SpringerLink
Log in

Multidimensional Poverty Among Adolescents in 38 Countries: Evidence from the Health Behaviour in School-aged Children (HBSC) 2013/14 Study

  • Published:
Child Indicators Research Aims and scope Submit manuscript

Abstract

This study applied UNICEF’s Multiple Overlap** Deprivation Analysis (MODA) framework to adolescents (aged 11, 13 and 15) in 37 European countries and Canada using data from the 2013/14 Health Behaviour in School-aged Children survey. It is one of the first applications of MODA based entirely on data collected from adolescents themselves rather than from household reference persons on their behalf. Unlike most other multidimensional child poverty studies, the present analysis focuses on non-material, relational aspects of child poverty. Substantial cross-country variation was found in the prevalence of adolescent deprivations in nutrition, perceived health, school environment, protection from peer violence, family environment and information access. These single dimensions of poverty did not closely relate to national wealth and income inequality. However, when we looked at deprivation in three or more dimensions (i.e., multidimensional poverty), we found association with income inequality. In most countries, girls were at a higher risk of multidimensional poverty than boys. In addition, adolescents who lived with both parents in the household or reported higher family wealth were consistently less poor than other adolescents, in both single and multiple dimensions. The results of this study show the interconnectedness of social (family, school support) and psychological (health and violence) dimensions of poverty for adolescents in higher income countries. Children poor in the domains of family and school environment are also likely to be poor in terms of perceived health and protection from peer violence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. See www.hbsc.org.

  2. We excluded Greenland from the analysis.

  3. The proportion of missing values was typically low (5% or less). The following countries had between 10% and 20% missing values: Armenia (perceived health, school environment, and family environment); Croatia (bullied); Denmark (family communication); Israel (nutrition, health, protection from violence), Norway (school environment); Portugal (family environment); Slovakia (information access); Spain (health; protection from violence; family environment; information access); Macedonia (nutrition); and England (family environment; information access).

  4. International Monetary Fund, World Economic Outlook Database, April 2016.

  5. Solt (2014). The Standardized World Income Inequality Database (SWIID) Version 5.0, October 2014.

  6. Logistic regression models produced nearly identical average marginal effects (results available on request).

  7. The cross-country correlation between the health deprivation rate and GDP per capita (PPP) is r = −0.31, (p = 0.06).

  8. When the information deprivation rate is regressed on both GDP per capita (PPP) and the Gini coefficient, the association with the later is no longer significant.

  9. Older adolescents were consistently less likely to be bullied, but in several countries they were at a higher risk of cyberbullying by pictures or messages. Since the rates of cyberbullying tend to be lower, older adolescents appeared to be less likely to be deprived in the protection from violence dimension (estimates available on request).

  10. However, if Luxembourg were excluded, there would still be a statistically significant negative partial correlation between multidimensional poverty and country wealth, even after controlling for income inequality.

  11. https://sustainabledevelopment.un.org/sdg10.

References

  • Alemán-Díaz, A. Y., Toczydlowska, E., Mazur, J., Frasquilho, D., Melkumova, M., & Holmqvist, G. (2016). Why income inequalities matter for young people’s health: A look at the evidence. In Innocenti working paper 2016–06. Florence: UNICEF Office of Research.

    Google Scholar 

  • Alkire, S., Foster, J., Seth, S., Santos, M. E., Roche, J. M., & Ballon, P. (2015). Multidimensional poverty measurement and analysis. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Bickham, D. S., Hswen, Y., & Rich, M. (2015). Media use and depression: Exposure, household rules, and symptoms among young adolescents in the United States. International Journal of Public Health, 60(2), 147–155. https://doi.org/10.1007/s00038-014-0647-6.

    Article  Google Scholar 

  • Bobakova, D., Hamrik, Z., Badura, P., Sigmundova, D., Nalecz, H., & Kalman, M. (2015). Test-retest reliability of selected physical activity and sedentary behaviour HBSC items in the Czech Republic, Slovakia and Poland. International Journal of Public Health, 60(1), 59–67. https://doi.org/10.1007/s00038-014-0628-9.

    Article  Google Scholar 

  • Boniel-Nissim, M., Tabak, I., Mazur, J., Borraccino, A., Brooks, F., Gommans, R., van der Sluijs, W., Zsiros, E., Craig, W., Harel-Fisch, Y., & Finne, E. (2015). Supporting communication withi parents moderates effects of electronic media use on life satisfaction during adolescence. International Journal of Public Health, 60(1), 189–198. https://doi.org/10.1007/s00038-014-0628-9.

    Article  Google Scholar 

  • Boyce, W., Davies, D., Gallupe, O., & Shelley, D. (2008). Adolescent risk taking, neighborhood social capital, and health. Journal of Adolescent Health, 43(3), 246–252. https://doi.org/10.1016/j.jadohealth.2008.01.014.

    Article  Google Scholar 

  • Bradshaw, J., & Richardson, D. (2009). An index of child well-being in Europe. Child Indicators Research, 2(3), 319–351. https://doi.org/10.1007/s12187-009-9037-7.

    Article  Google Scholar 

  • Brener, N., Kann, L., Shanklin, S., Kinchen, S., Eaton, D., Hawkins, J., et al. (2013). Methodology of the youth risk behavior surveillance system. MMWR. Recommendations and Reports: Morbidity and Mortality Weekly Report. Recommendations and Reports/Centers for Disease Control, 62(RR-1), 1–20.

    Google Scholar 

  • Brindova, D., Veselska, Z., Klein, D., Hamrik, Z., Sigmundova, D., van Dijk, J., et al. (2015). Is the association between screen-based behaviour and health complaints among adolescents moderated by physical activity? International Journal of Public Health, 60(2), 139–145. https://doi.org/10.1007/s00038-014-0627-x.

    Article  Google Scholar 

  • Brooks, F. M., Magnusson, J., Spencer, N., & Morgan, A. (2012). Adolescent multiple risk behaviour: An asset approach to the role of family, school and community. The Journal of Public Health, 34(suppl 1), i48–i56. https://doi.org/10.1093/pubmed/fds001.

    Article  Google Scholar 

  • Callaghan, M., Kelly, C., & Molcho, M. (2015). Exploring traditional and cyberbullying among Irish adolescents. International Journal of Public Health, 60(2), 199–206. https://doi.org/10.1007/s00038-014-0638-7.

    Article  Google Scholar 

  • Cavallo, F., Zambon, A., Raven-Sieberer, U., Torsheim, T., Lemma, P., & HBSC Positive Health Focus Group. (2006). Girls growing through adolescence have a higher risk of poor health. Quality of Life Research: an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 15(10), 1577–1585. https://doi.org/10.1007/s11136-006-0037-5.

    Article  Google Scholar 

  • Cavallo, F., Dalmasso, P., Ottova -Jordan, V., Brooks, F., Mazur, J., Välimaa, R., et al. (2015). Trends in self-rated health in European and North-American adolescents from 2002 to 2010 in 32 countries. The European Journal of Public Health, 25(suppl 2), 13–15. https://doi.org/10.1093/eurpub/ckv011.

    Article  Google Scholar 

  • Chzhen, Y., & Ferrone, L. (2017). Multidimensional child deprivation and poverty measurement: Case study of Bosnia and Herzegovina. Social Indicators Research, 131(3), 999–1014. https://doi.org/10.1007/s11205-016-1291-8.

  • Chzhen, Y., de Neubourg, C., de Milliano, M., & Plavgo, I. (2016a). Child poverty in the european union: The multiple overlap** deprivation analysis approach (EU-MODA). Child Indicators Research, 9(2), 335–336. https://doi.org/10.1007/s12187-015-9321-7.

    Article  Google Scholar 

  • Chzhen, Y., Moor, I., Pickett, W., Toczydlowska, E., & Stevens, G. (2016b). Family affluence and inequality in adolescent health and life satisfaction: Evidence from the HBSC study 2002-2014. In Innocenti working paper 2016–10. Florence: UNICEF Office of Research.

    Google Scholar 

  • Collins, W. A., & Laursen, B. (2004). Parent-adolescent relationships and influences. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology (Vol. 2, pp. 331–362). N.J: Willey.

    Google Scholar 

  • Currie, C., Molcho, M., Boyce, W., Holstein, B., Torsheim, T., & Richter, M. (2008). Researching health inequalities in adolescents: The development of the health behaviour in school-aged children (HBSC) family affluence scale. Social Science & Medicine, 66(6), 1429–1436. https://doi.org/10.1016/j.socscimed.2007.11.024.

    Article  Google Scholar 

  • Currie, C., Inchley, J., Molcho, M., Lenzi, M., Veselska, Z., & Wild, F. e. (2014). Health behaviour in school-aged children (HBSC) study protocol: Background, methodology and mandatory items for the 2013/14 survey. Edinburgh: Child and Adolescent Health Research Unit.

    Google Scholar 

  • de Milliano, M., & Handa, S. (2014). Child poverty and deprivation in Mali: The first national estimates. In Innocenti working paper 2014–20. Florence: UNICEF Office of Research.

    Google Scholar 

  • de Milliano, M., & Plavgo, I. (2014). Analysing child poverty and deprivation in sub-Saharan Africa: CC-MODA – Cross country multiple overlap** deprivation analysis. In Innocenti working paper 2014–19. Florence: UNICEF Office of Research.

    Google Scholar 

  • de Neubourg, C., Chai, J., de Milliano, M., Plavgo, I., & Wei, Z. (2012). Step-by-step guidelines to the multiple overlap** deprivation analysis (MODA). In Working Paper 2012–10. Florence: UNICEF Office of Research.

    Google Scholar 

  • de Neubourg, C., de Milliano, M., & Plavgo, I. (2014). Lost (in) dimensions: Consolidating progress in multidimensional poverty research. In Innocenti working paper 2014–04. Florence: UNICEF Office of Research.

    Google Scholar 

  • Dmitrieva, J. (2013). Socioeconomic influences on health and health behavior in adolescents. In W. T. O'Donohue, L. T. Benuto, & L. Woodward Tolle (Eds.), Handbook of adolescent health psychology (pp. 43–60). New York: Springer.

    Chapter  Google Scholar 

  • Due, P., Holstein, B., Lynch, J., Diderichsen, F., Gabhain, S., Scheidt, P., et al. (2005). Bullying and symptoms among school-aged children: International comparative cross sectional study in 28 countries. The European Journal of Public Health, 15(2), 128–132. https://doi.org/10.1093/eurpub/cki105.

    Article  Google Scholar 

  • Doku, D., Koivusilta, L. & Rimpelä, A. (2010). Indicators for measuring material affluence of adolescents in health inequality research in develo** countries. Child Indicators Research, 3(2), 243–260.

  • Elgar, F., McKinnon, B., Walsh, S., Freeman, J., Donnelly P, D., de Matos, M., et al. (2015a). Structural determinants of youth bullying and fighting in 79 countries. Journal of Adolescent Health, 57(6), 643–650.

    Article  Google Scholar 

  • Elgar, F., Pförtner, T.-K., Moor, I., De Clercq, B., Stevens, G. W., & Currie, C. (2015b). Socioeconomic inequalities in adolescent health 2002–2010: A time-series analysis of 34 countries participating in the health behaviour in school-aged children study. The Lancet, 385(9982), 2088–2095. https://doi.org/10.1016/S0140-6736(14)61460-4.

    Article  Google Scholar 

  • Evans, G. W., & Kim, P. (2010). Multiple risk exposure as a potential explanatory mechanism for the socioeconomic status-health gradient. Annals of the New York Academy of Sciences, 1186(1), 174–189. https://doi.org/10.1111/j.1749-6632.2009.05336.x.

    Article  Google Scholar 

  • Fairlie, R. W. (2012). The effects of home access to technology on computer skills: Evidence from a field experiment. Information Economics and Policy, 24(3–4), 243–253. https://doi.org/10.1016/j.infoecopol.2012.06.001.

    Article  Google Scholar 

  • Fairlie, R. W., Beltran, D. O., & Das, K. K. (2010). Home computers and educational outcomes: Evidence from the NLSY97 and CPS. Economic Inquiry, 48(3), 771–792. https://doi.org/10.1111/j.1465-7295.2009.00218.x.

    Article  Google Scholar 

  • Fletcher, A. C., Steinberg, L., & Williams-Wheeler, M. (2004). Parental influences on adolescent problem behavior: Revisiting Stattin and Kerr. Child Development, 75(3), 781–796. https://doi.org/10.1111/j.1467-8624.2004.00706.x.

    Article  Google Scholar 

  • Gariepy, G., Sentenac, M., McKinnon, B., & Elgar, F. (2016). Validity and reliability of a brief symptom checklist to measure psychological health in school-aged children. Child Indicators Research, 9(2), 471–484. https://doi.org/10.1007/s12187-015-9326-2.

    Article  Google Scholar 

  • Gordon, D., Nandy, S., Pantazis, C., Pemberton, S., & Townsend, P. (2003). The distribution of child poverty in the develo** world. Bristol: Centre for International Poverty Research.

    Google Scholar 

  • Guerra, N., & Hanish, L. D. (2004). Aggressive victims, passive victims, and bullies: Developmental continuity or developmental change? Merrill-Palmer Quarterly, 50(1), 17–38. https://doi.org/10.1353/mpq.2004.0003.

    Article  Google Scholar 

  • Guio, A.-C., Gordon, D., & Marlier, E. (2012). Measuring material deprivation in the EU: Indicators for the whole population and child-specific indicators. In Eurostat Methodologies and Working Papers. Luxembourg: Office for Official Publications of the European Communities.

    Google Scholar 

  • Hanson, M., & Chen, E. (2007). Socioeconomic status and health behaviors in adolescence: A review of the literature. Journal of Behavioral Medicine, 30(3), 263–285. https://doi.org/10.1007/s10865-007-9098-3.

    Article  Google Scholar 

  • Harel-Fisch, Y., Walsh, S. D., Fogel-Grinvald, H., Amitai, G., Pickett, W., Molcho, M., et al. (2011). Negative school perceptions and involvement in school bullying: A universal relationship across 40 countries. Journal of Adolescence, 34(4), 639–652. https://doi.org/10.1016/j.adolescence.2010.09.008.

    Article  Google Scholar 

  • Haugland, S., & Wold, B. (2001). Subjective health complaints in adolescence—Reliability and validity of survey methods. Journal of Adolescence, 24(5), 611–624. https://doi.org/10.1006/jado.2000.0393.

    Article  Google Scholar 

  • Haynie, D. L., Nansel, T., Eitel, P., Crump, A. D., Saylor, K., Yu, K., et al. (2001). Bullies, victims, and bully/victims: Distinct groups of at-risk youth. Journal of Early Adolescence, 21(1), 29–49. https://doi.org/10.1177/0272431601021001002.

    Article  Google Scholar 

  • Hemphill, S., Kotevski, A., & Heerde, J. (2015). Longitudinal associations between cyber-bullying perpetration and victimization and problem behavior and mental health problems in young Australians. International Journal of Public Health, 60(2), 227–237. https://doi.org/10.1007/s00038-014-0644-9.

    Article  Google Scholar 

  • Hong, J. S., & Espelage, D. L. (2012). A review of research on bullying and peer victimization in school: An ecological system analysis. Aggression and Violent Behavior, 17(4), 311–322. https://doi.org/10.1016/j.avb.2012.03.003.

    Article  Google Scholar 

  • Inchley, J., Currie, D., Young, T., Samdal, O., Torsheim, T., Augustson, L., et al. (2016). Growing up unequal: Gender and socioeconomic differences in young people’s health and well-being. Health behaviour in school-aged children (HBSC) study: International report from the 2013/2014 survey. Health policy for children and adolescents. Copenhagen: WHO Regional Office for Europe.

  • International Monetary Fund (2016). World Economic Outlook Database. https://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx. Accessed 11 April 2016.

  • Kamerman, S. B., Neuman, M., Waldfogel, J., & Brooks-Gunn, J. (2003). Social policies, family types and child outcomes in selected OECD countries. In OECD Social, Employment and Migration Working Papers, No. 6. OECD Publishing.

  • Kuhlemeier, H., & Hemker, B. (2007). The impact of computer use at home on students’ internet skills. Computers & Education, 49(2), 460–480. https://doi.org/10.1016/j.compedu.2005.10.004.

    Article  Google Scholar 

  • Lachytova, M., Katreniakova, Z., Mikula, P., & Nagyova, I. (2015). Self-rated health in relation to mental health, physical activity, sedentary behaviour in adolescents. The European Journal of Public Health, 25(suppl 3), ckv171. 081. https://doi.org/10.1093/eurpub/ckv171.081.

    Google Scholar 

  • Lau, M., & Bradshaw, J. (2016). Material well-being, social relationships and children's overall life satisfaction in Hong Kong. Child Indicators Research. https://doi.org/10.1007/s12187-016-9426-7.

  • Lemma, P., Borraccino, A., Berchialla, P., Dalmasso, P., Charrier, L., Vieno, A., et al. (2015). Well-being in 15-year-old adolescents: A matter of relationship with school. The Journal of Public Health, 37(4), 573–580. https://doi.org/10.1093/pubmed/fdu095.

    Google Scholar 

  • Levin, K. A., & Currie, C. (2010). Family structure, mother-child communication, father-child communication, and adolescent life satisfaction: A cross-sectional multilevel analysis. Health Education, 110(3), 152–168. https://doi.org/10.1108/09654281011038831.

    Article  Google Scholar 

  • Li, J., McMurray, A., & Stanley, F. (2008). Modernity’s paradox and the structural determinants of child health and well-being. Health Sociology Review, 17(1), 64–77. https://doi.org/10.5172/hesr.451.17.1.64.

    Article  Google Scholar 

  • Liu, Y., Wang, M., Tynjälä, J., Lv, Y., Villberg, J., Zhang, Z., et al. (2010). Test-retest reliability of selected items of health behaviour in school-aged children (HBSC) survey questionnaire in Bei**g, China. BMC Medical Research Methodology, 10(1), 1–9. https://doi.org/10.1186/1471-2288-10-73.

    Article  Google Scholar 

  • Machado-Rodrigues, A., Leite, N., Coelho-e-Silva, M., Enes, F., Fernandes, R., Mascarenhas, L., et al. (2015). Metabolic risk and television time in adolescent females. International Journal of Public Health, 60(2), 157–165. https://doi.org/10.1007/s00038-014-0625-z.

    Article  Google Scholar 

  • Mackenbach, J. P. (2012). The persistence of health inequalities in modern welfare states: The explanation of a paradox. Social Science & Medicine, 75(4), 761–769. https://doi.org/10.1016/j.socscimed.2012.02.031.

    Article  Google Scholar 

  • Main, G., & Bradshaw, J. (2012). A child material deprivation index. Child Indicators Research, 5(3), 503–521. https://doi.org/10.1007/s12187-012-9145-7.

    Article  Google Scholar 

  • Meireles, A., Xavier, C., de Souza, A. A., Proietti, F., & Caiaffa, W. (2015). Self-rated health among urban adolescents: The roles of age, gender, and their associated factors. PloS One, 10(7), e0132254. https://doi.org/10.1371/journal.pone.0132254.

    Article  Google Scholar 

  • Moreno, C., Muñoz-Tinoco, V., Pérez, P., & Sánchez-Queija, I. (2006). Los adolescentes españoles y sus familias: calidad en la comunicación con el padre y con la madre y conductas de riesgo relacionadas con el consumo de sustancias adictivas. Cultura y Educación, 18(3-4), 345–362. https://doi.org/10.1174/113564006779172975.

    Article  Google Scholar 

  • Moreno, C., Sánchez-Queija, I., Muñoz-Tinoco, V., Matos, M. G., Dallago, L., Bogt, T. T., et al. (2009). Cross-national associations between parent and peer communication and psychological complaints. International Journal of Public Health, 54(2), 235–242. https://doi.org/10.1007/s00038-009-5415-7.

    Article  Google Scholar 

  • Nuutinen, T., Roos, E., Ray, C., Villberg, J., Välimaa, R., Rasmussen, M., et al. (2014). Computer use, sleep duration and health symptoms: A cross-sectional study of 15-year olds in three countries. International Journal of Public Health, 59(4), 619–628. https://doi.org/10.1007/s00038-014-0561-y.

    Article  Google Scholar 

  • Olweus, D. (1996). The revised Olweus bully/victim questionnaire. Bergen: University of Bergen.

    Google Scholar 

  • Pedersen, T. P., Meilstrup, C., Holstein, B. E., & Rasmussen, M. (2012). Fruit and vegetable intake is associated with frequency of breakfast, lunch and evening meal: Cross-sectional study of 11-, 13-, and 15-year-olds. The International Journal of Behavioral Nutrition and Physical Activity, 9(1), 9. https://doi.org/10.1186/1479-5868-9-9.

    Article  Google Scholar 

  • Rampersaud, G., Pereira, M., Girard, B., Adams, J., & Metzl, J. (2005). Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. Journal of the American Dietetic Association, 105(5), 743–760. https://doi.org/10.1016/j.jada.2005.02.007.

    Article  Google Scholar 

  • Richter, M., Moor, I., & van Lenthe, F. J. (2012). Explaining socioeconomic differences in adolescent self-rated health: The contribution of material, psychosocial and behavioural factors. Journal of Epidemiology and Community Health, 66(8), 691–697. https://doi.org/10.1136/jech.2010.125500.

    Article  Google Scholar 

  • Ridolfo, H., & Maitland, A. (2011). Factors that influence the accuracy of adolescent proxy reporting of parental characteristics: A research note. Journal of Adolescence, 34(1), 95–103. https://doi.org/10.1016/j.adolescence.2010.01.008.

    Article  Google Scholar 

  • Rubin, D. B. (1987). Multiple imputation for non-response in surveys. New York: Wiley.

    Book  Google Scholar 

  • Schafer, J. L. (1997). Analysis of incomplete multivariate data. Boca Raton: Chapman & Hall/CRC.

    Book  Google Scholar 

  • Sinclair, K., Bauman, S., Poteat, V., Koenig, B., & Russell, S. (2012). Cyber and bias-based harassment: Associations with academic, substance use, and mental health problems. Journal of Adolescent Health, 50(5), 521–523. https://doi.org/10.1016/j.jadohealth.2011.09.009.

    Article  Google Scholar 

  • Smokowski, P. R., & Kopasz, K. H. (2005). Bullying in school: An overview of types, effects, family characteristics, and intervention strategies. Children and Schools, 27(2), 101–110. https://doi.org/10.1093/cs/27.2.101.

    Article  Google Scholar 

  • Solt, F. (2014). The standardized world income inequality database (SWIID) version 5.0., Harvard Dataverse, V14. https://dataverse.harvard.Edu/dataset.Xhtml?persistentId=hdl:1902.1/11992. Accesed 05 July 2016.

  • Sourander, A., Brunstein, K. A., Ikonen, M., Lindroos, J., Luntamo, T., Koskelainen, M., et al. (2010). Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Archives of General Psychiatry, 67(7), 720–728. https://doi.org/10.1001/archgenpsychiatry.2010.79.

    Article  Google Scholar 

  • Spears, B., Campbell, M. A., Tangen, D. J., Slee, P., & Cross, D. (2015). Australian pre-service teachers' knowledge and understanding of cyberbullying: Implications for school climate. Les Dossiers des Sciences de l'Éducation, 33, 109–130.

    Article  Google Scholar 

  • Steinberg, L., & Silk, J. S. (2002). Parenting adolescents. In M. Bornstein (Ed.), Handbook of parenting, 1 (Vol. 1, pp. 103–133). Mahwah: Lawrence Erlbaum Associates, Publishers.

    Google Scholar 

  • Story, M., & Stang, J. (2005). Nutrition needs of adolescents. In J. Stang & M. Story (Eds.), Guidelines for adolescent nutrition services (pp. 21–34). Minneapolis: University of Minnesota.

    Google Scholar 

  • Subrahmanyam, K., Greenfield, P., Kraut, R., & Gross, E. (2001). The impact of computer use on children's and adolescents' development. Journal of Applied Developmental Psychology, 22(1), 7–30. https://doi.org/10.1016/S0193-3973(00)00063-0.

    Article  Google Scholar 

  • Torsheim, T., Wold, B., & Samdal, O. (2000). The teacher and classmate support scale factor structure, test-retest reliability and validity in samples of 13-and 15-year-old adolescents. School Psychology International, 21(2), 195–212.

    Article  Google Scholar 

  • Tremblay, M. S., LeBlanc, A. G., Kho, M. E., Saunders, T. J., Larouche, R., Colley, R. C., et al. (2011). Systematic review of sedentary behaviour and health indicators in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 98. https://doi.org/10.1186/1479-5868-8-98.

    Article  Google Scholar 

  • UNICEF. (2007). Child poverty in perspective: An overview of child well-being in rich countries. In Innocenti report card 7. Florence: UNICEF Innocenti Research Centre.

    Google Scholar 

  • UNICEF. (2016). Fairness for children: A league table of inequality in child well-being in rich countries. In Innocenti report card 13. Florence: UNICEF Innocenti Research Centre.

    Google Scholar 

  • United Nations (1989). Convention on the rights of the child (CRC). http://www.ohchr.org/Documents/ProfessionalInterest/crc.pdf Accessed 05 July 2016.

  • Vereecken, C., Pedersen, T. P., Ojala, K., Krølner, R., Dzielska, A., Ahluwalia, N., et al. (2015). Fruit and vegetable consumption trends among adolescents from 2002 to 2010 in 33 countries. The European Journal of Public Health, 25(suppl 2), 16–19. https://doi.org/10.1093/eurpub/ckv012.

    Article  Google Scholar 

  • Viner, R. M., Ozer, E. M., Denny, S., Marmot, M., Resnick, M., Fatusi, A., et al. (2012). Adolescent health 2 adolescence and the social determinants of health. The Lancet, 379(9826), 1641–1652. https://doi.org/10.1016/s0140-6736(12)60149-4.

    Article  Google Scholar 

  • Voráčová, J., Sigmund, E., Sigmundová, D., & Kalman, M. (2015). Changes in eating behaviours among Czech children and adolescents from 2002 to 2014 (HBSC study). International Journal of Environmental Research and Public Health, 12(12), 15888–15899. https://doi.org/10.3390/ijerph121215028.

    Article  Google Scholar 

  • Walsh, S. D., Bruckauf, Z., & Gaspar, T. (2016). Adolescents at risk: Psychosomatic health complaints, low life satisfaction, excessive sugar consumption and their relationship with cumulative risks. In Innocenti working paper 2016–13. Florence: UNICEF Office of Research.

    Google Scholar 

  • Woodward, M., Oliphant, J., Lowe, G., & Tunstall-Pedoe, H. (2003). Contribution of contemporaneous risk factors to social inequality in coronary heart disease and all causes mortality. Preventive Medicine, 36(5), 561–568. https://doi.org/10.1016/S0091-7435(03)00010-0.

    Article  Google Scholar 

Download references

Acknowledgements

The Health Behaviour in School-aged Children (HBSC) study is a World Health Organization collaborative study and is supported by each member country of the HBSC network (www.hbsc.org). The HBSC study is coordinated internationally by Dr. Joanna Inchley, University of St. Andrews, Scotland, with international data coordination performed by Dr. Oddrun Samdal, University of Bergen, Norway.

Author information

Authors and Affiliations

  1. UNICEF Office of Research-Innocenti, Piazza SS Annunziata, 12, 50122, Florence, Italy

    Yekaterina Chzhen, Zlata Bruckauf & Emilia Toczydlowska

  2. Institute for Health and Social Policy and Douglas Institute, McGill University, 1130 Pine Avenue West, Montreal, QC, H3A 1A3, Canada

    Frank J. Elgar

  3. Department of Developmental and Educational Psychology, University of Seville, C/ Camilo José Cela, s/n, 41018, Seville, Spain

    Concepcion Moreno-Maldonado

  4. Utrecht Centre for Child and Adolescent Studies, Utrecht University, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands

    Gonneke W.J.M. Stevens

  5. Faculty of Physical Culture, Palacký University Olomouc, Tř. Míru 117, 77111, Olomouc, Czech Republic

    Dagmar Sigmundová

  6. Institute for Health and Social Policy, McGill University, 1130 Pine Ave West, Montreal, QC, H3A 1A3, Canada

    Geneviève Gariépy

Authors
  1. Yekaterina Chzhen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zlata Bruckauf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emilia Toczydlowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frank J. Elgar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Concepcion Moreno-Maldonado
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gonneke W.J.M. Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dagmar Sigmundová
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Geneviève Gariépy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yekaterina Chzhen.

Appendix

Appendix

Table 2 Gender, age, family structure and perceived family wealth as predictors of multidimensional poverty
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chzhen, Y., Bruckauf, Z., Toczydlowska, E. et al. Multidimensional Poverty Among Adolescents in 38 Countries: Evidence from the Health Behaviour in School-aged Children (HBSC) 2013/14 Study. Child Ind Res 11, 729–753 (2018). https://doi.org/10.1007/s12187-017-9489-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12187-017-9489-0

Keywords

Search

Navigation