Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey
Kopra, J., Härkänen, T., Tolonen, H., Jousilahti, P., Kuulasmaa, K., Reinikainen, J., & Karvanen, J. (2018). Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey. Scandinavian Journal of Public Health, 46(7), 758-766. https://doi.org/10.1177/1403494817734774
Published inScandinavian Journal of Public Health
© Author(s) 2017.
Aims: A common objective of epidemiological surveys is to provide population-level estimates of health indicators. Survey results tend to be biased under selective non-participation. One approach to bias reduction is to collect information about non-participants by contacting them again and asking them to fill in a questionnaire. This information is called recontact data, and it allows to adjust the estimates for non-participation. Methods: We analyse data from the FINRISK 2012 survey, where re-contact data were collected. We assume that the respondents of the recontact survey are similar to the remaining non-participants with respect to the health given their available background information. Validity of this assumption is evaluated based on the hospitalization data obtained through record linkage of survey data to the administrative registers. Using this assumption and multiple imputation, we estimate the prevalences of daily smoking and heavy alcohol consumption and compare them to es1 timates obtained with a commonly used assumption that the participants represent the entire target group. Results: This approach produces higher prevalence estimates than what is estimated from participants only. Among men, smoking prevalence estimate was 28.5% (23.2% for participants), heavy alcohol consumption prevalence was 9.4% (6.8% for participants). Among women, smoking prevalence was 19.0% (16.5% for participants) and heavy alcohol consumption 4.8% (3.0% for participants). Conclusion: Utilization of re-contact data is a useful method to adjust for non-participation bias on population estimates in epidemiological surveys. ...
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