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. 2020 Aug 10;15(8):e0237349.
doi: 10.1371/journal.pone.0237349. eCollection 2020.

Non-response in a national health survey in Germany: An intersectionality-informed multilevel analysis of individual heterogeneity and discriminatory accuracy

Philipp Jaehn  1 , Emily Mena  2   3 , Sibille Merz  1 , Robert Hoffmann  4 , Antje Gößwald  4 , Alexander Rommel  4 , Christine Holmberg  1   5 ; ADVANCE GENDER study group
Affiliations

Non-response in a national health survey in Germany: An intersectionality-informed multilevel analysis of individual heterogeneity and discriminatory accuracy

Philipp Jaehn et al. PLoS One. .

Abstract

Background: Dimensions of social location such as socioeconomic position or sex/gender are often associated with low response rates in epidemiological studies. We applied an intersectionality-informed approach to analyze non-response among population strata defined by combinations of multiple dimensions of social location and subjective health in a health survey in Germany.

Methods: We used data from the cross-sectional sample of the German Health Interview and Examination Survey for Adults (DEGS1) conducted between 2008 and 2011. Information about non-responders was available from a mailed non-responder questionnaire. Intersectional strata were constructed by combining all categories of age, sex/gender, marital status, and level of education in scenario 1. Subjective health was additionally used to construct intersectional strata in scenario 2. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to calculate measures of discriminatory accuracy, proportions of non-responders among intersectional strata, as well as stratum-specific total interaction effects (intersectional effects). Markov chain Monte Carlo methods were used to estimate multilevel logistic regression models.

Results: Data was available for 6,534 individuals of whom 36% were non-responders. In scenario 2, we found weak discriminatory accuracy (variance partition coefficient = 3.6%) of intersectional strata, while predicted proportions of non-response ranged from 20.6% (95% credible interval (CI) 17.0%-24.9%) to 57.5% (95% CI 48.8%-66.5%) among intersectional strata. No evidence for intersectional effects was found. These results did not differ substantially between scenarios 1 and 2.

Conclusions: MAIHDA revealed that proportions of non-response varied widely between intersectional strata. However, poor discriminatory accuracy of intersectional strata and no evidence for intersectional effects indicate that there is no justification to exclusively target specific intersectional strata in order to increase response, but that a combination of targeted and population-based measures might be appropriate to achieve more equal representation.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study population flowchart.
Fig 2
Fig 2. Predicted proportions of non-responders for each stratum from scenario 1.
Point estimates are proportions and 95% credible intervals.
Fig 3
Fig 3. Predicted intersectional effects for each stratum from scenario 1.
Point estimates are proportions and 95% credible intervals. Intersectional strata are ranked by the size of the predicted intersectional effect.
Fig 4
Fig 4. Predicted proportions of non-responders for each stratum from scenario 2.
Point estimates are proportions and 95% credible intervals. marr.: married, not m.: not married, high ed.: high education, low ed.: low education, bad health: moderate or bad health.
Fig 5
Fig 5. Predicted intersectional effects for each stratum from scenario 2.
Point estimates are proportions and 95% credible intervals. Intersectional strata are ranked by the size of the predicted intersectional effect.
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