# Weighting strategy and selection analysis in the panel ‘Health in Germany‘: methods and results for the 2024 annual survey

## TL;DR

This paper analyzes how sociodemographic factors affect participation in a German health panel and how weighting reduces bias in the 2024 survey.

## Contribution

The study introduces a weighting strategy to mitigate drop-out bias in a newly established health panel in Germany.

## Key findings

- Sociodemographic factors like age, education, and German citizenship strongly influence panel registration and participation.
- Weighting reduced most deviations in sample composition to below 0.5% points, except for German citizenship.
- Health-related factors had a smaller impact on drop-out compared to sociodemographic variables.

## Abstract

The panel `Health in Germany` has been established to gather nationwide health-related information, replacing cross-sectional surveys as primary data sources. However, panel designs involve multiple selection stages, potentially introducing additional nonresponse bias. This study aims to describe this drop-out bias and the weighting strategy used to improve representativeness.

Panelists were recruited through a recruitment study. At completion of the recruitment questionnaire, participants were invited to register for the panel. Registered panelists were subsequently invited to the first annual survey in 2024, which was divided into three sub-waves. Each included one of four questionnaires with different topics. Logistic regression models are used to estimate the probability for panel registration and participation in panel 2024 questionnaires, using sociodemographic and health-related variables from the recruitment study to illustrate drop-out bias. The weighting scheme and techniques are described. To assess the potential for drop-out bias and how it is reduced through weighting, unweighted and weighted estimates are compared to internal and external reference distributions using standardized differences.

Drop-out analysis showed that sociodemographic characteristics (age, education, German citizenship) had a stronger association with panel registration than health-related parameters (e.g., self-rated health, smoking status, sport activity, chronic diseases) among the participants of the recruitment study. Similar patterns as for registration were found for participation in the 2024 questionnaires, except for age, which showed a reverse effect. No differences were observed across questionnaire types. Standardized differences confirmed the findings: sociodemographic characteristics—particularly education and German citizenship—showed larger deviations than health-related parameters. The largest deviations occurred in the recruitment study. The weighting procedure reduced most standardized differences to below 0.5% points. An exception is German citizenship, which showed only slight improvement.

Drop-out within the first year of a newly established panel is mainly affected by sociodemographic variables, with minor effects due to health-related parameters. The additional recruitment steps did not lead to concerning deviations in sample composition compared to the recruitment study. Remaining differences were addressed through drop-out and calibration weighting, so the weighted panel 2024 sample does not substantially differ from what would be expected in a cross-sectional design such as the recruitment study. However, continued analyses are needed, as sample composition may change due to future panel attrition.

The online version contains supplementary material available at 10.1186/s12874-025-02740-w.