Accelerating and disproportionate burden of depression attributable to intimate partner violence among women in Low and Low-middle SDI regions

Accelerating and disproportionate burden of depression attributable to intimate partner violence among women in Low and Low-middle SDI regions | 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 Accelerating and disproportionate burden of depression attributable to intimate partner violence among women in Low and Low-middle SDI regions Dan Shan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9724901/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Intimate partner violence (IPV) is a major, preventable threat to women’s health and a key contributor to depression. The COVID-19 pandemic may have increased IPV exposure while disrupting support services. We examined long-term trends, inequalities, pandemic-related changes, and future burden of IPV-attributable depression across SDI regions. Methods We conducted a population-level analysis using GBD 2023 data for females aged ≥ 15 years across five SDI regions from 1990 to 2023. We assessed trends, age patterns, and decomposed recent changes into epidemiological, demographic, and ageing components. Socioeconomic inequalities were quantified using slope and concentration indices. Associations between COVID-19 incidence and IPV-attributable depression burden were analysed in lower SDI regions. Future trends to 2050 were projected using Bayesian age–period–cohort models. Results The burden of depression attributable to IPV increased across all SDI regions, with a faster rise after the late 2010s. The highest burden remained concentrated in low and low-middle SDI regions. Peak burden occurred in early to mid-adulthood, with earlier peaks in lower SDI settings. Inequalities persisted and widened over time. Recent increases were driven mainly by epidemiological change rather than demographic factors. Higher COVID-19 incidence was associated with increased burden in low SDI countries but not in low-middle SDI countries. Projections suggest the burden is likely to remain above pre-pandemic levels. Conclusions IPV-attributable depression among women continues to rise, with sustained inequalities across development levels. Targeted, integrated IPV prevention and mental health strategies are needed, particularly in lower SDI regions and among women in early and mid-adulthood. Psychiatry Intimate partner violence Depression Global Burden of Disease Socioeconomic inequality COVID-19 pandemic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What is already known on this topic Intimate partner violence (IPV) is a major cause of poor mental health among women and is strongly linked to depression. Previous studies have shown that the burden of IPV-related depression may be higher in low-resource settings, but evidence on how this burden changed during and after the COVID-19 pandemic has remained limited. Long-term projections and formal assessments of socioeconomic inequality have also been lacking. What this study adds Using Global Burden of Disease 2023 data, we found that the burden of depression attributable to IPV among women increased across all development levels from 1990 to 2023, with a marked acceleration after the late 2010s and especially during the COVID-19 period. The highest and fastest-rising burden was concentrated in Low and Low-middle SDI regions, particularly among women in early and mid-adulthood. Future projections suggest that the burden is likely to remain above pre-pandemic levels through 2050. How this study might affect research, practice or policy These findings highlight the need for integrated IPV prevention and mental health strategies, especially in lower-resource settings where inequalities are greatest. Strengthening access to mental health care, survivor support services, and crisis-resilient health systems may help reduce the long-term impact of IPV-related depression among women. Introduction Intimate partner violence (IPV) remains one of the most pervasive and preventable violations of women’s health and human rights worldwide ( 1 – 3 ). Exposure to physical or sexual violence by an intimate partner has been consistently linked to a wide range of adverse health outcomes, including physical injury, reproductive complications, chronic disease, and, most prominently, mental health disorders ( 4 , 5 ). Recent estimates from the Global Burden of Disease (GBD) Study 2023 position IPV among the leading risk factors for disability-adjusted life years (DALYs) among females aged 15 to 49 years globally, with depressive and anxiety disorders accounting for the largest share of the attributable burden ( 6 ). These findings underscore that the consequences of IPV extend far beyond immediate harm, contributing substantially to long-term mental health loss across the life course ( 3 ). The Coronavirus Disease 2019 (COVID-19) pandemic constituted an unprecedented global social and public health shock that further intensified both the occurrence of IPV and its psychological sequelae ( 7 , 8 ). Pandemic-related containment measures, including home confinement, mobility restrictions, economic disruption, and interruptions in social and protective services, created conditions that increased women’s exposure to abusive partners while simultaneously limiting access to external support ( 8 , 9 ). A growing body of evidence indicates that the pandemic period was accompanied by a marked escalation in violence against women, alongside a parallel deterioration in population mental health ( 10 , 11 ). Within this context, depression attributable to IPV has emerged as a critical yet underexamined component of the broader mental health consequences of the pandemic ( 3 ). The burden of IPV-related depression is not distributed evenly across populations and locations ( 12 ). Low- and middle-income countries (LMICs) bear a disproportionate share, reflecting structural vulnerabilities such as limited mental health service capacity, weak surveillance systems, and persistent social stigma surrounding both violence and mental illness ( 12 , 13 ). Data from the World Health Organization (WHO) World Mental Health Surveys indicate that treatment coverage for common mental disorders in low income settings remains substantially lower than in high income countries ( 13 ), resulting in widespread underdiagnosis and unmet need ( 12 , 13 ). These disparities are further shaped by broader socioeconomic gradients, suggesting that the mental health consequences of IPV are closely intertwined with social and developmental inequality ( 12 ). Within the GBD framework, socioeconomic development is commonly operationalised using the Socio-demographic Index (SDI). However, systematic quantification of these inequalities across SDI strata remains limited ( 14 ). Previous studies drawing on GBD 2019 and 2021 estimates have described long-term trends in depression attributable to IPV and highlighted substantial geographical variation ( 14 , 15 ). Nonetheless, several critical gaps persist. First, earlier analyses largely precede or only partially capture the COVID-19 period and therefore cannot quantify the full impact of the pandemic between 2020 and 2023 ( 15 ). Notably, the 2021 GBD-based analysis by Liu et al. acknowledged the possibility that COVID-19 may have exacerbated the IPV-related depressive burden, yet it did not provide empirical or analytic evidence to corroborate this claim ( 15 ). Second, although socioeconomic disparities are frequently acknowledged, few studies have applied formal inequality metrics to examine how the burden of IPV-related depression is distributed across development levels ( 14 ). Third, despite the growing recognition that IPV represents a sustained public health threat, no prior analysis has provided long-term projections specifically focused on depression attributable to IPV to inform future health system planning and violence prevention strategies ( 16 ). The release of the GBD 2023 provides a timely opportunity to address these gaps ( 17 ). In addition to updated prevalence and burden estimates, GBD 2023 expands the comparative risk assessment framework linking IPV to mental health outcomes and reinforces the central role of depressive disorders as the dominant contributors to attributable disability ( 6 ). Building on this evidence, we aimed to quantify the burden of depression attributable to IPV among women from 1990 to 2023 across SDI regions, with particular attention to Low and Low-middle SDI settings where vulnerability is likely greatest ( 2 , 16 , 18 ). We examine long-term trends and socioeconomic inequalities, evaluate changes in burden around the COVID-19 period, and project future trajectories of IPV-related depression to 2050. Together, these analyses aim to inform targeted prevention, equitable resource allocation, and the integration of violence and mental health considerations into responses to future public health emergencies. Methods Overview, data sources, and GBD 2023 framework This study is a population-level, secondary analysis based on estimates from GBD 2023, generated through the collaborative efforts of the global GBD network and coordinated by the Institute for Health Metrics and Evaluation (IHME) ( 17 , 19 , 20 ). GBD 2023 provides official age-sex–specific and age-sex–standardised estimates for 375 diseases and injuries, as well as risk-attributable burden for 88 risk factors, across 204 countries and territories from 1990 to 2023 ( 17 ). In accordance with IHME’s latest publication guidelines ( 17 , 19 , 20 ), all analyses in the present study were conducted at the SDI regional level. The study is led by a GBD Senior Collaborator (D.S.), who has contributed to multiple official GBD 2023 capstone publications, including those produced by the Causes of Death Collaborators ( 19 ), Demographics Collaborators ( 20 ), Disease, Injury and Risk Factor Collaborators ( 17 ), and the Intimate Partner Violence and Sexual Violence against Children Collaborators ( 6 ). Reporting follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) ( 21 ). Detailed descriptions of data sources and estimation procedures are available through the GBD 2023 Sources Tool hosted on the Global Health Data Exchange ( https://ghdx.healthdata.org/ ). All data sources underwent standardised quality assurance procedures, including data vetting for completeness and internal consistency, bias adjustment using MR-BRT (meta-regression Bayesian, regularized, trimmed), and cross-validation against alternative data streams ( 17 ). Methodological refinements in GBD 2023 relative to previous iterations improved trend stability, internal coherence, and harmonisation across heterogeneous data sources ( 17 ). The primary outcome of this study was DALYs, with age-standardised DALY rates used to facilitate comparisons across populations and over time. Disease and risk factor definitions In GBD 2023, depressive disorders comprise major depressive disorder (MDD) and dysthymia, representing disability due to persistent depressive symptomatology ( 17 , 19 , 20 ). MDD is characterized by persistent depressed mood or loss of interest or pleasure that is present for most of the day, nearly every day, for at least two weeks ( 22 ). Dysthymia presents with milder symptoms that are chronic in nature ( 23 ). To avoid ambiguity and improve clarity, the term depression in this article is used as a concise reference to depressive disorders unless otherwise specified. IPV was defined as any experience, from age 15 years onward, of physical or sexual violence perpetrated by a current or former intimate partner. An intimate partner includes a spouse, cohabiting partner, or, in settings where relevant, a non-cohabiting partner with whom the individual has had an intimate sexual relationship. Analyses were restricted to females because available evidence for risk-outcome associations among males did not meet GBD 2023 inclusion criteria ( 6 ). Further details on case definitions for depressive disorders and IPV, as well as procedures for modelling and estimating attributable burden, are available in the supplementary materials of the GBD 2023 capstone papers ( 17 , 19 , 20 ). Statistical analysis Age-specific and age-standardised DALYs attributable to IPV-related depression among women were extracted for five SDI regions (Low, Low-middle, Middle, High-middle, and High SDI) from 1990 to 2023, together with corresponding 95% uncertainty intervals (UI). Long-term temporal trends in age-standardised DALY rates were analysed using joinpoint regression, with piecewise log-linear models fitted to log-transformed rates. The number and location of joinpoints were identified using Monte Carlo permutation tests, with model complexity selected in a data-driven manner to identify statistically significant changes in annual percentage change (APC) across consecutive time segments ( 24 ). Age-specific patterns of disease burden were examined using DALYs across consecutive 5-year age groups among females aged 15 years and older in 2023. To quantify the drivers of change in IPV-related depression around the COVID-19 period (2019–2023), a decomposition analysis was conducted to estimate the relative contributions of epidemiological change, population growth, and population ageing across SDI regions ( 25 ). Socioeconomic inequality was assessed using age-standardised DALY rates to calculate the Slope Index of Inequality (SII) and the Concentration Index of Inequality (CII), capturing absolute and relative disparities across the SDI gradient in accordance with WHO recommendations ( 18 , 26 ). Associations between COVID-19 incidence and IPV-attributable depression burden were examined using country–year panel analyses from 2020 to 2023, restricted to low and low-middle SDI countries. Years lived with disability (YLDs), a component of DALYs, were used to quantify the non-fatal burden of IPV-attributable depression. Age-standardised COVID-19 incidence rates were modelled against age-standardised IPV-attributable depression YLD rates among females using two-way fixed-effects regression models with country and year fixed effects ( 27 , 28 ). Contemporaneous and one-year lagged COVID-19 incidence were analysed in separate specifications. Future trajectories of depression attributable to IPV among females were projected to 2050 across age groups and SDI levels using a Bayesian age–period–cohort (BAPC) modelling framework ( 29 ). The model was fitted to observed data from 1990 to 2023, incorporating age, period, and cohort effects as random components to address the inherent identification problem in age–period–cohort analyses. Projections were generated under alternative scenario assumptions regarding post-2023 period effects, including a continuation scenario and a conservative plateau scenario ( 29 , 30 ). Overall temporal patterns were summarised using age-standardised rates across SDI categories, while age-specific analyses were conducted in Low and Low-middle SDI regions to assess heterogeneity across age groups. Statistical significance was defined as a two-sided α of 0.05 where applicable. All analyses were conducted using R version 4.5.1. Results Temporal trends in IPV-attributable depression burden across SDI regions, 1990 to 2023 From 1990 to 2023, the burden of depression attributable to IPV among women increased steadily across all SDI regions ( Fig. 1 and Table 1 ) . In absolute terms, the High SDI region consistently contributed the largest number of DALYs, increasing from 738,623 (95% UI 273,996 to 1,487,215) in 1990 to 1,294,650 (546,829 to 2,219,150) in 2023 (Table 1 ). Over the same period, DALY counts in the Low SDI region rose from 285,231 (99,700 to 574,709) to 971,729 (404,454 to 1,679,422), representing a more than threefold increase. Age-standardised DALY rates changed relatively modestly between 1990 and 2019 across most SDI regions, followed by a marked increase during 2020–2023. By 2023, the highest age-standardised rates were observed in the Low SDI region at 127.2 per 100,000 (95% UI 51.6 to 222.9) and the Low-middle SDI region at 109.3 (46.6 to 193.5), compared with 90.9 (38.3 to 154.5) in the High SDI region. Between 1990 and 2023, the relative increase in age-standardised DALY rates was greatest in the Low-middle SDI region, rising from 69.9 (22.4 to 154.5) to 109.3 (46.6 to 193.5), whereas smaller relative increases were observed in the High SDI region (from 67.0 [24.9 to 134.7] to 90.9 [38.3 to 154.5]) and the High-middle SDI region (from 62.9 [23.3 to 131.8] to 76.1 [31.9 to 134.5]). Joinpoint regression identified a consistent shift from slow long-term increases to substantially steeper upward trends in age-standardised DALY rates of IPV-related depression across all SDI strata in the late 2010s (Fig. 1 ). In the Low SDI region, rates rose gradually from 1990 to 2018 (APC 0.33%) and then increased markedly from 2018 to 2023 (APC 5.92%). A similar pattern was observed in the Low-middle SDI region with a modest increase during 1990 to 2018 (APC 0.87%) followed by a pronounced rise during 2018 to 2023 (APC 4.96%). In Middle and High-middle SDI regions, the joinpoint occurred in 2017, with APCs increasing from 0.37% during 1990 to 2017 to 4.52% during 2017 to 2023 in the Middle SDI region, and from 0.13% during 1990 to 2017 to 3.70% during 2017 to 2023 in the High-middle SDI region. The High SDI region showed an additional earlier joinpoint, with no evidence of change during 1990 to 2006 (APC − 0.05%), a modest increase during 2006 to 2017 (APC 0.88%), and a sharper rise during 2017 to 2023 (APC 4.28%). Segment-specific APCs were statistically significant for all increasing phases after the late 2010s transition, indicating that the recent acceleration was robust across SDI levels, while the steepest post-joinpoint increases were observed in Low and Low-middle SDI regions. Age-standardised DALY rates were analysed using joinpoint regression with permutation tests to identify statistically significant temporal turning points, with the number of joinpoints determined by the data. APCs were estimated for each time segment. Points represent observed DALY rates and solid lines indicate fitted joinpoint trends. Asterisks (*) denote statistically significant APCs (p < 0·05). Across all SDI strata, DALY rates increased gradually from 1990 until the late 2010s, followed by a pronounced and largely synchronous acceleration beginning around 2017–2018. In the High SDI region, an additional earlier joinpoint reflects a prolonged period of stability followed by moderate growth before the recent acceleration. Table 1 DALY counts and age-standardised DALY rates (per 100,000) of depression attributable to IPV among women, by SDI region, 1990–2023 Year SDI regions High SDI High-middle SDI Middle SDI Low-middle SDI Low SDI DALY counts 1990 738623.4 (273996.4–1487214.9) 328906.5 (120100.0–701955.2) 125423.6 (40610.6–274083.2) 209718.4 (67469.4–458318.1) 285230.8 (99700.3–574709.3) 2000 812860.0 (309201.0–1506202.4) 393931.0 (145306.5–739481.7) 165523.7 (57040.2–332880.0) 288504.4 (103592.2–586745.3) 389830.6 (147313.8–724837.2) 2010 937646.5 (387872.5–1691304.7) 492800.8 (199260.2–892493.3) 208376.6 (82413.7–398617.0) 397134.4 (160548.3–742675.4) 521390.1 (217750.2–969013.3) 2019 1067658.1 (426704.1–1869295.6) 572391.7 (219424.5–1014460.6) 253887.7 (94169.1–464247.4) 482796.6 (188224.2–865393.8) 674375.1 (263952.7–1242349.8) 2020 1272812.8 (519012.6–2219658.3) 692639.8 (273050.6–1217012.2) 316191.9 (120108.0–572792.6) 616115.9 (248815.1–1098515.0) 871032.1 (356240.1–1563716.2) 2021 1272618.8 (526045.1–2196559.0) 682599.2 (276226.6–1205915.9) 321904.7 (127960.2–573001.4) 617816.8 (257616.3–1106987.5) 916113.1 (375205.4–1618910.4) 2022 1257665.8 (524254.6–2159329.4) 686507.1 (282900.7–1211377.8) 320146.5 (128184.5–567293.3) 628198.8 (265106.0–1119990.9) 918134.2 (377503.4–1602633.6) 2023 1294649.5 (546829.1–2219149.9) 702433.8 (295595.8–1238362.3) 332309.4 (132188.0–585540.2) 652263.0 (277347.0–1159904.7) 971729.3 (404454.5–1679421.9) Age-standardised DALY rates (per 100,000) 1990 67.0 (24.9–134.7) 62.9 (23.3–131.8) 47.4 (15.3–103.3) 69.9 (22.4–154.5) 90.7 (31.5–185.1) 2000 65.5 (25.0–121.3) 61.0 (22.3–115.2) 48.8 (16.9–98.8) 76.9 (27.6–155.9) 95.3 (35.5–183.7) 2010 67.9 (28.3–122.8) 62.4 (25.2–113.7) 49.9 (19.7–95.6) 84.5 (33.6–159.6) 97.2 (40.0–179.8) 2019 74.7 (29.7–130.2) 64.1 (24.5–113.4) 52.6 (19.4–96.5) 86.5 (33.6–158.1) 98.4 (37.8–181.0) 2020 89.2 (36.2–154.2) 76.9 (30.3–135.1) 64.7 (24.4–117.7) 108.6 (43.7–196.1) 123.6 (49.6–220.8) 2021 89.2 (36.8–152.2) 75.2 (30.5–132.5) 65.1 (25.7–117.2) 107.1 (44.8–191.9) 126.5 (50.5–225.3) 2022 88.2 (36.7–149.5) 75.0 (31.0–132.2) 64.0 (25.4–114.4) 107.0 (45.3–190.5) 123.3 (49.4–217.8) 2023 90.9 (38.3–154.5) 76.1 (31.9–134.5) 65.6 (25.9–116.1) 109.3 (46.6–193.5) 127.2 (51.6–222.9) Values are presented as estimates with 95% uncertainty intervals (UI). Age-specific distribution of IPV-attributable depression burden across SDI regions in 2023 In 2023, the age-specific distribution of DALYs attributable to IPV-related depression showed a consistent life-course pattern across all SDI regions, with burden increasing rapidly from adolescence, peaking in early to mid-adulthood, and declining thereafter ( Fig. 2 ) . In the Low SDI region, DALYs peaked at ages 25 to 29 years, reaching 155,206 DALYs with a 95% UI of 67,121 to 279,609. A similar early adult peak was observed in the Low-middle SDI region at ages 30 to 34 years, with 93,424 DALYs (41,051 to 166,004). In the Middle SDI region, the maximum burden occurred at ages 30 to 34 years, but at substantially lower absolute levels, with 46,391 DALYs (18,259 to 82,869). In contrast, High and High-middle SDI regions exhibited later peaks at ages 35 to 39 years, with 166,624 DALYs (70,646 to 285,920) in the High SDI region and 94,766 DALYs (38,754 to 166,928) in the High-middle SDI region. Beyond the peak ages, DALYs declined progressively with increasing age across all SDI strata. By ages 60 to 64 years, DALYs had fallen to 25,661 (5,043 to 58,146) in the Low SDI region, 23,127 (4,106 to 55,596) in the Low-middle SDI region, and 13,726 (4,747 to 27,213) in the Middle SDI region, while remaining comparatively higher in the High SDI region at 73,677 (28,152 to 137,747). DALYs were consistently lowest among adolescents aged 15 to 19 years and among the oldest age groups across all SDI regions. Panels show DALYs by 5-year age group for (a) High SDI, (b) High-middle SDI, (c) Middle SDI, (d) Low-middle SDI, and (e) Low SDI regions. Bars represent the total number of DALYs in each age group, shown as absolute numbers. The age distribution demonstrates consistent peaks in early to mid-adulthood across SDI regions, with the timing of peak burden varying by SDI level. Inequality and decomposition of IPV-attributable depression burden across SDI regions Inequality analyses across five SDI regions showed a modest socioeconomic gradient in age-standardised DALY rates of depression attributable to IPV, with point estimates consistently indicating higher burden in lower SDI regions over time ( Fig. 3 ) . The slope index of inequality suggested an increasing absolute difference, with SII point estimates of − 16.4 DALYs per 100,000 in 1990 (95% UI − 128.7 to 95.9), − 28.5 in 2019 (− 134.3 to 78.2), and − 46.3 in 2023 (− 168.8 to 78.9). Similarly, the concentration index of inequality remained negative across all years examined, with CII values of − 0.04 (95% UI − 0.36 to 0.21) in 1990, − 0.06 (− 0.30 to 0.17) in 2019, and − 0.08 (− 0.29 to 0.14) in 2023, indicating that point estimates consistently favoured a greater concentration of IPV-attributable depression burden among populations with lower SDI. Decomposition analyses of changes in total DALYs between 2019 and 2023 showed that increases across all SDI regions were driven predominantly by epidemiological change, with additional positive contributions from population growth and minimal or negative contributions from population ageing ( Fig. 4 ) . Epidemiological change accounted for the largest share of DALY increases in every SDI group, contributing 228,836 DALYs in the High SDI region, 106,874 in the High-middle SDI region, 64,206 in the Middle SDI region, 131,456 in the Low-middle SDI region, and 208,923 in the Low SDI region. Population growth contributed positively to increases in DALYs across all SDI strata, with the largest contributions observed in Low SDI (87,867 DALYs) and Low-middle SDI (37,339 DALYs) regions. In contrast, population ageing contributed negatively in High, High-middle, and Middle SDI regions, and only minimally in Low and Low-middle SDI regions. As a result of these combined effects, the largest absolute increases in total DALYs between 2019 and 2023 occurred in Low SDI (297,354 DALYs) and High SDI (226,991 DALYs) regions. Panel (a) shows the SII in age-standardised DALY rates of depression attributable to IPV across SDI ranks in 1990, 2019, and 2023. Circles represent observed regional estimates, with circle size proportional to population size. Solid lines indicate fitted regression slopes, and shaded areas denote 95% uncertainty intervals. Negative SII values indicate higher burden in lower SDI regions. Panel (b) shows concentration curves and corresponding CII for the same years. The dashed diagonal line represents equality. Curves above the line of equality and negative CII values indicate a disproportionate concentration of IPV-attributable depression burden among populations with lower SDI. Horizontal bars show the contributions of population ageing, population growth, and epidemiological change to the net change in total DALYs attributable to IPV-related depression within each SDI category between 2019 and 2023. Dots indicate the total absolute change in DALYs for each SDI group. Positive values denote increases in DALYs, whereas negative values indicate reductions. Association between COVID-19 incidence and IPV-attributable depression YLD rates in low and low-middle SDI countries, 2020–2023 Using a country–year panel design from 2020 to 2023, we assessed the association between age-standardised COVID-19 incidence rates and age-standardised IPV-attributable depression YLD rates among females in low and low-middle SDI countries (Supplementary Tables S1–S2, Appendix) . In low SDI countries (33 countries; 132 country–year observations), two-way fixed-effects models indicated that higher contemporaneous COVID-19 incidence was significantly associated with higher IPV-attributable depression YLD rates. Specifically, a 1% increase in COVID-19 incidence was associated with a 0.028% increase in IPV-attributable depression YLD rates (β = 0.028, SE = 0.007; p < 0.001), after controlling for country and year fixed effects (Table S3, Panel A) . By contrast, the one-year lagged COVID-19 incidence was inversely associated with IPV-attributable depression YLD rates in low SDI countries (β = −0.017, SE = 0.005; p < 0.01). Among low-middle SDI countries (43 countries; 172 country–year observations), no statistically significant associations were observed for either contemporaneous COVID-19 incidence (β = 0.003, SE = 0.002) or one-year lagged incidence (β = −0.002, SE = 0.003) (Table S3, Panel B) . All models incorporated country and year fixed effects with standard errors clustered at the country level. Model fit was high across specifications (R² range 0.997 to 0.999). Predicted future burden of IPV-attributable depression among females Using scenario-based projections derived from Bayesian age–period–cohort (BAPC) models, we estimated the future trajectory of age-standardised DALY rates of depression attributable to IPV among females across SDI levels through 2050 ( Fig. 5 ; Tables S4–S5) . Under the continuation scenario, in which age–period–cohort patterns observed up to 2023 were assumed to persist, age-standardised DALY rates were projected to increase substantially across all SDI categories between 2023 and 2050, with a clear and progressively widening gradient by SDI level ( Fig. 5 a; Figure S1; Table S4, panel a) . By 2050, the highest projected burdens were observed in Low SDI and Low-middle SDI regions, where age-standardised DALY rates were estimated to reach 377.6 (95% UI 44.6 to 2458.6) per 100 000 females and 302.4 (43.7 to 1732.2) per 100 000 females, respectively, compared with 224.2 (55.9 to 794.0) in the High SDI region. Across all SDI strata, projected increases accelerated after 2023, with the steepest absolute and relative rises occurring in lower SDI settings. In contrast, under the plateau scenario, which assumed no further period-related change after 2023 and stabilisation of rates at the mean levels observed during 2020–2023, age-standardised DALY rates were projected to remain constant over time but at persistently elevated levels across all SDI categories ( Fig. 5 b; Table S4, panel b) . Despite the absence of further increases, substantial socioeconomic inequalities persisted, with plateaued rates of 121.7 in the Low SDI region and 105.0 in the Low-middle SDI region, compared with 87.5 in the High SDI region. Under both the continuation and plateau scenarios, projected age-standardised DALY rates remain above pre-2020 levels through 2050. Age-specific projections further revealed marked heterogeneity in the future burden across age groups, particularly in Low and Low-middle SDI regions (Table S5) . Under the continuation scenario, the highest DALY rates by 2050 were consistently projected among women aged 25–49 years, with rates exceeding 300 per 100 000 females in several age groups, while lower but steadily increasing burdens were observed among adolescents and older age groups. Although uncertainty intervals widened with increasing projection horizons, the overall age distribution remained stable, indicating a persistent concentration of the future burden of IPV-attributable depression among women of reproductive and working ages in lower SDI settings. Solid lines indicate observed age-standardised DALY rates from 1990 to 2023, and dashed lines indicate projected rates from 2024 to 2050. Panel (a) presents the continuation scenario, assuming that age–period–cohort patterns observed up to 2023 continue beyond the observation period. Corresponding projections across five SDI regions with 95% uncertainty intervals are presented in Figure S1. Panel (b) presents a plateau scenario, in which no further period-related change is assumed after 2023, and age-standardised DALY rates are stabilised at the mean level observed during 2020–2023. The vertical dotted line marks the final year of observed data (2023). Estimates are shown for five SDI categories. Discussion Principal findings This study provides a comprehensive assessment of the burden of depression attributable to IPV among women over more than three decades, using updated population-level estimates from GBD 2023. Across five SDI regions, the burden of IPV-attributable depression increased gradually from 1990 to the late 2010s. Joinpoint analyses indicated a shift toward steeper upward trends beginning around 2017–2018; however, observed values remained largely stable through 2019. A pronounced and synchronous increase became evident after 2020, coinciding with the COVID-19 period ( 31 ). Women living in Low and Low-middle SDI regions bore a disproportionate share of this post-2020 increase. By 2023, these regions exhibited the highest age-standardised DALY rates and the steepest recent rises, despite contributing fewer absolute DALYs than the High SDI region. Age-specific analyses showed a consistent concentration of burden in early and mid-adulthood, with peak DALYs occurring at younger ages in lower SDI settings, highlighting critical life-course windows of vulnerability and persistent global inequalities, which are widening in absolute terms. Decomposition analyses showed that recent increases in IPV-attributable depression burden were driven primarily by epidemiological change, with smaller contributions from population growth and minimal or negative contributions from population ageing across SDI regions. Moreover, supporting evidence from country–year analyses indicated that, in low SDI countries, higher contemporaneous age-standardised COVID-19 incidence rates were associated with increased age-standardised YLD rates attributable to IPV-related depression. Finally, scenario-based projections suggest that, under both continuation and plateau assumptions, the burden of IPV-attributable depression is likely to remain elevated through 2050, particularly in lower SDI regions, underscoring the long-term public health and equity implications of the post-pandemic shift. Accelerating burden since the late 2010s The acceleration in IPV-attributable depression burden observed from the late 2010s represents a departure from the relatively slow and stable increases seen over the preceding decades. The synchronised timing of this inflection across all SDI regions suggests the influence of global forces rather than region-specific demographic or epidemiological transitions. Although our joinpoint analyses identified a structural change beginning around 2017–2018, the most pronounced increases occurred after 2020, indicating that the COVID-19 pandemic acted as a catalyst that amplified pre-existing vulnerabilities rather than as a singular cause of change. This pattern is consistent with evidence that the pandemic intensified known risk pathways for both IPV exposure and mental health deterioration through prolonged household confinement, economic disruption, and erosion of social support systems ( 8 – 10 ). Importantly, the post-2020 acceleration was evident in age-standardised rates as well as absolute DALYs, underscoring that the observed increases cannot be attributed to population ageing or growth alone. Instead, the findings point to a shift in underlying epidemiological conditions, including heightened exposure to violence, increased severity or persistence of depressive symptoms, and reduced access to protective and mental health services during and after the pandemic period. Emerging global evidence has documented substantial disruptions to IPV prevention, reporting, and mental health care during COVID-19, particularly in resource-constrained settings, lending plausibility to this interpretation ( 3 , 7 , 13 ). Together, these findings may suggest that the late-2010s acceleration reflects a structural worsening of IPV-related mental health risk that may persist beyond the immediate pandemic context. Socioeconomic disparities across SDI levels Marked socioeconomic disparities characterised the distribution of IPV-attributable depression burden across SDI levels, with women in Low and Low-middle SDI regions consistently experiencing higher age-standardised DALY rates than those in more developed settings. These gradients align with extensive evidence that socioeconomic disadvantage amplifies both exposure to IPV and vulnerability to its mental health consequences through intersecting pathways of poverty, gender inequality, and limited access to care. Large-scale syntheses, including the Lancet Commission on global mental health and sustainable development, have highlighted that treatment gaps for common mental disorders remain widest in low-resource settings, where mental health services are chronically underfunded and poorly integrated into primary care systems ( 12 , 13 ). Within this context, IPV-related depression is more likely to remain untreated, prolonged, and disabling, contributing to higher population-level burden despite lower absolute case counts. Importantly, the observed disparities reflect not only differences in baseline risk but also unequal capacity to absorb and recover from large-scale shocks. Health and social protection systems in low SDI settings are often less resilient to crises, with fewer safeguards to maintain IPV prevention, legal protection, and mental health services during periods of disruption ( 3 ). As a result, acute stressors such as the COVID-19 pandemic may translate into sustained increases in mental health burden rather than transient fluctuations ( 32 ). The persistence of higher age-standardised rates in lower SDI regions underscores that IPV-attributable depression is deeply embedded within broader structural inequalities, and that without targeted investments to reduce these gaps, global progress in violence prevention and mental health will remain uneven. Age-specific burden and life-course patterns The age distribution of IPV-attributable depression burden highlights a clear life-course pattern, with the highest concentration occurring during early and mid-adulthood. This period coincides with key transitions related to partnership formation, childbearing, and economic participation, which are known to shape both exposure to IPV and susceptibility to depressive disorders. Previous evidence suggests that IPV experienced during reproductive and working ages is often recurrent, may become severe, and is intertwined with caregiving responsibilities, thereby exerting a disproportionate and sustained impact on women’s mental health and functional capacity ( 2 , 33 – 36 ). The concentration of burden within these age groups underscores that IPV-related depression is not evenly distributed across the life span but clustered within socially and biologically critical periods. The earlier peak of burden observed in lower SDI settings further suggests that life-course vulnerability is shaped by contextual factors, including earlier age at union formation, reduced educational opportunities, and limited access to protective and mental health services ( 37 – 39 ). Previous research has shown that women exposed to IPV earlier in adulthood face a higher risk of chronic and recurrent depressive episodes, with effects that may persist even after cessation of violence ( 33 , 40 ). From a public health perspective, these findings reinforce the importance of adopting a life-course approach to prevention and care, prioritising early identification and integrated IPV and mental health interventions during young and middle adulthood, when the potential for averting long-term disability is greatest. Drivers of recent increases Decomposition analyses indicated that recent increases in IPV-attributable depression burden were driven predominantly by epidemiological change rather than by population growth or ageing, pointing to shifts in underlying risk conditions within the GBD attribution framework. Such changes may reflect alterations in IPV exposure, the severity or persistence of depressive symptoms, or the strength of the risk–outcome relationship linking IPV to depression during periods of widespread social disruption. Importantly, this interpretation does not rely on demographic explanations but instead highlights modifiable factors that influence how IPV translates into long-term mental health disability at the population level. Multiple lines of external evidence support the plausibility of these mechanisms. During the COVID-19 period, global studies documented substantial increases in the prevalence and severity of depressive symptoms, alongside prolonged symptom duration and reduced remission, particularly in settings with limited access to mental health care ( 41 – 43 ). In parallel, health systems experienced marked disruptions in essential mental health and social services, including IPV prevention, reporting, and support pathways, which are critical for mitigating the duration and disability associated with IPV-related depression ( 44 , 45 ). Together, these factors may have amplified the disabling consequences of IPV exposure—even in the absence of major demographic change—by increasing untreated morbidity and prolonging recovery. Within this context, the predominance of epidemiological change observed in our study underscores the importance of addressing service continuity, early intervention, and violence prevention as central strategies for reversing recent increases in IPV-attributable depression burden. COVID-19–related burden dynamics The COVID-19 period represents a distinct context in which social restrictions, economic insecurity, and disruptions to daily life converged to alter both violence dynamics and mental health vulnerability. Prior evidence indicates that lockdown measures and prolonged household confinement were associated with worsening psychological distress, reduced well-being, and slower recovery trajectories, even in populations without prior mental health conditions ( 46 , 47 ). In parallel, multiple analyses documented increases in indicators of domestic violence risk during periods of stringent mobility restrictions, suggesting that pandemic control measures may have unintentionally intensified exposure to IPV for some women ( 48 ). These overlapping stressors provide a plausible backdrop against which IPV-attributable depression burden could rise during the pandemic period, without implying a direct or exclusive causal pathway. Crucially, the pandemic also disrupted the systems intended to mitigate harm. Studies from diverse health-care settings reported substantial reductions in mental health service utilisation and delays in care-seeking during the early phases of COVID-19, with recovery remaining uneven over time ( 49 , 50 ). For women experiencing IPV, interruptions in access to protection, counselling, and treatment may have prolonged symptom duration and increased disability, thereby amplifying the burden attributable to IPV under the GBD counterfactual framework. These dynamics underscore that pandemic-related increases in IPV-attributable depression burden should be interpreted not simply as short-term shocks, but as the cumulative result of constrained coping capacity and weakened service responses during a period of sustained crisis. Future burden trajectories Scenario-based projections suggest that the burden of depression attributable to IPV is unlikely to return to pre-pandemic levels in the coming decades, even under conservative assumptions of stabilised rates. From a population health perspective, this persistence implies that recent increases may represent a lasting shift rather than a transient perturbation. Modelling studies of mental health outcomes suggest that major social and economic shocks may have enduring implications for depressive morbidity, particularly when recovery is constrained by limited service capacity and ongoing exposure to psychosocial stressors ( 41 , 51 ). Within this context, IPV-related depression may become increasingly entrenched in settings where prevention, protection, and treatment systems remain under-resourced. The widening divergence in projected burden across SDI levels further underscores the risk of accumulating long-term inequities. Prior evidence indicates that without targeted interventions, mental health burdens tend to concentrate disproportionately in populations facing persistent structural disadvantage ( 52 , 53 ). Interpreted through the GBD counterfactual framework, sustained elevation in IPV-attributable depression burden reflects not only continued exposure to violence but also delayed recovery and preventable disability over the life course. These projections therefore serve less as precise predictions than as signals for policy and system planning, highlighting the urgency of strengthening integrated IPV and mental health responses to avert the consolidation of post-pandemic mental health inequalities. Policy and programme implications Our findings have important implications for the evaluation and prioritisation of international policies addressing IPV and women’s mental health. Existing global frameworks, including the WHO RESPECT women strategy and international mental health action plans, emphasise violence prevention, survivor protection, and access to care, but implementation has remained uneven, particularly in low-resource settings ( 54 , 55 ). The sustained elevation of IPV-attributable depression burden observed in our analyses suggests that current policy approaches have been insufficient to buffer mental health consequences during large-scale crises. From a policy perspective, this gap underscores the need to move beyond siloed IPV or mental health strategies toward integrated programmes that explicitly address depressive morbidity as a core outcome of violence prevention efforts. Previous evidence indicates that interventions combining IPV screening, psychosocial support, and referral pathways within primary health care and reproductive health services are among the most feasible and scalable approaches in low- and middle-income settings ( 56 – 58 ). However, the effectiveness of such programmes depends on service continuity, workforce capacity, and legal and social protection mechanisms—elements that were frequently disrupted during the COVID-19 period. International policy responses should therefore prioritise strengthening system resilience, including task-sharing for mental health care, community-based survivor support, and crisis-contingent service delivery models. Interpreted through a global equity lens, reducing IPV-attributable depression burden will require not only expanding coverage of existing interventions, but also aligning violence prevention and mental health policies with preparedness planning to ensure that gains are not reversed during future social or public health emergencies. Strengths and limitations This study has several strengths. It draws on the most recent estimates from GBD 2023, enabling a consistent and comparable assessment of IPV-attributable depression burden across multiple decades and sociodemographic contexts. By integrating trend analyses, inequality metrics, decomposition methods, panel analyses, and scenario-based projections, the study provides a multidimensional perspective on how IPV-related mental health burden has evolved over time and how it may unfold in the future. Importantly, the use of age-standardised measures and SDI-stratified analyses allows for meaningful comparisons across populations with differing demographic structures and development levels, supporting a global equity-oriented interpretation of the findings. Several limitations should be considered when interpreting these results. First, as a secondary analysis based on GBD estimates, findings depend on the availability and quality of underlying data and modelling assumptions, including those related to IPV exposure measurement and risk–outcome relationships. Second, IPV-attributable depression burden reflects counterfactual attribution rather than causal decomposition, and overlapping pathways through which large-scale disruptions may concurrently influence IPV exposure and depressive morbidity cannot be disentangled. Third, analyses conducted at the SDI regional level may mask within-country and subnational heterogeneity, particularly in settings where social inequalities are pronounced. Finally, future projections are subject to increasing uncertainty over longer horizons and should be interpreted as indicative planning signals rather than precise forecasts. Despite these limitations, the overall patterns observed across methods and scenarios consistently point to sustained and unequal IPV-attributable depression burden, underscoring the robustness and policy relevance of the main conclusions. Conclusions In conclusion, this study shows that the burden of depression attributable to IPV among women has entered a phase of accelerated growth since the late 2010s, with a pronounced and persistent impact concentrated in Low and Low-middle SDI regions. Recent increases appear to be primarily driven by changes in epidemiological conditions that have amplified the mental health consequences of IPV. The persistence of elevated burden under multiple future scenarios suggests that recovery from the COVID-19 period is unlikely to occur spontaneously. These findings underscore the urgency of integrating IPV prevention with mental health care, strengthening service continuity during crises, and prioritising equitable, system-level responses to protect women’s mental health. Without targeted and sustained action, IPV-attributable depression is likely to remain a substantial and unevenly distributed contributor to global disability over the coming decades. Declarations Author contribution declaration The study was conceived and designed by DS. DS extracted the data, conducted the analyses, and drafted the initial manuscript, and critically reviewed and revised the manuscript. All authors approved the final version of the manuscript. DS take final responsibility for the decision to submit the manuscript for publication. Conflict of interest The authors declare no competing interests. Data sharing Data used in this study were obtained from the GBD 2023 study and are publicly available through the GBD Results Tool ( https://vizhub.healthdata.org/gbd-results/ ). Funding None Acknowledgments We thank the Institute for Health Metrics and Evaluation (IHME) and the Global Burden of Disease (GBD) Study collaborators for the initial development of the GBD 2023 estimates, which were funded by the Bill & Melinda Gates Foundation. 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Lancet 378(9802):1581–1591 Bangpan M, Felix L, Dickson K (2019) Mental health and psychosocial support programmes for adults in humanitarian emergencies: a systematic review and meta-analysis in low and middle-income countries. BMJ Glob Health 4(5):e001484 Additional Declarations The authors declare no competing interests. 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-9724901","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640877318,"identity":"87ee54bd-2c05-4fcb-9bbe-de231b310ec3","order_by":0,"name":"Dan Shan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDCCAwxsQNJGBkOYkJY0HggvgXgth0nQwneA/dmDnzvO8/DPbn748ecPm3wG9sMPmHnO4NYieYDH3LD3zG0eiTvHjKV5EtIsG3jSDJh5buDWYnCAh02Ct+02D8ONHAZphoTDBgwMOQzMPB/waWF/Jvm37RyP/I0c5p8/Ev4bMPC/IaSFwUyat+0Aj8GNHDYJnoQDBgwSIFvwOEzyMI+ZtGxbMo/hjTQza560ZAM2iWcGB+fg8T7f8fZnkm/b7OTkbiQ/vvnDxs6Anz/54YM3x3BrYWBGFwBF0wE8GkbBKBgFo2AUEAEAAU9L5JBgZSUAAAAASUVORK5CYII=","orcid":"","institution":"Lancaster University","correspondingAuthor":true,"prefix":"","firstName":"Dan","middleName":"","lastName":"Shan","suffix":""}],"badges":[],"createdAt":"2026-05-15 13:01:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9724901/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9724901/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109486543,"identity":"f938a684-021c-4a11-b680-256440c0d1f2","added_by":"auto","created_at":"2026-05-18 16:27:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSDI–specific joinpoint trends in age-standardised DALY rates of depression attributable to IPV, 1990–2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge-standardised DALY rates were analysed using joinpoint regression with permutation tests to identify statistically significant temporal turning points, with the number of joinpoints determined by the data. APCs were estimated for each time segment. Points represent observed DALY rates and solid lines indicate fitted joinpoint trends. Asterisks (*) denote statistically significant APCs (p\u0026lt;0·05). Across all SDI strata, DALY rates increased gradually from 1990 until the late 2010s, followed by a pronounced and largely synchronous acceleration beginning around 2017–2018. In the High SDI region, an additional earlier joinpoint reflects a prolonged period of stability followed by moderate growth before the recent acceleration.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9724901/v1/4cbc4fdf512c13d1af43798c.png"},{"id":109761269,"identity":"793522e9-cbb1-4eeb-8048-6182d38d1eb8","added_by":"auto","created_at":"2026-05-22 07:29:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-specific distribution of DALYs attributable to IPV–related depression across SDI levels in 2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanels show DALYs by 5-year age group for (a) High SDI, (b) High-middle SDI, (c) Middle SDI, (d) Low-middle SDI, and (e) Low SDI regions. Bars represent the total number of DALYs in each age group, shown as absolute numbers. The age distribution demonstrates consistent peaks in early to mid-adulthood across SDI regions, with the timing of peak burden varying by SDI level.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9724901/v1/3dec967f62ac7a4193e071a1.png"},{"id":109486545,"identity":"d76e2fcc-034a-47c4-a674-2cd2624a01c3","added_by":"auto","created_at":"2026-05-18 16:27:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSocioeconomic inequality in age-standardised DALY rates of depression attributable to IPV across SDI levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel (a) shows the SII in age-standardised DALY rates of depression attributable to IPV across SDI ranks in 1990, 2019, and 2023. Circles represent observed regional estimates, with circle size proportional to population size. Solid lines indicate fitted regression slopes, and shaded areas denote 95% uncertainty intervals. Negative SII values indicate higher burden in lower SDI regions. Panel (b) shows concentration curves and corresponding CII for the same years. The dashed diagonal line represents equality. Curves above the line of equality and negative CII values indicate a disproportionate concentration of IPV-attributable depression burden among populations with lower SDI.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9724901/v1/25162d78586323c353c41536.png"},{"id":109760115,"identity":"6f12caa3-0278-4ad9-9c97-ad4b5b4f28c5","added_by":"auto","created_at":"2026-05-22 07:28:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecomposition of changes in DALYs attributable to IPV–related depression across SDI levels, 2019–2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHorizontal bars show the contributions of population ageing, population growth, and epidemiological change to the net change in total DALYs attributable to IPV-related depression within each SDI category between 2019 and 2023. Dots indicate the total absolute change in DALYs for each SDI group. Positive values denote increases in DALYs, whereas negative values indicate reductions.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9724901/v1/0a7bafc790abf4c770567934.png"},{"id":109759958,"identity":"0701f265-48f3-4927-b0d5-8416a0ad8212","added_by":"auto","created_at":"2026-05-22 07:27:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScenario-based projections of age-standardised DALY rates of depression attributable to IPV among females, by SDI, 1990–2050\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSolid lines indicate observed age-standardised DALY rates from 1990 to 2023, and dashed lines indicate projected rates from 2024 to 2050. Panel (a) presents the continuation scenario, assuming that age–period–cohort patterns observed up to 2023 continue beyond the observation period. Corresponding projections across five SDI regions with 95% uncertainty intervals are presented in Figure S1. Panel (b) presents a plateau scenario, in which no further period-related change is assumed after 2023, and age-standardised DALY rates are stabilised at the mean level observed during 2020–2023. The vertical dotted line marks the final year of observed data (2023). Estimates are shown for five SDI categories.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9724901/v1/bd459e1709ae7b231029815e.png"},{"id":109800282,"identity":"a762c5c2-4240-4926-8308-1dfc4a8817de","added_by":"auto","created_at":"2026-05-22 15:37:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":694873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9724901/v1/0950e27a-7701-404e-9cc2-5d2e70192b0a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAccelerating and disproportionate burden of depression attributable to intimate partner violence among women in Low and Low-middle SDI regions\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"What is already known on this topic","content":"\u003cp\u003eIntimate partner violence (IPV) is a major cause of poor mental health among women and is strongly linked to depression. Previous studies have shown that the burden of IPV-related depression may be higher in low-resource settings, but evidence on how this burden changed during and after the COVID-19 pandemic has remained limited. Long-term projections and formal assessments of socioeconomic inequality have also been lacking.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003cbr\u003e Using Global Burden of Disease 2023 data, we found that the burden of depression attributable to IPV among women increased across all development levels from 1990 to 2023, with a marked acceleration after the late 2010s and especially during the COVID-19 period. The highest and fastest-rising burden was concentrated in Low and Low-middle SDI regions, particularly among women in early and mid-adulthood. Future projections suggest that the burden is likely to remain above pre-pandemic levels through 2050.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice or policy\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;These findings highlight the need for integrated IPV prevention and mental health strategies, especially in lower-resource settings where inequalities are greatest. Strengthening access to mental health care, survivor support services, and crisis-resilient health systems may help reduce the long-term impact of IPV-related depression among women.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eIntimate partner violence (IPV) remains one of the most pervasive and preventable violations of women\u0026rsquo;s health and human rights worldwide (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Exposure to physical or sexual violence by an intimate partner has been consistently linked to a wide range of adverse health outcomes, including physical injury, reproductive complications, chronic disease, and, most prominently, mental health disorders (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Recent estimates from the Global Burden of Disease (GBD) Study 2023 position IPV among the leading risk factors for disability-adjusted life years (DALYs) among females aged 15 to 49 years globally, with depressive and anxiety disorders accounting for the largest share of the attributable burden (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These findings underscore that the consequences of IPV extend far beyond immediate harm, contributing substantially to long-term mental health loss across the life course (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Coronavirus Disease 2019 (COVID-19) pandemic constituted an unprecedented global social and public health shock that further intensified both the occurrence of IPV and its psychological sequelae (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Pandemic-related containment measures, including home confinement, mobility restrictions, economic disruption, and interruptions in social and protective services, created conditions that increased women\u0026rsquo;s exposure to abusive partners while simultaneously limiting access to external support (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A growing body of evidence indicates that the pandemic period was accompanied by a marked escalation in violence against women, alongside a parallel deterioration in population mental health (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Within this context, depression attributable to IPV has emerged as a critical yet underexamined component of the broader mental health consequences of the pandemic (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe burden of IPV-related depression is not distributed evenly across populations and locations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Low- and middle-income countries (LMICs) bear a disproportionate share, reflecting structural vulnerabilities such as limited mental health service capacity, weak surveillance systems, and persistent social stigma surrounding both violence and mental illness (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Data from the World Health Organization (WHO) World Mental Health Surveys indicate that treatment coverage for common mental disorders in low income settings remains substantially lower than in high income countries (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), resulting in widespread underdiagnosis and unmet need (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These disparities are further shaped by broader socioeconomic gradients, suggesting that the mental health consequences of IPV are closely intertwined with social and developmental inequality (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Within the GBD framework, socioeconomic development is commonly operationalised using the Socio-demographic Index (SDI). However, systematic quantification of these inequalities across SDI strata remains limited (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies drawing on GBD 2019 and 2021 estimates have described long-term trends in depression attributable to IPV and highlighted substantial geographical variation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Nonetheless, several critical gaps persist. First, earlier analyses largely precede or only partially capture the COVID-19 period and therefore cannot quantify the full impact of the pandemic between 2020 and 2023 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Notably, the 2021 GBD-based analysis by Liu et al. acknowledged the possibility that COVID-19 may have exacerbated the IPV-related depressive burden, yet it did not provide empirical or analytic evidence to corroborate this claim (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Second, although socioeconomic disparities are frequently acknowledged, few studies have applied formal inequality metrics to examine how the burden of IPV-related depression is distributed across development levels (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Third, despite the growing recognition that IPV represents a sustained public health threat, no prior analysis has provided long-term projections specifically focused on depression attributable to IPV to inform future health system planning and violence prevention strategies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe release of the GBD 2023 provides a timely opportunity to address these gaps (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In addition to updated prevalence and burden estimates, GBD 2023 expands the comparative risk assessment framework linking IPV to mental health outcomes and reinforces the central role of depressive disorders as the dominant contributors to attributable disability (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Building on this evidence, we aimed to quantify the burden of depression attributable to IPV among women from 1990 to 2023 across SDI regions, with particular attention to Low and Low-middle SDI settings where vulnerability is likely greatest (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). We examine long-term trends and socioeconomic inequalities, evaluate changes in burden around the COVID-19 period, and project future trajectories of IPV-related depression to 2050. Together, these analyses aim to inform targeted prevention, equitable resource allocation, and the integration of violence and mental health considerations into responses to future public health emergencies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview, data sources, and GBD 2023 framework\u003c/h2\u003e \u003cp\u003eThis study is a population-level, secondary analysis based on estimates from GBD 2023, generated through the collaborative efforts of the global GBD network and coordinated by the Institute for Health Metrics and Evaluation (IHME) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). GBD 2023 provides official age-sex\u0026ndash;specific and age-sex\u0026ndash;standardised estimates for 375 diseases and injuries, as well as risk-attributable burden for 88 risk factors, across 204 countries and territories from 1990 to 2023 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn accordance with IHME\u0026rsquo;s latest publication guidelines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), all analyses in the present study were conducted at the SDI regional level. The study is led by a GBD Senior Collaborator (D.S.), who has contributed to multiple official GBD 2023 capstone publications, including those produced by the Causes of Death Collaborators (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Demographics Collaborators (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), Disease, Injury and Risk Factor Collaborators (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and the Intimate Partner Violence and Sexual Violence against Children Collaborators (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Reporting follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDetailed descriptions of data sources and estimation procedures are available through the GBD 2023 Sources Tool hosted on the Global Health Data Exchange (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All data sources underwent standardised quality assurance procedures, including data vetting for completeness and internal consistency, bias adjustment using MR-BRT (meta-regression Bayesian, regularized, trimmed), and cross-validation against alternative data streams (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Methodological refinements in GBD 2023 relative to previous iterations improved trend stability, internal coherence, and harmonisation across heterogeneous data sources (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The primary outcome of this study was DALYs, with age-standardised DALY rates used to facilitate comparisons across populations and over time.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDisease and risk factor definitions\u003c/h3\u003e\n\u003cp\u003eIn GBD 2023, depressive disorders comprise major depressive disorder (MDD) and dysthymia, representing disability due to persistent depressive symptomatology (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). MDD is characterized by persistent depressed mood or loss of interest or pleasure that is present for most of the day, nearly every day, for at least two weeks (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Dysthymia presents with milder symptoms that are chronic in nature (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). To avoid ambiguity and improve clarity, the term depression in this article is used as a concise reference to depressive disorders unless otherwise specified.\u003c/p\u003e \u003cp\u003eIPV was defined as any experience, from age 15 years onward, of physical or sexual violence perpetrated by a current or former intimate partner. An intimate partner includes a spouse, cohabiting partner, or, in settings where relevant, a non-cohabiting partner with whom the individual has had an intimate sexual relationship. Analyses were restricted to females because available evidence for risk-outcome associations among males did not meet GBD 2023 inclusion criteria (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Further details on case definitions for depressive disorders and IPV, as well as procedures for modelling and estimating attributable burden, are available in the supplementary materials of the GBD 2023 capstone papers (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAge-specific and age-standardised DALYs attributable to IPV-related depression among women were extracted for five SDI regions (Low, Low-middle, Middle, High-middle, and High SDI) from 1990 to 2023, together with corresponding 95% uncertainty intervals (UI). Long-term temporal trends in age-standardised DALY rates were analysed using joinpoint regression, with piecewise log-linear models fitted to log-transformed rates. The number and location of joinpoints were identified using \u003cem\u003eMonte Carlo\u003c/em\u003e permutation tests, with model complexity selected in a data-driven manner to identify statistically significant changes in annual percentage change (APC) across consecutive time segments (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Age-specific patterns of disease burden were examined using DALYs across consecutive 5-year age groups among females aged 15 years and older in 2023. To quantify the drivers of change in IPV-related depression around the COVID-19 period (2019\u0026ndash;2023), a decomposition analysis was conducted to estimate the relative contributions of epidemiological change, population growth, and population ageing across SDI regions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Socioeconomic inequality was assessed using age-standardised DALY rates to calculate the Slope Index of Inequality (SII) and the Concentration Index of Inequality (CII), capturing absolute and relative disparities across the SDI gradient in accordance with WHO recommendations (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssociations between COVID-19 incidence and IPV-attributable depression burden were examined using country\u0026ndash;year panel analyses from 2020 to 2023, restricted to low and low-middle SDI countries. Years lived with disability (YLDs), a component of DALYs, were used to quantify the non-fatal burden of IPV-attributable depression. Age-standardised COVID-19 incidence rates were modelled against age-standardised IPV-attributable depression YLD rates among females using two-way fixed-effects regression models with country and year fixed effects (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Contemporaneous and one-year lagged COVID-19 incidence were analysed in separate specifications. Future trajectories of depression attributable to IPV among females were projected to 2050 across age groups and SDI levels using a Bayesian age\u0026ndash;period\u0026ndash;cohort (BAPC) modelling framework (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The model was fitted to observed data from 1990 to 2023, incorporating age, period, and cohort effects as random components to address the inherent identification problem in age\u0026ndash;period\u0026ndash;cohort analyses. Projections were generated under alternative scenario assumptions regarding post-2023 period effects, including a continuation scenario and a conservative plateau scenario (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Overall temporal patterns were summarised using age-standardised rates across SDI categories, while age-specific analyses were conducted in Low and Low-middle SDI regions to assess heterogeneity across age groups. Statistical significance was defined as a two-sided α of 0.05 where applicable. All analyses were conducted using R version 4.5.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTemporal trends in IPV-attributable depression burden across SDI regions, 1990 to 2023\u003c/h2\u003e \u003cp\u003eFrom 1990 to 2023, the burden of depression attributable to IPV among women increased steadily across all SDI regions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In absolute terms, the High SDI region consistently contributed the largest number of DALYs, increasing from 738,623 (95% UI 273,996 to 1,487,215) in 1990 to 1,294,650 (546,829 to 2,219,150) in 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Over the same period, DALY counts in the Low SDI region rose from 285,231 (99,700 to 574,709) to 971,729 (404,454 to 1,679,422), representing a more than threefold increase.\u003c/p\u003e \u003cp\u003eAge-standardised DALY rates changed relatively modestly between 1990 and 2019 across most SDI regions, followed by a marked increase during 2020\u0026ndash;2023. By 2023, the highest age-standardised rates were observed in the Low SDI region at 127.2 per 100,000 (95% UI 51.6 to 222.9) and the Low-middle SDI region at 109.3 (46.6 to 193.5), compared with 90.9 (38.3 to 154.5) in the High SDI region. Between 1990 and 2023, the relative increase in age-standardised DALY rates was greatest in the Low-middle SDI region, rising from 69.9 (22.4 to 154.5) to 109.3 (46.6 to 193.5), whereas smaller relative increases were observed in the High SDI region (from 67.0 [24.9 to 134.7] to 90.9 [38.3 to 154.5]) and the High-middle SDI region (from 62.9 [23.3 to 131.8] to 76.1 [31.9 to 134.5]).\u003c/p\u003e \u003cp\u003eJoinpoint regression identified a consistent shift from slow long-term increases to substantially steeper upward trends in age-standardised DALY rates of IPV-related depression across all SDI strata in the late 2010s (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the Low SDI region, rates rose gradually from 1990 to 2018 (APC 0.33%) and then increased markedly from 2018 to 2023 (APC 5.92%). A similar pattern was observed in the Low-middle SDI region with a modest increase during 1990 to 2018 (APC 0.87%) followed by a pronounced rise during 2018 to 2023 (APC 4.96%). In Middle and High-middle SDI regions, the joinpoint occurred in 2017, with APCs increasing from 0.37% during 1990 to 2017 to 4.52% during 2017 to 2023 in the Middle SDI region, and from 0.13% during 1990 to 2017 to 3.70% during 2017 to 2023 in the High-middle SDI region. The High SDI region showed an additional earlier joinpoint, with no evidence of change during 1990 to 2006 (APC\u0026thinsp;\u0026minus;\u0026thinsp;0.05%), a modest increase during 2006 to 2017 (APC 0.88%), and a sharper rise during 2017 to 2023 (APC 4.28%). Segment-specific APCs were statistically significant for all increasing phases after the late 2010s transition, indicating that the recent acceleration was robust across SDI levels, while the steepest post-joinpoint increases were observed in Low and Low-middle SDI regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAge-standardised DALY rates were analysed using joinpoint regression with permutation tests to identify statistically significant temporal turning points, with the number of joinpoints determined by the data. APCs were estimated for each time segment. Points represent observed DALY rates and solid lines indicate fitted joinpoint trends. Asterisks (*) denote statistically significant APCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05). Across all SDI strata, DALY rates increased gradually from 1990 until the late 2010s, followed by a pronounced and largely synchronous acceleration beginning around 2017\u0026ndash;2018. In the High SDI region, an additional earlier joinpoint reflects a prolonged period of stability followed by moderate growth before the recent acceleration.\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\u003eDALY counts and age-standardised DALY rates (per 100,000) of depression attributable to IPV among women, by SDI region, 1990\u0026ndash;2023\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eSDI regions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh SDI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHigh-middle SDI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMiddle SDI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLow-middle SDI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLow SDI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDALY counts\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e738623.4\u003c/p\u003e \u003cp\u003e(273996.4\u0026ndash;1487214.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328906.5\u003c/p\u003e \u003cp\u003e(120100.0\u0026ndash;701955.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125423.6\u003c/p\u003e \u003cp\u003e(40610.6\u0026ndash;274083.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209718.4\u003c/p\u003e \u003cp\u003e(67469.4\u0026ndash;458318.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e285230.8\u003c/p\u003e \u003cp\u003e(99700.3\u0026ndash;574709.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e812860.0\u003c/p\u003e \u003cp\u003e(309201.0\u0026ndash;1506202.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393931.0\u003c/p\u003e \u003cp\u003e(145306.5\u0026ndash;739481.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165523.7\u003c/p\u003e \u003cp\u003e(57040.2\u0026ndash;332880.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288504.4\u003c/p\u003e \u003cp\u003e(103592.2\u0026ndash;586745.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e389830.6\u003c/p\u003e \u003cp\u003e(147313.8\u0026ndash;724837.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937646.5\u003c/p\u003e \u003cp\u003e(387872.5\u0026ndash;1691304.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492800.8\u003c/p\u003e \u003cp\u003e(199260.2\u0026ndash;892493.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208376.6\u003c/p\u003e \u003cp\u003e(82413.7\u0026ndash;398617.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e397134.4\u003c/p\u003e \u003cp\u003e(160548.3\u0026ndash;742675.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e521390.1\u003c/p\u003e \u003cp\u003e(217750.2\u0026ndash;969013.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1067658.1\u003c/p\u003e \u003cp\u003e(426704.1\u0026ndash;1869295.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572391.7\u003c/p\u003e \u003cp\u003e(219424.5\u0026ndash;1014460.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253887.7\u003c/p\u003e \u003cp\u003e(94169.1\u0026ndash;464247.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e482796.6\u003c/p\u003e \u003cp\u003e(188224.2\u0026ndash;865393.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e674375.1\u003c/p\u003e \u003cp\u003e(263952.7\u0026ndash;1242349.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1272812.8\u003c/p\u003e \u003cp\u003e(519012.6\u0026ndash;2219658.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e692639.8\u003c/p\u003e \u003cp\u003e(273050.6\u0026ndash;1217012.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316191.9\u003c/p\u003e \u003cp\u003e(120108.0\u0026ndash;572792.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e616115.9\u003c/p\u003e \u003cp\u003e(248815.1\u0026ndash;1098515.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e871032.1\u003c/p\u003e \u003cp\u003e(356240.1\u0026ndash;1563716.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1272618.8\u003c/p\u003e \u003cp\u003e(526045.1\u0026ndash;2196559.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682599.2\u003c/p\u003e \u003cp\u003e(276226.6\u0026ndash;1205915.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321904.7\u003c/p\u003e \u003cp\u003e(127960.2\u0026ndash;573001.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e617816.8\u003c/p\u003e \u003cp\u003e(257616.3\u0026ndash;1106987.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e916113.1\u003c/p\u003e \u003cp\u003e(375205.4\u0026ndash;1618910.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1257665.8\u003c/p\u003e \u003cp\u003e(524254.6\u0026ndash;2159329.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e686507.1\u003c/p\u003e \u003cp\u003e(282900.7\u0026ndash;1211377.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320146.5\u003c/p\u003e \u003cp\u003e(128184.5\u0026ndash;567293.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e628198.8\u003c/p\u003e \u003cp\u003e(265106.0\u0026ndash;1119990.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e918134.2\u003c/p\u003e \u003cp\u003e(377503.4\u0026ndash;1602633.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1294649.5\u003c/p\u003e \u003cp\u003e(546829.1\u0026ndash;2219149.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e702433.8\u003c/p\u003e \u003cp\u003e(295595.8\u0026ndash;1238362.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332309.4\u003c/p\u003e \u003cp\u003e(132188.0\u0026ndash;585540.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e652263.0\u003c/p\u003e \u003cp\u003e(277347.0\u0026ndash;1159904.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e971729.3\u003c/p\u003e \u003cp\u003e(404454.5\u0026ndash;1679421.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge-standardised DALY rates (per 100,000)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0 (24.9\u0026ndash;134.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.9 (23.3\u0026ndash;131.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.4 (15.3\u0026ndash;103.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.9 (22.4\u0026ndash;154.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.7 (31.5\u0026ndash;185.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.5 (25.0\u0026ndash;121.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0 (22.3\u0026ndash;115.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.8 (16.9\u0026ndash;98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.9 (27.6\u0026ndash;155.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.3 (35.5\u0026ndash;183.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.9 (28.3\u0026ndash;122.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.4 (25.2\u0026ndash;113.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.9 (19.7\u0026ndash;95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.5 (33.6\u0026ndash;159.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.2 (40.0\u0026ndash;179.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.7 (29.7\u0026ndash;130.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.1 (24.5\u0026ndash;113.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6 (19.4\u0026ndash;96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.5 (33.6\u0026ndash;158.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.4 (37.8\u0026ndash;181.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.2 (36.2\u0026ndash;154.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.9 (30.3\u0026ndash;135.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.7 (24.4\u0026ndash;117.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.6 (43.7\u0026ndash;196.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.6 (49.6\u0026ndash;220.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.2 (36.8\u0026ndash;152.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2 (30.5\u0026ndash;132.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.1 (25.7\u0026ndash;117.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.1 (44.8\u0026ndash;191.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126.5 (50.5\u0026ndash;225.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.2 (36.7\u0026ndash;149.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0 (31.0\u0026ndash;132.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.0 (25.4\u0026ndash;114.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.0 (45.3\u0026ndash;190.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.3 (49.4\u0026ndash;217.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.9 (38.3\u0026ndash;154.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.1 (31.9\u0026ndash;134.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.6 (25.9\u0026ndash;116.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.3 (46.6\u0026ndash;193.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127.2 (51.6\u0026ndash;222.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eValues are presented as estimates with 95% uncertainty intervals (UI).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAge-specific distribution of IPV-attributable depression burden across SDI regions in 2023\u003c/h2\u003e \u003cp\u003eIn 2023, the age-specific distribution of DALYs attributable to IPV-related depression showed a consistent life-course pattern across all SDI regions, with burden increasing rapidly from adolescence, peaking in early to mid-adulthood, and declining thereafter \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In the Low SDI region, DALYs peaked at ages 25 to 29 years, reaching 155,206 DALYs with a 95% UI of 67,121 to 279,609. A similar early adult peak was observed in the Low-middle SDI region at ages 30 to 34 years, with 93,424 DALYs (41,051 to 166,004). In the Middle SDI region, the maximum burden occurred at ages 30 to 34 years, but at substantially lower absolute levels, with 46,391 DALYs (18,259 to 82,869). In contrast, High and High-middle SDI regions exhibited later peaks at ages 35 to 39 years, with 166,624 DALYs (70,646 to 285,920) in the High SDI region and 94,766 DALYs (38,754 to 166,928) in the High-middle SDI region. Beyond the peak ages, DALYs declined progressively with increasing age across all SDI strata. By ages 60 to 64 years, DALYs had fallen to 25,661 (5,043 to 58,146) in the Low SDI region, 23,127 (4,106 to 55,596) in the Low-middle SDI region, and 13,726 (4,747 to 27,213) in the Middle SDI region, while remaining comparatively higher in the High SDI region at 73,677 (28,152 to 137,747). DALYs were consistently lowest among adolescents aged 15 to 19 years and among the oldest age groups across all SDI regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePanels show DALYs by 5-year age group for (a) High SDI, (b) High-middle SDI, (c) Middle SDI, (d) Low-middle SDI, and (e) Low SDI regions. Bars represent the total number of DALYs in each age group, shown as absolute numbers. The age distribution demonstrates consistent peaks in early to mid-adulthood across SDI regions, with the timing of peak burden varying by SDI level.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInequality and decomposition of IPV-attributable depression burden across SDI regions\u003c/h3\u003e\n\u003cp\u003eInequality analyses across five SDI regions showed a modest socioeconomic gradient in age-standardised DALY rates of depression attributable to IPV, with point estimates consistently indicating higher burden in lower SDI regions over time \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The slope index of inequality suggested an increasing absolute difference, with SII point estimates of \u0026minus;\u0026thinsp;16.4 DALYs per 100,000 in 1990 (95% UI\u0026thinsp;\u0026minus;\u0026thinsp;128.7 to 95.9), \u0026minus;\u0026thinsp;28.5 in 2019 (\u0026minus;\u0026thinsp;134.3 to 78.2), and \u0026minus;\u0026thinsp;46.3 in 2023 (\u0026minus;\u0026thinsp;168.8 to 78.9). Similarly, the concentration index of inequality remained negative across all years examined, with CII values of \u0026minus;\u0026thinsp;0.04 (95% UI\u0026thinsp;\u0026minus;\u0026thinsp;0.36 to 0.21) in 1990, \u0026minus;\u0026thinsp;0.06 (\u0026minus;\u0026thinsp;0.30 to 0.17) in 2019, and \u0026minus;\u0026thinsp;0.08 (\u0026minus;\u0026thinsp;0.29 to 0.14) in 2023, indicating that point estimates consistently favoured a greater concentration of IPV-attributable depression burden among populations with lower SDI.\u003c/p\u003e \u003cp\u003eDecomposition analyses of changes in total DALYs between 2019 and 2023 showed that increases across all SDI regions were driven predominantly by epidemiological change, with additional positive contributions from population growth and minimal or negative contributions from population ageing \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Epidemiological change accounted for the largest share of DALY increases in every SDI group, contributing 228,836 DALYs in the High SDI region, 106,874 in the High-middle SDI region, 64,206 in the Middle SDI region, 131,456 in the Low-middle SDI region, and 208,923 in the Low SDI region. Population growth contributed positively to increases in DALYs across all SDI strata, with the largest contributions observed in Low SDI (87,867 DALYs) and Low-middle SDI (37,339 DALYs) regions. In contrast, population ageing contributed negatively in High, High-middle, and Middle SDI regions, and only minimally in Low and Low-middle SDI regions. As a result of these combined effects, the largest absolute increases in total DALYs between 2019 and 2023 occurred in Low SDI (297,354 DALYs) and High SDI (226,991 DALYs) regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePanel (a) shows the SII in age-standardised DALY rates of depression attributable to IPV across SDI ranks in 1990, 2019, and 2023. Circles represent observed regional estimates, with circle size proportional to population size. Solid lines indicate fitted regression slopes, and shaded areas denote 95% uncertainty intervals. Negative SII values indicate higher burden in lower SDI regions. Panel (b) shows concentration curves and corresponding CII for the same years. The dashed diagonal line represents equality. Curves above the line of equality and negative CII values indicate a disproportionate concentration of IPV-attributable depression burden among populations with lower SDI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHorizontal bars show the contributions of population ageing, population growth, and epidemiological change to the net change in total DALYs attributable to IPV-related depression within each SDI category between 2019 and 2023. Dots indicate the total absolute change in DALYs for each SDI group. Positive values denote increases in DALYs, whereas negative values indicate reductions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between COVID-19 incidence and IPV-attributable depression YLD rates in low and low-middle SDI countries, 2020\u0026ndash;2023\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing a country\u0026ndash;year panel design from 2020 to 2023, we assessed the association between age-standardised COVID-19 incidence rates and age-standardised IPV-attributable depression YLD rates among females in low and low-middle SDI countries \u003cb\u003e(Supplementary Tables S1\u0026ndash;S2, Appendix)\u003c/b\u003e. In low SDI countries (33 countries; 132 country\u0026ndash;year observations), two-way fixed-effects models indicated that higher contemporaneous COVID-19 incidence was significantly associated with higher IPV-attributable depression YLD rates. Specifically, a 1% increase in COVID-19 incidence was associated with a 0.028% increase in IPV-attributable depression YLD rates (β\u0026thinsp;=\u0026thinsp;0.028, SE\u0026thinsp;=\u0026thinsp;0.007; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), after controlling for country and year fixed effects \u003cb\u003e(Table S3, Panel A)\u003c/b\u003e. By contrast, the one-year lagged COVID-19 incidence was inversely associated with IPV-attributable depression YLD rates in low SDI countries (β = \u0026minus;0.017, SE\u0026thinsp;=\u0026thinsp;0.005; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among low-middle SDI countries (43 countries; 172 country\u0026ndash;year observations), no statistically significant associations were observed for either contemporaneous COVID-19 incidence (β\u0026thinsp;=\u0026thinsp;0.003, SE\u0026thinsp;=\u0026thinsp;0.002) or one-year lagged incidence (β = \u0026minus;0.002, SE\u0026thinsp;=\u0026thinsp;0.003) \u003cb\u003e(Table S3, Panel B)\u003c/b\u003e. All models incorporated country and year fixed effects with standard errors clustered at the country level. Model fit was high across specifications (R\u0026sup2; range 0.997 to 0.999).\u003c/p\u003e\n\u003ch3\u003ePredicted future burden of IPV-attributable depression among females\u003c/h3\u003e\n\u003cp\u003eUsing scenario-based projections derived from Bayesian age\u0026ndash;period\u0026ndash;cohort (BAPC) models, we estimated the future trajectory of age-standardised DALY rates of depression attributable to IPV among females across SDI levels through 2050 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; \u003cb\u003eTables S4\u0026ndash;S5)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eUnder the continuation scenario, in which age\u0026ndash;period\u0026ndash;cohort patterns observed up to 2023 were assumed to persist, age-standardised DALY rates were projected to increase substantially across all SDI categories between 2023 and 2050, with a clear and progressively widening gradient by SDI level \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea; \u003cb\u003eFigure S1; Table S4, panel a)\u003c/b\u003e. By 2050, the highest projected burdens were observed in Low SDI and Low-middle SDI regions, where age-standardised DALY rates were estimated to reach 377.6 (95% UI 44.6 to 2458.6) per 100 000 females and 302.4 (43.7 to 1732.2) per 100 000 females, respectively, compared with 224.2 (55.9 to 794.0) in the High SDI region. Across all SDI strata, projected increases accelerated after 2023, with the steepest absolute and relative rises occurring in lower SDI settings.\u003c/p\u003e \u003cp\u003eIn contrast, under the plateau scenario, which assumed no further period-related change after 2023 and stabilisation of rates at the mean levels observed during 2020\u0026ndash;2023, age-standardised DALY rates were projected to remain constant over time but at persistently elevated levels across all SDI categories \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb; \u003cb\u003eTable S4, panel b)\u003c/b\u003e. Despite the absence of further increases, substantial socioeconomic inequalities persisted, with plateaued rates of 121.7 in the Low SDI region and 105.0 in the Low-middle SDI region, compared with 87.5 in the High SDI region. Under both the continuation and plateau scenarios, projected age-standardised DALY rates remain above pre-2020 levels through 2050.\u003c/p\u003e \u003cp\u003eAge-specific projections further revealed marked heterogeneity in the future burden across age groups, particularly in Low and Low-middle SDI regions \u003cb\u003e(Table S5)\u003c/b\u003e. Under the continuation scenario, the highest DALY rates by 2050 were consistently projected among women aged 25\u0026ndash;49 years, with rates exceeding 300 per 100 000 females in several age groups, while lower but steadily increasing burdens were observed among adolescents and older age groups. Although uncertainty intervals widened with increasing projection horizons, the overall age distribution remained stable, indicating a persistent concentration of the future burden of IPV-attributable depression among women of reproductive and working ages in lower SDI settings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSolid lines indicate observed age-standardised DALY rates from 1990 to 2023, and dashed lines indicate projected rates from 2024 to 2050. Panel (a) presents the continuation scenario, assuming that age\u0026ndash;period\u0026ndash;cohort patterns observed up to 2023 continue beyond the observation period. Corresponding projections across five SDI regions with 95% uncertainty intervals are presented in Figure S1. Panel (b) presents a plateau scenario, in which no further period-related change is assumed after 2023, and age-standardised DALY rates are stabilised at the mean level observed during 2020\u0026ndash;2023. The vertical dotted line marks the final year of observed data (2023). Estimates are shown for five SDI categories.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal findings\u003c/h2\u003e \u003cp\u003eThis study provides a comprehensive assessment of the burden of depression attributable to IPV among women over more than three decades, using updated population-level estimates from GBD 2023. Across five SDI regions, the burden of IPV-attributable depression increased gradually from 1990 to the late 2010s. Joinpoint analyses indicated a shift toward steeper upward trends beginning around 2017\u0026ndash;2018; however, observed values remained largely stable through 2019. A pronounced and synchronous increase became evident after 2020, coinciding with the COVID-19 period (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Women living in Low and Low-middle SDI regions bore a disproportionate share of this post-2020 increase. By 2023, these regions exhibited the highest age-standardised DALY rates and the steepest recent rises, despite contributing fewer absolute DALYs than the High SDI region. Age-specific analyses showed a consistent concentration of burden in early and mid-adulthood, with peak DALYs occurring at younger ages in lower SDI settings, highlighting critical life-course windows of vulnerability and persistent global inequalities, which are widening in absolute terms.\u003c/p\u003e \u003cp\u003eDecomposition analyses showed that recent increases in IPV-attributable depression burden were driven primarily by epidemiological change, with smaller contributions from population growth and minimal or negative contributions from population ageing across SDI regions. Moreover, supporting evidence from country\u0026ndash;year analyses indicated that, in low SDI countries, higher contemporaneous age-standardised COVID-19 incidence rates were associated with increased age-standardised YLD rates attributable to IPV-related depression. Finally, scenario-based projections suggest that, under both continuation and plateau assumptions, the burden of IPV-attributable depression is likely to remain elevated through 2050, particularly in lower SDI regions, underscoring the long-term public health and equity implications of the post-pandemic shift.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAccelerating burden since the late 2010s\u003c/h2\u003e \u003cp\u003eThe acceleration in IPV-attributable depression burden observed from the late 2010s represents a departure from the relatively slow and stable increases seen over the preceding decades. The synchronised timing of this inflection across all SDI regions suggests the influence of global forces rather than region-specific demographic or epidemiological transitions. Although our joinpoint analyses identified a structural change beginning around 2017\u0026ndash;2018, the most pronounced increases occurred after 2020, indicating that the COVID-19 pandemic acted as a catalyst that amplified pre-existing vulnerabilities rather than as a singular cause of change. This pattern is consistent with evidence that the pandemic intensified known risk pathways for both IPV exposure and mental health deterioration through prolonged household confinement, economic disruption, and erosion of social support systems (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the post-2020 acceleration was evident in age-standardised rates as well as absolute DALYs, underscoring that the observed increases cannot be attributed to population ageing or growth alone. Instead, the findings point to a shift in underlying epidemiological conditions, including heightened exposure to violence, increased severity or persistence of depressive symptoms, and reduced access to protective and mental health services during and after the pandemic period. Emerging global evidence has documented substantial disruptions to IPV prevention, reporting, and mental health care during COVID-19, particularly in resource-constrained settings, lending plausibility to this interpretation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Together, these findings may suggest that the late-2010s acceleration reflects a structural worsening of IPV-related mental health risk that may persist beyond the immediate pandemic context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic disparities across SDI levels\u003c/h2\u003e \u003cp\u003eMarked socioeconomic disparities characterised the distribution of IPV-attributable depression burden across SDI levels, with women in Low and Low-middle SDI regions consistently experiencing higher age-standardised DALY rates than those in more developed settings. These gradients align with extensive evidence that socioeconomic disadvantage amplifies both exposure to IPV and vulnerability to its mental health consequences through intersecting pathways of poverty, gender inequality, and limited access to care. Large-scale syntheses, including the Lancet Commission on global mental health and sustainable development, have highlighted that treatment gaps for common mental disorders remain widest in low-resource settings, where mental health services are chronically underfunded and poorly integrated into primary care systems (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Within this context, IPV-related depression is more likely to remain untreated, prolonged, and disabling, contributing to higher population-level burden despite lower absolute case counts.\u003c/p\u003e \u003cp\u003eImportantly, the observed disparities reflect not only differences in baseline risk but also unequal capacity to absorb and recover from large-scale shocks. Health and social protection systems in low SDI settings are often less resilient to crises, with fewer safeguards to maintain IPV prevention, legal protection, and mental health services during periods of disruption (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As a result, acute stressors such as the COVID-19 pandemic may translate into sustained increases in mental health burden rather than transient fluctuations (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The persistence of higher age-standardised rates in lower SDI regions underscores that IPV-attributable depression is deeply embedded within broader structural inequalities, and that without targeted investments to reduce these gaps, global progress in violence prevention and mental health will remain uneven.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAge-specific burden and life-course patterns\u003c/h2\u003e \u003cp\u003eThe age distribution of IPV-attributable depression burden highlights a clear life-course pattern, with the highest concentration occurring during early and mid-adulthood. This period coincides with key transitions related to partnership formation, childbearing, and economic participation, which are known to shape both exposure to IPV and susceptibility to depressive disorders. Previous evidence suggests that IPV experienced during reproductive and working ages is often recurrent, may become severe, and is intertwined with caregiving responsibilities, thereby exerting a disproportionate and sustained impact on women\u0026rsquo;s mental health and functional capacity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The concentration of burden within these age groups underscores that IPV-related depression is not evenly distributed across the life span but clustered within socially and biologically critical periods.\u003c/p\u003e \u003cp\u003eThe earlier peak of burden observed in lower SDI settings further suggests that life-course vulnerability is shaped by contextual factors, including earlier age at union formation, reduced educational opportunities, and limited access to protective and mental health services (\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Previous research has shown that women exposed to IPV earlier in adulthood face a higher risk of chronic and recurrent depressive episodes, with effects that may persist even after cessation of violence (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). From a public health perspective, these findings reinforce the importance of adopting a life-course approach to prevention and care, prioritising early identification and integrated IPV and mental health interventions during young and middle adulthood, when the potential for averting long-term disability is greatest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDrivers of recent increases\u003c/h2\u003e \u003cp\u003eDecomposition analyses indicated that recent increases in IPV-attributable depression burden were driven predominantly by epidemiological change rather than by population growth or ageing, pointing to shifts in underlying risk conditions within the GBD attribution framework. Such changes may reflect alterations in IPV exposure, the severity or persistence of depressive symptoms, or the strength of the risk\u0026ndash;outcome relationship linking IPV to depression during periods of widespread social disruption. Importantly, this interpretation does not rely on demographic explanations but instead highlights modifiable factors that influence how IPV translates into long-term mental health disability at the population level.\u003c/p\u003e \u003cp\u003eMultiple lines of external evidence support the plausibility of these mechanisms. During the COVID-19 period, global studies documented substantial increases in the prevalence and severity of depressive symptoms, alongside prolonged symptom duration and reduced remission, particularly in settings with limited access to mental health care (\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In parallel, health systems experienced marked disruptions in essential mental health and social services, including IPV prevention, reporting, and support pathways, which are critical for mitigating the duration and disability associated with IPV-related depression (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Together, these factors may have amplified the disabling consequences of IPV exposure\u0026mdash;even in the absence of major demographic change\u0026mdash;by increasing untreated morbidity and prolonging recovery. Within this context, the predominance of epidemiological change observed in our study underscores the importance of addressing service continuity, early intervention, and violence prevention as central strategies for reversing recent increases in IPV-attributable depression burden.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCOVID-19\u0026ndash;related burden dynamics\u003c/h2\u003e \u003cp\u003eThe COVID-19 period represents a distinct context in which social restrictions, economic insecurity, and disruptions to daily life converged to alter both violence dynamics and mental health vulnerability. Prior evidence indicates that lockdown measures and prolonged household confinement were associated with worsening psychological distress, reduced well-being, and slower recovery trajectories, even in populations without prior mental health conditions (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In parallel, multiple analyses documented increases in indicators of domestic violence risk during periods of stringent mobility restrictions, suggesting that pandemic control measures may have unintentionally intensified exposure to IPV for some women (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). These overlapping stressors provide a plausible backdrop against which IPV-attributable depression burden could rise during the pandemic period, without implying a direct or exclusive causal pathway.\u003c/p\u003e \u003cp\u003eCrucially, the pandemic also disrupted the systems intended to mitigate harm. Studies from diverse health-care settings reported substantial reductions in mental health service utilisation and delays in care-seeking during the early phases of COVID-19, with recovery remaining uneven over time (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). For women experiencing IPV, interruptions in access to protection, counselling, and treatment may have prolonged symptom duration and increased disability, thereby amplifying the burden attributable to IPV under the GBD counterfactual framework. These dynamics underscore that pandemic-related increases in IPV-attributable depression burden should be interpreted not simply as short-term shocks, but as the cumulative result of constrained coping capacity and weakened service responses during a period of sustained crisis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFuture burden trajectories\u003c/h2\u003e \u003cp\u003eScenario-based projections suggest that the burden of depression attributable to IPV is unlikely to return to pre-pandemic levels in the coming decades, even under conservative assumptions of stabilised rates. From a population health perspective, this persistence implies that recent increases may represent a lasting shift rather than a transient perturbation. Modelling studies of mental health outcomes suggest that major social and economic shocks may have enduring implications for depressive morbidity, particularly when recovery is constrained by limited service capacity and ongoing exposure to psychosocial stressors (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Within this context, IPV-related depression may become increasingly entrenched in settings where prevention, protection, and treatment systems remain under-resourced.\u003c/p\u003e \u003cp\u003eThe widening divergence in projected burden across SDI levels further underscores the risk of accumulating long-term inequities. Prior evidence indicates that without targeted interventions, mental health burdens tend to concentrate disproportionately in populations facing persistent structural disadvantage (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Interpreted through the GBD counterfactual framework, sustained elevation in IPV-attributable depression burden reflects not only continued exposure to violence but also delayed recovery and preventable disability over the life course. These projections therefore serve less as precise predictions than as signals for policy and system planning, highlighting the urgency of strengthening integrated IPV and mental health responses to avert the consolidation of post-pandemic mental health inequalities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePolicy and programme implications\u003c/h2\u003e \u003cp\u003eOur findings have important implications for the evaluation and prioritisation of international policies addressing IPV and women\u0026rsquo;s mental health. Existing global frameworks, including the WHO RESPECT women strategy and international mental health action plans, emphasise violence prevention, survivor protection, and access to care, but implementation has remained uneven, particularly in low-resource settings (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The sustained elevation of IPV-attributable depression burden observed in our analyses suggests that current policy approaches have been insufficient to buffer mental health consequences during large-scale crises. From a policy perspective, this gap underscores the need to move beyond siloed IPV or mental health strategies toward integrated programmes that explicitly address depressive morbidity as a core outcome of violence prevention efforts.\u003c/p\u003e \u003cp\u003ePrevious evidence indicates that interventions combining IPV screening, psychosocial support, and referral pathways within primary health care and reproductive health services are among the most feasible and scalable approaches in low- and middle-income settings (\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). However, the effectiveness of such programmes depends on service continuity, workforce capacity, and legal and social protection mechanisms\u0026mdash;elements that were frequently disrupted during the COVID-19 period. International policy responses should therefore prioritise strengthening system resilience, including task-sharing for mental health care, community-based survivor support, and crisis-contingent service delivery models. Interpreted through a global equity lens, reducing IPV-attributable depression burden will require not only expanding coverage of existing interventions, but also aligning violence prevention and mental health policies with preparedness planning to ensure that gains are not reversed during future social or public health emergencies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths. It draws on the most recent estimates from GBD 2023, enabling a consistent and comparable assessment of IPV-attributable depression burden across multiple decades and sociodemographic contexts. By integrating trend analyses, inequality metrics, decomposition methods, panel analyses, and scenario-based projections, the study provides a multidimensional perspective on how IPV-related mental health burden has evolved over time and how it may unfold in the future. Importantly, the use of age-standardised measures and SDI-stratified analyses allows for meaningful comparisons across populations with differing demographic structures and development levels, supporting a global equity-oriented interpretation of the findings.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these results. First, as a secondary analysis based on GBD estimates, findings depend on the availability and quality of underlying data and modelling assumptions, including those related to IPV exposure measurement and risk\u0026ndash;outcome relationships. Second, IPV-attributable depression burden reflects counterfactual attribution rather than causal decomposition, and overlapping pathways through which large-scale disruptions may concurrently influence IPV exposure and depressive morbidity cannot be disentangled. Third, analyses conducted at the SDI regional level may mask within-country and subnational heterogeneity, particularly in settings where social inequalities are pronounced. Finally, future projections are subject to increasing uncertainty over longer horizons and should be interpreted as indicative planning signals rather than precise forecasts. Despite these limitations, the overall patterns observed across methods and scenarios consistently point to sustained and unequal IPV-attributable depression burden, underscoring the robustness and policy relevance of the main conclusions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study shows that the burden of depression attributable to IPV among women has entered a phase of accelerated growth since the late 2010s, with a pronounced and persistent impact concentrated in Low and Low-middle SDI regions. Recent increases appear to be primarily driven by changes in epidemiological conditions that have amplified the mental health consequences of IPV. The persistence of elevated burden under multiple future scenarios suggests that recovery from the COVID-19 period is unlikely to occur spontaneously. These findings underscore the urgency of integrating IPV prevention with mental health care, strengthening service continuity during crises, and prioritising equitable, system-level responses to protect women\u0026rsquo;s mental health. Without targeted and sustained action, IPV-attributable depression is likely to remain a substantial and unevenly distributed contributor to global disability over the coming decades.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor contribution declaration\u003c/h2\u003e \u003cp\u003eThe study was conceived and designed by DS. DS extracted the data, conducted the analyses, and drafted the initial manuscript, and critically reviewed and revised the manuscript. All authors approved the final version of the manuscript. DS take final responsibility for the decision to submit the manuscript for publication.\u003c/p\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eData sharing\u003c/h2\u003e \u003cp\u003eData used in this study were obtained from the GBD 2023 study and are publicly available through the GBD Results Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank the Institute for Health Metrics and Evaluation (IHME) and the Global Burden of Disease (GBD) Study collaborators for the initial development of the GBD 2023 estimates, which were funded by the Bill \u0026amp; Melinda Gates Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrink J, Cullen P, Beek K, Peters SAE (2021) Intimate partner violence during the COVID-19 pandemic in Western and Southern European countries. 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Brain Behav 15(1):e70236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal prevalence and burden of (2021) depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 398(10312):1700\u0026ndash;1712\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEttman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S (2020) Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. JAMA Netw Open 3(9):e2019686\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAksunger N, Vernot C, Littman R, Voors M, Meriggi NF, Abajobir A et al (2023) COVID-19 and mental health in 8 low- and middle-income countries: A prospective cohort study. PLoS Med 20(4):e1004081\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH (2020) Pulse survey on continuity of essential health services during the COVID-19 pandemic: interim report, 27 August 2020. World Health Organization\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno C, Wykes T, Galderisi S, Nordentoft M, Crossley N, Jones N et al (2020) How mental health care should change as a consequence of the COVID-19 pandemic. Lancet Psychiatry 7(9):813\u0026ndash;824\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N et al (2020) The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 395(10227):912\u0026ndash;920\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaly M, Sutin AR, Robinson E (2022) Longitudinal changes in mental health and the COVID-19 pandemic: evidence from the UK Household Longitudinal Study. Psychol Med 52(13):2549\u0026ndash;2558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeslie E, Wilson R (2020) Sheltering in place and domestic violence: Evidence from calls for service during COVID-19. 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BMJ Glob Health 4(5):e001484\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":"Lancaster 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":"Intimate partner violence, Depression, Global Burden of Disease, Socioeconomic inequality, COVID-19 pandemic","lastPublishedDoi":"10.21203/rs.3.rs-9724901/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9724901/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntimate partner violence (IPV) is a major, preventable threat to women’s health and a key contributor to depression. The COVID-19 pandemic may have increased IPV exposure while disrupting support services. We examined long-term trends, inequalities, pandemic-related changes, and future burden of IPV-attributable depression across SDI regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a population-level analysis using GBD 2023 data for females aged ≥ 15 years across five SDI regions from 1990 to 2023. We assessed trends, age patterns, and decomposed recent changes into epidemiological, demographic, and ageing components. Socioeconomic inequalities were quantified using slope and concentration indices. Associations between COVID-19 incidence and IPV-attributable depression burden were analysed in lower SDI regions. Future trends to 2050 were projected using Bayesian age–period–cohort models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe burden of depression attributable to IPV increased across all SDI regions, with a faster rise after the late 2010s. The highest burden remained concentrated in low and low-middle SDI regions. Peak burden occurred in early to mid-adulthood, with earlier peaks in lower SDI settings. Inequalities persisted and widened over time. Recent increases were driven mainly by epidemiological change rather than demographic factors. Higher COVID-19 incidence was associated with increased burden in low SDI countries but not in low-middle SDI countries. Projections suggest the burden is likely to remain above pre-pandemic levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIPV-attributable depression among women continues to rise, with sustained inequalities across development levels. Targeted, integrated IPV prevention and mental health strategies are needed, particularly in lower SDI regions and among women in early and mid-adulthood.\u003c/p\u003e","manuscriptTitle":"Accelerating and disproportionate burden of depression attributable to intimate partner violence among women in Low and Low-middle SDI regions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 16:27:43","doi":"10.21203/rs.3.rs-9724901/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":"239344e7-80d8-4273-b20b-c2b01c983093","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68189181,"name":"Psychiatry"}],"tags":[],"updatedAt":"2026-05-18T16:27:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 16:27:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9724901","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9724901","identity":"rs-9724901","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}