Abstract
The health impacts of intimate partner violence against women and childhood sexual abuse are not fully understood. Here we conducted a systematic review by comprehensively searching seven electronic databases for literature on intimate partner violence-associated and childhood sexual abuse-associated health effects. Following the burden of proof methodology, we evaluated the evidence strength linking intimate partner violence and/or childhood sexual abuse to health outcomes supported by at least three studies. Results indicated a moderate association of intimate partner violence with major depressive disorder and with maternal abortion and miscarriage (63% and 35% increased risk, respectively). HIV/AIDS, anxiety disorders and self-harm exhibited weak associations with intimate partner violence. Fifteen outcomes were evaluated for their relationship to childhood sexual abuse, which was shown to be moderately associated with alcohol use disorders and with self-harm (45% and 35% increased risk, respectively). Associations between childhood sexual abuse and 11 additional health outcomes, such as asthma and type 2 diabetes mellitus, were found to be weak. Although our understanding remains limited by data scarcity, these health impacts are larger in magnitude and more extensive than previously reported. Renewed efforts on violence prevention and evidence-based approaches that promote healing and ensure access to care are necessary.
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Main
Violence against women, gender-based violence and violence against children are global health priorities and unacceptably pervasive human rights violations1,2,3. Intimate partner violence against women and childhood sexual abuse are two of the most prevalent and pernicious forms of violence, associated with substantial morbidity and mortality4,5,6,7,8. Globally, it is estimated that almost one in three ever-partnered women have experienced physical and/or sexual intimate partner violence in their lifetime and 20% of young women and almost 10% of young men have experienced some form of childhood sexual abuse4,9. Intimate partner violence is defined as any lifetime experience of physical or sexual violence perpetrated against women by a current or former intimate partner and childhood sexual abuse is defined as exposure of women and men before age 15 to any unwanted sexual contact10. Existing work points to the wide extent of associated serious health consequences. The immediate emotional and physical trauma of intimate partner violence and childhood sexual abuse too often leads to mental and other health consequences that can reverberate across lifetimes and over generations5,6,11,12,13,14. Although long treated separately, it is now established that intimate partner violence and childhood sexual abuse co-occur in the same families, with shared risk and protective factors and produce compounding consequences15,16,17 across the lifespan. For example, childhood sexual abuse is an acknowledged risk factor for later experience and perpetration of intimate partner violence18.
Intimate partner violence and childhood sexual abuse have been included within the Global Burden of Diseases, Injuries and Risk Factors (GBD) risk assessment framework since 2010. Intimate partner violence accounts for more disability-adjusted life years in women of reproductive ages than risk factors such as smoking19,20. Currently, the attributable health burden of both intimate partner violence and childhood sexual abuse are calculated in relation to relatively few health outcomes, likely underestimating their negative impacts. Nevertheless, the latest iteration of the GBD suggests that women exposed to intimate partner violence are 1.54 times as likely to experience depression and 1.60 times as likely to become infected with HIV, while individuals exposed to childhood sexual abuse are 2.21 times as likely to experience alcohol use disorder and 1.56 times as likely to experience depression10. There is an urgent need to update our understanding of the health burdens associated with both risk factors with the most recently available evidence.
In this study, we assessed all available literature on the health impacts of intimate partner violence against women and childhood sexual abuse by systematically searching seven databases for evidence on all forms of violence against women and children. Here, we constrained this larger dataset to evaluate health risks associated with existing GBD risk factors: intimate partner violence and childhood sexual abuse. We re-examine existing risk–outcome pairs in the GBD and evaluate the strength of evidence for new pairs, following the burden of proof risk function (BPRF) methodology developed by Zheng and colleagues21. Elucidating the consequences of violence against women and children is key to centering them as a global health priority and motivating investment in prevention and effective, multi-pronged support to survivors. Outcomes found to be substantially associated with either risk factor suggest areas of intervention to prevent and manage negative health consequences, whereas associations with weaker evidence highlight opportunities for further research (Table 1).
Results
Overview
After conducting a systematic review on seven databases and de-duplicating records, we considered 67,221 records published between 1 January 1970 and 31 January 2023 (Extended Data Fig. 1 shows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram). In total, 4,379 articles met inclusion criteria during title and abstract screening and 534 of these were accepted for extraction after full-text screens. In this paper, we assessed and analyzed studies reporting specifically on intimate partner violence (n = 57) and childhood sexual abuse (n = 172). Maps displaying the count of studies identified by geographical location are displayed in Extended Data Fig. 2 (intimate partner violence) and Extended Data Fig. 3 (childhood sexual abuse). To undertake an analysis using the BPRF methodology, the health outcome studied must correspond to a GBD cause definition and we must have identified a minimum of three studies reporting on the relationship. For intimate partner violence, our models are specific to women and represent women populations only. For childhood sexual abuse, our estimates reflect both men and women, drawing upon data from studies using combined, women-only and men-only samples.
Intimate partner violence
A total of five health outcomes were examined: major depressive disorder, maternal abortion and miscarriage, HIV/AIDS, anxiety disorders and self-harm. Estimates of the risk–outcome relationships are provided in Table 2. Forest plots are shown in Fig. 1 and funnel plots are shown in Extended Data Fig. 4.
The shape of the point indicates the type of intimate partner violence (physical, sexual and aggregate, including psychological) and the color indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dashed line reflects the null RR value (one) and the red vertical line is the burden of proof function at the fifth quantile for these harmful risk–outcome associations. The black data points and horizontal lines each correspond to an effect size and 95% UI from the included study identified on the y axis. We included multiple observations from a single study when effects were reported by form of violence, recall period and age group. Supplementary Table 15 contains more details on the included observations from each study.
Among the five outcomes assessed for a relationship with intimate partner violence, two associations were found to have a three-star rating, suggesting moderate evidence of the risk–outcome relationship: major depressive disorder (0.24 risk–outcome score (ROS)) and maternal abortion and miscarriage (0.15 ROS). For major depressive disorder, the strongest relationship, we extracted 16 observations from 12 studies22,23,24,25,26,27,28,29,30,31,32,33 (nine cohorts and three case–control) across nine locations (Supplementary Table 1). Based on our conservative BPRF analysis, we estimated at least a 63% increase in risk of major depressive disorder (1.63 BPRF). No bias covariates were detected as significant or included in our main model. Estimated ROSs were similar in sensitivity analyses in which we subset data to cohort studies (three case–controls25,28,31 excluded) and excluded studies with exposure definitions including psychological intimate partner violence in addition to physical and/or sexual intimate partner violence25,27,30,31. Without applying 10% trimming of outliers (a likelihood-based statistical approach that limits the influence of outliers and identifies the 90% most self-coherent observations), estimated between-study heterogeneity increased and the strength of the association was reduced (Supplementary Table 2).
The outcome with the second highest number of identified and included studies was maternal abortion and miscarriage, for which we extracted 11 observations from nine studies34,35,36,37,38,39,40,41 (three cohorts and six case–controls) across nine locations (Supplementary Table 1). The estimated BPRF was 1.35 (at least a 35% increase in risk). Our bias covariate that flagged studies using exposure definitions measuring recent/current intimate partner violence (rather than lifetime intimate partner violence) was detected as significant and adjusted for within our final model. Main results were sensitive to analyses run without trimming outliers and excluding studies reporting low case counts (<10) in exposed and/or unexposed groups39 (Supplementary Table 3).
A two-star rating suggests that exposure to intimate partner violence increases the risk of a given outcome by 0–15% and can be interpreted as weak evidence of an association. HIV/AIDS was found to have a two-star rating of the association with intimate partner violence based on our conservative interpretation of the evidence (at least a 13% increase in risk, 0.06 ROS, 1.13 BPRF). We extracted nine observations from six studies42,43,44,45,46,47 (three cohorts and three other designs) across five locations in sub-Saharan Africa (Supplementary Table 1). No bias covariates were selected as significant for inclusion in the final model. Results were consistent with sensitivity analyses in which we subset the input data to prospective cohort studies only (three other study designs excluded44,46,47) and did not trim outliers (Supplementary Table 4).
A one-star rating suggests that exposure to intimate partner violence is weakly associated with the outcome under study and that introduction of additional evidence in the future may lead to changes in our assessment of this relationship. Our BPRF analysis yielded a one-star rating for two of the five studied outcomes: anxiety disorders (−0.02 ROS) and self-harm (−0.42 ROS). For anxiety disorders, we extracted eight observations from five cohort studies23,24,26,30,32 across three locations (Supplementary Table 1). Our bias covariate that flagged studies using exposure definitions measuring recent/current intimate partner violence (rather than lifetime intimate partner violence) was detected as significant and adjusted for within our final model. The results were consistent across several sensitivity analyses: the one-star rating persisted when removing trimming and when excluding a study that included psychological violence in its exposure definition30 (Supplementary Table 5).
The smallest number of studies22,26,48,49 (three cohorts and one case–control across two locations) was reported for an association with self-harm (operationalized across all included studies as suicide attempt) (Supplementary Table 1). No bias covariates were detected as significant, and thus none were included in the final model. When trimming a single outlying study, the overall estimated between-study heterogeneity was much lower, resulting in a three-star association (0.17 ROS). When excluding the one study26 that used an aggregate outcome definition, including suicidal ideation, the ROS decreased (−0.70 ROS; Supplementary Table 6). Across each of the health outcomes analyzed in association with intimate partner violence, we did not detect publication bias within our model results, as determined using Egger’s regression test50.
Additional outcomes that were identified in our review but not able to be analyzed using the BPRF methodology for either not meeting minimum data availability criteria (alcohol use disorder32,51, maternal hypertensive disorders52,53, gestational diabetes52,53 and maternal hemorrhage53,54 and sexually transmitted infections, excluding HIV22,55) or not map** to an existing GBD cause (heavy episodic drinking56,57,58,59,60 and peripartum depression61,62,63,64,65,66,67,68) are presented in Supplementary Information 1.3. Extracted studies describing the risk of peripartum depression, heavy episodic drinking and alcohol use disorder are additionally visually summarized in Extended Data Fig. 5.
Childhood sexual abuse
A total of 15 health outcomes met our minimum data availability criteria to investigate their associations with childhood sexual abuse. These were alcohol use disorders, self-harm, major depressive disorder, anxiety disorders, asthma, type 2 diabetes mellitus, HIV/AIDS, sexually transmitted infections, maternal abortion and miscarriage, drug use disorders, conduct disorder, bulimia nervosa, schizophrenia, anorexia nervosa and ischemic heart disease. Relevant estimates of the risk–outcome relationships associated with childhood sexual abuse are provided in Table 2. Forest plots are shown in Figs. 2–4 and funnel plots are displayed in Extended Data Figs. 6–8.
The shape of the point indicates gender of the sample, and the color indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dashed line reflects the null RR value (one) and the red vertical line is the burden of proof function at the fifth quantile for these harmful risk–outcome associations. The black data points and horizontal lines each correspond to an effect size and 95% UI from the included study identified on the y axis. We included multiple observations from a single study when effects were reported by severity/frequency of exposure and/or separately by gender or other subgroups. Supplementary Table 16 provides more details on the included observations from each study.
The shape of the point indicates the sample gender and the color indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dotted line reflects the null RR value (one) and the red vertical line is the burden of proof function at the fifth quantile for these harmful risk–outcome associations. The black data points and horizontal lines each correspond to an effect size and 95% UI from the included study identified on the y axis. We included multiple observations from a single study when effects were reported by severity/frequency of exposure and/or separately by gender or other subgroups. Supplementary Table 16 provides more details on included observations from each study.
The shape of the point indicates the sample gender and the color indicates whether the point was detected and trimmed as an outlier. The light blue interval corresponds to the 95% UI incorporating between-study heterogeneity; the dark blue interval corresponds to the 95% UI without between-study heterogeneity. The black vertical dashed line reflects the null RR value (one) and the red vertical line is the burden of proof function at the fifth quantile for these harmful risk–outcome associations. The black data points and horizontal lines each correspond to an effect size and 95% UI from the included study identified on the y axis. We included multiple observations from a single study when effects were reported by severity/frequency of exposure and/or separately by gender or other subgroups. Supplementary Table 16 provides more details on the included observations from each study.
Among the outcomes assessed, we identified two with associations that yielded a three-star rating: alcohol use disorders (0.19 ROS) and self-harm (0.15 ROS). For alcohol use disorders, we extracted 15 observations from ten studies69,70,71,72,73,74,75,76,77,78 (nine cohorts and one case–control) across five locations (Supplementary Table 8). Alcohol use disorder was found to have a three-star rating of the association with childhood sexual abuse based on our conservative interpretation of the evidence (at least a 45% increase in risk, BPRF = 1.45). Two study-level bias covariates were found to be significant and adjusted for within the final model: geographic representativeness of the study and level of adjustment for confounding including age and sex. Results were sensitive to trimming in the model; without trimming, the estimated between-study heterogeneity increased substantially. We additionally undertook an analysis restricting our input dataset to only those studies that measured alcohol dependence (excluding studies using the accepted alternate definition of abuse and/or dependence69,72,75,77). When applying this restriction, results were consistent with our main analysis (0.20 ROS; Supplementary Table 9).
To estimate the association with self-harm (operationalized across all included studies as suicide attempt), we extracted 20 observations from 16 studies71,74,77,78,79,80,81,82,83,84,85,86,87,88,89,90 (14 cohorts and two case–controls) across nine locations (Supplementary Table 8). Our conservative interpretation of the evidence suggests at least 35% increase in self-harm risk given exposure to childhood sexual abuse with a BPRF of 1.35. Risk of reverse causation was found to be a significant bias covariate; thus, we conducted a sensitivity analysis restricting our input dataset to cohort studies only. Model results using cohort studies only77,78,80,81,84,85,86,87,89,90 were consistent with our main result (0.24 ROS; Supplementary Table 10).
We found a two-star rating for the association with three outcomes: major depressive disorder (0.09 ROS); anxiety disorders (0.08 ROS); and asthma (0.04 ROS). Childhood sexual abuse and major depressive disorder was the most data-rich risk–outcome pair across all health outcomes selected for analysis. We extracted 32 observations from 26 studies71,73,74,78,83,87,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,186 and was conducted in line with our previously published protocol (PROSPERO, CRD42022299831)188. Our search strings are reported in Supplementary Information 4.1 and have been previously published188.
In brief, our search strategy incorporated keyword and controlled vocabulary restrictions corresponding to (1) violence exposure; (2) study design and type; (3) measures of association and/or risk; and (4) publication year. Searches were not restricted to predetermined health outcomes in an effort to identify and extract all literature reporting on an association between GBV and/or VAC and health. During article screening and selection, we drew upon definitions of health outcomes from the cause, injury and risk factor case definitions used by the GBD study10,189. While we identified and extracted studies reporting on a variety of GBV and VAC case definitions, in the present study we report only results from studies that described exposure to intimate partner violence and/or childhood sexual abuse. We define intimate partner violence and childhood sexual abuse according to the GBD study case definitions: lifetime prevalence of physical and/or sexual violence by a current or former intimate partner since age 15 and lifetime prevalence of intercourse or other contact abuse (fondling or other sexual touching) when aged 15 years or younger in which the contact was unwanted or perpetrator was 5+ years older than the victim, for intimate partner violence and childhood sexual abuse, respectively.
We utilized the systematic review software Covidence to manage our review process, including the automated de-duplication of search results across different databases. Our inclusion criteria were case–control, cohort or case–crossover studies conducted in participant groups likely to be generalizable and reporting a relative measure of association or number of cases and non-cases among exposed groups (defined as any individual who has experienced a form of intimate partner violence and/or childhood sexual abuse throughout the lifetime) versus non-exposed comparators. Our exclusion criteria included cross-sectional, ecological, case series or case studies; studies conducted in subgroups identified via a shared characteristic associated with the exposure and/or outcome under study; studies that reported only aggregate measure of exposure combining violence exposure with other, non-eligible exposures; and studies missing essential data (effect sizes and/or appropriate uncertainty information; Supplementary Information 4.2). Studies reporting cross-sectional designs were accepted only when exposure ascertainment was retrospective, ensuring that exposure preceded the current health outcomes being evaluated.
We used the above-described criteria to title and abstract screen 67,221 identified articles. Each review step (title/abstract screening, full-text screening and data extraction) began with consensus-building exercises across the review team. After training and consensus-building, the first two-thirds of titles/abstracts were reviewed by two independent reviewers, with conflicts resolved by project leaders. Upon confirmation of a low rate of total conflicts (<5% of screened), the remainder of titles/abstracts were single screened. Non-English articles were screened by reviewers with proficiency in the language. Studies that met inclusion criteria during title/abstract screening (n = 4,379) were full-text screened and excluded if found to meet any exclusion criteria. Two independent reviewers full-text screened 10% of articles, with conflicts resolved by project leads. Upon confirming a low conflict rate (<5%), the remaining 90% of articles were single screened. In total, we accepted and extracted 496 articles reporting on health impacts of any form of GBV and/or VAC.
We supported our primary search results by identifying and citation searching systematic reviews/meta-analyses for additional references (Supplementary Information 4.3). Briefly, we screened systematic reviews/meta-analyses according to our review criteria and categorized included reviews by risk–outcome pair. We selected the highest-quality systematic review (determined based on recency, journal impact factor, adherence to PRISMA and GATHER guidelines and quality of search strategy) per risk–outcome pair to citation search. Extracted citations (n = 1,202) were cross-referenced against studies screened in our review, and newly identified articles (n = 584) were screened according to our review criteria, resulting in an additional 38 articles accepted for extraction.
In summary, these data formed the larger pool of studies from which we drew the inputs for the present investigation, which assessed studies measuring intimate partner violence (n = 57) or childhood sexual abuse (n = 172) specifically. All articles were extracted using a modified Covidence v.2.0 extraction template (Supplementary Table 20). Variables collected during data extraction corresponded to study characteristics; population and sample characteristics; exposure and outcome assessment; and effect sizes and uncertainty estimates. Our extraction procedure included collecting standardized information on sources of potential bias within studies, discussed further in our evaluation of publication bias methods section.
Data selection
Drawing upon all extracted studies, we identified health outcomes for which at least three studies were identified with a comparable exposure and outcome definition. Health outcomes were defined according to GBD reference and accepted alternate reference definitions (Supplementary Information 4.5), for which we incorporated covariates to detect whether their inclusion was a source of bias in final model results. For the purposes of the present study, we did not investigate associations between intimate partner violence during pregnancy and adverse birth outcomes. Low birthweight and short gestation are considered risk factors within the GBD and our analyses were restricted to GBD causes of disease and injury only. Relationships between risk factors (for example, intimate partner violence and low birthweight) are not currently incorporated within the BPRF methodology; however, these data were accepted in our broader review process and a separate study will investigate the health impacts of partner violence experienced during pregnancy and adverse birth outcomes. We also only accepted studies using highly specific diagnostic tools to diagnose specific mental disorders (rather than measuring general or overall psychological distress). For depressive and anxiety disorders specifically, we followed guidance from mental health research in the GBD and accepted studies measuring these outcomes via a list of acceptable diagnostic interviews and/or symptom scales (Supplementary Information 4.5.1). For other mental disorders, we followed GBD case definitions and accepted studies measuring outcomes by use of International Disease Classification and Diagnostic and Statistical Manual of Mental Disorder criteria. Evidence exists describing a bi-directional relationship between intimate partner violence and mental health outcomes11,12. We therefore only accepted study designs in which exposure preceded outcome.
In forming our input datasets for intimate partner violence models, we accepted author definitions of exposure matching the GBD case definition (physical and/or sexual intimate partner violence), those that measured physical intimate partner violence only and those that measured sexual intimate partner violence only. Due to data sparsity, we additionally accepted studies with author definitions that included psychological violence in addition to physical and/or sexual (defined exposure as any intimate partner violence involving physical, sexual and/or psychological abuse). Potential bias due to using an accepted alternate exposure definition was accounted for in our modeling process via two study-level bias covariates marking component exposure definitions and aggregate exposure definitions (Supplementary Table 23). We did not include author definitions measuring psychological intimate partner violence only, economic/financial intimate partner violence only or those reporting aggregate definitions incorporating economic intimate partner violence.
In forming our input datasets for childhood sexual abuse models, we accepted author definitions of exposure using any age threshold ≤18 years by any perpetrator. The GBD case definition considers the age for sexual abuse to be less than 15 years old; however, 18 is commonly accepted as the age of majority in many countries and is used in other global childhood sexual abuse indicators (for example, Sustainable Development Goal Indicator Target 16.2.3; ref. 190). Therefore, we sought to recognize that sexual violence experienced from ages 15–18 is considered childhood sexual abuse in many contexts. For definitions that used alternate ages of exposure, we incorporated a study-level bias covariate to test the impact of setting different upper bounds of the ages that constitute childhood. In addition, certain studies measured childhood sexual abuse perpetrated in the context of specific relationships (family member-perpetrated childhood sexual abuse); estimates from these studies were marked with a bias covariate indicating that a restricted perpetrator definition was used (Supplementary Table 24).
In the case that included studies for a given risk–outcome pair reported on the same underlying cohort or dataset, we compared the relative quality of each publication and selected only one publication to avoid undue influence of the specific cohort/sample in the model. Where possible, the least granular analyses were selected for use in modeling; however, for studies that only reported multiple effect sizes by non-overlap** subgroups (for example, reporting effects separately by age or sex, or gender strata), all effect sizes were included and were not adjusted as they reflected unique participants within the overall sample. If a study reported more than one effect size for multiple, non-mutually exclusive exposure/outcome definitions (for example, physical intimate partner violence and sexual intimate partner violence reported separately, with each exposure group formed without reference to the other type of exposure), we adjusted the standard errors of the observations by a factor matching the number of repeated measurements across the same sample to prevent overweighting these studies in model results (Supplementary Information 5.2). If studies reported effect sizes for repeated periods of exposure measurement (for example, measured intimate partner violence in the past year and over the lifetime), the effect size calculated using the exposure case definition closest to the GBD definition and/or with the best control for temporality between exposure and outcome was selected.
We did not observe consistent gender-specific effects of childhood sexual abuse across studies that reported effect sizes stratified by sex or gender; however, there is a potential compositional bias in childhood sexual abuse studies that represent women-only samples. Thus, for all childhood sexual abuse analyses, we included a bias covariate to indicate studies that reported on gender-specific samples. If this covariate was detected as significant within our selection algorithm, we undertook a sensitivity analysis constraining the input dataset to studies reporting effects across men and women.
For each risk–outcome pair meeting the three-study threshold, we used the MR–BRT tool to perform a meta-regression analysis to estimate the risk of the given outcome for those exposed to intimate partner violence and childhood sexual abuse relative to unexposed counterparts. Following the BPRF methodology, for risk–outcome pairs with sufficient data available (≥10 observations), we introduced likelihood-based trimming (10%) to detect and remove outliers that may otherwise over-influence the model.
Statistical analysis
Analyses were carried out using R v.4.0.5 and Python v.3.8.
Testing and adjusting for biases across study designs and characteristics
Following the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach191, the extracted risk of bias criteria for individual studies included (1) exposure measurement method (instrument or survey used) and data source (self-reported versus ascertained from administrative sources of information such as legal or healthcare databases); (2) outcome measurement method (instrument, survey or diagnostic criteria used) and data source (self-reported versus ascertained from administrative sources); (3) representativeness of study population; (4) control for confounding; (5) selection bias (risk of selection bias, based on percentage follow-up for longitudinal study designs and percentages of cases and controls for which exposure data could be ascertained for case–control designs); and (6) reverse causation (evaluated through study design and opportunity for recall bias (case–control studies)). Based on this extracted information, we created a series of binary covariates to capture potential sources of systematic bias within our input dataset. Across all risk–outcome pairs analyzed, the standard set of covariates included those measuring the representativeness of the study sample; whether the study sample represented a subpopulation only; risk of selection bias, defined as loss to follow-up or percentage for whom data not ascertained >20%; risk of reverse causation (case–control designs); measurement of ‘ever’ health outcome rather than current/recent; estimates uncontrolled for confounding; and odds ratio as an estimate of association (Supplementary Table 25).
Based on observed patterns in the input data, we additionally included two bias covariates for intimate partner violence analyses that captured the level of control for confounding within input studies, resulting in three categories of control for confounding: controlled for age and an additional three or more confounders (referent); controlled for age and one to two other confounders; uncontrolled for age, regardless of other confounders (Supplementary Table 26). Our intimate partner violence models additionally included two study-level bias covariates marking component exposure definitions and aggregate exposure definitions (Supplementary Table 23).
For childhood sexual abuse analyses, we additionally included bias covariates that captured whether a study reported on women only or men only and the level of control for confounding within input studies (whether a study controlled for age, gender and confounders beyond age and gender; Supplementary Table 27). Our childhood sexual abuse models additionally included two study-level bias covariates related to upper bounds of the age of exposure included in author case definitions and restricted perpetrator definitions used in author case definitions (Supplementary Table 24).
We additionally consulted with cause-specific research teams at the Institute for Health Metrics and Evaluation to gather expert guidance on accepted case definitions and best practices for measuring the relevant health outcome to inform outcome-specific bias covariates (for example, use of diagnostic interview versus symptom scale for measuring major depressive disorder; Supplementary Table 28).
The potential effect of bias covariates was tested using MR–BRT’s automated covariate selection process, which uses a Lasso strategy to identify statistically significant covariates at a threshold of 0.05 (ref. 21). Notably, for a covariate to be tested using this approach, there must be at least two rows of data for each value of the covariate (0 and 1). Owing to the small number of studies in our input datasets for several risk–outcome pairs, a limited set of bias covariates met the testing criteria (Supplementary Information 6). Covariates selected as significant by the stepwise Lasso strategy were adjusted for in the final model used to produce RR estimates.
Quantifying between-study heterogeneity
The MR–BRT tool quantifies between-study heterogeneity by accounting for within-study correlation, between-study heterogeneity and small number of studies. In this approach, the between-study heterogeneity parameter γ is estimated using the Fisher information matrix and the final uncertainty estimate reflects both the posterior uncertainty corresponding to the fixed effect (as in traditional meta-analytical approaches) as well as the 95th quantile of γ, which is sensitive to the number of studies and reported uncertainty of the effect size21.
Evaluating publication bias
Publication and reporting bias in the input data was detected and reported according to Egger’s regression test50, which assesses the degree to which the s.e.m. is correlated with effect size, in addition to visual inspection of funnel plots plotting the residuals of the risk function versus s.d.
Estimating the minimum risk exposure level
The theoretical minimum risk exposure level is the theoretically possible level of exposure that would minimize disease risk of the outcome, which, for intimate partner violence and childhood sexual abuse, was set at zero.
Estimating the burden of proof risk function
We estimated the BPRF, reflecting the most conservative estimate of the harmful association between intimate partner violence and childhood sexual abuse and the selected health outcomes that is consistent with the available evidence. For dichotomous risk factors, the BPRF is estimated as the fifth quantile of the model results, inclusive of between-study heterogeneity for harmful risks. ROSs are calculated from the BPRF as the signed log(BPRF) divided by two. A large positive ROS indicates strong and consistent evidence of an association, whereas a negative ROS suggests weak evidence of an association when accounting for between-study heterogeneity. BPRF values can be converted into measures of excess risk (Supplementary Information 7), which quantifies the additional risk of develo** a health outcome due to exposure to the risk factor. ROS can be further categorized into star rating categories ranging from zero to five based upon the estimated ROS (one star, ≤0.0 ROS; two stars, >0.0–0.14 ROS; three stars, >0.14–0.41 ROS; four stars, >0.41–0.62 ROS; and five stars, >0.62 ROS). A one-star rating indicates weak evidence of association, whereas a five-star rating indicates very strong evidence, and all risk–outcome pairs receiving a one- to five-star rating are eligible for inclusion in GBD. Conversely, a zero-star rating is assigned when the lower bound of the 95% UI that does not incorporate γ (between-study heterogeneity) crosses the null RR value of one. This result indicates insufficient evidence of an association between exposure and outcome and ROS values are not calculated for these risk–outcome pairs. Risk–outcome pairs with a zero-star association do not satisfy GBD inclusion criteria.
Model validation
The meta-analytical tool used here has been extensively evaluated and validated by Zheng and colleagues21. For the range of risk–outcome pairs presented here, we undertook several additional sensitivity analyses to evaluate our main results. Across all risk–outcome pairs for which our input modeling dataset was more than ten observations, we undertook a sensitivity analysis in which we did not apply 10% trimming. For certain intimate partner violence-related outcomes, we accepted author definitions encompassing psychological intimate partner violence in addition to physical and/or sexual intimate partner violence. To assess the specificity of our model results to physical and/or sexual intimate partner violence (the forms of the violence currently included in the referent GBD case definition), we undertook a priori sensitivity analyses restricting to studies that only used an author definition involving physical and/or sexual violence (excluding author definitions that also incorporated psychological violence).We additionally undertook several outcome-specific analyses in which we investigated the impact of excluding studies with certain characteristics identified a priori or via bias covariate selection (Supplementary Information 1.2 and 1.5).
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
The findings from this study are supported by data from the published literature. Details on data sources can be found on the burden of proof visualization tool (https://vizhub.healthdata.org/burden-of-proof/), including information about the data provider and links to where the data can be accessed or requested (where available). Study characteristics for all input data used in the analyses are also provided in Supplementary Information 1.
Code availability
All code used for these analyses is publicly available online (https://github.com/ihmeuw-msca/burden-of-proof/).
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Acknowledgements
Research reported in this publication was supported by the Bill and Melinda Gates Foundation (award 66-7813 to E.G.). The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the final report or the decision to publish.
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A.A., M.J.B., J.C., R.J.C.C.-A., S.C., J.K.C., C.V.N.C., G.N.d.A., F.M.D.d.A., B.H., M.H., R.Q.H.L., S.M., J.K.M., V.P., S.R., D.S., C.N.S., A.T. and N.V. were primarily responsible for seeking, cataloging, extracting or cleaning data; and designing or coding figures and tables. A.A., J.C., R.J.C.C.-A., J.S.C., F.M.D.d.A., A.N.D., L.S.F., E.G., B.H., F.N.K., R.Q.H.L., N.M., V.P., D.S., R.J.D.S., C.S., H.S. and N.V. provided data or critical feedback on data sources. A.Y.A., F.B., J.S.C., R.J.C.C.-A., L.S.F., E.G., S.I.H., F.N.K., N.M., C.J.L.M., L.S.F., C.N.S., C.S., H.S. and P.Z. provided critical feedback on methods or results. F.B., J.S.C., G.N.d.A., E.G., S.I.H., F.N.K., C.J.L.M., S.A.M., N.M., C.N.S. and H.S. drafted the work or revising it critically for important intellectual content. E.G., E.C.M., E.M.O. and C.N.S. managed the overall research enterprise. A.Y.A., S.C., C.J.L.M., R.J.D.S. and P.Z. developed methods or computational machinery. C.N.S. was primarily responsible for applying analytical methods to produce estimates. E.G., J.S.C., N.M. and C.N.S. wrote the first draft of the manuscript. E.G., S.I.H., E.C.M., E.M.O. and C.N.S. managed the estimation or publication process. M.K. was primarily responsible for seeking, cataloging, extracting or cleaning data; and designing or coding figures and tables.
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F.N.K. reports support for the present manuscript from Oak, Fondation Botnar and University of Miami. F.N.K. reports grants or contracts from Avon Cosmetics, US Cancer Pain Relief Committee, Medical Research Council, Merck KGaA/EMD Serono and WHO; consulting fees from Merck MGaA/EMD Serono and Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico; support for attending meetings and/or travel from Teconologico de Monterrery; and leadership or fiduciary roles in board, society, committee or advocacy groups, paid or unpaid with Women in Global Health as a Member of the Board of Directors, with Tómatelo a Pecho A.C. Mexico as the Founding President, with Mexican Health Foundation as a Senior Economist, with the International Association for Hospice and Palliative Care as a Member of the Board of Directors and with Esperanza as a Board Member; all outside the submitted work. J.S.C. reports grants or contracts from the National Institute for Health and Care Research, Youth Endowment Fund, College of Policing, University of Birmingham, and Birmingham City Council; and support for attending meetings and/or travel from University of Miami; all outside the submitted work. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Flow Diagram of systematic review on health effects of intimate partner violence against women and childhood sexual abuse.
The PRISMA flow diagram shows the data seeking approach undertaken as a part of our review, which sought to identify all literature reporting on the health effects of gender-based violence, violence against women, and violence against children and young people. Studies specifically measuring the health impacts of intimate partner violence against women (IPV) and childhood sexual abuse (CSA) were used for this analysis.
Extended Data Fig. 2 Data map of studies included in risk-outcome analyses for intimate partner violence against women and five health outcomes identified through the systematic review of the literature.
Map shows number of studies conducted in a location.
Extended Data Fig. 3 Data map of studies included in risk-outcome analyses for childhood sexual abuse and fifteen health outcomes identified through the systematic review of the literature.
Map shows number of studies conducted in each location.
Extended Data Fig. 4 Funnel plots for meta-analysis of the association between intimate partner violence against women and five health outcomes identified through the systematic review of the literature.
These modified funnel plots show the residuals of the reported mean relative risk (RR) relative to 0, the null value, on the x-axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y-axis. The light blue vertical interval corresponds to the 95% uncertainty interval (UI) incorporating between-study heterogeneity; the dark blue vertical interval corresponds to the 95% UI without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the grey dotted line reflects the null log(RR) of 0 (equivalent to a null RR of 1); the blue vertical line is the mean log(RR) for intimate partner violence and the outcome of interest; the red vertical line is the burden of proof function at the 5th quantile for these harmful risk-outcome associations.
Extended Data Fig. 5 Forest plots showing extracted studies describing the association between intimate partner violence against women and the risk for peripartum depression, heavy episodic drinking, and alcohol use disorder.
These forest plots present extracted mean relative risks (colored points) for each of the three outcomes for which we found studies in association with intimate partner violence exposure (peripartum depression, heavy episodic drinking, alcohol use disorder). The color of the point indicates type of intimate partner violence (that is, physical, sexual, aggregate including psychological) and the shape indicates the exposure temporality used in the author’s case definition of intimate partner violence. The horizontal lines indicate the 95% uncertainty interval from the included study. We included multiple observations from a single study when effects were reported by form of IPV, recall period, and age group. Okunala 2022 effect size has been extracted accurately as reported in the publication but should be considered with caution as lower and upper uncertainty interval bounds are not consistent with the mean estimate.
Extended Data Fig. 6 Funnel plots for meta-analysis of the association between childhood sexual abuse and five outcomes with an estimate three- or two-star association identified through the systematic review of the literature.
These modified funnel plots show the residuals of the reported mean relative risk (RR) relative to 0, the null value, on the x-axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y-axis. The light blue vertical interval corresponds to the 95% uncertainty interval (UI) incorporating between-study heterogeneity; the dark blue vertical interval corresponds to the 95% UI without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the grey dotted line reflects the null log(RR) of 0 (equivalent to a null RR of 1); the blue vertical line is the mean log(RR) for childhood sexual abuse and the outcome of interest; the red vertical line is the burden of proof function at the 5th quantile for these harmful risk-outcome associations.
Extended Data Fig. 7 Funnel plots for meta-analysis of the association between childhood sexual abuse and five outcomes with an estimated one-star association identified through the systematic review of the literature.
These modified funnel plots show the residuals of the reported mean relative risk (RR) relative to 0, the null value, on the x-axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y-axis. The light blue vertical interval corresponds to the 95% uncertainty interval (UI) incorporating between-study heterogeneity; the dark blue vertical interval corresponds to the 95% UI without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the grey dotted line reflects the null log(RR) of 0 (equivalent to a null RR of 1); the blue vertical line is the mean log(RR) for childhood sexual abuse and the outcome of interest; the red vertical line is the burden of proof function at the 5th quantile for these harmful risk-outcome associations.
Extended Data Fig. 8 Funnel plot for meta-analysis of the association between childhood sexual abuse and five outcomes with an estimated one- or zero-star association identified through the systematic review of the literature.
These modified funnel plots show the residuals of the reported mean relative risk (RR) relative to 0, the null value, on the x-axis and the residuals of the standard error, as estimated from both the reported standard error and gamma, relative to 0 on the y-axis. The light blue vertical interval corresponds to the 95% uncertainty interval (UI) incorporating between-study heterogeneity; the dark blue vertical interval corresponds to the 95% UI without between-study heterogeneity; the dots are each included observation; the red Xs are outliered observations; the grey dotted line reflects the null log(RR) of 0 (equivalent to a null RR of 1); the blue vertical line is the mean log(RR) for childhood sexual abuse and the outcome of interest; the red vertical line is the burden of proof function at the 5th quantile for these harmful risk-outcome associations.
Extended Data Fig. 9 Forest plots showing extracted studies describing the association between childhood sexual abuse and the risk for high body mass index, smoking, and high systolic blood pressure.
These forest plots present extracted mean relative risks (colored points) for each of the three outcomes for which we found studies in association with childhood sexual abuse exposure (high body mass index, smoking, high systolic blood pressure). In the first panel (high body mass index) the color of the point indicates whether the study describes obesity or overweight as an outcome. In all panels, the shape of the point indicates gender of the study sample. The horizontal lines indicate the 95% uncertainty interval from the included study. We included multiple observations from a single study when effects were reported by severity/frequency of exposure and/or separately by gender or other subgroups.
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Spencer, C.N., Khalil, M., Herbert, M. et al. Health effects associated with exposure to intimate partner violence against women and childhood sexual abuse: a Burden of Proof study. Nat Med 29, 3243–3258 (2023). https://doi.org/10.1038/s41591-023-02629-5
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DOI: https://doi.org/10.1038/s41591-023-02629-5
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