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Measuring Health Inequality in US: A Composite Index Approach

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Abstract

In this paper, we use the standardized mortality rates for 21 mutual exclusive causes of death to propose a composite index of US county-level health performances in 1980–2014 interval. We aggregate mortality rates by the stochastic multi-criteria acceptability analysis (SMAA), in order to avoid any a priori judgement on the importance given to a specific cause of death. The total observed inequality among counties is then decomposed to estimate the variability between and within states by means of the Theil index on SMAA outcomes. On average, there has been a decrease in the Composite Index of mortality from 1980 to 2014, but while the majority of counties had an increase in health conditions, some counties have shown a decrease in health performances in the same interval. This may be the reason of a persistent increase of total inequality among counties, with inequality within states constantly higher than inequality between states, both responsible of the growing inequality levels of health performances in the period analysed.

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Notes

  1. DEA originates from the work of Farrel (1957) and it has been further developed by Charnes et al. (1978).

  2. DEA originates from the work of Farrel (1957) and further developed by Charnes et al. (1978).

  3. The complete list is available as supplementary material.

  4. As it is known, Lorenz curves have a correspondence with the Gini index of inequality. Provided that Lorenz curves do not intersect, they rank distributions in the same way as the Gini index. However, where Lorenz curves intersect they can provide only a partial information on how to rank distributions, and this rank may differ from that provided by the Gini index. The reason is that Lorenz curves may provide only partial rankings when they intersect, while the Gini index provides a complete ranking even in the case where Lorenz curves intersect.

  5. The correlation coefficient is 0.001 in 1980.

  6. The correlation coefficient is − 0.063 in 2014.

  7. Using equal weighting would amount to use an all-cause mortality index, as the single mortality rates would just add up to the overall mortality rate. However, in the Global Health Data Exchange used in this paper, the all-cause mortality rate is calculated by using an age-standardized technique that is different from the age-standardized technique used to calculate the single mortality rates. Thus, in the dataset, the equal weighting of the single indicators does not add up to the all-cause mortality rate.

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Authors and Affiliations

  1. Department of Economics and Finance, University of Bari “Aldo Moro”, Largo Abbazia Santa Scolastica, 70124, Bari, Italy

    Raffaele Lagravinese

  2. Department of Economics, Roma Tre University, Via Silvio d’Amico, 77, 00145, Rome, Italy

    Paolo Liberati

  3. Italian National Research Council of Italy (CNR), Via G. Moruzzi, 1, 56124, Pisa, Italy

    Giuliano Resce

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  1. Raffaele Lagravinese
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Lagravinese, R., Liberati, P. & Resce, G. Measuring Health Inequality in US: A Composite Index Approach. Soc Indic Res 147, 921–946 (2020). https://doi.org/10.1007/s11205-019-02177-x

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