Related Experiment Video
Updated: Jun 28, 2025
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
Published on: October 27, 2023
Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods
Pravin Arun Sawant1, Sakshi Shantanu Hiralkar1, Yogita Purushottam Hulsurkar1
1Department of Health Sciences, School of Health Sciences, Savitribai Phule Pune University, Pune, India.
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.
Insights
Over-the-counter antibiotic use in rural Pune was predicted using machine learning. Key factors included perceived usefulness of direct pharmacy purchases and antibiotic use for eye issues, informing public health interventions.
Area of Science:
- Public Health
- Infectious Disease Epidemiology
- Health Informatics
Background:
- Over-the-counter (OTC) antibiotic use contributes to antimicrobial resistance, a significant global health threat.
- Understanding the drivers of OTC antibiotic consumption is crucial for developing targeted interventions.
- Rural populations may exhibit unique patterns of antibiotic access and usage.
Purpose of the Study:
- To identify key predictors of over-the-counter antibiotic use in rural Pune, India.
- To develop and validate a machine learning model for predicting OTC antibiotic use.
- To inform public health strategies aimed at reducing inappropriate antibiotic consumption.
Main Methods:
- Machine learning algorithms including stepwise logistic regression, lasso, random forest, XGBoost, and Boruta were employed for feature selection.
- Regression and tree-based models were constructed using identified features to predict OTC antibiotic use.
- Model performance was evaluated using five-fold cross-validation, focusing on Area Under the Receiver Operating Characteristic Curve (AUROC) and log-loss.
Main Results:
- The prevalence of OTC antibiotic use in rural Pune was 35.9%.
- Significant predictors identified by the Boruta algorithm included the perception of direct pharmacy purchases being useful, antibiotic use for eye complaints, and increased household antibiotic consumption.
- The XGBoost+Boruta model, incorporating 7 predictors, achieved a high predictive accuracy (AUROC: 0.934).
Conclusions:
- The XGBoost+Boruta model demonstrated superior accuracy in predicting OTC antibiotic use in the study population.
- Perceived utility of direct pharmacy purchases, antibiotic use for eye conditions, and higher consumption levels are critical factors influencing OTC antibiotic use.
- These findings provide a basis for designing targeted interventions to promote responsible antibiotic stewardship in rural settings.
Related Concept Videos
01:18Steps in Outbreak Investigation
01:12Analysis of Population Pharmacokinetic Data
01:02Prescription, Nonprescription and Orphan Drugs
The misuse and addiction to prescription drugs is a growing problem that can affect people of all age groups, specifically teenagers. This can happen when prescription medications are used in ways not intended by the prescriber, such as taking someone else's prescription or using medication for...
01:19MALDI-TOF Mass Spectrometry
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
01:19Pharmacovigilance
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...