Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey

TL;DR

This study demonstrates that using re-contact data in epidemiological surveys like FINRISK 2012 can effectively adjust for non-participation bias, leading to more accurate population health estimates.

Contribution

The paper introduces a method utilizing re-contact data and multiple imputation to correct for non-participation bias in health surveys.

Findings

Re-contact data increases prevalence estimates compared to participant-only data.

Adjusted smoking and alcohol consumption prevalences are higher among non-participants.

The approach improves the accuracy of population health indicators.

Abstract

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 re-contact 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 re-contact 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 estimates 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.