Magnitude and determinants of intimate partner violence against women in Somalia: evidence from the SDHS survey 2020 dataset | BMC Women’s Health

Magnitude and determinants of intimate partner violence against women in Somalia: evidence from the SDHS survey 2020 dataset | BMC Women’s Health

Descriptive statistics

Table 1 Univariate analysis of numerical variables of IPV in SDHS 2020 data

Table 1 shows that the average household size in Somalia is approximately five, which indicates that there are typically five people living in these women’s homes. The household size does, however, vary somewhat, as shown by the standard deviation of 2.332, with a minimum of zero and a maximum of 9 peoples living with women under the study. Additionally, the findings of this study indicate that, on average, 6.1 children are born to the questioned women in Somalia, with a standard deviation of 2.773. The distribution of births shows that women with larger families as well as those who have never given birth fall within the range of 0 to 16.

For a better understanding of the context of IPV against women in Somalia, the results in Table 2 shed light on the respondents’ demographic and socioeconomic characteristics. Foremost, the age distribution of the respondents shows that the majority of women under study are between the ages of 25 and 39. This age range appears to be particularly crucial in terms of IPV, since it may correspond to a time when women are more prone to violence or more likely to report such instances. In addition, the table shows the regional distribution of the respondents where Sanaag, Togdheer, and Sool, account for about 30% of the population. Bay, Middle Shabelle and Hiraan were the regions with the fewest respondents. Regional distribution is important for policymakers to shape efforts towards diminishing IPV against women in order to direct interventions and support services to individuals in greatest need. In addition, the above table shows the place of residence of respondents, where urban and nomad settings were equal in percentage, at about 36% each, and the rest were rural residents. Also, the table includes details regarding the educational backgrounds of the respondents and their partners. It shows that the majority of women did not attend school, with a percentage of 86.3%, while around 10% have a primary school-level education and barely 0.5% reported having a higher education. On the other hand, 82.43% of respondents reported their partners attended school, while around 13.78% said their partners did not attend school. The wealth quantile classified households into five groups based on their economic standing. Of the respondents, 23.80% and 22.20% belonged to the lowest and second lowest wealth index classes, respectively. 19.8%, 17.95%, and 4.7% of the population were categorized as middle class and fourth class, respectively, and 16.87% as the highest wealth index category. In terms of respondents’ and partners’ employment status, women were disproportionately unemployed, with almost 98.67 of them saying they had not worked in the previous 12 months. In contrast, 53.33% of the study’s female participants said their spouses had a job during the previous 12 months, while 45.95% said the opposite. Finally, the magnitude of domestic violence against women is about 4% in Somalia.

Table 2 Univariate analysis of categorical variables of IPV in SDHS 2020 data

Prevalence of IPV against wowen in Somalia

The study found that the prevalence of IPV against women in Somalia was 4.859% (95%CI: 3.850 − 5.4335) in this particular investigation.

Bivariate analysis of association between IPV and predictors

Table 3 shows the bivariate analysis using Chi-square tests, with a particular focus on the corresponding p-values (p). Significant associations (p < 0.05) were found between IPV and age, region, residence type, education level, household size, partner’s education and work, respondent’s work, and total children ever born.

In terms of age groups, there is a statistically significant association between age and IPV (χ² = 57.1166, df = 6, p < 0.001). The highest rate of IPV is found in the 35–39 age group (5.41%), while the lowest is in the 45–49 age group (2.49%). Examining the region variable, there is a significant association between region and IPV (χ² = 212.4546, df = 15, p < 0.001). The regions with the highest rates of IPV include Hiraan (6.07%), Middle Shabelle (6.02%), and Gedo (7.75%), while Sool (0.75%) has the lowest rate. Regarding the type of residence, there is a significant association between residence and IPV (χ² = 14.5493, df = 2, p = 0.001). The rural population has the highest rate of IPV (4.48%), followed by urban residents (4.41%), while the nomadic population has the lowest rate (3.47%). The highest educational level achieved by respondents also shows a significant association with IPV (χ² = 31.9454, df = 3, p < 0.001). Those with no education have the highest rate of IPV (4.35%), while those with higher education reported no incidents of IPV.

Other variables that show significant associations with IPV include the number of household members (χ² = 27.5678, df = 9, p = 0.001), husband/partner’s education (χ² = 6.7213, df = 2, p = 0.035), husband/partner’s work in the last 12 months (χ² = 26.6903, df = 2, p < 0.001), respondent’s work in the last 12 months (χ² = 4.8717, df = 2, p = 0.027), and total children ever born (χ² = 88.7376, df = 16, p < 0.001).

Table 3 Bivariate analysis of association between IPV and predictors

Multivariable logistic regression

Table 4 displays the results of the multivariable logistic regression analysis, controlling for potential confounders. Significant determinants of IPV included age (45–49 age group compared to 15–19), region (several regions showing higher risk compared to Awdal), type of residence (nomadic compared to rural), mother’s highest educational level (primary and secondary compared to no education), husband/partner’s education (no education compared to yes), husband/partner’s work (no work compared to yes), and respondent’s work (no work compared to yes).

Table 4 Results for the logistic regression analysis to identify the key determinants of the IPV in Somalia

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