Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021.

Autor: Aheto JMK; Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana. justiceaheto@yahoo.com.; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK. justiceaheto@yahoo.com.; College of Public Health, University of South Florida, Tampa, USA. justiceaheto@yahoo.com.; The West Africa Mathematical Modeling Capacity Development (WAMCAD) Consortium, Accra, Ghana. justiceaheto@yahoo.com., Menezes LJ; Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA., Takramah W; Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra, Ghana.; The West Africa Mathematical Modeling Capacity Development (WAMCAD) Consortium, Accra, Ghana., Cui L; Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Jazyk: angličtina
Zdroj: Malaria journal [Malar J] 2024 Apr 09; Vol. 23 (1), pp. 102. Date of Electronic Publication: 2024 Apr 09.
DOI: 10.1186/s12936-024-04918-x
Abstrakt: Background: Ghana is among the top 10 highest malaria burden countries, with about 20,000 children dying annually, 25% of which were under five years. This study aimed to produce interactive web-based disease spatial maps and identify the high-burden malaria districts in Ghana.
Methods: The study used 2016-2021 data extracted from the routine health service nationally representative and comprehensive District Health Information Management System II (DHIMS2) implemented by the Ghana Health Service. Bayesian geospatial modelling and interactive web-based spatial disease mapping methods were employed to quantify spatial variations and clustering in malaria risk across 260 districts. For each district, the study simultaneously mapped the observed malaria counts, district name, standardized incidence rate, and predicted relative risk and their associated standard errors using interactive web-based visualization methods.
Results: A total of 32,659,240 malaria cases were reported among children < 5 years from 2016 to 2021. For every 10% increase in the number of children, malaria risk increased by 0.039 (log-mean 0.95, 95% credible interval = - 13.82-15.73) and for every 10% increase in the number of males, malaria risk decreased by 0.075, albeit not statistically significant (log-mean - 1.82, 95% credible interval = - 16.59-12.95). The study found substantial spatial and temporal differences in malaria risk across the 260 districts. The predicted national relative risk was 1.25 (95% credible interval = 1.23, 1.27). The malaria risk is relatively the same over the entire year. However, a slightly higher relative risk was recorded in 2019 while in 2021, residing in Keta, Abuakwa South, Jomoro, Ahafo Ano South East, Tain, Nanumba North, and Tatale Sanguli districts was associated with the highest malaria risk ranging from a relative risk of 3.00 to 4.83. The district-level spatial patterns of malaria risks changed over time.
Conclusion: This study identified high malaria risk districts in Ghana where urgent and targeted control efforts are required. Noticeable changes were also observed in malaria risk for certain districts over some periods in the study. The findings provide an effective, actionable tool to arm policymakers and programme managers in their efforts to reduce malaria risk and its associated morbidity and mortality in line with the Sustainable Development Goals (SDG) 3.2 for limited public health resource settings, where universal intervention across all districts is practically impossible.
(© 2024. The Author(s).)
Databáze: MEDLINE
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