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Background: Female genital mutilation (FGM) is a serious health problem globally with various health, social and psychological consequences for women. In Ethiopia, the prevalence of female genital mutilation varied across different regions of the country. Therefore, this study aimed to investigate the trend and determinants of female genital mutilation among reproductive-age women over time. Methods: A secondary data analysis was done using 2000, 2005, and 2016 Demographic Health Surveys (DHSs) of Ethiopia. A total weighted sample of 36,685 reproductive-age women was included for analysis from these three EDHS Surveys. Logit based multivariate decomposition analysis was employed for identifying factors contributing to the decrease in FGM over time. The Bernoulli model was fitted using spatial scan statistics version 9.6 to identify hotspot areas of FGM, and ArcGIS version 10.6 was applied to explore the spatial distribution FGM across the country. Results: The trends of FGM practice has been decreased from 79.9% in 2000 to 70.4% in 2016 with an annual reduction rate of 0.8%. The multivariate decomposition analysis revealed that about 95% of the overall decrease in FGM practice from 2000 to 2016 was due to the difference in the effects of women’s characteristics between the surveys. The difference in the effects of residence, religion, occupation, education, and media exposure were significant predictors that contributed to the decrease in FGM over time. The spatial distribution of FGM showed variation across the country. The SaTScan analysis identified significant hotspot areas of FGM in Somali, Harari, and Afar regions consistently over the three surveys. Conclusion: Female genital mutilation practice has shown a remarkable decrease over time in Ethiopia. Public health programs targeting rural, non-educated, unemployed, and those women with no access to media would be helpful to maintain the decreasing trend of FGM practice. The significant Spatio-temporal clustering of FGM was observed across regions in Ethiopia. Public health interventions must target the identified clusters as well.
We used 2000, 2005, and 2016 Ethiopian Demographic and Health surveys (EDHSs). These EDHSs are nationally representative cross-sectional surveys performed in 9 regions and 2 country city administrations every five years (Fig. 1). In each of the surveys, stratified two-stage sampling of clusters was carried out. Stratification was achieved by separating each region into urban and rural areas. Accordingly, a total of 21 sampling strata have been created. In the first stage, a total of 539 Enumeration Areas (EAs) for EDHS 2000, 540 EAs for EDHS 2005, and 645 EAs for EDHS 2016 were randomly selected proportional to the EA size. At the second stage, on average 27 to 32 households per EA were selected. A total weighted sample of 36,685 (15,367 in EDHS 2000, 14,070 in EDHS 2005 and 7248 in EDHS 2016) reproductive-age women used for this study. The comprehensive procedure for sampling was described in the complete EDHS report [10, 11, 23]. Map of the study area (Source; Shape file from CSA, 2013, done using ArcGIS version 10.6 and SaTScan version 9.6) The outcome variable for this study was experienced FGM and coded as “Yes = 1” or “No = 0”. The EDHS asked women to answer the question “have you ever been circumcised?”. So, the response variable of the ith mother Yi was measured as a dichotomous variable with possible values Yi = Yes if ith mother had experienced circumcision and Yi = No if mother did not experience circumcision. The independent variables included in this study were: residence, religion, geographic region, responded age, maternal education, women occupation, media exposure, and wealth index. The data were extracted from the Individual Record (IR) data sets. Before any statistical analysis, the data were weighted using sampling weight, primary sampling unit, and strata, to restore the representativeness of the survey and get reliable statistical estimates. Trend analysis of FGM and decomposition of the decrease in the prevalence of FGM over time was done. The trend analysis has been done in three phases, phase 1 (2000–2005), phase 2 (2005–2016) and the overall trend (2000–2016), the trend and determinants was examined separately. For the trend analysis multivariate decomposition analysis for non-linear response outcome was employed to identify the factors contributed to the decrease in FGM practice across the surveys. For our study, Logit based decomposition analysis was employed. The Logit based multivariate decomposition analysis utilizes the output from the logistic regression model to parcel out the observed decrease in FGM over time into components. The main aim of multivariate decomposition is to identify the factors contributing to the decrease in FGM practice for the last 16 years. The decrease in FGM practice can be explained by the compositional difference between surveys (i.e. differences in characteristics) and/or the difference in effects of explanatory variables (i.e. differences in the coefficients) between the surveys. Hence, the observed decrease in FGM over time is additively decomposed into a characteristics (or endowments) component and a coefficient (or effects of characteristics) component. For logistic regression, the Logit or log-odd of FGM is taken as: E C The E component refers to the part of the differential owing to differences in endowments or characteristics. The C component refers to that part of the differential attributable to differences in coefficients or effects. The recently developed multivariate decomposition for the non-linear model was used for the decomposition analysis of female genital mutilation using the mvdcmp STATA command [24]. ArcGIS version 10.6 and SaTScan version 9.6 software were used for spatial analysis. The spatial autocorrelation (Global Moran’s I) statistic was used to assess whether there was significant clustering of FGM [25]. Moran’s I has a value ranging from-1 to 1. Positive Moran’s I value shows that FGM is clustered while negative Moran’s I indicates that FGM is dispersed [26]. The value of Moran’s I near zero has revealed that FGM is randomly distributed. Both Z-score and P-value are generated to assess the significance of the Moran index. In spatial scan statistical analysis, Bernoulli based model was employed to identify significant spatial high FGM clusters using Kuldorff’s SaTScan version 9.6 software. The SaTScan uses a circular scanning window that moves across the study area. Women who were circumcised were taken as cases whereas those who were not circumcised were taken as controls to fit the Bernoulli model. The default maximum spatial cluster size of < 50% of the population was used as an upper limit, which allowed both small and large clusters to be detected and ignored clusters that contained more than the maximum limit. For each potential cluster, a likelihood ratio test statistic and the p-value were used to determine significant clusters. The scanning window with maximum likelihood was the most likely performing cluster. The primary and secondary clusters were identified and ranked based on their likelihood ratio test, based on 999 Monte Carlo replications [27]. The Ordinary Kriging spatial interpolation method was used to predict the un-sampled/unmeasured values from the sampled measurements. Since the study was a secondary data analysis of publically available survey data from MEASURE DHS program, ethical approval and participant consent were not necessary for this particular study. We requested DHS Program and permission was granted to download and use the data for this study from http://www.dhsprogram.com. There are no names of individuals or household addresses in the data files. The geographic identifiers only go down to the regional level (where regions are typically very large geographical areas encompassing several states/provinces. In surveys that collect GIS coordinates in the field, the coordinates are only for the enumeration area (EA) as a whole, and not for individual households, and the measured coordinates are randomly displaced within a large geographic area so that specific enumeration areas cannot be identified.
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