Extent, Timing, and Predisposing Factors of Intimate Partner Violence in Sub‐Saharan Africa: A Cross‐Sectional Analysis Using Demography Health Surveys From 2015 to 2021.

Extent, Timing, and Predisposing Factors of Intimate Partner Violence in Sub‐Saharan Africa: A Cross‐Sectional Analysis Using Demography Health Surveys From 2015 to 2021 - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Health Sci Rep . 2026 Apr 19;9(4):e72392. doi: 10.1002/hsr2.72392 Search in PMC Search in PubMed View in NLM Catalog Add to search Extent, Timing, and Predisposing Factors of Intimate Partner Violence in Sub‐Saharan Africa: A Cross‐Sectional Analysis Using Demography Health Surveys From 2015 to 2021 Abel F Dadi Abel F Dadi 1 Menzies School of Health Research, Charles Darwin University, Casuarina, Australia 2 Addis Continental Institute of Public Health, Addis Ababa, Ethiopia Find articles by Abel F Dadi 1, 2, ✉ , Kedir Y Ahmed Kedir Y Ahmed 3 Rural Health Research Institute, Charles Sturt University, Orange, Australia Find articles by Kedir Y Ahmed 3 , Kayli Wild Kayli Wild 1 Menzies School of Health Research, Charles Darwin University, Casuarina, Australia Find articles by Kayli Wild 1 , Temesgen Yihunie Akalu Temesgen Yihunie Akalu 4 School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, Australia 5 Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Perth, Australia 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia Find articles by Temesgen Yihunie Akalu 4, 5, 6 , Adhanom Gebreegziabher Baraki Adhanom Gebreegziabher Baraki 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 7 School of Rehabilitation Therapy, Queen's University, Ontario, Canada Find articles by Adhanom Gebreegziabher Baraki 6, 7 , Achamyeleh Birhanu Teshale Achamyeleh Birhanu Teshale 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 8 School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia Find articles by Achamyeleh Birhanu Teshale 6, 8 , Tesfa Sewunet Alamneh Tesfa Sewunet Alamneh 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 9 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK Find articles by Tesfa Sewunet Alamneh 6, 9 , Zemenu Tadesse Tessema Zemenu Tadesse Tessema 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 8 School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia Find articles by Zemenu Tadesse Tessema 6, 8 , Robel Hussen Kabthymer Robel Hussen Kabthymer 10 Department of Medicine, School of Clinical Sciences, Monash University, Victoria, Australia 11 Department of Human Nutrition, School of Public Health, Dilla University, Dilla, Ethiopia Find articles by Robel Hussen Kabthymer 10, 11 , Koku Sisay Tamirat Koku Sisay Tamirat 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 12 School of Rural Health, Monash University, Victoria, Australia Find articles by Koku Sisay Tamirat 6, 12 , Getayeneh Antehunegn Tesema Getayeneh Antehunegn Tesema 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 8 School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia Find articles by Getayeneh Antehunegn Tesema 6, 8 Author information Article notes Copyright and License information 1 Menzies School of Health Research, Charles Darwin University, Casuarina, Australia 2 Addis Continental Institute of Public Health, Addis Ababa, Ethiopia 3 Rural Health Research Institute, Charles Sturt University, Orange, Australia 4 School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, Australia 5 Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Perth, Australia 6 Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia 7 School of Rehabilitation Therapy, Queen's University, Ontario, Canada 8 School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia 9 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK 10 Department of Medicine, School of Clinical Sciences, Monash University, Victoria, Australia 11 Department of Human Nutrition, School of Public Health, Dilla University, Dilla, Ethiopia 12 School of Rural Health, Monash University, Victoria, Australia * Correspondence: Abel F. Dadi ( [email protected] ) ✉ Corresponding author. Revised 2026 Mar 26; Received 2025 Dec 5; Accepted 2026 Apr 9; Collection date 2026 Apr. © 2026 The Author(s). Health Science Reports published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13092214  PMID: 42011274 ABSTRACT Background and Aims Reliable evidence on the extent, characteristics, and timing of intimate partner violence (IPV) is crucial for guiding targeted interventions in sub‐Saharan Africa (SSA). This study draws on the most recent demographic and health surveys (DHSs) conducted in SSA to identify approaches for preventing and responding effectively to women experiencing IPV. Methods We assessed sociodemographic data, maternal characteristics, and violence from the recent DHS (2015–2021) of 18 SSA countries. We performed a latent class analysis (LCA) to identify women experiencing similar levels of physical, sexual, and emotional violence and to examine their common characteristics. We fit a Cox proportional hazard model to determine predictors of short duration to the first episode of IPV after marriage. Results Our analysis included 84,717 women. We identified two distinct classes of IPV experience in SSA: a class with very high (class probability: physical (82.2%), sexual (77.6%), and emotional violence (33.8%) and a class with low violence. Six countries (Sierra Leone, Liberia, Uganda, Mali, Tanzania, and Zambia) constituted more than two‐thirds of women who experienced all forms of violence in SSA. Approximately 25% of women had their first episode of all forms of IPV in their first year of marriage. Two important variables, the husband drinking alcohol and the woman's lack of autonomy, were associated with all forms of IPV and predicted the shortest time to the first episode of IPV after marriage. A husband drinking alcohol alone predicted 61% of IPV cases (area under the receiver operating characteristic curve (ROC) = 0.613; 95% CI: 0.609, 0.616). Conclusions This study highlights the importance of comprehensive treatment and prevention efforts that include both criminal justice and public health strategies focusing on women who drink their partner and who have low authority in the first two to 3 years of marriage. Keywords: childbearing age, DHS, IPV, latent class analysis, sub‐Saharan countries 1. Background Violence against women is a major human rights violation and a global public health problem [ 1 ], increasingly gaining global attention [ 2 ]. Intimate partner violence (IPV) is any abusive behavior by a current or past intimate partner within the context of marriage, cohabitation, or any other formal or informal union and is the most common form of violence against women globally [ 3 ]. IPV encompasses physical, emotional or psychological, and sexual violence. Globally, 26% of ever‐married or partnered women over the age of 15 years have experienced physical and/or sexual violence in their lifetime, and 10% have experienced IPV in the past 12 months [ 4 ]. In SSA, these rates are even higher, with 33% of women experiencing IPV in their lifetime and 20% experiencing IPV in the past 12 months [ 5 ]. Women's experience of IPV starts early in life, increases in middle age, and tends to decline later in life. The coronavirus disease (COVID‐19) pandemic beginning in 2020 increased reports of IPV and brought new attention to addressing violence against women as a public health priority [ 6 , 7 ]. IPV has significant short‐, medium‐ and long‐term health and physical impacts on women, children, and families and has serious social and economic consequences, with significant geographic and demographic variations [ 4 , 5 ]. Evidence suggests a link between IPV and adverse maternal, perinatal, child and intergenerational outcomes [ 8 , 9 ]. Traumatic experiences, including witnessing IPV in early childhood, have been shown to predict greater vulnerability to adverse outcomes in adulthood, including mental illness, difficulties in parenting, and risks of experiencing and perpetrating IPV [ 8 ]. Many women who experienced IPV reported consequences such as physical injuries, chronic pain, and suicidal thoughts [ 10 ]. IPV is correlated with younger age, greater parity, a low wealth index, lower education levels, drinking or smoking habits, sexual autonomy, and rural residence [ 11 , 12 , 13 , 14 , 15 ]. Over the last three decades, there has been a strong call to address IPV as a human rights and public health issue in global consensus documents and regional conventions. The 2030 Sustainable Development Goals (SDGs) highlight the importance of eliminating all forms of violence against women and girls in the public and private spheres [ 16 ]. Long‐standing advocacy efforts by women's health and rights organizations urge all responsible agencies to engage with IPV and take urgent action. In sub‐Saharan Africa, several efforts have been also considered to reduce IPV and its consequences, including legal and policy reforms, community awareness programs, women's empowerment initiatives, gender‐transformative interventions, and the integration of IPV screening and support into health and social services. Although these approaches have shown some promise, their coverage, accessibility, and effectiveness remain uneven across settings. In addition, many interventions focus on response after violence has occurred, with limited attention to broader structural and contextual drivers of IPV. As such, large, multicounty studies are crucial for improving our understanding of IPV to inform targeted investments in effective and sustainable interventions. Despite the attention given by national and international actors to reducing IPV, limited evidence is available on the extent and characteristics of women with the highest prevalence of physical, sexual, and emotional violence; time to first episode of IPV since marriage; and predictors of IPV in SSA countries. This multicounty study combines DHS data from 18 SSA countries to provide new evidence on the level, timing, and predictors of IPV. 2. Methods 2.1. Data Source, Design, and Sampling The DHS is used nationally within low‐ and middle‐income (LMIC) countries to collect cross‐sectional data using a standard questionnaire, ideally every 5 years. This approach is then used to disseminate nationally representative data on a wide range of health and population indicators [ 17 ]. The DHS follows a two‐stage stratified cluster sampling design with the first administrative units (e.g., states) as urban and rural strata, a random selection of enumeration areas (EAs) at the first stage, and systematically selected households from EAs at the second stage [ 18 ]. We chose SSA countries based on the availability of domestic violence modules, which have been collected since 2015. Our analysis included ever‐married or cohabiting women aged 15–49 years who had complete IPV data. After approval by the DHS data custodians, we accessed the datasets of 18 SSA countries from the DHS program website ( https://dhsprogram.com/ ). 2.2. Variables and Measurement IPV is the outcome variable, and the DHS collects IPV data using standardized questions in the domestic violence module from one eligible woman per household, selected from every second or third household. The DHS uses the modified version of the Conflict Tactic Scale (CTS) to assess the emotional, physical and sexual dimensions of women's exposure to IPV [ 19 ]. The potential covariates adjusted in the model included maternal age (categorized as “15–19,” “20–24,“ “25–29,” “30–34,“ “35–39,“ “40–44,” or “45–49“), parity (as a continuous variable), women's level of education (categorized as “no education,“ “primary,“ “secondary,“ or “higher“), marital status (categorized as “married“ or “cohabited,“ occupation status (categorized as “yes“ or “no“), place of residence (categorized as “urban“ or “rural“), wealth quantile (categorized as “very low,” “low,” “middle,” “high,” or “very high”), partner's age (as a continuous variable), partner's level of education (categorized as “no education,” “primary,” “higher” and “unknown,” women's smoking status (categorized as “yes” or “no”), and partner's drinking status (categorized as “yes” or “no”). We generated two categories of media exposure from a set of three questions: frequency of watching television, reading newspaper/magazine, and listening to radio. The maternal responses to these questions were “not at all, less than once a week, at least once a week, and almost every day.” We categorized a woman who responded not at all to all the questions as having no media exposure (‘no’) or otherwise (“yes”). Women's autonomy was assessed using a set of four questions: i) who usually decides on health care? ii) who usually decides on large household purchases? iii) who usually decides on visits to family or relatives? These questions had four response options: “woman alone,” “woman and husband/partner,” “husband/partner alone,” and “someone else”; and iv) the fourth question, who usually decides what to do with money the husband earns? This question had the options “woman alone,” “woman and partner/husband,” “husband/partner alone,” “husband/partner has no earnings,” and “someone else.” We classified women as “not autonomous” if the responses of the women to the four questions of autonomy were “husband/partner alone” or “someone else,” or “autonomous” otherwise. We controlled the effect of time lag by adjusting for the year in which the DHS was conducted. 2.3. Statistical Analysis We appended sociodemographic characteristics and domestic violence module questions from 18 SSA DHS datasets. We checked for data completeness, calculated a weighted number of study participants, performed a descriptive analysis of variables included in the data, and presented our results using tables and figures. We checked for multicollinearity between explanatory variables using the variance inflation factor (VIF), and a VIF of less than five was used to rule out multicollinearity between predictors of IPV. We separately estimated the prevalence of physical, emotional, and sexual violence for each country. We then performed a latent class analysis (LCA) to identify the common characteristics of women living in different countries with different levels of physical, emotional, and sexual violence [ 20 , 21 ]. The LCA is an unsupervised machine learning statistical procedure that is used to qualitatively identify or detect latent (or unobserved) heterogeneity in samples and different subgroups, referred to as latent groups or classes within populations that share certain outward characteristics [ 22 ]. The LCA is becoming the most popular social science research method for capturing heterogeneity by integrating person‐centered and variable‐centered analyzes [ 23 , 24 , 25 ]. The LCA assumes that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. We performed stepwise analysis by fitting a one‐class model, two‐class model, and three‐class model. We used both statistical interpretability and theoretical interpretability to compare the models. A model with two classes fit the data better than the three‐ and one‐class models, as it had low BIC and AIC values as well as better theoretical interpretability [ 21 , 26 ]. We also extended an LCA to explore the common characteristics of women living in different countries with the highest prevalence of physical, sexual, and emotional violence. The domestic violence module contained a variable that recorded years to the first episode of IPV after marriage. We used the global Schoenfeld residuals test ( p value > 0.05) and graphical methods to determine whether the assumption for proportional hazard was met. We then fit the Cox proportional hazard model to identify factors associated with years to the first episode of IPV after marriage. We also estimated the predictive ability of a model with risk factors strongly associated with adverse birth outcomes by calculating the area under the receiver operating characteristic (ROC) curve (AUC) [ 27 ]. All analyzes accounted for the complex survey design using Stata's svyset command, and both p‐values and confidence intervals were reported [ 28 ]. The DHS dataset for all SSA countries is publicly available with no personal identifiers; thus, ethical approval was not needed. 3. Results Table 1 presents the characteristics of women of childbearing age combined across the 18 SSA countries. Approximately two‐thirds of the sample were from 2016 (28,979 [36.9%]) and 2018 (23,859 [30.4%]) DHSs; were living in rural areas (50,775 [64.7%])); had either no education (19,547 [31.9%]) or completed only primary school (29,331 [37.4%]); reported that they had no media exposure (51,989 [66.2%]); and were working at the time of the survey (52,929 [67.4%]). A very small proportion (798 [1%]) of women included in the survey smoked, while a significant minority (26,753 [35.4%]) of their husbands drank alcohol. The mean (± standard deviation) age of the partner and the number of children the woman had were 38.9 ( ± 11.4 years) and 3.7 ( ± 2.5 children), respectively. Most women were married (60,966 [77.7%]), and the majority (44,929 [57.2%]) lacked decision‐making autonomy. Table 1. Characteristics of woman of childbearing age (15–49 years) in SSA countries included in the study ( N = 84,717), 2015–2021. Variables # of woman with the violence module weighted # of woman with the violence module Weighted % DHS year 2015 4917 4593 5.8 2016 30,281 28,979 36.9 2017 6401 5551 7.1 2018 25,949 23,859 30.4 2019 3816 3357 4.3 2020 5478 4662 5.9 2021 7875 7500 9.5 Woman's age 15–19 5394 5019 6.4 20–24 15,375 13,357 17.0 25–29 18,744 16,420 20.9 30–34 16,799 15,008 19.1 35–39 13,107 12,686 16.2 40–44 8884 9221 11.7 45–49 6414 6789 8.8 Husband's age (mean, ±SD) 84,717 78,499 38.9 (11.4) Residence Urban 28,058 27,727 35.3 Rural 56,659 50,775 64.7 Highest education level attained by the woman No education 27,028 25,052 31.9 Primary 32,011 29,331 37.4 Secondary 22,119 20,463 26.1 Higher 3559 3655 4.7 Highest education level attained by the husband No education 21,290 19,547 24.9 Primary 28,188 25,805 32.9 Secondary 25,625 23,778 30.3 Higher 6490 6376 8.1 Unknown 3061 2955 3.8 Media exposure No 54,924 51,989 66.2 Yes 29,793 26,512 33.8 Wealth index Very low 18,460 15,286 19.5 Low 17,558 15,801 20.1 Middle 17,165 15,797 20.1 High 16,227 15,814 20.1 Very High 15,307 15,804 20.1 Number of children (mean, SD) 84,717 78,502 3.7 (2.5) Woman smokes cigarettes No 82,782 76,765 99.0 Yes 883 798 1.0 Marital status Married 66,328 60,966 77.7 Living with partner 18,389 17,535 22.3 Woman currently working No 28,092 25,572 32.6 Yes 56,625 52,929 67.4 Woman's autonomy Not autonomous 48,268 44,929 57.2 Autonomous 36,449 33,572 42.8 Husband/Partner drinks alcohol No 52,713 48,780 64.6 Yes 29,114 26,753 35.4 Open in a new tab Table 2 presents the prevalence of the three forms of IPV in the SSA countries included in the study. Sierra Leone (49.9%: 95% CI: 48.3, 51.6) and Liberia (43.7%: 95% CI: 41.3, 46.2) had the highest prevalence of physical IPV, and Mauritania had the lowest (4.8%: 95% CI: 4.1, 5.7), followed by South Africa (13.4%: 95% CI: 11.9, 15.1). Similarly, Sierra Leone (44.4%: 95% CI: 42.9, 46.1) and Liberia (39.0%: 95% CI: 36.6, 41.4) had the highest prevalence of emotional IPV, and Mauritania had the lowest (10.8%: 95% CI: 9.8, 12.0), followed by South Africa (11.7%: 95% CI: 10.3, 13.3). While sexual violence was generally the lowest type of IPV reported in SSA countries, Burundi had the highest rates (24.2%: 95% CI: 23.1, 25.4), and South Africa had the lowest (3.5%: 95% CI: 2.8, 4.5). Table 2. Prevalence of physical, emotional, and sexual violence by country in SSA (2015–2021). Countries Year Weighted # of woman Physical violence, 95%CI Emotional violence, 95%CI Sexual violence, 95%CI Burundi 2016–17 5551 37.9 (36.6, 39.2) 22.3 (21.2, 23.4) 24.2 (23.1, 25.4) Ethiopia 2016 3897 21.9 (20.6, 23.2) 21.8 (20.6, 23.2) 9.5 (8.6, 10.5) Madagascar 2021 4540 21.4 (20.3, 22.7) 28.2 (26.9, 29.5) 9.4 (8.6, 10.3) Malawi 2015–16 4171 23.5 (22.2, 24.8) 23.9 (22.6, 25.2) 17.8 (16.6, 19.0) Rwanda 2019–20 1430 31.9 (29.6,34.4) 28.6 (26.3, 31.0) 12.2 (10.6, 14.0) Tanzania 2015–16 5873 35.6 (34.4, 36.8) 32.2 (31.0, 33.4) 10.9 (10.1, 11.7) Uganda 2016 5642 37.2 (36.0, 38.5) 35.3 (34.0, 36.5) 21.2 (20.1, 22.2) Zambia 2018 5384 34.2 (32.9, 35.4) 25.7 (24.6, 26.9) 13.4 (12.5, 14.3) Zimbabwe 2015 4593 29.2 (27.9, 30.5) 28.2 (26.9, 29.5) 11.5 (10.6, 12.4) Angola 2016 7576 31.9 (30.8, 32.9) 25.8(24.8, 26.8) 7.4 (6.9, 8.1) Cameron 2018 3668 32.5 (31.0, 34.0) 23.5 (22.1, 24.9) 9.1 (8.2, 10.1) South Africa 2016 1816 13.4 (11.9, 15.1) 11.7 (10.3, 13.3) 3.5 (2.8, 4.5) Benin 2017–18 3831 18.8 (17.6,20.1) 36.1 (34.6, 37.6) 8.7 (7.8, 9.6) Gambia 2019–20 1623 28.4 (26.2, 30.6) 22.0 (20.0, 24.1) 5.1 (4.1, 6.3) Liberia 2019–20 1608 43.7 (41.3, 46.2) 39.0 (36.6, 41.4) 7.7 (6.5, 9.1) Mali 2018 3130 36.9 (35.2, 38.6) 36.9 (35.2, 38.6) 11.9 (10.8, 13.0) Nigeria 2018 7847 18.1 (17.5, 18.7) 30.3 (29.6, 31.0) 6.6 (6.2, 7.0) Sierra Leone 2019 3357 49.9 (48.3, 51.6) 44.4 (42.9, 46.1) 8.0 (7.1, 8.9) Mauritania 2021 2961 4.8 (4.1, 5.7) 10.8 (9.8, 12.0) 5.9 (5.1, 6.8) Open in a new tab The results of the latent class analysis revealed two classes of women living in SSA countries: a class with a very high prevalence and a class with a comparatively low prevalence of physical, emotional, and sexual violence. The class with a very high prevalence of IPV included women who experienced 82.2% physical violence, 77.6% emotional violence, and 33.8% sexual violence. The class with a low prevalence of IPV included a group of women who experienced 9.1% physical violence, 10.9% emotional violence, and 2.7% sexual violence. The probabilities of women being classified into low‐ and very‐high‐incidence IPV classes were 71.6% (95% CI: 70.5, 72.6) and 28.4% (95% CI: 27.4, 29.4), respectively. (Figure 1 and Table 3 ). Figure 1. Open in a new tab The prevalence of latent classes of IPV experienced by women in different sub‐Saharan African countries (2015–2021). Table 3. Latent class analysis showing the levels of IPV experienced by woman in SSA (2015–2021). Class name Types of violence Probability of experiencing the violence Probability of being categorized in the class Low prevalence of IPV group Physical violence 9.8 (9.1, 10.5) 71.6 (70.5, 72.6) Emotional violence 10.3 (9.7, 11.0) Sexual violence 3.0 (2.7, 3.3) Very high prevalence of IPV group Physical violence 82.7 (81.0, 84.2) 28.4 (27.4, 29.4) Emotional violence 76.8 (75.2, 78.3) Sexual violence 34.2 (33.0, 35.4) Open in a new tab Women classified in the very high IPV prevalence class were more likely to have a low education level (AOR: 1.92; 95% CI: 1.52, 2.43), be smokers (AOR: 1.52; 95% CI: 1.25, 1.85), not autonomous (AOR: 1.84; 95% CI: 1.73, 1.96), and drink alcohol from their partner (AOR: 3.62; 95% CI: 3.41, 3.85). (Table 4 ). Six SSA countries, Sierra Leone, Liberia, Uganda, Mali, Tanzania, and Zambia, had very high incidences of physical, sexual, and emotional violence, ranging from highest to lowest. Table 4. Factors associated with woman in different SSA countries being categorized in the very high IPV prevalence group (2015‐2021). Risk factors AOR, 95%CI Highest education level attained by the women No education 1.81 (1.42, 2.32) Primary 1.92 (1.52, 2.43) Secondary 1.75 (1.39, 2.19) Woman smokes cigarettes Yes 1.52 (1.25, 1.85) Woman's autonomy Not autonomous 1.84 (1.73, 1.96) Husband/partner drinks alcohol Yes 3.62 (3.41, 3.85) Open in a new tab Approximately 22.5%, 23.6%, and 27.1% of women experienced their first episode of physical, emotional, or sexual violence, respectively, in their first year of marriage. Half of the women experienced their first episode of all types of violence in their first two to 3 years of marriage, while approximately 73% experienced it in the first 5 years. Approximately 4.1%, 3.8%, and 2.8% of women experienced physical, emotional, and sexual violence, respectively, before starting to live with their partners. Several factors have been identified to predict the duration from marriage to the first episode of IPV in each country: maternal age, parity, alcohol consumption, lack of autonomy, smoking status, marital status, education level, wealth index, media exposure, and working status. (Table 5 ). However, three factors (the husband drinks alcohol, the woman has no autonomy, and parity) consistently predicted a short time to the first episode of IPV in most of the SSA countries included in the study where parity generally had a weak prediction. Husband alcohol consumption alone predicted 61% of the IPV cases (area under the receiver operating characteristic curve = 0.613; 95% CI: 0.609, 0.616). Adding autonomy to the model increased the model predictive ability to 64.1%, and no additional variable significantly changed the model predictive ability. A woman with a husband who drank alcohol had the shortest time to the first episode of IPV. For example, the risk of having a short time to the first episode of IPV among women with a husband who drank alcohol was 2.77 (95% CI: 2.39, 3.21), 2.70 (95% CI: 2.28, 3.20), and 2.32 (95% CI: 1.97, 2.73) times greater among women in Nigeria, Malawi, and Madagascar, respectively. The risk of having a short time to the first episode of IPV among women lacking autonomy was greater in Mali (AHR = 1.90; 95% CI: 1.30, 2.76) and lower in Angola and Burundi (AHR = 1.20; 95% CI: 1.07, 1.36). Table 5. Predictors of time to the first episode of IPV after marriage in different SSA countries (2015–2021). Countries Factors associated with time to the first episode of IPV, AHR (95%, CI) Maternal age categories Parity Husband takes alcohol Woman has no autonomy Woman smoking Unmarried* Husband education 20–24 25–29 45–49 No Primary secondary Burundi 1.29 (1.03, 1.62) 1.04 (1.01, 1.07) 2.23 (1.95, 2.56) 1.20 (1.07, 1.36) Ethiopia 1.96 (1.60, 2.41) 1.27 (1.01, 1.61) 2.62 (1.32, 5.19) 2.12 (1.22,3.68) 1.85 (1.01, 3.39) 1.93 (1.10, 3.40) Madagascar 1.05 (1.01, 1.10) 2.32 (1.97, 2.73) 1.37 (1.16, 1.62) 1.59 (1.01, 2.50) Malawi 2.70 (2.28, 3.20) Tanzania 1.48 (1.17, 1.88) 1.39 (1.08, 1.79) 1.07 (1.04, 1.10) 2.05 (1.84, 2.29) 1.25 (1.11, 1.41) 0.86 (0.77, 0.97) Uganda 1.30 (1.02, 1.66) Zambia 1.03 (1.01, 1.05) 1.85 (1.66, 2.05) 1.37 (1.24, 1.51) 0.88 (0.79, 0.98) Zimbabwe 1.09 (1.04, 1.14) 1.63(1.44,1.85) 1.49 (1.32,1.68) An gola 1.29 (1.03, 1.62) 1.04 (1.01, 1.07) 2.23 (1.95, 2.56) 1.20 (1.07, 1.36) Cameron 1.09 (1.05, 1.13) 2.31 (1.90, 2.81) 1.37 (1.17, 1.60) 1.37 (1.17, 1.61) Benin 1.12 (1.06, 1.19) 2.07 (1.72, 2.50) 1.62 (1.27, 2.07) 0.26 (0.1, 0.70) 1.69 (1.34, 2.13) Gambia 1.08 (1.02, 1.15) Liberia 0.48 (0.24, 0.94) 1.81 (1.50, 2.18) 1.25 (1.02, 1.53) Mali 1.06 (1.02, 1.10) 2.06 (1.61, 2.64) 1.90 (1.30, 2.76) Nigeria 1.43 (1.02, 2.02) 1.52 (1.09, 2.13) 2.77 (2.39, 3.21) Sierra Leone 1.70 (1.49,1.93) 1.38 (1.21, 1.58) 1.33 (1.10, 1.61) Note : * Living together without marriage. Countries Factors associated with time to the first episode of IPV, AHR (95%, CI) Wealth index () No media exposure Woman's education Woman working Lowest Lower Middle Higher No Primary Aecondary Madagascar 0.71 (0.52, 0.97) 1.27 (1.04, 1.55) Malawi 0.67 (0.50, 0.90) Rwanda Tanzania 1.35 (1.05, 1.75) 1.34 (1.05, 1.70) 1.16 (1.01, 1.33) Uganda 1.73 (1.21, (2.47) 2.12 (1.52, 2.96) 1.79 (1.28, 2.52) Zambia 1.82 (1.46, 2.27) 1.57 (1.26, 1.94) 1.38 (1.10, 1.72) 1.29 (1.05, 1.59) Zimbabwe 1.20 (1.03, 1.41) 1.77 (1.22, 2.57) 1.67 (1.18, 2.36) 1.38 (1.20, 1.58) Angola Cameron South Africa Benin Gambia 2.60 (1.12, 6.02) 2.23 (1.04, 4.80) Liberia Mali 1.40 (1.01, 1.93) 1.38 (1.18, 1.60) Nigeria 1.74 (1.03, 2.94) 1.64 (1.05, 2.58) 1.53 (1.07, 2.21) Sierra Leone 1.22 (1.10, 1.36) 1.21(1.02, 1.44) Mauritania Open in a new tab We also found that three predictors, smoking status, marital status, marital status, and wealth index, differentially predicted the time to the first episode of IPV in different countries. For example, a short duration of smoking predicted a first episode of IPV in Ethiopia (AHR = 2.62; 95% CI: 1.32, 5.19) and Sierra Leone (AHR = 1.33; 95% CI: 1.10, 1.61), whereas a longer duration of smoking predicted a first episode of IPV in Benin (AHR = 0.26; 95% CI: 0.1, 0.70). Similarly, having an unmarried relationship for a short time predicted the first episode of IPV in Ethiopia (AHR = 2.12: 95% CI: 1.22, 3.68), Gabon (AHR = 1.69; 95% CI: 1.34, 2.13), and Cameron (AHR = 1.37; 95% CI: 1.17, 1.61), whereas it predicted a longer time to the first episode of IPV in Zambia (AHR = 0.88; 95% CI: 0.79, 0.98) and Tanzania (AHR = 0.86: 95% CI: 0.77, 0.97). 4. Discussion Preventing violence against women and girls and ensuring responsive and inclusive societies are far‐reaching goals in the SDGs to ensure gender equity [ 29 ]. All World Health Organization (WHO) state countries are committed to eliminating all forms of violence against women and girls by 2030 [ 30 ]. This study contributes to informing SDG target 17.18, which calls for identifying different causes of IPV through detailed analysis of available data to support national and regional commitments to end all forms of IPV. In this sense, we explored the extent and common characteristics that explain the high IPV prevalence in SSA countries and the high‐risk timing of IPV incidence in married couples to inform targeted investments in eliminating IPV. Physical violence (up to 49.9%) was the most common type of violence experienced, followed by emotional (up to 44.4%) and sexual violence (24.2%). Sierra Leone had the highest proportion of women with physical and emotional violence, while Burundi had the highest proportion of women with sexual violence. Mauritania is the country with the lowest proportion of women who experienced the three forms of violence, followed by South Africa. We identified a subgroup of countries that had the highest proportion of physical, sexual, and emotional violence and a subgroup of women who experienced all types of violence at the highest rate. Six countries (Sierra Leone, Liberia, Uganda, Mali, Tanzania, and Zambia) constituted more than two‐thirds of women who experienced all forms of violence in the SSA countries. This implies that these subgroups of countries and women could benefit from a common intervention designed based on their shared characteristics. These shared characteristics of women most at risk in these countries included having a partner who drinks alcohol, having a low education level, lacking autonomy, and being smokers. Survival analysis also revealed that these women were more likely to experience all forms of IPV within a shorter period after marriage. All these findings are consistent with other studies conducted in similar contexts at different time points, suggesting that the findings are well‐established evidence requiring immediate and targeted intervention [ 12 , 14 , 15 , 31 ]. In this study, approximately one in five married or union women experienced their first episode of IPV in their first year of marriage, while nearly one in two women experienced IPV in their first two to 3 years of marriage. An analysis of IPV in 30 developing countries reported that approximately 38.5% and 67.5% of married women experienced their first episode within one and 3 years of marriage, respectively [ 32 ]. Another similar study pooling data from SSA countries reported that half of the women in unions had experienced their first episode of violence within 2 years of marriage [ 33 ]. The first few years after marriage, relationships develop, and couples face new ways of living and learn about new environments and experiences, which might bring both challenges and happiness to their lives and might involve different violent activities. This body of evidence reinforces the need for early intervention before couples are married and within the early years of union. Importantly, these interventions should be performed with individuals and specifically target abusive behavior, as couple‐focused therapy can marginalize power dynamics, provide opportunities for abuse to excuse behavior, and increase the risk of further violence [ 34 ]. Women living with a partner drinking alcohol were 3.62 times more likely to experience IPV, and their risk of experiencing all forms of IPV could be more than two times greater than that of women whose partner is not drinking alcohol in most of the SSA countries included in this analysis. Furthermore, a husband drinking alcohol alone predicted 61% of IPV cases, and adding autonomy to the model increased the model's predictive ability to 64.1%, making these two variables the most important predictors of IPV. Although the association between partner alcohol consumption and IPV is consistent with the findings of other similar studies [ 35 , 36 , 37 ], recent studies from developing countries have failed to consider partner alcohol consumption as a predictor of a short time to the first episode of IPV [ 33 ]. The bidirectional association between IPV and substance use has long been known where substance misuse could be a risk factor or a consequence of IPV victimization and perpetration [ 38 ]. This is potentially true because family addiction to substance misuse has significant economic, social, health, and psychological implications both for the family and society, resulting in couples' disagreement and creating barriers in the marital relationship over time. Our latent class analysis revealed that women with low education and low autonomy were 92% and 84% more likely to experience IPV, respectively, and these women were more likely to experience IPV earlier in marriage in most of the countries. Other studies have also found an association between IPV and women's low education and lack of autonomy [ 39 , 40 , 41 ]. Men who are abusive often feel a deep sense of entitlement, which is further entrenched in patriarchal social systems that give men more power and opportunity than women. Women's autonomy in marriage can be reflected in many situations, such as making decisions on family matters and household expenditures. This study revealed a substantial effect of women's economic autonomy, which has a direct or mediating effect on perpetrating IPV. Intergenerational cycles of violence can be perpetuated through systematic disadvantages and when violence affects the ability to bond with a child and to parent effectively. For example, men are more likely to use violence against their partner when they are insecurely attached or experience attachment anxiety, in which they attempt to regulate this insecurity through dominance and control [ 42 , 43 ]. Achieving economic autonomy has been a challenge for married women in low‐income countries where the male partner is dominant and is a primary source of income for the family, as explained by attachment theory [ 44 ]. While interventions that focus on the broad social goal of gender equality, women's economic independence, and access to justice are vital, there is also important work to be done with men about their early experiences of trauma and how this affects their dominance behavior within relationships [ 44 ]. Socioecological theories view IPV as an expression of conflict within the family that can best be understood through the examination of social structures that contribute to the use of violence [ 45 ]. On the other hand, feminist theory views IPV as an expression of the gender‐based domination of women by men [ 46 ]. The intergenerational transmission model and social learning theory view IPV as a social learning phenomenon in which children grow up in violent families and caregivers themselves as victims of domestic violence and neglect or as perpetrators as they grow to adulthood [ 47 , 48 ]. The psychological theory of violence looks at IPV as an instinct and a condition of human nature emersed in psychobiological and temperamental vulnerabilities and a result of a damaged psyche that affects self‐regulation, self‐concept, self‐esteem, attachment, and relationships [ 49 ]. In general, it is useful to comprehensively view all these theories of IPV from an individual's lifespan perspective, where violence born somewhere in life grows but does not occur among victims or perpetrators but rather passes to the next generation, where its nature, intent, and manifestation vary through the life of an individual. As such, effective treatment and prevention efforts should include both criminal justice and public health strategies starting in early childhood and focusing on multiple generations, but the most effective strategies and timing also need to be developed and tested. Through this lens, the research presented here provides insights into the individual‐ and country‐level predictors of IPV, illustrating the potential for targeted strategies that are most likely to reach the largest number of people at risk of perpetrating and experiencing violence. These strategies include targeting countries with the highest rates of physical, emotional and sexual abuse; intervening early in relationships; and working directly with men who drink alcohol and women who lack autonomy to address the underlying cycles of trauma that influence behavior over generations. 5. Strengths and Limitations A strength of this study is the use of nationally representative DHS data using standardized data collection procedures, which means that the findings are generalizable within and across SSA countries. We aimed to control changes in IPV incidence over time by including only the most recent surveys and adjusting for the year of the DHS survey. Two factors played the most important role in predicting the probability of IPV, with a predictive power of only 64.1%, indicating that the model did not include other important predictors of IPV, such as maternal mental health, childhood experience of domestic violence and neglect. The DHS used a cross‐sectional study design, which does not permit measurement of causality. The adjustment for complex data analysis uses the primary sampling unit to compute contextual variables that might create bias due to possible misclassification of respondents to incorrect administrative units. Proper class assignment may not be guaranteed because it is based on probabilities, and the exact number or percentage of sample members within each class cannot be determined. 6. Conclusion We identified two distinct classes of IPV experience in sub‐Saharan countries: a class with very high and low physical, sexual, and emotional violence, with probabilities of 28.4% and 71.6%, respectively. Physical violence (up to 49.9%) was the most prevalent type of violence, followed by emotional (up to 44.4%) and sexual violence (up to 24.2%). Six countries (Sierra Leone, Liberia, Uganda, Mali, Tanzania, and Zambia) constituted more than two‐thirds of the women who experienced all forms of violence in SSA countries. Approximately two‐thirds of women experienced their first episode of violence in the first 3 years of marriage. Two important variables, partner drinking alcohol and women's lack of autonomy, were found to increase the risk of IPV and predict the shortest time to first episode of IPV after marriage. Interventions that are most likely to reach the largest number of people at risk of perpetrating or experiencing IPV should focus on individual‐, family‐, and societal‐level factors in high‐prevalence countries to address early trauma experiences, men's drinking, and women's lack of autonomy. These strategies should be used early, prior to the first 3 years of marriage, when the risk of IPV onset is greatest. Author Contributions Kedir Y. Ahmed: conceptualization, methodology, validation, data curation, supervision. Kayli Wild: investigation, validation, writing – review and editing. Temesgen Yihunie Akalu: conceptualization, methodology, validation, writing – review and editing. Adhanom Gebreegziabher Baraki: conceptualization, methodology, validation, writing – review and editing. Achamyeleh Birhanu Teshale: conceptualization, methodology, validation, writing – review and editing. Tesfa Sewunet Alamneh: conceptualization, methodology, validation, writing – review and editing. Robel Hussen Kabthymer: conceptualization, methodology, validation, writing – review and editing. Koku Sisay Tamirat: conceptualization, methodology, validation, writing – review and editing. Getayeneh Antehunegn Tesema: conceptualization, methodology, validation, writing – review and editing, data curation. Funding The authors have nothing to report. Ethics Statement Approval of accessing and the use of DHS dataset was obtained from the Demographic and Health Surveys Program. The analysis used secondary data; there was no contact with the study participants, and informed consent was not available. Conflicts of Interest All other authors declare no competing interests. Transparency Statement The lead author Abel F. Dadi affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. Acknowledgments The authors are grateful to Measure DHS, ICF International, Rockville, MD, USA, for providing the data for analysis. This study received no grants from any funding agency in public, commercial or non‐for‐profit sectors. Data Availability Statement All DHS data are available at https://dhsprogram.com/data/available-datasets.cfm . The DHS provides open access to survey data files for legitimate academic research purposes. To initiate the download process, registration is mandatory. Researchers are required to provide their contact information, research title, and a brief description of the proposed analysis. Approval for dataset access is typically confirmed via email. It is important to note that these datasets are third‐party resources and are not owned or collected by the authors, who possess no special access privileges. The analysis files created from these data can be requested from the corresponding author. References 1. Sklansky D. A., A Pattern of Violence: How the Law Classifies Crimes and What it Means for Justice (Harvard University Press, 2021). [ Google Scholar ] 2. Miller E. and Mccaw B., “Intimate Partner Violence,” New England Journal of Medicine 380 (2019): 850–857. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Wallace R. 2015. Domestic Violence and Intimate Partner Violence: What's the Difference?” https://amuedge.com/domestic-violence-and-intimate-partner-violence-whats-the-difference/ . 4. WHO ., Violence Against Women Prevalence Estimates, 2018: Global, Regional and National Prevalence Estimates for Intimate Partner Violence Against Women and Global and Regional Prevalence Estimates For Non‐partner Sexual Violence Against Women (World Health Organization, 2021b). [ Google Scholar ] 5. WHO . 2021a. Devastatingly Pervasive: 1 in 3 Women Globally Experience Violence, https://www.who.int/news/item/09-03-2021-devastatingly-pervasive-1-in-3-women-globally-experience-violence . 6. Agüero J. M., “COVID‐19 and the Rise of Intimate Partner Violence,” World development 137 (2021): 105217. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Jetelina K. K., Knell G., and Molsberry R. J., “Changes in Intimate Partner Violence During the Early Stages of the COVID‐19 Pandemic in the USA,” Injury Prevention 27 (2021): 93–97. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Fulu E., Miedema S., Roselli T., et al., “Pathways Between Childhood Trauma, Intimate Partner Violence, and Harsh Parenting: Findings From the UN Multi‐Country Study on Men and Violence in Asia and the Pacific,” The Lancet Global Health 5 (2017): e512–e522. [ DOI ] [ PubMed ] [ Google Scholar ] 9. Howell K. H., Barnes S. E., Miller L. E., and Graham‐Bermann S. A., “Developmental Variations in the Impact of Intimate Partner Violence Exposure During Childhood,” Journal of Injury and Violence Research 8, no. 1 (2016): 43. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Bott S., Guedes A., Goodwin M., Mendoza J. A. 2012. Violence Against Women in Latin America and the Caribbean: A Comparative Analysis of Population‐Based Data from 12 Countries, Pan American Health Organization. [ Google Scholar ] 11. Aboagye R. G., Dadzie L. K., Arthur‐Holmes F., et al., “Intimate Partner Violence Against Married and Cohabiting Women in Sub‐Saharan Africa: Does Sexual Autonomy Matter?,” Reproductive Health 19 (2022): 79. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Izugbara C. O., Obiyan M. O., Degfie T. T., and Bhatti A., “Correlates of Intimate Partner Violence Among Urban Women in Sub‐Saharan Africa,” PLoS One 15 (2020): e0230508. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Nabaggala M. S., Reddy T., and Manda S., “Effects of Rural–Urban Residence and Education on Intimate Partner Violence Among Women in Sub‐Saharan Africa: A Meta‐Analysis of Health Survey Data,” BMC Women's Health 21 (2021): 149. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Ahinkorah B. O., Dickson K. S., and Seidu A.‐A., “Women Decision‐Making Capacity and Intimate Partner Violence Among Women in Sub‐Saharan Africa,” Archives of Public Health 76 (2018): 5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Wado Y. D., Mutua M. K., Mohiddin A., et al., “Intimate Partner Violence Against Adolescents and Young Women in Sub‐Saharan Africa: Who is Most Vulnerable?,” Reproductive health 18 (2021): 119. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. SDG . 2015. Sustainable Development Goals, https://www.un.org/sustainabledevelopment/inequality/ . 17. Corsi D. J., Neuman M., Finlay J. E., and Subramanian S., “Demographic and Health Surveys: A Profile,” International Journal of Epidemiology 41 (2012): 1602–1613. [ DOI ] [ PubMed ] [ Google Scholar ] 18. Croft Trevor N., Aileen M. J. M., Courtney K. A., et al. 2018. Guide to DHS Statistics: DHS‐7. Rockville, Maryland, USA. 19. Kishor S. and Johnson K. 2004. Profiling Domestic Violence: A Multi‐country Study, Measure DHS+, ORC Macro. 20. Lanza S. T., Dziak J. J., Huang L., et.al. 2015. LCA Stata Plugin Users' Guide (Version 1.2). University Park: The Methodology Center, Penn State. 21. Weller B. E., Bowen N. K., and Faubert S. J., “Latent Class Analysis: A Guide to Best Practice,” Journal of Black Psychology 46 (2020): 287–311. [ Google Scholar ] 22. Hagenaars J. A. and Mccutcheon A. L., Applied Latent Class Analysis (Cambridge University Press, 2002). [ Google Scholar ] 23. Killian M. O., Cimino A. N., Weller B. E., and Hyun Seo C., “A Systematic Review of Latent Variable Mixture Modeling Research in Social Work Journals,” Journal of Evidence‐Based Social Work 16 (2019): 192–210. [ Google Scholar ] 24. Muthén B. and Muthén L. K., “Integrating Person‐Centered and Variable‐Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes,” Alcoholism: Clinical and Experimental Research 24 (2000): 882–891. [ PubMed ] [ Google Scholar ] 25. Petersen K. J., Qualter P., and Humphrey N., “The Application of Latent Class Analysis for Investigating Population Child Mental Health: A Systematic Review,” Frontiers in Psychology 10 (2019): 1214. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Nylund K. L., Asparouhov T., and Muthén B. O., “Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study,” Structural Equation Modeling: A Multidisciplinary Journal 14 (2007): 535–569. [ Google Scholar ] 27. Nahm F. S., “Receiver Operating Characteristic Curve: Overview and Practical Use for Clinicians,” Korean Journal of Anesthesiology 75 (2022): 25–36. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Statacorp L. 1985. Stata Survey Data Reference Manual. Citeseer. 29. Hirsu L., Hashemi L., and Quezada‐Reyes Z. 2019. SDG 5: Achieve Gender Equality and Empower All Women and Girls. RMIT University, Melbourne. 30. WHO . 2017. Transformative Accountability for Adolescents: Accountability for the Health and Human Rights of Women, Children and Adolescents in the 2030 Agenda. 31. Sardinha L., Maheu‐Giroux M., Stöckl H., Meyer S. R., and García‐Moreno C., “Global, Regional, and National Prevalence Estimates of Physical or Sexual, or Both, Intimate Partner Violence Against Women in 2018,” Lancet 399 (2022): 803–813. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Peterman A., Bleck J., and Palermo T., “Age and Intimate Partner Violence: An Analysis of Global Trends Among Women Experiencing Victimization in 30 Developing Countries,” Journal of Adolescent Health 57 (2015): 624–630. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Akinyemi J. O., Somefun O., Olamijuwon E. O., De Wet N., and Odimegwu C. O., “How Long was the Honeymoon? Accelerated Failure Time Model of Spousal Violence Onset Among Women in Sub‐Saharan Africa,” Women and Health 60 (2020): 987–999. [ DOI ] [ PubMed ] [ Google Scholar ] 34. Schneider C. and Brimhall A. S., “From Scared to Repaired: Using an Attachment‐Based Perspective to Understand Situational Couple Violence,” Journal of Marital and Family Therapy 40 (2014): 367–379. [ DOI ] [ PubMed ] [ Google Scholar ] 35. Jatta J. W., Baru A., Fawole O. I., and Ojengbede O. A., “Intimate Partner Violence Among Pregnant Women Attending Antenatal Care Services in the Rural Gambia,” PLoS One 16 (2021): e0255723. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Lencha B., Ameya G., Baresa G., Minda Z., and Ganfure G., “Intimate Partner Violence and its Associated Factors Among Pregnant Women in Bale Zone, Southeast Ethiopia: A Cross‐Sectional Study,” PLoS One 14 (2019): e0214962. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Tusa B. S., Kebede S. A., and Weldesenbet A. B., “Spatial Distribution and Determinant Factors of Intimate Partner Violence Among Reproductive Age Group Women in Ethiopia: Using Generalized Structural Equation Modeling,” PLoS One 17 (2022): e0263811. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Cafferky B. M., Mendez M., Anderson J. R., and Stith S. M., “Substance Use and Intimate Partner Violence: A Meta‐Analytic Review,” Psychology of Violence 8 (2018): 110–131. [ Google Scholar ] 39. Sanz‐Barbero B., Barón N., and Vives‐Cases C., “Prevalence, Associated Factors and Health Impact of Intimate Partner Violence Against Women in Different Life Stages,” PLoS One 14 (2019): e0221049. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Sunmola A. M., Sambo M. N., Mayungbo O. A., and Morakinyo L. A., “Moderating Effect of Husband's Controlling Attitudes on the Relation Between Women's Household Decision‐Making Autonomy and Intimate Partner Violence Experience in Nigeria,” Journal of Interpersonal Violence 36 (2021): NP12125–NP12154. [ DOI ] [ PubMed ] [ Google Scholar ] 41. Tenkorang E. Y., “Women's Autonomy and Intimate Partner Violence in Ghana,” International Perspectives on Sexual and Reproductive Health 44 (2018): 51–61. [ DOI ] [ PubMed ] [ Google Scholar ] 42. Gilbar O., Taft C., and Dekel R., “Male Intimate Partner Violence: Examining the Roles of Childhood Trauma, PTSD Symptoms, and Dominance,” Journal of Family Psychology 34 (2020): 1004–1013. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Hunnicutt G., “Varieties of Patriarchy and Violence Against Women: Resurrecting “Patriarchy” as a Theoretical Tool,” Violence Against Women 15 (2009): 553–573. [ DOI ] [ PubMed ] [ Google Scholar ] 44. J. Park C., “Intimate Partner Violence: An Application of Attachment Theory,” Journal of Human Behavior in the Social Environment 26 (2016): 488–497. [ Google Scholar ] 45. Lawson J., “Sociological Theories of Intimate Partner Violence,” Journal of Human Behavior in the Social Environment 22 (2012): 572–590. [ Google Scholar ] 46. De Coster S. and Heimer K., “Unifying Theory and Research on Intimate Partner Violence: A Feminist Perspective,” Feminist Criminology 16 (2021): 286–303. [ Google Scholar ] 47. Ehrensaft M. K. and Langhinrichsen‐Rohling J., “Intergenerational Transmission of Intimate Partner Violence: Summary and Current Research on Processes of Transmission.” in Handbook of Interpersonal Violence and Abuse Across the Lifespan: A Project of the National Partnership to End Interpersonal Violence Across the Lifespan (NPEIV), eds. Geffner R., White J. W., Hamberger L. K., Rosenbaum A., Vaughan‐Eden V., and Vieth V. I. (Springer International Publishing, 2022). [ Google Scholar ] 48. Powers R. A., Cochran J. K., Maskaly J., and Sellers C. S., “Social Learning Theory, Gender, and Intimate Partner Violent Victimization: A Structural Equations Approach,” Journal of Interpersonal Violence 35 (2017): 3554–3580. [ DOI ] [ PubMed ] [ Google Scholar ] 49. King B., “Psychological Theories of Violence,” Journal of Human Behavior in the Social Environment 22 (2012): 553–571. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement All DHS data are available at https://dhsprogram.com/data/available-datasets.cfm . The DHS provides open access to survey data files for legitimate academic research purposes. To initiate the download process, registration is mandatory. Researchers are required to provide their contact information, research title, and a brief description of the proposed analysis. Approval for dataset access is typically confirmed via email. It is important to note that these datasets are third‐party resources and are not owned or collected by the authors, who possess no special access privileges. The analysis files created from these data can be requested from the corresponding author. Articles from Health Science Reports are provided here courtesy of Wiley ACTIONS View on publisher site PDF (360.5 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top