Association between climate variability and malaria epidemics in the East African highlands

TLDR

Recent reemergence of Plasmodium falciparum malaria in the East African highlands has been attributed to climate change, yet the influence of short‑term climate variability remains poorly understood because of high spatial and temporal variability and limited long‑term case data. This study aimed to quantify the association between autoregression, seasonality, and climate variability and monthly malaria outpatient numbers across seven highland sites. Using a nonlinear mixed‑regression model on 10–20 years of outpatient data from these sites, the authors examined how previous case counts, seasonal patterns, and climate fluctuations jointly predict current malaria incidence. The model explained 65–81% of the variance, revealed nonlinear synergistic effects of temperature and rainfall, attributed 12–63% of variance to climate variability, and demonstrated that climate fluctuations play a key role in triggering malaria epidemics with substantial spatial heterogeneity.

Abstract

The causes of the recent reemergence of Plasmodium falciparum epidemic malaria in the East African highlands are controversial. Regional climate changes have been invoked as a major factor; however, assessing the impact of climate in malaria resurgence is difficult due to high spatial and temporal climate variability and the lack of long-term data series on malaria cases from different sites. Climate variability, defined as short-term fluctuations around the mean climate state, may be epidemiologically more relevant than mean temperature change, but its effects on malaria epidemics have not been rigorously examined. Here we used nonlinear mixed-regression model to investigate the association between autoregression (number of malaria outpatients during the previous time period), seasonality and climate variability, and the number of monthly malaria outpatients of the past 10–20 years in seven highland sites in East Africa. The model explained 65–81% of the variance in the number of monthly malaria outpatients. Nonlinear and synergistic effects of temperature and rainfall on the number of malaria outpatients were found in all seven sites. The net variance in the number of monthly malaria outpatients caused by autoregression and seasonality varied among sites and ranged from 18 to 63% (mean = 38.6%), whereas 12–63% (mean = 36.1%) of variance is attributed to climate variability. Our results suggest that there was a high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in the highlands, and that climate variability played an important role in initiating malaria epidemics in the East African highlands.