Evidence map›Paper›PMID 26416106›Full record

ArticleThe American journal of tropical medicine and hygiene2015

Predictive Malaria Risk and Uncertainty Mapping in Nchelenge District, Zambia: Evidence of Widespread, Persistent Risk and Implications for Targeted Interventions.

Jessie Pinchoff, Mike Chaponda, Timothy Shields, James Lupiya, Tamaki Kobayashi, Modest Mulenga, William J Moss, Frank C Curriero, Southern Africa International Centers of Excellence for Malaria Research

Abstract read
In one paragraph

Article in The American journal of tropical medicine and hygiene, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Malaria in Refugee Children Resettled to a Holoendemic Area of Sub-Saharan Africa.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023
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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

9 authors.

Jessie PinchoffDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia jpincho1@jhu.edu.
Mike ChapondaDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
Timothy ShieldsDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
James LupiyaDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
Tamaki KobayashiDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
Modest MulengaDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
William J MossDepartment of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia.
Frank C Curriero
Southern Africa International Centers of Excellence for Malaria Research

Funding

TransmissionU19AI089680 · NIAID · JOHNS HOPKINS UNIVERSITY · PI WILLIAM J MOSS · 2010 to 2026
$25.6M
NIAID NIH HHS U19 AI089680
6 · The paper itself

Abstract

Malaria risk maps may be used to guide policy decisions on whether vector control interventions should be targeted and, if so, where. Active surveillance for malaria was conducted through household surveys in Nchelenge District, Zambia from April 2012 through December 2014. Households were enumerated based on satellite imagery and randomly selected for study enrollment. At each visit, participants were administered a questionnaire and a malaria rapid diagnostic test (RDT). Logistic regression models were used to construct spatial prediction risk maps and maps of risk uncertainty. A total of 461 households were visited, comprising 1,725 participants, of whom 48% were RDT positive. Several environmental features were associated with increased household malaria risk in a multivariable logistic regression model adjusting for seasonal variation. The model was validated using both internal and external evaluation measures to generate and assess root mean square error, as well as sensitivity and specificity for predicted risk. The final, validated model was used to predict and map malaria risk including a measure of risk uncertainty. Malaria risk in a high, perennial transmission setting is widespread but heterogeneous at a local scale, with seasonal variation. Targeting malaria control interventions may not be appropriate in this epidemiological setting.

Indexed as

AdolescentCross-Sectional StudiesEnvironmentFamily CharacteristicsFemaleHumansLogistic ModelsMalariaMaleMonte Carlo MethodRisk AssessmentRisk FactorsSatellite ImagerySeasonsSurveys and QuestionnairesUncertainty

Identifiers

PMID26416106
PMCPMC4674244

What OpenQuestion holds

Textmetadata
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.