Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

Abstract

Background: Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda.

Methods: This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs' catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence.

Results: Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4.

Conclusions: In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

Keywords: Covariates; DLNM; Effect; Environmental; Incidence; Malaria; Temporal.

Fig. 1

a Contour plots of the combined effect of time lags and Temperature on the incidence risk ratio of malaria. b Effect of specific Temperature and time lags on the incidence risk ratio of malaria. The blue lines are the mean relative risks, and the gray lines are 95% CI. c Effects of specific Temperature and time lags on the cumulative incidence risk ratio of malaria. The red lines are the mean incidence risk ratio, and the gray areas are 95% CI

Fig. 2

a Contour plots of the combined effect of time lags and rainfall amounts on the incidence risk ratio of malaria. b Effect of specific rainfall amounts and time lags on the incidence risk ratio of malaria. The blue lines are the mean incidence risk ratio, and the gray lines are 95% CI. c Effects of specific rainfall amounts and time lags on the cumulative incidence risk ratio of malaria. The red lines are the mean incidence risk ratio, and the gray areas are 95% CI

Fig. 3

a Contour plots of the combined effect of time lags and normalized vegetation index (NDVI) on the incidence risk ratio of malaria. b Effect of specific NDVI and time lags on the incidence risk ratio of malaria. The blue lines are the mean incidence risk ratio, and the gray lines are 95% CI. c Effects of specific NDVI and time lags on the cumulative incidence risk ratio of malaria. The red lines are the mean incidence risk ratio, and the gray areas are 95% CI