Building pharmacovigilance-ready datasets from resource-limited settings: Study of antiretroviral-associated ADRs in Melanesian patients

¹ Faculty of Pharmacy, Universitas Muhammadiyah Surakarta, Jalan Ahmad Yani, Sukoharjo, 57169, Central Java, Indonesia
² Faculty of Medicine, Universitas Muhammadiyah Surakarta (UMS), Jalan Ahmad Yani, Sukoharjo, 57169, Central Java, Indonesia

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Adverse drug reactions (ADRs) remain a major challenge in antiretroviral therapy (ART), particularly in underrepresented populations where real-world safety data are limited. This study analyzed the type, severity, and management of ADRs associated with first-line ART among Melanesian people living with HIV/AIDS as a structured foundation for future data-driven pharmacovigilance. A cross-sectional observational study was conducted among adult patients attending the Voluntary Counseling and Testing clinic at Central Papua Regional Hospital, Indonesia. Sociodemographic, clinical, behavioral, and ART-related variables were systematically linked with ADR characteristics, including onset timing, severity grading, and management approaches. Of 143 patients, 142 (99.3%) experienced at least one ADR, generating 645 ADR events. Dizziness (35.2%), nausea (19.1%), and drowsiness (9.0%) were the most frequently reported reactions, with most events classified as mild to moderate. Hepatotoxicity represented the most clinically significant higher-grade ADR. Bivariate analysis showed that shorter ART duration (1–6 months) was significantly associated with increased odds of moderate–severe ADRs (OR 2.63; p = 0.027). The majority of ADRs were managed through over- the-counter self-medication (67.8%), while prescription-based interventions accounted for 30.0% of cases. This study highlights how systematically structured real-world safety data from underrepresented populations can support scalable, data-oriented pharmacovigilance frameworks and inform future AI- and machine learning–based drug safety surveillance.