Evidence map›Paper›PMID 31778846›Full record

ArticleHealth & place2019

Extending multilevel spatial models to include spatially varying coefficients.

Mark Janko, Varun Goel, Michael Emch

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health & place, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. Article
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

3 authors.

Mark JankoInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Duke Global Health Institute, Duke University, Durham, NC, USA. Electronic address: mjanko@uw.edu.
Varun GoelDepartment of Geography, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA; Carolina Population Center, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
Michael EmchDepartment of Geography, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA; Carolina Population Center, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.

Funding

Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
NICHD NIH HHS P2C HD050924
6 · The paper itself

Abstract

Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo.

Indexed as

Models, StatisticalSpatial AnalysisBayes TheoremEpidemiologic MethodsGeography, MedicalHumansBayesian statisticsDisease ecologyHealth/medical geographyMultilevel modelsSpatially-varying coefficients

Identifiers

PMID31778846
PMCPMC6903407

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.