A biomarker feasibility study in the South East Asia Community Observatory health and demographic surveillance system.

Abstract

Background: Integration of biomarker data with information on health and lifestyle provides a powerful tool to enhance the scientific value of health research. Existing health and demographic surveillance systems (HDSSs) present an opportunity to create novel biodata resources for this purpose, but data and biological sample collection often presents challenges. We outline some of the challenges in developing these resources and present the outcomes of a biomarker feasibility study embedded within the South East Asia Community Observatory (SEACO) HDSS.

Methods: We assessed study-related records to determine the pace of data collection, response from potential participants, and feedback following data and sample collection. Overall and stratified measures of data and sample availability were summarised. Crude prevalence of key risk factors was examined.

Results: Approximately half (49.5%) of invited individuals consented to participate in this study, for a final sample size of 203 (161 adults and 42 children). Women were more likely to consent to participate compared with men, whereas children, young adults and individuals of Malay ethnicity were less likely to consent compared with older individuals or those of any other ethnicity. At least one biological sample (blood from all participants - finger-prick and venous [for serum, plasma and whole blood samples], hair or urine for adults only) was successfully collected from all participants, with blood test data available from over 90% of individuals. Among adults, urine samples were most commonly collected (97.5%), followed by any blood samples (91.9%) and hair samples (83.2%). Cardiometabolic risk factor burden was high (prevalence of elevated HbA1c among adults: 23.8%; of elevated triglycerides among adults: 38.1%; of elevated total cholesterol among children: 19.5%).

Conclusions: In this study, we show that it is feasible to create biodata resources using existing HDSS frameworks, and identify a potentially high burden of cardiometabolic risk factors that requires further evaluation in this population.