Looking into the black box of "Medical Innovation": rising health expenditures by illness type.

There is agreement among health economists that on the whole medical innovation causes health care expenditures (HCE) to rise. This paper analyzes for which diagnoses HCE per patient have grown significantly faster than average HCE. We distinguish decedents (patients in their last 4 years of life) from survivors and use a unique dataset comprising detailed HCE of all members of a regional health insurance fund in Upper Austria for the period 2005-2018. Our results indicate that among decedents in particular, the expenditures for treatment of neoplasms have exceeded the general trend in HCE. This confirms that medical innovation for this group of diseases has been particularly strong over the last 15 years. For survivors, we find a noticeable growth in cases and cost per case for pregnancies and childbirth, and also for treatment of mental and behavioral disorders. We discuss whether these findings contradict the widespread interpretation of cost-increasing innovations as "medical progress" and offer some policy recommendations.

Using Quantile and Asymmetric Least Squares Regression for Optimal Risk Adjustment.

In this paper, we analyze optimal risk adjustment for direct risk selection (DRS). Integrating insurers' activities for risk selection into a discrete choice model of individuals' health insurance choice shows that DRS has the structure of a contest. For the contest success function (csf) used in most of the contest literature (the Tullock-csf), optimal transfers for a risk adjustment scheme have to be determined by means of a restricted quantile regression, irrespective of whether insurers are primarily engaged in positive DRS (attracting low risks) or negative DRS (repelling high risks). This is at odds with the common practice of determining transfers by means of a least squares regression. However, this common practice can be rationalized for a new csf, but only if positive and negative DRSs are equally important; if they are not, optimal transfers have to be calculated by means of a restricted asymmetric least squares regression. Using data from German and Swiss health insurers, we find considerable differences between the three types of regressions. Optimal transfers therefore critically depend on which csf represents insurers' incentives for DRS and, if it is not the Tullock-csf, whether insurers are primarily engaged in positive or negative DRS. Copyright © 2016 John Wiley & Sons, Ltd.

The interaction of direct and indirect risk selection.

This paper analyzes the interaction of direct and indirect risk selection in health insurance markets. It is shown that direct risk selection - using measures unrelated to the benefit package like selective advertising or 'losing' applications of high risk individuals - nevertheless has an influence on the distortions of the benefit package caused by indirect risk selection. Direct risk selection (DRS) may either increase or decrease these distortions, depending on the type of equilibrium (pooling or separating), the type of DRS (positive or negative) and the type of cost for DRS (individual-specific or not). Regulators who succeed in reducing DRS by, e.g., banning excessive advertising or implementing fines for 'losing' applications, may therefore (unintendedly) mitigate or exacerbate the distortions of the benefit package caused by indirect risk selection. It is shown that the interaction of direct and indirect risk selection also alters the formula for optimal risk adjustment.

Health care expenditures and longevity: is there a Eubie Blake effect?

It is still an open question whether increasing life expectancy as such causes higher health care expenditures (HCE) in a population. According to the "red herring" hypothesis, the positive correlation between age and HCE is exclusively due to the fact that mortality rises with age and a large share of HCE is caused by proximity to death. As a consequence, rising longevity-through falling mortality rates-may even reduce HCE. However, a weakness of many previous empirical studies is that they use cross-sectional evidence to make inferences on a development over time. In this paper, we analyse the impact of rising longevity on the trend of HCE over time by using data from a pseudo-panel of German sickness fund members over the period 1997-2009. Using (dynamic) panel data models, we find that age, mortality and 5-year survival rates each have a positive impact on per-capita HCE. Our explanation for the last finding is that physicians treat patients more aggressively if the results of these treatments pay off over a longer time span, which we call "Eubie Blake effect". A simulation on the basis of an official population forecast for Germany is used to isolate the effect of demographic ageing on real per-capita HCE over the coming decades. We find that, while falling mortality rates as such lower HCE, this effect is more than compensated by an increase in remaining life expectancy so that the net effect of ageing on HCE over time is clearly positive.

Optimal cost reimbursement of health insurers to reduce risk selection.

In the absence of a perfect risk adjustment scheme, reimbursing health insurers' costs can reduce risk selection in community-rated health insurance markets. In this paper, we develop a model in which insurers determine the cost efficiency of health care and have incentives for risk selection. We derive the optimal cost reimbursement function, which balances the incentives for cost efficiency and risk selection. For health cost data from a Swiss health insurer, we find that an optimal cost reimbursement scheme should reimburse costs only up to a threshold.

Determinants of health care utilization by German sickness fund members--with application to risk adjustment.

In many countries, social health insurance systems are being reformed in favor of more competition among insurers, while premiums are community rated by regulation. The implicit incentives for insurers to engage in risk selection can only be curtailed using appropriate systems of risk-adjusted equalization payments among insurers. To develop these systems, predictors of individual utilization patterns have to be identified, e.g. via regression analysis using previous utilization data. In some countries such as Germany, such data are hardly ever available. In the early nineties, a number of sickness funds participated in an experiment in which individual utilization data were collected. Our data set covers more than 70,000 members of company sickness funds over a 5-year period. We analyze socio-demographic determinants of utilization which could be used as risk adjusters in a risk equalization scheme. Our results suggest that besides age and sex, the set of risk adjusters should include income, family status and a dummy for the last year of life.