Evaluation of generalised anxiety disorder surveillance in Ho Municipality, Ghana, 2020– 2024

Introduction: Anxiety disorders affect over 300 million people globally, causing disability, lost productivity, and poor quality of life. In Ghana, little is known about how well existing surveillance systems capture these cases. Generalised Anxiety Disorder (GAD) is reported through the District Health Information Management System (DHIMS2), yet the surveillance system in Ho Municipality has never been evaluated. We aimed to evaluate the system to determine whether it meets its objectives and key attributes. Methods: A descriptive cross-sectional study was conducted using the updated CDC guidelines for surveillance system evaluation. A purposive sample of 21 mental health workers involved in the Generalised Anxiety Disorder (GAD) surveillance system was selected in 8 facilities visited. An interview guide was used to collect information, and a checklist was used to assess key attributes. GAD cases were defined per Diagnostic-Statistical-Manual (DSM-5) criteria reported in DHIMS2. The system was evaluated to determine whether its objectives were met. Data were summarised as frequencies and proportions. Results: Of the 8 health facilities visited, 75% (6/8) recorded and reported GAD cases to the next level. Only 14% (3/21) of staff routinely analysed data, and 25% (2/8) of facilities had a case definition available. The system was simple because staff found reporting straightforward, flexible due to the use of both paper-based tools and DHIMS2, and acceptable as staff valued participation. However, stability was affected by incomplete entries (40%) and DHIMS2 interruptions, which led to reporting delays. Females accounted for 53% of reported cases, mostly aged 15–70 years. Conclusion: The GAD surveillance system in Ho Municipality is functional and useful, but limited by poor data analysis, incomplete reporting, and absent case definitions in most facilities. Ensuring case definitions and strengthening routine data review will improve data quality and enhance the system’s capacity to guide mental health interventions.