Patterns of antibiotic use in the community and challenges of antibiotic surveillance in a lower-middle-income country setting: a repeated cross-sectional study in Vellore, South India

Abstract

Objectives: There is considerable evidence linking antibiotic usage to bacterial resistance. Intervention strategies are needed to contain antibiotic use and thereby resistance. To plan appropriate strategies, it is imperative to undertake surveillance in the community to monitor antibiotic encounters and drivers of specific antibiotic misuse. Such surveillance is rarely in place in lower-middle-income countries (LMICs). This study describes antibiotic patterns and challenges faced while developing such surveillance systems in an LMIC.

Patients and methods: Surveillance of antibiotic encounters (prescriptions and dispensations) was carried out using a repeated cross-sectional design for 2 years in Vellore, south India. Every month, patients attending 30 health facilities (small hospitals, general practitioner clinics and pharmacy shops) were observed until 30 antibiotic encounters were attained in each. Antibiotic use was expressed as the percentage of encounters containing specific antibiotics and defined daily doses (DDDs)/100 patients. Bulk antibiotic sales data were also collected.

Results: Over 2 years, a total of 52,788 patients were observed and 21,600 antibiotic encounters (40.9%) were accrued. Fluoroquinolones and penicillins were widely used. Rural hospitals used co-trimoxazole more often and urban private hospitals used cephalosporins more often; 41.1% of antibiotic prescriptions were for respiratory infections. The main challenges in surveillance included issues regarding sampling, data collection, denominator calculation and sustainability.

Conclusions: Patterns of antibiotic use varied across health facilities, suggesting that interventions should involve all types of health facilities. Although challenges were encountered, our study shows that it is possible to develop surveillance systems in LMICs and the data generated may be used to plan feasible interventions, assess impact and thereby contain resistance.