The Relationship Between Climate Change and Indonesia's Malaria Burden

Abstract

ABSTRACTMalaria has been with humans for thousands of years. It is caused by parasites of the genus Plasmodium that are transmitted by female Anopheles mosquitoes. The variation of global malaria distribution has recently been associated with changing climatic conditions, such as temperature, precipitation, windspeed, and humidity. One country where malaria transmission remains high in select subnational areas is Indonesia. Founded upon previous findings on the relationship between climate change and malaria, this research delves into the same equation for the case of Indonesia through a structural model which overcomes the variable co‐interaction between temperature, precipitation, windspeed, and humidity. This study follows an ecological study design with yearly longitudinal data (t = 20, n = 432). The method of analysis employed is a structural equation modelling approach for panel datasets with an output of factor loading values to determine association levels. The independent variable is a climate change construct of maximum, minimum, and average values from temperature, windspeed, relative humidity and precipitation as observables taken from the NASA Langley Research Center (LaRC) POWER Project. Meanwhile, the dependent variable is yearly malaria incidence rates at the city and regency level extracted from the Malaria Atlas Project dataset. All variables are standardized to account for unit differences. The SEM results indicate a standardized relationship between a latent climate variable with malaria incidence in a statistically significant manner. However, differences in coefficient directions between the three models indicate that the relationship remains elusive. In the maximum value model, a standard deviation increase in the climate change construct from its mean is associated with a 0.04 standard deviation increase in malaria incidence from its own mean (p < 0.001). On the other hand, in the minimum and average value models, a standard deviation increase in limate change construct from its mean is associated with a 0.12 and 0.09 standard deviation decrease of malaria incidence from its own mean respectively (p < 0.001). Although statistical significance was established across all models which indicated relatively good fit across select indices, the standardized coefficient values presented in this study suggest that any associations between long term climatic variations (measured by yearly data) and malarial incidences are modest at best. The results of the structural equation models also indicate that other factors are at play when it comes to malaria case variations—as explained by the residual terms across all models.