Mapping malaria incidence using routine health facility surveillance data in Uganda

Bibliographic Data

ID21881003
AuthorsA Epstein (0000-0002-8253-6102, Liverpool School of Tropical Medicine, corresponding author), Jane Frances Namuganga (0000-0001-8452-4247, Infectious Diseases Research Collaboration), Isaiah Nabende (Infectious Diseases Research Collaboration), Emmanuel Victor Kamya (Infectious Diseases Research Collaboration), Moses R Kamya (0000-0001-9106-4234, Infectious Diseases Research Collaboration), Grant Dorsey (0000-0003-0740-0317, University of California, San Francisco), Hugh Sturrock (University of California, San Francisco), Hugh J W Sturrock (0000-0002-0029-3101, University of California, San Francisco), Samir Bhatt (0000-0002-0891-4611, University of Copenhagen), Isabel Rodriguez-Barraquer (0000-0001-6784-1021, University of California, San Francisco), Bryan Greenhouse (0000-0003-0287-9111, University of California, San Francisco)
Year2023
Volume8
Issue5
Pagese011137
Publication date2023-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBMJ Global Health (JOURNAL)
Journal identifiersISSN: 2059-7908 • E-ISSN: 2059-7908
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/bmjgh-2022-011137
PMID37208120
OpenAlexW4377095812
LanguageEN
References cited31

INTRODUCTION: Maps of malaria risk are important tools for allocating resources and tracking progress. Most maps rely on cross-sectional surveys of parasite prevalence, but health facilities represent an underused and powerful data source. We aimed to model and map malaria incidence using health facility data in Uganda. METHODS: Using 24 months (2019-2020) of individual-level outpatient data collected from 74 surveillance health facilities located in 41 districts across Uganda (n=445 648 laboratory-confirmed cases), we estimated monthly malaria incidence for parishes within facility catchment areas (n=310) by estimating care-seeking population denominators. We fit spatio-temporal models to the incidence estimates to predict incidence rates for the rest of Uganda, informed by environmental, sociodemographic and intervention variables. We mapped estimated malaria incidence and its uncertainty at the parish level and compared estimates to other metrics of malaria. To quantify the impact that indoor residual spraying (IRS) may have had, we modelled counterfactual scenarios of malaria incidence in the absence of IRS. RESULTS: Over 4567 parish-months, malaria incidence averaged 705 cases per 1000 person-years. Maps indicated high burden in the north and northeast of Uganda, with lower incidence in the districts receiving IRS. District-level estimates of cases correlated with cases reported by the Ministry of Health (Spearman's r=0.68, p<0.0001), but were considerably higher (40 166 418 cases estimated compared with 27 707 794 cases reported), indicating the potential for underreporting by the routine surveillance system. Modelling of counterfactual scenarios suggest that approximately 6.2 million cases were averted due to IRS across the study period in the 14 districts receiving IRS (estimated population 8 381 223). CONCLUSION: Outpatient information routinely collected by health systems can be a valuable source of data for mapping malaria burden. National Malaria Control Programmes may consider investing in robust surveillance systems within public health facilities as a low-cost, high benefit tool to identify vulnerable regions and track the impact of interventions

Environmental health · Health facility · Health services · Malaria · Medical emergency · Public health · Malaria Research and Control · Medicine · Mosquito-borne diseases and control · Nursing · Parasitic Diseases Research and Treatment · Immunology