Evaluation of the Hypertension Surveillance System at Pilot Hypertension Prevention and Control Health Facilities in Addis Ababa, Ethiopia, 2022.

IntroductionHypertension is a major cause of premature death worldwide. Evaluating a surveillance system promotes the best use of data collection resources and ensures that systems operate effectively. It allows us to determine whether a specific system is useful for a particular public health initiative and is achieving the goals of the public health program and the data collection objectives. Ethiopian hypertension control initiatives were recently launched (2019) and implemented in limited health facilities. Therefore, this evaluation is aimed at the hypertension surveillance system of Addis Ababa, Ethiopia. MethodA descriptive cross-sectional study was conducted from December 21 to 31, 2022. Eight health care facilities were evaluated based on updated CDC guidelines for evaluating public health surveillance systems, and one key informant from each facility was interviewed using semi structured questionnaire. The data were analyzed descriptively. The data were analyzed with Excel 2021 and summarized by the frequency mean, percentage and rate. ResultsThe system detected cases (8/8) and monitored the control rate, attendance rate and missed visits. All interviewed staff (8/8) stated that the case definition, diagnosis and treatment algorism of hypertension were easy. Regarding acceptability, all staff believed that stakeholders were engaged, and 7/8 of the facilities recorded and reported data in a timely manner, but there was no acknowledgment given to the staff (0/8). The stability and data quality were 95% and 72.9, respectively. The representativeness was 24% to 36% based on prevalence, and regarding sex, it ranged from 88% to 108%. predictive positive value (31.4%) and zero flexibility. ConclusionThe system was useful for detecting morbidities and retaining patients on treatment. It has good simplicity, acceptability and stability but requires improvement in its flexibility, representativeness, data quality and positive predictive value. The system is better able to scale up to other health facilities.