Association between intimate partner violence and utilization of facility delivery services in Nigeria: a propensity score matching analysis

Abstract

Background Intimate partner violence (IPV) has been shown to be associated with poor maternal healthcare utilisation and poor pregnancy outcomes. IPV can be seen both as the cause and result of low socioeconomic status and lack of maternal autonomy that can limit women’s access to resources and motivation necessary for seeking healthcare during pregnancy. This paper aims to study the relationship between intimate partner violence (IPV) and the utilisation of facility delivery services in Nigeria. Methods We applied propensity score matching (PSM) approach to examine the relationship between intimate partner violence (IPV) and the utilisation of facility delivery services. PSM is a popular strategy for reducing sampling bias through balancing sample characteristics, a technique that mimics randomization on cross-sectional data. Data were collected from Nigeria DHS surveys conducted in 2008 and 2013. IPV was the main explanatory variable of interest for delivery at health facility which was defined as delivering at any health institution including health clinics. Results PSM generated 20,446 cases distributed into two equal groups i.e. those who delivered at health facility versus those who did not. The prevalence of facility delivery in 2013 was 56.8% (95%CI 55.0–58.6) indicating a moderate increase from its 2008 level of 43.2% (41.4–45.0%). Lifetime prevalence of emotional, physical and sexual abuse was respectively 21.5%(95%CI 20.6, 22.4), 14.9% (14.2, 15.7) and 5.0% (4.6–5.4). In the multivariable analysis after adjusting for potential confounders, ever experiencing emotional abuse was associated increased odds of not delivering at a health facility. (AOR = 1.228, 95%CI, 1.095–1.679). Conclusion Women experiencing emotional violence are less likely to use institutional delivery services, and hence are susceptible to increased risk of reproductive complications. IPV is a complex issue that needs to be tackled by introducing evidence based strategies contextually relevant to local sociocultural environment. Further studies are required to understand the roots of IPV and the pathways through which it hindrances healthcare utilisation among women.

Determinants of prenatal care use and HIV testing during pregnancy: a population-based, cross-sectional study of 7080 women of reproductive age in Mozambique

“…The data were secondary and authors have no influence over the selection and measurement of the variables. As the data were self-reported, the chances of recall and reporting bias cannot be ignored [33,34].…”

Effect of education on attitude towards domestic violence in Nigeria: an exploration using Propensity score methodology

“…Only the use of PSM can effectively control for the selection bias (Arikan et al, 2018). Many studies have used PSM to address the problem of selection bias in quasiexperimental and cross-sectional study designs (Feng et al, 2012;Rubin, 1997;Shadish et al, 2006;Yang et al, 2016;Yaya et al, 2019). PSM is a statistical method that has proven useful for evaluating treatment effect when using non-experimental or observational data (Guo & Fraser, 2015).…”

“…(Yusuf et al, 2014). Also, IPV has been linked as a risk factor for maternal health care utilization and poor pregnancy outcome using PSM (Yaya et al, 2019).…”

“…PSM has been proved to be effective and it has been used in previous studies (Yang et al, 2016;Yaya et al, 2019;Yusuf et al, 2014). This study showed that lower proportion of men justified DV compared to women, although this rate was higher than that of Ukraine and Ghana, but almost in line with the percentage reported in Moldova and Namibia (Sardinha & Catalan, 2018).…”

Effect of Education on Attitude Towards Domestic Violence in Nigeria: An Exploration Using Propensity Score Methodology

“…Only the use of PSM can effectively control for the selection bias (Arikan, van de Vijver, & Yagmur, 2018). Many studies have used PSM to address the problem of selection bias in quasi-experimental and cross-sectional study designs (Feng, Zhou, Zou, Fan, & Li, 2012;Rubin, 1997;Shadish et al, 2006;Yang, Imbens, Cui, Faries, & Kadziola, 2016;Yaya, Gunawardena, & Bishwajit, 2019). PSM is a statistical method that has proven useful for evaluating treatment effects when using non-experimental or observational data (Guo & Fraser, 2015).…”