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Original Articles
- Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods
- Pravin Arun Sawant, Sakshi Shantanu Hiralkar, Yogita Purushottam Hulsurkar, Mugdha Sharad Phutane, Uma Satish Mahajan, Abhay Machindra Kudale
- Epidemiol Health. 2024;46:e2024044. Published online April 13, 2024
- DOI: https://doi.org/10.4178/epih.e2024044
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Abstract
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AbstractOBJECTIVES
Over-the-counter (OTC) antibiotic use can cause antibiotic resistance, threatening global public health gains. To counter OTC use, this study used machine learning (ML) methods to identify predictors of OTC antibiotic use in rural Pune, India.METHODSThe features of OTC antibiotic use were selected using stepwise logistic, lasso, random forest, XGBoost, and Boruta algorithms. Regression and tree-based models with all confirmed and tentatively important features were built to predict the use of OTC antibiotics. Five-fold cross-validation was used to tune the models’ hyperparameters. The final model was selected based on the highest area under the curve (AUROC) with a 95% confidence interval (CI) and the lowest log-loss.RESULTSIn rural Pune, the prevalence of OTC antibiotic use was 35.9% (95% CI, 31.6 to 40.5). The perception that buying medicines directly from a medicine shop/pharmacy is useful, using antibiotics for eye-related complaints, more household members consuming antibiotics, and longer duration and higher doses of antibiotic consumption in rural blocks and other social groups were confirmed as important features by the Boruta algorithm. The final model was the XGBoost+Boruta model with 7 predictors (AUROC, 0.934; 95% CI, 0.891 to 0.978; log-loss, 0.279) log-loss.CONCLUSIONSXGBoost+Boruta, with 7 predictors, was the most accurate model for predicting OTC antibiotic use in rural Pune. Using OTC antibiotics for eye-related complaints, higher consumption of antibiotics and the perception that buying antibiotics directly from a medicine shop/pharmacy is useful were identified as key factors for planning interventions to improve awareness about proper antibiotic use. -
Summary
- Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
- Jaehong Yoon, Ji-Hwan Kim, Yeonseung Chung, Jinsu Park, Glorian Sorensen, Seung-Sup Kim
- Epidemiol Health. 2021;43:e2021099. Published online November 17, 2021
- DOI: https://doi.org/10.4178/epih.e2021099
- 9,722 View
- 228 Download
- 1 Web of Science
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Abstract
Summary
PDFSupplementary Material
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AbstractOBJECTIVES
This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded “not applicable (NA)” to a question about hiring discrimination despite being eligible to answer.METHODSUsing data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using “yes” or “no” responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered “NA.” Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the “yes” or “no” group and the “NA” group.RESULTSBased on the predictions from the random forest model, we found that 58.8% of the “NA” group were predicted to have experienced hiring discrimination, while 19.7% of the “yes” or “no” group reported hiring discrimination. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.CONCLUSIONSThis study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms. -
SummaryKorean summary
본 연구는 한국노동패널조사(7차년도)에 포함된 3576명의 임금근로자의 자료를 이용해 성별에 따른 구직 과정 경험한 차별에 대한 과소보고의 규모를 확인하고자 하였다. 질문에 “예” 또는 “아니요”라고 응답한 임금근로자 3479명 데이터를 이용하여 고용 시 차별경험을 예측하는 머신러닝 모형을 구축하였고, 이를 활용해 이미 직장에서 일하고 있는 상태임에도 “해당사항 없음”이라고 응답한 임금근로자 97명이 차별을 경험했는지 여부를 예측하였다. 분석결과, “해당사항 없음”이라고 답한남성 근로자 64명 중 29명(45.3%), 여성 근로자 33명 중 28명(84.8%)가 실제로 차별을 경험한 것으로 추정되었다.Key Message
We examined gender differences in under-reporting hiring discrimination for wage workers who responded “not applicable(NA)” to a question about hiring discrimination despite being eligible to answer “yes” or “no.” Using data from 3,576 wage workers of the Korea Labor and Income Panel Study, we estimated the prevalence of hiring discrimination among those who answered “NA,” based on the best-performing machine learning prediction model for “yes” or “no” group. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.
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