Skip to main content

Advertisement

SpringerLink
Log in

Using Australian panel data to account for unobserved factors in measuring inequities for different channels of healthcare utilization

  • Original Paper
  • Published:
The European Journal of Health Economics Aims and scope Submit manuscript

Abstract

Inequity in healthcare utilization is typically measured as the unequal distribution of services by observable non-need indicators, such as income, after controlling for observable need indicators. However, important sources of unequal healthcare utilization are often unobserved. The unobserved element may reflect need factors, such as imperfectly measured severity of illness, that would predict greater utilization across different healthcare channels, but also based on choice, such as patient preferences to use a particular healthcare channel over an alternative one, which may differ in its effect between channels. Accounting for unobserved sources of utilization may, therefore, help to understand contradictory inequalities between different healthcare channels, such as pro-poor inequalities for general practitioner use and pro-rich inequalities for specialist visits. This paper uses survey data from the Household Income and Labour Dynamics in Australia and panel data methods to investigate if seemingly contradictory inequalities between different healthcare channels are explained by latent individual-level heterogeneity. Results show that unobserved individual-level heterogeneity affects inequities across different healthcare channels, providing indications that the unobserved element may primarily represent unobserved need.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. For a definition of different dimensions of access beyond utilization such as affordability, availability, and acceptability, see Penchansky and Thomas [20] or Silal et al. [22].

  2. Some unmet need may be self-chosen [2], corresponding to what we refer to as “preferences”.

  3. This definition of the Australian (reference) population does not include diplomatic personnel of overseas governments and members of non-Australian defence forces, or non-Australian individuals who stayed or intended to stay in Australia for less than one year. Institutions include hospitals, health care institutions, military and police installations, correctional and penal institutions, convents and monasteries, hotels, and motels.

  4. Data until wave 11 are, therefore, representative of the reference population in 2000 and data from wave 11 are representative of the reference population in 2010. However, due to cross-wave attrition and imperfect coverage of variables, a given sample may not be representative of either of these two reference populations.

  5. Our restriction of the sample to those over the age of 25 is used to account for the fact that education attainment typically increases until individuals are in their mid-20s, making it more difficult to interpret different individual circumstances that affect employment, income, and education, as some individuals may be studying or working, and the importance of these variables may take a different meaning depending on whether they live independently or with family. However, these transitory life stages are typical for individuals less than 25 years old.

  6. Hausman tests confirm that fixed effects are preferred over random effects for all models.

  7. We cluster standard errors at the individual level.

  8. Coefficient estimates for need are omitted, and their inclusion described by indicators. The SM includes the full results for all three channels of healthcare utilization.

  9. Our classification for being not in the labour force includes individuals not marginally attached to the labour force, hence individuals who are likely to look for work if the labour market circumstances change. By contrast, those outside the labour force but marginally attached, including individuals who are generally interested in working, are grouped with unemployed individuals.

  10. Note that we use HILDA, while Van Doorslaer et al., [27] and Hajizadeh et al., [10] use data from the Australian National Health Surveys. Both are representative of the population, but we do not use population weights to ensure representativeness of our results and, based on our understanding, also previous papers did not use population weighting, making results only partially comparable.

References

  1. Allanson, P., Gerdtham, U.G., Petrie, D.: Longitudinal analysis of income-related health inequality. J. Health Econ. 29(1), 78–86 (2010)

    Article  Google Scholar 

  2. Allin, S., Grignon, M., Le Grand, J.: Subjective unmet need and utilization of health care services in Canada: what are the equity implications? Soc. Sci. Med. 70(3), 465–472 (2010)

    Article  Google Scholar 

  3. Allin, S., Masseria, C., Mossialos, E.: Equity in health care use among older people in the UK: an analysis of panel data. Appl. Econ. 43(18), 2229–2239 (2011)

    Article  Google Scholar 

  4. Andersen, R.M.: Revisiting the behavioral model and access to medical care: does it matter? J. Health Soc. Behav. 36(1), 1–10 (1995)

    Article  CAS  Google Scholar 

  5. Australian Bureau of Statistics (1995) 4399.0 National Health Survey: SF-36 population norms, Canberra, https://www.abs.gov.au/ausstats/abs@.nsf/mf/4399.0, Accessed 1 Oct 2019

  6. Bago d’Uva, T., Jones, A.M., Van Doorslaer, E.: Measurement of horizontal inequity in health care utilisation using European panel data. J. Health Econ. 28(2), 280–289 (2009)

    Article  Google Scholar 

  7. Devaux, M., De Looper, M. (2012). Income-related inequalities in health service utilisation in 19 OECD countries, 2008–2009. OECD Health Working Papers. No 58. OECD Publishing, Paris

  8. Duckett, S., Willcox, S.: The Australian health care system, 5th edn. Oxford University Press, Oxford (2015)

    Google Scholar 

  9. Etilé, F., Milcent, C.: Income-related reporting heterogeneity in self-assessed health: evidence from France. Health Econ. 15(9), 965–981 (2006)

    Article  Google Scholar 

  10. Hajizadeh, M., Connelly, L.B., Butler, J.R.G.: Health policy and horizontal inequities of health-care utilization in Australia: 1983–2005. Appl. Econ. Lett. 19(18), 1765–1775 (2012)

    Article  Google Scholar 

  11. Hernández-Quevedo, C., Jones, A.M., López-Nicolás, A., Rice, N.: Socioeconomic inequalities in health: a comparative longitudinal analysis using the European Community Household Panel. Soc. Sci. Med. 63(5), 1246–1261 (2006)

    Article  Google Scholar 

  12. Jones, A.M., López-Nicolás, A.L.: Measurement and explanation of socioeconomic inequality in health with longitudinal data. Health Econ. 13(10), 1015–1030 (2004)

    Article  Google Scholar 

  13. Le Grand, J.: Equity and choice: an essay in economics and applied philosophy. Harper Collins Academic, London (1991)

    Book  Google Scholar 

  14. Lu, J.R., Leung, G.M., Kwon, S., Tin, K.Y., Van Doorslaer, E., O’Donnell, O.: Horizontal equity in health care utilization evidence from three high-income Asian economies. Soc. Sci. Med. 64(1), 199–212 (2007)

    Article  Google Scholar 

  15. McBride, D., Hardoon, S., Walters, K., Gilmour, S., Raine, R.: Explaining variation in referral from primary to secondary care: cohort study. BMJ 341, c6267 (2010)

    Article  Google Scholar 

  16. Morris, S., Sutton, M., Gravelle, H.: Inequity and inequality in the use of health care in England: an empirical investigation. Soc. Sci. Med. 60(6), 1251–1266 (2005)

    Article  Google Scholar 

  17. Muurinen, J.M., Le Grand, J.: The economic analysis of inequalities in health. Soc. Sci. Med. 20(10), 1029–1035 (1985)

    Article  CAS  Google Scholar 

  18. O’Donnell, O., Propper, C.: Equity and the distribution of UK National Health Service resources. J. Health Econ. 10(1), 1–19 (1991)

    Article  CAS  Google Scholar 

  19. O’Donnell, O., Van Doorslaer, E., Wagstaff, A., Lindelow, M.: Analyzing health equity using household survey data: a guide to techniques and their implementation. The World Bank, Washington (2008)

    Google Scholar 

  20. Penchansky, R., Thomas, J.W.: The concept of access: definition and relationship to consumer satisfaction. Med Care 19(2), 127–140 (1981)

    Article  CAS  Google Scholar 

  21. Propper, C.: The demand for private health care in the UK. J. Health Econ. 19(6), 855–876 (2000)

    Article  CAS  Google Scholar 

  22. Silal, S.P., Penn-Kekana, L., Harris, B., Birch, S., McIntyre, D.: Exploring inequalities in access to and use of maternal health services in South Africa. BMC Health Serv. Res. 12(1), 120 (2012)

    Article  Google Scholar 

  23. Scott, A., Shiell, A., King, M.: Is general practitioner decision making associated with patient socio-economic status? Soc. Sci. Med. 42(1), 35–46 (1996)

    Article  CAS  Google Scholar 

  24. Sørensen, T.H., Olsen, K.R., Vedsted, P.: Association between general practice referral rates and patients’ socioeconomic status and access to specialised health care: a population-based nationwide study. Health Policy 92(2–3), 180–186 (2009)

    Article  Google Scholar 

  25. Summerfield, M., Freidin, S., Hahn, M., Ittak, P., Li, N., Macalalad, N., et al.: HILDA user manual–release 10. In: Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Melbourne (2011)

    Google Scholar 

  26. Van Doorslaer, E., Masseria, C.: Income-related inequality in the use of medical care in 21 OECD countries (109–166). In: Organisation for Economic Cooperation and Development. Towards high-Performing Health Systems. OECD, Paris (2004)

  27. Van Doorslaer, E., Clarke, P., Savage, E., Hall, J.: Horizontal inequities in Australia’s mixed public/private health care system. Health Policy 86(1), 97–108 (2008)

    Article  Google Scholar 

  28. Van Ourti, T.: Measuring horizontal inequity in Belgian health care using a Gaussian random effects two part count data model. Health Econ. 13(7), 705–724 (2004)

    Article  Google Scholar 

  29. Wagstaff, A., Van Doorslaer, E.: Equity in health care finance and delivery. Handb. Health Econ. 1, 1803–1862 (2000)

    Article  Google Scholar 

  30. Wagstaff, A., Van Doorslaer, E., Paci, P.: On the measurement of horizontal inequity in the delivery of health care. J. Health Econ. 10(2), 169–205 (1991)

    Article  CAS  Google Scholar 

  31. Watson, N., Wooden, M., Re-Engaging with Survey Non-Respondents: The BHPS, SOEP and HILDA Survey Experience (February 2011). Melbourne Institute Working Paper No. 2/11. https://doi.org/10.2139/ssrn.1792983. Accessed 15 Oct 2021

  32. World Health Organization: Closing the gap in a generation: health equity through action on the social determinants of health: commission on social determinants of health final report. World Health Organization, Geneva (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for the Business and Economics of Health, The University of Queensland, Sir Llew Edwards Building (#14), Cnr University Drive and Campbell Road, St Lucia, QLD, 4067, Australia

    Jonas Fooken

  2. Macquarie University Centre for the Health Economy, Level 1 EMC2 Building, 3 Innovation Rd, Macquarie Park, NSW, 2109, Australia

    Varinder Jeet

Authors
  1. Jonas Fooken
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Varinder Jeet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonas Fooken.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 183 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fooken, J., Jeet, V. Using Australian panel data to account for unobserved factors in measuring inequities for different channels of healthcare utilization. Eur J Health Econ 23, 717–728 (2022). https://doi.org/10.1007/s10198-021-01391-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10198-021-01391-0

Keywords

JEL

Search

Navigation