SPATIAL ANALYSIS OF UNDER-FIVE MORTALITY IN KENYA

  • P. Kimani Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • A. Koech Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • S. Koinange Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • C. Mugo Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Keywords: cold spots, hotspots, relative risk, under-five mortality

Abstract

The Sustainable Development Goals (SDGs) target 3.2 aims to have under-five mortality of at least 25 deaths per 1000 live births by the year 2030. Kenya is still far from reaching the SDG 3.2 target with under-five mortality reported in 2014 of 52 deaths per 1000 live births. This study aimed to identify the hotspots and cold spots of under-five mortality at the constituency level and develop a risk map using data from Kenya Demographic and Health Surveys (KDHS) conducted in 2014. A Besag, York, and Mollie (BYM) model was used to model the under-five mortality relative risk. Local Moran’s I and Ord-Getis Gi statistics were used to identify constituencies that were hot spots and cold spots of underfive mortality. Based on Ord-Getis Gi statistic 29 constituencies mostly from Migori, Tana River, Meru, and Garissa Counties were hotspots whereas, 27 constituencies mostly from Kiambu and Nairobi counties were cold spots. Constituencies that had high children under-five years deaths also had a high under-five relative risk. By providing an insight into the hotspots, cold spots and the relative risk of under-five mortality in Kenya at the constituency level; this study provided information that is helpful in policy formulation of policies and programs aiming at reducing the under-five mortality in Kenya and to achieve the SDG target 3.2.

Published
2019-07-08