Evidence map›Paper›PMID 38531921›Full record

ArticleScientific reports2024

Estimating the size of populations at risk for malaria: a case study in cattle herders and agricultural workers in Northern Namibia.

Francois Rerolle, Jerry O Jacobson, Cara Smith Gueye, Adam Bennett, Sidney Carrillo, Henry Ntuku, Jennifer L Smith

Abstract read
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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
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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

7 authors.

Francois RerolleMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA. rerollefrancois@gmail.com.
Jerry O JacobsonMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA.
Cara Smith GueyeMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA.
Adam BennettMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA.
Sidney CarrilloMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA.
Henry NtukuMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA.
Jennifer L SmithMalaria Elimination Initiative, The Global Health Group, University of California, San Francisco, CA, USA.

Funding

Genetic and social network analysis to target interventions for malaria eliminationK01AI153555 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SMITH, JENNIFER LINNEA · 2020 to 2024
$725k
NIAID NIH HHS K01 AI153555
6 · The paper itself

Abstract

Cattle herders and agricultural workers have been identified has key high-risk populations for malaria in northern Namibia. Population size estimates for these groups are lacking but are important for planning, monitoring and evaluating the effectiveness of targeted strategies towards malaria elimination in the region. In this analysis, we extend population size estimation methods routinely used in HIV research, specifically social mapping and multiple source capture-recapture, to the context of malaria to estimate how many cattle herders and agricultural workers lived in two regions of northern Namibia over the course of the 2019-2020 malaria season. Both methods estimated two to three times more agricultural workers than cattle herders but size estimates based on the multiple source capture-recapture method were two to three times greater than the mapping-based, highlighting important methodological considerations to apply such methods to these highly mobile populations. In particular, we compared open versus closed populations assumptions for the capture-recapture method and assessed the impact of sensitivity analyses on the procedure to link records across multiple data sources on population size estimates. Our results are important for national control programs to target their resources and consider integrating routine population size estimation of high risk populations in their surveillance activities.

Indexed as

FarmersMalariaAnimalsCattleHumansNamibiaPopulation DensityRisk Factors

Identifiers

PMID38531921
PMCPMC10965975

What OpenQuestion holds

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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.