Reducing Boarding in a Post‐Anesthesia Care Unit

When operating room schedules in hospitals are produced, the constraints and preferences of surgeons and hospital workers are a primary consideration. The downstream impact on post‐operative bed availability is often ignored. This can lead to the boarding of patients overnight in the post‐anesthesia care unit (PACU) because intensive care unit beds are unavailable. In this paper, we apply integer programming and simulation to develop improved surgical scheduling assignments. We want to balance new surgeries with hospital discharges in order to reduce the variability of occupied beds from one day to the next and, as a result, to reduce boarding in the PACU.     

Life Is All about Timing: An Examination of Differences in Treatment Quality for Trauma Patients Based on Hospital Arrival Time

In health care, most quality transparency and improvement programs focus on the quality variation across hospitals, while we know much less about within‐hospital quality variation. This study examines one important factor that is associated with the fluctuation of quality of care in the same hospital—the timing of patient arrival. We analyze data from the National Trauma Data Bank and find that patients arriving at the hospital during off‐hours (6 PM–6 AM) receive significantly lower quality care than those who arrive during the daytime, as reflected in higher mortality rates, among other measures. More importantly, we try to uncover the mechanism for the quality variation. Interestingly, we find consistent evidence that the inferior care received during off‐hours is not likely due to unobserved heterogeneity, disruptions in circadian rhythms, or delays in receiving treatment. Instead, it is more likely due to the limited availability of high‐quality resources. This leads to a higher surgical complication rate, a higher likelihood of multiple surgeries, and longer patient length of stay in the intensive care unit. These findings have important implications for optimal resource allocation in hospitals to improve the quality‐of‐care delivery.     

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.     

Least Squares Model Averaging

This paper considers the problem of selection of weights for averaging across least squares estimates obtained from a set of models. Existing model average methods are based on exponential Akaike information criterion (AIC) and Bayesian information criterion (BIC) weights. In distinction, this paper proposes selecting the weights by minimizing a Mallows criterion, the latter an estimate of the average squared error from the model average fit. We show that our new Mallows model average (MMA) estimator is asymptotically optimal in the sense of achieving the lowest possible squared error in a class of discrete model average estimators. In a simulation experiment we show that the MMA estimator compares favorably with those based on AIC and BIC weights. The proof of the main result is an application of the work of Li (1987).     

Leadership and change in the Tasmanian environment movement

Semi-structured interviews with key environmentalists conducted in 2002 suggest strategic decision makers, organisational spokespeople and Green politicians are key leaders of the Tasmanian environmental movement, although charismatic leadership persists following movement routinisation. Routinisation is associated with the rise of non-protest environmental movement organisations addressing mainstream issues such as soil erosion, while protest oriented organisations champion the protection of old growth forests. Disproportionate federal government funding of non-protest organisations underpins tensions between movement leaders. An environmental council representing the diverse interests of the groups and organisations comprising the movement mitigates tensions and strengthens leader integration. Entrenched leaders from the ‘baby boom’ generation hinder the rise of younger leaders. Leader mentoring schemes adopted by movement organisations offer a potential solution to leadership succession in a mature social movement.     

False Belief and Emotion Understanding in Post-institutionalized Children

Deficits in social cognition may impair the ability to negotiate social transactions and relationships and contribute to socio emotional difficulties experienced by some post‐institutionalized children. We examined false belief and emotion understanding in 40 institutional care‐adopted children, 40 foster care‐adopted children and 40 birth children. Both groups of adopted children were adopted internationally. Controlling for verbal ability, post‐institutionalized children scored lower than birth children on a false belief task. Almost half of the post‐institutionalized children performed below chance levels. The foster care group did not differ from either group on false belief understanding. The groups did not differ on emotion understanding after controlling for verbal ability. The results suggest that some post‐institutionalized children are delayed in false belief understanding.     

Does Religion Make You Healthier and Longer Lived? Evidence for Germany

Researchers in the US have consistently reported substantial—not just statistically significant—links between religious belief and practice, and improved health and longevity. In this paper we report evidence for Germany, using data from the long-running, nationally representative German Socio-Economic Panel (SOEP 1984). The SOEP dataset includes multiple measures of health, plus many ‘controls’ which it is appropriate to use in assessing links between religious practice, health and longevity. These controls include personality traits known to be associated with better health (notably conscientiousness), and also the age of death of parents of the survey respondents. Initial results suggested that religious practice (church attendance) may be linked only to subjective (self-rated) measures of health, not to more objective measures. It seemed possible that results in some previous research could be due to what may be termed satisfaction bias or positivity bias; the known tendency of religious people to report higher than average satisfaction with almost all aspects of life. Further investigation indicated that relationships between church attendance and subjective measures of health were weaker, when a control for satisfaction bias was in place. However, there was countervailing evidence that the subjective measures in SOEP may actually be more not less valid than the objective measures; they are better not worse predictors of mortality. It was also clear that religious belief and church attendance are associated with health-protective behaviors and attitudes, including taking more exercise, not smoking and higher life satisfaction. At the end of the paper we estimate a structural equation model which maps links between religious practice, these protective behaviors and attitudes, and improved health outcomes.     

Parents Transmit Happiness Along with Associated Values and Behaviors to Their Children: A Lifelong Happiness Dividend?

There are strong two-way links between parent and child happiness (life satisfaction), even for ‘children’ who have grown up, moved to their own home and partnered themselves. German panel evidence shows that transmission of (un)happiness from parents to children is partly due to transmission of values and behaviors known to be associated with happiness (Headey et al. in Proc Natl Acad Sci 107(42):17922–17926, 2010, in Soc Indic Res doi:10.1007/s11205-012-0079-8,2012). These values and behaviors include giving priority to pro-social and family values, rather than material values, maintaining a preferred balance between work and leisure, active social and community participation, and regular exercise. Both parents have about equal influence on the values and behaviors which children adopt. However, the life satisfaction of adult ‘children’ continues to be directly influenced by the life satisfaction of their mothers, with the influence of fathers being only indirect, via transmission of values and behaviors. There appears to be a lifelong happiness dividend (or unhappiness dividend) due to parenting. Structural equation models with two-way causation indicate that the life satisfaction of offspring can significantly affect the satisfaction of their parents, as well as vice versa, long after the ‘children’ have left home. Data come from 25 waves of the German Socio-Economic Panel Survey (SOEP 1984–2008). SOEP is the only panel survey worldwide in which data on life satisfaction have been obtained from parents and an adequate sub-sample of children no longer living in the parental home.     

Quantile estimation from grouped data: the cell midpoint

Data are often collected in histogram form, especially in the context of computer simulation. While requiring less memory and computation than saving all observations, the grouping of observations in the histogram cells complicates statistical estimation of parameters of interest. In this paper the mean and variance of the cell midpoint estimator of the p^th quantile are analyzed in terms of distribution, cell width, and sample size. Three idiosyncrasies of using cell midpoints to estimate quantiles are illustrated. The results tend to run counter to previously published results for grouped data.