A Survey of Jewish Attitudes and Experiences Relating to End-of-Life Care and the "Right to Die".

We conducted a survey of Jewish attitudes towards, and experiences with, end-of-life care. Questions fell into three areas: (1) Expectations for Jewish end-of-life care; (2) Experiences with such care; and (3) Attitudes toward the "right to die." Examining denominational differences in belief in, and adherence to, Halakha (Jewish law), we confirm many expectations described in the literature. We find notable nuances in specific areas of need across Jewish denomination, and in terms of acceptance of the withdrawal of life support vs assisted suicide. Care for the nuances of Jewish belief is indicated for effective and satisfying Jewish end-of-life care.

Seasonal incidence of biopsy-proven giant cell arteritis: a 20-year retrospective study of the University of California Davis Medical System.

OBJECTIVES: Giant cell arteritis (GCA) is a vasculitis that affects large and medium sized arteries. The aetiology of GCA is unknown and numerous risk factors have been proposed. In this article, we evaluate the incidence of biopsy-positive GCA in Northern California and assess for seasonal variation. METHODS: We performed a retrospective review based on billing codes of temporal artery biopsies performed at the University of California, Davis from 2003 to 2014. RESULTS: We identified 174 biopsies (119 female, 55 male). Of these, 21 positive biopsies were female while 8 were male. Although three times as many women had a positive biopsy compared to men, twice as many biopsies were performed on women. Women were not found to have a significantly higher risk of developing GCA over men. Patients with a positive biopsy averaged 76.4±8.9 years of age. The odds of having a positive biopsy increased significantly with age. Positive biopsies were significantly more likely to occur in the months of May through July than the rest of the year (p<0.028). CONCLUSIONS: Our retrospective study is the first report of the seasonal incidence of biopsy-proven GCA in California. Our data suggest that increased age and summer months are risk factors for developing biopsy-proven GCA in our region.

Do ethnic differences still exist in pain assessment and treatment in the emergency department?

Although the provision of timely and appropriate analgesia is a primary goal of Emergency Department (ED) staff, pain continues to be undertreated and some evidence supports the existence of pain treatment disparities. Despite strong incentives from accreditation organizations, pain management in the ED may still be inconsistent and problematic. The purpose of this research study was to conduct a retrospective chart review to investigate pain assessment and treatment for 200 adults (≥18 years old) admitted to the ED suffering from long-bone fractures. An additional purpose was to investigate demographic variables, including ethnicity, to determine if they influenced pain assessment, pain treatment, and wait times in the ED. Although assessment and treatment of pain is universally recognized as being important and necessary to provide optimal patient care, only 52% of patients in this study were assessed using a pain intensity scale, with 43% of those assessed reporting pain as ≥5 on a 0-10 pain intensity instrument. Pain medication was administered to 75% of the patients, but 25% of the patients received no medication. Only 24% of those receiving a pain medication were reassessed to determine pain relief. Compounding these problems were wait times for analgesia of >1 hour. Although the influence on pain management related to ethnicity was not a factor in this study, other findings revealed that undertreatment of pain, inadequate assessment, lack of documentation of pain, and lengthy wait times persist in the ED.

A hierarchical Bayesian mixture model for repeated dietary records.

Many dietary consumption variables show strong positive skewness or large proportions of zeros. Attempts to normalize such data using transformations such as powers and logarithms can be unsuccessful: this results in poor estimates of their probability distributions, and hence of the proportions of the population whose consumption is beyond recommended limits. As an alternative to such transformations, the use of finite mixtures of standard distributions offers flexible modeling of data having skewed or multi-modal distributions, such as data on dietary consumption. In many dietary studies, individuals are asked to report their consumptions on several days. The use of finite-mixture models for such repeated data requires generalization to take account of the resulting hierarchical structure in the data. We first consider how finite mixture models might be extended to data with repeated records, and then apply a Bayesian version of one such extension to data on the consumption of retinol (Vitamin A) by British adults over 7 consecutive days. We also illustrate how factors such as sex and age may be included in the model. The mixture-model approach is found to provide better estimates than alternative methods of the probability distributions of daily consumptions and of maximum consumption over 7days.

Exposure assessment for pesticide intake from multiple food products: a Bayesian latent-variable approach.

Pesticide risk assessment for food products involves combining information from consumption and concentration data sets to estimate a distribution for the pesticide intake in a human population. Using this distribution one can obtain probabilities of individuals exceeding specified levels of pesticide intake. In this article, we present a probabilistic, Bayesian approach to modeling the daily consumptions of the pesticide Iprodione though multiple food products. Modeling data on food consumption and pesticide concentration poses a variety of problems, such as the large proportions of consumptions and concentrations that are recorded as zero, and correlation between the consumptions of different foods. We consider daily food consumption data from the Netherlands National Food Consumption Survey and concentration data collected by the Netherlands Ministry of Agriculture. We develop a multivariate latent-Gaussian model for the consumption data that allows for correlated intakes between products. For the concentration data, we propose a univariate latent-t model. We then combine predicted consumptions and concentrations from these models to obtain a distribution for individual daily Iprodione exposure. The latent-variable models allow for both skewness and large numbers of zeros in the consumption and concentration data. The use of a probabilistic approach is intended to yield more robust estimates of high percentiles of the exposure distribution than an empirical approach. Bayesian inference is used to facilitate the treatment of data with a complex structure.