Differences of Rainfall–Malaria Associations in Lowland and Highland in Western Kenya

Naohiko Matsushita, Yoonhee Kim, Chris Fook Sheng Ng, Masao Moriyama, Tamotsu Igarashi, Kazuhide Yamamoto, Wellington Otieno, Noboru Minakawa and Masahiro Hashizume
Additional contact information
Naohiko Matsushita: Department of Paediatric Infectious Diseases, Institute of Tropical Medicine (NEKKEN), Nagasaki University. Nagasaki 852-8523, Japan
Yoonhee Kim: Department of Global Environmental Health, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
Chris Fook Sheng Ng: School of Tropical Medicine and Global Health (TMGH), Nagasaki University, Nagasaki 852-8523, Japan
Masao Moriyama: Division of Electrical Engineering and Computer Science, Graduate School of Engineering, Nagasaki University, Nagasaki 852-8521, Japan
Tamotsu Igarashi: Remote Sensing Technology Center of Japan (RESTEC), Tokyo 105-0001, Japan
Kazuhide Yamamoto: Japan Aerospace Exploration Agency (JAXA), Tokyo 101-8008, Japan
Wellington Otieno: Centre for Research and Technology Development Maseno University, Kisumu 40100, Kenya
Noboru Minakawa: Department of Vector Ecology and Environment, Institute of Tropical Medicine, Nagasaki University, Nagasaki 852-8523, Japan
Masahiro Hashizume: Department of Paediatric Infectious Diseases, Institute of Tropical Medicine (NEKKEN), Nagasaki University. Nagasaki 852-8523, Japan

IJERPH, 2019, vol. 16, issue 19, 1-13

Abstract: Many studies have reported a relationship between climate factors and malaria. However, results were inconsistent across the areas. We examined associations between climate factors and malaria in two geographically different areas: lowland (lakeside area) and highland in Western Kenya. Associations between climate factors (rainfall, land surface temperature (LST), and lake water level (LWL)) and monthly malaria cases from 2000 to 2013 in six hospitals (two in lowland and four in highland) were analyzed using time-series regression analysis with a distributed lag nonlinear model (DLNM) and multivariate meta-analysis. We found positive rainfall–malaria overall associations in lowland with a peak at 120 mm of monthly rainfall with a relative risk (RR) of 7.32 (95% CI: 2.74, 19.56) (reference 0 mm), whereas similar associations were not found in highland. Positive associations were observed at lags of 2 to 4 months at rainfall around 100–200 mm in both lowland and highland. The RRs at 150 mm rainfall were 1.42 (95% CI: 1.18, 1.71) in lowland and 1.20 (95% CI: 1.07, 1.33) in highland (at a lag of 3 months). LST and LWL did not show significant association with malaria. The results suggest that geographical characteristics can influence climate–malaria relationships.

Keywords: time-series analysis; distributed lag nonlinear model (DLNM), lagged effect; heterogeneity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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