Spatial distribution and predictors of intimate partner violence among women in Nigeria – BMC Women's Health – BioMed Central

Advertisement
BMC Women's Health volume 22, Article number: 245 (2022)
1 Altmetric
Metrics details
Globally, intimate partner violence is one of the major health problems women face every day. Its consequences are enormous. However, our search of the available literature revealed that no study had examined the spatial distribution of intimate partner violence and the predictors of intimate partner violence among women in Nigeria using current nationally representative data. This study, therefore, sought to examine the spatial distribution of intimate partner violence and its predictors among women in Nigeria.
We sourced data from the 2018 Nigeria Demographic and Health Survey for this study. A sample size of 8,968 women was considered for this study. We employed both multilevel and spatial analyses to ascertain the factors associated with intimate partner violence and its spatial clustering.
The hot spot areas for intimate partner violence in Nigeria were Gombe, Bauchi, Adamawa, Plateau, Kogi, Edo, Ebonyi, and Rivers. The likelihood of experiencing intimate partner violence among women in Nigeria was high among women with primary education, those that were previously married, women currently working, women who were Yoruba, women with parity of four and above and women who were exposed to mass media while low odds of intimate partner violence was reported among women who were Muslims. Women who resided in the North East region and those who lived in communities with medium socioeconomic status were more likely to experience intimate partner violence, while women who were within the richest wealth index and those residing in the South West region were less likely to experience intimate partner violence.
The study found regional variations in the prevalence of intimate partner violence among women in Nigeria. Therefore, policymakers should focus their attention on the hotspots for intimate partner violence in the country. There is also the need to consider the factors identified in this study to reduce intimate partner violence among women in Nigeria. Empowering women would yield a significant improvement in the fight against gender-based violence.
Peer Review reports
Intimate partner violence (IPV) is one of the major health problems that women face [1, 2]. IPV is a situation where an individual subjects a spouse or a partner in a romantic relationship to physical, sexual, emotional, or psychological torture [3, 4]. IPV is gender-based violence with no boundary in terms of nationality, religion, culture, or class [5, 6]. The World Health Organisation (WHO) estimated in 2013 that every one in three women in the world has ever been beaten, forced into sexual intercourse or abused in their lifetime [1], this estimate still persist in the recent report published in 2021 and got exacerbated with the advent of 2019 Coronavirus disease (COVID-19) [7]. A common form of violence perpetrated against women is abuse from their intimate partner [5].
IPV could have devastating effects on the physical and mental health of victims. For instance, IPV affects the reproductive health of women, which may lead to an increase in their likelihood of having miscarriages, pregnancy termination, stillbirths, induced abortions, unplanned pregnancies, and pregnancy-related complications [6, 8,9,10,11]. Aside from the instant or short-term traumatic consequences, IPV can result in chronic health problems such as physical disability, drug and alcohol abuse and addiction, depression, suicidal ideation, and even death [4, 12, 13].
Despite the devastating effects of IPV on the health of women, the family cultural norms in Nigeria which has been reported to be oligarchy and patriarchy have seemingly favored perpetrators of gender-based violence, making victims shy away from reporting such issues to law enforcement agencies and health care providers [5, 12, 14, 15]. Previous studies conducted in Nigeria have shown that factors such as employment status, educational attainment, spouse’s educational attainment, religion, marital status, and wealth status were predictors of IPV among women [8, 16, 17]. In the same vein, most of the available studies indicated that the prevalence of IPV among women in Nigeria is high [3, 8, 18]. For example, Benebo, Schumann, and Vaezghasemi [19] reported that one out of four women in Nigeria had ever experienced IPV [19]. Therefore, it is important to focus more research attention on the factors that predict the perpetration of IPV against women, especially in recent times.
Up until now, our search of the available literature revealed that none of the studies had examined the spatial distribution of IPV and the predictors of IPV among women in Nigeria using current nationally representative data from the Nigeria Demographic and Health Survey (NDHS) [20, 21]. As a result, designing and implementing policies specific to Nigeria’s various regions or clusters has become a daunting task for policymakers. This creates a significant gap in extant literature that needs urgent research attention. Therefore, this study examined the spatial distribution and predictors of IPV among women in Nigeria using the recent NDHS data conducted in 2018. Findings from the study could help direct policies that would help reduce gender-based violence against women, which is in line with the United Nations’ (UN) Sustainable Development Goal (SDG) 5 of achieving gender equality and empowering women and girls by the year 2030 [22].
This is a cross-sectional analysis of population-based data from the 2018 NDHS. The NDHS is a nationally representative survey used to gather sociodemographic and other health-related indicators such as IPV [23]. A two-stage sampling procedure was used to gather data from 36 administrative units and the Federal Capital Territory (FCT). The survey’s primary sampling unit was made up of samples drawn at random from clusters. A total of 41,821 women aged 15–49 participated in the 2018 study. From this number, 8968 women who had complete information and participated in the domestic violence module were considered in this study. The sampling, pretesting and the general methodology of the 2018 NDHS have been published elsewhere [24, 25]. In writing this manuscript, we adopted the guidelines for improving the reporting of observational studies in Epidemiology [26]. The dataset utilised in this study is available in the public domain and can be downloaded from https://dhsprogram.com/data/available-datasets.cfm.
IPV was the outcome variable. It was obtained from the following variables: sexual violence, emotional violence, and physical violence. These three variables were derived from a series of questions in the domestic violence module that were related to a variety of violent acts that a woman had experienced. Previous studies include details on the questions for each aspect of three forms of IPV [27, 28]. There were Yes, or No response questions asked for each element of IPV. Therefore, a woman who had undergone at least one of the acts was regarded as ever experienced physical, emotional, or sexual abuse. From the questions asked on the experience of physical, emotional, and sexual abuse, IPV was created with respondents experiencing at least one of these violent acts regarded as ever had IPV and otherwise [9, 29].
Based on theoretical and practical significance and the availability of the variables in the dataset, we considered both individual and contextual factors in our study [23]. These were also influenced by their association with IPV in several previous studies in Nigeria and sub-Saharan (SSA) in general [9, 17].
The individual-level factors were age (15–24, 25–34, 35+), educational level (No education, primary, secondary/higher), husband/partner’s educational level (No education, primary, secondary/higher), marital status (currently married, cohabiting, previously married), working status (not working, working), ethnicity (Hausa, Yoruba, Igbo, Others), religion (Christianity, Islam, Traditionalist & Others), parity (0, 1–3, 4+), and exposure to mass media (yes, no) [9, 17].
The contextual factors were place of residence (urban and rural), wealth index (poorest, poorer, middle, richer, richest), region (North Central, North East, North West, South East, South South, South West), sex of household head (male, female), community literacy level (low, medium, high), community socioeconomic status (low, medium, high) [9, 17].
We employed both spatial and multilevel analyses in analyzing the data.
Different statistical software like Excel, SaTScan, ArcGIS, and Stata 16 were utilized for spatial distribution of IPV in Nigeria. A total of 1400 clusters or Enumerations Areas (EAs) were considered for this study. Among these clusters, seven were dropped because they had no measured longitude and latitude data. The data were weighted with v005 (weighing variable) and geographic coordinate data were merged in Stata 16 and then exported to excel, which was finally imported to ArcGIS 10.7 for spatial analysis.
To check whether there is clustering effect in IPV in Nigeria, spatial autocorrelation analysis was done. This analysis result gives Global Moran’s I value, Z-score and p-value for deciding whether the data is dispersed or random or clustered. Moran’s I value close to positive 1 indicates there is clustering effect, close to negative one indicates dispersed and close to zero indicates random. If p-value is significant and Moran’s I value is close to mean, that means IPV had clustering effect [30].
The hot spot analysis tool gives a Getis_Ord or Gi* statistics for cluster in the dataset. Statistical values like Z-score and p-value is computed to determine the statistical significance of the clusters. Results of the analysis with high GI* value means hot spot areas and low GI* value means cold spot areas [31].
Spatial prediction is one of the techniques of furcating unsampled areas based on sampled areas. In Nigeria, a total of 1400 enumeration areas were selected to take a sample for this areas that believed to be representative of the country. A total of seven clusters had no enumeration longitude and latitude were dropped. Based on 1393 sampled areas, it is possible to predict the remaining parts of Nigeria. Ordinary Kriging prediction methods were used for this study to predict IPV in unobserved areas of Nigeria.
Bernoulli purely spatial model was applied to identify IPV clusters using 1393 enumeration areas. SatTscan Software was used for the analysis. First, the dataset was managed as appropriate for the SaTScan software. Women who faced IPV were taken as cases and women who did not face IPV were taken as controls. The Cluster number, longitude and latitude data were obtained from GPS dataset. The cluster size less than 50% of the population was taken as upper bound. A Monte Carlo replication was used for this study. Based on the above criteria, primary clusters were identified.
A two-level multilevel binary logistic regression models were fitted to evaluate the individual and contextual (household and community level) factors linked to IPV experience among women in Nigeria. In the modelling, women were nested within households; then households were nested within clusters. To account for the unexplained variability at the community level, clusters were proposed as a random effect. A total of four models were fitted. Firstly, we fitted an empty model, model 0, which contained no predictors (random intercept). Following that, model I only included individual-level variables, model II only included contextual-level variables, and model III included both individual-level and contextual-level variables. The odds ratio and related 95% confidence intervals were provided for all models. These models were fitted using a Stata command “melogit” for the identification of predictors with the outcome variable (IPV). The log-likelihood ratio (LLR), Akaike Information Criteria (AIC) was used to compare models. The best fit model has the highest log-likelihood and the lowest AIC [32]. The multicollinearity test, which used the variance inflation factor (VIF), revealed no evidence of collinearity among the independent variables (Mean VIF = 1.83, Maximum VIF = 1.17, and Minimum VIF = 3.09). The domestic violence module sample weight (d005/1,000,000) was used in all analyses to account for over-and under-sampling, while the svy command was used to account for the complex survey design and generalizability of the results. All the analyses were carried out using Stata version 16.0 (Stata Corporation, College Station, TX, USA).
Since the authors of this manuscript did not collect the data, we sought permission from the MEASURE DHS website and access to the data was provided after our intent for the request was assessed and approved on the 6th of April 2021. The DHS surveys are ethically accepted by the ORC Macro Inc. Ethics Committee and the Ethics Boards of partner organizations in different countries, such as the Ministries of Health. All methods were performed in accordance with the relevant guidelines and regulations. The women who were interviewed gave informed consent during each of the surveys.
A total weighted sample of 8968 women was included in the study. At the individual level, 3807 (42.44%) of the respondents were aged 25–34, 4154 (46.31%) of women had secondary education and above, 4697 (52.37%) of husbands/partners had secondary school and above, 8128 (90.63%) were currently married, 6472 (72.16%) were currently working, 3046 (33.97%) of women ethnicity was Hausa, 4667 (52.03%) of the respondents practiced Islam, and 6183 (68.94%) had mass media exposure. At household/community level, 4905 (54.69%) of women resided in the rural area, 1952 (21.76%) were from the richest household, 2434 (27.14%) were residing in North West, 3124 (34.83%) were from a community with high literacy level, and 5131 (57.21%) were from a community with low socioeconomic status (Table 1).
Out of 1400 total clusters, 1,393 clusters were used for spatial analysis of IPV in Nigeria. The Enumeration areas on the Nigeria map is located by points. Each enumeration area had a proportion of IPV ranges from zero to hundred percent. High proportion of IPV were represented by red colors whereas the low proportion of IPV were represented by green colors (Fig. 1).
Geographical location of IPV among women in Nigeria, 2018
The spatial autocorrelation analysis was done using ArcGIS software to check whether there is clustering effect on IPV distribution in Nigeria. The statistical analysis results found were Global Moran’s I = 0.26, p ≤ 0.001 and Z-score 32.77. This indicates that there is clustering effect in IPV distribution in Nigeria (Fig. 2).
The autocorrelation spatial result of IPV reproductive age women in Nigeria, 2018
The hot spot analysis revealed a high proportion of women who faced IPV (hot spot) and low-proportion women who faced IPV (cold spot area). Risk areas were represented by red colors (high rate of IPV). The hot spot areas were located in Gombe, Bauchi, Adamawa, Plateau, Kogi, Edo, Ebonyi, and Rivers (p < 0.010) (Fig. 3).
Hot spot analysis of IPV among women in Nigeria, 2018
Travel from blue to red-colored areas, interpolated IPV over the area increases, red color reveals the predicted IPV risk area maps, and blue color reveals the predicted low IPV risk areas. The high predicted IPV areas were located in Kogi, Plateau, Kaduna, Adamawa, Gombe, and Niger. The low predicted IPV areas were located in Oyo, Ogun, Lagos, Katsina, and Jigawa (Fig. 4).
Prediction of IPV among reproductive age women in Nigeria, 2018
Primary clusters of IPV were detected. 1095 primary significant clusters were located. Primary spatial windows were located in the Eastern and Southern parts of Nigeria, which located at (7.347914 N, 10.176090 E)/492.24 kilo meter radius, and LLR of 143 and RR 1.64 with significant p value. It was revealed that women within the spatial window had a 1.64 times higher risk of IPV than women outside the window. The secondary clusters spatial window was typically located in the Western part of Nigeria, but it was not significant (Table 2; Fig. 5).
The SaTScan analysis result of IPV among women in Nigeria, 2018
The significant predictors at the individual level were women’s education, marital status, working status, ethnicity, religion, parity, and mass media exposure. The likelihood of experiencing IPV among women in Nigeria was higher among women with primary education [aOR = 1.32; 95%(CI = 1.09–1.60)], those that were previously married [aOR = 1.73; 95%(CI = 1.33–2.25)], women currently working [aOR = 1.29; 95%(CI = 1.14–1.47)], women who were Yoruba [aOR = 1.39; 95%(CI = 0.99–1.94)], women with parity from four and above [aOR = 1.78; 95%(CI = 1.40–2.25)], women exposed to mass media [aOR = 1.41;95%(CI = 1.00–1.31)], compared to women who were without education, women that were not yet married, women not currently working, women whose ethnicity was Hausa, women without any children and those who were not exposed to mass media. Lower odds of IPV were reported among women who were practicing Islam [aOR = 0.47; 95%(CI = 0.47–0.69)] compared to those practicing Christianity (Table 3).
At the household/community level, the significant predictors were wealth index, region, and community socioeconomic status. Women who resided in North East [aOR = 1.50;95%(CI = 1.16–1.92)], and in communities with medium socioeconomic status [aOR = 1.45;95%(CI = 1.06–2.00)], were more likely to experience IPV compared to those residing in the North Central and in communities with low socioeconomic status. Women who were within the richest wealth index [aOR = 0.45; 95%(CI = 0.34–0.61)], and those residing in the South West region [aOR = 0.25; 95%(CI = 0.18–0.34)] were less likely to experience IPV compared to those of the poorest wealth index and those residing in North Central (Table 3).
The empty model (Model 0), as shown below in Table 3, depicted a substantial variation in the likelihood of IPV among women in Nigeria across the clustering of the Primary Sampling Units (PSUs) [σ2 = 1.23; 95%(CI = 1.06–1.44)]. The “model 0” indicated that 27% of the variation in IPV among women in Nigeria was attributed to the variation between Intra-Class Correlation, i.e., (ICC = 0.27). The between-cluster variation decreased to 23% (0.23) in Model I (individual level only). In the household/community-level only (Model II), the ICC decreased further to 20%, while the ICC declined to 19% in the complete model with both the individual and household/community factors (Model III). This further reiterates that the variations in the likelihood of IPV in Nigeria are attributed to the variation in PSUs. The Akaike’s Information Criterion (AIC) values showed a successive reduction, which means a substantial improvement in each of the models over the previous model and also affirmed the goodness of fit of Model III. Therefore, Model III, the complete model with both the selected individual and household/community factors, was selected to predict the likelihood of IPV among women in Nigeria (Table 1).
The study examined the spatial distribution and predictors of IPV among women in Nigeria using the recent NDHS data conducted in 2018. We found that the lower and higher proportions of IPV ranged from 0 to 29% and 64% to 100%, respectively. The high predicted IPV areas were located in Kogi, Plateau, Kaduna, Adamawa, Gombe, and Niger, whereas the low predicted IPV areas were located in Oyo, Ogun, Lagos, Katsina, and Jigawa. A possible reason for this finding could be the insurgency of Boko Haram, which increased the rates of violence against women in the conflict-affected areas (i.e., Northeastern states) [33]. It is also possible that the Boko Haram insurgency might have reduced the household autonomy of women by not engaging in any socioeconomy activities, thereby increasing their susceptibility to IPV [34]. The finding indicates that conflicts play a significant role in the rate of IPV against women. Therefore, efforts to reduce the rate of IPV among women in Nigeria should consider preventing the occurrences of conflicts and empowering women as this will help curtail the surge in IPV rates among women residing in the Northeastern region of the country.
The study also found that women who were previously married were more likely to experience IPV. The likely explanation for this finding could be that women who were previously married were abused, leading to their decision to opt-out of the marriage [35]. Additionally, perhaps these women believed that once they have been abused, there is a likelihood that their spouse would continue to abuse them; hence, leaving the marriage becomes the best option for them.
Corroborating the findings of other previous studies [36, 37], the study found that Yoruba were more likely to experience women IPV than women from the Hausa ethnic group. A possible reason for this finding could be that women from the Hausa ethnic group see violence against them as a norm, resulting in their reduction in the report rates of IPV perpetrated against them [36].
Women with parity from four and above and those exposed to mass media were more likely to experience IPV. The reasons for these findings could be that women who have four and above children with a male spouse may depend solely on the spouse for their needs, increasing their susceptibility to experiencing IPV [38]. It could also be that women exposed to mass media have been educated; hence, they are more empowered to fight for their rights, exposing them to be abused [39].
Similar to other previous studies by Ahinkorah et al., [17] and Memiah et al. [34], lower odds of IPV were reported among women who were practicing Islam compared to those practicing Christianity. A possible reason for this finding could be the existence of certain strict norms in the Islamic religion regarding how diligently women should be treated, decreasing the likelihood of Islamic women experiencing IPV [17]
Women who reside in North East and medium community socioeconomic status were more likely to experience IPV compared to those residing in the North Central and community with low socioeconomic status. Probably, the low level of socioeconomic status of women residing in the North East region of Nigeria predisposes them to be abused by their spouses [40]. Therefore, it is valid to assume that increasing the socioeconomic status of women protects them from being abused.
Akin to other previous studies [17, 41], women within the richest wealth index and those residing in the South West region were less likely to experience IPV compared to those residing in the South West region who were poorest and those residing in North Central. A possible reason for this finding could be that women who are from rich households are more empowered and have access to needed resources, and they can help fight for their rights and the rights of other marginalized women in the communities, reducing their chances of been abused [17, 41]. It is also possible that women residing in the South West region of Nigeria are more empowered than their counterparts in the North Central region of Nigeria, protecting them from been abused [42].
The findings of this study are relevant to policy and public health. The inequality in IPV experience based on wealth index, favouring women who belonged to the richest wealth quintile, calls for the attention of policymakers and key stakeholders in policy formulation and implementation to have policies on IPV that are pro-poor. Emphasis must be placed on the poor’s special needs to empower them and eliminate the macro-level factors like poverty that permeate IPV perpetration. Public health-wise, the findings identified the hotspots of IPV and mapped out the spatial distribution of IPV. This should provide public health providers with the blueprint that will guide their programs and interventions. Furthermore, it will help to ensure optimal utilization of scarce resources in the fight against IPV in Nigeria, as priority will be given to provinces that are hotspots for IPV. Future research could seek to explore how in-depth Nigeria women wealth quintile can further be used in fighting against IPV in Nigeria by conducting qualitative research among the local communities.
Our study has several strengths. First, the use of nationally representative data boosts the capacity of our findings to be generalized to women in Nigeria. Additionally, the use of Geographical Information System (GIS) in the analysis of the spatial distribution enabled us to identify the hotspots of IPV in Nigeria, and this is a major contribution to the web of literature on IPV in Nigeria. Moreover, identifying these IPV hotspots would be beneficial to program designers and implementers in their design on context-specific and population-targeted interventions to alleviate IPV. Nevertheless, the study was not without some limitations. A major limitation to this study was that the data used was cross-sectional in design, limiting us from establishing causality. Also, the data was self-reported, making it highly susceptible to recall bias and social desirability bias since IPV in itself is not socially acceptable.
The study found regional variations in the prevalence of IPV among women in Nigeria. The high prevalent IPV areas were located in Kogi, Plateau, Kaduna, Adamawa, Gombe, and Niger, whereas the low prevalent IPV areas were located in Oyo, Ogun, Lagos, Katsina, and Jigawa. Further, the study has identified the individual and community level factors that predict IPV perpetration among Nigerian women. Therefore, policymakers should consider the factors identified in this study to reduce IPV prevalence among women in Nigeria. Chiefly, empowering women would yield a significant improvement in the fight against gender-based violence.
The datasets utilized in this study can be accessed at https://dhsprogram.com/data/available-datasets.cfm.
Intimate partner violence
Nigeria Demographic Health Survey
World Health Organization
United Nations
Sustainable Development Goals
Federal Capital Territory
National Population Commission
Sub-Saharan
The log-likelihood ratio
Akaike information criteria
Variance inflation factor
Enumerations areas
Relative risk
Intra-class correlation
Geographical information system
World Health Organization (WHO). Responding to intimate partner violence and sexual violence against women: WHO clinical and policy guidelines: World Health Organization, Geneva.; 2021.
Daoud N, Matheson FI, Pedersen C, Hamilton-Wright S, Minh A, Zhang J, et al. Pathways and trajectories linking housing instability and poor health among low-income women experiencing intimate partner violence (IPV): toward a conceptual framework. Women Health. 2016;56(2):208–25.
Article  Google Scholar 
Balogun MO, John-Akinola YO. A qualitative study of intimate partner violence among women in Nigeria. J Interpers Violence. 2015;30(14):2410–27.
Article  Google Scholar 
Ogum Alangea D, Addo-Lartey AA, Sikweyiya Y, Chirwa ED, Coker-Appiah D, Jewkes R, et al. Prevalence and risk factors of intimate partner violence among women in four districts of the central region of Ghana: baseline findings from a cluster randomised controlled trial. PLoS ONE. 2018;13(7):e0200874.
Article  Google Scholar 
Ilika AL, Okonkwo PI, Adogu P. Intimate partner violence among women of childbearing age in a primary health care Centre in Nigeria. Afr J Reprod Health. 2002;6:53–8.
Article  Google Scholar 
Murshid NS, Lemke M, Hussain A, Siddiqui S. Combatting gender-based violence: perspectives from social work, education, interdisciplinary studies, and medical anthropology. Berlin: Springer; 2020. p. 83–96.
Google Scholar 
WHO. Addressing violence against women in health and multisectoral policies: a global status report. 2021; 9240040455.
Iliyasu Z, Galadanci HS, Abubakar S, Auwal MS, Odoh C, Salihu HM, et al. Phenotypes of intimate partner violence among women experiencing infertility in Kano, Northwest Nigeria. Int J Gynecol Obstet. 2016;133(1):32–6.
Article  Google Scholar 
Ahinkorah BO. Intimate partner violence against adolescent girls and young women and its association with miscarriages, stillbirths and induced abortions in sub-Saharan Africa: evidence from demographic and health surveys. SSM Popul Health. 2021;13:100730.
Article  Google Scholar 
Tenkorang EY. Intimate partner violence and the sexual and reproductive health outcomes of women in Ghana. Health Educ Behav. 2019;46(6):969–80.
Article  Google Scholar 
Bolarinwa OA, Tessema ZT, Frimpong JB, Babalola TO, Ahinkorah BO, Seidu A-A. Spatial distribution and factors associated with adolescent pregnancy in Nigeria: a multi-level analysis. Arch Public Health. 2022;80(1):1–13.
Article  Google Scholar 
Tanimu TS, Yohanna S, Omeiza SY. The pattern and correlates of intimate partner violence among women in Kano, Nigeria. Afr J Primary Health Care Family Med. 2016;8(1):1.
Google Scholar 
Rao N, Norris Turner A, Harrington B, Nampandeni P, Banda V, Norris A. Correlations between intimate partner violence and spontaneous abortion, stillbirth, and neonatal death in rural Malawi. Int J Gynecol Obstet. 2017;138(1):74–8.
Article  Google Scholar 
Ilika AL. Women’s perception of partner violence in a rural Igbo community. Afr J Reprod Health. 2005;9:77–88.
Article  Google Scholar 
Chernyak E. A comparative study of intimate partner violence in post-Soviet countries: evidence from national surveys: University of Windsor (Canada); 2016.
Onigbogi MO, Odeyemi KA, Onigbogi OO. Prevalence and factors associated with intimate partner violence among married women in an urban community in Lagos State, Nigeria. Afr J Reprod Health. 2015;19(1):91–100.
PubMed  Google Scholar 
Ahinkorah BO, Dickson KS, Seidu A-A. Women decision-making capacity and intimate partner violence among women in sub-Saharan Africa. Arch Public Health. 2018;76(1):1–10.
Article  Google Scholar 
Aduloju PO, Olagbuji NB, Olofinbiyi AB, Awoleke JO. Prevalence and predictors of intimate partner violence among women attending infertility clinic in south-western Nigeria. Eur J Obst Gynecol Reprod Biol. 2015;188:66–9.
Article  Google Scholar 
Benebo FO, Schumann B, Vaezghasemi M. Intimate partner violence against women in Nigeria: a multilevel study investigating the effect of women’s status and community norms. BMC Womens Health. 2018;18(1):1–17.
Article  Google Scholar 
Oyediran KA, Feyisetan B. Prevalence and contextual determinants of intimate partner violence in Nigeria. Afr Popul Stud. 2017;31:1.
Google Scholar 
Uchendu O, Olabumuyi O, Awosika O. 346 Experience of intimate partner violence among Nigerian women: evidence from 2018 NDHS. Int J Epidemiol. 2021;50(168):670.
Google Scholar 
United Nations (UN). Transforming our world: the 2030 Agenda for Sustainable Development. 2015.
Corsi DJ, Neuman M, Finlay JE, Subramanian S. Demographic and health surveys: a profile. Int J Epidemiol. 2012;41(6):1602–13.
Article  Google Scholar 
National Population Commission (NPC) & ICF. Nigeria Demographic and Health Survey 2018; Abuja, Nigeria, and Rockville, Maryland, USA: NPC and ICF; 2019.
Bolarinwa OA, Ahinkorah BO, Okyere J, Seidu A-A, Olagunju OS. A multilevel analysis of prevalence and factors associated with female child marriage in Nigeria using the 2018 Nigeria Demographic and Health Survey data. BMC Womens Health. 2022;22(1):158. https://doi.org/10.1186/s12905-022-01733-x.
Article  PubMed  PubMed Central  Google Scholar 
Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495–9.
Article  Google Scholar 
Kishor S, Johnson K. Profiling domestic violence: a multi-country study. Stud Fam Plann. 2005;36(3):259–61.
Article  Google Scholar 
Straus MA. Measuring intrafamily conflict and violence: the conflict tactics (CT) scales. J Marriage Family. 1979;41:75–88.
Article  Google Scholar 
Samad N, Das P, Ahinkorah BO, Seidu A-A, Frimpong JB, Okyere J, et al. Intimate partner violence and pregnancy termination in Armenia: evidence from nationally-representative survey data. Eur J Investig Health Psychol Educ. 2021;11(2):294–302.
Article  Google Scholar 
Waldhör T. The spatial autocorrelation coefficient Moran’s I under heteroscedasticity. Stat Med. 1996;15(7–9):887–92.
Article  Google Scholar 
Tsai P-J, Lin M-L, Chu C-M, Perng C-H. Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006. BMC Public Health. 2009;9(1):1–13.
Article  Google Scholar 
Goldstein H. Multilevel statistical models. New York: Wiley; 2011.
Google Scholar 
Ekhator-Mobayode UE, Hanmer LC, Rubiano EC. The effect of armed conflict on intimate partner violence (IPV): evidence from the Boko Haram (BH) Insurgency in Nigeria. 2020.
Memiah P, Ah Mu T, Prevot K, Cook CK, Mwangi MM, Mwangi EW, et al. The prevalence of intimate partner violence, associated risk factors, and other moderating effects: findings from the Kenya National Health Demographic Survey. J Interpers Violence. 2018:0886260518804177.
Krebs C, Breiding MJ, Browne A, Warner T. The association between different types of intimate partner violence experienced by women. J Family Violence. 2011;26(6):487–500.
Article  Google Scholar 
Nwabunike C, Tenkorang EY. Domestic and marital violence among three ethnic groups in Nigeria. J Interpers Violence. 2017;32(18):2751–76.
Article  Google Scholar 
Ameh N, Kene T, Onuh S, Okohue J, Umeora O, Anozie O. Burden of domestic violence amongst infertile women attending infertility clinics in Nigeria. Niger J Med. 2007;16(4):375–7.
CAS  PubMed  Google Scholar 
Pathak N, Dhairyawan R, Tariq S. The experience of intimate partner violence among older women: a narrative review. Maturitas. 2019;121:63–75.
Article  Google Scholar 
Slakoff DC, Aujla W, PenzeyMoog E. The role of service providers, technology, and mass media when home isn’t safe for intimate partner violence victims: best practices and recommendations in the era of CoViD-19 and beyond. Arch Sex Behav. 2020;49(8):2779–88.
Article  Google Scholar 
Antai D. Traumatic physical health consequences of intimate partner violence against women: what is the role of community-level factors? BMC Womens Health. 2011;11(1):1–14.
Article  Google Scholar 
Dim EE. Differentials and predictors of IPV against Nigerian women in rural and urban areas. J Aggress Maltreat Trauma. 2020;29(7):785–807.
Article  Google Scholar 
Obayelu OA, Chime AC. Dimensions and drivers of women’s empowerment in rural Nigeria. Int J Soc Econ. 2020;47:315.
Article  Google Scholar 
Download references
The authors are grateful to MEASURE DHS for granting access to the dataset used in this study.
There is no specific funding received for this study.
Department of Global Public Health, Canterbury Christ Church University, Canterbury, CT1 1QU, United Kingdom
Obasanjo Afolabi Bolarinwa
Department of Public Health Medicine, University of KwaZulu-Natal, Durban, 4049, South Africa
Obasanjo Afolabi Bolarinwa
School of Public Health, University of Technology Sydney, Sydney, NSW, 2007, Australia
Bright Opoku Ahinkorah
Department of Health, Physical Education and Recreation, University of Cape Coast, PMB TF0494, Cape Coast, Ghana
James Boadu Frimpong
Centre for Gender and Advocacy, Takoradi Technical University, Takoradi, Ghana
Abdul-Aziz Seidu
College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD, 4811, Australia
Abdul-Aziz Seidu
Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
Zemenu Tadesse Tessema
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
OAB developed the study’s concept and performed the multilevel analysis. JBF, BOA, OAB & AS drafted the introduction and abstract, wrote the discussion, and the conclusion sections of the study, AS drafted the methodology, and ZTT & OAB performed the spatial analysis. All authors proofread the first draft of the manuscript and contributed intellectually to the overall development and approved the manuscript’s final version for submission.
Correspondence to Obasanjo Afolabi Bolarinwa.
Not applicable.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Reprints and Permissions
Bolarinwa, O.A., Ahinkorah, B.O., Frimpong, J.B. et al. Spatial distribution and predictors of intimate partner violence among women in Nigeria. BMC Women’s Health 22, 245 (2022). https://doi.org/10.1186/s12905-022-01823-w
Download citation
Received: 11 May 2021
Accepted: 10 June 2022
Published: 20 June 2022
DOI: https://doi.org/10.1186/s12905-022-01823-w
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
ISSN: 1472-6874
By using this website, you agree to our Terms and Conditions, California Privacy Statement, Privacy statement and Cookies policy. Manage cookies/Do not sell my data we use in the preference centre.
© 2022 BioMed Central Ltd unless otherwise stated. Part of Springer Nature.

source

Leave a Comment

Your email address will not be published.