Waning success: a 2013-2022 spatial and temporal trend analysis of malaria in Ethiopia.

Abstract

Background: Despite consecutive decades of success in reducing malaria transmission, Ethiopia went off track towards its goal of malaria elimination by 2030, as outlined in the NMCP malaria strategy. Recent malaria outbreaks in Ethiopia are attributed to the emergence and spread of diagnostic and drug-resistant Plasmodium falciparum, increased insecticide resistance in major vectors and the spread of invasive Anopheles stephensi. The effects of the COVID-19 pandemic, environmental anomalies and internal conflicts have also potentially played a role in increasing malaria transmission. This study aimed to evaluate the contribution of environmental factors and An. stephensi to the spatiotemporal trends of recent malaria cases in Ethiopia.

Methods: Clinical malaria case data reported weekly between January 2013 and January 2023 were obtained from the Ethiopian Public Health Institute (EPHI), Addis Ababa. A negative binomial regression model was used to explain the variability and potential overdispersion in the weekly number of malaria cases reported across Ethiopian administrative zones. This model incorporated fixed effects for selected environmental factors and random effects to capture temporal trends, zone specific seasonal patterns, spatial trends at the zone level, and the presence of An. stephensi and its impact.

Results: Our negative binomial regression model highlighted 56% variability in the data and slightly more than half (55%) was due to environmental factors, while the remainder was captured by random effects. A significant nationwide decline in malaria risk was observed between 2013 and 2018, followed by a sharp increase in early 2022. Malaria risk was higher in western and northwestern zones of Ethiopia compared to other zones. Zone-specific seasonal patterns, not explained by environmental factors, were grouped into four clusters of seasonal behaviours. The presence of An. stephensi was not shown to have any significant impact on malaria risk.

Conclusions: Understanding the spatial and temporal drivers of malaria transmission and therefore identifying more appropriate malaria control strategies are key to the success of any malaria elimination and eradication programmes in Ethiopia. Our study found that approximately 50% of malaria risk variability could be explained by environmental, temporal, and spatial factors included in the analysis, while the remaining variation was unexplained and may stem from other factors not considered in this study. This highlights the need for a better understanding of underlying factors driving local malaria transmission and outbreaks, to better tailor regional programmatic responses.