Abstract
Background
Women’s empowerment may influence non-communicable disease (NCD) risk. However, evidence on these associations among women in Bangladesh remains limited. This study examined the associations between women’s empowerment and the prevalence of selected non-communicable conditions among ever-married reproductive-aged women in Bangladesh.
Methods
Data from the 2022 Bangladesh Demographic and Health Survey were analyzed. Women’s empowerment was assessed using the survey-based women’s empowerment index (SWPER), encompassing three domains: attitudes toward violence, social independence, and decision-making. Selected non-communicable conditions included overweight/obesity, hypertension, diabetes, anxiety symptoms, and depression symptoms, and were categorized into composite physical NCDs (overweight/obesity, hypertension, or diabetes) and mental conditions (anxiety or depression symptoms). Anxiety and depression symptoms were screened using the GAD-7 and PHQ-9 scales. Both individual outcomes and composite outcomes were assessed. Modified Poisson regression was used to assess the associations between each empowerment domain and the different forms of NCDs while controlling for potential confounders and presented as adjusted prevalence ratio (APR).
Results
The prevalence of overweight/obesity was 55.4%, hypertension 16.2%, diabetes 14.4%, anxiety 18.6%, and depression 4.7%. Overall, 63.2% of women had at least one physical NCD, and 19.4% had any mental symptoms. High empowerment was observed in the attitude toward violence (85.4%) and decision-making (59.5%) but was low in the social independence domain (16.9%). High empowerment in attitude toward violence was associated with lower prevalence of overweight/obesity (APR: 0.90, 95%CI: 0.82–1.00), any physical NCD (APR: 0.89, 95%CI: 0.79–1.00), anxiety symptoms (APR: 0.68, 95%CI: 0.58–0.79), depression symptoms (APR: 0.65, 95%CI: 0.45–0.94), and any mental symptoms (APR: 0.69, 95%CI: 0.59–0.80). Similarly, high empowerment in social independence was inversely associated with overweight/obesity (APR: 0.94, 95%CI: 0.88–1.00), anxiety symptoms (APR: 0.87, 95%CI: 0.78–0.97), and any mental symptoms (APR: 0.88, 95%CI: 0.80–0.98). However, higher decision-making empowerment was associated with higher prevalence of overweight/obesity (APR: 1.24, 95%CI: 1.15–1.33), hypertension (APR: 1.29, 95%CI: 1.01–1.64), and any physical NCD (APR: 1.12, 95%CI: 1.03–1.21).
Conclusions
Women’s empowerment domains exhibited associations with selected health outcomes. Promoting gender-equitable attitudes and targeted interventions addressing social independence might improve the health and well-being of women in Bangladesh.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27084-y.
Keywords: Women’s empowerment, Survey-based women’s empowerment index (SWPER), Non-communicable diseases, Hypertension, Diabetes, Mental health, Anxiety, Depression, Demographic and health survey, Bangladesh.
Background
Non-communicable diseases (NCDs) remain a major global health challenge, responsible for an estimated 43.8 million deaths and 1.73 billion disability-adjusted life years (DALYs) in 2021 [1]. Nearly two-thirds of these deaths arise in low-and middle-income countries (LMICs) such as Bangladesh, where premature mortality (before age 70) from NCDs represents about 82% of all early deaths [2]. Physical NCDs such as cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes continue to dominate NCD-related mortality globally [2]. However, increasing attention is now being given to mental health disorders, particularly anxiety and depression, which affect more than 1.1 billion people [3]. Mental disorders rank among the top ten contributors to the global disease burden in terms of prevalence and DALYs, with a rising trend since 1990 [4]. Recognizing their importance, the 2018 United Nations Political Declaration on the Prevention and Control of NCDs officially broadened the NCDs agenda to include the promotion of mental health and well-being alongside the traditional NCDs [5]. Moreover, individuals with mental health conditions experience a disproportionately higher burden of physical NCDs, particularly in South Asia [6]. Notably, gender disparities are evident: women globally bear a greater burden of both mental disorders [4] and physical NCDs [1].
Bangladesh is undergoing a rapid epidemiological transition, with NCDs now accounting for approximately 70% of all deaths [7]. In 2019, five of the top ten risk factors for all-cause mortality and DALYs were physical NCD-related metabolic factors, including high blood pressure, high fasting plasma glucose, elevated cholesterol, and high body mass index (BMI)—conditions that contribute to hypertension, diabetes, and overweight/obesity [8]. Mental health problems are also an increasing concern here. Women, in particular, experience a more complex health trajectory across their life course due to both physiological and socioeconomic factors, which exacerbate their vulnerability to physical NCDs and mental disorders. Evidence suggests that Bangladeshi women face higher rates of these conditions compared to men [9, 10]. In 2022, 38% of women were overweight/obese, 23% had hypertension, 17% had diabetes, 5% experienced depression, and 20% reported anxiety [10].
Women’s empowerment is intrinsically linked to improved health outcomes and is enshrined in the Sustainable Development Goals (SDGs), particularly Goal 5, which seeks to “empower all women and girls” [11]. Empowerment encompasses multidimensional processes that enhance an individual’s capacity to make choices and take actions that positively influence their health and well-being [12]. Empowered women generally enjoy increased access to resources, greater decision-making authority, and enhanced agency, all of which are associated with better health behaviors and more effective engagement with preventive and curative health services [13]. Evidence consistently demonstrates that higher levels of empowerment yield substantial benefits for reproductive, maternal, newborn, and child health, including increased use of modern contraception, improved maternal care uptake, and better child nutrition and immunization coverage [14–19].
Despite growing recognition of the pivotal role of women’s empowerment in shaping health trajectories, empirical data on the association between empowerment and NCD risk among Bangladeshi women remain scarce. The challenge of rigorously measuring empowerment—given its abstract and multidimensional nature—has led to diverse frameworks and tools, limiting comparability across studies. The survey-based women’s empowerment index (SWPER), developed and validated using demographic and health survey data, overcomes many of these limitations by capturing key domains of empowerment (attitude toward violence, social independence, and decision-making) using robust, individual-level measures [20].
This study addresses critical knowledge gaps by examining the association between women’s empowerment and selected physical and mental non-communicable conditions among women of reproductive age (WRA) in Bangladesh, using the latest nationally representative data. Findings from this research are expected to offer insights that inform transformative approaches for reducing the NCDs burden and improving women’s health in Bangladesh and similar LMICs settings.
Methods
Data source and study design
This study analyzed secondary data from the 2022 Bangladesh demographic and health survey (BDHS). This cross-sectional survey followed a two-stage, stratified sampling for enumeration areas (EAs) selection covering both rural and urban areas from eight administrative divisions. Firstly, probability proportional to size sampling was employed to select 675 EAs (urban areas: 237, rural areas: 438). Subsequently, from each EA using systematic sampling, 45 households were selected, among which 30 households with ever-married women aged 15–49 years were randomly chosen for a detailed interview. For biomarker measurements, a subsample of households was selected systematically, with half of the surveyed households included for anthropometric assessment. From this subsample, eight households were further selected for additional biomarker measurements, including anthropometry, blood pressure (BP), and blood glucose assessments.
A total of 19,987 eligible WRA were interviewed using the long questionnaire. Among them, 10,053 WRA were eligible for biomarker measurements (10,053 for height and weight, 5,137 for blood pressure, and 5,188 for blood glucose testing). In this study, pregnant women and participants with missing information on biomarkers or women’s empowerment variables were excluded. The final analytical samples included 18,954 women for mental health analysis, 8,840 for anthropometric outcomes, 4,599 for blood pressure, and 4,496 for blood glucose analysis. In addition, 4,493 women had complete data for all three biomarkers (Fig. 1).
Fig. 1.
Schematic diagram of sample selection
BP and fasting blood glucose were measured by trained health technicians following standardized protocols. BP was recorded three times at intervals of at least five minutes using the Multi-User Upper Arm Blood Pressure Monitor (model UA-767 F/FAC), and the mean of the second and third readings was considered. Fasting blood glucose was assessed using a HemoCue 201 RT analyzer. Capillary blood samples were obtained from the middle or ring finger after an overnight fast, with the first two drops discarded and the third used for analysis. Anthropometric measurements were taken using calibrated equipment: weight was measured with a SECA digital scale (model 874U), and height was recorded using a ShorrBoard measuring board.
Anxiety was evaluated using the Generalized Anxiety Disorder-7 (GAD-7) scale [21]. Participants indicated the frequency of their symptoms on a scale from 0 (never) to 3 (always) over the past two weeks, with responses to the seven items combined to provide a total score ranging from 0 to 21. The GAD-7 showed a strong internal consistency (Cronbach’s α = 0.827). Depression was evaluated using the Patient Health Questionnaire-9 (PHQ-9) scale [22]. Participants indicated the frequency of their symptoms on a scale from 0 (never) to 3 (always), with responses to the 9 items combined to provide a total score ranging from 0 to 27. The PHQ-9 scale also showed a strong internal consistency (Cronbach’s α = 0.815). Detailed descriptions of the sampling design and data collection are available in the report [10].
Outcome variables
The primary outcome of this study was the presence of selected non-communicable conditions, which were further classified into physical and mental NCDs based on their availability in the BDHS dataset. In this study, physical NCDs included overweight/obesity, hypertension, and diabetes, whereas mental health conditions included anxiety and depression symptoms. Both individual and composite outcomes were assessed.
Women were classified as overweight/obese if their BMI was ≥ 23 kg/m², following the Asian cut-off for BMI [23]. Hypertension was defined according to World Health Organization (WHO) criteria as an average systolic blood pressure ≥ 140 mmHg, an average diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication [24]. Similarly, diabetes was defined based on WHO guidelines as a fasting blood glucose level ≥ 7 mmol/L or current use of glucose-lowering medication [25]. Different studies have used varying cut points to determine anxiety symptoms. In this study, a GAD-7 score ≥ 6 was used to identify anxiety symptoms to align with the BDHS report [10, 21]. We also conducted sensitivity analysis using another established cut point (GAD-7 scores ≥ 10) of moderate/severe anxiety symptoms [21]. Furthermore, depression symptoms were defined as a PHQ-9 score ≥ 10 [10, 22].
Two composite outcomes were constructed. Any physical NCD was defined as the presence of overweight/obesity, hypertension, or diabetes [26, 27]. Any mental health symptom was defined as having either anxiety or depression symptoms.
Exposure variables
The primary exposure variable in this study was women’s empowerment, assessed using the SWPER Global Index. This standardized and validated index, developed from DHS data, measures women’s empowerment across LMICs through three different domains: attitude toward violence, social independence, and decision-making [20]. The attitude toward violence dimension was constructed from five DHS items assessing whether a woman believes wife beating is justified under specific circumstances, such as going out without informing the husband, arguing with him, refusing sex, neglecting children, or burning food. The social independence domain incorporates indicators such as the frequency of reading newspapers or magazines, years of schooling, age at first cohabitation/marriage, age at first birth, as well as age and educational differences between the woman and her husband. The decision-making domain includes women’s participation in household decisions and was based on three questions concerning who usually decides about the woman’s healthcare, major household purchases, and visits to family or relatives.
Following the recommended SWPER Global methodology, the index was constructed using the published principal component analysis (PCA) loadings and item weights [20]. Domain-specific scores were calculated by applying these predefined weights to the corresponding DHS items. To ensure global comparability, the resulting scores were standardized using the global mean and standard deviation derived from previous studies of LMICs [20], according to the formula:
 |
These standardized scores, which range from negative to positive values, indicate levels of empowerment, with positive values representing above-average empowerment, negative values indicating below-average empowerment, and zero representing the global average. Subsequently, these scores were classified into three categories: low, medium, and high empowerment, following previous methodology [20].
Covariates
Covariate selection was guided by their association with both outcome and exposure variables in the prior literature [28–35]. The following covariates were included: women age in years (15–29, 30–39, 40–49), women education (no education, primary, secondary, higher), women employment status (unemployed, employed), number of children (up to 2, 3 or more), area of residence (urban, rural), and administrative region (Barisal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Sylhet). The household wealth index was constructed using PCA of the key assets owned by households [10]. Households were further ranked based on wealth scores and divided into quintiles (poorest, poorer, middle, richer, richest).
Statistical analyses
Descriptive analyses were performed to summarize the study variables. Proportions were determined for categorical variables, while means were presented for continuous variables. Bivariate associations between the outcome variables and sociodemographic as well as empowerment characteristics were examined using chi-square tests of independence. To explore the association of each of the empowerment domains and non-communicable conditions, modified Poisson regression with robust variance procedure to estimate standard errors was used [36–38]. Adjusted prevalence ratio (APR) with corresponding 95% confidence intervals (CIs) was estimated, controlling for all covariates, including women’s age, employment status, number of children, wealth index, area of residence, and administrative division, simultaneously. However, education was not adjusted in the regression models to prevent over-adjustment, as it was an intrinsic component of the SWPER domain. Before running the models, multicollinearity was assessed using variance inflation factors (VIF). The observed VIF values ranged from 1.01 to 2.20, all of which were below the threshold of 10 [39]. All statistical tests were two-tailed, and a p-value < 0.05 was considered statistically significant. “svy” command was used to account for clustering, stratification, and sampling weights in all analyses. Statistical analyses were conducted using Stata version 15.0 (StataCorp LLC, College Station, TX, USA).
Results
This study included a total of 8,840 women for overweight/obesity, 4,599 for hypertension, 4,496 for diabetes, and 18,954 for mental illness. Most of the women were aged 30–39 years. Regarding educational attainment, over half of the participants had secondary or higher education, while over one in ten had no formal education. Around two-thirds of women were unemployed and had up to two children. Most women resided in rural areas. Geographically, participants were spread across all divisions, with the highest proportion from Dhaka and Chattogram. The SWPER standardized score indicated relatively higher empowerment in the “attitude toward violence” 0.55 and “decision-making” domain 0.36 to 0.38 compared to “social independence” −0.35 to − 0.38 (Table 1). Furthermore, participants’ characteristics for hypertension/diabetes and any physical NCDs are presented in Supplementary Table 1.
Table 1.
Sociodemographic characteristics of the studied participants
| Variables | Overweight/obesity, N = 8,840 | Hypertension, N = 4,599 | Diabetes, N = 4,496 |
Anxiety/Depression, N = 18,954 |
|---|---|---|---|---|
| n (%) | n (%) | n (%) | n (%) | |
| Women age in years | ||||
| 15–29 | 3550 (40.6) | 1744 (37.8) | 1707 (37.8) | 8172 (43.7) |
| 30–39 | 3133 (35.5) | 1666 (36.7) | 1621 (36.5) | 6475 (34.0) |
| 40–49 | 2157 (23.9) | 1189 (25.5) | 1168 (25.7) | 4307 (22.3) |
| Women education | ||||
| No education | 1172 (13.3) | 648 (14.2) | 634 (14.2) | 2431 (13.0) |
| Primary | 2304 (26.0) | 1231 (27.0) | 1206 (27.0) | 4886 (25.8) |
| Secondary | 4040 (47.1) | 2023 (45.2) | 1982 (45.4) | 8786 (47.4) |
| Higher | 1324 (13.6) | 697 (13.6) | 674 (13.4) | 2851 (13.8) |
| Women employment status | ||||
| Not employed | 6047 (67.0) | 3134 (66.4) | 3062 (66.3) | 13,254 (68.8) |
| Employed | 2793 (33.0) | 1465 (33.6) | 1434 (33.7) | 5700 (31.2) |
| Number of children | ||||
| Up to 2 | 5967 (67.2) | 3007 (64.8) | 2937 (64.8) | 13,173 (69.4) |
| 3 or more | 2,873 (32.8) | 1,592 (35.2) | 1559 (35.2) | 5781 (30.6) |
| Wealth index | ||||
| Poorest | 1583 (17.7) | 804 (17.4) | 797 (17.7) | 3350 (17.6) |
| Poorer | 1674 (19.6) | 890 (20.1) | 881 (20.5) | 3721 (20.2) |
| Middle | 1794 (20.9) | 892 (20.0) | 876 (20.0) | 3777 (20.6) |
| Richer | 1833 (21.0) | 958 (21.0) | 939 (21.0) | 3936 (21.0) |
| Richest | 1956 (20.8) | 1055 (21.5) | 1003 (20.8) | 4170 (20.6) |
| Area of residence | ||||
| Urban | 3087 (28.3) | 1620 (28.5) | 1568 (28.0) | 6611 (28.2) |
| Rural | 5753 (71.7) | 2979 (71.5) | 2928 (72.0) | 12,343 (71.8) |
| Division | ||||
| Barishal | 957 (6.2) | 524 (6.5) | 516 (6.6) | 2030 (6.0) |
| Chattogram | 1279 (18.1) | 689 (19.0) | 672 (19.0) | 2811 (18.7) |
| Dhaka | 1322 (25.2) | 663 (24.4) | 619 (23.6) | 2866 (25.3) |
| Khulna | 1129 (11.7) | 592 (12.0) | 580 (12.0) | 2474 (12.0) |
| Mymensingh | 971 (7.8) | 499 (7.8) | 495 (8.0) | 2049 (7.6) |
| Rajshahi | 1169 (13.6) | 591 (13.2) | 583 (13.4) | 2436 (13.2) |
| Rangpur | 1094 (11.8) | 551 (11.3) | 548 (11.5) | 2294 (11.5) |
| Sylhet | 919 (5.6) | 490 (5.8) | 483 (5.9) | 1994 (5.7) |
| Standardized attitude toward violence SWPER score, mean (95%CI) | 0.55 (0.53, 0.56) | 0.55 (0.53, 0.56) | 0.55 (0.53, 0.56) | 0.55 (0.54, 0.56) |
| Standardized social independence SWPER score, mean (95%CI) | −0.37 (− 0.39, − 0.34) | −0.38 (− 0.41, − 0.35) | −0.38 (− 0.41, − 0.35) | −0.35 (− 0.37, − 0.33) |
| Standardized decision-making SWPER score, mean (95%CI) | 0.37 (0.34, 0.39) | 0.38 (0.35, 0.41) | 0.38 (0.35, 0.41) | 0.36 (0.34, 0.38) |
Overall, most women (85.4%) reported high empowerment in the attitude toward violence domain, followed by the decision-making domain (59.5%), while high empowerment was much lower in the social independence domain (16.9%). Empowerment patterns varied across sociodemographic groups: younger women were more empowered in attitude toward violence, while older women showed greater empowerment in decision-making. Higher levels of education corresponded with greater proportions of women reporting high empowerment across all domains. Moreover, wealthier and urban women exhibited higher levels of empowerment, and notable regional variations were observed (Table 2).
Table 2.
Level of women's empowerment by sociodemographic characteristics (N = 18,954)
| Variables | Level of empowermenta | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Attitude toward violence | Social independence | Decision-making | ||||||||||
| Low | Medium | High | p-value | Low | Medium | High | p-value | Low | Medium | High | p-value | |
| Overall | 678 (3.9) | 1932 (10.7) | 16,344 (85.4) | - | 7996 (43.3) | 7509 (39.8) | 3449 (16.9) | - | 2827 (14.1) | 5048 (26.4) | 11,079 (59.5) | - |
| Women age in years | ||||||||||||
| 15–29 | 254 (3.5) | 746 (9.5) | 7172 (87.0) | < 0.001 | 2849 (35.8) | 3856 (47.1) | 1477 (17.1) | < 0.001 | 1631 (18.8) | 2424 (29.5) | 4117 (51.7) | < 0.001 |
| 30–39 | 236 (3.9) | 696 (11.4) | 5543 (84.7) | 2713 (43.1) | 2451 (38.3) | 1311 (18.6) | 703 (10.3) | 1597 (24.3) | 4175 (65.4) | |||
| 40–49 | 188 (4.6) | 490 (12.0) | 3629 (83.4) | 2444 (58.3) | 1202 (27.6) | 661 (14.1) | 493 (10.9) | 1027 (23.5) | 2787 (65.6) | |||
| Women education | ||||||||||||
| No education | 142 (6.2) | 310 (13.1) | 1979 (80.7) | < 0.001 | 1971 (81.7) | 375 (14.9) | 85 (3.4) | < 0.001 | 363 (14.2) | 610 (24.6) | 1458 (61.2) | < 0.001 |
| Primary | 236 (4.9) | 573 (12.3) | 4077 (82.8) | 3090 (64.1) | 1539 (30.8) | 257 (5.1) | 731 (14.0) | 1252 (24.9) | 2903 (61.1) | |||
| Secondary | 271 (3.4) | 874 (10.5) | 7641 (86.1) | 2886 (33.6) | 4698 (53.4) | 1202 (13.0) | 1398 (15.1) | 2420 (27.3) | 4968 (57.6) | |||
| Higher | 29 (1.3) | 175 (6.1) | 2647 (92.6) | 49 (1.7) | 897 (33.1) | 1905 (65.2) | 335 (11.0) | 766 (27.9) | 1750 (61.1) | |||
| Women employment status | ||||||||||||
| Not employed | 451 (3.6) | 1299 (10.3) | 11,504 (86.1) | 0.004 | 5162 (40.0) | 5587 (42.4) | 2505 (17.6) | < 0.001 | 2288 (16.4) | 3677 (27.3) | 7289 (56.3) | < 0.001 |
| Employed | 227 (4.3) | 633 (11.7) | 4840 (84.0) | 2834 (50.4) | 1922 (34.1) | 944 (15.5) | 539 (9.2) | 1371 (24.3) | 3790 (66.5) | |||
| Number of children | ||||||||||||
| Up to 2 | 418 (3.5) | 1268 (10.1) | 11,487 (86.4) | < 0.001 | 4435 (34.9) | 5677 (43.5) | 3061 (21.6) | < 0.001 | 2101 (15.2) | 3656 (27.6) | 7416 (57.2) | < 0.001 |
| 3 or more | 260 (4.7) | 664 (12.1) | 4857 (83.2) | 3561 (62.3) | 1832 (31.2) | 388 (6.5) | 726 (11.7) | 1392 (23.6) | 3663 (64.7) | |||
| Wealth index | ||||||||||||
| Poorest | 181 (5.5) | 391 (11.9) | 2778 (82.6) | < 0.001 | 1820 (54.9) | 1256 (37.2) | 274 (7.9) | < 0.001 | 551 (16.0) | 911 (26.4) | 1888 (57.6) | < 0.001 |
| Poorer | 162 (4.8) | 433 (11.9) | 3126 (83.3) | 1817 (49.3) | 1468 (39.2) | 436 (11.5) | 600 (15.7) | 994 (26.1) | 2127 (58.2) | |||
| Middle | 130 (3.8) | 414 (11.4) | 3233 (84.8) | 1680 (45.0) | 1557 (41.1) | 540 (13.9) | 599 (14.6) | 970 (25.8) | 2208 (59.6) | |||
| Richer | 127 (3.4) | 380 (10.3) | 3429 (86.3) | 1590 (41.9) | 1621 (40.9) | 725 (17.2) | 591 (13.8) | 1041 (25.7) | 2304 (60.5) | |||
| Richest | 78 (2.1) | 314 (8.1) | 3778 (89.8) | 1089 (27.2) | 1607 (40.0) | 1474 (32.8) | 486 (11.0) | 1132 (27.9) | 2552 (61.1) | |||
| Area of residence | ||||||||||||
| Urban | 157 (2.7) | 601 (9.8) | 5853 (87.5) | 0.004 | 2375 (36.7) | 2559 (38.6) | 1677 (24.7) | < 0.001 | 850 (11.1) | 1758 (26.1) | 4003 (62.8) | < 0.001 |
| Rural | 521 (4.3) | 1331 (11.1) | 10,491 (84.6) | 5621 (45.9) | 4950 (40.2) | 1772 (13.9) | 1977 (15.3) | 3290 (26.5) | 7076 (58.2) | |||
| Division | ||||||||||||
| Barishal | 50 (2.8) | 187 (9.2) | 1793 (88.0) | 0.004 | 817 (42.0) | 837 (42.3) | 376 (15.7) | < 0.001 | 352 (18.5) | 639 (31.6) | 1039 (49.9) | < 0.001 |
| Chattogram | 89 (3.3) | 274 (9.9) | 2448 (86.8) | 979 (35.6) | 1320 (47.7) | 512 (16.7) | 347 (13.4) | 646 (22.1) | 1818 (64.5) | |||
| Dhaka | 119 (4.1) | 347 (11.5) | 2400 (84.4) | 1158 (40.0) | 1119 (39.2) | 589 (20.8) | 335 (11.2) | 849 (29.7) | 1682 (59.1) | |||
| Khulna | 118 (4.7) | 294 (12.1) | 2062 (83.2) | 1196 (50.1) | 885 (35.4) | 393 (14.5) | 457 (19.1) | 651 (25.8) | 1366 (55.1) | |||
| Mymensingh | 80 (4.0) | 177 (8.8) | 1792 (87.2) | 888 (44.3) | 819 (40.1) | 342 (15.6) | 320 (15.5) | 468 (22.7) | 1261 (61.8) | |||
| Rajshahi | 94 (4.5) | 295 (13.0) | 2047 (82.5) | 1208 (52.1) | 850 (34.6) | 378 (13.3) | 303 (11.9) | 684 (27.0) | 1449 (61.1) | |||
| Rangpur | 61 (3.0) | 214 (9.8) | 2019 (87.2) | 1143 (51.7) | 801 (35.0) | 350 (13.3) | 270 (11.9) | 552 (24.5) | 1472 (63.6) | |||
| Sylhet | 67 (3.7) | 144 (7.4) | 1783 (88.9) | 607 (31.3) | 878 (44.1) | 509 (24.6) | 443 (23.2) | 559 (28.4) | 992 (48.4) | |||
aValues were reported as number (%)
Over half of the participants (55.4%) were overweight/obese, while 16.2% had hypertension and 14.4% had diabetes. Anxiety and depression affected 18.6% and 4.7% of participants, respectively. Overall, 19.4% of women had at least one mental symptom, whereas 63.2% had at least one physical NCD (Table 3). Besides, the prevalence of anxiety symptoms using the GAD-7 ≥ 10 cut point, and hypertension/diabetes are presented in Supplementary Table 2.
Table 3.
Prevalence of physical, and mental NCDs by sociodemographic factors and SWPER domains
| Variables | Individual outcomes | Composite outcomes | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overweight/obesity, N = 8840 | Hypertension, N = 4599 | Diabetes, N = 4496 | Anxiety symptoms, N = 18,954 | Depression symptoms, N = 18,954 | Any mental symptoms, N = 18,954 | Any physical NCDs, N = 4490 | ||||||||
| n (%) | p-value | n (%) | p-value | n (%) | p-value | n (%) | p-value | n (%) | p-value | n (%) | p-value | n (%) | p-value | |
| Overall | 4906 (55.4) | - | 773 (16.2) | - | 650 (14.4) | - | 3550 (18.6) | - | 912 (4.7) | - | 3695 (19.4) | - | 2845 (63.2) | - |
| Women age in years | ||||||||||||||
| 15–29 | 1568 (44.0) | < 0.001 | 96 (5.0) | < 0.001 | 142 (8.7) | < 0.001 | 1169 (14.0) | < 0.001 | 318 (3.7) | < 0.001 | 1232 (14.8) | < 0.001 | 869 (51.0) | < 0.001 |
| 30–39 | 1968 (63.0) | 301 (17.3) | 230 (14.2) | 1375 (21.5) | 329 (5.0) | 1416 (22.1) | 1110 (68.1) | |||||||
| 40–49 | 1370 (63.4) | 376 (31.1) | 278 (23.1) | 1006 (23.3) | 265 (5.9) | 1047 (24.2) | 866 (74.4) | |||||||
| Women education | ||||||||||||||
| No education | 581 (49.4) | < 0.001 | 148 (22.5) | < 0.001 | 101 (17.0) | 0.135 | 629 (25.8) | < 0.001 | 166 (6.7) | < 0.001 | 651 (26.8) | < 0.001 | 389 (62.3) | 0.294 |
| Primary | 1214 (54.4) | 226 (18.3) | 189 (15.5) | 1043 (21.2) | 258 (5.0) | 1074 (21.9) | 737 (62.8) | |||||||
| Secondary | 2268 (55.6) | 304 (14.1) | 265 (13.1) | 1515 (17.2) | 390 (4.4) | 1575 (17.8) | 1253 (62.7) | |||||||
| Higher | 843 (62.3) | 95 (12.4) | 95 (13.9) | 363 (12.2) | 98 (2.9) | 395 (13.2) | 466 (67.0) | |||||||
| Women employment status | ||||||||||||||
| Not employed | 3350 (55.2) | 0.698 | 530 (16.0) | 0.681 | 459 (15.1) | 0.099 | 2415 (18.3) | 0.242 | 630 (4.6) | 0.744 | 2523 (19.1) | 0.347 | 1928 (63.3) | 0.966 |
| Employed | 1556 (55.7) | 243 (16.5) | 191 (13.0) | 1135 (19.3) | 282 (4.7) | 1172 (19.9) | 917 (63.2) | |||||||
| Number of children | ||||||||||||||
| Up to 2 | 3268 (53.9) | < 0.001 | 451 (14.6) | < 0.001 | 403 (13.9) | 0.267 | 2179 (16.3) | < 0.001 | 571 (4.1) | < 0.001 | 2286 (17.1) | < 0.001 | 1833 (61.8) | 0.017 |
| 3 or more | 1638 (58.4) | 322 (19.1) | 247 (15.3) | 1371 (23.8) | 341 (5.8) | 1409 (24.4) | 1012 (65.9) | |||||||
| Wealth index | ||||||||||||||
| Poorest | 592 (37.6) | < 0.001 | 110 (13.8) | 0.007 | 67 (7.8) | < 0.001 | 700 (20.9) | < 0.001 | 179 (5.1) | 0.071 | 724 (21.6) | < 0.001 | 369 (46.2) | < 0.001 |
| Poorer | 814 (49.8) | 136 (14.2) | 110 (12.3) | 790 (21.1) | 188 (5.0) | 817 (21.9) | 497 (57.3) | |||||||
| Middle | 950 (53.4) | 137 (14.9) | 120 (13.0) | 699 (18.4) | 200 (5.2) | 729 (19.2) | 542 (63.0) | |||||||
| Richer | 1126 (61.1) | 181 (18.6) | 141 (15.2) | 730 (18.1) | 186 (4.4) | 762 (18.8) | 657 (69.7) | |||||||
| Richest | 1424 (71.9) | 209 (18.8) | 212 (22.7) | 631 (15.1) | 159 (3.6) | 663 (15.9) | 780 (77.3) | |||||||
| Area of residence | ||||||||||||||
| Urban | 1970 (63.6) | < 0.001 | 320 (19.1) | 0.001 | 294 (20.3) | < 0.001 | 1181 (16.9) | 0.024 | 297 (4.0) | 0.126 | 1235 (17.6) | 0.024 | 1107 (70.9) | < 0.001 |
| Rural | 2936 (52.1) | 453 (15.0) | 356 (12.1) | 2369 (16.3) | 615 (4.9) | 2460 (20.1) | 1738 (60.3) | |||||||
| Division | ||||||||||||||
| Barishal | 555 (54.9) | < 0.001 | 74 (13.8) | 0.178 | 72 (14.3) | < 0.001 | 358 (17.9) | < 0.001 | 87 (4.5) | < 0.001 | 385 (19.2) | < 0.001 | 336 (62.6) | < 0.001 |
| Chattogram | 785 (59.9) | 124 (17.1) | 100 (14.7) | 571 (20.4) | 113 (4.0) | 587 (21.0) | 465 (68.4) | |||||||
| Dhaka | 770 (59.0) | 94 (14.8) | 126 (20.7) | 458 (15.9) | 120 (3.9) | 479 (16.6) | 401 (65.7) | |||||||
| Khulna | 695 (60.0) | 104 (17.0) | 77 (11.7) | 491 (19.1) | 162 (6.2) | 511 (19.8) | 394 (66.2) | |||||||
| Mymensingh | 418 (41.9) | 79 (15.3) | 56 (10.8) | 329 (16.6) | 70 (3.5) | 348 (17.4) | 256 (50.7) | |||||||
| Rajshahi | 685 (56.3) | 108 (18.2) | 64 (8.2) | 415 (17.0) | 83 (3.4) | 434 (17.7) | 383 (63.3) | |||||||
| Rangpur | 561 (49.0) | 88 (14.2) | 77 (13.2) | 543 (24.8) | 158 (7.3) | 561 (25.7) | 333 (59.1) | |||||||
| Sylhet | 437 (45.5) | 102 (20.5) | 78 (15.5) | 385 (18.9) | 119 (5.9) | 390 (19.1) | 277 (55.8) | |||||||
| Attitude toward violence | ||||||||||||||
| Low | 181 (58.7) | 0.509 | 36 (18.4) | 0.402 | 23 (12.9) | 0.584 | 192 (27.4) | < 0.001 | 49 (7.0) | 0.001 | 196 (27.9) | < 0.001 | 114 (68.9) | 0.162 |
| Medium | 497 (55.2) | 86 (18.1) | 70 (16.0) | 456 (22.8) | 123 (6.1) | 478 (24.2) | 295 (66) | |||||||
| High | 4228 (55.3) | 651 (15.9) | 557 (14.3) | 2902 (17.7) | 740 (4.4) | 3021 (18.4) | 2436 (62.6) | |||||||
| Social independence | ||||||||||||||
| Low | 2069 (55.4) | 0.101 | 373 (18.2) | 0.011 | 277 (14.6) | 0.193 | 1691 (20.7) | < 0.001 | 420 (5.1) | 0.018 | 1749 (21.5) | < 0.001 | 1219 (64.3) | 0.019 |
| Medium | 1889 (54.2) | 264 (14.4) | 241 (13.3) | 1318 (17.8) | 351 (4.5) | 1371 (18.4) | 1075 (60.7) | |||||||
| High | 948 (58.1) | 136 (14.9) | 132 (16.5) | 541 (15.4) | 141 (3.7) | 575 (16.3) | 551 (66.5) | |||||||
| Decision-making | ||||||||||||||
| Low | 572 (42.6) | < 0.001 | 86 (11.6) | < 0.001 | 82 (10.9) | 0.028 | 448 (16.1) | 0.007 | 111 (4.1) | 0.300 | 469 (17.0) | 0.016 | 372 (54.2) | < 0.001 |
| Medium | 1295 (55.2) | 183 (14.8) | 179 (15.8) | 934 (18.2) | 220 (4.5) | 978 (19.0) | 731 (62.8) | |||||||
| High | 3039 (58.4) | 504 (17.8) | 389 (14.7) | 2168 (19.4) | 581 (4.9) | 2248 (20.1) | 1742 (65.4) | |||||||
The prevalence of all individual and composite physical NCDs increased significantly with age, greater household wealth, urban residence, and higher levels of empowerment in decision-making domains. Women with higher education, greater parity, and higher empowerment in social independence had a lower prevalence of hypertension. Furthermore, all forms of mental NCDs were more prevalent among older women, those with lower education, higher parity, and lower empowerment regarding attitude toward violence and social independence. Women living in urban areas, from poorer households, and with higher decision-making empowerment had a higher prevalence of anxiety. Furthermore, a significant regional disparity in the prevalence of all forms of NCDs was observed (Table 3).
Supplementary Tables 3–4 present the unadjusted prevalence ratio for the associations of women’s empowerment domains and different forms of NCD outcomes. When examining individual physical NCD outcomes, in the adjusted model, women with high empowerment in the attitude toward violence (APR: 0.90, 95%CI: 0.82–1.00, p = 0.047) and social independence domain (APR: 0.94, 95%CI: 0.88–1.00, p = 0.046) had significantly lower prevalence of overweight/obesity. However, high empowerment in the decision-making domain was significantly associated with higher prevalence of overweight/obesity (APR: 1.24, 95%CI: 1.15–1.33, p < 0.001) and hypertension (APR: 1.29, 95%CI: 1.01–1.64, p = 0.042). In terms of composite outcome, any physical NCDs had a significant inverse association with a higher level of empowerment in the attitude toward violence domain (APR: 0.89, 95%CI: 0.79–1.00, p = 0.048) and a positive association with a higher level of empowerment in the decision-making domain (APR: 1.12, 95%CI: 1.03–1.21, p = 0.008) (Table 4). Furthermore, women with medium empowerment in the decision-making domain had significantly higher prevalence of diabetes/hypertension (APR: 1.21, 95%CI: 1.01–1.46, p = 0.045) (Supplementary Table 5).
Table 4.
Associations between women’s empowerment domains and different forms of physical NCDs
| Variables | Overweight/obesity | Hypertension | Diabetes | Any physical NCDs | ||||
|---|---|---|---|---|---|---|---|---|
| APR (95%CI) | p-value | APR (95%CI) | p-value | APR (95%CI) | p-value | APR (95%CI) | p-value | |
| Attitude toward violence | ||||||||
| Low | Ref. | Ref. | Ref. | Ref. | ||||
| Medium | 0.92 (0.82, 1.03) | 0.152 | 1.00 (0.69, 1.46) | 0.995 | 1.17 (0.73, 1.86) | 0.515 | 0.95 (0.83, 1.09) | 0.495 |
| High | 0.90 (0.82, 1.00) | 0.047 | 0.90 (0.65, 1.26) | 0.543 | 1.00 (0.65, 1.52) | 0.985 | 0.89 (0.79, 1.00) | 0.048 |
| Social independence | ||||||||
| Low | Ref. | Ref. | Ref. | Ref. | ||||
| Medium | 0.98 (0.94, 1.03) | 0.387 | 0.93 (0.79, 1.08) | 0.340 | 0.92 (0.77, 1.11) | 0.390 | 0.95 (0.90, 1.01) | 0.086 |
| High | 0.94 (0.88, 1.00) | 0.046 | 0.82 (0.66, 1.01) | 0.058 | 0.91 (0.72, 1.17) | 0.467 | 0.96 (0.90, 1.03) | 0.275 |
| Decision-making | ||||||||
| Low | Ref. | Ref. | Ref. | Ref. | ||||
| Medium | 1.21 (1.12, 1.30) | < 0.001 | 1.19 (0.92, 1.55) | 0.190 | 1.30 (1.00, 1.70) | 0.052 | 1.10 (1.00, 1.20) | 0.043 |
| High | 1.24 (1.15, 1.33) | < 0.001 | 1.29 (1.01, 1.64) | 0.042 | 1.14 (0.90, 1.46) | 0.281 | 1.12 (1.03, 1.21) | 0.008 |
All models were adjusted for women age, employment status, number of children, wealth index, area of residence, and division
APR Adjusted prevalence ratio, CI Confidence interval, Ref Reference
For mental health outcomes, higher empowerment in the attitude toward violence domain was significantly associated with a lower prevalence of anxiety symptoms (APR: 0.68, 95%CI: 0.58–0.79, p < 0.001), depression symptoms (APR: 0.65, 95%CI: 0.45–0.94, p = 0.021), and any mental symptoms (APR: 0.69, 95%CI: 0.59–0.80, p < 0.001). Similarly, high social independence empowerment showed significantly inverse associations with anxiety symptoms (APR: 0.87, 95%CI: 0.78–0.97, p = 0.011) and any mental symptoms (APR: 0.88, 95%CI: 0.80–0.98, p = 0.016). However, decision making domain had no significant association with any of the mental symptoms (Table 5). Besides, when applying the alternative cut-off for anxiety (GAD-7 ≥ 10), only high empowerment in the attitude toward violence domain remained inversely associated with anxiety symptoms (APR: 0.60, 95%CI: 0.43–0.84, p = 0.003) (Supplementary Table 6).
Table 5.
Associations between women’s empowerment domains and different forms of mental symptoms
| Variables | Anxiety symptoms | Depression symptoms | Any mental symptoms | |||
|---|---|---|---|---|---|---|
| APR (95%CI) | p-value | APR (95%CI) | p-value | APR (95%CI) | p-value | |
| Attitude toward violence | ||||||
| Low | Ref. | Ref. | Ref. | |||
| Medium | 0.84 (0.71, 1.00) | 0.044 | 0.87 (0.57, 1.32) | 0.517 | 0.87 (0.73, 1.03) | 0.110 |
| High | 0.68 (0.58, 0.79) | < 0.001 | 0.65 (0.45, 0.94) | 0.021 | 0.69 (0.59, 0.80) | < 0.001 |
| Social independence | ||||||
| Low | Ref. | Ref. | Ref. | |||
| Medium | 0.96 (0.89, 1.04) | 0.324 | 1.00 (0.85, 1.18) | 0.982 | 0.96 (0.89, 1.03) | 0.257 |
| High | 0.87 (0.78, 0.97) | 0.011 | 0.86 (0.68, 1.09) | 0.208 | 0.88 (0.80, 0.98) | 0.016 |
| Decision-making | ||||||
| Low | Ref. | Ref. | Ref. | |||
| Medium | 1.11 (0.98, 1.27) | 0.110 | 1.12 (0.85, 1.48) | 0.405 | 1.11 (0.97, 1.26) | 0.120 |
| High | 1.12 (0.99, 1.28) | 0.078 | 1.19 (0.91, 1.56) | 0.205 | 1.10 (0.98, 1.25) | 0.113 |
All models were adjusted for women age, employment status, number of children, wealth index, area of residence, and division
APR Adjusted prevalence ratio, CI Confidence interval, Ref Reference
Discussion
To our knowledge, this is among the first studies to explore the association between women’s empowerment and several NCD-related conditions among WRA in Bangladesh. The majority of the women exhibited high empowerment in the attitude toward violence and decision-making domains, while a few showed social independence. A substantial prevalence of physical NCDs (overweight/obesity, hypertension, or diabetes), as well as symptoms of mental NCDs (anxiety or depression), was observed. Additionally, higher empowerment in the attitude toward violence domain was associated with lower prevalence of overweight/obesity and all forms of mental symptoms. High empowerment in social independence was also inversely associated with overweight/obesity, anxiety symptoms, and any mental symptoms. On the other hand, high decision-making empowerment was associated with a higher prevalence of overweight/obesity, hypertension, and any physical NCDs.
Patterns of women’s empowerment
Consistent with earlier studies in Bangladesh [31, 40] and other South Asian countries [20, 41], this study found higher empowerment in the domains of decision-making and attitude toward violence, but lower empowerment in social independence. Women’s decision-making autonomy in Bangladesh increased from ~ 40% in 2011 to ~ 60% in 2022 [10, 42], while the proportion of women justifying wife-beating declined from 37% to 14% over the same period [10, 43]. These positive trends align with the high empowerment levels observed in our analysis. These improvements reflect the country’s progress in gender equality [44] and women’s participation in education and economic activities [45]. Legal frameworks such as the Prevention of Women and Children Repression Act, the Domestic Violence Act, the National Women Development Policy, and various programs led by the Ministry of Women and Children Affairs might have contributed to reducing violence and increasing women’s autonomy [46]. Nevertheless, progress in social independence remains limited. Many women continue to marry early, have restricted access to mass media, face adolescent childbirth, and a considerable proportion still lack formal education [45]. These persistent challenges highlight the need for continued policy and community-level interventions.
Empowerment in the attitude toward violence domain and NCDs
This study demonstrated that women with high empowerment in the attitude toward violence domain were associated with lower prevalence of all forms of mental health symptoms, overweight/obesity, and the composite “any physical NCDs”, while no significant associations were found with hypertension or diabetes.
Intimate partner violence (IPV) is a major variable for this empowerment domain. The physiological pathways linking violence to these health outcomes are complex and include chronic activation of the stress response, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, alterations in cortisol levels, increased allostatic load, inflammatory responses, and imbalances in neurotransmitters [47, 48]. These processes manifest more rapidly in mental symptoms through acute HPA-driven emotional dysregulation [47] and in overweight/obesity via cortisol-mediated appetite stimulation and visceral fat deposition [49]. A previous study in Bangladesh also linked the association between obesity and anxiety [50]. Hypertension and diabetes, by contrast, require prolonged exposure and additional genetic, behavioral, dietary, or metabolic factors to emerge clinically, explaining the null associations here. Furthermore, due to the high prevalence of overweight/obesity, the association in the composite outcome should be interpreted cautiously.
Sociocultural context is critical for such an association. In patriarchal settings such as Bangladesh, deeply entrenched cultural norms and unwritten social expectations often legitimize violence against women as an accepted component of family life [51]. Within this context, abusive behavior may be interpreted not as a violation of rights but as a justified expression of male authority, which women may internalize as being in their own best interest [52]. This normalization of violence can generate chronic psychological distress. Furthermore, IPV constrains women’s access to household resources, nutrition, healthcare, and financial autonomy, while stigma and low mental health literacy further impede care-seeking [53–55]. Conversely, women who reject violence may be less likely to internalize such norms and may experience greater autonomy, psychological resilience, and access to social and health resources, thereby reducing vulnerability to mental symptoms and adiposity.
These findings align with prior studies in Bangladesh [55–57], other LMICs [58, 59], and global analyses [60–62], linking IPV exposure to elevated mental disorders. However, evidence linking attitudes toward violence to physical NCDs remains mixed. An Indian study found an inverse association with hypertension [63], whereas research from Nepal observed null associations for hypertension [34] and mental health [35]. A systematic review further reported associations between IPV and diabetes and cardiovascular disease [64]. Furthermore, studies in LMICs found a positive association with IPV and overweight/obesity [65, 66]. Such divergence may reflect contextual heterogeneity in sociocultural norms, measurement of empowerment and IPV, variation in healthcare access, and diagnostic coverage across settings.
Empowerment in the social independence domain and NCDs
Women’s empowerment in the social independence domain was inversely associated with overweight/obesity, anxiety symptoms, and the composite “any mental symptoms”. Furthermore, all other outcomes showed consistent inverse trends, though the association was not significant. Our findings align with prior evidence supporting a protective role of social independence on mental symptoms [35], but contrast with a study showing hypertension reductions in other settings [34].
Social independence—measured through education, access to information, age at first birth, and spousal differences—is critical for women’s autonomy and health behaviors. Women with higher social independence are more likely to delay marriage/childbirth, gain health knowledge, achieve elevated social status, seek healthcare proactively, and maintain social networks [14, 67]. These factors reduce stressor exposure and support healthier behaviors, plausibly underlying the significant inverse associations with mental health symptoms and overweight/obesity via improved stress management, dietary patterns, and physical activity. Non-significant yet directionally consistent trends for hypertension/diabetes likely reflect their longer latency. Overweight/obesity typically develops earlier as a direct consequence of behavioral and stress-related pathways, whereas prolonged obesity increases risk for these downstream cardiometabolic conditions, requiring extended exposure to manifest clinically [68, 69].
Despite these benefits, structural and sociocultural barriers in Bangladesh may attenuate social independence’s protective effects beyond observed outcomes. Constraints such as limited resources, economic opportunities, restricted mobility, physical activity, and persistent gender norms [70] could hinder translation of autonomy into broader health gains for non-significant outcomes. Multivariable adjustment for several variables may also represent over-adjustment, while a smaller sample size may mask true effects. This interplay underscores the need to address both individual empowerment and systemic barriers to optimize health benefits in Bangladesh and similar settings.
Empowerment in the decision-making domain and NCDs
This study found a significant positive association between women’s empowerment in the decision-making domain and overweight/obesity, hypertension, and the composite physical NCD outcome, while no statistically significant associations were observed with mental symptoms. Similar positive associations with diabetes and anxiety were observed in unadjusted models. Evidence linking decision-making empowerment to physical NCDs remains limited and mixed. For instance, a study from Bangladesh reported higher odds of overweight/obesity among women with greater autonomy [71], whereas research from India found that women’s decision-making power had a protective effect against hypertension [72]. Furthermore, a study in Nepal found a null association with hypertension [34]. Regarding mental health, several studies from Africa [73–75] indicate a protective effect of decision-making empowerment, whereas some South Asian studies suggest an increase in mental health symptoms with greater empowerment [35, 76].
Bangladesh’s unique sociocultural context, particularly traditional gender norms, financial independence, and reliance on patriarchal approval, shapes health outcomes in ways that differ from other countries. Greater involvement in household decisions may increase role strain and psychosocial stress rather than alleviate it, particularly when empowerment is not accompanied by parallel gains in economic security or structural support [35, 77], potentially contributing to NCD risk. Furthermore, women with higher decision‑making autonomy may simultaneously gain control over health‑related choices and be more exposed to sedentary lifestyles, contributing to reduced energy expenditure and increased NCD risk. Empowered women might navigate physical healthcare more effectively, but mental health issues remain less acknowledged, possibly due to persistent stigma, cultural beliefs, and limited mental health literacy, which discourage women from recognizing or seeking formal care [78], limiting the observable associations between empowerment and mental health outcomes in this context.
These findings should be interpreted cautiously considering potential detection bias and temporal ambiguity. Women with greater decision-making power may have higher health awareness and better recognition of physical symptoms, leading to increased diagnosis and reporting of physical NCDs [79], which could produce higher observed prevalence through detection bias rather than higher underlying disease burden. Furthermore, the possibility of reverse causation could affect the interpretation, whereby existing NCDs alter household decision-making roles or increase engagement with health services, thereby inflating empowerment scores. Therefore, the observed association reflects a complex interplay of sociocultural context, behavioral pathways, and measurement limitations rather than a direct causal effect of decision-making empowerment on physical NCD risk.
Policy implication
This study highlights the need to integrate women’s empowerment into public health strategies to mitigate the NCD burden in Bangladesh. To address gender-equitable attitudes toward violence, interventions should leverage existing platforms such as Community Clinics, Union Health and Family Welfare Centres (UHFWCs), and Upazila Health Complexes (UHCs), which already deliver maternal health, family planning, and basic services. These can incorporate community awareness campaigns, school-based education involving teachers, and advocacy through religious leaders and local government opinion leaders to promote mental health, reduce psychosocial stress, and facilitate early NCD detection. Enhancing social independence requires financial literacy and health education via community health workers, microfinance programs, and participatory workshops within reproductive health services. Given potential detection bias, where empowered women access healthcare more readily and receive earlier NCD diagnoses, empowerment efforts should pair with expanded NCD screening and management. This aligns with the government’s efforts to integrate mental health services into primary healthcare, including peer support groups and counseling in Community Clinics and maternal-child health programs. Policies targeting multiple empowerment dimensions alongside preventive NCD care would reduce health inequities and advance SDGs.
Strengths and limitations
This study has several notable strengths. It utilized nationally representative data, ensuring the broad generalizability of the findings. The use of standardized survey methodologies and validated questionnaires enhanced the reliability and comparability of the data. Moreover, women’s empowerment was measured using the SWPER index—a globally recognized and validated tool—allowing for cross-country comparability and robust assessment across key empowerment domains. The inclusion of both physical and mental non-communicable conditions provides a more comprehensive understanding of how empowerment influences women’s health.
Despite these strengths, certain limitations should be acknowledged. The SWPER index, while widely used, does not fully capture the multidimensional nature of women’s empowerment, which also includes sociocultural, economic, legal, familial, political, and psychosocial dimensions. In addition, the analysis was restricted to ever-married women, limiting the generalizability of the findings to unmarried women. Mental health outcomes were assessed using the PHQ-9 and GAD-7 instruments, which are screening tools rather than diagnostic instruments. As a result, the prevalence of depression and anxiety symptoms may be overestimated or underestimated compared with clinically confirmed disorders. These outcomes were self-reported and may be subject to reporting and social desirability bias. Besides, the cross-sectional design precludes causal inference and raising the possibility of reverse causation. Women with existing mental or physical NCDs may experience changes in empowerment, household roles, or attitudes toward violence. In addition, greater interaction with health services among women with diagnosed conditions may influence both the reporting of empowerment-related variables and the likelihood of disease detection. Furthermore, residual confounding cannot be excluded, as several important factors were not available in the dataset, including dietary patterns, tobacco and alcohol use, physical activity, psychosocial stress, and social support. In addition, the selection of variables included in the adjustment models may have influenced the estimated associations. The analysis involved multiple comparisons across three empowerment domains and multiple NCD outcomes, increasing the possibility of type I error. Hence, these findings should be interpreted cautiously. Differences in sample size between mental health and physical NCD outcomes also limited statistical power for disease-specific analyses. A small number of observations were excluded due to missing data on key variables, which may introduce potential selection bias. Furthermore, the absence of a biomarker specific sampling weight in the BDHS dataset may slightly affect prevalence estimates. Longitudinal studies are needed to clarify temporality and causal pathways between women’s empowerment and NCD outcomes.
Conclusions
Women’s empowerment influences the health outcomes of women in Bangladesh. Higher empowerment in the attitude toward violence was associated with lower prevalence of overweight/obesity and all forms of mental symptoms, while greater social independence was inversely associated with overweight/obesity, anxiety symptoms, and any mental symptoms. In contrast, higher decision-making empowerment showed positive associations with overweight/obesity, hypertension, and any physical NCDs—likely reflecting detection bias alongside lifestyle or socioeconomic mediators. These findings reveal complex pathways linking empowerment dimensions to health outcomes, underscoring the need for nuanced, domain-specific approaches in women’s health research and intervention design. Future studies should explore mediating mechanisms and longitudinal effects to inform targeted strategies for reducing Bangladesh’s growing NCD burden among women.
Supplementary Information
Acknowledgements
We sincerely acknowledge the Bangladesh Bureau of Statistics (BBS) and the Bangladesh Demographic and Health Survey for granting access to the nationally representative data utilized in this study.
Abbreviations
- AOR
Adjusted Odds Ratio
- BDHS
Bangladesh Demographic and Health Survey
- BMI
Body Mass Index
- CI
Confidence Intervals
- GAD-7
Generalized Anxiety Disorder-7
- IPV
Intimate partner violence
- LMICs
Low- and Middle-Income Countries
- NCD
Non-Communicable Disease
- PHQ-9
Patient Health Questionnaire-9
- SDGs
Sustainable Development Goals
- SWPER
Survey-based Women’s Empowerment
- VIF
Variance Inflation Factor
- WRA
women of reproductive age
Authors’ contributions
TA, and RH contributed equally to the study. TA, RH, and SS conceptualized the study. TA, and RH analyzed and interpreted the data. TA, RH, SS, and MSH drafted the manuscript. All authors critically reviewed and approved the manuscript.
Funding
Open access funding provided by Lund University. This research did not receive any specific funding from commercial or non-commercial funding agencies.
Data availability
All data are publicly accessible from the DHS database: https://www.dhsprogram.com/Countries/Country-Main.cfm?ctryid=1&c=Bangladesh&Country=Bangladesh&cn=&r=4.
Declarations
Ethics approval and consent to participate
This study utilized secondary data from the 2022 BDHS. A formal request was submitted to the Data Archivist of the DHS Program to obtain access to the data. After getting approval, full access to the data was obtained from www.dhsprogram.com, in compliance with the data sharing policy. The survey authority received ethical approval from the Bangladesh Medical Research Council (BMRC) and ICF. All survey respondents provided informed consent prior to participation. Because this study analyzed publicly available, de-identified data, no additional ethical approval was required.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Li J, Pandian V, Davidson PM, Song Y, Chen N, Fong DYT. Burden and attributable risk factors of non-communicable diseases and subtypes in 204 countries and territories, 1990–2021: a systematic analysis for the global burden of disease study 2021. Int J Surg. 2025;111(3):2385–97. 10.1097/JS9.0000000000002260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.World Health Organization. Noncommunicable diseases. 2025. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. Accessed 2 October 2025.
- 3.World Health Organization. Mental disorders. 2025. https://www.who.int/news-room/fact-sheets/detail/mental-disorders. Accessed 2 October 2025.
- 4.Fan Y, Fan A, Yang Z, Fan D. Global burden of mental disorders in 204 countries and territories, 1990–2021: results from the global burden of disease study 2021. BMC Psychiatry. 2025;25(1):486. 10.1186/s12888-025-06932-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.World Health Organization. UN Political Declaration on Prevention and Control of NCDs. 2018. https://www.emro.who.int/noncommunicable-diseases/publications/un-political-declaration-on-prevention-and-control-of-ncds.html. Accessed 12 September 2025.
- 6.Uphoff EP, Newbould L, Walker I, Ashraf N, Chaturvedi S, Kandasamy A, et al. A systematic review and meta-analysis of the prevalence of common mental disorders in people with non-communicable diseases in Bangladesh, India, and Pakistan. J Glob Health. 2019;9(2):020417. 10.7189/jogh.09.020417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.World Bank. Cause of death, by non-communicable diseases (% of total)-Bangladesh Data. 2019. https://data.worldbank.org/indicator/SH.DTH.NCOM.ZS?locations=BD. Accessed 15 September 2025.
- 8.Islam SMS, Uddin R, Das S. The burden of diseases and risk factors in Bangladesh, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Glob Health. 2023;11(12):e1931–42. 10.1016/S2214-109X(23)00432-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.National Institute of Mental Health. National Mental Health Survey. 2019. 2021. https://nimh.gov.bd/wp-content/uploads/2021/11/Mental-Health-Survey-Report.pdf. Accessed 15 September 2025.
- 10.National Institute of Population Research and Training (NIPORT), ICF. Bangladesh demographic and health survey 2022: final report. Dhaka, bangladesh, and Rockville. Maryland, USA: NIPORT and ICF; 2024. [Google Scholar]
- 11.United Nations. Goal 5: Achieve gender equality and empower all women and girls. 2025. https://sdgs.un.org/goals/goal5. Accessed 16 September 2025.
- 12.Alsop R, Bertelsen MF, Holland J. Empowerment in practice: From analysis to implementation. World Bank; 2006.
- 13.Pratley P. Associations between quantitative measures of women’s empowerment and access to care and health status for mothers and their children: A systematic review of evidence from the developing world. Soc Sci Med. 2016;169:119–31. 10.1016/j.socscimed.2016.08.001. [DOI] [PubMed] [Google Scholar]
- 14.Aboagye RG, Essuman MA, Salihu T, Seidu AA, Hagan JE Jr, Baiden F, et al. Association between the Survey-based Women’s Empowerment (SWPER) index and barriers to healthcare in sub-Saharan Africa. Int Health. 2025;17(5):734–44. 10.1093/inthealth/ihaf023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Eom YJ, Chi H, Bhatia A, Lee HY, Subramanian SV, Kim R. Individual- and community-level women’s empowerment and complete use of maternal healthcare services: A multilevel analysis of 34 sub-Saharan African countries. Soc Sci Med. 2025;370:117816. 10.1016/j.socscimed.2025.117816. [DOI] [PubMed] [Google Scholar]
- 16.Ewerling F, Wehrmeister FC, Victora CG, Raj A, McDougal L, Barros AJ. Is women’s empowerment associated with coverage of RMNCH interventions in low- and middle-income countries? An analysis using a survey-based empowerment indicator, the SWPER. J Glob Health. 2021;11:04015. 10.7189/jogh.11.04015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wendt A, Santos TM, Cata-Preta BO, Costa JC, Mengistu T, Hogan DR, et al. Children of more empowered women are less likely to be left without vaccination in low- and middle-income countries: A global analysis of 50 DHS surveys. J Glob Health. 2022;12:04022. 10.7189/jogh.12.04022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Komakech JJ, Walters CN, Rakotomanana H, Hildebrand DA, Stoecker BJ. The associations between women’s empowerment measures, child growth and dietary diversity: Findings from an analysis of demographic and health surveys of seven countries in Eastern Africa. Matern Child Nutr. 2022;18(4):e13421. 10.1111/mcn.13421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kabir A, Rashid MM, Hossain K, Khan A, Sikder SS, Gidding HF. Women’s empowerment is associated with maternal nutrition and low birth weight: evidence from Bangladesh Demographic Health Survey. BMC Womens Health. 2020;20(1):93. 10.1186/s12905-020-00952-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ewerling F, Raj A, Victora CG, Hellwig F, Coll CV, Barros AJ. SWPER Global: A survey-based women’s empowerment index expanded from Africa to all low- and middle-income countries. J Glob Health. 2020;10(2):020343. 10.7189/jogh.10.020434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]
- 22.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157–63. 10.1016/S0140-6736(03)15268-3. [DOI] [PubMed] [Google Scholar]
- 24.Whitworth JA, World Health Organization, International Society of Hypertension Writing Group. 2003 World Health Organization (WHO)/International Society of Hypertension (ISH) statement on management of hypertension. J Hypertens. 2003;21(11):1983–1992. 10.1097/00004872-200311000-00002 [DOI] [PubMed]
- 25.World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: Report of a WHO/IDF consultation. 2006. https://www.who.int/publications/i/item/definition-and-diagnosis-of-diabetes-mellitus-and-intermediate-hyperglycaemia
- 26.Ngo VK, Rubinstein A, Ganju V, Kanellis P, Loza N, Rabadan-Diehl C, et al. Grand challenges: Integrating mental health care into the non-communicable disease agenda. PLoS Med. 2013;10(5):e1001443. 10.1371/journal.pmed.1001443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ali M, Amin MR, Jarl J, Saha S. Prevalence, trends, and inequality in noncommunicable diseases in Bangladesh: Evidence from Bangladesh Demographic and Health Surveys 2011 and 2017–2018. Public Health Chall. 2024;3(1):e148. 10.1002/puh2.148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Chowdhury MH, Khan NM, Rahman MM. Analysing risk factors for diabetes in Bangladesh: a Bayesian hierarchical approach using a nationwide cross-sectional study. BMC Public Health. 2025;25(1):3252. 10.1186/s12889-025-23762-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ferdausi F, Al-Zubayer MA, Keramat SA, Ahammed B. Prevalence and associated factors of underweight and overweight/obesity among reproductive-aged women: A pooled analysis of data from South Asian countries (Bangladesh, Maldives, Nepal and Pakistan). Diabetes Metab Syndr. 2022;16(3):102428. 10.1016/j.dsx.2022.102428. [DOI] [PubMed] [Google Scholar]
- 30.Das NC, Ghosh PK, Hossain MA, Shuvo UA, Talukder NR, Khatun F, et al. Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach. PLoS ONE. 2025;20(10):e0335442. 10.1371/journal.pone.0335442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Alam MB, Khanam SJ, Kabir MA, Hassen TA, Khan MN. Association between women’s empowerment and use of modern contraception methods in Bangladesh: evidence from Bangladesh demographic and health survey 2022. Contracept Reprod Med. 2025;10(1):41. 10.1186/s40834-025-00383-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Akter S, Hosen MS, Khan MS, Pal B. Assessing the pattern of key factors on women’s empowerment in Bangladesh: Evidence from Bangladesh Demographic and Health Survey, 2007 to 2017-18. PLoS ONE. 2024;19(3):e0301501. 10.1371/journal.pone.0301501. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Kirkwood EK, Raihana S, Alam NA, Dibley MJ. Women’s participation in decision-making: Analysis of Bangladesh Demographic and Health Survey data 2017–2018. J Int Dev. 2024;36(1):26–42. 10.1002/jid.3805. [Google Scholar]
- 34.Shawon MSR, Rahman MR, Fahima T, Jannat F, Hossain FB. Women empowerment and hypertension in Nepal: a nationally representative survey analysis. J Public Health Policy. 2025;46(4):778–94. 10.1057/s41271-025-00593-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Shawon MSR, Hossain FB, Ahmed R, Poly IJ, Hasan M, Rahman MR. Role of women empowerment on mental health problems and care-seeking behavior among married women in Nepal: secondary analysis of nationally representative data. Arch Womens Ment Health. 2024;27(4):527–36. 10.1007/s00737-024-01433-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3:21. 10.1186/1471-2288-3-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Coutinho LM, Scazufca M, Menezes PR. Methods for estimating prevalence ratios in cross-sectional studies. Rev Saude Publica. 2008;42(6):992–8. 10.1590/S0034-89102008000600003. [PubMed] [Google Scholar]
- 38.Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–6. 10.1093/aje/kwh090. [DOI] [PubMed] [Google Scholar]
- 39.O’Brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673–90. 10.1007/s11135-006-9018-6. [Google Scholar]
- 40.Rafi MA, Anika US, Hasan MT, Hossain MG. Association between women’s empowerment and mental health help-seeking behaviour in Bangladesh: findings from a nationally representative survey. BMJ Open. 2025;15(9):e099770. 10.1136/bmjopen-2025-099770. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Rahman MM, Jampaklay A. Women’s empowerment and intentions for additional children among reproductive-aged women: an analysis of demographic and health surveys in South Asia. BMC Public Health. 2025;25(1):1864. 10.1186/s12889-025-23000-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Kibria GMA, Albrecht J, Lane W, Stafford KA, Jones L, Vesselinov R, et al. Prevalence, trends, and factors associated with maternal autonomy regarding healthcare, finances, and mobility in Bangladesh: Analysis of Demographic and Health Surveys 1999–2018. PLOS Glob Public Health. 2024;4(2):e0002816. 10.1371/journal.pgph.0002816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Islam MS, Siddiquee MS, Shohag MR. Trends and patterns in women’s attitude towards wife-beating in Bangladesh: insights from three waves nationally representative survey. SN Social Sci. 2025;5(7):1–21. 10.1007/S43545-025-01133-6. [Google Scholar]
- 44.World Economic Forum. Gender Gap Report 2025. 2025. Available at: https://www.weforum.org/publications/global-gender-gap-report-2025. Accessed 16 September 2025.
- 45.World Bank Group. Gender Data Portal: Bangladesh. 2025. https://genderdata.worldbank.org/en/economies/bangladesh. Accessed 16 September 2025.
- 46.Budget Analysis and Monitoring Unit. Gender Equality and Women Empowerment in Bangladesh. 2022. https://cpd.org.bd/publication/gender-equality-and-women-empowerment-in-bangladesh/. Accessed 16 September 2025.
- 47.Dutton MA. Mental Health and Intimate Partner Violence. 2025;502–26. 10.1093/oso/9780197758991.003.0018
- 48.Alhalal E, Falatah R. Intimate partner violence and hair cortisol concentration: A biomarker for HPA axis function. Psychoneuroendocrinology. 2020;122:104897. 10.1016/j.psyneuen.2020.104897. [DOI] [PubMed] [Google Scholar]
- 49.Jackson SE, Kirschbaum C, Steptoe A. Hair cortisol and adiposity in a population-based sample of 2,527 men and women aged 54 to 87 years. Obes (Silver Spring). 2017;25(3):539–44. 10.1002/oby.21733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Duty FA, Rahman MH, Salma N. Prevalence of anxiety disorder and its association with BMI: an analysis of women’s experiences in Bangladesh using BDHS-2022 data. BMC Public Health. 2025;25(1):1144. 10.1186/s12889-025-22427-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Rahman F, Sakib ABMN. The Normalization of Domestic Violence in Bangladesh: Analysing through the Lens of Social Learning Theory. 2022. 10.3329/ssr.v39i1.64917
- 52.Schuler SR, Lenzi R, Badal SH, Nazneen S. Men’s perspectives on women’s empowerment and intimate partner violence in rural Bangladesh. Cult Health Sex. 2018;20(1):113–27. 10.1080/13691058.2017.1332391. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.World Health Organization. Violence against women. 2024. https://www.who.int/news-room/fact-sheets/detail/violence-against-women. Accessed 28 September 2025.
- 54.Weitzman A, Goosby BJ. Intimate partner violence, circulating glucose, and non-communicable Disease: Adding insult to injury? SSM Popul Health. 2020;13:100701. 10.1016/j.ssmph.2020.100701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Koly KN, Saba J, Mallick T, Rashid F, Watson J, Neves BB. Exploring the pattern of mental health support-seeking behaviour and related barriers among women experiencing intimate partner violence in urban slums of Bangladesh: perspectives from multiple level stakeholders. PLOS Glob Public Health. 2025;5(5):e0004568. 10.1371/journal.pgph.0004568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Nipa SI, Islam A, Disha FA, Raigangar V. Association Between Intimate Partner Violence (IPV) and Physical, Psychological, and Social Health Outcomes for Women in Bangladesh. J Women’s Pelvic Heal Phys Ther. 2024;48:4–10. 10.1097/jwh.0000000000000296. [Google Scholar]
- 57.De PK, Murshid NS. Associations of intimate partner violence with screening for mental health disorders among women in urban Bangladesh. Int J Public Health. 2018;63(8):913–21. 10.1007/s00038-018-1139-x. [DOI] [PubMed] [Google Scholar]
- 58.Stark L, Seff I, Mutumba M, Fulu E. Intimate Partner Violence and Mental Health: Deepening Our Understanding of Associations, Pathways, and Prevention in Low- and Middle-Income Countries. Int J Environ Res Public Health. 2023;20(2):1505. 10.3390/ijerph20021505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Stark L, Seff I, Weber AM, Cislaghi B, Meinhart M, Bermudez LG, et al. Perpetration of intimate partner violence and mental health outcomes: sex- and gender-disaggregated associations among adolescents and young adults in Nigeria. J Glob Health. 2020;10(1):010708. 10.7189/jogh.10.010708. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.White SJ, Sin J, Sweeney A, Salisbury T, Wahlich C, Montesinos Guevara CM, et al. Global Prevalence and Mental Health Outcomes of Intimate Partner Violence Among Women: A Systematic Review and Meta-Analysis. Trauma Violence Abuse. 2024;25(1):494–511. 10.1177/15248380231155529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.World Health Organization. Global and regional estimates of violence against women. 2021. https://www.who.int/publications/i/item/9789241564625
- 62.Bacchus LJ, Ranganathan M, Watts C, Devries K. Recent intimate partner violence against women and health: a systematic review and meta-analysis of cohort studies. BMJ Open. 2018;8(7):e019995. 10.1136/bmjopen-2017-019995. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Chhabra P, Behera S, Sharma R, Malhotra RK, Mehta K, Upadhyay K, et al. Gender-specific factors associated with hypertension among women of childbearing age: Findings from a nationwide survey in India. Front Cardiovasc Med. 2022;9:999567. 10.3389/fcvm.2022.999567. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Jones W, Rossi B, Goralski SL, Martin JL. Associations between IPV and non-communicable diseases: a systematic review. BMC Public Health. 2025;25(1):3216. 10.1186/s12889-025-24498-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Mukamana JI, Machakanja P, Zeeb H, Yaya S, Adjei NK. Investigating the associations between intimate partner violence and nutritional status of women in Zimbabwe. PLoS ONE. 2022;17(7):e0272038. 10.1371/journal.pone.0272038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Ferdos J, Rahman M. Exposure to intimate partner violence and malnutrition among young adult Bangladeshi women: cross-sectional study of a nationally representative sample. Cad Saude Publica. 2018;34(7):e00113916. 10.1590/0102-311X00113916. [DOI] [PubMed] [Google Scholar]
- 67.Anik AI, Ghose B, Rahman MM. Relationship between maternal healthcare utilisation and empowerment among women in Bangladesh: evidence from a nationally representative cross-sectional study. BMJ Open. 2021;11(8):e049167. 10.1136/bmjopen-2021-049167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Norris T, Cole TJ, Bann D, Hamer M, Hardy R, Li L, et al. Duration of obesity exposure between ages 10 and 40 years and its relationship with cardiometabolic disease risk factors: A cohort study. PLoS Med. 2020;17(12):e1003387. 10.1371/journal.pmed.1003387. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Cheung BM, Li C. Diabetes and hypertension: is there a common metabolic pathway? Curr Atheroscler Rep. 2012;14(2):160–6. 10.1007/s11883-012-0227-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Akhter S, Kamruzzaman M, Anwar I, Banu MS, Reidpath DD, Cameron AJ. Knowledge of gendered needs among the planners and policy makers for prevention of NCDs in Bangladesh: a qualitative exploration. Int J Equity Health. 2024;23(1):110. 10.1186/s12939-024-02186-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Moushumi NS, Hassan R, Hossain MS, Konok AH, Amin MR. Prevalence and risk factors of malnutrition among women of reproductive age in Bangladesh: A secondary data analysis of BDHS 2022. Bioresearch Commun. 2025;11(01):1711–27. 10.3329/brc.v11i1.78885. [Google Scholar]
- 72.Stroope S. Seclusion, decision-making power, and gender disparities in adult health: Examining hypertension in India. Soc Sci Res. 2015;53:288–99. 10.1016/j.ssresearch.2015.05.013. [DOI] [PubMed] [Google Scholar]
- 73.Antabe R, Antabe G, Sano Y, Pienaah CKA. Women’s household decision-making autonomy and mental health outcomes in Mozambique. Glob Ment Health (Camb). 2025;12:e40. 10.1017/gmh.2025.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Fielding D, Lepine A. Women’s empowerment and wellbeing: evidence from Africa. J Dev Stud. 2017;53:826–40. 10.1080/00220388.2016.1219345. [Google Scholar]
- 75.Leight J, Pedehombga A, Ganaba R, Gelli A. Women’s empowerment, maternal depression, and stress: Evidence from rural Burkina Faso. SSM Ment Health. 2022;2:100160. 10.1016/j.ssmmh.2022.100160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Richardson RA, Harper S, Bates LM, Nandi A. The effect of agency on women’s mental distress: A prospective cohort study from rural Rajasthan, India. Soc Sci Med. 2019;233:47–56. 10.1016/j.socscimed.2019.05.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Kermode M, Herrman H, Arole R, White J, Premkumar R, Patel V. Empowerment of women and mental health promotion: a qualitative study in rural Maharashtra, India. BMC Public Health. 2007;7:225. 10.1186/1471-2458-7-225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Koly KN, Tasnim Z, Ahmed S, Saba J, Mahmood R, Farin FT, et al. Mental healthcare-seeking behavior of women in Bangladesh: content analysis of a social media platform. BMC Psychiatry. 2022;22(1):797. 10.1186/s12888-022-04414-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.McClintock HF, Peacock V, Nkiri Asong R. Social determinants of health and hypertension screening among women in The Gambia: an evaluation of 2019–2020 demographic and health survey data. J Hum Hypertens. 2025;39(2):148–54. 10.1038/s41371-024-00945-y. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data are publicly accessible from the DHS database: https://www.dhsprogram.com/Countries/Country-Main.cfm?ctryid=1&c=Bangladesh&Country=Bangladesh&cn=&r=4.