RESUMO
Background: The use of a Geographic Information System in identifying meningitis hotspots in the Upper West Region (UWR) remains underutilized, making spatial targeting of meningitis hotspots difficult. We therefore utilized surveillance data enabled with GIS technology to target meningitis outbreaks in the UWR. Methods: Secondary data analysis was conducted in the study. The dynamics of bacterial meningitis in space and time were studied using epidemiological data from 2018 to 2020. Spot map and choropleths were used to depict the distribution of cases in the region. Moran's I statistics were used to assess spatial autocorrelation. Getis-Ord Gi*(d) and Anselin Local Moran's statistics were used to identify hotspots and spatial outliers within the study area. A Geographic Weighted Regression model was also used to examine how socio bio-climatic conditions influence the spread of meningitis. Results: There were 1176 cases of bacterial meningitis, 118 deaths, and 1058 survivors between 2018 and 2020. Nandom municipality had the highest Attack Rate (AR) at 492/100,000 persons, followed by Nadowli-Kaleo district at 314/100,000 persons. Jirapa had the highest case fatality rate (CFR) at 17%. The spatio-temporal analysis showed spatial diffusion of meningitis prevalence from the western half of the UWR to the east with a significant number of hotpots and cluster outliers. Conclusion: Bacterial meningitis does not occur at random. Populations (10.9%) under sub-districts identified as hotspots are exceptionally at higher risk of outbreaks. Targeted interventions should be directed towards clustered hotspots, focusing on zones with low prevalence fenced off by high prevalence zones.
RESUMO
Ghana Health Service (GHS) has strengthened community-based surveillance (CBS) to facilitate early detection and rapid reporting of health events of all origins. Since June 2017, GHS has employed an event-based surveillance approach at the community level in a phased manner. CBS coverage has broadened from 2 to 30 districts across Ghana. Through this effort, capacity was built across all administrative levels in these districts to detect, report, triage, and verify signals, and to perform risk assessment and investigate events. Data were collected and analyzed during an evaluation of initial 2-district implementation in March 2018 and during expanded 30-district implementation in March 2019. Between September 2018 and March 2019, 317 health events were detected through CBS. These events included vaccine-preventable disease cases, acute hemorrhagic conjunctivitis outbreaks, clusters of unexpected animal deaths, and foodborne illness clusters. Eighty-nine percent of the 317 events were reported to district-level public health staff within 24 hours of detection at the community level, and 87% of all detected events were responded to within 48 hours of detection. CBS detected 26% of all suspected vaccine-preventable disease cases that were reported from implementing districts through routine disease surveillance. GHS strengthened CBS in Ghana to function as an early warning system for health events of all origins, advancing the Global Health Security Agenda.