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Identifying hotspots of malaria incidence and mortality for tailored interventions in Cameroon using routine data from 2011 to 2021: A Bayesian space-time variability modeling

par Arnold, Fottsoh Fokam;Kirakoya, Fati ;Marcelin, Ateba;Jean, Fosso;Ye, Yazoume;Samadoulougou, Sekou
Référence Spatial and spatio-temporal epidemiology, 54, 100733
Publication Publié, 2025-08-01

Article révisé par les pairs

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Résumé :

Malaria remains a health challenge globally, particularly in Cameroon. Despite the emphasis of recent studies to provide estimates of spatio-temporal variations in malaria in the country, these studies have overlooked an important aspect of disease surveillance, which is the monitoring and assessing of spatio-temporal risk in hotspots. This study addresses this gap by examining the ex-ante dynamics of malaria risk clustering. It identifies health districts in Cameroon with a high potential to be hotspots based on uncomplicated incidence, severe incidence, and deaths. The authors used the malaria routine data from 189 contiguous health districts in Cameroon from January 2011 - December 2021 to achieve this. By fitting a Bayesian spatiotemporal model, the classification of the spatial trend into hotspots, coldspots, and neutral-spots, and the classification of the differential time trends into increasing, decreasing, and stable trends were defined to assess the ex-ante risk of hotspot. As findings, 42.9 % (81), 44.9 % (85) and 28.6 % (54) districts show a decreasing trend for the uncomplicated, severe, and malaria deaths, respectively. However, 44.9 % (85), 37.6 % (71), and 32.8 % (62) districts show an increasing trend for the uncomplicated, severe, and malaria deaths, respectively, including 24.3 % (46), 23.3 % (44), and 21.7 % (41) identified with a high likelihood of becoming hotspots for uncomplicated, severe, and malaria deaths, respectively. This trend suggests that these neutral-spot and coldspots health districts, especially those with increasing trends, are at risk of becoming hotspots. Although aggregated health facility data may not accurately reflect individual-level risk and could be influenced by varying surveillance and case management practices, integrating a Bayesian space-time variability modeling into Cameroon's malaria surveillance system can help pinpoint ex-ante hotspots and facilitate a proactive and targeted interventions.