Data analysis

The data analysis was largely planned at the point of commencement of the work on the paper. Authors EF and RJ were involved in the research from its inception and had planned the questionnaire so that it would be possible to undertake an analysis of prevalence of violence and risk factors. They ensured as much as possible that the main variables previously described in the literature [5] were included in the dataset. We planned the analysis to test the relationships between the independent variables and the outcomes. This study is reported as per the STROBE guidelines (S1 STROBE Checklist).

We combined the datasets and analysed the data using Stata, version 13. All procedures took into account the multistage structure of the dataset, with stratification by site within a country and enumeration areas as clusters. The sample was self-weighting. Women’s experiences of violence and male partner violence perpetration, as well as the independent variables, were summarized as percentages (or means), with 95% confidence limits calculated using standard methods (Taylor linearization).

We categorised the type of violence exposure according to the most severe type experienced, where greatest severity was considered as exposure to physical and/or sexual IPV, as this is the category that has been the basis of most health consequences research [1] and is consistent with the paper on male risk factors for IPV published from the same dataset [10]. It is currently common practice in the field not to model a combined variable with sexual and physical IPV and economic and emotional abuse, although this has been sometimes done [13]. This is because the field’s understanding of the latter is at a much earlier stage, with limited agreement on how to measure it, how to prevent it, and the implications (of emotional abuse alone) for health and development outcomes. It is important for the field that the issue is not ignored, hence its inclusion here, but we do not feel that the field is quite ready for it to be meaningfully pooled with sexual and physical violence for risk factor modelling and interventions. This approach has been followed by other authors, for example, Mahenge et al. [14].

The multiple emotional/economic abuse category consisted of women who had experienced more than 1 act of economic or emotional abuse but never experienced sexual or physical abuse. All ever-partnered women and men were classified into 5 violence exposure categories: none, emotional/economic without sexual or physical (henceforth referred to as ‘emotional/economic’), sexual without physical and with or without emotional/economic (henceforth referred to as ‘sexual’), physical without sexual and with or without emotional/economic (henceforth referred to as ‘physical’), or sexual and physical with or without emotional/economic (henceforth referred to as ‘physical/sexual’).

We also evaluated the relationship between the outcome (IPV) and nonresponse (missing data) in putative risk factors. No association was found between a woman’s IPV status and her nonresponse to any of the possible risk factors. However, to increase the sample of women with responses to scale measurements (e.g., gender attitudes and relationship control), women with partial responses to scale items were also included. Three methods for imputing for missing data were initially compared. These involved imputing for missing scale items using either (1) a woman’s responses to other items in the scale (individual respondent mean) or (2) the average for each item adjusted for IPV status, or (3) the average of the overall score adjusted for IPV status. There were no significant differences in the 3 methods for both gender attitudes and relationship control scores. We used ‘the average of the overall score (adjusted for IPV status)’ to impute for missing scores.

The exercise of testing variables and building model drew on current theories about risk factors and drivers of violence against women. The selection of variables as putative risk factors was informed by the state of knowledge in the field. Drawing on a life-course modified ecological model of violence risk [15], we conceptualized possible risk factors as (1) structural, (2) those pertaining to the women (including stemming from her childhood), (3) those pertaining to her partner, and (4) those pertaining to their relationship. We further were informed in our thinking by research on masculinities that views a range of male behaviours as indicator variables for hegemonic masculinity [16]. The connections between hegemonic masculinity and violence against women have been extensively theorized. In building the structural equation model, we drew on our extensive knowledge base on gender-based violence. It is well recognized that IPV is strongly associated with poverty and that poverty increases the likelihood of experience of adversity in childhood and influences access to education [9,17–19]. Research with men has shown that childhood trauma exposure influences ideas about gender equity, which is why we hypothesized this direction of effect for women [19]. Further research has shown that women’s ideas about gender influence partner selection, as does exposure to childhood trauma [20].

To show associations between independent variables that were putative risk factors, we first conducted a bivariable analysis with a (by type) lifetime IPV exposure measure and a multinomial regression with no physical, sexual, or severe economic/financial violence as the comparison group. A maximum likelihood multinomial logit model, which adjusted for the survey design, was used to compare factors associated with different types of IPV experienced with the no-violence reference category. We initially fitted bivariable models and then included all factors that were significantly associated with IPV experience in the bivariate models into an overall model, which was adjusted for the country and age group of the woman.

We examined factors associated with past-year experience of IPV considering the same independent variables, but with a past-year exposure to any physical and/or sexual IPV as the outcome, due to sample size considerations, we did not perform multinomial modelling. We sought to model 19 covariates in the logistic regression model, which, according to generally accepted rules of thumb [21], would require a total of 190 events. Because the category ‘physical and sexual IPV’ contained only 124 events, having this as an outcome in a multinomial model could have resulted in overfitting. We therefore decided to fit a regression model specifying the combined outcome of ‘any exposure to physical and/or sexual IPV’. Since this combined outcome contained 2,765 x 16.7% = 461 events, this decision allowed us to proceed with less concern about overfitting.

Multivariable logistic regression was used to determine risk factors associated with past-year physical and/or sexual IPV experience in women, with those not experiencing this as the reference group. To enable the use of a variable on frequency of quarrelling, which was not measured in Cambodia, a dummy level for Cambodia was created for the quarrelling variable for use in the logistic regression model. All variables were included in the multivariate analysis. We focus the discussion on variables with P < or = 0.05 in the model, which is adjusted for country/site and age-group of the woman.

The population attributable fractions (PAFs) for each category of IPV were calculated using the formula PAF = ((RRR − 1) / RRR) * Pe, where RRR is the adjusted relative risk ratio from the adjusted model and Pe is the proportion of women who had experienced that particular IPV type and who had the exposure.

Structural equation modelling (SEM) was conducted using Stata 13.0 to assess the interrelationship between variables associated with physical and/or sexual IPV in the multinomial regression model. The model outcome was a past-year IPV variable that had 4 levels drawn from the physical and sexual IPV questions: no exposure, sexual IPV, physical IPV, and physical or sexual IPV. The correlation between each hypothesized variable and the IPV variable was then tested by building variable pairs. All associations were tested by running a full-information maximum likelihood method to deal with missing values. This method was chosen over multiple imputations because it has been shown to yield superior results in structural equation modelling [22]. As a next stage, a measurement model was fitted with the variables allowed to freely correlate. To assess model fit of the observed data, we used the comparative fit index (CFI) (>0.95); Tucker-Lewis Index (TLI) (>0.9) for acceptable fit and (>0.95) as indicative of good fit [23]; and root mean square error of approximation (RMSEA) (of 0.05 or less) [24,25].

We fitted a path model using full information maximum likelihood (FIML) estimation to model all available data. The final model was built based on theory and statistically meaningful modifications using backwards elimination to exclude endogenous variables that did not mediate any path (with significance set at the P < 0.05 level) from the exogenous variables to IPV in order to ensure model parsimony. Before adjusting standard errors for clustering of participants in countries, model fit was very good (p(χ²) = 0.519, RMSEA < 0.001, CFI = 1.000, and TLI = 1.001). After adjusting for clustering, the coefficient of determination (CD) was 0.215. The model did not include any error covariances.

Comparison of prevalence

Comparing lifetime reports of women’s experiences and men’s reports of IPV by type from the 4 countries (Table 2) reveals that sexual IPV was quite similarly reported by men and women, except women less often disclosed lifetime sexual IPV in Cambodia (9.1% versus 21%) and China (8.3% versus 19.4%) and men reported less past-year sexual IPV in Sri Lanka. Men reported less lifetime and past-year physical IPV than women in Cambodia, but much more in China. Men reported more lifetime physical IPV than women in Bougainville, but past-year reports were similar. In Sri Lanka, the overall level of violence reported by men and women and the rates for each type were similar. In every country, women reported much more past-year emotional and financial IPV than men.

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Table 2. Among partnered women and men, past 12-month prevalence of women’s and men’s experience of different types of violence perpetrated by their intimate partners, by country.

https://doi.org/10.1371/journal.pmed.1002381.t002

In Cambodia, 0.4% (95% CI 0.1%–1.73%) of women had experienced nonpartner rape in the past year, and 1.9% (95% CI 1.12%–2.70%) of men disclosed perpetration. In China, 2.3% (95% CI 1.49%–3.43%) of women had experienced nonpartner rape in the past year, and 1.7% (95% CI 0.94%–2.52%) of men disclosed perpetration. In Bougainville, 5.7% (95% CI 4.21%–7.75%) of women had experienced nonpartner rape in the past year, and 11.7% (95% CI 9.02%–14.30%) of men disclosed perpetration. In Sri Lanka, 0.5% (95% CI 0.15%–1.40%) of women had experienced nonpartner rape in the past year, and 0.8% (95% CI 0.22%–1.43%) of men disclosed perpetration.

The prevalence of past-year physical and/or sexual IPV experience increased with age (Table 3). Poverty, indicated by present food insecurity and problems finding money for an emergency, was associated with a greater risk of IPV, as was the women being the main breadwinner. Families in which the wife provided most of the money for the home were twice as likely to have food insecurity (P < 0.001) as those in which the husband provided, another provided, or both the husband and wife shared equally in providing.

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Table 3. Prevalence and distribution of factors associated with women’s past-year experience of sexual or physical intimate partner violence (N = 2,765).

https://doi.org/10.1371/journal.pmed.1002381.t003

Risk factor analysis

All 3 forms of childhood abuse (sexual, physical, and emotional) and witnessing abuse of mother were more common among women with past-year physical or sexual IPV experience. Women whose partners earned more than them had a lower past-year IPV prevalence than those earning the same as their partners or women who earned more. Partner characteristics associated with women’s past-year IPV experience were the male partner’s regular alcohol use, ever or past-year drug use, lack of fidelity, and unemployment. Women who were highly controlled by their partner were more likely to have experienced past-year IPV, as were those who quarrelled more often and those holding less gender-inequitable views.

S1 Table shows the prevalence of women’s social characteristics, victimisation history, partner characteristics, and gender attitudes and relationship factors by lifetime IPV exposure category for the combined dataset (all 4 countries), with the unadjusted associations and the adjusted associations shown in S2 Table. These tables show very similar patterns of associated factors as was seen in the past-year physical or sexual IPV exposure analysis.

Table 3 shows the logistic regression models of factors associated with past-year IPV. In the past 12 months, 461/ 2,765 (16.7%) women had experienced sexual or physical (or both forms of) IPV. The risk factors shown are experiencing more poverty; having experienced abuse in childhood (sexual, physical, or emotional); having a partner who drinks alcohol, uses drugs, may be unfaithful, is unemployed, or is highly controlling; and having more frequent quarrelling in the relationship. The PAF was the largest for quarrelling often, but the second greatest PAF was for the group related to exposure to violence in childhood, followed by the PAF for the group related to the woman being controlled by her partner. The partner characteristics (substance abuse, unemployment, and infidelity) had the next highest PAFs. In the backwards/forwards elimination model, currently married women were at much higher risk.

Structural model

Results for the structural equation model are presented in Fig 1 and Table 4 and follow recommended guidelines outlined by Mueller and Hancock [26]. The paths between socioeconomic status and IPV were mediated by childhood trauma exposure (i.e., poorer women had a higher trauma exposure) and increased IPV risk or by women’s educational attainment (i.e., wealthier women had been in school for longer) and having more equitable gender attitudes, which conveyed IPV protection, unless associated with more quarrelling. Childhood trauma was linked to IPV through 4 pathways. One was direct, such that childhood trauma increased the risk of IPV. One was mediated by partner alcohol use and frequency of quarrelling, such that childhood trauma reduced the chance of having a low-alcohol-using partner and thus lower quarrelling. One path was mediated by (more inequitable) attitudes to gender equity. The fourth path was mediated by partner fidelity such that risk was associated with greater confidence in him being faithful. Witnessing abuse of the woman’s mother was more common in women exposed to trauma in childhood and was included to improve model fit but did not mediate a pathway. A figure with all significant and nonsignificant paths and standard errors is presented in S1 Fig.

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Fig 1. Final structural model of final factors influencing women’s experience of intimate partner violence (IPV) (standardized path coefficients [only statistically significant paths shown]).

https://doi.org/10.1371/journal.pmed.1002381.g001

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Table 4. Women's path model: Direct effects, disturbance variances, and equation-level goodness of fit.

https://doi.org/10.1371/journal.pmed.1002381.t004

Comparing reports

Between a quarter and two-thirds of women in the 4 countries studied had experienced IPV, and 1.7% and 15.9% had experienced nonpartner rape. There was very great diversity in the prevalence of IPV between countries, as previously reported in Asia and the Pacific [1]. Reports by men and women show much similarity, but overall, women’s reported prevalence of lifetime physical and sexual violence experience was lower than men’s reports of perpetration, notably in sexual violence reporting. Men’s reporting of past-year nonpartner rape was much higher than women’s in Bougainville. A different pattern was seen in past-year reports that were not clearly patterned with respect to those of men, except in the area of emotional/financial abuse, for which in all countries women reported much more.

We would not necessarily expect men’s and women’s reports of nonpartner sexual violence to concur, and some women are at much higher risk than others in the population and may experience multiple rapes [27]. Although we did not have couples’ reports on partner violence, we do expect the acts/experiences of violence of men and women to be similar at a population level for past-year violence, as 75% of men had had only 1 sexual partner in the last year, and most women were married (77.7%) or cohabiting (2.9%). It is possible that women tended to minimise or forget some lifetime experiences of partner violence, but it may also be the case that higher levels of reports by men are explained by men using violence on some types of female partners more often than on their wives. Given the differences in men’s and women’s lifetime reports, we must conclude that the current global lifetime prevalence rates that are based on women’s reported experiences may underestimate the lifetime perpetration of IPV and nonpartner rape by men.

Risk factors and drivers

We saw 4 important groups of risk factors for IPV experience. First, our results confirm that past-year IPV victimisation is more common in a context of poverty [6]. Secondly, exposure to physical, sexual, and/or emotional childhood trauma was very strongly associated with experience of all forms of IPV (past year or lifetime). This advances current research that has focused on sexual violence or on witnessing maternal abuse [5,6]. In the structural model, childhood trauma had a direct pathway to IPV experience, and it mediated several indirect paths. This helps explain why childhood trauma exposure is such an important risk factor (as shown by the PAF). The analysis of factors associated with IPV perpetration by men has also shown the importance of all forms of childhood trauma [10]. We observed also that childhood trauma exposure was associated with a more conservative position towards gender equity. It is possible that this is easier for women to adopt if they have lower self-esteem and more insecurity after trauma, as it generally is socially rewarded and normative.

Witnessing abuse of one’s mother has been found to be associated with both experience of and perpetration of IPV in many studies [17,28–35]. We confirmed this, but in the structural model, it was not as important as childhood trauma. Since previous research has often focused on witnessing abuse rather than more thoroughly measuring childhood trauma, it is possible that assumptions that there is a direct intergenerational learning process normalising IPV victimisation among women and girls are overemphasising this 1 traumatic experience, and witnessing abuse may be better interpreted as an indicator of exposure to wider childhood experiences of emotional and other trauma, all of which elevate IPV risk. The latter explanation fits better with the knowledge that witnessing abuse of one’s mother is traumatic and repulsive, which has long been an observation that fits uncomfortably with a direct learning explanation.

The third variable group consists of partner characteristics: his drinking, past-year drug use, controlling behaviour, unemployment, and fidelity. Generally, these are previously well-established risk factors, although research with men has not confirmed associations with drug use in Asia and the Pacific, except in relation to perpetration of multiple perpetrator rape [8,10]. Alcohol abuse combines a direct impact on behaviour, financial tensions, and gender-inequitable masculinity; the fidelity measure reflects the male sexual entitlement dimension of the latter [6,8,36,37]. Highly controlling behaviour is an abusive practice that is closely related to the use of physical and sexual violence [38] and is viewed by some authors as part of the concept of emotional abuse. In the structural model, male partner alcohol consumption and infidelity both mediated pathways between childhood trauma and IPV experience—in the former case, mediated by frequency of quarrelling. These partner variables may highlight the potential for enhanced prevention intervention impact if men and women are both involved in interventions to reduce violence [39]. Partner unemployment was significant on 1 of the models and would generally be interpreted as contributing to poverty in the relationship, with associated tensions, but it may also impact on self-perceived manliness, and violence may be used as a response to this [39].

The frequency of quarrelling was very strongly associated with IPV, as it was in the models of men’s perpetration in the 4 countries [10]. Although quarrelling is linked to men’s and women’s ideas about gender equity, intervention research shows that it can be reduced within relationships by training in communication skills, and this can reduce partner violence [40].

One of the most important findings of the structural model was a pathway that can be interpreted as indicating variables that build women’s resilience to violence. This linked higher wealth, higher educational attainment, and having more gender-equitable attitudes. This is very important because all of these factors are amenable to intervention, and it highlights the role of poverty reduction and interventions to enhance girls’ schooling, which may be supported for many reasons related to development and the general upliftment of women, in IPV prevention. In this study, the Gender Equitable Men (GEM) scale was used to measure women’s gender attitudes. This is a broad measure that includes attitudes towards the use of violence against women. The latter alone have been shown to be very strongly associated with risk of violence [41,42]; however, we found strong correlations with IPV in a version of the scale without the question about attitudes towards violence.

Economic empowerment has been shown to be a fruitful area of intervention with women [43], but more consistently so when combined with a gender empowerment intervention [44]. Our analysis suggests that interventions with adult women would do better to include a focus on gender empowerment and relationship dynamics in order to ensure that empowerment alone does not result in greater quarrelling and violence. Our structural model provides some indication of why interventions that impact on several variables in the resilience pathway for women (economic status and gender attitudes/relationship skills) may be much better than single-component interventions.

Reducing childhood trauma exposure is ultimately critical to reducing women’s experience of violence and is strongly related to poverty. Whilst there is much work on early interventions in childhood to reduce the experience of trauma and IPV in the next generation, it is possible that poverty reduction will have the greatest impact.

Limitations

The study findings reflect the sampled sites; generalizability beyond this is unclear, and the combined dataset analysed here does not reflect the whole region. Since the research was cross-sectional, temporality may be questioned, but since this was recent violence, this is not likely to be a great problem. All the prevalence estimates for violence were compared with estimates weighted for the number of eligible men and women per household. The latter were not significantly different in any site, and thus, we have used unweighted estimates. The main analysis was on past-year IPV exposure, and because this is less common than lifetime exposure, the power of the analysis was inevitably impacted. However, the focus has strengthened the interpretability of the results for programming, as it is the goal of IPV prevention to reduce exposure of women at risk in the future and recent abuse is the best measure of this. A study limitation is that we do not have a comparison of men’s and women's reports from the same relationship. In accordance with WHO ethics and safety guidelines, we did not interview men and women in the same location, much less in couples. The motivation is to avoid the (to our knowledge small) possibility of retaliatory violence associated with partners learning of the interview content. This risk is not justified in cross-sectional research but prevents comparison of couples’ reports.