A microeconometric analysis of health care utilization in Europe

  • Maria Cristina Majo Maria Cristina Majo

This thesis consists of three separately readable chapters that were independently written.

Chapter 1 addresses the question how income affects health care utilization by the population aged 50 and over in the United States and a number of European countries with varying health care systems. The probabilities that individuals receive several medical services (visits to general practitioner, specialist, dentist, inpatient, or outpatient services) are analyzed separately using probit models. In addition to controls for income and demographic characteristics, controls for health status (both subjective and objective measures of health) are used. We analyze how the relationship between income and health care utilization varies across countries and relate these cross country differences to characteristics of the health care system, i.e., per capita total and public expenditure on health care, gate-keeping for specialist care, and co-payments.

In Chapter 2 we deal with the question how preventive clinical service utilization by the population aged 50 and over is related to socio-economic status in a number of European countries with varying health care systems. The probabilities that individuals receive preventive clinical services (influenza vaccination, blood check, colonoscopy, blood stool test, eye exam, and mammogram for women) are analyzed separately using probit models. In addition to controls for education and demographic characteristics, controls for economic factors and health status (both subjective and objective measures of health) are used. The analysis of education first, and then of all three indicators of socio economic status – education, income, and work status – suggests that economic and social resources are associated with whether respondents use preventive services. The main result is that education level emerges as a very important determinant for the uptake of preventive care.

Chapter 3 is devoted to the analysis of response variables that are scored as counts and that present a large number of zeros, which often arises in quantitative health care analysis. A zero-inflated Poisson model with fixed-effects is defined to identify respondent- and health-related characteristics associated with health care demand. This is a new model that is proposed to model count measures of health care utilization and account for the panel structure of the data. Parameter estimation is achieved by conditional maximum likelihood. An application of the new model is implemented using SHARE data from the 2004–2006 waves, and compared to existing panel data models for count data. Results show that separately controlling for whether outcomes are zero or positive in one of the two years does make a difference for counts with a larger number of zeros.

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