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Health system characteristics and unmet care needs in Europe: an analysis based on EU-SILC data

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Abstract

Using survey data from the 2009 wave of the European Union Statistics on Income and Living Conditions, this study examines the determinants of unmet needs for medical and dental care in European countries. Special emphasis is put on the impact of health system characteristics. Four factors are taken into account: the density of doctors or dentists, the rules governing access to practitioners, the method of paying primary care physicians, and the amount of out-of-pocket payments. The analysis is carried out using multilevel logistic regression models. Separate regressions are estimated for medical and dental services. The dependent variable is whether respondents reported that, at least once in the last 12 months, they needed care but did not receive it. The estimation results show that the probability of experiencing unmet medical or dental needs varies noticeably across countries. This inter-country variability seems to be partly explained by the differences in the financing of health care. Indeed, a positive link is found between the share of households’ out-of-pocket payments in total health expenditure and the probability of unmet needs. The other contextual factors do not seem to play a significant role.

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Notes

  1. The only factor relating to the health care system that was taken into account in one of these studies (by Tchicaya and Lorentz [41], investigating social inequalities in access to dental care) was the level of availability of dental services (as measured by the number of dentists per 10,000 inhabitants).

  2. This study covers thirteen European countries: Belgium, Denmark, England, Estonia, France, Germany, Hungary, Ireland, Italy, Latvia, Norway, Portugal and the Netherlands.

  3. In the fourth country, namely in Bulgaria, the proportion of unmet needs due to financial reasons is close to 70 %.

  4. These are unweighted means.

  5. For an introduction to these models, see for example Singer [40].

  6. We used quintiles rather than deciles in this step because our attempts to estimate a model with random coefficients for all income decile dummies were unsuccessful in the case of dental care.

  7. The averages of the individual probabilities, computed from Model I, are equal to 0.079 and 0.110, respectively, for the former group, as against 0.040 and 0.042 for the latter.

  8. However, in the case of parents with young children, this higher propensity to forgo or delay care may as well be explained by the difficulties in balancing work, family responsibilities and personal life.

  9. This was computed as the average of individual probabilities.

  10. This remains true when these characteristics are introduced separately (results available upon request).

  11. Probability changes were calculated for each individual (using Model III) and then averaged across the sample.

  12. The significant impact of out-of-pocket payments on the prevalence of unmet health care needs was confirmed by a complementary analysis based on country-level panel data (same set of countries as in the multilevel models; at least three annual observations over the period 2004–2011). Indeed, using fixed-effects regressions, and controlling for a number of other factors (proportion of the population aged 50 and over, proportion of adults assessing their health as “very good”, percentage of people with upper secondary or higher education, GDP per capita, unemployment rate, and population density), we found that increases in out-of-pocket payments were associated with increased rates of unmet needs for medical and dental care. The detailed results of this analysis are available from the authors.

  13. Private health insurance is likely to affect (negatively) the occurrence of unmet needs mainly through its impact on the out-of-pocket cost of care. Including this variable is therefore equivalent to indirectly taking into account the effect of out-of-pocket payments. In the baseline specification, we chose to introduce the level of these payments explicitly rather than indirectly. This explains why the insurance variable was only used in the discussion section.

  14. Using data from the 2007 wave of EU-SILC, Tchicaya and Lorentz [41], for their part, found a significant relationship between dentist density and the probability of reporting unmet dental needs, but not in the expected direction.

  15. To limit the size of the table, the estimated parameters of Model IV are not presented here, but are available upon request.

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Acknowledgments

This study was carried out with the financial support of the Region Lorraine and the University of Lorraine. The authors would like to thank two anonymous referees and participants at the IMPALLA-ESPANET international conference on ‘Building blocks for an inclusive society: empirical evidence from social policy research’ (Esch-Belval, 18–19 April 2013) and the 33rd annual meeting of the French association for social economics (Marne-la-Vallée, 12–13 September 2013), for helpful comments and suggestions.

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  1. Bureau d’Economie Théorique et Appliquée, CNRS, University of Strasbourg, and University of Lorraine, 13, Place Carnot, C.O. n°70026, 54035, Nancy Cedex, France

    Sabine Chaupain-Guillot & Olivier Guillot

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  1. Sabine Chaupain-Guillot
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  2. Olivier Guillot
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Correspondence to Olivier Guillot.

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Chaupain-Guillot, S., Guillot, O. Health system characteristics and unmet care needs in Europe: an analysis based on EU-SILC data. Eur J Health Econ 16, 781–796 (2015). https://doi.org/10.1007/s10198-014-0629-x

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