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A spatial analysis of inter-regional patient mobility in Italy

Author

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  • Emanuela Marrocu

    ()

  • Silvia Balia
  • Rinaldo Brau

Abstract

Free patient mobility among autonomous providers has been often considered an effective stimulus for enhancing healthcare. However, some jurisdictions may underperform due to the existence of economies of scale and spatial spillovers. Where regions assume the costs of providing care to residents, this could challenge the sustainability of regional budgets in a decentralised National Health Service (NHS) and put at risk universalism and equity of health care. We use a ten years (2001-2010) panel of Italian data on hospital discharges to assess the determinants of inter-regional mobility and to distinguish between factors related with policies pursued by the regional health authorities from extra-regional (neighbouring regions or national-level) factors. Data on hospital discharges are merged with a set of variables on salient features of hospital care services in each Regional Health System (RHS) and with information on demographic and economic characteristics of Italian regions. We analyse bilateral Origin-to-Destination (OD) flows between any two regions by means of a gravity regression model that includes a rich set of push and pull factors. Compared to previous studies, mainly performed on cross-section samples, the longitudinal dimension of the data enables us to estimate a nonlinear conditionally correlated random effects dynamic model that accounts for region-pair-specific unobservable heterogeneity. Moreover, we address the issue of cross-regional dependence arising from the existence of regional spillovers by applying recent advances in spatial econometrics (Elhorst, 2014; Vega and Elhorst, 2015). The model is estimated for total inter-regional patient flows and for specific types of hospital admission, namely surgery, medicine and cancers. Finally, the estimation results are used to analyse specific what-if scenarios relevant to the health authorities for the national and sub-national management of services. Our main results suggest that, beside regional population and income, local supply factors such as hospital capacity and technology endowment, clinical specialization and performance indicators are important drivers of patient mobility. Moreover, geography matters and spatial proximity plays a relevant role in reinforcing inter-regional mobility patterns. Our econometric analysis has also detected a mildly explosive dynamics in inter-regional patient mobility over time. This result, coupled with the significant role played by factors not directly controlled by regional policy-makers and RHS managers (e.g. population, GDP per capita and spatial spillovers), might induce a polarisation between the group of the richest, most populated and best performing regions, which are increasingly capable of attracting more patients, and the group of the weakest regions, with growing patient outflows and severe financial and organizational problems. These considerations call for a thorough assessment of the long-run sustainability of the current decentralised NHS. RHS budget autonomy could not be entirely consistent with free patient choice. This opens a more general discussion on whether and to what extent the health financing system would require the introduction of appropriate equalising compensation schemes aimed at neutralising the financial consequences of mobility and, eventually, at guaranteeing universalism and equity in healthcare.

Suggested Citation

  • Emanuela Marrocu & Silvia Balia & Rinaldo Brau, 2016. "A spatial analysis of inter-regional patient mobility in Italy," ERSA conference papers ersa16p127, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa16p127
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    References listed on IDEAS

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    1. Daniele Fabbri & Silvana Robone, 2010. "The geography of hospital admission in a national health service with patient choice," Health Economics, John Wiley & Sons, Ltd., vol. 19(9), pages 1029-1047, September.
    2. Rosella Levaggi & Francesco Menoncin, 2013. "Soft budget constraints in health care: evidence from Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(5), pages 725-737, October.
    3. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
    4. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    5. Brekke, Kurt R. & Levaggi, Rosella & Siciliani, Luigi & Straume, Odd Rune, 2014. "Patient mobility, health care quality and welfare," Journal of Economic Behavior & Organization, Elsevier, vol. 105(C), pages 140-157.
    6. James P. LeSage & Christine Thomas-Agnan, 2015. "Interpreting Spatial Econometric Origin-Destination Flow Models," Journal of Regional Science, Wiley Blackwell, vol. 55(2), pages 188-208, March.
    7. Shinjo, Daisuke & Aramaki, Toshiharu, 2012. "Geographic distribution of healthcare resources, healthcare service provision, and patient flow in Japan: A cross sectional study," Social Science & Medicine, Elsevier, vol. 75(11), pages 1954-1963.
    8. Nicolas Debarsy, 2012. "The Mundlak Approach in the Spatial Durbin Panel Data Model," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 109-131, March.
    9. Rosella Levaggi & Roberto Zanola, 2004. "Patients' migration across regions: the case of Italy," Applied Economics, Taylor & Francis Journals, vol. 36(16), pages 1751-1757.
    10. James P. LeSage & R. Kelley Pace, 2008. "Spatial Econometric Modeling Of Origin‐Destination Flows," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 941-967, December.
    11. Marcello Montefiori, 2005. "Spatial competition for quality in the market for hospital care," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(2), pages 131-135, June.
    12. David Cantarero, 2006. "Health care and patients’ migration across Spanish regions," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 7(2), pages 114-116, June.
    13. Matteo Lippi Bruni & Lucia Nobilio & Cristina Ugolini, 2008. "The analysis of a cardiological network in a regulated setting: a spatial interaction approach," Health Economics, John Wiley & Sons, Ltd., vol. 17(2), pages 221-233, February.
    14. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    15. Simona Baldi & Davide Vannoni, 2017. "The impact of centralization on pharmaceutical procurement prices: the role of institutional quality and corruption," Regional Studies, Taylor & Francis Journals, vol. 51(3), pages 426-438, March.
    16. Adolph, Christopher & Greer, Scott L. & Massard da Fonseca, Elize, 2012. "Allocation of authority in European health policy," Social Science & Medicine, Elsevier, vol. 75(9), pages 1595-1603.
    17. Brekke, Kurt Richard & Cellini, Roberto & Siciliani, Luigi & Straume, Odd Rune, 2008. "Competition and quality in regulated markets with sluggish demand," CEPR Discussion Papers 6938, C.E.P.R. Discussion Papers.
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    More about this item

    Keywords

    regional health systems; hospital admissions; gravity model; nonlinear gravity panel model; spatial spillovers;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R50 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - General

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