The relationship between environmental covariates and malaria burden is complex, as the effect is not only determined in the current period but may also be influenced by preceding time points. This study investigated the quantitative relationship between environmental covariates and malaria incidence in high malaria transmission areas in Uganda. In these settings, temperature, rainfall and NDVI significantly affected the temporal distribution of malaria incidence. High (greater than the observed median) temperature values increased the IRR of malaria significantly in month lag 4 and the cumulative IRR at month lags 1-4 as compared to the median observed temperature. Similarly, high rainfall increased the IRR of malaria significantly at the month lag 0 and the cumulative IRR at month lags 1-4 as compared to the median observed rainfall. High values of NDVI increased the IRR of malaria at month lag 2 and the cumulative IRR significantly at month lags 2-4 as compared to the median observed NDVI.
Malaria control remains a priority in the national health agenda, requiring planning and efficient allocation of the limited resources available [31]. Efficient allocation of resources relies not only current measures of malaria burden but also predicting future malaria burden. Surveillance data has been used to monitor trends in malaria burden and visualization of prior seasonal peaks in different transmission settings. The addition of place of residence as part of routine surveillance data collection tool has enabled estimation of health facility catchment areas and generation of malaria incidence estimates to derive a direct measure of disease burden. Combining health facility surveillance data with environmental covariates such as rainfall, temperature and vegetation coverage available through remote-sensing sources may benefit malaria control efforts, as environmental covariates are reported to facilitate malaria transmission[32].
The relationship between environmental covariates and malaria incidence may form a strong basis for malaria early warning systems, as such prediction tools may guide planning and control of malaria outbreaks. For instance rainfall and sea surface temperature have been used for monitoring malaria early warnings in Botswana with the success of the malaria control program in reducing malaria incidence attributed to the early warnings [26]. Similarly in South Africa, prediction of malaria based on the seasonal climate forecasts showed that short-term predictions coincided closely with the observed malaria cases, which may also benefit the malaria early warning system [33]. In this study, high temperature increased the IRR of malaria at month lag 4. Knowing temperature as a key parameter in mosquito development, biting and survival with warmer temperatures increasing the infection rates as the vector reproduces faster, the likelihood of infection after a mosquito bite is amplified [34]. Even if the specific effect of temperature on the IRR of malaria increased in month lag 2, the cumulative IRR increased significantly at month lags 1-4. The increased cumulative IRR could possibly be explained by the increased multiplication rate presented by global warming increasing the length of mosquito breeding season [34]. The month lagged effects of temperature would avail time long enough to design interventions to interrupt malaria transmission, despite temperature values used in the current study being high as compared to the optimal temperature for malaria transmission of 29oC [35]. However, this finding was consistent with previous studies which have demonstrated how temporal disease risk shifts in response to temperature changes and increase in maximum temperature increases the incidence rate of malaria significantly of the current month and later [36–38].
The current study also found high values of rainfall to significantly increase the IRR of malaria at month lag 0 in these settings. Comparable to the specific rainfall effect, the cumulative IRR of malaria was increased significantly at month lag 1-4 at approximately 200 mm. Rainfall provides avenues that facilitate mosquito breeding suggesting that these areas retain water after rains presenting suitable places for mosquito fertilization and increasing the risk of malaria infections and transmission. Although not all mosquitoes need stagnant water, they require at least some form of water to hatch eggs increasing the risk in preceding time points. The preceding time points’ malaria IRR is increased by the transcended adult mosquitoes. This finding was consistent with earlier studies. For instance a study conducted in Kenya showed positive associations between rainfall and malaria burden at lags of 2 to 4 months at rainfall approximately 100–200 mm in both lowland and highland [39].
This study also found a significant increased cumulative IRR of malaria at month lags 2-4 for high values of NDVI approximately 0.72 indicating dense vegetation. Vegetation around household residences may serve as refuge for outdoor resting of mosquitoes[40]. Conversely, sparse vegetation may limit the biting rates reducing the likelihood of malaria transmission. Deforestation which is an indicator of low vegetation cover has been shown to reduce malaria transmission significantly[41]. This study was implemented in Amazon basin which is well known to be drained by the Amazon River and its tributaries. The possible explanation is that in the current month, deforestation reduces the outdoor resting places for mosquitoes driving the mosquitoes away reducing the risk of malaria burden. Contrary with a study conducted in Kenya that have demonstrated the associations between NDVI values of 0.35 and malaria burden, the current study used 0.24 which are all in the same range of 0.2-0.5 and did not realize any specific effect significant association at any month lags [42,43]. The possible explanation could be the difference in the transmission intensities between the current study and the former study in Kenya. The current study only considered high transmission settings while the former study compared lowlands and highlands. Ofnote highlands are prone to low mosquito population as the conditions are not friendly resulting to low infection rates.
In the present study, despite the increasing cumulative IRR for high values of environmental covariates in month lags 0-4, the rate of increase in the cumulative IRR was more in month lags 1-2 as compared to 3-4. For instance the cumulative IRR more than doubled in month lags 1-3 as compared to month lags 3-4 at temperature approximately 35oC, more than doubled in month lags 0-2 as compared to 2-4 at rainfall approximately 200 mm, and more than doubled in month lags 0-2 as compared to 2-4 at NDVI of approximately 0.72. This may be well explained by the saturation effect, as when environmental conditions are sufficient for mosquito cycle completion, an additional value of the covariates may have little impact on the development of mosquito or parasite. This seems to suggest that interventions may be more effective if implemented in the earliest time as much as possible in order the interrupt mosquito cycle supporting the current World Health Organization recommendations on early accurate diagnosis and treatment of malaria[44]. The current study had practical implications as the advance warnings of approaching situations advantageous to malaria epidemics will afford national malaria control programmes the freedom needed to stock commodities required to deal with impending surges or epidemics.
This study has several limitations. First, as this study was a population level study which involved environmental covariates and malaria, it is possible that some confounders may not have been considered which may have influenced the results such as socio-economic and community practices[45,46]. Second, the data available was limited to a 24-month period, as data from previous years was only health facility cases of malaria rather than incidence as catchment areas were not available. This limited the ability to control for long-term trends. Such long-term trends in rainfall have been shown to influence malaria burden[47]. Third, this study was unable to encompass the entirety of environmental covariates, for instance because altitude did not vary over time, it was not considered as a covariate in this analysis. However, adding a health facility random variable in the model catered for the variability that was site-specific. Fourth, the study was conducted around health facilities whose data is prone to missingness may have influenced the result. Health facilities with less than 5 percent missing data on the village of residence for each month were included. Finally, the current study explored the associations between environmental covariates with malaria incidence in high transmission settings, and these findings may only be applicable and generalizable to these settings.