Key result
Routine hypertension data integration enables tracking but faces significant under-reporting and recording errors.
Why the study?
HMIS in LMICs have generally not been adapted to systematically integrate quality data on NCDs despite a growing burden of cardiovascular diseases.
Integrating hypertension data into routine health information systems like DHIS2 is a critical step for improving cardiovascular population health management in low and middle income countries.
Functional Health Information Systems are critical to provide decision-makers with reliable and quality data for real-time adaptation of the health system. The District Health Information System (DHIS) 2, the world’s largest Health Information Management System (HMIS), facilitates routine health data collection and reporting in many countries globally through its flexible digital platform. In a majority of low and middle income countries (LMICs), there is usually a good availability of indicators on communicable diseases, and maternal and child health. However, although LMICs now face a rapid epidemiological transition with the growing burden of cardiovascular diseases, their HMIS have generally not been adapted to systematically integrate quality data on non-communicable diseases (NCDs) which is essential to quality of care and to assess health systems performance along the chronic disease continuum of care. Here we describe the steps that Senegal took to develop and implement a systematic data collection system for hypertension data using DHIS2. We draw important lessons on opportunities and challenges for integration of NCD data into HMIS and discuss future perspectives for the continued improvement and sustainability of cardiovascular population health data management.
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Sécula et al. (2025) studied Hypertension. Integration of hypertension data into DHIS2 was evaluated. The integration of hypertension data into the routine health data collection system (DHIS2) in Dakar, Senegal, facilitated systematic data tracking but faced significant data quality challenges including under-reporting and recording errors.
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