Abstract
In this paper, we apply a two-part model to estimate the effect of health literacy on the demand for physician visits under different institutional settings. Using a constructed measure of health information, we find evidence for supplier-induced demand in some parts of Switzerland. While the level of health information is uncorrelated with the likelihood of visiting a physician (contact decision), the conditional number of visits (frequency decision) depends on the individual’s information status and the regulation of physician drug dispensing. In cantons with a drug prescription scheme, we do not find a significant difference in the number of visits between well-informed individuals and people with relatively little health literacy. In contrast, the existence of self-dispensing general practitioner and specialists is associated with a gap in demand that is strongly related to health literacy: Compared to cantons with prescription schemes, uninformed patients exhibit a higher number of outpatient visits in the cantons that (partly) allow the dispensation of drugs by physicians. However, patients with a high level of health information seem to be rather unaffected by physician drug dispensing. As a consequence, we observe an information-related gap in the number of outpatient contacts that only prevails in areas where doctors are entitled to sell drugs themselves. These findings suggest that self-dispensing doctors succeed in inducing demand that affects the number of physician-patient contacts. Health literacy, on the other hand, tends to counter these incentives.
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Notes
McGuire (2000) provides an extensive overview of empirical studies on fees and inducement.
The proof can be found in Appendix.
In Switzerland, prescribing physicians are also compensated for medical prescriptions according to a nationwide fee-for-service catalogue. In absolute terms, however, the prescription fee is rather small and does not depend on the amount of medication prescribed.
An extract of the SHS 2007 questionnaire can be found in Fig. 2 in Appendix.
At a first glance, it might seem adequate to use the interview language (i.e., German, French, Italian) as a proxy for cultural differences across individuals. Language, however, is strongly related to the canton of residence and thus the dispensing policy. The inclusion of these highly correlated variables could evoke a situation of substantial multicollinearity. We consider the resulting bias to be more severe than the marginal gain in precision from including the language variable in our model. Religion, on the other hand, is less related to the canton of residence.
For reasons of statistical analysis, the FSO has divided Switzerland and its 26 cantons into 106 MS regions (mobilité spatiale; French, “spatial mobility”).
See Mihaylova et al. (2011) for a review of statistical methods in health economics.
We test the hypothesis \(H_{0}:\) \(SD=MIXED=INFO=INFO_{SD}=INFO_{MIXED}=0\). The respective \(\chi ^{2}(5)\) statistic reports a test value of 7.71 (p\(=\)0.17).
Following Winkelmann (2003), the marginal effect of any right-hand variable \(x_{i}\) is given by the sum of the two partial effects, \(\frac{\partial E\left[ y_{i}\mid \mathbf {x}_{i}\right] }{\partial x_{i}}=\frac{\partial \Pr \left[ y_{i}>0\mid \mathbf {x}_{i}\right] }{\partial x_{i}}E\left[ y_{i}\mid y_{i}>0,\mathbf {x}_{i}\right] +\frac{\partial E\left[ y_{i}\mid y_{i}>0,\mathbf {x}_{i}\right] }{\partial x_{i}}\Pr \left[ y_{i}>0\mid \mathbf {x}_{i}\right] \).
To calculate the Mill’s ratio, the first stage has to be estimated by means of a Probit model instead of using the logit specification.
The number of observations in the second part is somewhat lower than in the standard regression. This is due to the fact that the inverse Mill’s ratio is not identified for pregnant woman in the Probit model, as all of them exhibited at least one physician visit.
We test the joint hypotheses that \(SD=SD_{\lambda }\), \(MIXED=MIXED_{\lambda }\), \(INFO=INFO_{\lambda }\), \(INFO_{SD}=INFO_{SD}^{^{\lambda }}\), and \(INFO_{MIXED}=INFO_{MIXED}^{^{\lambda }}\).
The additional results can be found in Table 12 in Appendix.
References
Aigner, D. J. (1973). Regression with a binary independent variable subject to errors of observation. Journal of Econometrics, 1, 49–59.
Arrow, K. J. (1963). Uncertainty and the welfare economics of medical care. American Economic Review, 53, 941–973.
Cameron, A. C., & Trivedi, P. K. (2013). Regression Analysis of Count Data (2nd ed.). Cambridge: Cambridge University Press.
De Jaegher, K. (2012). The value of private patient information in the physician-patient relationship: A game-theoretic account. Computational and Mathematical Methods in Medicine, 2012, 847396. doi:10.1155/2012/847396.
Deb, P., & Holmes, A. M. (2000). Estimates of use and costs of behavioural health care: A comparison of standard and finite mixture models. Health Economics, 9, 475–489.
DiMatteo, M. R. (2004). Variations in patients’ adherence to medical recommendations. Medical Care, 42, 200–209.
Dwyer, D. S., & Liu, H. (2013). The impact of consumer health information on the demand for health services. The Quarterly Review of Economics and Finance, 53, 1–11.
Feldman, R., & Sloan, F. (1988). Competition among physicians, revisited. Journal of Health Politics, Policy and Law, 13, 239–261.
Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80, 223–255.
Hadley, J., & Lee, R. (1978). Toward a physician payment policy: Evidence from the economic stabilization program. Policy Sciences, 10, 105–120.
Hay, J., & Leahy, M. J. (1982). Physician-induced demand: An empirical analysis of the consumer information gap. Journal of Health Economics, 1, 231–244.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.
HLS-EU-Consortium.(2012). Comparative report of health literacy in eight EU member states. http://www.health-lteracy.eu.
Hurley, J., & Labelle, R. (1995). Relative fees and the utilization of physicians’ services in Canada. Health Economics, 4, 419–438.
Hurley, J., Labelle, R., & Rice, T. (1990). The relationship between physician fees and the utilization of medical services in Ontario. Advances in Health Economics and Health Services Research, 11, 49–78.
Iizuka, T. (2012). Physician agency and adoption of generic pharmaceutical. The American Economic Review, 102, 2826–2858.
Kaiser, B., & Schmid, C. (2013). Does Physician Dispensing Increase Drug Expenditures?. Discussion Paper 13-03 University of Bern, Department of Economics.
Kenkel, D. (1990). Consumer health information and the demand for medical care. The Review of Economics and Statistics, 72, 587–595.
Leung, S. F., & Yu, S. (1996). On the choice between sample selection and two-part models. Journal of Econometrics, 72, 197–229.
Liu, Y.-M., Yang, Y.-H. K., & Hsieh, C.-R. (2009). Financial incentives and physicians’ prescription decisions on the choice between brand-name and generic drugs: Evidence from Taiwan. Journal of Health Economics, 28, 341–349.
Manning, W. G., Morris, C. N., Newhouse, J. P., Orr, L. L., Duan, N., Keeler, E., et al. (1981). A two-part model of the demand for medical care: Preliminary results from the health insurance study. In J. van der Gaag & M. Perlman (Eds.), Health, Economics, and health economics. Amsterdam: North Holland.
McGuire, T. G. (2000). Physician agency. In A. J. Culyer & J. P. Newhouse (Eds.), Handbook of health economics chapter 9 (Vol. 1, pp. 461–536). Philadelphia, PA: Elsevier.
Mihaylova, B., Briggs, A., O’Hagan, A., & Thompson, S. G. (2011). Review of statistical methods for analysing healthcare resources and costs. Health Economics, 20, 897–916.
Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of Econometrics, 33, 341–365.
Nguyen, N. X., & Derrick, F. W. (1997). Physician behavioral response to a Medicare price reduction. Health Services Research, 32, 283–298.
Pletscher, M. (2012). Health literacy and outpatient physician visits in Switzerland. Health and Ageing Newsletter, 27, 9–12.
Pohlmeier, W., & Ulrich, V. (1995). An econometric model of the two-part decisionmaking process in the demand for health care. Journal of Human Resources, 30, 339–361.
Rice, T., & McCall, N. (1983). Factors influencing physician assignment decisions under Medicare. Inquiry, 20, 45–56.
Rischatsch, M. (2014). Lead me not into temptation: Drug price regulation and dispensing physicians in Switzerland. European Journal of Health Economics, 15, 697–708.
Schmid, C. (2014). Consumer health information and the demand for physician visits. Health Economics, 24, 1–21.
Trottmann, M., Früh, M., Reich, O., & Telser H. (2015). Auswirkungen der Medikamentenabgabe durch die Ärzteschaft (Selbstdispensation) auf den Arzneimittelkonsum und die Kosten zu Lasten der OKP. Schlussbericht Studie im Auftrag des Bundesamtes für Gesundheit.
Winkelmann, R. (2003). Econometric analysis of count data (4th ed.). Berlin: Springer.
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Appendix
Appendix
Proofs of the model results in “theoretical background” section
The three partial derivatives (3), (4), and (5) can be derived by applying the implicit function theorem to (1) and (2). In a first step, we replace m in (1) by m(p), and by \(m(\varphi )\), respectively, and using (2), we write s(y) instead of s to obtain the following three equations:
We now have to differentiate \(\partial m/\partial p\), \(\partial m/\partial \varphi \), and \(\partial s/\partial \varphi \). Knowing that \(N_{mn}=N_{nm}=0\), the three equations above can then be written as:
Without loss of generality, we set \(N_{nn}=0\) and rearrange the three terms to obtain (3), (4), and (5):
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Meyer, S. Dispensing physicians, asymmetric information supplier-induced demand: evidence from the Swiss Health Survey. Int J Health Econ Manag. 16, 215–245 (2016). https://doi.org/10.1007/s10754-016-9187-3
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DOI: https://doi.org/10.1007/s10754-016-9187-3