Associations of Internet Use and Pregnancy Loss with Depression and Anxiety among Women in Bangladesh: Evidence from the 2022 BDHS

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Salek Miah, Mohammad Ohid Ullah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7546370/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2025 Read the published version in BMC Women's Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Bangladeshi married women experience a high prevalence of mental health disorders such as depression and anxiety, but the intersection of reproductive life, digital connectivity, and geographical disparities has been less researched. The research examines associations between internet use, pregnancy loss, and mental health symptoms with spatial and temporal trends. Methods The study employed nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), which included 19,987 ever-married women. Depression was assessed using PHQ-9 scores, and anxiety was evaluated using GAD-7 scores. Stepwise multinomial logistic regression was used for this study. Spatial analyses highlight division-wise prevalence of mental health outcomes, exposure to the internet status, and pregnancy loss. Findings Depression and anxiety were present in 5.13% and 4.48% of women, respectively. Near one-fifth (23.3%) experienced pregnancy loss, and 28.5% reported internet use. In comparison with women with no loss, women with one loss had significantly higher odds of anxiety (AOR 1.31, 95% CI 1.20–1.43) and depression (AOR 1.29, 95% CI 1.18–1.41), while women with two or more losses had significantly higher risk (anxiety AOR 1.82, CI 1.55–2.14; depression AOR 1.43, CI 1.24–1.68). Internet use during the past 12 months was associated with reduced odds of anxiety (AOR 0.65, CI 0.59–0.71) and depression (AOR 0.77, CI 0.69–0.85). Regional disparities were observed; Rangpur had the highest mental health burden and Dhaka the lowest. Temporal trends showed declining rural-urban inequalities in internet use and call termination rates, which reflected growing rural access and evolving norms. Conclusion Pregnancy loss is a significant risk to poor mental health, but recent internet use has a protective effect. Geospatial disparities in mental health outcomes are consistent with trends in digital access and reproductive burden. The findings suggest the need for inclusive digital, reproductive, and psychosocial health programs, particularly in high-risk regions such as Rangpur. Building digital inclusion could be a powerful and scalable solution to counteract mental health disparities in low-resource settings. Mental health Pregnancy loss Internet use Spatial analysis Trend analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Mental illnesses are a major public health concern worldwide, cutting across all levels of socioeconomic status 1 . Anxiety and depression are two of the most common mental illnesses, with a substantial impact on quality of life, maternal health, and child development 2 . In particular, women who have experienced pregnancy loss a traumatic event involving miscarriage, stillbirth, or neonatal loss are at higher risk of adverse mental health outcomes, including anxiety and depression 3 . The psychological consequences of pregnancy loss affect maternal health, family functioning, and subsequent pregnancies, making urgent research and intervention imperative 4 . Globally, the burden of mental disorders is disproportionately placed on low- and middle-income countries (LMICs), where healthcare systems are often under-resourced and mental illness still carries stigma. Pregnancy loss, miscarriage, and stillbirth are a significant global public health concern. In total, among all clinically diagnosed pregnancies, the estimated 15.3% equate to about 23 million yearly miscarriages 5 . Stillbirth also contributes a significant burden at an estimated almost 1.9 million yearly stillbirths, or close to one stillbirth per 72 total births 6 . The global distribution of pregnancy loss is extremely unequal: SubSaharan Africa accounts for around 47% of all stillbirths and South Asia for around 32% 7 . In contrast, the high-income regions, such as Europe, have very low rates of stillbirths typically less than 5 per 1,000 births 8 . Low- and middle-income country surveillance data report that miscarriage or abortion 9 , pregnancy loss, takes place in about 3.2% of pregnancies, with stillbirth rates rising to as high as 35 per 1,000 live births in South Asia, and estimated at 17 per 1,000 in Sub-Saharan Africa 1 0 . While affluent countries have come a long way in identification and responsiveness to maternal mental health, most LMICs in Asia 11 and Africa lag because of inadequate mental health infrastructure and limited epidemiological data 12 . Bangladesh, a densely populated South Asian country transitioning from the low- to middle-income economy category, is one such example of the challenge. While recent socioeconomic progress and maternal health improvement are underway, mental health remains a weak public health component 13 . Cultural norms, gender inequalities, and limitations of resources contribute to the compounded nature of access and utilization of mental health services, especially in women who undergo pregnancy loss 14 . In Bangladesh, pregnancy loss may also have a larger bearing on family structure and social harmony. Women undergoing pregnancy loss are at higher risk of facing domestic violence, marital conflict, and even divorce 15 . Social stigma of pregnancy loss, added to gender inequality and patriarchal family organization, typically doubles the vulnerability for these women 11 . Social impacts not only intensify adverse mental health consequences but also undermine the social and economic well-being of these women and create a cycle of disadvantage that is often neglected within public health policy 16 . Evidence in industrialized countries shows that there is a clear association between pregnancy loss and subsequent depression and anxiety 17 . Simultaneously, internet use and digitalization have emerged as important social determinants of health in the LMICs. Internet use can play a dual, it can improve to access health information, social support, and mental health resources, but may also expose women to unattainable social expectations, cyberbullying, or misinformation 18 . However, such data are scarce and often not representative of South Asian LMICs like Bangladesh. Physical health outcomes of pregnancy loss are primarily addressed in national surveys and studies, with little attention paid to mental health outcomes or the broader social impacts, such as violence and marital instability 19 . Besides, this is first time Bangladesh Demographic and Health survey (BDHS) collected mental health data using two valid screening tools Generalized Anxiety Disorder (GAD-7) , and Patient Health Questionnaire-9 (PHQ-9) 20 . Apart from that, mental health determinants in Bangladesh are multifaceted and are influenced by the socioeconomic status of a person, religious and cultural environment, geographical disparities, media exposure, and access to health information 21 . Few studies have exhaustively examined the risk factors of pregnancy loss 22 or the risk factors of mental health symptoms through national-level data, and no studies have utilized spatial analysis to identify regional disparities in pregnancy loss and internet use associated mental health symptoms 23 . The aim of the study is 1) To examine the association between mental health symptoms and pregnancy loss. 2) To examine the association between mental health symptoms and internet use 3) To examine the spatial variations of mental health symptoms, pregnancy loss, and internet use. 4) To examine the trend line of pregnancy loss by rural – urban disparities from 1997 to 2022, and the trend line of internet use by rural urban disparities from 2011 to 2022. The study contributes to fair health outcomes and aligns with international Sustainable Development Goals (SDG) on health and well-being. Lastly, the research emphasizes the need for culturally sensitive, context-specific mental health and social protection policy strategies in maternal health systems of Bangladesh and other similar LMICs. Methods Data Sources and Study Design This cross-sectional study employed nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS). The survey employed a two-stage stratified sampling strategy to enroll ever-married women aged 15–49 years from all eight administrative divisions of Bangladesh. This study used 19, 987 ever -married women in Bangladesh. The complex survey design, including stratification, clustering, and sampling weights, was entirely corrected in all analyses to provide nationally representative estimates. The Fig. 1 highlights the study methodology. The investigation follows STROBE guidelines. Outcomes The primary outcomes were depression and anxiety, measured with the sanctioned Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) screening questionnaires, respectively. Both outcomes were categorized into four levels none, mild, moderate, and severe based on prevalent cutoff scores. In regression modeling, groups were examined using multinomial logistic regression to assess the symptoms of mental health disorders. Exposures The key exposures for consideration in this analysis were internet use and pregnancy loss. Pregnancy loss was assessed as any history of miscarriage, stillbirth, or abortion and as a binary variable (no loss or any loss). Internet use in the past 12 months was a binary variable (user or not a user). These indicators were derived from relevant BDHS questions and re-coded to enhance interpretation. Covariates The study selected theory-driven and previously researched covariates that included individual, household, reproductive health, and empowerment factors. Care for reproductive health was ascertained through whether women received adequate antenatal visits (4 or more), delivered in a health facility, and had postnatal checkups, which was classified as inadequate, partial, or adequate. Sociodemographic correlates were age groups (15–49 years), level of education, religion, income in the household, size of household, region, and urban or rural location. Obstetric and family included children ever given birth to, current pregnancy status, number of times married, recent menstruation status, pressure to have a baby, cesarean section, and possession of a health card. Health services access seen as barriers like distance or lack of desire to travel alone, merged into yes/no variable for major problems getting care. Exposure to mass media represented the possession of a cell phone and the frequency with which women read newspapers, viewed TV, or listened to the radio; combined, it meant whether or not they received any media exposure. Women's empowerment was measured by their decision-making on health, money, and visit, use of contraceptives, and a composite empowerment score (high/low). Finally, we analyzed whether ever the women justified intimate partner violence under any situation, with a yes/no variable. Statistical Analysis All analyses were performed with Stata version 17 (StataCorp, College Station, TX, USA) and R studio 4.5.1 , utilizing the svy family of commands to account for the complex survey design. Descriptive statistics gave a snapshot of sample characteristics as weighted frequencies and proportions. To assess model performance the study applied Likelihood Ratio Test (LRT) . Stepwise multinomial logistic regression was employed to test associations, starting with unadjusted models for anxiety outcome variable and pregnancy loss only exposures (Model 1), then applied among 12 stepwise selection-based model 12th number is model shows better based on lower BIC (Bayesian Information Criteria) = 23006.54 but slightly higher (Akaike Information Criteria) AIC = 22832.68 compared to model 11, AIC = 22910.69, Variance Inflation Factor (VIF = 1.8) 24 . Similarly, for anxiety and internet use procedures, among 12 model, the 12th number model reveals better fit compared to rest of models including matrix AIC = 22817.29, BIC = 22983.25 and (VIF = 1.42). Additional outcome variable depression and for one of the main exposure pregnancies losses, among 12 models the 10th number model highlights better model with matrix AIC = 23905.67, BIC = 24016.31, (VIF = 1.06). Similarly for depression and internet user model, among 12 model the 10th number models represent better fit with matrix AIC = 23915.37, BIC = 24018.11, and (VIF = 1.10). Adjusted odds ratio with 95% confidence intervals were reported, and moderate-to-severe anxiety and depression division-level prevalence data were extracted from STATA 17 and prepared in Microsoft Excel (xlsx format) to investigate geographic inequalities in mental disorders. They were read into RStudio version 4.1.1 for geographical mapping. Bangladesh's administrative boundary shapefile was downloaded from the Humanitarian Data Exchange (HDX) platform ( https://data.humdata.org/dataset/cod-ab-bgd ) and division-level (level 1) boundaries. Using R packages sf, tmap, dplyr, and readr, the shapefile was merged with mental health prevalence data at the division level. Choropleth maps were produced to visually display the spatial distribution of anxiety and depression by the eight divisions. The maps enabled the identification of regional clusters and geographic disparities and were important for informing targeted public health interventions. Results Internet use declines with depression severity in women. Approximately 30% of non-depressed women are internet users, falling to about 22% for moderately or severely depressed women. On the other hand, the proportion of non-users goes up with rising depression severity as the evidence indicates that those with higher depressive symptoms shun digital spaces. This suggests a mental health status overlap digital divide with important consequences for designing accessible digital mental health interventions. Depression severity worsens with each increasing pregnancy loss. Women without pregnancy loss have the highest proportion with no depression (72%), while women with one or more losses have progressively lower proportions with no depression and higher proportions with mild to severe depression. Of special interest, women with two or more losses have almost twice as much severe depression as women with no loss. This highlights the psychological impact of pregnancy loss and indicates the need for certain psychological intervention among these women. Internet use is highest in non-anxious women (about 31%) and much lower in mildly or moderately anxious women (~ 22%). Interestingly, internet use is only marginally higher in severely anxious women (26.5%) but still lower than in the no-anxiety group. The consistently greater proportion of women failing to use the internet across all anxiety categories suggests that symptoms of anxiety are perhaps linked with reduced digital activity, possibly limiting exposure to online facilities that could help promote mental health. The more anxious individuals have a greater incidence of pregnancy loss. Among non-anxious women, 22% have experienced one or more pregnancy losses, rising to nearly 31% among the moderate-anxiety group. Less linear than depression, though, the data suggest that level of anxiety is related to increased likelihood of prior pregnancy loss, showing marked correlation of reproductive-health–mental-health [ see Supplementary Figure S1 -S5 ]. Among 19,987 married women who were interviewed, anxiety (GAD-7) and depression (PHQ-9) rates were 4.48% and 5.13%, respectively. Pregnancy loss was experienced by nearly one-quarter (23.3%) of participants with 4.9%, with two or more losses. Internet exposure was reported by 28.5%, and 40.8% had been exposed to family planning information. Household decision-making was by most women (83.2%), and 13% employed justification for intimate partner violence. The sample was predominantly Muslim (89.7%), and rural residents made up 65%. Nearly 40% were illiterate, and over half (54%) had extreme difficulty accessing healthcare. These psychosocial and demographic traits provide relevant background information about mental health and reproductive outcomes for this sample [ see Supplementary Table S1 ]. Pregnancy loss was strongly associated with having greater likelihoods of anxiety and depression. A single loss women had 31% higher odds of anxiety (OR 1.31, 95% CI 1.20–1.43) and 29% higher odds of depression (OR 1.29, 95% CI 1.18–1.41), while two or more losses women had 82% higher odds of anxiety (OR 1.82, 95% CI 1.55–2.13) and 45% higher odds of depression (OR 1.45, 95% CI 1.25–1.69), all statistically significant (p < 0.001). Justification of intimate partner violence increased odds by 37% for anxiety (OR 1.37, 95% CI 1.22–1.54) and 25% for depression (OR 1.25, 95% CI 1.12–1.39). Decision-making autonomy was protective against anxiety (OR 0.83, 95% CI 0.75–0.91). Significant healthcare access barriers increased odds by around 30% for both conditions. Pressure to have a child nearly doubled the odds for anxiety (OR 1.93, 95% CI 1.59–2.34) and depression (OR 1.83, 95% CI 1.49–2.24). Regional differences were elevated; Dhaka women, for example, had reduced odds of anxiety (OR 0.77, 95% CI 0.62–0.95) and depression (OR 0.82, 95% CI 0.68–0.99), whereas women from Rangpur had elevated odds (anxiety OR 1.38, 95% CI 1.11–1.71; depression OR 1.30, 95% CI 1.06–1.58)[ see supplementary Table S2 ]. Pregnancy loss was highly associated with a higher risk of depression and anxiety. Women who had one pregnancy loss had 29% increased risk of depression (adjusted OR 1.29, 95% CI 1.17–1.41) and 31% increased risk of anxiety (adjusted OR 1.31, 95% CI 1.19–1.43) compared to those who had no loss. Those who experienced two or more losses had even greater probabilities 43% higher for depression (adjusted OR 1.43, 95% CI 1.24–1.68) and 82% higher for anxiety (adjusted OR 1.82, 95% CI 1.55–2.14), all highly significant (p < 0.001). Conversely, internet use within the last 12 months was associated with significantly reduced odds for both depression (adjusted OR 0.77, 95% CI 0.71–0.83) and anxiety (adjusted OR 0.66, 95% CI 0.59–0.73), and suggests a protective effect. Such associations persisted even after adjustment for a number of sociodemographic and psychosocial confounders [Table 1 ] . Table 1 Associations Between Pregnancy Loss, Internet Use and Mental Health Outcomes Characteristics Depression (PHQ-9 ≥ 10) Unadjusted OR (95% CI) Depression (PHQ-9 ≥ 10) Adjusted OR (95% CI) Anxiety (GAD-7 ≥ 10) Unadjusted OR (95% CI) Anxiety (GAD-7 ≥ 10) Adjusted OR (95% CI) Pregnancy loss No loss (Ref) 1.00 1.00 One loss 1.29 (1.17–1.41)*** 1.29 (1.17–1.41)*** 1.30 (1.20–1.41)*** 1.31 (1.19–1.43)*** Two or more losses 1.45 (1.25–1.69)*** 1.43 (1.24–1.68)*** 1.82 (1.55–2.14)*** 1.82 (1.55–2.14)*** Internet use No use (Ref) 1.00 1.00 Used before last 12 months 0.76 (0.69–0.84)** 0.77 (0.71–0.83)*** 0.65 (0.60–0.70)** 0.6 (0.59–0.73)* Note: Adjusted models control for IPV justification, decision autonomy, problems during pregnancy, religion, wealth, household size, pregnancy pressure, abstinence status, residence status, number of unions, media exposure, and household division. * p < 0.05 , ** p < 0.01 , *** p < 0.001. OR = Odds Ratio, CI = Confidence Interval, Ref = Reference category. Pressure to conceive and two or more pregnancy losses emerged as the most influential risk factors, both with significantly higher odds of anxiety with confidence intervals not equaling unity. Regional variation exists with women living in Rangpur at greater risk. Additional risk factors are Muslim religion, having more than one marriage, and considering pregnancy loss as a significant issue. Justifying intimate partner violence and residing in divisions like Chattogram and Sylhet are associated with comparatively higher odds of anxiety. Protective factors increased socioeconomic status (middle and rich wealth), decision-making autonomy, abstinence in the present (e.g., for drugs), rural dwelling, and living in divisions Mymensingh and Dhaka. Internet use addiction also displays a robust protective effect, and the results suggest that online connectedness can buffer anxiety. Results indicate intersections of reproductive stress, social context, and empowerment in risk of anxiety and where focused mental health intervention should be aimed [ see Supplementary Figure S6 -S 7 ]. The symptom depression model also reflects pressure to become pregnant and pregnancy loss as key risk factors, with both loss of one or more fetuses raising depression risk significantly. Muslim women, those with multiple marriages, or those who justify intimate partner violence have slightly elevated depression risk. Unlike for anxiety, some factors like exposure to media and participation in decision-making have weaker or non-significant associations. Protective factors are derived from past abstaining behaviors, larger household size (4 + persons), higher wealth status, and living in rural areas. Internet usage also demonstrates a trend towards a protective role against depression, though with less statistical confidence than for anxiety. The implications of these results draw attention to the mental health consequences of reproductive challenges within broader socio-cultural and economic frameworks. The results endorse integrated reproductive and mental health care, particularly in vulnerable subgroups facing compounded adversity [ see Supplementary Fig. 8 ]. The map indicates dramatic regional differences in rates of pregnancy loss across Bangladesh. Sylhet exhibits the highest rate at nearly 32%, with it being a hot zone. Rangpur, Khulna, and Dhaka also show high rates between 28% and 30%. Barisal and Mymensingh show medium levels, while Chittagong and Rajshahi show the lowest rates below 25%. This geographical pattern suggests corresponding health, socioeconomic, or environmental differences which may be underlying causes of pregnancy loss. Interventions in the high-prevalence sites by public health are therefore warranted to reduce these risks [Figure 2 ]. The maximum percentage of internet use is in Dhaka at 40.6%, followed by Chattogram (33.7%) and Khulna (29%), indicating higher digital connectivity in the central and southeastern regions. Sylhet (26.9%) and Rajshahi (24%) exhibit mediocre usage, and Barishal (22.3%) and Mymensingh (17.1%) have lower utilization. The lowest rate is from Rangpur at 12.3%. These variations reflect extensive spatial inequality in digital access, which can be assumed to be due to differences in urbanization, infrastructure, and socio-economic conditions. The coverage of the data is overall high in all the divisions, calling for targeted efforts for increasing connectivity in low-coverage regions [Figure 3 ]. Prevalence of anxiety is equally uniform in regional pattern with varying percentage rates of 22.0–33.6%. Rangpur again has the highest rate of 33.6%, followed by Chattogram (28.9%) and Barisal (26.8%). Sylhet, Rajshahi, and Khulna have a moderate prevalence of anxiety (26.3–27.5%), and Mymensingh (24.1%) and Dhaka (22.0%) have the lowest rate. The complete data for all divisions are available. These geographic trends show a consistent pattern of mental health problems, especially in northern Bangladesh, and suggest geographically targeted mental health interventions [Figure 4 ]. The prevalence of depression in the population is substantially varied by the administrative regions of Bangladesh from 25.3–34.8%. Maximum prevalence is in Rangpur with 34.8%, presented by the darkest color, followed by Chattogram (30.5%) and Khulna (30.1%) with higher prevalence. Moderate prevalence rates are for Sylhet (29.2%), Rajshahi (29.1%), and Barisal (27.4%), while lowest rates are in Dhaka (25.4%) and Mymensingh (25.3%), depicted in the lightest color band. There are no missing data, suggesting complete geographical coverage. The results point towards considerable geographic variations in depression burden, with proportional impact across northern and southeastern regions [Figure 5 ]. Internet use in 2011 was low at 7.5% in urban areas and virtually zero at 0.5% in rural areas. In 2014, urban penetration had roughly doubled to 13%, and rural penetration had increased modestly to 1.8%. Between 2014 and 2018, there was explosive growth: urban penetration increased to 28%, and rural penetration increased threefold to 6%. Between 2018 and 2022, rural use of the internet increased spectacularly to 16.6%, while use in urban areas increased modestly to 27.3%. These trends have been an information revolution with rapid rural adoption following urban pioneering. Infrastructure roll-out, mobile technology availability, and digital literacy campaigns are likely to have contributed. The closing rural-urban divide in internet access has tremendous potential for building women's empowerment, health communication, and economic participation across Bangladesh [Figure 7 ]. Urban women report higher rates of terminations than rural women over all 25 years. At 24% in 1997, urban rates fell marginally before more acutely rising to 31% in 2004, while the highest rural rates at 26% occurred in the same year. Both of these rates fell below 2004, rural prevalence decreasing between 19% in 2014 and urban rates increasing to approximately 26%. Both the urban and rural rates increased modestly from 2014 and largely converged by 2022 (24.1% urban and 22.3% rural) [Figure 6 ]. Discussion The present article investigates mental health symptom associations with pregnancy loss, use of the internet and mental health, space variation in use of the internet, pregnancy loss, and mental health, and temporal trends in internet use and termination of pregnancy by urban- rural residence. In line with global meta-analyses of increasing depression and anxiety after perinatal loss (RR ≈ 2.14 for depression, 1.75 for anxiety) 12,25 , our adjusted analyses show strong graded associations: one loss is associated with elevated odds of depression (AOR 1.29; 95% CI 1.17–1.41) and anxiety (AOR 1.31; 95% CI 1.19–1.43), with two or more losses further increasing risk (although with overlap in CI for depression) depression (AOR 1.43; 95% CI 1.24–1.68) and anxiety (AOR 1.82; CI 1.55-2.14) 26 . The study finds novel evidence from an LMIC that prior internet use is significantly associated with reduced chances of anxiety (AOR 0.65; 95% CI 0.59–0.71) and depression (AOR 0.77; 95% CI 0.69–0.85). This protective association is explored to large global data documenting reduced depressive symptoms and increased life satisfaction among older internet users 27 . It is one of the first country-level research studies in Bangladesh to explore digital access to mental well-being, and it calls for longitudinal studies to define causality and processes 28 . This studies spatial analyses reveal striking geographic differences between administrative areas: Rangpur has much greater odds of both anxiety (AOR 1.38; 95% CI 1.11–1.71) and depression (AOR 1.30; CI 1.06-1,58). Dhaka has much lower odds of anxiety (AOR 0.77; 95% CI 0.62–0.95) and depression (AOR 0.82; CI 0.68-0.99). These gradients indicate pregnancy loss and internet exposure trends. For instance, regions with high levels of pregnancy loss (e.g., Rangpur at ~32%) are also those of elevated mental health risk, while those with higher internet usage (e.g., Dhaka at ~40.6%) experience lower mental health burden 29,30 . Geospatial mental health disparities in Bangladesh's rural environment have been similarly noted but only infrequently linked to digital exposure or reproductive outcomes 29 . Termination of abortion was consistently much greater in urban (reaching ~31% in 2004) than rural, though the rural–urban gap was closing by 2022 (~24.1% vs. 22.3%). The Internet use increased exponentially from 2014 to 2022, with rural women's adoption trebling (~6% to ~ 16.6%), whereas urban usage plateaued (28%) 31 . These trends highlight structural change in health provision and diffusion of the digital. Convergence across rural termination rates over time may reflect improved rural access or changing reproductive values 32 . At the same time, faster rural take-up of internet use offers promise in leveraging digital connectivity to benefit mental wellbeing and reproductive health interventions 33 . Encouraging digital inclusion particularly in poor-resource divisions can be a scalable mental health strategy 34 . Divisions like Rangpur have to be integrated with reproductive health, psychosocial care, and digital outreach programs 35,36 . The investigation findings are comprehensively in line with systematic reviews of heightened depression/anxiety after pregnancy loss (e.g., meta-analysis RR≈2.14). Internet use's protective relationship is consistent with global evidence for older people but adds this to younger LMIC women, making a point of the growing importance of digital determinants in mental health 37–39 . But unlike some cohort studies of high-income groups, we did not find evidence of moderate adverse effects of exposure to media other than internet 40 . Furthermore, our cross-sectional design limits causal inference to some degree, whereas prospective cohort studies (e.g., in Norway) 41 found elevated miscarriage risk for women with pre-existing psychiatric illness but not exposure to the internet 35,42,43 . Strengths and Limitations Strengths: National, large dataset, comparable survey weights, control for a range of confounders, equivalent associations in models, and combined spatial and temporal dimensions. Limitations : Cross-sectional design limits causal inference. Self-reported loss of pregnancy and mental health symptoms may produce recall bias. Internet use is measured retrospectively only and does not specify frequency or purpose. Spatial analyses reflect division-level aggregates and squelch intra-divisional heterogeneity. Conclusion In conclusion, this investigation reveals to all four objectives, and compelling evidence that pregnancy loss does increase the risk of depression and anxiety, and internet use is associated with reducing mental health symptoms. Spatial and temporal analyses reveal persistent inequities but also emerging digital opportunities, particularly in rural locations. Public health initiatives for reproductive health, digital equity, and psychosocial care need to become more accessible to overcome these converging burdens among married women in Bangladesh. Future research needs to examine targeted digital interventions and long-term effects to inform sustainable, equity-focused mental health policy. The government needs to accord the highest priority to expanding mental health services in rural regions by integrating reproductive health care with counseling. In addition, affordable internet access policies and digital literacy programs could be efficient cost-saving measures to reduce mental health disparities across the country. Declarations This is not clinical trials. Funding No funding was obtained for this study. Author Contribution M.O.U. and M.S.M. conceptualized and designed the study. M.S.M. conducted the data analysis, prepared tables and figures, and drafted the initial manuscript. M.O.U. supervised the study, provided critical revisions, and reviewed the manuscript. Both authors interpreted the results, contributed to writing and editing, and approved the final version of the manuscript for submission. Acknowledgement We thank Bangladesh for the Demographic and Health Survey (BDHS) 2022. Data Availability The 2022 Bangladesh Demographic and Health Survey (BDHS) dataset is available [https://dhsprogram.com/data/dataset/Bangladesh\_Standard-DHS\_2022.cfm?flag=0](https:/dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0) Ethical Considerations The BDHS 2022 survey protocol received the ethical approval of Bangladesh Medical Research Council and ICF International. Informed consent was obtained from all participants before data collection. The secondary analysis used de-identified publicly available data and therefore did not require any additional institutional ethical approval. References Ghodke J, Vora N, Gupta A. 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Sserunkuuma J et al. Problematic use of the internet, smartphones, and social media among medical students and relationship with depression: An exploratory study. PLoS ONE 18, (2023). Magnus MC, Havdahl A, Morken N-H, Wensaas K-A, Håberg SE. Risk Miscarr Women Psychiatric Disorders 2 3 . Amin MT et al. Prevalence and correlates of anxiety and depression among ever-married reproductive-aged women in Bangladesh: national-level insights from the 2022 Bangladesh Demographic and Health Survey. BMC Public Health 25, (2025). Koly KN et al. Depressive symptoms and anxiety among women with a history of abortion living in urban slums of Bangladesh. BMC Psychol 11, (2023). Das S et al. Trend and risk factors of fatal pregnancy termination: A long-term nationwide population-based cross-section survey in Bangladesh. PLoS ONE 18, (2023). Jia L, Li W, Liu Y, Wang L. Psychologic Sequelae in Early Pregnancy Complications. International Journal of Women’s Health vol. 15 51–57 Preprint at https://doi.org/10.2147/IJWH.S382677 (2023). Burnham KP, Anderson DR. Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research vol. 33 261–304 Preprint at https://doi.org/10.1177/0049124104268644 (2004). Díaz-Pérez E, Haro G, Echeverria I. Psychopathology Present in Women after Miscarriage or Perinatal Loss: A Systematic Review. Psychiatry International vol. 4 126–135 Preprint at https://doi.org/10.3390/psychiatryint4020015 (2023). Vlachou F et al. Fetal loss and long-term maternal morbidity and mortality: A systematic review and meta-analysis. PLoS Med 21, (2024). Díaz-Pérez E, Haro G, Echeverria I. Psychopathology Present in Women after Miscarriage or Perinatal Loss: A Systematic Review. Psychiatry International vol. 4 126–135 Preprint at https://doi.org/10.3390/psychiatryint4020015 (2023). Arafat SMY, Rajkumar RP. Mental disorders during pregnancy and postpartum in Bangladesh: A narrative review. Health Science Reports vol. 7 Preprint at https://doi.org/10.1002/hsr2.70027 (2024). Koly KN et al. Depressive symptoms and anxiety among women with a history of abortion living in urban slums of Bangladesh. BMC Psychol 11, (2023). Kulsum U et al. RISK FACTORS ASSOCIATED WITH PREGNANCY OUTCOMES IN PATIENTS WITH RECURRENT PREGNANCY LOSS AFTER TREATMENT IN FETOMATERNAL MEDICINE DEPARTMENT RISK FACTORS ASSOCIATED WITH PREGNANCY OUTCOMES IN PATIENTS WITH RECURRENT PREGNANCY LOSS AFTER TREATMENT IN FETOMATERNAL MEDICINE DEPARTMENT, BSMMU, DHAKA, BANGLADESH. vol. 2 (2025). Hoq MI, Hossain MM, Sayeed MA, Jakaria M. Trends in the prevalence of antenatal and postnatal depression in Bangladesh: A systematic review and meta-analysis. Heliyon 11, (2025). Say L et al. Global causes of maternal death: A WHO systematic analysis. Lancet Glob Health 2, (2014). Blencowe H, et al. National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: A systematic analysis. Lancet Glob Health. 2016;4:e98–108. Cuenca D. Pregnancy loss: Consequences for mental health. Frontiers in Global Women’s Health vol. 3 Preprint at https://doi.org/10.3389/fgwh.2022.1032212 (2022). Bodunde EO et al. Pregnancy and birth complications and long-term maternal mental health outcomes: A systematic review and meta-analysis. BJOG: An International Journal of Obstetrics and Gynaecology vol. 132 131–142 Preprint at https://doi.org/10.1111/1471-0528.17889 (2025). Mamun MA et al. Exploring mental health literacy among prospective university students using GIS techniques in Bangladesh: an exploratory study. Global Mental Health 11, (2024). Al-Mamun F, Abdullah AM, ALmerab MM, Al Mamun M, Mamun MA. Prevalence and factors associated with depression and anxiety among the Bangladeshi university entrance test-taking students using GIS technology. Sci Rep 14, (2024). Mudiyanselage KWW et al. The effectiveness of mental health interventions involving non-specialists and digital technology in low-and middle-income countries – a systematic review. BMC Public Health 24, (2024). Kim J et al. Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis. JMIR Mental Health vol. 10 Preprint at https://doi.org/10.2196/43066 (2023). Insan N, Forrest S, Jaigirdar A, Islam R, Rankin J. Social Determinants and Prevalence of Antenatal Depression among Women in Rural Bangladesh: A Cross-Sectional Study. Int J Environ Res Public Health 20, (2023). Özen-Dursun B, Kaptan SK, Giles S, Husain N, Panagioti M. Understanding self-harm and suicidal behaviours in South Asian communities in the UK: systematic review and meta-synthesis. BJPsych Open 9, (2023). Wani C, McCann L, Lennon M, Radu C. Digital Mental Health Interventions for Adolescents in Low- and Middle-Income Countries: Scoping Review. Journal of Medical Internet Research vol. 26 Preprint at https://doi.org/10.2196/51376 (2024). Westby CL, Erlandsen AR, Nilsen SA, Visted E, Thimm JC. Depression, anxiety, PTSD, and OCD after stillbirth: a systematic review. BMC Pregnancy Childbirth 21, (2021). Additional Declarations No competing interests reported. Supplementary Files SuuplementaryTables.docx Supplementaryfigures.docx STROBEchecklistcrosssectional.docx GA.png Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2025 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 11 Sep, 2025 Editor invited by journal 09 Sep, 2025 Editor assigned by journal 08 Sep, 2025 Submission checks completed at journal 08 Sep, 2025 First submitted to journal 05 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Salek Miah","email":"","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Salek","lastName":"Miah","suffix":""},{"id":514994075,"identity":"ed6c0896-719d-4fcd-9a5c-e6009d1c4dcf","order_by":1,"name":"Mohammad Ohid Ullah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCcYGCRDND+UzNhCpxYBBEqT0AHFawMiAweAAsVr4pZsbbzD8+SNvfP6M8ecPDDayGw4Q0CI552CzBWObgeG2GzlmEgcY0owJajG4kdgm/bfBgHHbDR4zoMMOJxKlRYLhj4H95v4zxh8OMPwnVgubQeIGhhwDoMMOENYiOSMR5Bfj5Bk30sokzhgkG88kpIVfIv0hMMTkbPv7D2/+UFFhJ9tHSAu6O0lTPgpGwSgYBaMABwAAj3NFCLcpIgwAAAAASUVORK5CYII=","orcid":"","institution":"Shahjalal University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Ohid","lastName":"Ullah","suffix":""}],"badges":[],"createdAt":"2025-09-05 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4","display":"","copyAsset":false,"role":"figure","size":121350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial variation of anxietty\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/109518d528a7f0714480190f.png"},{"id":91685166,"identity":"877dc311-f6c7-45dc-bbbc-4f27bf59fdde","added_by":"auto","created_at":"2025-09-19 07:28:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial variation of Depression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/0c8c979512f7d51dc421929b.png"},{"id":91686587,"identity":"986791f3-6f8a-43bd-bb8c-91081d1f802a","added_by":"auto","created_at":"2025-09-19 07:52:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend Analysis of pregnancy loss\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/57ce27cd5dfa352da74d79d5.png"},{"id":91686410,"identity":"b5d6d0bd-0d47-46e5-8058-c7e606783924","added_by":"auto","created_at":"2025-09-19 07:44:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":61660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003etrend Analysis of Internet use\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/fd8d8617d5af6f5a759832f4.png"},{"id":97178546,"identity":"8cab5dac-820b-4f66-9587-f44cbdfe1de1","added_by":"auto","created_at":"2025-12-01 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07:28:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23663549,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/2e7025e1b502778c779c99b0.docx"},{"id":91686412,"identity":"16ce4557-16f7-46a6-be5f-be76579dd9a6","added_by":"auto","created_at":"2025-09-19 07:44:04","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":33439,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcrosssectional.docx","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/7320cccf0f74726618371d41.docx"},{"id":91685165,"identity":"05702d00-1314-404b-af3e-06eb3adac9f7","added_by":"auto","created_at":"2025-09-19 07:28:04","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":62048,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7546370/v1/90126f3e0fdadd03a756b634.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of Internet Use and Pregnancy Loss with Depression and Anxiety among Women in Bangladesh: Evidence from the 2022 BDHS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental illnesses are a major public health concern worldwide, cutting across all levels of socioeconomic status\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Anxiety and depression are two of the most common mental illnesses, with a substantial impact on quality of life, maternal health, and child development\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In particular, women who have experienced pregnancy loss a traumatic event involving miscarriage, stillbirth, or neonatal loss are at higher risk of adverse mental health outcomes, including anxiety and depression\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The psychological consequences of pregnancy loss affect maternal health, family functioning, and subsequent pregnancies, making urgent research and intervention imperative\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Globally, the burden of mental disorders is disproportionately placed on low- and middle-income countries (LMICs), where healthcare systems are often under-resourced and mental illness still carries stigma. Pregnancy loss, miscarriage, and stillbirth are a significant global public health concern. In total, among all clinically diagnosed pregnancies, the estimated 15.3% equate to about 23\u0026nbsp;million yearly miscarriages\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Stillbirth also contributes a significant burden at an estimated almost 1.9\u0026nbsp;million yearly stillbirths, or close to one stillbirth per 72 total births\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe global distribution of pregnancy loss is extremely unequal: SubSaharan Africa accounts for around 47% of all stillbirths and South Asia for around 32%\u003csup\u003e7\u003c/sup\u003e. In contrast, the high-income regions, such as Europe, have very low rates of stillbirths typically less than 5 per 1,000 births\u003csup\u003e8\u003c/sup\u003e. Low- and middle-income country surveillance data report that miscarriage or abortion\u003csup\u003e9\u003c/sup\u003e, pregnancy loss, takes place in about 3.2% of pregnancies, with stillbirth rates rising to as high as 35 per 1,000 live births in South Asia, and estimated at 17 per 1,000 in Sub-Saharan Africa\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e0\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile affluent countries have come a long way in identification and responsiveness to maternal mental health, most LMICs in Asia\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and Africa lag because of inadequate mental health infrastructure and limited epidemiological data\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Bangladesh, a densely populated South Asian country transitioning from the low- to middle-income economy category, is one such example of the challenge. While recent socioeconomic progress and maternal health improvement are underway, mental health remains a weak public health component\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Cultural norms, gender inequalities, and limitations of resources contribute to the compounded nature of access and utilization of mental health services, especially in women who undergo pregnancy loss\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In Bangladesh, pregnancy loss may also have a larger bearing on family structure and social harmony. Women undergoing pregnancy loss are at higher risk of facing domestic violence, marital conflict, and even divorce\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Social stigma of pregnancy loss, added to gender inequality and patriarchal family organization, typically doubles the vulnerability for these women\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Social impacts not only intensify adverse mental health consequences but also undermine the social and economic well-being of these women and create a cycle of disadvantage that is often neglected within public health policy\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Evidence in industrialized countries shows that there is a clear association between pregnancy loss and subsequent depression and anxiety\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Simultaneously, internet use and digitalization have emerged as important social determinants of health in the LMICs. Internet use can play a dual, it can improve to access health information, social support, and mental health resources, but may also expose women to unattainable social expectations, cyberbullying, or misinformation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, such data are scarce and often not representative of South Asian LMICs like Bangladesh. Physical health outcomes of pregnancy loss are primarily addressed in national surveys and studies, with little attention paid to mental health outcomes or the broader social impacts, such as violence and marital instability\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Besides, this is first time Bangladesh Demographic and Health survey (BDHS) collected mental health data using two valid screening tools \u003cb\u003eGeneralized Anxiety Disorder (GAD-7)\u003c/b\u003e, and \u003cb\u003ePatient Health Questionnaire-9 (PHQ-9)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Apart from that, mental health determinants in Bangladesh are multifaceted and are influenced by the socioeconomic status of a person, religious and cultural environment, geographical disparities, media exposure, and access to health information\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Few studies have exhaustively examined the risk factors of pregnancy loss\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e or the risk factors of mental health symptoms through national-level data, and no studies have utilized spatial analysis to identify regional disparities in pregnancy loss and internet use associated mental health symptoms\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe aim of the study is 1) To examine the association between mental health symptoms and pregnancy loss. 2) To examine the association between mental health symptoms and internet use 3) To examine the spatial variations of mental health symptoms, pregnancy loss, and internet use. 4) To examine the trend line of pregnancy loss by rural \u0026ndash; urban disparities from 1997 to 2022, and the trend line of internet use by rural urban disparities from 2011 to 2022.\u003c/p\u003e\u003cp\u003eThe study contributes to fair health outcomes and aligns with international Sustainable Development Goals (SDG) on health and well-being. Lastly, the research emphasizes the need for culturally sensitive, context-specific mental health and social protection policy strategies in maternal health systems of Bangladesh and other similar LMICs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources and Study Design\u003c/h2\u003e\u003cp\u003eThis cross-sectional study employed nationally representative data from the \u003cb\u003e2022 Bangladesh Demographic and Health Survey (BDHS).\u003c/b\u003e The survey employed a two-stage stratified sampling strategy to enroll ever-married women aged 15\u0026ndash;49 years from all eight administrative divisions of Bangladesh. This study used 19, 987 ever -married women in Bangladesh. The complex survey design, including stratification, clustering, and sampling weights, was entirely corrected in all analyses to provide nationally representative estimates. The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights the study methodology. The investigation follows \u003cb\u003eSTROBE\u003c/b\u003e guidelines.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes were depression and anxiety, measured with the \u003cb\u003esanctioned Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7)\u003c/b\u003e screening questionnaires, respectively. Both outcomes were categorized into four levels none, mild, moderate, and severe based on prevalent cutoff scores. In regression modeling, groups were examined using \u003cb\u003emultinomial logistic regression\u003c/b\u003e to assess the symptoms of mental health disorders.\u003c/p\u003e\n\u003ch3\u003eExposures\u003c/h3\u003e\n\u003cp\u003eThe key exposures for consideration in this analysis were internet use and pregnancy loss. Pregnancy loss was assessed as any history of miscarriage, stillbirth, or abortion and as a binary variable (no loss or any loss). Internet use in the past 12 months was a binary variable (user or not a user). These indicators were derived from relevant BDHS questions and re-coded to enhance interpretation.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe study selected theory-driven and previously researched covariates that included individual, household, reproductive health, and empowerment factors. Care for reproductive health was ascertained through whether women received adequate antenatal visits (4 or more), delivered in a health facility, and had postnatal checkups, which was classified as inadequate, partial, or adequate. Sociodemographic correlates were age groups (15\u0026ndash;49 years), level of education, religion, income in the household, size of household, region, and urban or rural location. Obstetric and family included children ever given birth to, current pregnancy status, number of times married, recent menstruation status, pressure to have a baby, cesarean section, and possession of a health card. Health services access seen as barriers like distance or lack of desire to travel alone, merged into yes/no variable for major problems getting care. Exposure to mass media represented the possession of a cell phone and the frequency with which women read newspapers, viewed TV, or listened to the radio; combined, it meant whether or not they received any media exposure. Women's empowerment was measured by their decision-making on health, money, and visit, use of contraceptives, and a composite empowerment score (high/low). Finally, we analyzed whether ever the women justified intimate partner violence under any situation, with a yes/no variable.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were performed with \u003cb\u003eStata version 17 (StataCorp, College Station, TX, USA)\u003c/b\u003e and \u003cb\u003eR studio 4.5.1\u003c/b\u003e, utilizing the svy family of commands to account for the complex survey design. Descriptive statistics gave a snapshot of sample characteristics as weighted frequencies and proportions. To assess model performance the study applied \u003cb\u003eLikelihood Ratio Test (LRT)\u003c/b\u003e. Stepwise multinomial logistic regression was employed to test associations, starting with unadjusted models for anxiety outcome variable and pregnancy loss only exposures (Model 1), then applied among 12 stepwise selection-based model 12th number is model shows better based on lower BIC (Bayesian Information Criteria)\u0026thinsp;=\u0026thinsp;23006.54 but slightly higher (Akaike Information Criteria) AIC\u0026thinsp;=\u0026thinsp;22832.68 compared to model 11, AIC\u0026thinsp;=\u0026thinsp;22910.69, Variance Inflation Factor (VIF\u0026thinsp;=\u0026thinsp;1.8)\u003csup\u003e24\u003c/sup\u003e. Similarly, for anxiety and internet use procedures, among 12 model, the 12th number model reveals better fit compared to rest of models including matrix AIC\u0026thinsp;=\u0026thinsp;22817.29, BIC\u0026thinsp;=\u0026thinsp;22983.25 and (VIF\u0026thinsp;=\u0026thinsp;1.42). Additional outcome variable depression and for one of the main exposure pregnancies losses, among 12 models the 10th number model highlights better model with matrix AIC\u0026thinsp;=\u0026thinsp;23905.67, BIC\u0026thinsp;=\u0026thinsp;24016.31, (VIF\u0026thinsp;=\u0026thinsp;1.06). Similarly for depression and internet user model, among 12 model the 10th number models represent better fit with matrix AIC\u0026thinsp;=\u0026thinsp;23915.37, BIC\u0026thinsp;=\u0026thinsp;24018.11, and (VIF\u0026thinsp;=\u0026thinsp;1.10). Adjusted odds ratio with 95% confidence intervals were reported, and moderate-to-severe anxiety and depression division-level prevalence data were extracted from STATA 17 and prepared in Microsoft Excel (xlsx format) to investigate geographic inequalities in mental disorders. They were read into RStudio version 4.1.1 for geographical mapping. Bangladesh's administrative boundary shapefile was downloaded from the Humanitarian Data Exchange (HDX) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.humdata.org/dataset/cod-ab-bgd\u003c/span\u003e\u003cspan address=\"https://data.humdata.org/dataset/cod-ab-bgd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and division-level (level 1) boundaries. Using R packages sf, tmap, dplyr, and readr, the shapefile was merged with mental health prevalence data at the division level. Choropleth maps were produced to visually display the spatial distribution of anxiety and depression by the eight divisions. The maps enabled the identification of regional clusters and geographic disparities and were important for informing targeted public health interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eInternet use declines with depression severity in women. Approximately 30% of non-depressed women are internet users, falling to about 22% for moderately or severely depressed women. On the other hand, the proportion of non-users goes up with rising depression severity as the evidence indicates that those with higher depressive symptoms shun digital spaces. This suggests a mental health status overlap digital divide with important consequences for designing accessible digital mental health interventions.\u003c/p\u003e\n\u003cp\u003eDepression severity worsens with each increasing pregnancy loss. Women without pregnancy loss have the highest proportion with no depression (72%), while women with one or more losses have progressively lower proportions with no depression and higher proportions with mild to severe depression. Of special interest, women with two or more losses have almost twice as much severe depression as women with no loss. This highlights the psychological impact of pregnancy loss and indicates the need for certain psychological intervention among these women.\u003c/p\u003e\n\u003cp\u003eInternet use is highest in non-anxious women (about 31%) and much lower in mildly or moderately anxious women (~\u0026thinsp;22%). Interestingly, internet use is only marginally higher in severely anxious women (26.5%) but still lower than in the no-anxiety group. The consistently greater proportion of women failing to use the internet across all anxiety categories suggests that symptoms of anxiety are perhaps linked with reduced digital activity, possibly limiting exposure to online facilities that could help promote mental health.\u003c/p\u003e\n\u003cp\u003eThe more anxious individuals have a greater incidence of pregnancy loss. Among non-anxious women, 22% have experienced one or more pregnancy losses, rising to nearly 31% among the moderate-anxiety group. Less linear than depression, though, the data suggest that level of anxiety is related to increased likelihood of prior pregnancy loss, showing marked correlation of reproductive-health\u0026ndash;mental-health [\u003cstrong\u003esee Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-S5\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003eAmong 19,987 married women who were interviewed, anxiety (GAD-7) and depression (PHQ-9) rates were 4.48% and 5.13%, respectively. Pregnancy loss was experienced by nearly one-quarter (23.3%) of participants with 4.9%, with two or more losses. Internet exposure was reported by 28.5%, and 40.8% had been exposed to family planning information. Household decision-making was by most women (83.2%), and 13% employed justification for intimate partner violence. The sample was predominantly Muslim (89.7%), and rural residents made up 65%. Nearly 40% were illiterate, and over half (54%) had extreme difficulty accessing healthcare. These psychosocial and demographic traits provide relevant background information about mental health and reproductive outcomes for this sample [\u003cstrong\u003esee Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003ePregnancy loss was strongly associated with having greater likelihoods of anxiety and depression. A single loss women had 31% higher odds of anxiety (OR 1.31, 95% CI 1.20\u0026ndash;1.43) and 29% higher odds of depression (OR 1.29, 95% CI 1.18\u0026ndash;1.41), while two or more losses women had 82% higher odds of anxiety (OR 1.82, 95% CI 1.55\u0026ndash;2.13) and 45% higher odds of depression (OR 1.45, 95% CI 1.25\u0026ndash;1.69), all statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Justification of intimate partner violence increased odds by 37% for anxiety (OR 1.37, 95% CI 1.22\u0026ndash;1.54) and 25% for depression (OR 1.25, 95% CI 1.12\u0026ndash;1.39). Decision-making autonomy was protective against anxiety (OR 0.83, 95% CI 0.75\u0026ndash;0.91). Significant healthcare access barriers increased odds by around 30% for both conditions. Pressure to have a child nearly doubled the odds for anxiety (OR 1.93, 95% CI 1.59\u0026ndash;2.34) and depression (OR 1.83, 95% CI 1.49\u0026ndash;2.24). Regional differences were elevated; Dhaka women, for example, had reduced odds of anxiety (OR 0.77, 95% CI 0.62\u0026ndash;0.95) and depression (OR 0.82, 95% CI 0.68\u0026ndash;0.99), whereas women from Rangpur had elevated odds (anxiety OR 1.38, 95% CI 1.11\u0026ndash;1.71; depression OR 1.30, 95% CI 1.06\u0026ndash;1.58)[\u003cstrong\u003esee supplementary Table S2\u003c/strong\u003e]. Pregnancy loss was highly associated with a higher risk of depression and anxiety. Women who had one pregnancy loss had 29% increased risk of depression (adjusted OR 1.29, 95% CI 1.17\u0026ndash;1.41) and 31% increased risk of anxiety (adjusted OR 1.31, 95% CI 1.19\u0026ndash;1.43) compared to those who had no loss. Those who experienced two or more losses had even greater probabilities 43% higher for depression (adjusted OR 1.43, 95% CI 1.24\u0026ndash;1.68) and 82% higher for anxiety (adjusted OR 1.82, 95% CI 1.55\u0026ndash;2.14), all highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, internet use within the last 12 months was associated with significantly reduced odds for both depression (adjusted OR 0.77, 95% CI 0.71\u0026ndash;0.83) and anxiety (adjusted OR 0.66, 95% CI 0.59\u0026ndash;0.73), and suggests a protective effect. Such associations persisted even after adjustment for a number of sociodemographic and psychosocial confounders [Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e]\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations Between Pregnancy Loss, Internet Use and Mental Health Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepression (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) Unadjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepression (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;10) Adjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnxiety (GAD-7\u0026thinsp;\u0026ge;\u0026thinsp;10) Unadjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnxiety (GAD-7\u0026thinsp;\u0026ge;\u0026thinsp;10) Adjusted OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnancy loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo loss (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29 (1.17\u0026ndash;1.41)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29 (1.17\u0026ndash;1.41)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30 (1.20\u0026ndash;1.41)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31 (1.19\u0026ndash;1.43)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo or more losses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45 (1.25\u0026ndash;1.69)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43 (1.24\u0026ndash;1.68)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82 (1.55\u0026ndash;2.14)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82 (1.55\u0026ndash;2.14)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo use (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsed before last 12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.69\u0026ndash;0.84)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77 (0.71\u0026ndash;0.83)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65 (0.60\u0026ndash;0.70)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6 (0.59\u0026ndash;0.73)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: Adjusted models control for IPV justification, decision autonomy, problems during pregnancy, religion, wealth, household size, pregnancy pressure, abstinence status, residence status, number of unions, media exposure, and household division.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e, \u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/strong\u003e, \u003csup\u003e\u003cstrong\u003e***\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001. OR\u0026thinsp;=\u0026thinsp;Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval, Ref\u0026thinsp;=\u0026thinsp;Reference category.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePressure to conceive and two or more pregnancy losses emerged as the most influential risk factors, both with significantly higher odds of anxiety with confidence intervals not equaling unity. Regional variation exists with women living in Rangpur at greater risk. Additional risk factors are Muslim religion, having more than one marriage, and considering pregnancy loss as a significant issue. Justifying intimate partner violence and residing in divisions like Chattogram and Sylhet are associated with comparatively higher odds of anxiety. Protective factors increased socioeconomic status (middle and rich wealth), decision-making autonomy, abstinence in the present (e.g., for drugs), rural dwelling, and living in divisions Mymensingh and Dhaka. Internet use addiction also displays a robust protective effect, and the results suggest that online connectedness can buffer anxiety. Results indicate intersections of reproductive stress, social context, and empowerment in risk of anxiety and where focused mental health intervention should be aimed [ \u003cstrong\u003esee Supplementary Figure S6\u003c/strong\u003e-S\u003cstrong\u003e7\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003eThe symptom depression model also reflects pressure to become pregnant and pregnancy loss as key risk factors, with both loss of one or more fetuses raising depression risk significantly. Muslim women, those with multiple marriages, or those who justify intimate partner violence have slightly elevated depression risk. Unlike for anxiety, some factors like exposure to media and participation in decision-making have weaker or non-significant associations. Protective factors are derived from past abstaining behaviors, larger household size (4\u0026thinsp;+\u0026thinsp;persons), higher wealth status, and living in rural areas. Internet usage also demonstrates a trend towards a protective role against depression, though with less statistical confidence than for anxiety. The implications of these results draw attention to the mental health consequences of reproductive challenges within broader socio-cultural and economic frameworks. The results endorse integrated reproductive and mental health care, particularly in vulnerable subgroups facing compounded adversity [\u003cstrong\u003esee Supplementary Fig.\u0026nbsp;8\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003eThe map indicates dramatic regional differences in rates of pregnancy loss across Bangladesh. Sylhet exhibits the highest rate at nearly 32%, with it being a hot zone. Rangpur, Khulna, and Dhaka also show high rates between 28% and 30%. Barisal and Mymensingh show medium levels, while Chittagong and Rajshahi show the lowest rates below 25%. This geographical pattern suggests corresponding health, socioeconomic, or environmental differences which may be underlying causes of pregnancy loss. Interventions in the high-prevalence sites by public health are therefore warranted to reduce these risks [Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe maximum percentage of internet use is in Dhaka at 40.6%, followed by Chattogram (33.7%) and Khulna (29%), indicating higher digital connectivity in the central and southeastern regions. Sylhet (26.9%) and Rajshahi (24%) exhibit mediocre usage, and Barishal (22.3%) and Mymensingh (17.1%) have lower utilization. The lowest rate is from Rangpur at 12.3%. These variations reflect extensive spatial inequality in digital access, which can be assumed to be due to differences in urbanization, infrastructure, and socio-economic conditions. The coverage of the data is overall high in all the divisions, calling for targeted efforts for increasing connectivity in low-coverage regions [Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e].\u003c/strong\u003e Prevalence of anxiety is equally uniform in regional pattern with varying percentage rates of 22.0\u0026ndash;33.6%. Rangpur again has the highest rate of 33.6%, followed by Chattogram (28.9%) and Barisal (26.8%). Sylhet, Rajshahi, and Khulna have a moderate prevalence of anxiety (26.3\u0026ndash;27.5%), and Mymensingh (24.1%) and Dhaka (22.0%) have the lowest rate. The complete data for all divisions are available. These geographic trends show a consistent pattern of mental health problems, especially in northern Bangladesh, and suggest geographically targeted mental health interventions [Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe prevalence of depression in the population is substantially varied by the administrative regions of Bangladesh from 25.3\u0026ndash;34.8%. Maximum prevalence is in Rangpur with 34.8%, presented by the darkest color, followed by Chattogram (30.5%) and Khulna (30.1%) with higher prevalence. Moderate prevalence rates are for Sylhet (29.2%), Rajshahi (29.1%), and Barisal (27.4%), while lowest rates are in Dhaka (25.4%) and Mymensingh (25.3%), depicted in the lightest color band. There are no missing data, suggesting complete geographical coverage. The results point towards considerable geographic variations in depression burden, with proportional impact across northern and southeastern regions [Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e]. Internet use in 2011 was low at 7.5% in urban areas and virtually zero at 0.5% in rural areas. In 2014, urban penetration had roughly doubled to 13%, and rural penetration had increased modestly to 1.8%. Between 2014 and 2018, there was explosive growth: urban penetration increased to 28%, and rural penetration increased threefold to 6%. Between 2018 and 2022, rural use of the internet increased spectacularly to 16.6%, while use in urban areas increased modestly to 27.3%. These trends have been an information revolution with rapid rural adoption following urban pioneering. Infrastructure roll-out, mobile technology availability, and digital literacy campaigns are likely to have contributed. The closing rural-urban divide in internet access has tremendous potential for building women\u0026apos;s empowerment, health communication, and economic participation across Bangladesh [Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e]. Urban women report higher rates of terminations than rural women over all 25 years. At 24% in 1997, urban rates fell marginally before more acutely rising to 31% in 2004, while the highest rural rates at 26% occurred in the same year. Both of these rates fell below 2004, rural prevalence decreasing between 19% in 2014 and urban rates increasing to approximately 26%. Both the urban and rural rates increased modestly from 2014 and largely converged by 2022 (24.1% urban and 22.3% rural) [Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present article investigates mental health symptom associations with pregnancy loss, use of the internet and mental health, space variation in use of the internet, pregnancy loss, and mental health, and temporal trends in internet use and termination of pregnancy by urban- rural residence.\u003c/p\u003e\n\u003cp\u003eIn line with global meta-analyses of increasing depression and anxiety after perinatal loss (RR ≈ 2.14 for depression, 1.75 for anxiety)\u003csup\u003e12,25\u003c/sup\u003e , our adjusted analyses show strong graded associations: one loss is associated with elevated odds of depression (AOR 1.29; 95% CI 1.17–1.41) and anxiety (AOR 1.31; 95% CI 1.19–1.43), with two or more losses further increasing risk (although with overlap in CI for depression) \u0026nbsp;depression (AOR 1.43; 95% CI 1.24–1.68) and anxiety (AOR 1.82; CI 1.55-2.14)\u003csup\u003e26\u003c/sup\u003e. The study finds novel evidence from an LMIC that prior internet use is significantly associated with reduced chances of anxiety (AOR\u0026nbsp;0.65; 95% CI\u0026nbsp;0.59–0.71) and depression (AOR\u0026nbsp;0.77; 95% CI\u0026nbsp;0.69–0.85). This protective association is explored to large global data documenting reduced depressive symptoms and increased life satisfaction among older internet users\u003csup\u003e27\u003c/sup\u003e. It is one of the first country-level research studies in Bangladesh to explore digital access to mental well-being, and it calls for longitudinal studies to define causality and processes\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis studies spatial analyses reveal striking geographic differences between administrative areas:\u003cbr\u003e\u0026nbsp;Rangpur has much greater odds of both anxiety (AOR 1.38; 95% CI 1.11–1.71) and depression (AOR 1.30; CI 1.06-1,58). Dhaka has much lower odds of anxiety (AOR 0.77; 95% CI 0.62–0.95) and depression (AOR 0.82; CI 0.68-0.99).\u003c/p\u003e\n\u003cp\u003eThese gradients indicate pregnancy loss and internet exposure trends. For instance, regions with high levels of pregnancy loss (e.g., Rangpur at ~32%) are also those of elevated mental health risk, while those with higher internet usage (e.g., Dhaka at ~40.6%) experience lower mental health burden\u003csup\u003e29,30\u003c/sup\u003e. Geospatial mental health disparities in Bangladesh's rural environment have been similarly noted but only infrequently linked to digital exposure or reproductive outcomes\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Termination of abortion was consistently much greater in urban (reaching ~31% in 2004) than rural, though the rural–urban gap was closing by 2022 (~24.1% vs. 22.3%).\u003cbr\u003e\u0026nbsp;The Internet use increased exponentially from 2014 to 2022, with rural women's adoption trebling\u003c/p\u003e\n\u003cp\u003e(~6% to ~ 16.6%), whereas urban usage plateaued (28%)\u003csup\u003e31\u003c/sup\u003e. These trends highlight structural change in health provision and diffusion of the digital. Convergence across rural termination rates over time may reflect improved rural access or changing reproductive values\u003csup\u003e32\u003c/sup\u003e. At the same time, faster rural take-up of internet use offers promise in leveraging digital connectivity to benefit mental wellbeing and reproductive health interventions\u003csup\u003e33\u003c/sup\u003e. Encouraging digital inclusion particularly in poor-resource divisions can be a scalable mental health strategy\u003csup\u003e34\u003c/sup\u003e. Divisions like Rangpur have to be integrated with reproductive health, psychosocial care, and digital outreach programs\u003csup\u003e35,36\u003c/sup\u003e. The investigation findings are comprehensively in line with systematic reviews of heightened depression/anxiety after pregnancy loss (e.g., meta-analysis RR≈2.14). Internet use's protective relationship is consistent with global evidence for older people but adds this to younger LMIC women, making a point of the growing importance of digital determinants in mental health\u003csup\u003e37–39\u003c/sup\u003e. But unlike some cohort studies of high-income groups, we did not find evidence of moderate adverse effects of exposure to media other than internet\u003csup\u003e40\u003c/sup\u003e. Furthermore, our cross-sectional design limits causal inference to some degree, whereas prospective cohort studies (e.g., in Norway) \u003csup\u003e41\u003c/sup\u003efound elevated miscarriage risk for women with pre-existing psychiatric illness but not exposure to the internet\u003csup\u003e35,42,43\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths:\u0026nbsp;\u003c/strong\u003eNational, large dataset, comparable survey weights, control for a range of confounders, equivalent associations in models, and combined spatial and temporal dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e: Cross-sectional design limits causal inference. Self-reported loss of pregnancy and mental health symptoms may produce recall bias. Internet use is measured retrospectively only and does not specify frequency or purpose. Spatial analyses reflect division-level aggregates and squelch intra-divisional heterogeneity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this investigation reveals to all four objectives, and compelling evidence that pregnancy loss does increase the risk of depression and anxiety, and internet use is associated with reducing mental health symptoms. Spatial and temporal analyses reveal persistent inequities but also emerging digital opportunities, particularly in rural locations. Public health initiatives for reproductive health, digital equity, and psychosocial care need to become more accessible to overcome these converging burdens among married women in Bangladesh.\u003cbr\u003e\u0026nbsp;Future research needs to examine targeted digital interventions and long-term effects to inform sustainable, equity-focused mental health policy. The government needs to accord the highest priority to expanding mental health services in rural regions by integrating reproductive health care with counseling. In addition, affordable internet access policies and digital literacy programs could be efficient cost-saving measures to reduce mental health disparities across the country.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis is not clinical trials.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding was obtained for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.O.U. and M.S.M. conceptualized and designed the study. M.S.M. conducted the data analysis, prepared tables and figures, and drafted the initial manuscript. M.O.U. supervised the study, provided critical revisions, and reviewed the manuscript. Both authors interpreted the results, contributed to writing and editing, and approved the final version of the manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Bangladesh for the Demographic and Health Survey (BDHS) 2022.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe 2022 Bangladesh Demographic and Health Survey (BDHS) dataset is available [https://dhsprogram.com/data/dataset/Bangladesh\\_Standard-DHS\\_2022.cfm?flag=0](https:/dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2022.cfm?flag=0)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BDHS 2022 survey protocol received the ethical approval of Bangladesh Medical Research Council and ICF International. Informed consent was obtained from all participants before data collection. The secondary analysis used de-identified publicly available data and therefore did not require any additional institutional ethical approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGhodke J, Vora N, Gupta A. Women\u0026rsquo;s Mental Health: Narrative Rev. 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlagarajah J, Ceccolini D, Butler S. Digital mental health interventions for treating mental disorders in young people based in low-and middle-income countries: A systematic review of the literature. \u003cem\u003eGlobal Mental Health\u003c/em\u003e vol. 11 Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/gmh.2024.71\u003c/span\u003e\u003cspan address=\"10.1017/gmh.2024.71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhodke J, Vora N, Gupta A. 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BMC Pregnancy Childbirth 21, (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental health, Pregnancy loss, Internet use, Spatial analysis, Trend analysis","lastPublishedDoi":"10.21203/rs.3.rs-7546370/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7546370/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBangladeshi married women experience a high prevalence of mental health disorders such as depression and anxiety, but the intersection of reproductive life, digital connectivity, and geographical disparities has been less researched. The research examines associations between internet use, pregnancy loss, and mental health symptoms with spatial and temporal trends.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe study employed nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS), which included 19,987 ever-married women. Depression was assessed using PHQ-9 scores, and anxiety was evaluated using GAD-7 scores. Stepwise multinomial logistic regression was used for this study. Spatial analyses highlight division-wise prevalence of mental health outcomes, exposure to the internet status, and pregnancy loss.\u003c/p\u003e\u003ch2\u003eFindings\u003c/h2\u003e\u003cp\u003eDepression and anxiety were present in 5.13% and 4.48% of women, respectively. Near one-fifth (23.3%) experienced pregnancy loss, and 28.5% reported internet use. In comparison with women with no loss, women with one loss had significantly higher odds of anxiety (AOR 1.31, 95% CI 1.20\u0026ndash;1.43) and depression (AOR 1.29, 95% CI 1.18\u0026ndash;1.41), while women with two or more losses had significantly higher risk (anxiety AOR 1.82, CI 1.55\u0026ndash;2.14; depression AOR 1.43, CI 1.24\u0026ndash;1.68). Internet use during the past 12 months was associated with reduced odds of anxiety (AOR 0.65, CI 0.59\u0026ndash;0.71) and depression (AOR 0.77, CI 0.69\u0026ndash;0.85). Regional disparities were observed; Rangpur had the highest mental health burden and Dhaka the lowest. Temporal trends showed declining rural-urban inequalities in internet use and call termination rates, which reflected growing rural access and evolving norms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePregnancy loss is a significant risk to poor mental health, but recent internet use has a protective effect. Geospatial disparities in mental health outcomes are consistent with trends in digital access and reproductive burden. The findings suggest the need for inclusive digital, reproductive, and psychosocial health programs, particularly in high-risk regions such as Rangpur. Building digital inclusion could be a powerful and scalable solution to counteract mental health disparities in low-resource settings.\u003c/p\u003e","manuscriptTitle":"Associations of Internet Use and Pregnancy Loss with Depression and Anxiety among Women in Bangladesh: Evidence from the 2022 BDHS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 07:27:59","doi":"10.21203/rs.3.rs-7546370/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T09:58:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T05:32:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151706520797468796252739301138856071246","date":"2025-10-12T19:38:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T08:47:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245290122293794864122639160608352749121","date":"2025-10-06T14:38:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T19:08:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T14:15:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-08T08:40:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-08T08:39:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-09-05T17:40:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ad86848-e77b-4456-8718-e9f3029dd792","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:04:16+00:00","versionOfRecord":{"articleIdentity":"rs-7546370","link":"https://doi.org/10.1186/s12905-025-04166-4","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-11-29 15:58:23","publishedOnDateReadable":"November 29th, 2025"},"versionCreatedAt":"2025-09-19 07:27:59","video":"","vorDoi":"10.1186/s12905-025-04166-4","vorDoiUrl":"https://doi.org/10.1186/s12905-025-04166-4","workflowStages":[]},"version":"v1","identity":"rs-7546370","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7546370","identity":"rs-7546370","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}