Armed conflict, alcohol misuse, decision-making, and intimate partner violence among women in Northeastern Uganda: a population level study

Abstract

BackgroundRelations among and interactions between exposure to armed conflict, alcohol misuse, low socioeconomic status, gender (in)equitable decision-making, and intimate partner violence (IPV) represent serious global health concerns. Our objective was to determine extent of exposure to these variables and test pathways between these indicators of interest.MethodsWe surveyed 605 women aged 13 to 49 who were randomly selected via multistage sampling across three districts in Northeastern Uganda in 2016. We used Mplus 7.4 to estimate a moderated structural equation model of indirect pathways between armed conflict and intimate partner violence for currently partnered women (n = 558) to evaluate the strength of the relationships between the latent factors and determine the goodness-of-fit of the proposed model with the population data.ResultsMost respondents (88.8%) experienced conflict-related violence. The lifetime/ past 12 month prevalence of experiencing intimate partner violence was 65.3%/ 50.9% (psychological) and 59.9%/ 43.8% (physical). One-third (30.7%) of women’s partners reportedly consumed alcohol daily. The relative fit of the structural model was superior (CFI = 0.989; TLI = 0.989). The absolute fit (RMSEA = 0.029) closely matched the population data. The partner and joint decision-making groups significantly differed on the indirect effect through partner alcohol use (a1b1 = 0.209 [0.017: 0.467]).ConclusionsThis study demonstrates that male partner alcohol misuse is associated with exposure to armed conflict and intimate partner violence—a relationship moderated by healthcare decision-making. These findings encourage the extension of integrated alcohol misuse and intimate partner violence policy and emergency humanitarian programming to include exposure to armed conflict and gendered decision-making practices.

Gender Based Violence against Women in Sub-Saharan Africa: A Systematic Review and Meta-Analysis of Cross-Sectional Studies

“…This finding is consistent with findings of other studies [2,6,15,27]. This might be attributed to factors such as socioeconomic class, women's disempowerment, community acceptance for wife beating and the type of community in which the study was conducted [4,6,27,29,108,109].…”

“…The sub region analyses found that Eastern Africa (42%) including Ethiopia and Uganda were the most affected by all forms of IPV [29,47], followed by Western Africa (41.7%). In line with our findings, the two regions that experienced high prevalence rates of IPV in comparison to other African regions [7,25] was also consistent with other studies conducted in SSA countries [4,5].…”

“…Overall, pooled prevalence of sexual violence was 18.61% (95% CI 15.21, 22.00) with a high disparity among studies detected ( Figure S6). The highest prevalence report was found in women in Northern Uganda (50%) (95% CI 46, 53%) [29], followed by a study conducted amongst women (39.7%) (95% CI 32.2, 47.2) in the Democratic Congo [31]. The lowest prevalence was found in Ghana (4%) (95% CI 3.1, 5.1) [37] and Nigeria (6.6%) (95% CI 6.3, 6.9) [45] amongst women of reproductive age.…”

“…Prevalence of physical violence was found in 23 studies ranging from 5.5% (95% CI 3.9, 7.9%) [47] among nurses in Ethiopia and 5.9% (95% CI 3.6-9.3%) [66] among HIV positive pregnant women in Nigeria to 59.9% (95% CI 55.5-63.8%) [29] (Figure S5). Eleven studies showed that over the past year, the prevalence of IPV ranged from 9.3% (95% CI 8.3, 10.6%) in a Tanzanian study [96] to 43.8% (95% CI 39.5, 47.8%) [29] in Uganda. The pooled past year prevalence of physical IPV was estimated at 21.59% (95% CI 15.84, 27.33%).…”

Navigating risk and protective factors for family violence during and after the COVID-19 ‘perfect storm’

“…In this section, we examine an accumulating international body of knowledge, which indicates that FV is more prevalent during and often beyond pandemics, conflicts, and disasters (eg, see Guruge et al, 2017;Kelly, Colantuoni, Robinson, & Decker, 2018;Mootz et al, 2018;Parkinson & Zara, 2013;Peterman et al, 2020;Rao, 2020). In terms of explanatory pathways, Peterman and colleagues identified nine pathways linked to increased VAW/C during pandemics.…”