Non-response in a national health survey in Germany: An intersectionality-informed multilevel analysis of individual heterogeneity and discriminatory accuracy.
Adolescent
Adult
Aged
Cross-Sectional Studies
Educational Status
Female
Germany
Health Status
Health Surveys
/ statistics & numerical data
Humans
Male
Marital Status
/ statistics & numerical data
Middle Aged
Multilevel Analysis
Refusal to Participate
/ statistics & numerical data
Sex Factors
Young Adult
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2020
2020
Historique:
received:
20
04
2020
accepted:
23
07
2020
entrez:
11
8
2020
pubmed:
11
8
2020
medline:
8
10
2020
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 32776957
doi: 10.1371/journal.pone.0237349
pii: PONE-D-20-11368
pmc: PMC7416954
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0237349Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
J Adv Nurs. 1999 Jun;29(6):1520-6
pubmed: 10354249
Soc Sci Med. 2014 Jun;110:10-7
pubmed: 24704889
Eur J Epidemiol. 2005;20(6):489-96
pubmed: 16121757
Am J Epidemiol. 2001 Aug 15;154(4):373-84
pubmed: 11495861
Gesundheitswesen. 1999 Dec;61 Spec No:S57-61
pubmed: 10726397
Public Health Rev. 2016 Jul 30;37:4
pubmed: 29450046
Int J Epidemiol. 2013 Aug;42(4):1012-4
pubmed: 24062287
Health Promot J Austr. 2006 Dec;17(3):260-3
pubmed: 17176244
Ann Epidemiol. 2007 Sep;17(9):643-53
pubmed: 17553702
Soc Sci Med. 2017 Mar;177:213-222
pubmed: 28189024
Ann Epidemiol. 1996 Nov;6(6):498-506
pubmed: 8978880
Ann Epidemiol. 2003 Feb;13(2):105-10
pubmed: 12559669
Image J Nurs Sch. 1994 Fall;26(3):185-90
pubmed: 7989060
Soc Sci Med. 2018 Apr;203:64-73
pubmed: 29199054
SSM Popul Health. 2018 Mar 20;4:334-346
pubmed: 29854918
Soc Sci Med. 2019 Jan;221:95-105
pubmed: 30578943
Health Place. 2019 Nov;60:102214
pubmed: 31563833
BMC Public Health. 2013 Apr 09;13:320
pubmed: 23570559
Gesundheitswesen. 1998 Dec;60 Suppl 2:S59-68
pubmed: 10063725
PLoS One. 2018 Dec 10;13(12):e0208624
pubmed: 30532244
Soc Sci Med. 2019 Apr;226:260-262
pubmed: 30914246
Arthritis Care Res (Hoboken). 2019 Nov 15;:
pubmed: 31733042
BMC Med Res Methodol. 2011 May 23;11:77
pubmed: 21605357
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2013 May;56(5-6):620-30
pubmed: 23703478
Gesundheitswesen. 2004 May;66(5):326-36
pubmed: 15141353
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2013 May;56(5-6):611-9
pubmed: 23703477
PLoS One. 2019 Aug 27;14(8):e0220322
pubmed: 31454361
Soc Sci Med. 2020 Jan;245:112500
pubmed: 31492490
BMC Public Health. 2012 Sep 01;12:730
pubmed: 22938722
Eur J Epidemiol. 2019 Mar;34(3):301-317
pubmed: 30830562
Soc Sci Med. 2012 Jun;74(11):1712-20
pubmed: 22361090
Women Health. 2018 Apr;58(4):365-386
pubmed: 28332953
Int J Surg. 2014 Dec;12(12):1500-24
pubmed: 25046751
Psychol Aging. 2001 Sep;16(3):414-26
pubmed: 11554520
Scand J Public Health. 2006;34(6):623-31
pubmed: 17132596
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2019 Jan;62(1):102-107
pubmed: 30498848
Am J Public Health. 2012 Jul;102(7):1267-73
pubmed: 22594719
Soc Sci Med. 2019 Apr;226:249-253
pubmed: 30691972
Ethn Health. 2015;20(6):611-32
pubmed: 25411892
Cad Saude Publica. 2015 Nov;31(11):2259-74
pubmed: 26840808
Soc Sci Med. 2020 Jan;245:112499
pubmed: 31542315
Soc Sci Med. 2018 Apr;203:74-80
pubmed: 29305018