Optimizing Routine Malaria Surveillance Data in Urban Environments: A Case Study in Maputo City, Mozambique

Area of Science:

• * Public Health
• * Epidemiology
• * Urban Health

Background:

• * Malaria incidence in urban areas of endemic countries is often poorly understood, with an assumption of low, imported case numbers.
• * Effective malaria control requires surveillance systems with adequate spatial and temporal resolution for targeted interventions.

Purpose of the Study:

• * To assess spatial and temporal trends of malaria cases in Maputo City, Mozambique.
• * To optimize malaria surveillance for characterizing the local epidemiology and guiding localized responses.

Main Methods:

• * Utilized an expanded case notification form for confirmed malaria cases in KaMavota District, Maputo City (November 2019 - August 2021).
• * Administered questionnaires to cases and retrospectively geolocated households using local landmarks.
• * Analyzed 2,380 reported malaria cases and spatial patterns of 1,314 geolocated cases.

Main Results:

• * A total of 2,380 malaria cases were reported, predominantly uncomplicated (97.7%), with a median age of 21 years.
• * A significant proportion (70.8%) of cases reported recent travel outside the city, including international travel for nine individuals.
• * Geolocated cases displayed distinct spatial patterns, indicating potential areas of endemic transmission within the city.

Conclusions:

• * An expanded case notification form provides a detailed overview of urban malaria epidemiology in Maputo City.
• * Geolocation data are crucial for identifying malaria transmission hotspots and prioritizing resource allocation.
• * Optimizing routine data collection is essential for characterizing urban malaria and informing decision-making in rapidly urbanizing endemic areas.