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BMC Public Health
Published in: BMC Public Health 1/2024

Open Access 01-12-2024 | Type 2 Diabetes | Research

Intersectional analysis of social disparities in type 2 diabetes risk among adults in Germany: results from a nationwide population-based survey

Authors: Francesca Färber, Enrique Alonso-Perez, Christin Heidemann, Yong Du, Gertraud Stadler, Paul Gellert, Julie Lorraine O’Sullivan

Published in: BMC Public Health | Issue 1/2024

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Abstract

Background

Differences in type 2 diabetes risk have been reported for several sociodemographic determinants including sex/gender or socioeconomic status. From an intersectional perspective, it is important to not only consider the role of social dimensions individually, but also their intersections. This allows for a deeper understanding of diabetes risk and preventive needs among diverse population groups.

Methods

As an intersectionality-informed approach, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was used in a population-based sample of adults without known diabetes in Germany from the cross-sectional survey “Disease knowledge and information needs– Diabetes mellitus (2017)”. Diabetes risk was assessed by the German Diabetes Risk Score (GDRS, range 0-122 points), estimating the individual risk of developing type 2 diabetes within the next 5 years based on established self-reported risk factors. Nesting individuals in 12 intersectional strata defined by combining sex/gender, educational level, and history of migration, we calculated measures to quantify the extent to which individual differences in diabetes risk were explained at strata level, and how much this was due to additive or multiplicative intersectional effects of social determinants.

Results

Drawing on data of 2,253 participants, we found good discriminatory accuracy of intersectional strata (variance partition coefficient = 14.00% in the simple intersectional model). Model-predicted GDRS means varied between 29.97 (corresponding to a “low risk” of < 2%) in women with high educational level and a history of migration, and 52.73 (“still low risk” of 2–5%) in men with low educational level without a history of migration. Variance in GDRS between strata was mainly explained by additive effects of social determinants (proportional change in variance to intersectional interaction model = 77.95%) with being male and having low educational level being associated with higher GDRS. There was no evidence of multiplicative effects in individual strata.

Conclusions

Type 2 diabetes risk differed between intersectional strata and can to some extent be explained at strata level. The role of intersectional effects was minor and needs to be further investigated. Findings suggest a need for specific preventive measures targeted at large groups with increased diabetes risk, such as men and persons with low educational level.
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Literature
1.
2.
Heidemann C, Scheidt-Nave C, Beyer AK, Baumert J, Thamm R, Maier B, et al. Health situation of adults in Germany - results for selected indicators from GEDA 2019/2020-EHIS. J Health Monit. 2021;6(3):3–25.PubMedPubMedCentral
3.
Porst M, von der Lippe E, Leddin J, Anton A, Wengler A, Breitkreuz J, et al. The burden of disease in Germany at the national and regional level. Dtsch Arztebl International. 2022;119(46):785–92.
4.
5.
Schulze MB, Hu FB. Primary prevention of diabetes: what can be done and how much can be prevented? Annu Rev Public Health. 2005;26(1):445–67.PubMedCrossRef
6.
Bellou V, Belbasis L, Tzoulaki I, Evangelou E. Risk factors for type 2 diabetes mellitus: an exposure-wide umbrella review of meta-analyses. PLoS ONE. 2018;13(3):e0194127.PubMedPubMedCentralCrossRef
7.
8.
NCD Risk Factor Collaboration (NCD-RisC), Zhou B, Lu Y, Hajifathalian K, Bentham J, Di Cesare M, et al. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. The Lancet. 2016;387(10027):1513–30.CrossRef
9.
Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes Mellitus. Endocr Rev. 2016;37(3):278–316.PubMedPubMedCentralCrossRef
10.
Richter A, Schienkiewitz A, Starker A, Krug S, Domanska O, Kuhnert R, et al. Health-promoting behaviour among adults in Germany– results from GEDA 2019/2020-EHIS. J Health Monit. 2021;6(3):26–44.
11.
Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol. 2011;40(3):804–18.PubMedCrossRef
12.
Kyrou I, Tsigos C, Mavrogianni C, Cardon G, Van Stappen V, Latomme J, et al. Sociodemographic and lifestyle-related risk factors for identifying vulnerable groups for type 2 diabetes: a narrative review with emphasis on data from Europe. BMC Endocr Disord. 2020;20(Suppl 1):134.PubMedPubMedCentralCrossRef
13.
Heidemann C, Du Y, Baumert J, Paprott R, Lampert T, Scheidt-Nave C. Social inequality and diabetes mellitus– developments over time among the adult population in Germany. J Health Monit. 2019;4(2):11–28.PubMedPubMedCentral
14.
Geyer S, Hemström Ö, Peter R, Vågerö D. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. J Epidemiol Commun Health. 2006;60(9):804–10.CrossRef
15.
Safieddine B, Sperlich S, Beller J, Lange K, Epping J, Tetzlaff J, et al. Socioeconomic inequalities in type 2 diabetes in employed individuals, nonworking spouses and pensioners. SSM - Population Health. 2020;11:100596.PubMedPubMedCentralCrossRef
16.
Schienkiewitz A, Kuhnert R, Blume M, Mensink GBM. Overweight and obesity among adults in Germany - results from GEDA 2019/2020-EHIS. J Health Monit. 2022;7(3):21–8.PubMedPubMedCentral
17.
Meeks KA, Freitas-Da-Silva D, Adeyemo A, Beune EJ, Modesti PA, Stronks K, et al. Disparities in type 2 diabetes prevalence among ethnic minority groups resident in Europe: a systematic review and meta-analysis. Intern Emerg Med. 2016;11(3):327–40.PubMedCrossRef
18.
Vandenheede H, Deboosere P, Stirbu I, Agyemang CO, Harding S, Juel K, et al. Migrant mortality from diabetes mellitus across Europe: the importance of socio-economic change. Eur J Epidemiol. 2012;27(2):109–17.PubMedCrossRef
19.
Robert Koch-Institute (RKI), editor. Health in Germany– the most important trends.: Federal Health Reporting. Jointly provided by RKI and Destatis. Berlin: RKI; 2016.
20.
Dyck M, Wenner J, Wengler A, Bartig S, Fischer F, Wandschneider L, et al. Migration Und Gesundheit in Deutschland– Eine Bestandsaufnahme Der Datenquellen. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 2019;62(8):935–42.PubMedCrossRef
21.
Razum O, Karrasch L, Spallek J. Migration. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 2016;59(2):259–65.PubMedCrossRef
22.
Bauer GR. Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Soc Sci Med. 2014;110:10–7.PubMedCrossRef
23.
Crenshaw K. Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics. University of Chicago Legal Forum. 1989;1989:139-67.
24.
Bowleg L. The problem with the phrase women and minorities: intersectionality-an important theoretical framework for public health. Am J Public Health. 2012;102(7):1267–73.PubMedPubMedCentralCrossRef
25.
Bauer GR, Churchill SM, Mahendran M, Walwyn C, Lizotte D, Villa-Rueda AA. Intersectionality in quantitative research: a systematic review of its emergence and applications of theory and methods. SSM - Population Health. 2021;14:100798.PubMedPubMedCentralCrossRef
26.
Evans CR, Williams DR, Onnela JP, Subramanian SV. A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Soc Sci Med. 2018;203:64–73.PubMedCrossRef
27.
Wemrell M, Bennet L, Merlo J. Understanding the complexity of socioeconomic disparities in type 2 diabetes risk: a study of 4.3 million people in Sweden. BMJ Open Diabetes Res Care. 2019;7(1):e000749.PubMedPubMedCentralCrossRef
28.
Merlo J. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework. Soc Sci Med. 2018;203:74–80.PubMedCrossRef
29.
Rodriguez-Lopez M, Leckie G, Kaufman JS, Merlo J. Multilevel modelling for measuring interaction of effects between multiple categorical variables: An illustrative application using risk factors for preeclampsia. Paediatric and Perinatal Epidemiology. 2022;37:154– 64.
30.
Evans CR, Leckie G, Merlo J. Multilevel versus single-level regression for the analysis of multilevel information: the case of quantitative intersectional analysis. Soc Sci Med. 2020;245:112499.PubMedCrossRef
31.
Holman D, Salway S, Bell A. Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults. Sci Rep. 2020;10(1):13522.PubMedPubMedCentralCrossRefADS
32.
Paprott R, Heidemann C, Stühmann LM, Baumert J, Du Y, Hansen S, et al. First results from the study ‘Disease knowledge and information needs - diabetes mellitus (2017)’. J Health Monit. 2018;3(S3):22–60.PubMedPubMedCentral
33.
Schmich P, Lemcke J, Zeisler M-L, Müller A, Allen J, Wetzstein M. Ad hoc surveys at the Robert Koch Institute. J Health Monit. 2018;3(3):70–80.PubMedPubMedCentral
34.
Stühmann LM, Paprott R, Heidemann C, Ziese T, Hansen S, Zahn D, et al. Psychometric properties of a nationwide survey for adults with and without diabetes: the disease knowledge and information needs - diabetes mellitus (2017) survey. BMC Public Health. 2020;20(1):192.PubMedPubMedCentralCrossRef
35.
Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Möhlig M, et al. An accurate risk score based on Anthropometric, Dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care. 2007;30(3):510–5.PubMedCrossRef
36.
Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke (DIfE). DIfE– Deutscher Diabetes-Risiko-Test®. Der Diabetologe. 2021;17(5):592–4.CrossRef
37.
Mühlenbruch K, Ludwig T, Jeppesen C, Joost H-G, Rathmann W, Meisinger C, et al. Update of the German diabetes risk score and external validation in the German MONICA/KORA study. Diabetes Res Clin Pract. 2014;104(3):459–66.PubMedCrossRef
38.
Paprott R, Mühlenbruch K, Mensink GB, Thiele S, Schulze MB, Scheidt-Nave C, et al. Validation of the German diabetes risk score among the general adult population: findings from the German Health Interview and examination surveys. BMJ Open Diabetes Res Care. 2016;4(1):e000280.PubMedPubMedCentralCrossRef
39.
Mühlenbruch K, Joost HG, Boeing H, Schulze MB. Risk prediction for type 2 diabetes in the German population with the updated German diabetes risk score (GDRS). Ernaehrungs Umschau. 2014;61(6):90–3.
40.
Heidemann C, Paprott R, Stühmann LM, Baumert J, Mühlenbruch K, Hansen S, et al. Perceived diabetes risk and related determinants in individuals with high actual diabetes risk: results from a nationwide population-based survey. BMJ Open Diabetes Res Care. 2019;7(1):e000680.PubMedPubMedCentralCrossRef
41.
Scheidt-Nave C, Kamtsiuris P, Gößwald A, Hölling H, Lange M, Busch MA, et al. German health interview and examination survey for adults (DEGS) - design, objectives and implementation of the first data collection wave. BMC Public Health. 2012;12(1):730.PubMedPubMedCentralCrossRef
42.
Brauns H, Scherer S, Steinmann S. The CASMIN Educational classification in International Comparative Research. In: Hoffmeyer-Zlotnik JHP, Wolf C, editors. Advances in cross-national comparison: a European Working Book for demographic and socio-economic variables. Boston, MA: Springer US; 2003. pp. 221–44.CrossRef
43.
Lumley T. Analysis of Complex Survey samples. J Stat Softw. 2004;9(8):1–19.CrossRef
44.
45.
Axelsson Fisk S, Mulinari S, Wemrell M, Leckie G, Perez Vicente R, Merlo J. Chronic obstructive Pulmonary Disease in Sweden: an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy. SSM - Population Health. 2018;4:334–46.PubMedPubMedCentralCrossRef
46.
47.
Bates D, Mächler M, Bolker B, Walker S. Fitting Linear mixed-effects models using lme4. J Stat Softw. 2015;67(1).
48.
Duursma R. bootpredictlme4: Predict Method For lme4 With Bootstrap. R package version 0.1. 2023. Accessed 21 Apr 2023.
49.
Patil I, Makowski D, Ben-Shachar MS, Wiernik BM, Bacher E, Lüdecke D. Datawizard: an R Package for Easy Data Preparation and statistical transformations. J Open Source Softw. 2022;7(78).
50.
Keller L, Lüdtke O, Preckel F, Brunner M. Educational inequalities at the intersection of multiple social categories: an introduction and Systematic Review of the Multilevel Analysis of Individual Heterogeneity and discriminatory accuracy (MAIHDA) Approach. Educational Psychol Rev. 2023;35(1):31.CrossRef
51.
52.
Abouzeid M, Philpot B, Janus ED, Coates MJ, Dunbar JA. Type 2 diabetes prevalence varies by socio-economic status within and between migrant groups: analysis and implications for Australia. BMC Public Health. 2013;13:252.PubMedPubMedCentralCrossRef
53.
54.
Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P. WHO European review of social determinants of health and the health divide. The Lancet. 2012;380(9846):1011–29.CrossRef
55.
56.
Hoebel J, Starker A, Jordan S, Richter M, Lampert T. Determinants of health check attendance in adults: findings from the cross-sectional German Health Update (GEDA) study. BMC Public Health. 2014;14(1):913.PubMedPubMedCentralCrossRef
57.
Jordan S, von der Lippe E. [Participation in health behaviour change programmes: results of the German health interview and examination survey for adults (DEGS1)]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2013;56(5–6):878–84.PubMedCrossRef
58.
Robertson C, Avenell A, Boachie C, Stewart F, Archibald D, Douglas F, et al. Should weight loss and maintenance programmes be designed differently for men? A systematic review of long-term randomised controlled trials presenting data for men and women: the ROMEO project. Obes Res Clin Pract. 2016;10(1):70–84.PubMedCrossRef
59.
Jackson MC, Dai S, Skeete RA, Owens-Gary M, Cannon MJ, Smith BD, et al. An examination of gender differences in the National Diabetes Prevention Program’s Lifestyle Change Program. Diabetes Educ. 2020;46(6):580–6.PubMedPubMedCentralCrossRef
60.
Harreiter J, Kautzky-Willer A. Sex and Gender Differences in Prevention of Type 2 diabetes. Front Endocrinol (Lausanne). 2018;9:220.PubMedCrossRef
61.
Reitzle L, Heidemann C, Jacob J, Pawlowska-Phelan D, Ludwig M, Scheidt-Nave C. Incidence of type 1 and type 2 diabetes before and during the COVID-19 pandemic in Germany: analysis of routine data from 2015 to 2021. J Health Monit. 2023;8(S5):2–25.PubMedPubMedCentral
62.
Nam GE, Kim W, Han K, Jung JH, Han B, Kim J, et al. Association between living alone and incident type 2 diabetes among middle-aged individuals in Korea: a nationwide cohort study. Sci Rep. 2021;11:3659.PubMedPubMedCentralCrossRefADS
Metadata
Title
Intersectional analysis of social disparities in type 2 diabetes risk among adults in Germany: results from a nationwide population-based survey
Authors
Francesca Färber
Enrique Alonso-Perez
Christin Heidemann
Yong Du
Gertraud Stadler
Paul Gellert
Julie Lorraine O’Sullivan
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-024-17903-5

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