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

Abstract

Abstract Background Ghana is among the top 10 highest malaria burden countries, with about 20000 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 data in 2016–2021 from the routine health service nationally representative and comprehensive District Health Information Management System II (DHIMS2) implemented by the Ghana Health Service. Bayesian geospatial modeling 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. Factors associated with malaria risk are the log number of children (log-mean − 0.99, 95% credible interval = -1.06 – -0.92) and the log number of males (log-mean 0.21, 95% credible interval = 0.18–0.23). The study found substantial spatial and temporal differences in malaria risk across the 260 districts. The predicted national relative risk was 1.23 (SE: 0.0084) with a range of 0.0012 to 4.8291. Using the 2021 data, 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. It provides an effective, actionable tool to arm policymakers and program managers in their efforts to reduce malaria risk and its associated morbidity and mortality in line with the Sustainable Development Goals (SDG) 3.2 in a setting with limited public health resources, where universal intervention across all districts is practically impossible.