Abstract
This chapter examines patient mobility across Italian regions using data on hospital discharges that occurred in 2008. The econometric analysis is based on Origin–Destination (OD) flow data. Since patient mobility is a crucial phenomenon in contexts of hospital competition based on quality and driven by patient choice, as is the case in Italy, it is crucial to understand its determinants. What makes the Italian case more interesting is the decentralization of the National Health Service that yields large regional variation in patient flows in favor of Centre-Northern regions, which typically are ‘net exporters’ of hospital treatments. We present results from gravity models estimated using count data estimators, for total and specific types of flows (ordinary admissions, surgical DRGs and medical DRGs). We model cross-section dependence by specifically including features other than geographical distance for OD pairs, such as past migration flows and the share of surgical DRGs. Most of the explanatory variables exhibit the expected effect, with distance and GDP per capita at origin showing a negative impact on patient outflows. Past migrations and indicators of performance at destination are effective determinants of patient mobility. Moreover, we find evidence of regional externalities due to spatial proximity effects at both origin and destination.
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Notes
- 1.
- 2.
Somewhat surprisingly, process factors are found to play a major role, and outcome factors a residual one (Rademakers et al. 2011).
- 3.
In fact, health care is a public and merit good which the central regulators want to provide equitably in the whole federation.
- 4.
As remarked by Lippi Bruni et al. (2008), the importance of distance differs significantly among treatments, as demand for health services is far from homogeneous. In particular, patients generally show a greater willingness to travel for major treatments. Hence, the intensity of the distance decay effect cannot be generalized.
- 5.
Approximately 33.6 % of public hospitals are run by LHAs; 11 % are autonomous public enterprises; 4.4 % are scientific institutes for research, hospitalization and health care and 2 % are medical school hospitals. Private accredited hospitals represent about 45 % of the total number of providers but only 16 % of total admissions.
- 6.
Ordinary admissions imply at least one night spent at the hospital and exclude admissions in long-term care and rehabilitation wards, as well as admissions of healthy babies born at the hospital.
- 7.
The inflow rate is the percentage ratio between non-enrolees admitted in region j (inflows) and the total number of admissions in region j. The outflow rate is the percentage ratio between enrolees of region i admitted in other regions (outflows) and the total number of admissions of enrolees of region i.
- 8.
The devices are magnetic resonance imaging, linear accelerator in radiotherapy, computed axial tomography (CT), gamma CT, mammogram, gamma camera, positron emission tomography (PET) or CT-integrated PET, hyperbaric chamber, digital angiography, automated immunochemistry analyzer.
- 9.
The case-mix standardization controls for regional differences in the complexity of the admissions. Consequently, the indicator of regional standardized average lengths of stays assumes that each region share the same complexity of cases as the national average.
- 10.
In the yearly report on hospitals activity based on the discharges database, the Italian Ministry of Health assesses the efficiency of RHS on the basis of data on ordinary admissions with stays longer than one day. Stays are typically longer and cases can be more clinically complex in long-term and rehabilitation wards and neonatal care units.
- 11.
In our sample data we just have one pair of regions, Val d’Aosta and Basilicata, featuring a zero patient flow.
- 12.
Note that this specification is referred to as negative binomial 2 (NegBin2); the negative binomial 1 entails a linear variance function. The NegBin2 specification is typically preferred because the quadratic form has been proven to provide a very good approximation to more general variance functions. This is a remarkable advantage because the maximum likelihood estimators for negative binomial models are not consistent when the variance specification is incorrect.
- 13.
In a preliminary analysis we also estimated the Poisson model, but we found overwhelming evidence in contrast with the equidispersion assumption.
- 14.
Fabbri and Robone (2010) claim that past residential migration is one of the most relevant variable that can generate network autocorrelation in patient flows.
- 15.
In order to account for the particular morphology of the Italian territory, an undisputed factor in driving the well-known North–South divide, we also include a dummy variable to discriminate between Centre-Northern and Southern regions. The dummy turned out to be highly significant with the expected negative sign, but it makes negative the coefficient of per capita GDP. This is due to the fact that the two variables are significantly negatively correlated. For these reasons we prefer the specification that includes per capita GDP only.
- 16.
- 17.
The selection of the lagged regressors was carried out on the basis of a preliminary analysis performed on all the origin and destination covariates.
- 18.
Moreover, note that the “hospital capacity” variable turn out to be significant in model (6).
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Acknowledgments
The research leading to these results received funding from the Sardinian Region (LR7). The authors would also like to thank Daniela Moro for valuable assistance in preparing the database. We thank Lucia Lispi and Pietro Granella from the Italian Ministry of Health for offering access to the hospital discharge data.
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Balia, S., Brau, R., Marrocu, E. (2014). What Drives Patient Mobility Across Italian Regions? Evidence from Hospital Discharge Data. In: Levaggi, R., Montefiori, M. (eds) Health Care Provision and Patient Mobility. Developments in Health Economics and Public Policy, vol 12. Springer, Milano. https://doi.org/10.1007/978-88-470-5480-6_6
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