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Background: Owing to the severe repercussions associated with female genital mutilation (FGM) and its illicit status in many countries, the WHO, human rights organisations and governments of most sub-Saharan African countries have garnered concerted efforts to end the practice. This study examined the socioeconomic and demographic factors associated with FGM among women and their daughters in sub-Saharan Africa (SSA). Methods: We used pooled data from current Demographic and Health Surveys (DHS) conducted between January 1, 2010 and December 31, 2018 in 12 countries in SSA. In this study, two different samples were considered. The first sample was made up of women aged 15–49 who responded to questions on whether they had undergone FGM. The second sample was made up of women aged 15–49 who had at least one daughter and responded to questions on whether their daughter(s) had undergone FGM. Both bivariate and multivariable analyses were performed using STATA version 13.0. Results: The results showed that FGM among women and their daughters are significantly associated with household wealth index, with women in the richest wealth quintile (AOR, 0.51 CI 0.48–0.55) and their daughters (AOR, 0.64 CI 0.59–0.70) less likely to undergo FGM compared to those in the poorest wealth quintile. Across education, the odds of women and their daughters undergoing FGM decreased with increasing level of education as women with higher level of education had the lowest propensity of undergoing FGM (AOR, 0.62 CI 0.57–0.68) as well as their daughters (AOR, 0.32 CI 0.24–0.38). FGM among women and their daughters increased with age, with women aged 45–49 (AOR = 1.85, CI 1.73–1.99) and their daughters (AOR = 12.61, CI 10.86–14.64) more likely to undergo FGM. Whiles women in rural areas were less likely to undergo FGM (AOR = 0.81, CI 0.78–0.84), their daughters were more likely to undergo FGM (AOR = 1.09, CI 1.03–1.15). Married women (AOR = 1.67, CI 1.59–1.75) and their daughters (AOR = 8.24, CI 6.88–9.87) had the highest odds of undergoing FGM. Conclusion: Based on the findings, there is the need to implement multifaceted interventions such as advocacy and educational strategies like focus group discussions, peer teaching, mentor–mentee programmes at both national and community levels in countries in SSA where FGM is practiced. Other legislative instruments, women capacity-building (e.g., entrepreneurial training), media advocacy and community dialogue could help address the challenges associated with FGM. Future studies could consider the determinants of intention to discontinue or continue the practice using more accurate measures in countries identified with low to high FGM prevalence.
The study used pooled data from current Demographic and Health Surveys (DHS) conducted between January 1, 2010 and December 31, 2018 in 12 countries in SSA. The countries are Burkina Faso, Chad, Ethiopia, Guinea, Kenya, Mali, Niger, Nigeria, Senegal, Sierra Leone, Tanzania and Togo. These 12 countries were included in the study because their surveys had information on FGM and had questions on whether the woman herself had undergone FGM; and whether she had daughter(s) who have also undergone FGM. We excluded two countries (Côte d’Ivoire and Gambia) because although they had data on FGM, data for daughters of women aged 15–49 were non-existent. The 12 countries were considered to provide a holistic and in-depth evidence of FGM in SSA. DHS is a nationwide survey executed every five years across low-and-middle-income countries (LMICs). It is representative of each of these countries. Women’s files that have responses by women aged 15–49 were used in the study. The surveys targeted core maternal and child health indicators such as FGM, unintended pregnancy, contraceptive use, skilled birth attendance, immunisation among under-fives and intimate partner violence. Stratified dual-stage sampling approach was employed and the same questions were posed to women of all these countries and thus make it feasible for multi-country study. The study involved cluster sampling process (i.e. enumeration areas [EAs]), followed by systematic household sampling within the selected EAs. The sample frame usually excludes nomadic and institutional groups such as prisoners and hotel occupants. In this study, two different samples were considered. The first sample was made up of 130,605 women aged 15–49 who responded to questions on whether they had undergone FGM. The second sample was made up of 122,941 women aged 15–49 who had at least one daughter and responded to questions on whether their daughter(s) had undergone FGM. We followed the ‘Strengthening the Reporting of Observational Studies in Epidemiology’ (STROBE) statement in conducting this study. The dependent variable in this study was “has had FGM or undergone FGM’. To derive this variable, respondents were asked if their genital area was “nicked with nothing removed;” “something removed,” or “sewn shut”. The responses were ‘Yes’ and ‘No’. These were coded as follows; No = 0, Yes = 1. Respondents who had daughters were further asked how many of their daughter(s) had their genital area “nicked with nothing removed;” “something removed,” or “sewn shut”. The response ranged from ‘no daughter’ to ‘1, 2, 3, 4, 5, 6, 7 daughters’. To provide a binary outcome, women who said none of their daughters went through FGM were coded as ‘No = 0’ and those who had at least one daughter going through FGM were coded ‘Yes = 1’. The main explanatory variable was ‘socio-economic status’. Following some previous studies [18–20], we used wealth quintile and maternal education as proxy measures of socio-economic status. In the standard DHS, wealth quintile is computed from data on household ownership of selected assets such as bicycle, materials used for house construction, television, type of water access and sanitation facilities. A composite variable, wealth status, is created from these assets through Principal Component Analysis (PCA) by placing households on a continuous measure of relative wealth after which households are categorized into five wealth quintiles namely poorest, poorer, middle, richer and richest [21]. Maternal education, on the other hand is a standardized variable of highest education attained and offers level of education in these four categories: No education, Primary, Secondary, and Higher [21]. We maintained the original categorization and coding of these two variables, (i. e. wealth quintile and maternal education). Apart from these independent variables, we controlled for country of survey and demographic variables like age, residence, marital status, occupation, frequency of reading newspaper, frequency of listening to radio and frequency of watching television. The coding of these variables are found in Table Table1.1. Apart from country of survey, which was included a priori, selection of all the explanatory variables was influenced by previous studies [14, 22, 23] and their availability in the datasets. Socio-demographic characteristics of respondents (Weighted) The analyses begun with computation of FGM among women aged 15–49 and their daughters. Secondly, we appended the datasets and this generated a total sample of 130,605 women aged 15–49 with data on FGM and 122,941 of women aged 15–49 who had at least one daughter and answered questions on FGM among their daughters. After appending, we presented the weighted socio-demographic characteristics of women aged 15–49 and those who had at least one daughter (see Table Table1).1). After this, we calculated the prevalence of FGM among women aged 15–49 and their daughters and presented them using charts (see Figs. 1, ,2).2). We also calculated the prevalence of FGM among women and their daughters across their socio-economic status and other socio-demographic characteristics. We presented these using proportions, chi-square and p values. Finally, two binary logistic regression models were built. The first model (Model I) reports on womens’ FGM whilst the second model (Model II) reports on daughters’ FGM. Proportion of women aged 15–49 who have undergone FGM in SSA Proportion of daughters of women aged 15–49 who have undergone FGM in SSA The model fitness specification was done with the Hosmer–Lemeshow test while multicollinearity was checked using the variance inflation factor (VIF). The multicollinearity test for the explanatory variables for FGM among women (Mean VIF = 1.46, Max VIF = 1.90, Minimum = 1.06) and that of FGM among daughters (Mean VIF = 1.45, Max VIF = 1.80, Minimum = 1.06) showed no evidence of collinearity among the independent variables. Binary logistic regression was employed because our dependent variables were measured using a binary factor. Results for the regression analysis were presented as crude odds ratios (COR) and adjusted odds ratios (AOR), with their corresponding 95% confidence intervals (CI) signifying precision. The analyses were carried out with STATA version 13.0 with inherent sample weight applied. Sample weight was applied and the survey command (svy) was used to account for the complex sampling design of the survey. The DHS surveys obtain ethical clearance from the Ethics Committee of ORC Macro Inc. as well as Ethics Boards of partner organisations of the various countries such the Ministries of Health. During each of the surveys, either written or verbal consent was provided by the women. Since the data was not collected by the authors of this manuscript, we sought permission from MEASURE DHS’s website and access to the data was provided after our intent for the request was assessed and approved on 3rd April, 2019. The dataset is freely available at https://dhsprogram.com/data/available-datasets.cfm.
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