Evidence map›Paper›PMID 24330615›Full record

ArticleParasites & vectors2013

Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast.

Martin Walker, Peter Winskill, María-Gloria Basáñez, Joseph M Mwangangi, Charles Mbogo, John C Beier, Janet T Midega

Open access · goldAbstract read
In one paragraph

Article in Parasites & vectors, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

22 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
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  7. Seasonal dynamics, resting behaviour and insecticide susceptibility ofCurrent research in parasitology & vector-borne diseases · 2024
    Article
  8. Article
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  10. Frontiers in immunology · 2022
    Article
  11. Article
  12. Characterizing the Spatial Determinants and Prevention of Malaria in Kenya.International journal of environmental research and public health · 2019
    Article
  13. Article
  14. Article
  15. Article
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  20. Review
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

7 authors at 3 institutions in 3 countries.

Martin Walker
Peter Winskill
María-Gloria Basáñez
Joseph M Mwangangi
Charles Mbogo
John C Beier
Janet T Midega
Imperial College London · GBKenya Medical Research Institute · KEUniversity of Miami · US

Funding

Outdoor Resting and Sugar Feeding Behaviors of African Malaria VectorsR01AI100968 · NIAID · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI BEIER, JOHN C. · 2013 to 2017
$2.9M
Environmental Changes and Mosquito-Borne Diseases in Arid EnvironmentsR01GM093345 · NIGMS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI BEIER, JOHN C. · 2010 to 2013
$2.1M
Biotechnology and Biological Sciences Research Council BB/I015957/1NIAID NIH HHS R01AI100968NIGMS NIH HHS R01 GM093345NIGMS NIH HHS R01GM093345Wellcome Trust 092654
6 · The paper itself

Abstract

backgroundThe distribution of anopheline mosquitoes is determined by temporally dynamic environmental and human-associated variables, operating over a range of spatial scales. Macro-spatial short-term trends are driven predominantly by prior (lagged) seasonal changes in climate, which regulate the abundance of suitable aquatic larval habitats. Micro-spatial distribution is determined by the location of these habitats, proximity and abundance of available human bloodmeals and prevailing micro-climatic conditions. The challenge of analysing--in a single coherent statistical framework--the lagged and distributed effect of seasonal climate changes simultaneously with the effects of an underlying hierarchy of spatial factors has hitherto not been addressed.

methodsData on Anopheles gambiae sensu stricto and A. funestus collected from households in Kilifi district, Kenya, were analysed using polynomial distributed lag generalized linear mixed models (PDL GLMMs).

resultsAnopheline density was positively and significantly associated with amount of rainfall between 4 to 47 days, negatively and significantly associated with maximum daily temperature between 5 and 35 days, and positively and significantly associated with maximum daily temperature between 29 and 48 days in the past (depending on Anopheles species). Multiple-occupancy households harboured greater mosquito numbers than single-occupancy households. A significant degree of mosquito clustering within households was identified.

conclusionsThe PDL GLMMs developed here represent a generalizable framework for analysing hierarchically-structured data in combination with explanatory variables which elicit lagged effects. The framework is a valuable tool for facilitating detailed understanding of determinants of the spatio-temporal distribution of Anopheles. Such understanding facilitates delivery of targeted, cost-effective and, in certain circumstances, preventative antivectorial interventions against malaria.

Indexed as

AnimalsAnophelesHumansInsect VectorsKenyaMalariaPopulation DensityTime FactorsWeather

Identifiers

PMID24330615
PMCPMC3843567
OpenAlexW2133920690

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.