Abstract
We provide a novel approach to evaluate access to health care based on the monetization of access barriers individuals face. This approach allows to distinguish the opportunities individuals enjoy from their utilization of health services, permitting a better assessment of potential, as opposed to revealed, access. An application to Italian data on heart valve replacement shows that the methodology we provide can be easily applied to quantify the share of the population to which access is precluded and the key determinants of denied access.
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Notes
Traditionally, the economic literature sees access as depending on supply as well as demand factors (e.g. Mooney 1983). Supply factors affecting access to health care relate to the spatial distribution of providers, the production technology as well as other factors influencing the cost and the appropriateness of health services. Demand factors are generally related to the individual capacity of obtaining and processing the necessary information; the burden of disease; the individual’s skills and attitudes; the diffusion of self-care practices (Andersen 1995). Levesque et al. (2013) outline five dimensions capturing supply as well as demand-side determinants of access to care: approachability, acceptability, availability and accommodation, affordability and appropriateness.
First, even if some inequalities in the health status might be unproblematic (e.g. due to bad lifestyles), health care is a social primary good, whose accessibility is a prerequisite for the exercise of basic rights and liberties (Daniels 1981, 1985); it turns out that, by rewarding lifestyle decisions in the context of health care, one would inevitably jeopardize the liberal principle of equal political and civil rights, because a bad health status would limit individual possibilities to exercise these rights. Second, the principle of responsibility states that individuals should be held responsible for their choices, not for the consequences of their choices; it is only in the special case in which outcomes depend solely (or sensibly) on personal choices (what does not seem to be the case with health) that individuals can be held responsible. As actual consequences of a choice partly depend on factors beyond the individual's control, those who make the same choices may not have the same need for treatment. Forcing only the subset of people in need to pay the cost of irresponsible choices is at the hearth of what might be called a fairness objection (Cappelen and Norheim 2005). In addition to these, a humanitarian argument is also worth mentioning, according to which, any society has a moral obligation to help people in need. Given these objections, one may agree that Roemer’s ideal of levelling the playing field, widely accepted in the literature on income inequality, can be seriously questioned as far as disparities in health or in health care consumption are concerned.
The low no access probability associated with Latina finds an explanation in its low cost of access due to its proximity to Rome (where a health treatment of appropriate quality is provided).
Notice that, although important, our paper does not report the result of the simulation exercise when differences in needs for heart valve replacement at the provincial level are accounted for, since this does not affect the findings in any significant way. Indeed, by proxying the risk of heart valve substitution with data on general cardiovascular risk, we have computed the distribution of the probability of access weighted by the risk. Results remain basically unchanged (the linear correlation between the weighted and non-weighted probabilities is indeed 0.96). Data are available upon request.
We wish to thank one of the reviewers for raising this point.
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Acknowledgements
We wish to thank Paolo Li Donni, Massimiliano Piacenza, Silvana Robone and all the participants to the XXXI Conference of the Italian Society of Public Economics (Turin, September 2019) for helpful suggestions and comments. The usual disclaimers apply.
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Funding was provided by University of Naples Federico II (Grant No. PG_2017_0016542_00016542).
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Appendix 1: Computing Accessible Resources
Appendix 1: Computing Accessible Resources
The accessible resources of individual i in province j, can be written as
where Yij denotes the accessible resources available to individual i in province j. Such resources are given by the difference between equivalent consumption CEij and the equivalent poverty line PEij , where equivalent means that use is made of the equivalence scales provided by ISTAT to take into account the composition of the household. That is \(C_{ij}^{E} = \frac{{C_{hj} }}{{K_{hj} }}\), \(h = 1, \ldots ,H\), where Chj is the overall consumption of household h, to which individual i belongs, in province j, and Khj is the equivalence scale associated to household h. CEij is therefore the per-capita consumption within the household taking into consideration economies of scale in consumption. \(P_{ij}^{E}\) is calculated in the same way, \(P_{ij}^{E} = \frac{{P_{hj} }}{{K_{hj} }}\). The difference \(C_{ij}^{E} - P_{ij}^{E}\) gives the resources that an individual can rely upon in case of need together with the overall savings of family h, Shj, and other financial (immediately available) assets, FAhj.
Notice that the survey on Household Income and Wealth provides information concerning only the region where a given household lives. As our purpose is to carry on the analysis at the provincial level, we have employed an incremental nearest-neighbourhood strategy to place individuals in the right province.
To this end we have first computed the average income in each of the Italian provinces, then we have considered each region in turn. For any given region, we have performed a fuzzy clustering analysis providing, as a probability vector, the likelihood that a given household belongs to a certain province. Individuals are so randomly associated to provinces. The analysis has been designed in such a way as to respect the two following conditions: (1) the proportion of sampled individuals assigned to a given province respects the real proportion of the province population over the regional one; (2) the expected income in the pool of individuals associated to a given province must be consistent with the real mean income of the province.
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Abatemarco, A., Aria, M., Beraldo, S. et al. Measuring Disparities in Access to Health Care: A Proposal Based on an Ex-ante Perspective. Soc Indic Res 150, 549–568 (2020). https://doi.org/10.1007/s11205-020-02305-y
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DOI: https://doi.org/10.1007/s11205-020-02305-y