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

Artikel i vetenskaplig tidskrift
Författare J. Bjork
Ulf Strömberg
Annika Rosengren
K. Toren
Björn Fagerberg
Anna Grimby-Ekman
Göran Bergström
Publicerad i Scandinavian Journal of Public Health
Volym 45
Sidor 45-49
ISSN 1403-4948
Publiceringsår 2017
Publicerad vid Institutionen för medicin
Institutionen för medicin, avdelningen för molekylär och klinisk medicin
Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa, enheten för hälsometri
Sidor 45-49
Språk en
Länkar 10.1177/1403494817702326
Ämnesord Bias correction, inverse probability weighting, population-based study, propensity score, register, score
Ämneskategorier Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Allmän medicin

Sammanfattning

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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