Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods.

Publisher: Korean Society of Epidemiology Country of Publication: Korea (South) NLM ID: 101519472 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2092-7193 (Electronic) Linking ISSN: 20927193 NLM ISO Abbreviation: Epidemiol Health Subsets: MEDLINE

Original Publication: Seoul : Korean Society of Epidemiology

Machine Learning* ; Nonprescription Drugs*/therapeutic use ; Anti-Bacterial Agents*/therapeutic use ; Rural Population*/statistics & numerical data; India/epidemiology ; Humans ; Adult ; Male ; Female ; Middle Aged

Objectives: 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.
Methods: The 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.
Results: In 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.
Conclusions: XGBoost+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.

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Keywords: Algorithm; Antibiotic; Antibiotic resistance; India; Machine learning; Pharmacy

0 (Nonprescription Drugs)
0 (Anti-Bacterial Agents)

Date Created: 20240419 Date Completed: 20240809 Latest Revision: 20250212

20250213

PMC11417445

10.4178/epih.e2024044

38637971