The Association of Weather Variability and Under Five Malaria Mortality in KEMRI/CDC HDSS in Western Kenya 2003 to 2008: A Time Series Analysis

Maquins Sewe, Joacim Rocklöv, John Williamson, Mary Hamel, Amek Nyaguara, Frank Odhiambo and Kayla Laserson
Additional contact information
Maquins Sewe: KEMRI Centre for Global Health Research, Kisumu, Kenya, Box 1578, Kisumu 40100, Kenya
Joacim Rocklöv: Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, Umeå SE-901 85, Sweden
John Williamson: Centers for Disease Control and Prevention, Centre for Global Health, 600 Clifton Rd, Atlanta, GA 30333, USA
Mary Hamel: Centers for Disease Control and Prevention, Centre for Global Health, 600 Clifton Rd, Atlanta, GA 30333, USA
Amek Nyaguara: KEMRI Centre for Global Health Research, Kisumu, Kenya, Box 1578, Kisumu 40100, Kenya
Frank Odhiambo: KEMRI Centre for Global Health Research, Kisumu, Kenya, Box 1578, Kisumu 40100, Kenya
Kayla Laserson: Centers for Disease Control and Prevention, Centre for Global Health, 600 Clifton Rd, Atlanta, GA 30333, USA

IJERPH, 2015, vol. 12, issue 2, 1-15

Abstract: Malaria is among the leading causes of mortality in the younger under-five group of children zero to four years of age. This study aims at describing the relationship between rainfall and temperature on under-five malaria or anaemia mortality in Kenya Medical Research Institute and United States Centers for Disease Control (KEMRI/CDC) Health and Demographic Surveillance System (HDSS). This study was conducted through the ongoing KEMRI and CDC collaboration. A general additive model with a Poisson link function was fit to model the weekly association of lagged cumulative rainfall and average temperature on malaria/anemia mortality in KEMRI/CDC HDSS for the period 2003 to 2008. A trend function was included in the model to control for time trends and seasonality not explained by weather fluctuations. 95% confidence intervals was presented with estimates. Malaria or anemia mortality was found to be associated with changes in temperature and rainfall in the KEMRI HDSS, with a delay up to 16 weeks. The empirical estimates of associations describe established biological relationships well. This information, and particularly, the strength of the relationships over longer lead times can highlight the possibility of developing a predictive forecast with lead times up to 16 weeks in order to enhance preparedness to high transmission episodes.

Keywords: malaria mortality; KEMRI/CDC HDSS; general additive model; rainfall; temperature; lag; Kenya (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
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