Top

BMC Public Health

Published in:

Open Access 01-12-2021 | Public Health | Research

Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake

Authors: Emily Mena, Gabriele Bolte, on behalf of the Advance Gender study group

Published in: BMC Public Health | Issue 1/2021

Abstract

Background

Daily vegetable intake is considered an important behavioural health resource associated with improved immune function and lower incidence of non-communicable disease. Analyses of population-based data show that being female and having a high educational status is most strongly associated with increased vegetable intake. In contrast, men and individuals with a low educational status seem to be most affected by non-daily vegetable intake (non-DVI). From an intersectionality perspective, health inequalities are seen as a consequence of an unequal balance of power such as persisting gender inequality. Unravelling intersections of socially driven aspects underlying inequalities might be achieved by not relying exclusively on the male/female binary, but by considering different facets of gender roles as well. This study aims to analyse possible interactions of sex/gender or sex/gender related aspects with a variety of different socio-cultural, socio-demographic and socio-economic variables with regard to non-DVI as the health-related outcome.

Method

Comparative classification tree analyses with classification and regression tree (CART) and conditional inference tree (CIT) as quantitative, non-parametric, exploratory methods for the detection of subgroups with high prevalence of non-DVI were performed. Complete-case analyses (n = 19,512) were based on cross-sectional data from a National Health Telephone Interview Survey conducted in Germany.

Results

The CART-algorithm constructed overall smaller trees when compared to CIT, but the subgroups detected by CART were also detected by CIT. The most strongly differentiating factor for non-DVI, when not considering any further sex/gender related aspects, was the male/female binary with a non-DVI prevalence of 61.7% in men and 42.7% in women. However, the inclusion of further sex/gender related aspects revealed a more heterogenous distribution of non-DVI across the sample, bringing gendered differences in main earner status and being a blue-collar worker to the foreground. In blue-collar workers who do not live with a partner on whom they can rely on financially, the non-DVI prevalence was 69.6% in men and 57.4% in women respectively.

Conclusions

Public health monitoring and reporting with an intersectionality-informed and gender-equitable perspective might benefit from an integration of further sex/gender related aspects into quantitative analyses in order to detect population subgroups most affected by non-DVI.

Available only for authorised users

Hosseini B, Berthon BS, Saedisomeolia A, Starkey MR, Collison A, Wark PAB, et al. Effects of fruit and vegetable consumption on inflammatory biomarkers and immune cell populations: a systematic literature review and meta-analysis. Am J Clin Nutr. 2018;108(1):136–55. https://doi.org/10.1093/ajcn/nqy082.CrossRefPubMed

Kalmpourtzidou A, Eilander A, Talsma EF. Global vegetable intake and supply compared to recommendations: a systematic review. Nutrients. 2020;12(6):1558. https://doi.org/10.3390/nu12061558.CrossRefPubMedCentral

Angelino D, Godos J, Ghelfi F, Tieri M, Titta L, Lafranconi A, et al. Fruit and vegetable consumption and health outcomes: an umbrella review of observational studies. Int J Food Sci Nutr. 2019;70(6):652–67. https://doi.org/10.1080/09637486.2019.1571021.CrossRefPubMed

Forouzanfar MH, Afshin A, Alexander LT, Anderson HR, Bhutta ZA, Biryukov S, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the global burden of disease study 2015. Lancet. 2016;388(10053):1659–724. https://doi.org/10.1016/S0140-6736(16)31679-8.CrossRef

Stea TH, Nordheim O, Bere E, Stornes P, Eikemo TA. Fruit and vegetable consumption in Europe according to gender, educational attainment and regional affiliation-A cross-sectional study in 21 European countries. PLoS One. 2020;15(5):e0232521–e.CrossRef

Prättälä R, Paalanen L, Grinberga D, Helasoja V, Kasmel A, Petkeviciene J. Gender differences in the consumption of meat, fruit and vegetables are similar in Finland and the Baltic countries. Eur J Pub Health. 2006;17(5):520–5. https://doi.org/10.1093/eurpub/ckl265.CrossRef

Mensink GBM, Schienkiewitz A, Lange C. Gemüsekonsum bei Erwachsenen in Deutschland. Journal of Health Monitoring. 2017;2(2).

WHO Fruit and Vegetable Promotion Initiative – report of the meeting. Geneva: World Health Organization; 2003.

Johnson J, Repta R. Sex and Gender: Beyond the Binaries. In: Oliffe JL GL, editor. Designing and conducting gender, sex, and health research Los Angeles/London/New Dehli/Singapore/ Washington DC: SAGE Publications; 2012. p. 17–37.

10.

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. https://doi.org/10.1016/j.socscimed.2014.03.022.CrossRefPubMed

11.

Breiman L, Friedman J, Olshen R, Stone C. Classification and regression trees. Wadsworth and Brooks/Cole: Monterey, CA; 1984.

12.

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 Popul Health. 2021;14:100798. https://doi.org/10.1016/j.ssmph.2021.100798.CrossRefPubMedPubMedCentral

13.

Mubayi A. Chapter 10 - Computational Modeling Approaches Linking Health and Social Sciences: Sensitivity of Social Determinants on the Patterns of Health Risk Behaviors and Diseases. In: Srinivasa Rao ASR, Pyne S, Rao CR, editors. Handbook of Statistics. 36: Elsevier; 2017. p. 249–304.

14.

Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006;15(3):651–74. https://doi.org/10.1198/106186006X133933.CrossRef

15.

Henrard S, Speybroeck N, Hermans C. Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia. Haemophilia. 2015;21(6):715–22. https://doi.org/10.1111/hae.12778.CrossRefPubMed

16.

Lemon SC, Roy J, Clark MA, Friedmann PD, Rakowski W. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann Behav Med. 2003;26(3):172–81. https://doi.org/10.1207/S15324796ABM2603_02.CrossRefPubMed

17.

Friel SN. John; Kelleher, Cecily. Who eats four or more servings of fruit and vegetables per day? Multivariate classification tree analysis of data from the 1998 survey of lifestyle, attitudes and nutrition in the Republic of Ireland. Public Health Nutr. 2004;8(2):159–69.CrossRef

18.

Kim S, Choi M-K. Factors associated with fruit and vegetable consumption of subjects having a history of stroke: using 5th Korea National Health and nutrition examination survey (2010, 2011). Korean J Community Nutr. 2014;19(5):468. https://doi.org/10.5720/kjcn.2014.19.5.468.CrossRef

19.

RKI. Gesundheit in Deutschland Aktuell 2009. public use file. GEDA 2009 Dokumentation des Datensatzes 2011.

20.

RKI. Daten und Fakten: Ergebnisse der Studie „Gesundheit in Deutschland aktuell 2010″. Berlin: RKI; 2012.

21.

Schenk L, Ellert U, Neuhauser H. Kinder und Jugendliche mit Migrationshintergrund in Deutschland. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2007;50(5):590–9. https://doi.org/10.1007/s00103-007-0220-z.CrossRefPubMed

22.

Johnson JL, Greaves, L., & Repta, R. Better Science with Sex and Gender: A Primer for Health Research2007.

23.

Pelletier R, Ditto B, Pilote L. A composite measure of gender and its association with risk factors in patients with premature acute coronary syndrome. Psychosom Med. 2015;77(5):517–26. https://doi.org/10.1097/PSY.0000000000000186.CrossRefPubMed

24.

Mena E, Bolte G. on behalf of the ADVANCE GENDER Study Group. Intersectionality-based quantitative health research and sex/gender sensitivity: a scoping review. Int. J. Equity Health. 2019;18(1):199.CrossRef

25.

Mena E. Bolte G, on behalf of the ADVANCE GENDER study group. CART-analysis embedded in social theory: A case study comparing quantitative data analysis strategies for intersectionality-based public health monitoring within and beyond the binaries SSM Popul Health. 2021;13:100722. https://doi.org/10.1016/j.ssmph.2020.100722.CrossRefPubMed

26.

Dalgard OS, Bjørk S, Tambs K. Social support, negative life events and mental health. Br J Psychiatry. 1995;166(1):29–34. https://doi.org/10.1192/bjp.166.1.29.CrossRefPubMed

27.

Meltzer H. Development of a common instrument for mental health. In: A. Nosikov, Gudex C, editors. EUROHIS: developing common instruments for health surveys. Amsterdam: IOS Press; 2003.

28.

Therneau TM. Atkinson. Foundation, M. An Introduction to Recursive Partitioning Using the RPART Routines: E.J; 2019.

29.

Hothorn T, Zeileis A. Partykit: a modular toolkit for recursive Partytioning in R. J Mach Learn Res. 2015;16(118):3905–9.

30.

R Core Team. A language and environment for statistical computing. Vienna, Austria 2013. http://www.R-project.org/.

31.

Venkatasubramaniam A, Wolfson J, Mitchell N, Barnes T, JaKa M, French S. Decision trees in epidemiological research. Emerg Themes Epidemiol. 2017;14(1):11. https://doi.org/10.1186/s12982-017-0064-4.CrossRefPubMedPubMedCentral

32.

Cairney J, Veldhuizen, S., Vigod, S., Streiner, D.L., Wade, T.J., & Kurdyak, P. Exploring the social determinants of mental health service use using intersectionality theory and CART analysis. J Epidemiol Community Health 2014;68:145–150, 2, DOI: https://doi.org/10.1136/jech-2013-203120.

33.

Emanuel AS, McCully SN, Gallagher KM, Updegraff JA. Theory of planned behavior explains gender difference in fruit and vegetable consumption. Appetite. 2012;59(3):693–7. https://doi.org/10.1016/j.appet.2012.08.007.CrossRefPubMedPubMedCentral

34.

Turrell G, Hewitt B, Patterson C, Oldenburg B. Measuring socio-economic position in dietary research: is choice of socio-economic indicator important? Public Health Nutr. 2003;6(2):191–200. https://doi.org/10.1079/PHN2002416.CrossRefPubMed

35.

Galobardes B, Morabia A, Bernstein MS. Diet and socioeconomic position: does the use of different indicators matter? Int J Epidemiol. 2001;30(2):334–40. https://doi.org/10.1093/ije/30.2.334.CrossRefPubMed

36.

Barrington WE, Ceballos RM, Bishop SK, McGregor BA, Beresford SAA. Perceived stress, behavior, and body mass index among adults participating in a worksite obesity prevention program, Seattle, 2005–2007. Prev Chronic Dis. 2012;9:E152–E.CrossRef

37.

Gardiner CK, Hagerty SL, Bryan AD. Stress and number of servings of fruit and vegetables consumed: buffering effects of monetary incentives. J Health Psychol 2019;0(0):1359105319884620, 26, 10, 1359105319881763, DOI: https://doi.org/10.1177/1359105319884620.

38.

Dėdelė A, Miškinytė A, Andrušaitytė S, Bartkutė Ž. Perceived stress among different occupational groups and the interaction with sedentary behaviour. Int J Environ Res Public Health. 2019;16(23):4595. https://doi.org/10.3390/ijerph16234595.CrossRefPubMedCentral

39.

Guinea-Martin D, Mora R, Ruiz-Castillo J. The evolution of gender segregation over the life course. Am Sociol Rev. 2018;83(5):983–1019. https://doi.org/10.1177/0003122418794503.CrossRef

40.

Campos-Serna J, Ronda-Pérez E, Artazcoz L, Moen BE, Benavides FG. Gender inequalities in occupational health related to the unequal distribution of working and employment conditions: a systematic review. Int J Equity Health. 2013;12(1):57. https://doi.org/10.1186/1475-9276-12-57.CrossRefPubMedPubMedCentral

41.

Hammarström A, Johansson K, Annandale E, Ahlgren C, Alex L, Christianson M, et al. Central gender theoretical concepts in health research: the state of the art. J Epidemiol Community Health. 2014;68(2):185–90. https://doi.org/10.1136/jech-2013-202572.CrossRefPubMed

42.

Elser H, Falconi AM, Bass M, Cullen MR. Blue-collar work and women's health: a systematic review of the evidence from 1990 to 2015. SSM Popul Health. 2018;6:195–244. https://doi.org/10.1016/j.ssmph.2018.08.002.CrossRefPubMedPubMedCentral

43.

Arias-de la Torre J, Molina AJ, Fernández-Villa T, Artazcoz L, Martín V. Mental health, family roles and employment status inside and outside the household in Spain. Gac Sanit. 2019;33(3):235–41. https://doi.org/10.1016/j.gaceta.2017.11.005.CrossRefPubMed

44.

Al AD, Anıl İ. The comparison of the individual performance levels between full-time and part-time employees: the role of job satisfaction. Procedia Soc Behav Sci. 2016;235:382–91. https://doi.org/10.1016/j.sbspro.2016.11.048.CrossRef

45.

Cheung KL, ten Klooster PM, Smit C, de Vries H, Pieterse ME. The impact of non-response bias due to sampling in public health studies: a comparison of voluntary versus mandatory recruitment in a Dutch national survey on adolescent health. BMC Public Health. 2017;17(1):276. https://doi.org/10.1186/s12889-017-4189-8.CrossRefPubMedPubMedCentral

46.

Magallares A. Drive for thinness and pursuit of muscularity: the role of gender ideologies. Univ Psychol. 2016;15(2):353–60. https://doi.org/10.11144/Javeriana.upsy15-2.dtpm.CrossRef

47.

Pivonka E, Seymour J, McKenna J, Baxter SD, Williams S. Development of the Behaviorally Focused Fruits & Veggies—More Matters Public Health Initiative. J Am Diet Assoc. 2011;111(10):1570–7. https://doi.org/10.1016/j.jada.2011.07.001.CrossRefPubMed

48.

Pöge K, Rommel A, Mena E, Holmberg C, Saß A-C, Bolte G. AdvanceGender – Verbundprojekt für eine geschlechtersensible und intersektionale Forschung und Gesundheitsberichterstattung. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2019;62(1):102–7. https://doi.org/10.1007/s00103-018-2855-3.CrossRefPubMed

49.

Lange C, Jentsch F, Allen J, Hoebel J, Kratz AL, von der Lippe E, et al. Data resource profile: German health update (GEDA)—the health interview survey for adults in Germany. Int J Epidemiol. 2015;44(2):442–50. https://doi.org/10.1093/ije/dyv067.CrossRefPubMed

Title: Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
Authors: Emily Mena
Gabriele Bolte
on behalf of the Advance Gender study group
Publication date: 01-12-2021
Publisher: BioMed Central
Keyword: Public Health
Published in: BMC Public Health / Issue 1/2021
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-021-12043-6

At a glance: The STEP trials

Springer Medicine

Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake

Abstract

Background

Method

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Method

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Associations of physical performance and physical activity with mental well-being in middle-aged women

Lifetime commercial heterosexual behavior among HIV negative elderly men from rural Chengdu, China: a modified knowledge-attitude-practice perspective

Racial/ethnic, gender, and age group differences in cardiometabolic risks among adults in a Northern California health plan: a cross-sectional study

HPV vaccine: uptake and understanding among global Indigenous communities – a qualitative systematic review

Health indices for the evaluation and monitoring of health in children and adolescents in prevention and health promotion: a scoping review

Health care provided to recent asylum-seeking and non-asylum-seeking pediatric patients in 2016 and 2017 at a Swiss tertiary hospital - a retrospective study