Attributing Causes to Disability

  • Wilma J. NusselderEmail author
  • Caspar C. Looman
  • Herman Van Oyen
  • Renata Tiene De Carvalho Yokota
Part of the International Handbooks of Population book series (IHOP, volume 9)


Diseases play a major role in the disablement process, especially at older ages. This chapter focuses on the attribution method, which uses cross-sectional data to partition the disability prevalence into additive contribution of causes, taking into account multimorbidity and that disability can occur in the absence of diseases. We present a detailed description of the attribution method, including the definition of the additive hazard models for binary and multinomial disability outcomes. The method is applied to cross-sectional data from Brazil to illustrate the interpretation of the cumulative hazard rates of disability and how the method accounts for multimorbidity and independence. A summary of previous studies that have applied the method are also provided. Finally, the limitations and strengths of this approach compared to alternative methods using cross-sectional data are outlined.


Attribution Disability Chronic diseases Additive hazard model Binary Multinomial 


  1. Chiang, C. L. (1961). On the probability of death from specific causes in the presence of competing risks. In Proceedings of the fourth Berkley symposium on mathematical statistics and probability (Vol. 4, pp. 169–180). Berkeley: University of California Press.Google Scholar
  2. Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. Palto Alto: Morgan Kaufmann.Google Scholar
  3. Eide, G. E. (2008). Attributable fractions for partitioning risk and evaluating disease prevention: A practical guide. The Clinical Respiratory Journal, 2(Suppl 1), 92–103.CrossRefGoogle Scholar
  4. Eide, G. E., & Gefeller, O. (1995). Sequential and average attributable fractions as aids in the selection of preventive strategies. Journal of Clinical Epidemiology, 48(5), 645–655.CrossRefGoogle Scholar
  5. Ferguson, J., Alvarez-Iglesias, A., Newell, J., et al. (2018). Estimating average attributable fractions with confidence intervals for cohort and case-control studies. Statistical Methods in Medical Research, 27(4), 1141–1152.CrossRefGoogle Scholar
  6. Hardy, S. E., & Gill, T. M. (2004). Recovery from disability among community-dwelling older persons. JAMA, 291(13), 1596–1602.CrossRefGoogle Scholar
  7. Klijs, B., Nusselder, W. J., Looman, C. W., et al. (2011). Contribution of chronic disease to the burden of disability. PLoS One, 6(9), e25325.CrossRefGoogle Scholar
  8. Klijs, B., Nusselder, W. J., Looman, C. W., et al. (2014). Educational disparities in the burden of disability: Contributions of disease prevalence and disabling impact. American Journal of Public Health, 104(8), e141–e148.CrossRefGoogle Scholar
  9. Llorca, J., & Delgado-Rodriguez, M. (2004). A new way to estimate the contribution of a risk factor in populations avoided no additivity. Journal of Clinical Epidemiology, 57(5), 479–483.CrossRefGoogle Scholar
  10. Manton, K. G., & Stallard, E. (1984). Recent trends in mortality analysis. Orlando: Academic.Google Scholar
  11. McElduff, P., Attia, J., Ewald, B., et al. (2002). Estimating the contribution of individual risk factors to disease in a person with more than one risk factor. Journal of Clinical Epidemiology, 55(6), 588–592.CrossRefGoogle Scholar
  12. Nusselder, W. J., & Looman, C. W. (2004). Decomposition of differences in health expectancy by cause. Demography, 41(2), 315–334.CrossRefGoogle Scholar
  13. Nusselder, W. J., Looman, C. W., Mackenbach, J. P., et al. (2005). The contribution of specific diseases to educational disparities in disability-free life expectancy. American Journal of Public Health, 95(11), 2035–2041.CrossRefGoogle Scholar
  14. Nusselder, W. J., van der Velden, K., van Sonsbeek, J. L., et al. (1996). The elimination of selected chronic diseases in a population: The compression and expansion of morbidity. American Journal of Public Health, 86(2), 187–194.CrossRefGoogle Scholar
  15. Nusselder, W. J., Wapperom, D., Looman, C. W. N., et al. (2018). Contribution of chronic conditions to disability in men and women in France. European Journal of Public Health, 29(1), 99–104.CrossRefGoogle Scholar
  16. Palazzo, C., Ravaud, J. F., Trinquart, L., et al. (2012). Respective contribution of chronic conditions to disability in France: Results from the national disability-health survey. PLoS One, 7(9), e44994.CrossRefGoogle Scholar
  17. Ruckinger, S., von Kries, R., & Toschke, A. M. (2009). An illustration of and programs estimating attributable fractions in large scale surveys considering multiple risk factors. BMC Medical Research Methodology, 9, 7.CrossRefGoogle Scholar
  18. Strobl, R., Muller, M., Emeny, R., et al. (2013). Distribution and determinants of functioning and disability in aged adults – Results from the German KORA-Age study. BMC Public Health, 13, 137.CrossRefGoogle Scholar
  19. Szwarcwald, C. L., Malta, D. C., Pereira, C. A., et al. (2014). Pesquisa Nacional de Saude no Brasil: concepcao e metodologia de aplicacao. Ciênc saúde coletiva, 19(2), 333–342.CrossRefGoogle Scholar
  20. Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science & Medicine, 38(1), 1–14.CrossRefGoogle Scholar
  21. Verbrugge, L. M., Lepkowski, J. M., & Imanaka, Y. (1989). Comorbidity and its impact on disability. Milbank Quarterly, 67(3–4), 450–484.CrossRefGoogle Scholar
  22. Vos, T., Flaxman, A. D., Naghavi, M., et al. (2012). Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: A systematic analysis for the global burden of disease study 2010. Lancet, 380(9859), 2163–2196.CrossRefGoogle Scholar
  23. World Health Organisation. (2001). International classification of functioning, disability and health: ICF. Geneva.Google Scholar
  24. World Health Organization. (2013). WHO methods and data sources for global burden of disease estimates 2000–2011. Geneva.Google Scholar
  25. Yee, T., & Hastie, T. (2003). Reduced-rank vector generalized linear models. Statistical Modelling, 3(1), 15–41.CrossRefGoogle Scholar
  26. Yokota, R. T., Berger, N., Nusselder, W. J., et al. (2015a). Contribution of chronic diseases to the disability burden in a population 15 years and older, Belgium, 1997-2008. BMC Public Health, 15, 229.CrossRefGoogle Scholar
  27. Yokota, R. T., de Moura, L., Andrade, S. S., et al. (2016a). Contribution of chronic conditions to gender disparities in disability in the older population in Brazil, 2013. International Journal of Public Health, 61(9), 1003–1012.CrossRefGoogle Scholar
  28. Yokota, R. T., Nusselder, W. J., Robine, J. M., et al. (2016b). Contribution of chronic conditions to the disability burden across smoking categories in middle-aged adults, Belgium. PLoS One, 11(4), e0153726.CrossRefGoogle Scholar
  29. Yokota, R. T., Van der Heyden, J., Demarest, S., et al. (2015b). Contribution of chronic diseases to the mild and severe disability burden in Belgium. Archives of Public Health, 73(1), 37.CrossRefGoogle Scholar
  30. Yokota, R. T., Van der Heyden, J., Nusselder, W. J., et al. (2016c). Impact of chronic conditions and multimorbidity on the disability burden in the older population in Belgium. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 71(7), 903–909.CrossRefGoogle Scholar
  31. Yokota, R. T. C., Looman, C. W. N., Nusselder, W. J., et al. (2016d). Addhaz: Binomial and multinomial additive hazards model. in R Package.Google Scholar
  32. Yokota, R. T. C., Nusselder, W. J., Robine, J. M., et al. (2017a). Contribution of chronic conditions to functional limitations using a multinomial outcome: Results for the older population in Belgium and Brazil. Archives of Public Health, 75, 68.CrossRefGoogle Scholar
  33. Yokota, R. T. C., Van Oyen, H., Looman, C. W. N., et al. (2017b). Multinomial additive hazard model to assess the disability burden using cross-sectional data. Biometrical Journal, 59(5), 901–917.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wilma J. Nusselder
    • 1
    Email author
  • Caspar C. Looman
    • 1
  • Herman Van Oyen
    • 2
    • 3
  • Renata Tiene De Carvalho Yokota
    • 2
    • 4
  1. 1.Department of Public HealthErasmus University Medical CentreRotterdamThe Netherlands
  2. 2.Department of Epidemiology and Public HealthSciensanoBrusselsBelgium
  3. 3.Department of Public Health and Primary CareGhent UniversityGhentBelgium
  4. 4.Department of Sociology, Interface DemographyVrije Universiteit BrusselBrusselsBelgium

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