Differential Influence of Environmental Factors on Malaria Due to Vector Control Interventions in Uganda

Abstract

Background: Few studies have explored how vector control interventions may modify associations between environmental factors and malaria. Methods: We used weekly malaria cases reported from six public health facilities in Uganda. Environmental variables (temperature, rainfall, humidity, and vegetation) were extracted from remote sensing sources. The non-linearity of environmental variables was investigated, and negative binomial regression models were used to explore the influence of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) on associations between environmental factors and malaria incident cases for each site as well as pooled across the facilities, with or without considering the interaction between environmental variables and vector control interventions. Results: An average of 73.3 weekly malaria cases per site (range: 0–597) occurred between 2010 and 2018. From the pooled model, malaria risk related to environmental variables was reduced by about 35% with LLINs and 63% with IRS. Significant interactions were observed between some environmental variables and vector control interventions. There was site-specific variability in the shape of the environment–malaria risk relationship and in the influence of interventions (6 to 72% reduction in cases with LLINs and 43 to 74% with IRS). Conclusion: The influence of vector control interventions on the malaria–environment relationship need to be considered at a local scale in order to efficiently guide control programs.

Predicting malaria risk considering vector control interventions under climate change scenarios

“…Eighth, although we used two contemporary greenhouse gas emissions scenarios (intermediate – RCP.5 and pessimistic – RCP8.5) and a set of 14 different climate projections to reduce the uncertainty inherent to each model, it is not guaranteed that the actual future scenario will be captured among the simulated conditions. Finally, the projections were established from a pooled model (combining data from six subregions of Uganda) which could therefore mask subregional disparities in the effects of interventions and environmental factors; however similar predictions were obtained with the pooled and the subregional-specific models in our previous study 32 . Nonetheless, establishing projections at the subregional level with higher resolution (ex., 0.11°) RCMs’ simulations available through the updated CORDEX CMIP6 project (with the more recent Shared Socioeconomic Pathways – SSP- scenarios used in the last AR6 IPCC report 33 ), would improve predictions and be relevant to better inform decision-makers on the future risk of malaria.…”

“…In this study, we used a statistical model developed previously 32 to estimate the number of malaria cases. Briefly, a generalized linear model based on a negative binomial distribution was used to estimate the association between malaria incident cases and environmental factors (rainfall, minimum and maximum temperature, specific humidity, and vegetation).…”