survey 2022: Final Report [Internet]. Dhaka, Bangladesh, and Rockville, Maryland, USA: NIPORT and ICF; 2024. Available from: https://www.dhsprogram.com/pubs/pdf/FR386/FR386.pdf \u003c/li\u003e\n\u003cli\u003eMental Health Atlas [Internet]. [cited 2026 Feb 17]. Available from: https://www.who.int/teams/mental-health-and-substance-use/data-research/mental-health-atlas \u003c/li\u003e\n\u003cli\u003eLund C, Breen A, Flisher AJ, Kakuma R, Corrigall J, Joska JA, et al. Poverty and common mental disorders in low and middle income countries: A systematic review. Social Science \u0026amp; Medicine. 2010 Aug 1;71(3):517\u0026ndash;28. doi:10.1016/j.socscimed.2010.04.027 \u003c/li\u003e\n\u003cli\u003ePoverty and Inequality Platform [Internet]. [cited 2026 Feb 17]. Available from: https://pip.worldbank.org/country-profiles/BGD \u003c/li\u003e\n\u003cli\u003eCumbe VFJ, Muanido A, Manaca MN, Fumo H, Chiruca P, Hicks L, et al. Validity and item response theory properties of the Patient Health Questionnaire-9 for primary care depression screening in Mozambique (PHQ-9-MZ). BMC Psychiatry. 2020 Jul 22;20(1):382. doi:10.1186/s12888-020-02772-0 \u003c/li\u003e\n\u003cli\u003eThe DHS Program - Bangladesh: Standard DHS, 2022 Dataset [Internet]. [cited 2026 Feb 23]. Available from: https://www.dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0 \u003c/li\u003e\n\u003cli\u003eKroenke K, Spitzer RL, Williams JBW. The PHQ-9. Journal of General Internal Medicine. 2001;16(9):606\u0026ndash;13. doi:10.1046/j.1525-1497.2001.016009606.x \u003c/li\u003e\n\u003cli\u003eFisher J, Mello MC de, Patel V, Rahman A, Tran T, Holton S, et al. Prevalence and determinants of common perinatal mental disorders in women in low-and lower-middle-income countries: a systematic review. Bulletin of the World Health Organization. 2012;90:139\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eShorey S, Chee CYI, Ng ED, Chan YH, Tam WWS, Chong YS. Prevalence and incidence of postpartum depression among healthy mothers: A systematic review and meta-analysis. Journal of Psychiatric Research. 2018 Sep 1;104:235\u0026ndash;48. doi:10.1016/j.jpsychires.2018.08.001 \u003c/li\u003e\n\u003cli\u003eGausia K, Fisher C, Ali M, Oosthuizen J. Antenatal depression and suicidal ideation among rural Bangladeshi women: a community-based study. Archives of women\u0026rsquo;s mental health. 2009;12(5):351\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eNasreen HE, Kabir ZN, Forsell Y, Edhborg M. Prevalence and associated factors of depressive and anxiety symptoms during pregnancy: a population based study in rural Bangladesh. BMC women\u0026rsquo;s health. 2011;11(1):22. \u003c/li\u003e\n\u003cli\u003eLorant V, Deli\u0026egrave;ge D, Eaton W, Robert A, Philippot P, Ansseau M. Socioeconomic Inequalities in Depression: A Meta-Analysis. Am J Epidemiol. 2003 Jan 15;157(2):98\u0026ndash;112. doi:10.1093/aje/kwf182 \u003c/li\u003e\n\u003cli\u003ePatel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, et al. The Lancet Commission on global mental health and sustainable development. The Lancet. 2018 Oct 27;392(10157):1553\u0026ndash;98. doi:10.1016/S0140-6736(18)31612-X PubMed PMID: 30314863. \u003c/li\u003e\n\u003cli\u003eRaza S, Banik R, Noor STA, Sayeed A, Saha A, Jahan E, et al. Anxiety and depression among reproductive-aged women in Bangladesh: burden, determinants, and care-seeking practices based on a nationally representative demographic and health survey. Arch Womens Ment Health. 2025 Oct 1;28(5):1125\u0026ndash;41. doi:10.1007/s00737-025-01564-3 \u003c/li\u003e\n\u003cli\u003eVillegas L, McKay K, Dennis CL, Ross LE. Postpartum Depression Among Rural Women From Developed and Developing Countries: A Systematic Review. The Journal of Rural Health. 2011;27(3):278\u0026ndash;88. doi:10.1111/j.1748-0361.2010.00339.x \u003c/li\u003e\n\u003cli\u003eUpadhyay UD, Karasek D. Women\u0026rsquo;s empowerment and achievement of desired fertility in Sub-Saharan Africa. DHS Working Papers No. 80 [Internet]. Calverton, Maryland, USA: ICF Macro; 2010. Available from: http://dhsprogram.com/pubs/pdf/WP80/WP80.pdf \u003c/li\u003e\n\u003cli\u003eGelaye B, Rondon MB, Araya R, Williams MA. Epidemiology of maternal depression, risk factors, and child outcomes in low-income and middle-income countries. The Lancet Psychiatry. 2016 Oct 1;3(10):973\u0026ndash;82. doi:10.1016/S2215-0366(16)30284-X PubMed PMID: 27650773. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Postpartum depression, Maternal mental health, Socioeconomic inequality, Multilevel analysis, BDHS 2022","lastPublishedDoi":"10.21203/rs.3.rs-9100287/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9100287/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePostpartum depression (PPD) is a common maternal mental health disorder with significant implications for mothers and children, particularly in low- and middle-income countries (LMICs). Nationally representative evidence on postpartum depressive symptoms in Bangladesh has been limited. This study aimed to estimate the national prevalence of postpartum depressive symptoms and examine individual- and community-level determinants using data from the 2022 Bangladesh Demographic and Health Survey (BDHS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used data from the 2022 Bangladesh Demographic and Health Survey (BDHS), including 3,117 women aged 15\u0026ndash;49 years who had a live birth within the 12 months preceding the survey. Postpartum depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), with a score of \u0026ge;\u0026thinsp;5 indicating the presence of depressive symptoms. Weighted descriptive analyses were conducted, and multilevel mixed-effects logistic regression models with random intercepts for clusters were applied to examine associated factors. Results are presented as adjusted odds ratios (AORs) with 95% confidence intervals (CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe weighted prevalence of postpartum depressive symptoms was 16.17%. Prevalence varied across administrative divisions, ranging from 13.4% in Mymensingh to 18.1% in Rajshahi. In the fully adjusted multilevel model, household wealth index was significantly associated with postpartum depressive symptoms. Compared with women in the poorest quintile, those in the poorer (AOR 0.59; 95% CI 0.39\u0026ndash;0.88), middle (AOR 0.62; 95% CI 0.41\u0026ndash;0.95), richer (AOR 0.56; 95% CI 0.34\u0026ndash;0.90), and richest (AOR 0.48; 95% CI 0.27\u0026ndash;0.86) quintiles had progressively lower odds of depressive symptoms. Community education was associated with lower odds in the community-level model but was not statistically significant in the fully adjusted model. No significant associations were observed for maternal age, education, parity, pregnancy intention, antenatal care visits, delivery characteristics, media exposure, place of residence, or community wealth.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePPD affects about one in six postpartum women in Bangladesh and is strongly patterned by socioeconomic disadvantage, geographic and community context. Integrating mental health screening into routine maternal health services and addressing socioeconomic disparities may help reduce the burden of postpartum depressive symptoms in Bangladesh.\u003c/p\u003e","manuscriptTitle":"Socioeconomic Inequality and Postpartum Depression in Bangladesh: Evidence from BDHS 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:20:01","doi":"10.21203/rs.3.rs-9100287/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T05:41:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245290122293794864122639160608352749121","date":"2026-04-29T06:54:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T06:34:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T04:38:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T09:36:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T16:41:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-03-24T16:35:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d09cdf4e-d335-492d-b808-39b26e50e73c","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T05:41:40+00:00","index":57,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67360470,"name":"Health sciences/Diseases"},{"id":67360471,"name":"Health sciences/Health care"},{"id":67360472,"name":"Health sciences/Medical research"},{"id":67360473,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-11T06:20:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:20:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9100287","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9100287","identity":"rs-9100287","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}