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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

Bonander, Carl ; Nilsson, Anton LU ; Björk, Jonas LU ; Blomberg, Anders ; Engström, Gunnar LU ; Jernberg, Tomas ; Sundström, Johan ; Östgren, Carl Johan LU ; Bergström, Göran and Strömberg, Ulf (2022) In PLoS ONE 17(3).
Abstract

Objectives To 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). Methods We 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... (More)

Objectives To 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). Methods We 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. Results Combining 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. Conclusions Both 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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
17
issue
3
article number
e0265088
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:35259202
  • scopus:85126077455
ISSN
1932-6203
DOI
10.1371/journal.pone.0265088
language
English
LU publication?
yes
id
17b96f9a-e4b5-4977-a4b0-f03263638cc0
date added to LUP
2022-04-27 08:23:02
date last changed
2024-03-23 20:08:48
@article{17b96f9a-e4b5-4977-a4b0-f03263638cc0,
  abstract     = {{<p>Objectives To 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). Methods We 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 &gt;0.10 were considered meaningful. Results Combining 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. Conclusions Both 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.</p>}},
  author       = {{Bonander, Carl and Nilsson, Anton and Björk, Jonas and Blomberg, Anders and Engström, Gunnar and Jernberg, Tomas and Sundström, Johan and Östgren, Carl Johan and Bergström, Göran and Strömberg, Ulf}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{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}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0265088}},
  doi          = {{10.1371/journal.pone.0265088}},
  volume       = {{17}},
  year         = {{2022}},
}