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

Journal article
Authors J. Bjork
Ulf Strömberg
Annika Rosengren
K. Toren
Björn Fagerberg
Anna Grimby-Ekman
Göran Bergström
Published in Scandinavian Journal of Public Health
Volume 45
Pages 45-49
ISSN 1403-4948
Publication year 2017
Published at Institute of Medicine
Institute of Medicine, Department of Molecular and Clinical Medicine
Institute of Medicine, Department of Public Health and Community Medicine, Health Metrics
Pages 45-49
Language en
Links 10.1177/1403494817702326
Keywords Bias correction, inverse probability weighting, population-based study, propensity score, register, score
Subject categories Public Health, Global Health, Social Medicine and Epidemiology, Family Medicine

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 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.

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