Abstract

The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

Highlights

  • Introduction219 million incidences and 435,000 deaths worldwide in 2017 [1]

  • Malaria is one of the leading causes of morbidity and mortality in the world, with an estimated219 million incidences and 435,000 deaths worldwide in 2017 [1]

  • To estimate the spatial clustering of malaria incidence rate per 1000 from the Demographic and Health Surveys (DHS) covariate database across counties, Local indicators of spatial association (LISA) and Getis-Ord G were deployed to measure the extent of spatial autocorrelation among the neighboring counties

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Summary

Introduction

219 million incidences and 435,000 deaths worldwide in 2017 [1]. About 92% of malaria incidence in 2017 was in sub-Saharan Africa [1]. Children aged under five years are the most vulnerable group accounting for 61% of all malaria deaths worldwide, with the African region accounting for 93%. Of all malaria deaths in 2017 [1]. Malaria is caused by the parasite Plasmodium that is transmitted to human hosts through a vector, the infected female Anopheles mosquitoes [2]. The two species of Plasmodium—P. falciparum (Africa and SE Asia) and P. vivax (Americas) pose the greatest threat, while a diverse group of Anopheles (30 to 40 species) serves as vectors for this disease vector biology [2].

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