Bridging the gap in epilepsy surveillance: Evaluation of the epilepsy surveillance system in Ho Municipality, Ghana

Introduction: Epilepsy poses a public health challenge in Ghana, affecting an estimated 1% of the population. Although it is widespread, there is a lack of comprehensive surveillance data. This results in inconsistent implementation and information sharing, ultimately undermining effective planning and intervention efforts. This study evaluated the epilepsy surveillance and reporting system in Ho Municipality, Volta Region, to assess its usefulness, characteristics, and role in filling data gaps. Methods: A cross-sectional assessment was carried out from March to April 2025, following the CDC’s 2001 Updated Guidelines for Evaluating Public Health Surveillance Systems, adapted for the local setting. Data collection involved reviewing DHIMS2 records from 2020 to 2024, examining health facility registers, and conducting stakeholder interviews at eight facilities. Quantitative indicators were rated on a 3-point scale (1 = poor, 2 = fair, 3 = good) across attributes such as simplicity, flexibility, representativeness, stability, timeliness, and data quality. These scores were converted into percentages and categorised as poor (<60%), average (60–80%), or good (>80%). Data analysis was primarily descriptive, with results presented as frequencies and alongside thematic summaries of qualitative findings. Results: A total of 965 epilepsy cases were reported from 2020 to 2024; 196 (20.3%) received treatment, 7 (0.7%) were referred, and none resulted in death. Outpatient visits constituted 724 (75.0%) of the cases, with follow-up achieved in 772 (80.0%) of the cases. Case definition use was consistent in 2/8 (25%) facilities, while 7/8 (87.5%) submitted reports on time. Overall, simplicity (80%), flexibility (78%), and acceptability (82%) were rated as good; stability (65%) and data quality (68%) were rated as average. Conclusion: The Ho Municipal epilepsy surveillance system showed moderate effectiveness, characterised by ease of use, acceptability, and promptness. However, data quality and case definition for diagnosis need strengthening to improve stability, representativeness, and overall system performance.