Advanced

Predicting participation in the population-based Swedish cardiopulmonary bio-image study (SCAPIS) using register data

Björk, Jonas LU ; Strömberg, Ulf; Rosengren, Annika; Toren, Kjell; Fagerberg, Björn; Grimby-Ekman, Anna and Bergström, Göran M.L. (2017) In Scandinavian Journal of Public Health 45(17_suppl). p.45-49
Abstract

Aims: To illustrate the importance of access to register data on determinants and predictors of study participation to assess validity of population-based studies. In the present investigation, we use data on sociodemographic conditions and disease history among individuals invited to the Swedish cardiopulmonary bio-image study (SCAPIS) in order to establish a model that predicts study participation. Methods: The pilot study of SCAPIS was conducted within the city of Gothenburg, Sweden, in 2012, with 2243 invited individuals (50% participation rate). An anonymous data set for the total target population (n = 24,502) was made available by register authorities (Statistics Sweden and the National Board of Health and Welfare) and included... (More)

Aims: To illustrate the importance of access to register data on determinants and predictors of study participation to assess validity of population-based studies. In the present investigation, we use data on sociodemographic conditions and disease history among individuals invited to the Swedish cardiopulmonary bio-image study (SCAPIS) in order to establish a model that predicts study participation. Methods: The pilot study of SCAPIS was conducted within the city of Gothenburg, Sweden, in 2012, with 2243 invited individuals (50% participation rate). An anonymous data set for the total target population (n = 24,502) was made available by register authorities (Statistics Sweden and the National Board of Health and Welfare) and included indicators of invitation to and participation in SCAPIS along with register data on residential area, sociodemographic variables, and disease history. Propensity scores for participation were estimated using logistic regression. Results: Residential area, country of birth, civil status, education, occupational status, and disposable income were all associated with participation in multivariable models. Adding data on disease history only increased overall classification ability marginally. The associations with disease history were diverse with some disease groups negatively associated with participation whereas some others tended to increase participation. Conclusions: The present investigation stresses the importance of a careful consideration of selection effects in population-based studies. Access to detailed register data also for non-participants can in the statistical analysis be used to control for selection bias and enhance generalizability, thereby making the results more relevant for policy decisions.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bias correction, inverse probability weighting, population-based study, propensity score, register data, residential area, validity
in
Scandinavian Journal of Public Health
volume
45
issue
17_suppl
pages
5 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85022219986
  • wos:000405007800009
ISSN
1403-4948
DOI
10.1177/1403494817702326
language
English
LU publication?
yes
id
1fa57cf5-df23-423c-9b4d-61cda882124b
date added to LUP
2017-07-25 12:37:24
date last changed
2017-09-18 11:39:16
@article{1fa57cf5-df23-423c-9b4d-61cda882124b,
  abstract     = {<p>Aims: To illustrate the importance of access to register data on determinants and predictors of study participation to assess validity of population-based studies. In the present investigation, we use data on sociodemographic conditions and disease history among individuals invited to the Swedish cardiopulmonary bio-image study (SCAPIS) in order to establish a model that predicts study participation. Methods: The pilot study of SCAPIS was conducted within the city of Gothenburg, Sweden, in 2012, with 2243 invited individuals (50% participation rate). An anonymous data set for the total target population (n = 24,502) was made available by register authorities (Statistics Sweden and the National Board of Health and Welfare) and included indicators of invitation to and participation in SCAPIS along with register data on residential area, sociodemographic variables, and disease history. Propensity scores for participation were estimated using logistic regression. Results: Residential area, country of birth, civil status, education, occupational status, and disposable income were all associated with participation in multivariable models. Adding data on disease history only increased overall classification ability marginally. The associations with disease history were diverse with some disease groups negatively associated with participation whereas some others tended to increase participation. Conclusions: The present investigation stresses the importance of a careful consideration of selection effects in population-based studies. Access to detailed register data also for non-participants can in the statistical analysis be used to control for selection bias and enhance generalizability, thereby making the results more relevant for policy decisions.</p>},
  author       = {Björk, Jonas and Strömberg, Ulf and Rosengren, Annika and Toren, Kjell and Fagerberg, Björn and Grimby-Ekman, Anna and Bergström, Göran M.L.},
  issn         = {1403-4948},
  keyword      = {Bias correction,inverse probability weighting,population-based study,propensity score,register data,residential area,validity},
  language     = {eng},
  month        = {07},
  number       = {17_suppl},
  pages        = {45--49},
  publisher    = {Taylor & Francis},
  series       = {Scandinavian Journal of Public Health},
  title        = {Predicting participation in the population-based Swedish cardiopulmonary bio-image study (SCAPIS) using register data},
  url          = {http://dx.doi.org/10.1177/1403494817702326},
  volume       = {45},
  year         = {2017},
}