1 Department of Mathematics and Statistics, University of Jyvaskyla, Finland.
2 Department of Public Health Solutions, National Institute for Health and Welfare, Finland.
Scand J Public Health. 2018 Nov;46(7):758-766. doi: 10.1177/1403494817734774. Epub 2017 Oct 26.
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.
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 hospitalisation 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.
When adjusting for non-participation using re-contact data, higher prevalence estimates were observed compared to prevalence estimates based on participants only. Among men, the smoking prevalence estimate was 28.5% (23.2% for participants) and heavy alcohol consumption prevalence was 9.4% (6.8% for participants). Among women, smoking prevalence was 19% (16.5% for participants) and heavy alcohol consumption was 4.8% (3% for participants).
The utilisation of re-contact data is a useful method to adjust for non-participation bias on population estimates in epidemiological surveys.
流行病学调查的一个共同目标是提供人群健康指标的估计值。在选择性不参与的情况下，调查结果往往存在偏差。减少偏差的一种方法是通过再次联系非参与者并要求他们填写问卷来收集有关非参与者的信息。这些信息称为再联系数据，并允许对不参与进行调整。
我们分析了来自 FINRISK 2012 调查的再联系数据。我们假设，再联系调查的受访者在给定其可用背景信息的情况下，与剩余的非参与者在健康方面相似。根据通过将调查数据与行政登记册链接获得的住院数据，评估此假设的有效性。使用此假设和多重插补，我们估计了每日吸烟和大量饮酒的患病率，并将其与仅使用参与者的常见假设获得的估计值进行了比较。
当使用再联系数据调整不参与时，与仅基于参与者的估计值相比，观察到更高的患病率估计值。在男性中，吸烟患病率估计为 28.5%（参与者为 23.2%），大量饮酒的患病率为 9.4%（参与者为 6.8%）。在女性中，吸烟的患病率为 19%（参与者为 16.5%），大量饮酒的患病率为 4.8%（参与者为 3%）。
利用再联系数据是调整流行病学调查中人群估计值的非参与偏差的有用方法。
