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How Do the Determinants of Demand for GP Visits Respond to Higher Supply? An Analysis of Grouped Counts

  • Paulos Teckle
  • Matt Sutton
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    Although there is a substantial literature on the determinants of demand for primary care, few studies have been able to examine how these determinants respond to higher supply. Some demand studies include supply variables or regional dummy variables to allow for different supply conditions. A few have tested for marginal effects of supply variables attributed at a highly aggregated geographic level. However, relatively little is known about whether there is a supply constraint and how demand responses differ across population groups. We used information from a household survey of 60'806 individuals for whom we had detailed information on supply and access conditions. As in many surveys, the annual measure of utilisation is a grouped count and we estimate a grouped negative binomial model (NegBin2) of the determinants of demand for general practitioner (GP) visits by Maximum Likelihood. We exploit a variable on which respondents were asked to report the convenience with which they were able to access GP services. We demonstrate the significance of this variable in determining the number of GP visits. We then examine which demand determinants are correlated with reported convenience. Finally, we compare the demand equations for respondents reporting unconstrained access to GPs with respondents reporting constrained access. We find that being unemployed has a significant positive effect on GP visits for individuals who reported poor access. People who own a car and reported a good access to GPs have significantly higher visits.

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    Article provided by Swiss Society of Economics and Statistics (SSES) in its journal Swiss Journal of Economics and Statistics.

    Volume (Year): 144 (2008)
    Issue (Month): III (September)
    Pages: 495-513

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    Handle: RePEc:ses:arsjes:2008-iii-10
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    1. Cameron, A Colin & Windmeijer, Frank A G, 1996. "R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 209-20, April.
    2. Andrew M. Jones, 2012. "health econometrics," The New Palgrave Dictionary of Economics, Palgrave Macmillan.
    3. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    4. Morris, Stephen & Sutton, Matthew & Gravelle, Hugh, 2005. "Inequity and inequality in the use of health care in England: an empirical investigation," Social Science & Medicine, Elsevier, vol. 60(6), pages 1251-1266, March.
    5. Sisira Sarma & Wayne Simpson, 2006. "A microeconometric analysis of Canadian health care utilization," Health Economics, John Wiley & Sons, Ltd., vol. 15(3), pages 219-239.
    6. Ignacio Abasolo & Rob Manning & Andrew Jones, 2001. "Equity in utilization of and access to public-sector GPs in Spain," Applied Economics, Taylor & Francis Journals, vol. 33(3), pages 349-364.
    7. Jorgen Lauridsen & Terkel Christiansen & Unto Häkkinen, 2004. "Measuring inequality in self-reported health-discussion of a recently suggested approach using Finnish data," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 725-732.
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