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Measuring Avoidable Health Inequality with Realization of Conditional Potential Life Years (RCPLY)

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Author Info

  • Dennis Petrie
  • Kam Ki Tang
  • D.S. Prasada Rao

Abstract

In a series of papers (Tang, Chin and Rao, 2008; and Tang, Petrie and Rao 2006 & 2007), we have tried to improve on a mortality-based health status indicator, namely age-at-death (AAD), and its associated health inequality indicators that measure the distribution of AAD. The main contribution of these papers is to propose a frontier method to separate avoidable and unavoidable mortality risks. This has facilitated the development of a new indicator of health status, namely the Realization of Potential Life Years (RePLY). The RePLY measure is based on the concept of a “frontier country” that, by construction, has the lowest mortality risks for each age-sex group amongst all countries. The mortality rates of the frontier country are used as a proxy for the unavoidable mortality rates, and the residual between the observed mortality rates and the unavoidable mortality rates are considered as avoidable morality rates. In this approach, however, countries at different levels of development are benchmarked against the same frontier country without considering their heterogeneity. The main objective of the current paper is to control for national resources in estimating (conditional) unavoidable and avoidable mortality risks for individual countries. This allows us to construct a new indicator of health status – Realization of Conditional Potential Life Years (RCPLY). The paper presents empirical results from a dataset of life tables for 167 countries from the year 2000, compiled and updated by the World Health Organization. Measures of national average health status and health inequality based on RePLY and RCPLY are presented and compared.

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Bibliographic Info

Paper provided by Economic Studies, University of Dundee in its series Dundee Discussion Papers in Economics with number 224.

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Length: 41 pages
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:dun:dpaper:224

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Keywords: Mortality risk; avoidable deaths; health inequality; data envelopment analysis; stochastic frontier analysis.;

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References

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  1. K.K.Tang & Prasada Rao, . "Avoidable Mortality Risks and Measurement of Wellbeing and Inequality," MRG Discussion Paper Series 0806, School of Economics, University of Queensland, Australia.
  2. Wagstaff, Adam, 2009. "Correcting the concentration index: A comment," Journal of Health Economics, Elsevier, vol. 28(2), pages 516-520, March.
  3. Erreygers, Guido, 2009. "Correcting the Concentration Index: A reply to Wagstaff," Journal of Health Economics, Elsevier, vol. 28(2), pages 521-524, March.
  4. Erreygers, Guido, 2009. "Correcting the Concentration Index," Journal of Health Economics, Elsevier, vol. 28(2), pages 504-515, March.
  5. Allison, R. Andrew & Foster, James E., 2004. "Measuring health inequality using qualitative data," Journal of Health Economics, Elsevier, vol. 23(3), pages 505-524, May.
  6. Tang, Kam Ki & Petrie, Dennis & Rao, D.S. Prasada, 2009. "The income-climate trap of health development: A comparative analysis of African and Non-African countries," Social Science & Medicine, Elsevier, vol. 69(7), pages 1099-1106, October.
  7. Dennis Petrie & Kam Ki Tang, 2008. "A Rethink on Measuring Health Inequalities Using the Gini Coefficient," Discussion Papers Series 381, School of Economics, University of Queensland, Australia.
  8. Adam Wagstaff, 2005. "The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 429-432.
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Cited by:
  1. Adriana Castelli & Olena Nizalova, 2011. "Avoidable mortality: what it means and how it is measured," Working Papers 063cherp, Centre for Health Economics, University of York.

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