Azongnibo K. R. M., Guindo-Coulibaly N., Bonnet Emmanuel, Kokro-Djahouri M. N. W., Assouho K. F., Niamke M. G., Fournet Florence, Anoh P. K., Assi S. B., Adja A. M. (2023). Spatiotemporal analysis of malaria incidence in Cote d'Ivoire from 2015 to 2019. Transactions of the Royal Society of Tropical Medicine and Hygiene, [Early access], p. [9 p.]. ISSN 0035-9203.
Titre du document
Spatiotemporal analysis of malaria incidence in Cote d'Ivoire from 2015 to 2019
Azongnibo K. R. M., Guindo-Coulibaly N., Bonnet Emmanuel, Kokro-Djahouri M. N. W., Assouho K. F., Niamke M. G., Fournet Florence, Anoh P. K., Assi S. B., Adja A. M.
Source
Transactions of the Royal Society of Tropical Medicine and Hygiene, 2023,
[Early access], p. [9 p.] ISSN 0035-9203
Background The collection of malaria cases over time allows the identification of areas with the highest incidence. Our objective was to characterize the spatial distribution of malaria in Cote d'Ivoire from 2015 to 2019 at the health district level. Methods Data on the number of reported malaria cases confirmed by rapid diagnostic test (RDT) in the general population, the number of patients attending medical consultations and the total population by health district and year were collected from the National Malaria Control Program in Cote d'Ivoire. Crude and adjusted incidence rates were estimated for each health district and year. Adjusted incidence rates were used to perform global (Moran's index) and local indicators of spatial autocorrelation (LISA) analyses. Results Between 2015 and 2019, mean crude incidence rates increased from 155.5 parts per thousand to 229.8 parts per thousand. We observed significant heterogeneity in malaria incidence rates across the study period and within a given year. The overall Moran index showed spatial autocorrelation for every year analysed except 2017. The LISA analysis showed that the health districts with high incidence rates were concentrated in the western zone of Cote d'Ivoire. Conclusions The use of spatial analyses to identify the areas with the highest malaria incidence rates is a relevant approach to optimize control measures in targeted areas.
