Abstract:
Abstract
Introduction: Globally, 38% of contraceptive users discontinue the use of
contraceptives. In Ethiopia, about 35% of contraceptive users discontinue within
twelve months. Discontinuation reduces contraceptive coverage, family planning
program effectiveness and contributes to undesired fertility. Hence understanding
the spatial distribution and potential predictors of contraceptive discontinuation is
crucial to reducing its undesired outcomes. Thus, this study could enable to
determine the spatial heterogeneity and determinant factors for contraceptive
discontinuation in Ethiopia.
Objective: This study aimed to determine the spatial distribution and predictors of
contraceptive discontinuation among reproductive-age women in Ethiopia.
Methodology: A population-based cross-sectional study was conducted using
secondary data analysis from 2016 Ethiopian Demographic Health Survey. ArcGIS
Pro version 2.8 was used to handle mapping, hotspot, and spatial autocorrelation
analysis. Bernoulli model was used to analyze the purely spatial cluster detection
through SaTScan version 10.0.2 software. Eight machine learning algorithms were
employed and evaluated using performance metrics. The best-performing model
was applied to predict and identify important predictors of contraceptive
discontinuation. Furthermore, the most important factors identified through the best
predictive model were used to predict contraceptive discontinuation in originally
unsampled areas. Finally, association rule mining was applied to discover the
relationship between contraceptive discontinuation and its top predictors.
Result: Spatial distribution of contraceptive discontinuation was clustered in
Ethiopia with a global Moran’s I index value of 0.3(p-value <0.001). Accordingly,
enumeration areas in South Wollo, South Gondar, East and West Gojjam zones of
Amhara region, and Illubabor zone of Oromia region were detected as hot spot
areas. Random Forest was the best predictive model with 68% accuracy and the
top ten predictors of contraceptive discontinuation were identified. Association rule
mining identified women's age, women’s education level, family size, husband’s
desire for children, husband’s education level, and women’s fertility preference as
the most frequently associated factors with contraceptive discontinuation.
