Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach

BACKGROUND

Globally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia.

METHODOLOGY

Secondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software.

RESULT

Random forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. 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 predictors most frequently associated with contraceptive discontinuation.

CONCLUSION

Results have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.

Spatial distribution of antenatal care utilization and associated factors in Ethiopia: evidence from Ethiopian demographic health surveys

BACKGROUND

Antenatal care (ANC) is one of the components of care to be provided to pregnant women. In Ethiopia, characterizing the spatial distribution of antenatal care utilization is essential to prioritize risk areas where ANC is needed and facilitate interventions. Therefore, this spatial analysis was performed to assess the spatial distribution of ANC utilization between 2000 and 2011 and to identify factors associated with ANC utilization in Ethiopia.

METHODS

A total of 23,179 women who had a live birth in the five years preceding the surveys were included in the study. The spatial data were created in ArcGIS10.1 for each study clusters. The Bernoulli model was used by applying Kulldorff methods using the SaTScan™ software to analyze the purely spatial clusters of ANC utilization. Multiple logistic regression analysis was used to identify predictors affecting ANC utilization.

RESULTS

ANC utilization had spatial variations across the country. Spatial scan statistics identified 49 high performing clusters (LLR = 111.92, P < 0.001) in 2000, 51 (LLR = 114.49, P < 0.001) in 2005 and, 86 (LLR = 121.53, P < 0.001) in 2011. ANC utilization was higher among mothers; with richest wealth quintiles, lowest number of birth order, who are living in urban areas, younger and educated.

CONCLUSION

These results provide further insight into differences in ANC utilization in the country and highlight high and modest performing clusters. This could enable efficient and timely spatial targeting to improve ANC service up take in Ethiopia.