The Use of Chemical Restraints for Older Long-Term Hospital Patients: A Case Report from Finland

The purpose of this study was to describe the use and concomitant use of psychotropics and other drugs as chemical restraints in the aged in long-term hospital care. The study consisted of 154 patients (42 men, 112 women) hospitalized in five long-term care wards in Pori City Hospital, Finland. Three or more psychotropics were regularly given to 33% of the patients and regularly or irregularly to 53% of the patients. Two or more benzodiazepine derivatives or related drugs were regularly given to 24% of the patients and regularly or irregularly to 46% of the patients. The very poor cognitive and functional abilities of the patients, the common concomitant use of psychotropic drugs, the use of psychotropics to control the behavior of the patients, and the lack of documentation of the effects and side effects of the drugs give rise to the conclusion that psychotropics were used as chemical restraints in these long-term care wards.     

Histogram-Based Interpolation of the Lorenz Curve and Gini Index for Grouped Data

In grouped data, the estimation of the Lorenz curve without taking into account the within-class variability leads to an overestimation of the curve and an underestimation of the Gini index. We propose a new strictly convex estimator of the Lorenz curve derived from a linear interpolation-based approximation of the cumulative distribution function. Integrating the Lorenz curve, a correction can be derived for the Gini index that takes the intraclass variability into account.     

Robust adaptive Metropolis algorithm with coerced acceptance rate

The adaptive Metropolis (AM) algorithm of Haario, Saksman and Tamminen (Bernoulli 7(2):223?C242, 2001) uses the estimated covariance of the target distribution in the proposal distribution. This paper introduces a new robust adaptive Metropolis algorithm estimating the shape of the target distribution and simultaneously coercing the acceptance rate. The adaptation rule is computationally simple adding no extra cost compared with the AM algorithm. The adaptation strategy can be seen as a multidimensional extension of the previously proposed method adapting the scale of the proposal distribution in order to attain a given acceptance rate. The empirical results show promising behaviour of the new algorithm in an example with Student target distribution having no finite second moment, where the AM covariance estimate is unstable. In the examples with finite second moments, the performance of the new approach seems to be competitive with the AM algorithm combined with scale adaptation.