Extent and structure of health insurance expenditures for complementary and alternative medicine in Swiss primary care

BACKGROUND: The study is part of a nationwide evaluation of complementary and alternative medicine (CAM) in primary care in Switzerland. The goal was to evaluate the extent and structure of basic health insurance expenditures for complementary and alternative medicine in Swiss primary care. METHODS: The study was designed as a cross-sectional evaluation of Swiss primary care providers and included 262 certified CAM physicians, 151 noncertified CAM physicians and 172 conventional physicians. The study was based on data from a mailed questionnaire and on reimbursement information obtained from health insurers. It was therefore purely observational, without interference into diagnostic and therapeutic procedures applied or prescribed by physicians. Main outcome measures included average reimbursed costs per patient, structured into consultation- and medication-related costs, and referred costs. RESULTS: Total average reimbursed cost per patient did not differ between CAM physicians and conventional practitioners, but considerable differences were observed in cost structure. The proportions of reimbursed costs for consultation time were 56% for certified CAM, 41% for noncertified CAM physicians and 40% for conventional physicians; medication costs--including expenditures for prescriptions and directly dispensed drugs--respectively accounted for 35%, 18%, and 51% of costs. CONCLUSION: The results indicate no significant difference for overall treatment cost per patient between CAM and COM primary care in Switzerland. However, CAM physicians treat lower numbers of patients and a more cost-favourable patient population than conventional physicians. Differences in cost structure reflect more patient-centred and individualized treatment modalities of CAM physicians.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

Prehistoric genomes reveal the genetic foundation and cost of horse domestication

The domestication of the horse revolutionized warfare, trade, and the exchange of people and ideas. This at least 5,500-y-long process, which ultimately transformed wild horses into the hundreds of breeds living today, is difficult to reconstruct from archeological data and modern genetics alone. We therefore sequenced two complete horse genomes, predating domestication by thousands of years, to characterize the genetic footprint of domestication. These ancient genomes reveal predomestic population structure and a significant fraction of genetic variation shared with the domestic breeds but absent from Przewalski’s horses. We find positive selection on genes involved in various aspects of locomotion, physiology, and cognition. Finally, we show that modern horse genomes contain an excess of deleterious mutations, likely representing the genetic cost of domestication.