Using electronic health record data for chronic disease surveillance in low- and middle-income countries: the example of hypertension in rural Guatemala

Hypertension is the leading preventable cause of death worldwide. Two-thirds of people with hypertension live in Low- and Middle-Income Countries (LMIC). However, epidemiological data necessary to address the growing burden of hypertension and other Non-Communicable Diseases (NCDs) in LMICs are severely lacking. Electronic Health Records (EHRs) are an emerging source of epidemiological data for LMICs, but have been underutilized for NCD monitoring. The objective of this study was to estimate the prevalence of hypertension in a rural Indigenous community in Guatemala using EHR data, describe hypertension risk factors and current treatment in this population, and demonstrate the feasibility of using EHR data for epidemiological surveillance of NCDs in LMIC. We conducted a cross-sectional analysis of 3646 adult clinic visits. We calculated hypertension prevalence using physician diagnosis, antihypertensive treatment, or Blood Pressure (BP) ≥140/90 mmHg. We noted antihypertensives prescribed and BP control (defined as BP<140/90 mmHg) for a total of 2496 unique patients (21% of whom were men). We constructed mixed-effects models to investigate the relationship between BP and hypertension risk factors. The estimated hypertension prevalence was 16.7%. Two-thirds of these patients had elevated BP, but were not diagnosed with or treated for hypertension. Most patients receiving treatment were prescribed monotherapy and only 31.0% of those with recognized hypertension had controlled BP. Male sex, older age, increasing weight, and history of hypertension were associated with increasing systolic BP, while history of hypertension, history of diabetes, and increasing weight were associated with increasing diastolic BP. Using EHR data, we estimated comparable hypertension prevalence and similar risk factor associations to prior studies conducted in Guatemala, which used traditional epidemiological methods. Hypertension was underrecognized and undertreated in our study population, and our study was more efficient than traditional methods and provided additional data on treatment and outcomes; insights gleaned from this analysis were essential in developing a sustainable intervention. Our experience demonstrates the feasibility and advantages of using EHR-derived data for NCD surveillance and program planning in LMICs.