Evidence map›Paper›PMID 39181610›Full record

ArticleSpatial and spatio-temporal epidemiology2024

Bayesian group testing regression models for spatial data.

Rongjie Huang, Alexander C McLain, Brian H Herrin, Melissa Nolan, Bo Cai, Stella Self

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Spatial and spatio-temporal epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

6 authors.

Rongjie HuangDepartment of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, 29208, SC, USA.
Alexander C McLainDepartment of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, 29208, SC, USA.
Brian H HerrinCollege of Veterinary Medicine, Kansas State University, 1700 Denison Ave, Manhattan, 66502, KS, USA.
Melissa NolanDepartment of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, 29208, SC, USA.
Bo CaiDepartment of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, 29208, SC, USA.
Stella SelfDepartment of Epidemiology and Biostatistics, University of South Carolina, 915 Greene Street, Columbia, 29208, SC, USA. Electronic address: scwatson@mailbox.sc.edu.

Funding

Targeting Behavioral Adjustment and Healthy Lifestyle in Preschool-Age Children Using an Integrated Family-Based InterventionP20GM130420 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI PRINZ, RONALD J · 2020 to 2024
$11.2M
Developing New Statistical Methods for Vector-Borne Disease Surveillance to Improve Accuracy while Reducing CostR01AI179840 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI Stella Coker Watson Self · 2023 to 2026
$3.0M
NIAID NIH HHS R01 AI179840NIGMS NIH HHS P20 GM130420
6 · The paper itself

Abstract

Spatial patterns are common in infectious disease epidemiology. Disease mapping is essential to infectious disease surveillance. Under a group testing protocol, biomaterial from multiple individuals is physically combined into a pooled specimen, which is then tested for infection. If the pool tests negative, all contributing individuals are generally assumed to be uninfected. If the pool tests positive, the individuals are usually retested to determine who is infected. When the prevalence of infection is low, group testing provides significant cost savings over traditional individual testing by reducing the number of tests required. However, the lack of statistical methods capable of producing maps from group testing data has limited the use of group testing in disease mapping. We develop a Bayesian methodology that can simultaneously map disease prevalence using group testing data and identify risk factors for infection. We illustrate its real-world utility using two datasets from vector-borne disease surveillance.

Indexed as

Bayes TheoremCommunicable DiseasesHumansModels, StatisticalPrevalenceRisk FactorsSpatial AnalysisConditional autoregressive priorGaussian predictive processGeneralized linear mixed effects spatial regressionGroup testing

Identifiers

PMID39181610
PMCPMC11347770

What OpenQuestion holds

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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.