Evidence map›Paper›PMID 31575076›Full record

SynthesisInternational journal of environmental research and public health2019

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, Masahiro Hashizume

Abstract readMeta-Analysis
In one paragraph

Synthesis in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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  17. Long-Term PMInternational journal of environmental research and public health · 2022
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  20. Characterizing the Spatial Determinants and Prevention of Malaria in Kenya.International journal of environmental research and public health · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Naohiko MatsushitaDepartment of Paediatric Infectious Diseases, Institute of Tropical Medicine (NEKKEN), Nagasaki University. Nagasaki 852-8523, Japan. naomatsu2@yahoo.co.jp.
Yoonhee KimDepartment of Global Environmental Health, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan. yoonheekim@m.u-tokyo.ac.jp.
Chris Fook Sheng NgSchool of Tropical Medicine and Global Health (TMGH), Nagasaki University, Nagasaki 852-8523, Japan. chrisng@nagasaki-u.ac.jp.
Masao MoriyamaDivision of Electrical Engineering and Computer Science, Graduate School of Engineering, Nagasaki University, Nagasaki 852-8521, Japan. matsu@cis.nagasaki-u.ac.jp.
Tamotsu IgarashiRemote Sensing Technology Center of Japan (RESTEC), Tokyo 105-0001, Japan. igarashi_tamotsu@restec.or.jp.
Kazuhide YamamotoJapan Aerospace Exploration Agency (JAXA), Tokyo 101-8008, Japan. yamamoto.kazuhide@jaxa.jp.
Wellington OtienoCentre for Research and Technology Development Maseno University, Kisumu 40100, Kenya. restechmaseno@yahoo.com.
Noboru MinakawaDepartment of Vector Ecology and Environment, Institute of Tropical Medicine, Nagasaki University, Nagasaki 852-8523, Japan. minakawa@nagasaki-u.ac.jp.
Masahiro HashizumeDepartment of Paediatric Infectious Diseases, Institute of Tropical Medicine (NEKKEN), Nagasaki University. Nagasaki 852-8523, Japan. hashizum@nagasaki-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

RainClimateEcosystemHumansKenyaMalariaRisk FactorsTemperaturedistributed lag nonlinear model (DLNM), lagged effectheterogeneitytime-series analysis

Identifiers

PMID31575076
PMCPMC6801446

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.