Mapping of Intimate Partner Violence: Evidence From a National Population Survey

Abstract

Evidence on the relative importance of geographical distribution and associated factors with intimate partner violence (IPV) can inform regional and national health programs on women’s health. Four thousand seven hundred and twenty married women aged 15-49 years were interviewed in 2016 about IPV and this data was extracted from the Ethiopian Demographic Health Survey (EDHS) in 2020. The sample was selected by a two-staged cluster survey of women. The analysis was conducted using logistic regression that adjusted for clustering and sampling weights. Moreover, weighted proportions of IPV were exported to ArcGIS to conduct autocorrelations to assess the clustering of IPV. Amongst the 4469 married women who were 15 to 49 years of age included in the analysis, 34% (95% CI, 31.4%-36.3%) experienced IPV, 23.5% ( 95% CI, 21.5%-25.7%) experienced physical violence, 10.1% (95% CI, 8.7%- 11.7 %) experienced sexual violence and 24% (95% CI, 21.7%-26.4 %) experienced emotional violence. Partners’ controlling behaviour [AOR: 3.94; 95% CI, 3.03- 5.12], partner’s alcohol consumption [AOR: 2.59; 95% CI, 1.80- 3.71], partner educational qualifications [AOR: 2.16; 95% CI, 1.26- 3.71], a woman birthing more than five children [AOR: 1.70; 95% CI, 1.12- 2.56] and a history of the woman’s father being physically violent towards her mother [AOR: 1.99; 95% CI, 1.52- 2.59] were associated with an increased risk of IPV amongst married women in Ethiopia. Western and Central Oromia, Western Amhara, Gambella and Central Tigray and Hararri were identified as hot spot areas in Ethiopia (p<0.001). In this study, there was a significant geographic clustering of IPV in Ethiopia. Controlling and drinking behaviour and partners’ unemployment status were identified as important factors for married women experiencing IPV. Hence, there is a need for a context- driven evidence-based design intervention to reduce the impact of IPV.

Keywords

domestic violence sexual assault cultural contexts alcohol and drugs assessment female offenders

Introduction

Intimate partner violence (IPV) is a global health issue and a human rights violation that affects women from different backgrounds and social groups. IPV includes physical, sexual, or emotional violence (Abrahams et al., 2014). A multi-country study conducted by the World Health Organization (WHO) among women found that IPV is the most common type of gender-based violence (GBV) with an overall prevalence of 30% reported globally and with high variability in prevalence among countries (WHO, 2013). According to a systematic and meta-analysis study conducted in sub-Saharan Africa (SSA), which is one of the worst regions reporting IPV in comparison to other regions of the world (Muluneh et al., 2020; WHO, 2013), in 2020, the prevalence rate of IPV was as high as 44% (Muluneh et al., 2020). The high prevalence of IPV is similar in Ethiopia (Deyessa et al., 2010; Yenealem et al., 2019). Additionally, Ethiopia has one of the lowest gender equality performance indicators in SSA countries (Dessalegn et al., 2020; Hausmann et al., 2011).

Studies indicate that IPV affects women’s mental and physical functioning and increases the chances of poor health outcomes, which intensifies depression, anxiety, chronic pain, and feelings of disempowerment (Choudhary et al., 2011; Rahme et al., 2020). In 2016, WHO released a global plan of action to address interpersonal violence, particularly against women, adolescent females, and children (WHO, 2013). It is important to identify the different risk factors that increase the chances of IPV in order to set priorities to address prevention and mitigation measures that are paramount in achieving the Sustainable Development Goals (SDGs) 2030 target for eradication of IPV (United Nations General Assembly, 2015; WHO, 2013). Various studies including WHO have recommended using the ecological framework as an explanatory tool to identify associated risk factors of IPV (WHO, 2012). The ecological model considers the complex interplay between individual, relationship, community, and societal factors that may lead to GBV. There is a dearth of research that focuses on the comprehensive ecological framework approach, which usually only highlights individual and relationship factors (Abrahams et al., 2014) without community and societal factors. Additionally, IPV studies have not focused on geographic influences. This information has received far less attention in comparison to other health research, and minimal work has been employed using geographic information systems (GIS) (Beyer et al., 2015a, 2015b) in countries including Ethiopia. Therefore, there is a need for a geolocation analysis to improve our understanding of IPV to target and develop tailored interventions (Bell & Naugle, 2008). Most recently, a national representative survey in Ethiopia was conducted to reveal some of these important results (CSA, 2016). The survey report and previous researchers have not attempted more in-depth analyses, such as patterns, geographical locations, correlations or regression models of various individual, relationship, community, and social factors in Ethiopia, limiting information available to provide a tailored response to IPV.

Moreover, to our knowledge, there have been no studies focusing on the spatial distribution of IPV (physical, sexual, or emotional) in Ethiopia. Hence, identifying the hot spot areas of various forms of IPV will assist in designing and prioritizing tailored interventions and appropriate resources. The findings reported in this study will provide vital evidence to inform policy and guide health investments to prevent violence, which is in alignment with a unified national response that takes into account the prioritization of an approach tailored toward women, partners, communities, and societies in Ethiopia and to achieve the SDGs by 2030. In conclusion, this study will conduct a geospatial analysis and investigate factors associated with IPV among women of reproductive ages that is, between 15 and 49 years, in Ethiopia.

Methodology

Study Setting

Ethiopia is an independent and the second most populous country located in Eastern SSA with a population of more than 110 million (Encyclopaedia, 2019; UN, 2019). Ethiopia has one of the lowest gender equality performance indicators in SSA countries. Ethiopia ranks 121 out of 134 countries in terms of the magnitude and scope of gender disparities (Dessalegn et al., 2020; Hausmann et al., 2010). Women in Ethiopia have the lowest socioeconomic status within the population; moreover, they lack adequate social support networks (CSA, 2016; FMOH, 2015; Misganaw et al., 2017; WHO et al., 2015).

Data Source

The Ethiopia Demographic and Health Survey (EDHS) 2016 is the fourth population cross-sectional survey that was conducted by the Ethiopian Central Statistical Agency (CSA, 2016). During the survey collection period, all individuals who spent the night in that particular area were interviewed (de facto data collection method) (CSA, 2016). Prior to this, the survey was conducted in 2000, 2005, and 2011. For the first time, EDHS 2016 included a module of questions related to IPV at an individual level (CSA, 2016).

The surveys were developed by Measure DHS. The 2016 EDHS datasets were downloaded from the Measure DHS website (https://www.dhsprogram.com/data/available-datasets.cfm) in STATA format with permission. Overall, 5,860 women were interviewed about their experiences of GBV with a response rate of 97%. The remaining 3% of women were not interviewed due to a lack of privacy and presence of a male spouse. Women were asked whether or not they had experienced a number of violent acts within their current or previous relationships (CSA, 2016). Only women who were married (n = 4,720), who were asked all questions related to reproductive health, and who responded to the spousal violence questionnaire were included in the analysis. The sampling is in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, which randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire. It is considered as representative, as women were selected randomly. Background characteristics among women selected for IPV and the general female population in the selected households were shown to be similar (CSA, 2016). The details of the sampling women experienced for IPV justification are written in each country that distributes the DHS, as well as the WHO (CSA, 2016; WHO, 2001).

Variables for Identifying Confounding Factors for Intimate Partner Violence

The main outcome variable was IPV, which is defined as any physical, sexual, or emotional violence experience that was inflicted by the male partner. Additionally, physical, sexual, and emotional violence were considered as outcome variables. IPV was measured by dichotomous forms (1 = yes and 0 = no) generating at least one of the experiences from the three forms. Women experiencing IPV can be defined by one of the following: (a) physical spousal violence—push you, shake you, or throw something at you; slap you; twist your arm, or pull your hair; punch you with his/her fist or with something that could hurt you; kick you, drag you, or beat you up; try to choke you or burn you on purpose; or threaten or attack you with a knife, gun, or any other weapon; (2) sexual spousal violence—physically force you to have sexual intercourse with him even when you do not want to; physically force you to perform any other sexual acts you do not want to; force you with threats or in any other way to perform sexual acts you do not want to; (3) emotional spousal violence—say or do something to humiliate you in front of others; threaten to hurt or harm you or someone close to you; insult you or make you feel bad about yourself. The main outcome variable (IPV) was examined against four levels of confounding variables, which included individual variables (women’s characteristics), partner/family characteristics, and community- and societal-level variables. Some variables such as the wealth index variable were constructed using household facilities and assets, which were weighted, using principal component analysis (Filmer & Pritchett, 2001). The range of assets considered included a television, radio, fridge, and ownership of a car, bicycle, or motorcycle. Similarly, variables for decision-making skills and Women justifying physical violence were constructed from various dimensions. The selected variables were identified based on another systematic review that was conducted on factors associated with GBV in SSA that used EDHS datasets (Muluneh et al., 2020; WHO, 2013).

Analysis Plan

STATA/MP Version 16 (Stata Corp, College Station, TX, USA) was used for all statistical analyses as recommended by Hosmer and Lemeshow (Nattino et al., 2020). To allow for adjustments for the cluster sampling design and weighting, the “Svy” commands were employed. Initially, we conducted frequency tabulations to describe the data used in this study. We then created a contingency table for examining all independent variables associated with IPV. To estimate the confidence intervals (CIs) for prevalence, we used the Taylor series linearization method (Hightower et al., 1988). We then calculated the bivariate and binary logistic regressions to adjust for clustering and sampling weights to assess the unadjusted odds ratios of any forms of IPV, including physical, sexual, and emotional violence. A stage modeling process determined the adjusted odds ratios of IPV as part of the binary logistic regression analysis. In the final model, we tested and reported any co-linearity. We then calculated the odds ratios with 95% CIs to assess the adjusted risk of possible confounding variables.

Spatial Analysis

Data related to the proportion of IPV, including physical, sexual, and emotional violence, were exported to ArcGIS 10.3.4 to visualize estimations. Geospatial analysis was conducted to determine whether the overall IPV, including physical, sexual, and emotional violence, was significantly clustered or randomly clustered in particular locations of Ethiopia. Spatial autocorrelations in the prevalence of IPV, including physical, sexual, and emotional violence, were measured using the Global Moran’s I statistic (Esri, 2020). The Global Moran’s I tool was used to determine the overall pattern and trend of the data.

The geographic distribution and clustering of IPV that included physical, emotional and sexual violence were assessed using Z-score values and p-values. A positive Moran’s I index value indicated a tendency toward clustering, while a negative Moran’s I index value indicated a tendency toward dispersion (Esri, 2020), and p-values were set for statistical significance as p < 0.05. A hot spot analysis was then conducted using Getis-Ord Gi* tool in ArcGIS (Ord & Getis, 1995). Hot spot analyses displayed high or low clusters that identified areas with either high or low values. The hot spot analysis image conveyed that there are areas within Ethiopia that had higher spatial clustering than would be assumed by pure randomness.

Results

Figure 1 presents the prevalence of IPV and various forms of IPV, among 4,469 ever-married women (weighted) aged between 15 years and 49 years. Thirty-four percent of women have experienced at least one form of IPV by their partners. Emotional (24%) and physical (23.5%) violence were the most frequently reported experiences, while sexual violence (10%) was the least reported (see Figure 1).

Figure 1.

Prevalence and 95%CI of gender-based violence and emotional, sexual, and physical violence.

Prevalence of Intimate Partner Violence, Individual and Partner Characteristics Among Reproductive Age Groups of Women

The prevalence of IPV varied depending upon women’s socioeconomic status and partner attributes. IPV occurred most often among illiterate women (35.5%) [32.5, 38.6] and women whose partners were illiterate (35.9%) (32.1, 39.9). Similarly, IPV was highly prevalent among women who were married before the age of 18 years (35.1%) (32.3, 38.0) and women who consumed alcohol (36.4%) (32.8, 40.2) in comparison to women who were married after the age of 18 years and women who did not consume alcohol. Women who could access various channels of communication were at lower risk for IPV in comparison to those who had minimal access (see Table 1 ). As shown in Table 1, women experienced higher IPV if their partner was unemployed, exerted controlling behaviors, consumed alcohol, and was uneducated; for example, almost half (46.2%) (41.8, 50.6) of the women experienced GBV if their partner exerted controlling behaviors (see Table 1).

Table 1.

Prevalence and Unadjusted Odd Ratios of IPV Among Women Aged 15–49 Years, Women and Partner Characteristics (N = 4,720).

Variables		N*	Pr 95% CI	Unadjusted OR	P-value
Women’s age	15–24	958	32.6 [28.6, 37.0]	1.00
	25–34	1,882	33.1 [29.7, 36.7]	1.02 [0.79, 1.32]	0.561
	35–49	1,629	33.8 [31.4, 36.3]	1.12 [0.87, 1.43]
Woman’s education	Secondary+	665	23.2 [16.7, 31.3]	1.00
	Primary	1,320	34.4 [30.3, 38.7]	1.73 [1.12, 2.66]	0.022
	No schooling	2,735	35.5 [32.5, 38.6]	1.81 [1.2, 2.79]
Women’s age at first marriage	19–49 years	1,891	31.6 [27.9, 35.9]	1.00	0.127
Women’s age at first marriage	15–18 years	2,829	35.1[32.39,38.0]	1.17 [0.95, 1.43]
Numbers of union/partners of women	1	3,901	32.8 [30.2, 35.5]	1.00	0.050
Numbers of union/partners of women	2 or more	819	38.2 [33.2, 43.5]	1.26 [0.99, 1.60]
Current marital status	Currently married	4,123	32.3 [29.7, 35.0]	1.00
Current marital status	Formerly married	597	43.9 [37.4, 50.6]	1.64 [1.22, 2.21]	0.001
Women ever drank alcohol	No	3,188	32.3 [29.5, 35.6]	1.00
Women ever drank alcohol	Yes	1,532	36.4 [32.8, 40.2]	1.19 [0.96, 1.48]	0.097
Access to communication/media	Yes	1,406	28.8 [23.9, 34.2]	1.00
Access to communication/media	No	3,314	35.6 [32.9, 38.3]	1.37 [1.04, 1.79]	0.025
Age of partner(years)	15–24	213	25.5 [16.5, 37.4]	1.00
	25–35	1,411	33 [29.2, 37.1]	1.44[0.0.80, 2.58]
	36–50	1,996	31.20 [27.9, 34.7]	1.32 [0.74, 2.38]	0.002
	50+	1,100	40.6 [35.9, 45.3]	1.99 [1.12, 3.51]
	0–4	918	30.4 [25.8, 35.4]	1.00
Partner’s education	Secondary +	883	19.9 [15.4, 25.3]	1.00
	Primary	1,287	33.5 [29.7, 37.5]	2.02 [1.44, 2.86]	<0.001
	No Schooling	1,909	35.9 [32.1, 39.8]	2.25 [1.62, 3.2]
	Working	3,688	32.3 [29.5, 35.1]	1.00
Husband’s employment status	Not Working	1,032	39.5 [34.6, 44.7]	1.37 [1.07, 1.76]	0.013
Husband’s employment status	0–2	1,954	31.7 [28.2, 35.4]	1.00
Number of children	3–5	1,637	32.8 [29.2, 36.5]	1.05 [0.85, 1.29]	0.092
Number of children	5+	1,129	38.0 [33.4, 42.8]	1.32[1.02, 1.71]
Husband exerted controlling behaviors	No	2,295	17.0 [14.5, 19.9]	1.00
Husband exerted controlling behaviors	Yes	2,425	46.7 [43.5, 49.7]	4.25 [3.41, 5.28]	<0.001
Woman’s father violent toward her mother	No	3,269	27.6 [25.1, 30.3]	1.00	<0.001
Woman’s father violent toward her mother	Yes	1,451	46.2 [41.8, 50.6]	2.25 [1.84, 2.76]
Partner ever drank alcohol	No	3502	28.2 [25.4, 31.3]	1.00	<0.001
Partner ever drank alcohol	Yes	1218	45.9 [41.7, 50.1]	2.16 [1.72, 2.69]

Note. N* = Unweighted N, weighted Pr = prevalence, weighted CI = confidence intervals, weighted OR = odds ratios.

Prevalence of Intimate Partner Violence by Community and Societal Characteristics Among Reproductive Age Groups of Women

Table 2 demonstrates that the prevalence of IPV was highest among women residing in rural areas (35.2%), those who reported a poor wealth index (35.5%), those with minimal decision-making power, and those who justified their partner’s physical violence toward them. Similarly, Oromia (38%), Harari (35%), and Amhara (35%) were the three regions in Ethiopia that reported the highest levels of IPV among married women, whilst the lowest cases were reported in Somali and Afar (see Table 2 ), despite these two areas reporting the highest levels of female genital mutilation in the country (Dessalegn et al., 2020).

Table 2.

Prevalence and Unadjusted Odds Ratios of IPV Among Women Aged 15–49 Years Against Community- and Societal-level Characteristics (N = 4,720).

Variables		N*	Pr 95% CI	Unadjusted OR	p-Value
Place of residence	Urban	1,211	27.6 [22.9, 32.8]	1.00
	Rural	3,509	35.2 [32.4, 37.9]	1.42 [1.07, 1.87]	0.012
	Tigray	493	33.5 [28.3, 39.1]	1.00
Region	Afar	387	19.8 [14.9, 25.9]	0.49 [0.32, 0.75]
	Amhara	572	35.1 [29.9, 40.6]	1.07 [0.76, 1.50]
	Oromiya	649	38.4 [33.9, 42.9]	1.23 [0.91, 1.68]	<0.001
	Benishangul-Gumuz	383	31.8 [26.3, 37.8]	0.92 [0.65, 1.33]
	SNNP	564	29.3 [24.4, 34.6]	0.82 [0.58, 1.16]
	Gambella	343	34.4 [26.4, 43.5]	1.04 [0.66, 1.64]
	Harari	281	37.3 [29.9, 45.44]	1.18[0.78, 1.78]
	Addis Ababa	288	25.8 [19.8, 32.9]	0.69 [0.45, 1.06]
	Dire Dawa	296	28.9 [23.8, 34.5]	0.81 [0.57, 1.15]
Wealth index	Rich	1,309	25.7 [21.1, 30.8]	1.00
	Middle	660	37.5 [3.4, 43.2]	1.75 [1.23, 2.48]
	Poor	2141	35.5 [31.9, 39.3]	1.59 [1.19, 2.14]	<0.001
Decision-making power	Full power	996	30.7 [27.7, 33.9]	1.00
	Moderate power	780	37.7 [31.8, 43.4]	1.35 [1.02, 1.879	0.005
	No power	2,944	40.3 [35.0, 45.7]	1.52 [1.16, 1.99]
Justified women beating	No	1,866	30.5 [26.6, 34.7]	1	0.005
Justified women beating	Yes	2,854	35.4 [32.54, 38.36]	1.24 [1.00, 1.6]

Note. N* = Unweighted N, weighted Pr = prevalence, weighted CI = confidence intervals, weighted OR = odds ratios.

Logistic Regression Analysis

The overall hierarchical logistic regression models are presented in Table 3 . The regression results show the important explanatory variables that increase the chances of any form of IPV, including physical, sexual, and emotional violence, among women. The results show that women who have more than five children have higher chances of experiencing IPV compared to women who have two or less children (AOR: 1.70, 95% CI, 1.12–2.58, p = 0.013). Similarly, women who had a history of their father being physically violent toward their mother were twice as likely to experience IPV in comparison to those women who had no history of violence (see Table 3 ).

Table 3.

Adjusted Odds Ratio (AOR) of Individual, Partner, Community, and Societal Associated Factors of Various Forms of IPV Among Married Women of Age 15–49 Years.

Variables		Any Form of IPV		Physical Violence		Sexual Violence		Emotional Violence
Variables		AOR [95% CI]	P	AOR [95% CI]	P	AOR [95% CI]	P	AOR [95% CI]	p
	Women’s characteristics
Women’s education	Secondary	1.00		1.00		1.00		1.00
	Primary	1.47 [0.75, 2.88]	0.259	1.5 [0.73, 3.15]	0.264	1.73 [0.43, 6.89]	0.436	1.89 [0.99, 3.59]	0.052
	Illiterate	1.24 [0.59, 2.61]	0.574	1.6 [0.49, 2.70]	0.739	1.96 [0.45, 8.62]	0.372	1.62 [0.84, 3.13]	0.147
Women drinking alcohol	Yes	0.90 [0.60, 1.34]	0.597	1.00		1.00		1.00
Women drinking alcohol	No	1.00		0.90 [0.56, 1.44]	0.657	0.53 [0.32, 0.88	0.014*	0.88 [0.59, 1.32]	0.541
Access to communication	No	1.10 [0.60, 1.34]	0.667	1.00		1.00		1.00
Access to communication	Yes	1.00		1.30 [0.77, 2.19]	0.328	0.74 [0.41, 1.33]	0.319	1.49 [0.92, 2.40]	0.102
	Partner characteristics
Age of partner	15–24	1.00		1.00		1.00		1.00
	25–35	1.71 [0.88, 3.32]	0.112	1.30 [0.58, 2.79]	0.542	3.21 [1.15, 8.87]	0.025*	1.07 [0.54, 2.11]	0.85
	36–50	1.33 [0.66, 2.67]	0.428	0.98 [0.43, 0.99	0.955	2.38 [0.84, 6.73]	0.103	0.99 [0.48, 2.04]	0.982
	50+	1.64 [0.79, 3.39]	0.184	0.99 [0.41, 2.42]	0.988	3.89 [116, 13.01]	0.028*	1.76 [0.86, 3.61]	0.12
Number of children	≤2	1.00		1.00		1.00		1.00
	3–5	1.08 [0.77, 1.53]	0.648	0.98 [0.66, 1.46]	0.938	1.141 [0.68, 1.89]	0.611	1.10 [0.76, 1.59]	0.598
	5+	1.70 [1.12 , 2.58]	0.013*	1.49 [0.89, 2.49]	0.126	1.53 [0.85, 2.73]	0.149	1.92 [1.22, 3.02]	0.005*
Husband’s employment	Working	1.00		1.00		1.00		1.00
Husband’s employment	Not working	1.06 [0.67, 1.70]	0.792	1.07 [0.66, 1.47]	0.786	1.10 [0.49, 2.44]	0.824	0.91 [0.50, 1.67]	0.76
Partner’s education	Secondary	1.00		1.00		1.00		1.00
	Primary	1.65 [1.01, 2.70]	0.046*	1.66 [0.92, 3.00]	0.091	0.91 [0.37, 2.22]	0.826	1.39 [0.86, 2.27]	0.18
	illiterate	2.16 [1.26, 3.71]	0.005*	2.41 [1.3, 4.47]	0.006*	0.87 [0.33, 2.34]	0.787	1.64 [0.93, 2.90]	0.09
Partner exerted controlling behaviors	No	1.00		1.00		1.00		1.00
Partner exerted controlling behaviors	Yes	3.94 [3.03, 5.12]	<001*	3.56 [2.64, 4.81]	<001*	3.12 [1.91, 5.11]	<001*	4.62 [3.28, 6.50	<001*
Woman’s father violent toward her mother	No	1.00		1.00		1.00		1.00
Woman’s father violent toward her mother	Yes	1.99 [1.52, 2.59]	<001*	1.85 [1.39; 2.46]	<001*	1.32 [0.85, 2.05]	0.219	1.99 [1.46, 2.71]	<001*
Partner drinks alcohol	No	1.00		1.00		1.00		1.00
Partner drinks alcohol	Yes	2.59 [1.80, 3.71	<001*	3.041 [2.11; 4.38]	<001*	3.86 [2.48, 6.02]	<001*	2.29 [1.6, 3.26]	<001*
	Community characteristics
Place of residence	Urban	1.00		1.00		1.00		1.00
Place of residence	Rural	0.88 [0.51, 1.52]	0.642	1.37 [0.72, 2.66]	0.338	0.69 [0.22, 2.16]	0.521	0.73 [0.40, 1.34]	0.306
Region	Tigray	1.00		1.00		1.00		1.00
	Afar	0.73 [0.42, 1.26]	0.253	1.16 [0.61, 2.19]	0.657	0.24 [0.09, 0.63]	0.004	0.62 [0.32, 1.21]	0.161
	Amhara	1.09 [0.68, 1.73]	0.73	1.50 [0.95, 2.36]	0.079	0.68 [0.39, 1.19]	0.175	1.15 [0.73, 1.83]	0.544
	Oromia	1.55 [0.99, 2.44]	0.055	3.23 [1.97, 5.31]	0	1.16 [0.68, 1.96]	0.583	1.04 [0.66, 1.66]	0.849
	Somali	0.39 [0.22, 0.70]	0.002	0.92 [0.45, 1.89]	0.823	0.05 [0.01, 0.25]	0	0.36 [0.19, 0.66]	0.001
	Benishangul-Gumuz	1.37 [0.85, 2.20]	0.192	2.08 [1.3, 3.33]	0.002	0.59 [0.29, 1.18]	0.134	1.31 [0.78, 2.19]	0.311
	SNNP	1.25 [0.81, 1.95]	0.311	2.11 [1.29, 3.44]	0.003	0.49 [0.26, 0.93]	0.029	1.02 [0.63, 1.68]	0.91
	Gambella	1.75 [0.92, 3.35]	0.088	3.623 [1.78, 7.38]	0.000	0.56 [0.27, 1.33]	0.207	1.48 [0.79, 2.78]	0.216
	Harrari	2.52 [1.44, 4.41]	0.001	4.76 [2.41, 9.42]	0.000	0.58 [0.17, 2.02]	0.394	2.51 [1.44, 4.40]	0.001
	Addis Ababa	2.01 [1.06, 3.79]	0.032	4.82 [2.36, 9.84]	0.000	1.03 [0.30, 3.55]	0.96	1.63 [0.85, 3.10]	0.141
	Dire Dawa	2.42 [1.48, 3.94]	<001	4.84 [2.76, 8.5]	0.000	1.13 [0.44, 2.89]	0.797	1.76 [1.02, 3.03]	0.043
	Societal characteristics
Wealth Index	Rich	1.00		1.00		1.00		1.00
	Middle	1.55 [0.88, 2.76]	0.132	1.17 [0.62, 2.18]	0.63	3.59 [1.19, 10.8]	0.023	1.2 [0.63, 2.36]	0.557
	Poor	1.64 [0.97, 2.75]	0.063	1.23 [0.69, 2.18]	0.478	5.15 [1.78, 14,93]	0.003	1.42 [0.77, 2.61]	0.267
Decision-making power	Full power	1.00		1. 00		1.00		1.00
	No power	1.17 [0.84, 1.64]	0.572	0.82 [0.56, 1.20]	0.312	1.18 [0.69, 1.79]	0.642	1.30 [0.90, 1.89]	0.167
	Moderate	1.09 [0.72, 1.66]	0.673	0.99 [0.64, 1.56]	0.996	0.95 [0.48, 1.88]	0.885	1.72 [1.14, 2.59]	0.009*
Justified women beating	Yes	1.15 [0.86, 1.52]	0.344	1.00		1.00		1.00
Justified women beating	No	1.00		1.06 [0.77, 1.46]	0.714	2.46 [1.49, 4.04]	0.00	0.84 [0.61, 1.15]	0.279

Notes. p = p-value, AOR = adjusted odds ratios.

*significant at p < 0.05.

In addition, those women whose partners were illiterate were twice as likely to experience IPV in comparison to those whose partners were more educated (AOR: 2.16, 95% CI 1.26–3.71, p = 0.005). Moreover, the husband exerting controlling behavior was another factor that increased the chances of IPV among married women. Women were four times more likely to experience IPV if their partner exerted controlling behavior in comparison to women whose partners did not (AOR: 3.94, 95% CI 3.03–5.12, p < 0.001). Women were three times more likely to experience IPV if their partner consumed alcohol. There were significant differences in regions for women experiencing IPV, with higher rates of IPV particularly in rural areas (see Table 3 ).

Educational achievements of male partners, controlling behaviors of male partners, a history of the woman’s father being violent toward her mother, and alcohol consumption by male partners were directly associated with women’s experiences of physical violence. Similarly, the age of partners, alcohol consumption, controlling behaviors of partners, and women’s educational attainment were associated with sexual violence toward married women in Ethiopia. Moreover, married women’s emotional violence experiences were influenced by having more children, controlling behaviors of partners, a history of the woman’s father being violent toward her mother, alcohol consumption by partners, and low levels of women’s decision-making power (See Table 3).

Geographic Distribution and Spatial Clustering of Intimate Partner Violence Among Married Women

In this study, the low prevalence areas of IPV were reported in Somalia, Afar, and parts of the Eastern Amhara region. High IPV spots were observed in Western and Central areas of the Oromia region, Western Amhara, Central Tigray, Eastern Benishangul, Gambella, Dire Dawa, and Harari (Figure 2). The spatial distribution of IPV showed significant geographic variations among regions in Ethiopia (Moran’s I index = 3.2; Z score = 47.3; p < 0.001).

Figure 2.

Map of prevalence of IPV among married women aged 15–49 years in Ethiopia, 2016.

Further spatial analysis of IPV by type (physical, emotional, and sexual) showed various types of clustering in the country. There was evidence of spatial clustering of physical violence (Global Moran’s I = 1.9, Z score = 28.8, p < 0.001), sexual violence (Global Moran’s I = 1.4; Z score = 20.9, p < 0.001), and emotional violence (Global Moran’s I = 2.8, Z score = 42.1, p < 0.001).

Geographic Distribution of Physical Violence

The spatial analysis of physical violence identified eight clusters as hot spot areas. High spot prevalence of physical violence was mapped in the Western and Central parts of Oromia, Southern and Eastern Behishangul-Gumz, Addis Ababa, Dire Dawa, Harari, and Gambella regions. Somali, Afar, and Tigray identified the lowest prevalence of IPV (see Figure 3).

Figure 3.

Map of prevalence of physical partner violence among married women aged 15–49 years in Ethiopia, 2016.

Geographic Distribution of Sexual Violence

The Getis-Ord Gi* statistics showed that the prevalence of sexual violence among women aged 15–49 years had statistically significant variations among regions in Ethiopia. Hotspots of sexual violence experiences among women of reproductive age were clustered around the majority of Central and South Amhara, Western and Eastern Oromia, and North-Eastern Gambela regions. Addis Ababa, Somali, and Afar showed the lowest prevalence of IPV (see Figure 4).

Figure 4.

Map of prevalence of intimate sexual violence among married women aged 15–49 years in Ethiopia, 2016.

Geographic Distribution of Emotional Violence

Furthermore, hots spots of emotional violence among women of reproductive age were observed in clusters located in Central Tigray, Western Amhara, the majority of Benishangul, except for the Northern part, Western Oromia, Gambela, Harari, and Dire Dawa. The prevalence of IPV remained low in the regions of Somali and Afar (see Figure 5).

Figure 5.

Map of prevalence of emotional violence among married women aged 15–49 years in Ethiopia, 2016.

Discussion

This study has demonstrated an analysis of the spatial distribution, reported rates, and factors associated with IPV. The use of spatial analysis successfully identified clusters of IPV rates among women and demonstrated that the phenomenon was heterogeneous in the regions in Ethiopia, with clusters distributed mainly in the regions of Oromia, Gambella, Amhara, Tigray, and Harari. Moreover, the geographical variation was significantly different between regional areas, and it varied depending on the type of violence (physical, sexual, and emotional violence). Western Oromia showed the highest clustering of IPV in the country. Furthermore, identification of cluster analysis showed that the highest hot spots of physical violence were reported in Oromia, Gambella, Benishangul-Gumz, Addis Ababa, Harari, Tigray, Amhara, and Western and Central Oromia. All Gambella and Eastern Benishangul-Gumz regions identified the highest clustered cases for sexual violence.

Emotional violence was identified in hot spot clustered areas in Central Tigray, Benishangul-Gumz, Gambella, and Harari. The possible geographical variation of IPV in the regions of Ethiopia may be related to the patriarchal nature of the community that promotes male dominance, which is consistent with other studies (Dessalegn et al., 2020; Deyessa et al., 2010; Muluneh et al., 2020). This study showed that the partner characteristics are key predictors for women experiencing IPV in their lifetime. Partners’ controlling behaviors, consumption of alcohol by partners, coupled with family background (exposure to violence in the family of origin) and low educational status, were instrumental factors associated with married women experiencing IPV in Ethiopia. This patriarchal and power relationship aligns with feminist or gender perspectives theories and further emphasizes the effect that male dominance exerts on society (Heise, 2013).

Controlling behaviors within relationships are related to an unequal social distribution of power between genders and diminish economic independence for women. Patriarchal societies that work directly or indirectly to endorse a male-dominated social order and family structure often result in men exercising power and control over women in several ways, as demonstrated by IPV (Johnson, 2004). Moreover, evidence shows that controlling behaviors are highly typical of coercive controlling abuse (Myhill, 2015) and intimate intimidation (Johnson, 2004), which are considered a form of psychological aggression (Henning & Klesges, 2003).

Controlling behaviors are exercised by the perpetrator through acts of physical abuse, intimidation, or through verbal threats of serious aggression (Tanha et al., 2009). Emotional threats and being controlled by an intimate partner have adverse effects on well-being (Coker et al., 2000). Emotional abuse warrants just as much focus as other forms of violence, such as physical and sexual violence (Antai, 2011; Johnson, 2004).

Increased levels of alcohol consumption have been associated with increased IPV in Ethiopia (Muluneh et al., 2020; WHO et al., 2015). Possible reasons of how alcohol consumption contributes to IPV include the following: (a) When consuming alcohol, both partners are more likely to engage in highly provocative or aggressive behavior without thinking about the consequences of their actions. (b) The acceptance and tolerance of alcohol-related behaviors may influence drinkers’ expectations. Social and cultural perceptions of alcohol may also play a role as some people who drink alcohol may intentionally engage in aggression or violence toward an intimate partner, assuming that their behavior will be excused on the basis that they were consuming alcohol at the time of the violence. (c) Insults or aggression by the partner may be interpreted as a threat to their masculinity or social identity and requires an aggressive response to reassert this identity (Abrahams et al., 2014; Antai, 2011). It also may be that the reverse is true for women in abusive relationships who have been shown to consume increased amounts of alcohol as a form of self-therapy in order to cope with the abuse (Abrahams et al., 2014; WHO, 2013).

Women who have experienced previous family abuse are more likely to experience IPV as adult women. In this study, we found that women who witnessed their father abusing their mother doubled their chances of experiencing IPV as an adult. This cycle of violence might be attributed to continual exposure to violence during their childhood and increased their chances of experiencing IPV later in their adult life (Abrahams et al., 2014; Adjah & Agbemafle, 2016; Admassu Wossen & Tilahun Degfie, 2016).

Limitations of the Study

This study has both strengths and limitations. Strengths include the use of a gold standard measurement of developing countries (DHS measure), and the sampling was based on the WHO recommendation, that is, a nationally representative survey demonstrated by use of a large dataset of Ethiopia DHS 2016. The survey used a complex two-stage-stratified cluster sampling design and used hierarchical logistic regression analysis to account for clustering and weighting correlations. However, this study does not show any causality of effects as the design of the study is cross-sectional in nature. Additionally, women may have answered their questions and underreported their experiences of IPV due to fear of stigma, discrimination, and retaliation. Additionally, this study is only limited to EDHS questionnaires and lacks some characteristics related to refugees, internally displaced people, and people who have traveled from other parts of the country.

Conclusion and Recommendations

In summary, this study provides evidence for geographic clustering of IPV in Ethiopia and has identified priority regions, administrative zones, and clusters where IPV collaborative interventions should be introduced and strengthened to reduce the impact of IPV. In particular, Western and Central Oromia, Western Amhara, Gambella, Central Tigray, and Harari need special attention in response to IPV. Most importantly, exertion of controlling behaviors by partners, increased levels of alcohol consumption by partners, educational qualifications of partners, increased number of children birthed by women, and a history of physical violence increased the chances of IPV among married women in Ethiopia. Therefore, early prevention of excessive alcohol consumption and controlling behaviors of partners may help to deter and promote healthier relationships and may reduce various forms of IPV. Moreover, the study highlights the need for policy and program interventions to reduce gender imbalance and eradicate various forms of IPV.

Ethical Clearance

This study used secondary data that are available in the public domain of DHS. All DHS surveys were approved by the Inner City Fund (ICF) international and an institutional review board. In addition, ethical approval was secured from Western Sydney University Ethics Committee as a requirement for academic study.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Muluken Dessalegn Muluneh

Author Biographies

Muluken Dessalegn Muluneh, MPH, is a PhD candidate at Western Sydney University in Australia. His research focuses on answering the public health and human right issues focusing on gender-based violence in sub-Saharan African countries. Currently, he is based in Ethiopia and conducting his PhD research project. He has ample experience working in Monitoring Evaluation and Research (MER). He led and co-lead various projects in maternal, adolescent, and child health at Amref Health Africa in Ethiopia.

Lyn Francis, PhD, is a lecturer in School of Nursing and Midwifery, Western Sydney University. She has experience and qualifications in nursing, midwifery, child and family, law, and health management. She has been in academia for 15 years, which includes University of Newcastle, Charles Darwin University, and Griffith University. She has experience and interest in gender-based violence and law in here research.

Kingsley Agho, PhD, is senior lecturer in Biostatistics at the School of Science and Health, Western Sydney University, Australia. Before joining Western Sydney University, Kingsley has had research and teaching experience from the University of Sydney and University of Newcastle. He has over 15 years of experience in the design and analysis of large epidemiological studies including longitudinal and cluster Randomized Clinical Trial (RCT) data. Other key research interests of Kingsley are in epidemiology, global health, and water management and engineering.

Virginia Stulz, PhD, has vast experience in supervising PhD students. She supervises Higher Degree by Research (HDR) students, conducts research in the area, and teaches different courses in the university. Associate Professor Virginia Stulz is currently working in a conjoint position between Nepean Blue Mountains Local Health District and Western Sydney University. Virginia is currently the Chief Investigator of four research projects in her district, which investigate innovative models of maternity care, influencing better birth outcomes and alternative pain relief methods for women experiencing back pain in labour.

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