Evidence map›Paper›PMID 40538103›Full record

ArticleStatistical methods in medical research2025

Spatiotemporal effects on dengue incidence based on a large cluster randomized study.

Jerome Johnson, Xiangyu Yu, Suzanne M Dufault, Nicholas P Jewell

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Jerome JohnsonInstitute of Clinical Trials and Methodology, MRC Clinical Trials Unit at University College London, London, UK.ORCID 0000-0001-8104-0650
Xiangyu YuDivision of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
Suzanne M DufaultDivision of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.ORCID 0000-0002-6316-6548
Nicholas P JewellDepartment of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.

Funding

Emerging methods and applications for test-negative studies of of infectious disease interventionsR01AI148127 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI JEWELL, NICHOLAS P · 2020 to 2023
$1.8M
NIAID NIH HHS R01 AI148127
6 · The paper itself

Abstract

A recent large-scale cluster randomized test-negative study assessed the impact of a mosquito-based intervention on the incidence of clinical dengue showing a protective efficacy of 77.1% (95% CI: (65.3%, 84.9%)). While the intervention was randomized at a cluster-level, human and mosquito movement suggest potential violations in assumptions necessary for intention-to-treat analyses to produce accurate estimates of the full intervention effect due to spatial clustering of dengue cases, and/or potential non-independence in the intervention arising from spillover of the intervention (or control) across cluster boundaries. We address these distinct but related effects using two approaches. First, we examine whether a clustering effect exists, that is, whether the presence of a recent dengue case in the sample within a specified distance from a residence raises the risk of dengue. Second, we use cluster reallocation techniques to examine intervention spillover effects. We find strong spatial effects of the presence of dengue cases on the risk of clinical dengue that exhibit both serospecificity and a dose response, more evident in control than intervention clusters at least on an additive scale. Contrarily, there is no evidence of any appreciable local spillover effect from intervention to control clusters, or vice versa, in terms of either the risk of dengue infection or the level of disease clustering.

Indexed as

DengueSpatio-Temporal AnalysisAnimalsCluster AnalysisHumansIncidenceCluster-randomized trialcluster reallocationdenguedisease clusteringspatiotemporal point processspillovertest-negative studies

Identifiers

PMID40538103
PMCPMC12308035

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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.