Population Modifiable Risk Factors for Depression and Anxiety among Reproductive-aged Women in Nepal: an analysis from 2022 Nepal demographic health survey data

Population Modifiable Risk Factors for Depression and Anxiety among Reproductive-aged Women in Nepal: an analysis from 2022 Nepal demographic health survey data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population Modifiable Risk Factors for Depression and Anxiety among Reproductive-aged Women in Nepal: an analysis from 2022 Nepal demographic health survey data Santosh Giri, Nancy Ross, Rachel Kornhaber, Allen G. Ross, Kedir Y Ahmed, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5830806/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Identifying the critical modifiable risk factors for anxiety and depression is crucial for reducing the increasing burden of mental illness among reproductive-aged women 15–49 years in Nepal. We investigated Population Attributable Fractions (PAFs) of generalized anxiety disorder and major depressive disorder attributable to potentially modifiable risk factors among reproductive-age women. This cross-sectional study analysed the data from the Nepal Demographic Health Survey in 2022. Multilevel logistic regression analyses determined odds ratio (ORs) for risk factors associated with depression and anxiety. PAFs adjusted for communality were calculated using adjusted ORs and prevalence estimates for each risk factor. This study included a weighted sample of 7,410 women, with a mean age of 30 (± 10) years. Highest PAFs of depression were associated with women who experienced emotional abuse (PAF: 18.2%; 95%CI: 15.4–20.2), physical violence (PAF: 12.1%; 95%CI: 5.1–16.7), and sexual abuse (PAF: 9.0%; 95%CI: 5.9–11.5), functional difficulty (PAF: 6.9%; 95%CI: 2.8–10.1) and food insecurity (PAF: 6.6%; 95%CI: 4.4–8.4). These five potentially modifiable risk factors accounted for 52.8% (95%CI: 33.7–67.0) of depression cases. Highest PAFs for anxiety were associated with women who experienced emotional abuse (PAF: 10.8%; 95%CI: 8.7–12.7), functional impairment (PAF: 7.8%; 95%CI: 5.7–9.6), physical violence (PAF: 7.8%; 95%CI: 4.4–10.6), sexual abuse (PAF: 5.6%; 95%CI: 3.9–7.3), and food insecurity (PAF: 3.7%; 95%CI: 2.4–4.9). These five potentially modifiable risk factors accounted for 35.7% (95%CI: 25.2–45.1) of anxiety cases. The results of this study highlight the necessity of targeted strategies at the community and household levels to address violence against women. Couple-based approaches involving men are particularly relevant to break the cycle of intergenerational violence and fostering environments conducive for better mental health. Epidemiology Generalized anxiety disorder major depressive disorder emotional abuse sexual abuse population attributable fraction violence against women Figures Figure 1 Introduction In 2019, approximately 970 million people worldwide experienced mental disorders, with anxiety and depression as the most common cause. 1 Mental disorders are highly prevalent across the globe, but an estimated 80% of people with mental disorders reside in low- and middle-income countries (LMICs). 1 In South Asia, between 1990 and 2019, the incidence of depression increased from 2.8 million to 5.2 million (increased by 85%), and anxiety disorders increased from 24.1 million to 42.6 million (increased by 77%). 2 This burden not only causes lower quality of life among the affected individuals, but it also has social and economic consequences for families, communities, and countries. Among South Asian countries, Nepal is facing an increasing burden of mental disorders among various age groups, including reproductive age women. 3 Nepal's first ever National Mental Health Survey conducted in 2019–20 reported the prevalence of any mental disorder (e.g., major depressive disorders, generalized anxiety disorders, post-traumatic stress disorders, alcohol and substance use disorder) among individuals of 18 years and above was 5.1% among women while it was only 3.4% for men. 4 The observed higher burden of anxiety and depression among reproductive age women is perhaps attributed to patriarchal norms, women’s socio-economic and educational disadvantages, limited support services, normalization of violence against women, and limited access to legal protections for women. 5 Underlying social-contextual disadvantages and living with mental disorders are further associated with poor lifestyles, increased rates of chronic health conditions, suicidal tendencies, and premature deaths among reproductive age women. 6 Accessing mental health care is a significant challenge in Nepal, particularly for individuals from rural areas and low-income communities, including women, the elderly, and children. 4 These barriers to access ranges from an individual level encompassing an inadequate knowledge about accessing healthcare, cost of medical treatments, to structural levels including poor institutional capacity to provide mental health services. 4 , 7 As suggested by the Lancet Commission on global mental health and sustainable development, LMICs including Nepal should make a shift from its focus on the ‘treatment gap’ to mental health as a ‘public good’. 8 Progress in promoting mental health, and preventing mental disorders is unlikely to occur if efforts are not concentrated on addressing the social determinants of mental health. 9 Population Attributable Fractions (PAFs) estimates for depression and anxiety offer crucial insights for prioritizing interventions, allocating resources effectively, and designing tailored prevention strategies. Therefore, this study aimed to calculate the PAFs of major depressive disorder and generalized anxiety disorder, attributable to modifiable risk factors among reproductive age women in Nepal. Methods Study design and data sources This cross-sectional study analysed data from Nepal Demographic Health Survey (NDHS) conducted in 2022 10 . The survey of 2022 was the eighth demographic health survey conducted between January 5 and June 22, 2022. The NDHS gathers data on the demographics and health of individuals, encompassing topics such as maternal and child health, mortality, nutrition, and the social determinants of health. A standardized Mental Health Module (MHM) was added to the survey questionnaire, for the first time, providing the opportunity to conduct this study. Sampling procedures and sample size NDHS uses a two-stage cluster sampling technique to select the study participants. In the first stage, seven provinces were stratified into 15 sampling strata, with 476 primary sample units chosen. A total of 14,280 households were interviewed, with 14,845 women and 4,913 men. The mental health module was completed by 12,323 individuals out of which 7,410 were women of reproductive age. 10 Outcome variables The outcome variables were major depressive disorder and generalized anxiety disorder. Major depressive disorder was measured using Patient Health Questionnaire 9 (PHQ-9) (sensitivity = 88%, specificity = 88%) and generalized anxiety disorder was measured using Generalized Anxiety Disorder 7 (GAD-7) (sensitivity = 92%, specificity = 76%). 10 Based on the cutoff measure used by NDHS 2022, PHQ-9 scores of 10 or more was defined as having depression and GAD-7 scores of 6 or more was defined as having anxiety symptoms. 10 Modifiable risk factors The modifiable risk factors included: socio-economic factors, health risk behaviours, and gender-related factors. The socio-economic factors included: household wealth index (‘poorest’, ‘poorer’, ‘middle’, ‘richer’, ‘richest’), highest education attainment (‘never attended school or primary education’, ‘secondary or higher’), employment (‘not employed’, ‘employed’), functional difficulty (‘no’, ‘yes’), and food insecurity (‘no’, ‘yes’). The health risk behaviours included current smoking status (‘no’, ‘yes’) and alcohol consumption in the last month (‘no’, ‘yes’). The gender-related factors included: traditional separation practices during menstrual period (‘no’, ‘yes’), justification of wife beating (‘no’, ‘yes’), sexual negotiation (‘no’, ‘yes’), involvement in decision-making (‘no’, ‘yes’), experience of physical violence since the age of 15 (‘no’, ‘yes’), ever experienced sexual abuse (‘no’, ‘yes’) and emotional abuse by the partner (‘no’, ‘yes’). For this study potential covariates including age, marital status (grouped as married or unmarried) and province we considered. The household wealth index is a composite measure of relative economic status estimated using household-level information on asset ownership and access to services from individual questionnaires. In NDHS, the household wealth index was computed using principal component analysis (PCA), considering various aspects like the possession of household amenities such as toilets, electricity, television, radio, fridge, and bicycle, as well as the availability of a source of drinking water and the type of flooring material used in the main house. Food insecurity is measured as moderate or severe food insecurity. Moderate food insecurity referred to having to reduce the quality and/or quantity of food and having uncertainty about the ability to obtain food due to lack of money or other resources during the last 12 months. Severe food insecurity referred to running out of food and, at the most extreme, going a day (or days) without eating during the last 12 months. Functional difficulty is defined based on respondents' self-reported limitations in performing basic activities of daily living due to physical, mental, cognitive, or sensory impairment. Sexual negotiation refers to women's ability to refuse sex and negotiate safer sex practices within their relationships. The justification of domestic violence against wives, or wife beating, was derived from certain circumstances when women normalize assault from their husband for activities, such as burning food, arguing with their husband, going out without informing them, neglecting children, or refusing sex. The separation practice during menstrual period was measured by asking women about whether they were excluded, or not allowed, to participate in various social activities outside and at home during menstrual period. Statistical analysis We used weighted analysis accounting for the complex survey design using the ‘survey’ package 11 and presented the categorical variables as frequency and percentage (%) with 95% confidence interval (CI) while numerical variables as mean with 95% CI. Multilevel logistic regression model with clusters as random intercepts were used to separately calculate the Odds Ratio (OR) and 95% CI for depression and anxiety disorders. The reference group was those individuals who had GAD-7 scores less than 6 for anxiety and PHQ-9 scores less than 10 for depression. For the individual risk factors significant in the multilevel regression model of potentially modifiable risk factors for anxiety and depression outcomes, we calculated the unadjusted PAF using Miettinen’s formula 12 : PAF e = P e (OR e – 1)/OR e , where P e is the prevalence of the risk factor e and OR e is the adjusted odds ratio of the anxiety or depression associated with the risk factor e . The prevalence of specific risk factors was calculated using the weighted survey analysis for the reduced sample size in this study. Communality weights were computed to account for the overlap of risk factors across individuals, as adding up the PAFs for each risk factor would lead to an overestimation of their collective impact on anxiety or depression. 13 Using the method outlined by Lee et al., the communality calculation involved performing a pairwise tetrachoric correlation analysis on the risk factors. This was followed by a principal component analysis (PCA) of the tetrachoric correlation matrix. For each risk factor, we calculated the sum of the squares of the loadings in all principal components with an eigenvector greater than 1. The weighting of each risk factor was determined using the formula 13 : W e = 1 – communality e . The combined PAF was then calculated using 12 , 13 : PAF = 1 – [(1 – W 1 *PAF 1 ) * (1 – W 2 *PAF 2 ) * (1 – W 3 *PAF 3 )…]. Finally, the adjusted PAF for each risk factors were calculated using the formula 12 , 13 : PAF e = ([PAF e /∑PAF e ] * combined PAF). All analyses were performed in R version 4.3.2. 14 All methods were conducted in compliance with the principles outlined in the Declaration of Helsinki. Results Study participants The study sample comprised of a weighted sample of 7,410 women (Table 1 ) with an average age of 30 (± 10) years. A total of 68.3% (5,064) of women resided in urban areas, and 26.2% (1,944) had no education. Only 4.3% (319) of women smoked and 23.5% (1,739) consumed alcohol in the last month. A total of 21.3% (1,581) had some form of functional difficulty and 7.8% (573) had moderate-to-severe food insecurity in their households. Table 1 Characteristics of the study participants. Total population (N = 7,410) Major Depressive Disorder Generalized Anxiety Disorder No (N = 7,008) Yes (N = 403) No (N = 5,785) Yes (N = 1,626) n (%) n (%) n (%) n (%) n (%) Province Bagmati 1,493 (20.2) 1,428 (95.6) 65 (4.4) 1,209 (81.0) 284 (19.0) Koshi 1,241 (16.7) 1,161 (93.5) 80 (6.5) 939 (75.6) 302 (24.4) Madhesh 1,512 (20.4) 1,436 (95.0) 76 (5.0) 1,179 (77.9) 333 (22.1) Gandaki 704 (9.5) 676 (96.0) 28 (4.0) 579 (82.2) 125 (17.8) Lumbini 1,360 (18.4) 1,293 (95.1) 67 (4.9) 1,062 (78.1) 298 (21.9) Karnali 458 (6.2) 415 (90.7) 43 (9.3) 331 (72.2) 127 (27.8) Sudurpaschim 641 (8.6) 598 (93.2) 43 (6.8) 486 (75.8) 155 (24.2) Place of residence Urban 5,064 (68.3) 4,812 (95.0) 252 (5.0) 3,965 (78.3) 1,099 (21.7) Rural 2,347 (31.7) 2,196 (93.6) 151 (6.4) 1,820 (77.5) 527 (22.5) Household wealth index Poorest 1,344 (18.1) 1,257 (93.6) 86 (6.4) 1,051 (78.2) 293 (21.8) Poorer 1,372 (18.5) 1,285 (93.7) 87 (6.3) 1,021 (74.5) 350 (25.5) Middle 1,512 (20.4) 1,415 (93.6) 96 (6.4) 1,139 (75.4) 373 (24.6) Richer 1,704 (23.0) 1,621 (95.1) 83 (4.9) 1,341 (78.7) 363 (21.3) Richest 1,479 (20.0) 1,429 (96.6) 50 (3.4) 1,233 (83.3) 247 (16.7) Education level No education 1,944 (26.2) 1,821 (93.7) 122 (6.3) 1,454 (74.8) 489 (25.2) Basic 2,256 (30.4) 2,109 (93.5) 147 (6.5) 1,719 (76.2) 537 (23.8) Secondary 2,931 (39.6) 2,802 (95.6) 129 (4.4) 2,369 (80.8) 562 (19.2) Higher 280 (3.8) 275 (98.4) 5 (1.6) 243 (86.7) 37 (13.3) Currently married No 1,893 (25.5) 1,796 (94.9) 97 (5.1) 1,514 (80.0) 380 (20.0) Yes 5,517 (74.5) 5,211 (94.5) 306 (5.5) 4,271 (77.4) 1,246 (22.6) Employment status Not employed 2,033 (27.4) 1,945 (95.7) 88 (4.3) 1,629 (80.1) 405 (19.9) Employed 5,377 (72.6) 5,063 (94.2) 315 (5.8) 4,156 (77.3) 1,221 (22.7) Functional difficulty No 5,830 (78.7) 5,563 (95.4) 266 (4.6) 4,711 (80.8) 1,119 (19.2) Yes 1,581 (21.3) 1,444 (91.4) 137 (8.6) 1,074 (68.0) 507 (32.0) Food insecurity No 6,821 (92.2) 6,500 (95.3) 321 (4.7) 5,408 (79.3) 1,413 (20.7) Yes 573 (7.8) 493 (86.0) 80 (14.0) 364 (63.5) 209 (36.5) Current smoking status No 7,091 (95.7) 6,715 (94.7) 376 (5.3) 5,559 (78.4) 1,532 (21.6) Yes 319 (4.3) 292 (91.6) 27 (8.4) 226 (70.8) 93 (29.2) Alcohol consumption in the last month No 5,672 (76.5) 5,378 (94.8) 293 (5.2) 4,473 (78.9) 1,198 (21.1) Yes 1,739 (23.5) 1,629 (93.7) 110 (6.3) 1,312 (75.4) 427 (24.6) Traditional separation practices during menstrual period No 903 (12.2) 855 (94.7) 48 (5.3) 690 (76.4) 213 (23.6) Yes 6,507 (87.8) 6,152 (94.5) 355 (5.5) 5,095 (78.3) 1,412 (21.7) Wife beating justified No 6,040 (81.5) 5,720 (94.7) 320 (5.3) 4,719 (78.1) 1,321 (21.9) Yes 1,371 (18.5) 1,287 (93.9) 83 (6.1) 1,066 (77.8) 304 (22.2) Sexual negotiation No 2,279 (30.8) 2,145 (94.1) 134 (5.9) 1,778 (78.0) 501 (22.0) Yes 5,131 (69.2) 4,862 (94.8) 269 (5.2) 4,007 (78.1) 1,124 (21.9) Household decision-making No 2,909 (52.6) 2,732 (93.9) 176 (6.1) 2,230 (76.6) 679 (23.4) Yes 2,624 (47.4) 2,494 (95.1) 129 (4.9) 2,055 (78.3) 569 (21.7) Experienced physical violence since age 15 No 3,920 (76.2) 3,792 (96.7) 128 (3.3) 3,237 (82.6) 683 (17.4) Yes 1,227 (23.8) 1,074 (87.5) 153 (12.5) 778 (63.5) 448 (36.5) Ever experienced sexual abuse No 4,741 (92.1) 4,540 (95.8) 200 (4.2) 3,815 (80.5) 925 (19.5) Yes 406 (7.9) 326 (80.2) 81 (19.8) 200 (49.3) 206 (50.7) Emotional violence by the partner No 3,729 (86.3) 3,598 (96.5) 131 (3.5) 3,040 (81.5) 689 (18.5) Yes 592 (13.7) 473 (79.9) 119 (20.1) 296 (50.0) 296 (50.0) A total of 87.8% (6,507) had practiced traditional separation practices during menstrual at any point of their lives. Eighteen percent (18.5%; 1,371) of women justified wife beating, and 30.8% (2,279) were not able to negotiate sex with their spouse. Fifty-three percent (52.6%; 2,909) of women were not involved in making household decisions. A total of 23.8% (1,227) experienced physical violence after the age of 15, while 7.9% (406) reported being sexually abused in their life and 13.7% (592) were emotionally abused by their spouse. The prevalence of major depressive disorder and generalized anxiety disorder among reproductive aged women was 5.4% (95% CI: 4.9–6.0) and 21.9% (95% CI: 20.9–23.0) respectively (Table 1 ). Population attributable fractions for major depressive disorder As shown in Table 2 , the highest PAFs of depression were associated with women who experienced emotional abuse (PAF: 18.2%; 95% CI: 15.4–20.2), physical violence (PAF: 12.1%; 95% CI: 5.1–16.7), sexual abuse (PAF: 9.0%; 95% CI: 5.9–11.5), functional difficulty (PAF: 6.9%; 95% CI: 2.8–10.1) and food insecurity (PAF: 6.6%; 95% CI: 4.4–8.4). These five potentially modifiable risk factors accounted for 52.8% (95% CI: 33.7–67.0) of depression among women of reproductive age. Table 2 Population Attributable Fractions for major depressive disorder and generalized anxiety disorder among reproductive age women years in Nepal (n = 4,202). Risk factors Major Depressive Disorder Generalized Anxiety Disorder Risk factor prevalence % (95% CI) OR (95% CI) Unadjusted PAF % (95% CI) Adjusted PAF % (95% CI) Risk factor prevalence % (95% CI) OR (95% CI) Unadjusted PAF % (95% CI) Adjusted PAF % (95% CI) Functional difficulty No 66.1 (60.9, 70.9) Ref Ref Ref 68.8 (66.3, 71.3) Ref Ref Ref Yes 33.9 (29.1, 39.1) 1.67 (1.2, 2.34) 13.7 (4.8, 22.4) 6.9 (2.8, 10.1) 31.2 (28.7, 33.7) 1.86 (1.53, 2.27) 14.5 (9.9, 18.9) 7.8 (5.7, 9.6) Food insecurity No 79.9 (75.6, 83.7) Ref Ref Ref 87.1 (85.3, 88.7) Ref Ref Ref Yes 20.1 (16.3, 24.4) 2.84 (1.86, 4.36) 13 (7.5, 18.8) 6.6 (4.4, 8.4) 12.9 (11.3, 14.7) 2.15 (1.61, 2.87) 6.9 (4.3, 9.6) 3.7 (2.4, 4.9) Experienced physical violence since age 15 No 45.6 (39.6, 51.8) Ref Ref Ref 60.4 (57.1, 63.5) Ref Ref Ref Yes 54.4 (48.2, 60.4) 1.78 (1.22, 2.6) 23.9 (8.7, 37.2) 12.1 (5.1, 16.7) 39.6 (36.5, 42.9) 1.57 (1.27, 1.95) 14.4 (7.8, 20.9) 7.8 (4.4, 10.6) Ever experienced sexual abuse No 71.3 (65.4, 76.6) Ref Ref Ref 81.8 (79.1, 84.2) Ref Ref Ref Yes 28.7 (23.4, 34.6) 2.61 (1.76, 3.88) 17.7 (10.1, 25.7) 9.0 (5.9, 11.5) 18.2 (15.8, 20.9) 2.35 (1.76, 3.14) 10.5 (6.8, 14.2) 5.6 (3.9, 7.3) Emotional abuse by the partner No 52.4 (45.9, 58.8) Ref Ref Ref 69.9 (66.6, 73.1) Ref Ref Ref Yes 47.6 (41.2, 54.1) 4.06 (2.77, 5.94) 35.9 (26.3, 45) 18.2 (15.4, 20.2) 30.1 (26.9, 33.4) 3.02 (2.33, 3.9) 20.1 (15.4, 24.8) 10.8 (8.7, 12.7) CI: Confidence interval; PAF: population attributable fraction; OR: odds ratio Adjusted PAF is the relative contribution of each risk factor to the overall PAF when adjusted for communality. PAF for the results significant in the full model were calculated and are presented here. Population attributable fractions for generalized anxiety disorder According to Table 2 , the highest PAFs of anxiety were associated with women who experienced emotional abuse (PAF: 10.8%; 95% CI: 8.7–12.7), physical violence (PAF: 7.8%; 95% CI: 4.4–10.6), functional difficulty (PAF: 7.8%; 95% CI: 5.7–9.6), sexual abuse (PAF: 5.6%; 95% CI: 3.9–7.3), and food insecurity (PAF: 3.7%; 95% CI: 2.4–4.9). The highest PAFs of five risk factors accounted for 35.7% (95% CI: 25.2–45.1) of anxiety. Figure 1 presents the adjusted PAFs for individual risk factors contributing to depression and anxiety, ranked by descending impact, with emotional abuse showing the highest PAF for both conditions. The result from the multilevel logistic regressions is also provided in Supplementary file (Table 1 – 2 ). Discussion This is the first study to investigate the PAFs of major depressive disorder and generalized anxiety disorder among reproductive-age women in Nepal. The findings from our study revealed that 53% of major depressive disorder was associated with women who experienced emotional abuse, physical violence, sexual abuse, functional difficulty, and food insecurity. A total of 36% of generalized anxiety disorder was associated with women who experienced emotional abuse, physical violence, functional difficulty, sexual abuse, and food insecurity. Previous studies on depression and anxiety among women of reproductive age identified more general risk factors, including occupational risk factors (e.g., job strain), health risk factors (e.g., obesity) and past traumatic events (e.g., child maltreatment). 15 , 16 Due to strong patriarchal norms and values in the Nepalese communities, women’s access to financial and educational opportunities is limited compared to men. Recognizing the influence of such contextual factors, our study also took into account, additional risk factors, such as traditional menstrual practices, functional difficulty, women’s involvement in household decision making, and violence against women, for analysis. 5 , 17 The PAF estimates presented in this study offer new insights into anxiety and depression that can be prevented by addressing the modifiable risk factors. Globally, national governments spend an average of 2% of their total healthcare budgets on mental health. However, most LMICs, given resource limitations, prioritize a substantial portion of their budgets on treating only severe mental health conditions. 1 In Nepal, between 2008 and 2020, both the total annual health budget (from 6.5% in 2008 to 6.1% in 2020) and the annual budget allocated for mental health services as a proportion of the total healthcare budget (from 0.8% in 2008 to 0.2% in 2020) decreased. 18 LMICs could potentially achieve cost savings in treating mental health patients if they also prioritize their resources on the prevention of mental health disorders. 19 However, within LMICs in Asia, the value of mental health prevention and promotion has been recognized only in selected countries, such as Thailand. 9 , 20 The link between violence against women (physical, sexual, and emotional abuse) and mental disorders, as indicated by our cross-sectional study, highlights a critical area for prevention that to date has been neglected. 6 Micro-enterprise schemes for women to increase household income, along with education opportunities for girls, are critical for economic independence and for developing the ability to challenge the traditional gender norms in day-to-day life. 21 , 22 Additionally, the value of community women’s groups, and health and social resources (e.g., female community health volunteers) within local communities are key existing assets for women’s mental health resilience in Nepal which should be promoted. 20 Women with prior traumatic experience should have access to mental health screening and treatment. 23 However, community-based mental health services, rural mental health services and trauma informed care are all in an infancy stage of development in Nepal, indicating a substantial mental health service gap throughout the country. 24 This gap is further exacerbated by the lack of trained mental health professionals, insufficient mental health facilities, lack of training on mental health screening at primary care level, and high out-of-pocket expenditure associated with accessing psychiatric services. 25 Without the political will of the government, it is hard to overcome these challenges. Violence against women has long-term, intergenerational effect on children’s mental health and children who experience or witness violence in their families are more likely to perpetuate similar behaviours as adults. 26 , 27 Moreover, individuals who experience adverse events during childhood are more likely to become smokers and heavy or problematic drinkers, be diagnosed with chronic diseases, have mental illness, be victims or perpetrators of violence, and attempt suicide in adulthood. 28 Interventions to break intergenerational cycle of violence against women and children are crucial and couple-based approaches are particularly relevant. Strengths and limitations of this study The main strength of this study is that it relies on a nationally representative data encompassing 7,410 women of reproductive age to investigate the PAFs for key risk factors of generalized anxiety disorder and major depressive disorder. The present study also had some limitations. It is limited in its ability to establish a causal association between the risk factors and the mental health outcomes examined due to the cross-sectional design of the data. Although the NDHS employs various strategies to mitigate possible recall bias, the second limitation of our study is the possibility of recall bias inherent in the data collected, especially for the events or behaviours that occurred a long time ago or are sensitive in nature. Finally, the PAF calculations assume causation, independence of modifiable risk factors, and consistent relationships throughout time. 29 However, generalizations about women's mental health may be unrealistic due to complex socio-economic, cultural, healthcare, and behavioural issues. Despite this complexity, PAFs provide a simple straightforward and intuitive metric to identify modifiable risk factors for policy intervention, complementing other methodological approaches. Conclusions A total of 53% of major depressive disorder and 36% of generalized anxiety disorder is attributable to experiences of physical, sexual, and emotional abuse, household food insecurity and functional difficulty. Experiences of abuse and food insecurity extend beyond individual mental health outcomes and encompass broader societal implications. Unmanaged mental health conditions can impede economic productivity, and strain healthcare systems, perpetuating cycles of poverty and inequality. It is crucial to develop and implement targeted gender and economic empowerment interventions that aim to prevent and address violence against women within families and communities. Couple-based approaches or interventions involving men are particularly relevant in breaking intergenerational cycles of violence and fostering environments conducive to the mental health and well-being of women, children, and families. The political will of the government is essential to overcoming the gaps in mental health services. Abbreviations CI Confidence Interval GAD Generalised Anxiety Disorder MHM Mental Health Module NDHS Nepal Demographic and Health Survey OR Odds Ratio PAF Population Attributable Fraction PCA Principal Component Analysis PHQ Patient Health Questionnaire Declarations Authors’ Contribution SG: Conceptualization, Methodology, Investigation, Software, Formal analysis, Writing - Original Draft; NR: Writing- Reviewing and Editing; RK: Writing- Reviewing and Editing; AGR: Investigation, Writing- Reviewing and Editing; KYA: Methodology, Software, Validation, Formal analysis, Writing- Reviewing and Editing; PA: Investigation, Writing- Reviewing and Editing; MMH: Writing- Reviewing and Editing; AEA: Writing- Reviewing and Editing; FHA: Writing- Reviewing and Editing; SM: Writing- Reviewing and Editing; ST: Conceptualization, Methodology, Validation, Formal analysis, Writing- Reviewing and Editing, Supervision Competing Interests: None reported Funding The authors did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for this work. Ethics approval The NDHS received ethical approval from the Ethical Review Board of Nepal Health Research Council (Reference number: 678, Date: Sep 30, 2021) and the institutional review board of ICF International (Reference number: 180657.0.001.NP.DHS.01, Date: April 28, 2022). Before each interview was conducted, an informed consent statement was read to the respondent to decide their participation in the survey. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Patient consent for publication Not applicable Data availability statement The datasets supporting the conclusions of this article are available in the DHS repository, (https://dhsprogram.com/data/available-datasets.cfm). The DHS provides open access to survey data files for legitimate academic research purposes. To initiate the download process, registration is mandatory. Researchers are required to provide their contact information, research title, and a brief description of the proposed analysis. Approval for dataset access is typically confirmed via email. It is important to note that these datasets are third-party resources and not under the ownership or collection of the authors, who possess no special access privileges. Acknowledgements The authors are grateful to Measure Demographic Health Survey, ICF International, Rockville, MD, USA, for providing the data for analysis. References World Health Organization, R. O. f. S.-E. A. World mental health report: transforming mental health for all (2022) Institute for Health Metrics and Evaluation (IHME) (2020) GBD Compare Data Visualization. Global Burden of Disease (GBD) Study 2019 , http://vizhub.healthdata.org/gbd-compare Dhungana RR et al (2023) The burden of mental disorders in Nepal between 1990 and 2019: Findings from the Global Burden of Disease Study 2019. Glob Ment Health (Camb) 10:e61. https://doi.org/10.1017/gmh.2023.55 NHRC (2021) Report of National Mental Health Survey 2020 , https://nhrc.gov.np/wp-content/uploads/2022/10/National-Mental-Health-Survey-Report2020.pdf > Sapkota BD, Simkhada P, Newton D, Parker S (2024) Domestic Violence Against Women in Nepal: A Systematic Review of Risk Factors. Trauma Violence Abuse 15248380231222230. https://doi.org/10.1177/15248380231222230 Oram S, Khalifeh H, Howard LM (2017) Violence against women and mental health. Lancet Psychiatry 4:159–170. https://doi.org/10.1016/S2215-0366(16)30261-9 Lemon CA et al (2024) Priorities for research promoting mental health in the south and east of Asia. Lancet Reg Health - Southeast Asia 23. https://doi.org/10.1016/j.lansea.2023.100287 Patel V et al (2018) The Lancet Commission on global mental health and sustainable development. Lancet 392:1553–1598. https://doi.org/10.1016/S0140-6736(18)31612-X Lund C et al (2018) Social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews. Lancet Psychiatry 5:357–369. https://doi.org/10.1016/s2215-0366(18)30060-9 Ministry of Health and Population (Nepal), N. E., ICF (2023) Nepal Demographic and Health Survey 2022. Ministry of Health and Population [Nepal], Kathmandu, Nepal Lumley T (2024) survey: analysis of complex survey samples. , https://cran.r-project.org/web//packages/survey/survey.pdf Khosravi A, Nazemipour M, Shinozaki T, Mansournia MA (2021) Population attributable fraction in textbooks: Time to revise. Glob Epidemiol 3:100062. https://doi.org/10.1016/j.gloepi.2021.100062 Lee M et al (2022) Variation in Population Attributable Fraction of Dementia Associated With Potentially Modifiable Risk Factors by Race and Ethnicity in the US. JAMA Netw Open 5:e2219672. https://doi.org/10.1001/jamanetworkopen.2022.19672 R Core Team (2023) R: A Language and Environment for Statistical Computing , https://www.R-project.org/ Dragioti E et al (2022) Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review. Mol Psychiatry 27:3510–3519. https://doi.org/10.1038/s41380-022-01586-8 Li M, D'Arcy C, Meng X (2016) Maltreatment in childhood substantially increases the risk of adult depression and anxiety in prospective cohort studies: systematic review, meta-analysis, and proportional attributable fractions. Psychol Med 46:717–730. https://doi.org/10.1017/s0033291715002743 Thapa S, Bhattarai S, Aro AR (2019) Menstrual blood is bad and should be cleaned': A qualitative case study on traditional menstrual practices and contextual factors in the rural communities of far-western Nepal. SAGE Open Med 7:2050312119850400. https://doi.org/10.1177/2050312119850400 Rai Y, Gurung D, Gautam K (2021) Insight and challenges: mental health services in Nepal. BJPsych Int 18:E5. https://doi.org/10.1192/bji.2020.58 Chisholm D et al (2016) Scaling-up treatment of depression and anxiety: a global return on investment analysis. Lancet Psychiatry 3:415–424. https://doi.org/10.1016/S2215-0366(16)30024-4 Mathias K et al (2024) Inverting the deficit model in global mental health: An examination of strengths and assets of community mental health care in Ghana, India, Occupied Palestinian territories, and South Africa. PLOS Glob Public Health 4:e0002575. https://doi.org/10.1371/journal.pgph.0002575 Doustmohammadian A et al (2022) Community-based participatory interventions to improve food security: A systematic review. Front Nutr 9:1028394. https://doi.org/10.3389/fnut.2022.1028394 Shawon MSR et al (2024) Role of women empowerment on mental health problems and care-seeking behavior among married women in Nepal: secondary analysis of nationally representative data. Arch Women Ment Health. https://doi.org/10.1007/s00737-024-01433-5 Oram S et al (2022) The Lancet Psychiatry Commission on intimate partner violence and mental health: advancing mental health services, research, and policy. Lancet Psychiatry 9:487–524. https://doi.org/10.1016/S2215-0366(22)00008-6 WHO Team (2022) Situational Assessment - WHO Special Initiative for Mental Health. World Health Organization (WHO) Singh R, Khadka S (2022) Mental health law in Nepal. BJPsych Int 19:24–26. https://doi.org/10.1192/bji.2021.52 Gartland D, Giallo R, Woolhouse H, Mensah F, Brown SJ (2019) Intergenerational Impacts of Family Violence - Mothers and Children in a Large Prospective Pregnancy Cohort Study. EClinicalMedicine 15, 51–61 https://doi.org/10.1016/j.eclinm.2019.08.008 Meyer S, Reeves E, Fitz-Gibbon K (2021) The intergenerational transmission of family violence: Mothers' perceptions of children's experiences and use of violence in the home. Child Fam Soc Work 26:476–484. https://doi.org/10.1111/cfs.12830 Hughes K et al (2017) The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet Public Health 2:e356–e366. https://doi.org/10.1016/S2468-2667(17)30118-4 Eide GE (2008) Attributable fractions for partitioning risk and evaluating disease prevention: a practical guide. Clin Respir J 2(1):92–103. https://doi.org/10.1111/j.1752-699X.2008.00091.x Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5830806","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402260456,"identity":"a6444e6b-2b96-42aa-abf9-313d7291a4fc","order_by":0,"name":"Santosh Giri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYFACxgaGhAoIU4IELWdI0wLS1UaKFvn+xW0fHs6zszc4wHzwNg+DXWIDIS0GNx42z0jclpy44QBbsjUPQzIRWiQONjMkbjuQYHCAx0yah4GZsBb5GSAtcw4AHcb/DailnrAWhvONQC0NBxg3HOBhA2o5TIxfGJsZEo4lJ848zGZsOcfguDFhh/Uff8z4o8bOnu9488MbbyqqZQk7TCIBymAGW0pQPRDwHyBG1SgYBaNgFIxoAAAWHDumleXL9AAAAABJRU5ErkJggg==","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":true,"prefix":"","firstName":"Santosh","middleName":"","lastName":"Giri","suffix":""},{"id":402260457,"identity":"de86664c-f5b8-4f65-a602-2c8af89932a6","order_by":1,"name":"Nancy Ross","email":"","orcid":"","institution":"School of Social Work, Dalhousie University, K’jipuktuk Halifax, Nova Scotia, Canada","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"","lastName":"Ross","suffix":""},{"id":402260458,"identity":"059d3fb5-a406-4a1b-b9a2-2aab82c1d512","order_by":2,"name":"Rachel Kornhaber","email":"","orcid":"","institution":"School of Nursing, Paramedicine and and Healthcare Sciences, Charles Sturt University, Bathurst, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Kornhaber","suffix":""},{"id":402260459,"identity":"82f53d2d-7981-474d-b475-5530a4711bd4","order_by":3,"name":"Allen G. Ross","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Allen","middleName":"G.","lastName":"Ross","suffix":""},{"id":402260460,"identity":"76fc7023-78c1-4daf-bdeb-50400c1291e7","order_by":4,"name":"Kedir Y Ahmed","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Kedir","middleName":"Y","lastName":"Ahmed","suffix":""},{"id":402260461,"identity":"8efa9650-cc68-44d8-b055-8f0b8a50e7d9","order_by":5,"name":"Pushpanjali Adhikari","email":"","orcid":"","institution":"Department of Community Programs, Dhulikhel Hospital, Dhulikhel, Kavrepalanchowk, Nepal","correspondingAuthor":false,"prefix":"","firstName":"Pushpanjali","middleName":"","lastName":"Adhikari","suffix":""},{"id":402260462,"identity":"4a3ee2ad-843b-41b7-8a9c-939d0bf6af17","order_by":6,"name":"M. Mamum Huda","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Mamum","lastName":"Huda","suffix":""},{"id":402260463,"identity":"4b70d020-f259-46c6-9977-35b19527acba","order_by":7,"name":"Anayochukwu E. Anyasodor","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Anayochukwu","middleName":"E.","lastName":"Anyasodor","suffix":""},{"id":402260464,"identity":"34c91760-70c1-40fb-9922-3c3783c80457","order_by":8,"name":"Feleke H. Astawesegn","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Feleke","middleName":"H.","lastName":"Astawesegn","suffix":""},{"id":402260465,"identity":"2ce4dc5a-eacd-4dd8-970f-a50e9a52cc98","order_by":9,"name":"Shakeel Mahmood","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Shakeel","middleName":"","lastName":"Mahmood","suffix":""},{"id":402260466,"identity":"4603b3ed-0d57-4e51-9df8-a101f43e59a2","order_by":10,"name":"Subash Thapa","email":"","orcid":"","institution":"Rural Health Research Institute (RHRI), Charles Sturt University, Orange, NSW, Australia","correspondingAuthor":false,"prefix":"","firstName":"Subash","middleName":"","lastName":"Thapa","suffix":""}],"badges":[],"createdAt":"2025-01-15 03:14:54","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5830806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5830806/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74073239,"identity":"910c93fa-f239-4177-9634-98c7f12f2879","added_by":"auto","created_at":"2025-01-17 13:17:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39665,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation Attributable Fractions (PAFs) for major depressive disorder and generalized anxiety disorder among women of reproductive age (15-49 years) in Nepal\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5830806/v1/1e426916198b44852c89a591.png"},{"id":74075101,"identity":"ca72419e-9675-4f5d-84a0-b4b6c3ffaa16","added_by":"auto","created_at":"2025-01-17 13:33:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1449298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5830806/v1/82356464-96ba-4eab-8457-e63566bb38cb.pdf"},{"id":74073240,"identity":"64a236e1-eb32-4fce-93d8-5f7006727d60","added_by":"auto","created_at":"2025-01-17 13:17:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39908,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5830806/v1/63d0333b57c69d11363505b5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePopulation Modifiable Risk Factors for Depression and Anxiety among Reproductive-aged Women in Nepal: an analysis from 2022 Nepal demographic health survey data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2019, approximately 970\u0026nbsp;million people worldwide experienced mental disorders, with anxiety and depression as the most common cause.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Mental disorders are highly prevalent across the globe, but an estimated 80% of people with mental disorders reside in low- and middle-income countries (LMICs).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In South Asia, between 1990 and 2019, the incidence of depression increased from 2.8\u0026nbsp;million to 5.2\u0026nbsp;million (increased by 85%), and anxiety disorders increased from 24.1\u0026nbsp;million to 42.6\u0026nbsp;million (increased by 77%).\u003csup\u003e2\u003c/sup\u003e This burden not only causes lower quality of life among the affected individuals, but it also has social and economic consequences for families, communities, and countries.\u003c/p\u003e \u003cp\u003eAmong South Asian countries, Nepal is facing an increasing burden of mental disorders among various age groups, including reproductive age women.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Nepal's first ever National Mental Health Survey conducted in 2019\u0026ndash;20 reported the prevalence of any mental disorder (e.g., major depressive disorders, generalized anxiety disorders, post-traumatic stress disorders, alcohol and substance use disorder) among individuals of 18 years and above was 5.1% among women while it was only 3.4% for men.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The observed higher burden of anxiety and depression among reproductive age women is perhaps attributed to patriarchal norms, women\u0026rsquo;s socio-economic and educational disadvantages, limited support services, normalization of violence against women, and limited access to legal protections for women.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Underlying social-contextual disadvantages and living with mental disorders are further associated with poor lifestyles, increased rates of chronic health conditions, suicidal tendencies, and premature deaths among reproductive age women.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAccessing mental health care is a significant challenge in Nepal, particularly for individuals from rural areas and low-income communities, including women, the elderly, and children.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e These barriers to access ranges from an individual level encompassing an inadequate knowledge about accessing healthcare, cost of medical treatments, to structural levels including poor institutional capacity to provide mental health services.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e As suggested by the Lancet Commission on global mental health and sustainable development, LMICs including Nepal should make a shift from its focus on the \u0026lsquo;treatment gap\u0026rsquo; to mental health as a \u0026lsquo;public good\u0026rsquo;.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProgress in promoting mental health, and preventing mental disorders is unlikely to occur if efforts are not concentrated on addressing the social determinants of mental health.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Population Attributable Fractions (PAFs) estimates for depression and anxiety offer crucial insights for prioritizing interventions, allocating resources effectively, and designing tailored prevention strategies. Therefore, this study aimed to calculate the PAFs of major depressive disorder and generalized anxiety disorder, attributable to modifiable risk factors among reproductive age women in Nepal.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data sources\u003c/h2\u003e \u003cp\u003eThis cross-sectional study analysed data from Nepal Demographic Health Survey (NDHS) conducted in 2022 \u003csup\u003e10\u003c/sup\u003e. The survey of 2022 was the eighth demographic health survey conducted between January 5 and June 22, 2022. The NDHS gathers data on the demographics and health of individuals, encompassing topics such as maternal and child health, mortality, nutrition, and the social determinants of health. A standardized Mental Health Module (MHM) was added to the survey questionnaire, for the first time, providing the opportunity to conduct this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling procedures and sample size\u003c/h3\u003e\n\u003cp\u003eNDHS uses a two-stage cluster sampling technique to select the study participants. In the first stage, seven provinces were stratified into 15 sampling strata, with 476 primary sample units chosen. A total of 14,280 households were interviewed, with 14,845 women and 4,913 men. The mental health module was completed by 12,323 individuals out of which 7,410 were women of reproductive age.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eThe outcome variables were major depressive disorder and generalized anxiety disorder. Major depressive disorder was measured using Patient Health Questionnaire 9 (PHQ-9) (sensitivity\u0026thinsp;=\u0026thinsp;88%, specificity\u0026thinsp;=\u0026thinsp;88%) and generalized anxiety disorder was measured using Generalized Anxiety Disorder 7 (GAD-7) (sensitivity\u0026thinsp;=\u0026thinsp;92%, specificity\u0026thinsp;=\u0026thinsp;76%).\u003csup\u003e10\u003c/sup\u003e Based on the cutoff measure used by NDHS 2022, PHQ-9 scores of 10 or more was defined as having depression and GAD-7 scores of 6 or more was defined as having anxiety symptoms.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eModifiable risk factors\u003c/h3\u003e\n\u003cp\u003eThe modifiable risk factors included: socio-economic factors, health risk behaviours, and gender-related factors. The socio-economic factors included: household wealth index (\u0026lsquo;poorest\u0026rsquo;, \u0026lsquo;poorer\u0026rsquo;, \u0026lsquo;middle\u0026rsquo;, \u0026lsquo;richer\u0026rsquo;, \u0026lsquo;richest\u0026rsquo;), highest education attainment (\u0026lsquo;never attended school or primary education\u0026rsquo;, \u0026lsquo;secondary or higher\u0026rsquo;), employment (\u0026lsquo;not employed\u0026rsquo;, \u0026lsquo;employed\u0026rsquo;), functional difficulty (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;), and food insecurity (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;). The health risk behaviours included current smoking status (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;) and alcohol consumption in the last month (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;). The gender-related factors included: traditional separation practices during menstrual period (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;), justification of wife beating (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;), sexual negotiation (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;), involvement in decision-making (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;), experience of physical violence since the age of 15 (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;), ever experienced sexual abuse (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;) and emotional abuse by the partner (\u0026lsquo;no\u0026rsquo;, \u0026lsquo;yes\u0026rsquo;). For this study potential covariates including age, marital status (grouped as married or unmarried) and province we considered.\u003c/p\u003e \u003cp\u003eThe household wealth index is a composite measure of relative economic status estimated using household-level information on asset ownership and access to services from individual questionnaires. In NDHS, the household wealth index was computed using principal component analysis (PCA), considering various aspects like the possession of household amenities such as toilets, electricity, television, radio, fridge, and bicycle, as well as the availability of a source of drinking water and the type of flooring material used in the main house. Food insecurity is measured as moderate or severe food insecurity. Moderate food insecurity referred to having to reduce the quality and/or quantity of food and having uncertainty about the ability to obtain food due to lack of money or other resources during the last 12 months. Severe food insecurity referred to running out of food and, at the most extreme, going a day (or days) without eating during the last 12 months.\u003c/p\u003e \u003cp\u003eFunctional difficulty is defined based on respondents' self-reported limitations in performing basic activities of daily living due to physical, mental, cognitive, or sensory impairment. Sexual negotiation refers to women's ability to refuse sex and negotiate safer sex practices within their relationships. The justification of domestic violence against wives, or wife beating, was derived from certain circumstances when women normalize assault from their husband for activities, such as burning food, arguing with their husband, going out without informing them, neglecting children, or refusing sex. The separation practice during menstrual period was measured by asking women about whether they were excluded, or not allowed, to participate in various social activities outside and at home during menstrual period.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used weighted analysis accounting for the complex survey design using the \u0026lsquo;survey\u0026rsquo; package \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and presented the categorical variables as frequency and percentage (%) with 95% confidence interval (CI) while numerical variables as mean with 95% CI. Multilevel logistic regression model with clusters as random intercepts were used to separately calculate the Odds Ratio (OR) and 95% CI for depression and anxiety disorders. The reference group was those individuals who had GAD-7 scores less than 6 for anxiety and PHQ-9 scores less than 10 for depression.\u003c/p\u003e \u003cp\u003eFor the individual risk factors significant in the multilevel regression model of potentially modifiable risk factors for anxiety and depression outcomes, we calculated the unadjusted PAF using Miettinen\u0026rsquo;s formula\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e: PAF\u003csub\u003ee\u003c/sub\u003e = P\u003csub\u003ee\u003c/sub\u003e(OR\u003csub\u003ee\u003c/sub\u003e \u0026ndash; 1)/OR\u003csub\u003ee\u003c/sub\u003e, where P\u003csub\u003ee\u003c/sub\u003e is the prevalence of the risk factor \u003cem\u003ee\u003c/em\u003e and OR\u003csub\u003ee\u003c/sub\u003e is the adjusted odds ratio of the anxiety or depression associated with the risk factor \u003cem\u003ee\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe prevalence of specific risk factors was calculated using the weighted survey analysis for the reduced sample size in this study. Communality weights were computed to account for the overlap of risk factors across individuals, as adding up the PAFs for each risk factor would lead to an overestimation of their collective impact on anxiety or depression.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Using the method outlined by Lee et al., the communality calculation involved performing a pairwise tetrachoric correlation analysis on the risk factors. This was followed by a principal component analysis (PCA) of the tetrachoric correlation matrix. For each risk factor, we calculated the sum of the squares of the loadings in all principal components with an eigenvector greater than 1. The weighting of each risk factor was determined using the formula \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e: W\u003csub\u003ee\u003c/sub\u003e = 1 \u0026ndash; communality\u003csub\u003ee\u003c/sub\u003e. The combined PAF was then calculated using \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e: PAF\u0026thinsp;=\u0026thinsp;1 \u0026ndash; [(1 \u0026ndash; W\u003csub\u003e1\u003c/sub\u003e*PAF\u003csub\u003e1\u003c/sub\u003e) * (1 \u0026ndash; W\u003csub\u003e2\u003c/sub\u003e*PAF\u003csub\u003e2\u003c/sub\u003e) * (1 \u0026ndash; W\u003csub\u003e3\u003c/sub\u003e*PAF\u003csub\u003e3\u003c/sub\u003e)\u0026hellip;].\u003c/p\u003e \u003cp\u003eFinally, the adjusted PAF for each risk factors were calculated using the formula\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e: PAF\u003csub\u003ee\u003c/sub\u003e = ([PAF\u003csub\u003ee\u003c/sub\u003e/\u0026sum;PAF\u003csub\u003ee\u003c/sub\u003e] * combined PAF). All analyses were performed in R version 4.3.2.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e All methods were conducted in compliance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThe study sample comprised of a weighted sample of 7,410 women (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with an average age of 30 (\u0026plusmn;\u0026thinsp;10) years. A total of 68.3% (5,064) of women resided in urban areas, and 26.2% (1,944) had no education. Only 4.3% (319) of women smoked and 23.5% (1,739) consumed alcohol in the last month. A total of 21.3% (1,581) had some form of functional difficulty and 7.8% (573) had moderate-to-severe food insecurity in their households.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7,410)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMajor Depressive Disorder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGeneralized Anxiety Disorder\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7,008)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5,785)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1,626)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBagmati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,493 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,428 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,209 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e284 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKoshi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,241 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,161 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e939 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e302 (24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMadhesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,512 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,436 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,179 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e333 (22.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGandaki\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e704 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e676 (96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e579 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e125 (17.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumbini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,360 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,293 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,062 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e298 (21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarnali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e458 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e415 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e331 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e127 (27.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSudurpaschim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e641 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e598 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e486 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e155 (24.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,064 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,812 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e252 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,965 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,099 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,347 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,196 (93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,820 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e527 (22.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold wealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,344 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,257 (93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,051 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e293 (21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,372 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,285 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,021 (74.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e350 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,512 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,415 (93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,139 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e373 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,704 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,621 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,341 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e363 (21.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,479 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,429 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,233 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e247 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,944 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,821 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,454 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e489 (25.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,256 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,109 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,719 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e537 (23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,931 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,802 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,369 (80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e562 (19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e243 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37 (13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,893 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,796 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,514 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e380 (20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,517 (74.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,211 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e306 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,271 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,246 (22.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,033 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,945 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,629 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e405 (19.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,377 (72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,063 (94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e315 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,156 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,221 (22.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,830 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,563 (95.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e266 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,711 (80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,119 (19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,581 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,444 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,074 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e507 (32.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood insecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,821 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,500 (95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e321 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,408 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,413 (20.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e573 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e493 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e364 (63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e209 (36.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,091 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,715 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e376 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,559 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,532 (21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e292 (91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93 (29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption in the last month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,672 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,378 (94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e293 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,473 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,198 (21.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,739 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,629 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,312 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e427 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional separation practices during menstrual period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e903 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e855 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e690 (76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e213 (23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,507 (87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,152 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e355 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,095 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,412 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWife beating justified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,040 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,720 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e320 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,719 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,321 (21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,371 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,287 (93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,066 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e304 (22.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual negotiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,279 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,145 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,778 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e501 (22.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,131 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,862 (94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e269 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,007 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,124 (21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold decision-making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,909 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,732 (93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,230 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e679 (23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,624 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,494 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,055 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e569 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced physical violence since age 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,920 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,792 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,237 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e683 (17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,227 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,074 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e778 (63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e448 (36.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver experienced sexual abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,741 (92.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,540 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,815 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e925 (19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e406 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e326 (80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206 (50.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional violence by the partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,729 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,598 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,040 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e689 (18.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e592 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e473 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e296 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e296 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 87.8% (6,507) had practiced traditional separation practices during menstrual at any point of their lives. Eighteen percent (18.5%; 1,371) of women justified wife beating, and 30.8% (2,279) were not able to negotiate sex with their spouse. Fifty-three percent (52.6%; 2,909) of women were not involved in making household decisions. A total of 23.8% (1,227) experienced physical violence after the age of 15, while 7.9% (406) reported being sexually abused in their life and 13.7% (592) were emotionally abused by their spouse. The prevalence of major depressive disorder and generalized anxiety disorder among reproductive aged women was 5.4% (95% CI: 4.9\u0026ndash;6.0) and 21.9% (95% CI: 20.9\u0026ndash;23.0) respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation attributable fractions for major depressive disorder\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the highest PAFs of depression were associated with women who experienced emotional abuse (PAF: 18.2%; 95% CI: 15.4\u0026ndash;20.2), physical violence (PAF: 12.1%; 95% CI: 5.1\u0026ndash;16.7), sexual abuse (PAF: 9.0%; 95% CI: 5.9\u0026ndash;11.5), functional difficulty (PAF: 6.9%; 95% CI: 2.8\u0026ndash;10.1) and food insecurity (PAF: 6.6%; 95% CI: 4.4\u0026ndash;8.4). These five potentially modifiable risk factors accounted for 52.8% (95% CI: 33.7\u0026ndash;67.0) of depression among women of reproductive age.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation Attributable Fractions for major depressive disorder and generalized anxiety disorder among reproductive age women years in Nepal (n\u0026thinsp;=\u0026thinsp;4,202).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMajor Depressive Disorder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eGeneralized Anxiety Disorder\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk factor prevalence\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnadjusted PAF\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted PAF\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRisk factor prevalence\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnadjusted PAF\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdjusted PAF\u003c/p\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.1 (60.9, 70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.8 (66.3, 71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.9 (29.1, 39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67 (1.2, 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7 (4.8, 22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9 (2.8, 10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.2 (28.7, 33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.86 (1.53, 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.5 (9.9, 18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.8 (5.7, 9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood insecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.9 (75.6, 83.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.1 (85.3, 88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.1 (16.3, 24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.84 (1.86, 4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (7.5, 18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.6 (4.4, 8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.9 (11.3, 14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.15 (1.61, 2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.9 (4.3, 9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7 (2.4, 4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperienced physical violence since age 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.6 (39.6, 51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.4 (57.1, 63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.4 (48.2, 60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78 (1.22, 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9 (8.7, 37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.1 (5.1, 16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.6 (36.5, 42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57 (1.27, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.4 (7.8, 20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.8 (4.4, 10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver experienced sexual abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.3 (65.4, 76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.8 (79.1, 84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.7 (23.4, 34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61 (1.76, 3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7 (10.1, 25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0 (5.9, 11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.2 (15.8, 20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.35 (1.76, 3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.5 (6.8, 14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.6 (3.9, 7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional abuse by the partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.4 (45.9, 58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.9 (66.6, 73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.6 (41.2, 54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06 (2.77, 5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9 (26.3, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.2 (15.4, 20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.1 (26.9, 33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.02 (2.33, 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.1 (15.4, 24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.8 (8.7, 12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCI: Confidence interval; PAF: population attributable fraction; OR: odds ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdjusted PAF is the relative contribution of each risk factor to the overall PAF when adjusted for communality.\u003c/p\u003e \u003cp\u003ePAF for the results significant in the full model were calculated and are presented here.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation attributable fractions for generalized anxiety disorder\u003c/h2\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the highest PAFs of anxiety were associated with women who experienced emotional abuse (PAF: 10.8%; 95% CI: 8.7\u0026ndash;12.7), physical violence (PAF: 7.8%; 95% CI: 4.4\u0026ndash;10.6), functional difficulty (PAF: 7.8%; 95% CI: 5.7\u0026ndash;9.6), sexual abuse (PAF: 5.6%; 95% CI: 3.9\u0026ndash;7.3), and food insecurity (PAF: 3.7%; 95% CI: 2.4\u0026ndash;4.9). The highest PAFs of five risk factors accounted for 35.7% (95% CI: 25.2\u0026ndash;45.1) of anxiety.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the adjusted PAFs for individual risk factors contributing to depression and anxiety, ranked by descending impact, with emotional abuse showing the highest PAF for both conditions. The result from the multilevel logistic regressions is also provided in Supplementary file (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to investigate the PAFs of major depressive disorder and generalized anxiety disorder among reproductive-age women in Nepal. The findings from our study revealed that 53% of major depressive disorder was associated with women who experienced emotional abuse, physical violence, sexual abuse, functional difficulty, and food insecurity. A total of 36% of generalized anxiety disorder was associated with women who experienced emotional abuse, physical violence, functional difficulty, sexual abuse, and food insecurity.\u003c/p\u003e \u003cp\u003ePrevious studies on depression and anxiety among women of reproductive age identified more general risk factors, including occupational risk factors (e.g., job strain), health risk factors (e.g., obesity) and past traumatic events (e.g., child maltreatment).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Due to strong patriarchal norms and values in the Nepalese communities, women\u0026rsquo;s access to financial and educational opportunities is limited compared to men. Recognizing the influence of such contextual factors, our study also took into account, additional risk factors, such as traditional menstrual practices, functional difficulty, women\u0026rsquo;s involvement in household decision making, and violence against women, for analysis.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The PAF estimates presented in this study offer new insights into anxiety and depression that can be prevented by addressing the modifiable risk factors.\u003c/p\u003e \u003cp\u003eGlobally, national governments spend an average of 2% of their total healthcare budgets on mental health. However, most LMICs, given resource limitations, prioritize a substantial portion of their budgets on treating only severe mental health conditions.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In Nepal, between 2008 and 2020, both the total annual health budget (from 6.5% in 2008 to 6.1% in 2020) and the annual budget allocated for mental health services as a proportion of the total healthcare budget (from 0.8% in 2008 to 0.2% in 2020) decreased.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e LMICs could potentially achieve cost savings in treating mental health patients if they also prioritize their resources on the prevention of mental health disorders.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, within LMICs in Asia, the value of mental health prevention and promotion has been recognized only in selected countries, such as Thailand.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe link between violence against women (physical, sexual, and emotional abuse) and mental disorders, as indicated by our cross-sectional study, highlights a critical area for prevention that to date has been neglected.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Micro-enterprise schemes for women to increase household income, along with education opportunities for girls, are critical for economic independence and for developing the ability to challenge the traditional gender norms in day-to-day life.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Additionally, the value of community women\u0026rsquo;s groups, and health and social resources (e.g., female community health volunteers) within local communities are key existing assets for women\u0026rsquo;s mental health resilience in Nepal which should be promoted.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWomen with prior traumatic experience should have access to mental health screening and treatment.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e However, community-based mental health services, rural mental health services and trauma informed care are all in an infancy stage of development in Nepal, indicating a substantial mental health service gap throughout the country.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e This gap is further exacerbated by the lack of trained mental health professionals, insufficient mental health facilities, lack of training on mental health screening at primary care level, and high out-of-pocket expenditure associated with accessing psychiatric services.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Without the political will of the government, it is hard to overcome these challenges.\u003c/p\u003e \u003cp\u003eViolence against women has long-term, intergenerational effect on children\u0026rsquo;s mental health and children who experience or witness violence in their families are more likely to perpetuate similar behaviours as adults.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Moreover, individuals who experience adverse events during childhood are more likely to become smokers and heavy or problematic drinkers, be diagnosed with chronic diseases, have mental illness, be victims or perpetrators of violence, and attempt suicide in adulthood.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Interventions to break intergenerational cycle of violence against women and children are crucial and couple-based approaches are particularly relevant.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations of this study\u003c/h2\u003e \u003cp\u003eThe main strength of this study is that it relies on a nationally representative data encompassing 7,410 women of reproductive age to investigate the PAFs for key risk factors of generalized anxiety disorder and major depressive disorder. The present study also had some limitations. It is limited in its ability to establish a causal association between the risk factors and the mental health outcomes examined due to the cross-sectional design of the data. Although the NDHS employs various strategies to mitigate possible recall bias, the second limitation of our study is the possibility of recall bias inherent in the data collected, especially for the events or behaviours that occurred a long time ago or are sensitive in nature. Finally, the PAF calculations assume causation, independence of modifiable risk factors, and consistent relationships throughout time.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e However, generalizations about women's mental health may be unrealistic due to complex socio-economic, cultural, healthcare, and behavioural issues. Despite this complexity, PAFs provide a simple straightforward and intuitive metric to identify modifiable risk factors for policy intervention, complementing other methodological approaches.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA total of 53% of major depressive disorder and 36% of generalized anxiety disorder is attributable to experiences of physical, sexual, and emotional abuse, household food insecurity and functional difficulty. Experiences of abuse and food insecurity extend beyond individual mental health outcomes and encompass broader societal implications. Unmanaged mental health conditions can impede economic productivity, and strain healthcare systems, perpetuating cycles of poverty and inequality. It is crucial to develop and implement targeted gender and economic empowerment interventions that aim to prevent and address violence against women within families and communities. Couple-based approaches or interventions involving men are particularly relevant in breaking intergenerational cycles of violence and fostering environments conducive to the mental health and well-being of women, children, and families. The political will of the government is essential to overcoming the gaps in mental health services.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eCI Confidence Interval\u003c/p\u003e \u003cp\u003eGAD Generalised Anxiety Disorder\u003c/p\u003e \u003cp\u003eMHM Mental Health Module\u003c/p\u003e \u003cp\u003eNDHS Nepal Demographic and Health Survey\u003c/p\u003e \u003cp\u003eOR Odds Ratio\u003c/p\u003e \u003cp\u003ePAF Population Attributable Fraction\u003c/p\u003e \u003cp\u003ePCA Principal Component Analysis\u003c/p\u003e \u003cp\u003ePHQ Patient Health Questionnaire\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSG: Conceptualization, Methodology, Investigation, Software, Formal analysis, Writing - Original Draft; NR: Writing- Reviewing and Editing; RK: Writing- Reviewing and Editing; AGR: Investigation, Writing- Reviewing and Editing; \u0026nbsp;KYA: Methodology, Software, Validation, Formal analysis, Writing- Reviewing and Editing; PA: Investigation, Writing- Reviewing and Editing; MMH: Writing- Reviewing and Editing; AEA: Writing- Reviewing and Editing; FHA: Writing- Reviewing and Editing; SM: Writing- Reviewing and Editing; ST: Conceptualization, Methodology, Validation, Formal analysis, Writing- Reviewing and Editing, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone reported\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors for this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NDHS received ethical approval from the Ethical Review Board of Nepal Health Research Council (Reference number: 678, Date: Sep 30, 2021) and the institutional review board of ICF International (Reference number: 180657.0.001.NP.DHS.01, Date: April 28, 2022). Before each interview was conducted, an informed consent statement was read to the respondent to decide their participation in the survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the DHS repository, (https://dhsprogram.com/data/available-datasets.cfm). The DHS provides open access to survey data files for legitimate academic research purposes. To initiate the download process, registration is mandatory. Researchers are required to provide their contact information, research title, and a brief description of the proposed analysis. Approval for dataset access is typically confirmed via email. It is important to note that these datasets are third-party resources and not under the ownership or collection of the authors, who possess no special access privileges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Measure Demographic Health Survey, ICF International, Rockville, MD, USA, for providing the data for analysis.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization, R. O. f. S.-E. A. World mental health report: transforming mental health for all (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstitute for Health Metrics and Evaluation (IHME) (2020) \u003cem\u003eGBD Compare Data Visualization. Global Burden of Disease (GBD) Study 2019\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vizhub.healthdata.org/gbd-compare\u003c/span\u003e\u003cspan address=\"http://vizhub.healthdata.org/gbd-compare\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhungana RR et al (2023) The burden of mental disorders in Nepal between 1990 and 2019: Findings from the Global Burden of Disease Study 2019. Glob Ment Health (Camb) 10:e61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/gmh.2023.55\u003c/span\u003e\u003cspan address=\"10.1017/gmh.2023.55\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHRC (2021) \u003cem\u003eReport of National Mental Health Survey 2020\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nhrc.gov.np/wp-content/uploads/2022/10/National-Mental-Health-Survey-Report2020.pdf\u003c/span\u003e\u003cspan address=\"https://nhrc.gov.np/wp-content/uploads/2022/10/National-Mental-Health-Survey-Report2020.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026amp;gt\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapkota BD, Simkhada P, Newton D, Parker S (2024) Domestic Violence Against Women in Nepal: A Systematic Review of Risk Factors. Trauma Violence Abuse 15248380231222230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/15248380231222230\u003c/span\u003e\u003cspan address=\"10.1177/15248380231222230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOram S, Khalifeh H, Howard LM (2017) Violence against women and mental health. Lancet Psychiatry 4:159\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(16)30261-9\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(16)30261-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemon CA et al (2024) Priorities for research promoting mental health in the south and east of Asia. Lancet Reg Health - Southeast Asia 23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lansea.2023.100287\u003c/span\u003e\u003cspan address=\"10.1016/j.lansea.2023.100287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel V et al (2018) The Lancet Commission on global mental health and sustainable development. Lancet 392:1553\u0026ndash;1598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(18)31612-X\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(18)31612-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLund C et al (2018) Social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews. Lancet Psychiatry 5:357\u0026ndash;369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2215-0366(18)30060-9\u003c/span\u003e\u003cspan address=\"10.1016/s2215-0366(18)30060-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health and Population (Nepal), N. E., ICF (2023) Nepal Demographic and Health Survey 2022. Ministry of Health and Population [Nepal], Kathmandu, Nepal\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumley T (2024) \u003cem\u003esurvey: analysis of complex survey samples.\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web//packages/survey/survey.pdf\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web//packages/survey/survey.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhosravi A, Nazemipour M, Shinozaki T, Mansournia MA (2021) Population attributable fraction in textbooks: Time to revise. Glob Epidemiol 3:100062. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloepi.2021.100062\u003c/span\u003e\u003cspan address=\"10.1016/j.gloepi.2021.100062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee M et al (2022) Variation in Population Attributable Fraction of Dementia Associated With Potentially Modifiable Risk Factors by Race and Ethnicity in the US. JAMA Netw Open 5:e2219672. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2022.19672\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2022.19672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2023) \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDragioti E et al (2022) Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review. Mol Psychiatry 27:3510\u0026ndash;3519. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41380-022-01586-8\u003c/span\u003e\u003cspan address=\"10.1038/s41380-022-01586-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, D'Arcy C, Meng X (2016) Maltreatment in childhood substantially increases the risk of adult depression and anxiety in prospective cohort studies: systematic review, meta-analysis, and proportional attributable fractions. Psychol Med 46:717\u0026ndash;730. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/s0033291715002743\u003c/span\u003e\u003cspan address=\"10.1017/s0033291715002743\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapa S, Bhattarai S, Aro AR (2019) Menstrual blood is bad and should be cleaned': A qualitative case study on traditional menstrual practices and contextual factors in the rural communities of far-western Nepal. SAGE Open Med 7:2050312119850400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2050312119850400\u003c/span\u003e\u003cspan address=\"10.1177/2050312119850400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRai Y, Gurung D, Gautam K (2021) Insight and challenges: mental health services in Nepal. BJPsych Int 18:E5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/bji.2020.58\u003c/span\u003e\u003cspan address=\"10.1192/bji.2020.58\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChisholm D et al (2016) Scaling-up treatment of depression and anxiety: a global return on investment analysis. Lancet Psychiatry 3:415\u0026ndash;424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(16)30024-4\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(16)30024-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathias K et al (2024) Inverting the deficit model in global mental health: An examination of strengths and assets of community mental health care in Ghana, India, Occupied Palestinian territories, and South Africa. PLOS Glob Public Health 4:e0002575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgph.0002575\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgph.0002575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoustmohammadian A et al (2022) Community-based participatory interventions to improve food security: A systematic review. Front Nutr 9:1028394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnut.2022.1028394\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2022.1028394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShawon MSR et al (2024) Role of women empowerment on mental health problems and care-seeking behavior among married women in Nepal: secondary analysis of nationally representative data. Arch Women Ment Health. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00737-024-01433-5\u003c/span\u003e\u003cspan address=\"10.1007/s00737-024-01433-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOram S et al (2022) The Lancet Psychiatry Commission on intimate partner violence and mental health: advancing mental health services, research, and policy. Lancet Psychiatry 9:487\u0026ndash;524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(22)00008-6\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(22)00008-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO Team (2022) Situational Assessment - WHO Special Initiative for Mental Health. World Health Organization (WHO)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh R, Khadka S (2022) Mental health law in Nepal. BJPsych Int 19:24\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/bji.2021.52\u003c/span\u003e\u003cspan address=\"10.1192/bji.2021.52\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGartland D, Giallo R, Woolhouse H, Mensah F, Brown SJ (2019) Intergenerational Impacts of Family Violence - Mothers and Children in a Large Prospective Pregnancy Cohort Study. \u003cem\u003eEClinicalMedicine\u003c/em\u003e 15, 51\u0026ndash;61 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eclinm.2019.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2019.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer S, Reeves E, Fitz-Gibbon K (2021) The intergenerational transmission of family violence: Mothers' perceptions of children's experiences and use of violence in the home. Child Fam Soc Work 26:476\u0026ndash;484. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cfs.12830\u003c/span\u003e\u003cspan address=\"10.1111/cfs.12830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes K et al (2017) The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet Public Health 2:e356\u0026ndash;e366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2468-2667(17)30118-4\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(17)30118-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEide GE (2008) Attributable fractions for partitioning risk and evaluating disease prevention: a practical guide. Clin Respir J 2(1):92\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1752-699X.2008.00091.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1752-699X.2008.00091.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Rural Health Research Institute, Charles Sturt University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generalized anxiety disorder, major depressive disorder, emotional abuse, sexual abuse, population attributable fraction, violence against women","lastPublishedDoi":"10.21203/rs.3.rs-5830806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5830806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIdentifying the critical modifiable risk factors for anxiety and depression is crucial for reducing the increasing burden of mental illness among reproductive-aged women 15\u0026ndash;49 years in Nepal. We investigated Population Attributable Fractions (PAFs) of generalized anxiety disorder and major depressive disorder attributable to potentially modifiable risk factors among reproductive-age women. This cross-sectional study analysed the data from the Nepal Demographic Health Survey in 2022. Multilevel logistic regression analyses determined odds ratio (ORs) for risk factors associated with depression and anxiety. PAFs adjusted for communality were calculated using adjusted ORs and prevalence estimates for each risk factor. This study included a weighted sample of 7,410 women, with a mean age of 30 (\u0026plusmn;\u0026thinsp;10) years. Highest PAFs of depression were associated with women who experienced emotional abuse (PAF: 18.2%; 95%CI: 15.4\u0026ndash;20.2), physical violence (PAF: 12.1%; 95%CI: 5.1\u0026ndash;16.7), and sexual abuse (PAF: 9.0%; 95%CI: 5.9\u0026ndash;11.5), functional difficulty (PAF: 6.9%; 95%CI: 2.8\u0026ndash;10.1) and food insecurity (PAF: 6.6%; 95%CI: 4.4\u0026ndash;8.4). These five potentially modifiable risk factors accounted for 52.8% (95%CI: 33.7\u0026ndash;67.0) of depression cases. Highest PAFs for anxiety were associated with women who experienced emotional abuse (PAF: 10.8%; 95%CI: 8.7\u0026ndash;12.7), functional impairment (PAF: 7.8%; 95%CI: 5.7\u0026ndash;9.6), physical violence (PAF: 7.8%; 95%CI: 4.4\u0026ndash;10.6), sexual abuse (PAF: 5.6%; 95%CI: 3.9\u0026ndash;7.3), and food insecurity (PAF: 3.7%; 95%CI: 2.4\u0026ndash;4.9). These five potentially modifiable risk factors accounted for 35.7% (95%CI: 25.2\u0026ndash;45.1) of anxiety cases. The results of this study highlight the necessity of targeted strategies at the community and household levels to address violence against women. Couple-based approaches involving men are particularly relevant to break the cycle of intergenerational violence and fostering environments conducive for better mental health.\u003c/p\u003e","manuscriptTitle":"Population Modifiable Risk Factors for Depression and Anxiety among Reproductive-aged Women in Nepal: an analysis from 2022 Nepal demographic health survey data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 13:17:09","doi":"10.21203/rs.3.rs-5830806/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00b2d082-9b69-4d88-ae86-ba313d21a3d9","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42873906,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-01-17T13:17:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 13:17:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5830806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5830806","identity":"rs-5830806","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}