Evidence mapPaperPMID 35259202Full record

ArticlePloS one2022

The value of combining individual and small area sociodemographic data for assessing and handling selective participation in cohort studies: Evidence from the Swedish CardioPulmonary bioImage Study.

Carl Bonander, Anton Nilsson, Jonas Björk, Anders Blomberg, Gunnar Engström, Tomas Jernberg, Johan Sundström, Carl Johan Östgren, Göran Bergström, Ulf Strömberg

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.4field-weighted citation impact, top 8% of its field
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

21 citing papers in PubMed, 24 citations in OpenAlex.

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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

10 authors at 9 institutions in 2 countries.

Carl BonanderSchool of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0002-1189-9950
Anton NilssonEpidemiology, Population Studies and Infrastructures (EPI@LUND), Lund University, Lund, Sweden.ORCID 0000-0001-5774-7189
Jonas BjörkEpidemiology, Population Studies and Infrastructures (EPI@LUND), Lund University, Lund, Sweden.
Anders BlombergDepartment of Public Health and Clinical Medicine, Section of Medicine, Umeå University, Umeå, Sweden.
Gunnar EngströmDepartment of Clinical Sciences, Lund University, Malmö, Sweden.
Tomas JernbergDepartment of Clinical Sciences, Danderyd University Hospital, Karolinska Institutet, Stockholm, Sweden.
Johan SundströmDepartment of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden.
Carl Johan ÖstgrenDepartment of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Göran BergströmDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Ulf StrömbergDepartment of Research and Development, Region Halland, Halmstad, Sweden.
Lund University · SEHallands sjukhus Halmstad · SEKarolinska University Hospital · SELinköping University · SESahlgrenska University Hospital · SEStatistics Sweden · SEUmeå University · SEUniversity of Gothenburg · SEUppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo study the value of combining individual- and neighborhood-level sociodemographic data to predict study participation and assess the effects of baseline selection on the distribution of metabolic risk factors and lifestyle factors in the Swedish CardioPulmonary bioImage Study (SCAPIS).

methodsWe linked sociodemographic register data to SCAPIS participants (n = 30,154, ages: 50-64 years) and a random sample of the study's target population (n = 59,909). We assessed the classification ability of participation models based on individual-level data, neighborhood-level data, and combinations of both. Standardized mean differences (SMD) were used to examine how reweighting the sample to match the population affected the averages of 32 cardiopulmonary risk factors at baseline. Absolute SMDs >0.10 were considered meaningful.

resultsCombining both individual-level and neighborhood-level data gave rise to a model with better classification ability (AUC: 71.3%) than models with only individual-level (AUC: 66.9%) or neighborhood-level data (AUC: 65.5%). We observed a greater change in the distribution of risk factors when we reweighted the participants using both individual and area data. The only meaningful change was related to the (self-reported) frequency of alcohol consumption, which appears to be higher in the SCAPIS sample than in the population. The remaining risk factors did not change meaningfully.

conclusionsBoth individual- and neighborhood-level characteristics are informative in assessing study selection effects. Future analyses of cardiopulmonary outcomes in the SCAPIS cohort can benefit from our study, though the average impact of selection on risk factor distributions at baseline appears small.

Indexed as

Alcohol DrinkingCohort StudiesHumansMiddle AgedRisk FactorsSelf ReportSweden

Identifiers

PMID35259202
PMCPMC8903292
OpenAlexW4220689175

What Socratic holds

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LicenceCC BY
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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 Socratic graph.