Evidence map›Paper›PMID 26415959›Full record

ArticleMalaria journal2015

Associations between urbanicity and malaria at local scales in Uganda.

Simon P Kigozi, Deepa K Pindolia, David L Smith, Emmanuel Arinaitwe, Agaba Katureebe, Maxwell Kilama, Joaniter Nankabirwa, Steve W Lindsay, Sarah G Staedke, Grant Dorsey and 2 more

Abstract read
In one paragraph

Article in Malaria journal, 2015. 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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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

12 authors.

Simon P KigoziInfectious Diseases Research Collaboration, Kampala, Uganda. skigozi@yahoo.com.
Deepa K PindoliaClinton Health Access Initiative, Boston, USA. dpindolia@gmail.com.
David L SmithFogarty International Center, National Institutes of Health, Bethesda, USA. david.smith@zoo.ox.ac.uk.
Emmanuel ArinaitweInfectious Diseases Research Collaboration, Kampala, Uganda. emmy3md@yahoo.com.
Agaba KatureebeInfectious Diseases Research Collaboration, Kampala, Uganda. agabaik@gmail.com.
Maxwell KilamaInfectious Diseases Research Collaboration, Kampala, Uganda. kilamam@gmail.com.
Joaniter NankabirwaInfectious Diseases Research Collaboration, Kampala, Uganda. jnankabirwa@yahoo.co.uk.
Steve W LindsaySchool of Biological and Biomedical Sciences, Durham University, Durham, UK. s.w.lindsay@durham.ac.uk.
Sarah G StaedkeLondon School of Hygiene and Tropical Medicine, London, UK. sarah.staedke@lshtm.ac.uk.
Grant DorseyDepartment of Medicine, San Francisco General Hospital, University of California, San Francisco, CA, USA. gdorsey@medsfgh.ucsf.edu.
Moses R KamyaInfectious Diseases Research Collaboration, Kampala, Uganda. mkamya@infocom.co.ug.
Andrew J TatemFogarty International Center, National Institutes of Health, Bethesda, USA. A.J.Tatem@soton.ac.uk.

Funding

Transmission ProjectU19AI089674 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MATTHEW G DORSEY · 2010 to 2026
$29.3M
NIAID NIH HHS U19 AI089674NIAID NIH HHS U19AI089674
6 · The paper itself

Abstract

backgroundSub-Saharan Africa is expected to show the greatest rates of urbanization over the next 50 years. Urbanization has shown a substantial impact in reducing malaria transmission due to multiple factors, including unfavourable habitats for Anopheles mosquitoes, generally healthier human populations, better access to healthcare, and higher housing standards. Statistical relationships have been explored at global and local scales, but generally only examining the effects of urbanization on single malaria metrics. In this study, associations between multiple measures of urbanization and a variety of malaria metrics were estimated at local scales.

methodsCohorts of children and adults from 100 households across each of three contrasting sub-counties of Uganda (Walukuba, Nagongera and Kihihi) were followed for 24 months. Measures of urbanicity included density of surrounding households, vegetation index, satellite-derived night-time lights, land cover, and a composite urbanicity score. Malaria metrics included the household density of mosquitoes (number of female Anopheles mosquitoes captured), parasite prevalence and malaria incidence. Associations between measures of urbanicity and malaria metrics were made using negative binomial and logistic regression models.

resultsOne site (Walukuba) had significantly higher urbanicity measures compared to the two rural sites. In Walukuba, all individual measures of higher urbanicity were significantly associated with a lower household density of mosquitoes. The higher composite urbanicity score in Walukuba was also associated with a lower household density of mosquitoes (incidence rate ratio = 0.28, 95 % CI 0.17-0.48, p < 0.001) and a lower parasite prevalence (odds ratio, OR = 0.44, CI 0.20-0.97, p = 0.04). In one rural site (Kihihi), only a higher density of surrounding households was associated with a lower parasite prevalence (OR = 0.15, CI 0.07-0.34, p < 0.001). And, in only one rural site (Nagongera) was living where NDVI ≤0.45 associated with higher incidence of malaria (IRR = 1.35, CI 1.35-1.70, p = 0.01).

conclusionsUrbanicity has been shown previously to lead to a reduction in malaria transmission at large spatial scales. At finer scales, individual household measures of higher urbanicity were associated with lower mosquito densities and parasite prevalence only in the site that was generally characterized as being urban. The approaches outlined here can help better characterize urbanicity at the household level and improve targeting of control interventions.

Indexed as

Geographic Information SystemsAdultAnimalsAnophelesChildChild, PreschoolCohort StudiesFemaleHumansIncidenceInfantMalaria, FalciparumPlasmodium falciparumResidence CharacteristicsUgandaUrban Population

Identifiers

PMID26415959
PMCPMC4587721

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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.