The Impact of Variability and Patient Information on Health Care System Performance

In the delivery of health care services, variability in the patient arrival and service processes can cause excessive patient waiting times and poor utilization of facility resources. Based on data collected at a large primary care facility, this paper investigates how several sources of variability affect facility performance. These sources include ancillary tasks performed by the physician, patient punctuality, unscheduled visits to the facility's laboratory or X‐ray services, momentary interruptions of a patient's examination, and examination time variation by patient class. Our results indicate that unscheduled visits to the facility's laboratory or X‐ray services have the largest impact on a physician's idle time. The average patient wait is most affected by how the physician prioritizes completing ancillary tasks, such as telephone calls, relative to examining patients. We also investigate the improvement in system performance offered by using increasing levels of patient information when creating the appointment schedule. We find that the use of policies that sequence patients based on their classification improves system performance by up to 25.5%.     

Tracking national and regional spatial-temporal mortality risk associated with NO2 concentrations in Canada: a Bayesian hierarchical two-level model

The association between daily variations in urban air quality and mortality has been well documented using time series statistical methods. This approach assumes a constant association over time. We develop a space-time dynamic model that relaxes this assumption, thus more directly examining the hypothesis that improvements in air quality translate into improvements in public health. We postulate a Bayesian hierarchical two-level model to estimate annual mortality risks at regional and national levels and to track both risk and heterogeneity of risk within and between regions over time. We illustrate our methods using daily nitrogen dioxide concentrations (NO2) and nonaccidental mortality data collected for 1984-2004 in 24 Canadian cities. Estimates of risk and heterogeneity are compared by cause of mortality (cardio-pulmonary [CP] versus non-CP) and season, respectively. Over the entire period, the NO2 risk for CP mortality was slightly lower but with a narrower credible interval than that for non-CP mortality, mainly due to an unusually low risk for a single year (1998). Warm season NO2 risk was higher than cold season risk for both CP and non-CP mortality. For 21 years overall there were no significant differences detected among the four regional NO2 risks. We found overall that there was no strong evidence for time trends in NO2 risk at national or regional levels. However, an increasing linear time trend in the annual between-region heterogeneities was detected, which suggests the differences in risk among the four regions are getting larger, and further studies are necessary to understand the increasing heterogeneity.     

美国环境哲学的两种对立思潮

作为美国环境哲学领域中的两支重要流派,社会生态学与深生态学在生态危机的根本原因、人类自然与非人类自然的关系、人类在自然中的地位和作用、化解当前生态危机的路径以及理性在解决生态危机过程中的作用等六个重要议题方面,都存在着明显而有启示意义的差异和对立.     

The Research Potential of Mental-Health Social Workers: A Qualitative Study of the Views of Senior Mental-Health Service Managers

Social workers have undoubtedly played a significant role inmental-health care in Britain. Yet their contribution to themental-health knowledge base is decidedly meagre compared tothe prolific research activity of psychiatry and allied healthprofessions. A qualitative study was conducted on the researchpotential of mental-health social work, based on the views ofsenior service managers in London boroughs. Social-work researchwas found to suffer from a combination of structural, economicand academic constraints. The impact of social work on mental-healthpolicy and practice developments, particularly in the lightof a growing emphasis on evidence-based practice, is likelyto remain weak until this issue is addressed.     

A comparison of efficiencies between quasi-likelihood and pseudo-likelihood estimates in non-separable spatial–temporal binary data

The goal of this paper is to compare the performance of two estimation approaches, the quasi-likelihood estimating equation and the pseudo-likelihood equation, against model mis-specification for non-separable binary data. This comparison, to the authors’ knowledge, has not been done yet. In this paper, we first extend the quasi-likelihood work on spatial data to non-separable binary data. Some asymptotic properties of the quasi-likelihood estimate are also briefly discussed. We then use the techniques of a truncated Gaussian random field with a quasi-likelihood type model and a Gibbs sampler with a conditional model in the Markov random field to generate spatial–temporal binary data, respectively. For each simulated data set, both of the estimation methods are used to estimate parameters. Some discussion about the simulation results are also included.     

Sterilizing the "Feeble-minded": Eugenics in Alberta, Canada, 1929–1972

Abstract Between 1929 and 1972, the Alberta Eugenics Board recommended that 4739 residents of the province be sterilized. However, only 60% of these individuals, 2834 in total, were ultimately sterilized since the legislation under which the Eugenics Board operated required patient consent to be obtained unless the individual recommended for sterilization was diagnosed as "mentally defective." Women, teenagers and young adults, and Aboriginals were particularly targeted by the Alberta Eugenics Board. The Board pursued its sterilization mandate extremely aggressively and, because of a unique set of social, political and economic circumstances in the province, continued to operate long after other political jurisdictions in North America had set aside their involuntary sterilization programs.     

Using the Summed Rank Cusum for monitoring environmental data from industrial processes

Environmental issues have become a hot topic recently, especially those surrounding industrial outputs. Effluents, emissions, outflows, by-products, waste materials, product de-commissioning, land reclamation and energy consumption are all the subject of monitoring, either under new legislation or through economic necessity. Many types of environmental data are often difficult to understand or measure because of their unusual distribution of values however. Standard methods of monitoring these data types often fail or are unwieldy. The scarcity of events, small volume measurements and the unusual time scales sometimes involved add to the complexity of the task. One recently developed monitoring technique is the Summed Rank Cusum (SRC) that applies non-parametric methods to a standard chart. The SRC can be used diagnostically and this paper describes the application of this new tool to three data sets, each derived from a different problem area. These are measuring industrial effluent, assessing the levels of potentially harmful proteins produced by an industrial process and industrial land reclamation in the face of harmful waste materials. The use of the SRC to spot change points in time retrospectively is described. The paper also shows the use of SRC in the significant-difference testing mode, which is applied via the use of spreadsheets. Links to other similar methods described in the literature are given and formulae describing the statistical nature of the transformation are shown. These practical demonstrations illustrate that the graphical interpretation of the method appears to help considerably in practice when trying to find time-series change points. The charts are an effective graphical retrospective monitoring technique when dealing with non-normal data. The method is easy to apply and may help considerably in dealing with environmental data in the industrial setting when standard methods are not appropriate. Further work is continuing on the more theoretical aspects of the method.     

The dangers posed by saddle points,and other problems,when using central composite designs

This paper discusses two problems, which can occur when using central composite designs (CCDs), that are not generally covered in the literature but can lead to wrong decisions-and therefore incorrect models-if they are ignored. Most industrialbased experimental designs are sequential. This usually involves running as few initial tests as possible, while getting enough information as is needed to provide a reasonable approximation to reality (the screening stage). The CCD design strategy generally requires the running of a full or fractional factorial design (the cube or hypercube) with one or more additional centre points. The cube is augmented, if deemed necessary, by additional experiments known as star-points. The major problems highlighted here concern the decision to run the star points or not. If the difference between the average response at the centre of the design and the average of the cube results is significant, there is probably a need for one or more quadratic terms in the predictive model. If not, then a simpler model that includes only main effects and interactions is usually considered sufficient. This test for 'curvature' in a main effect will often fail if the design space contains or surrounds a saddle-point. Such a point may disguise the need for a quadratic term. This paper describes the occurrence of a real saddle-point from an industrial project and how this was overcome. The second problem occurs because the cube and star point portions of a CCD are sometimes run as orthogonal blocks. Indeed, theory would suggest that this is the correct procedure. However in the industrial context, where minimizing the total number of tests is at a premium, this can lead to designs with star points a long way from the cube. In such a situation, were the curvature test to be found non-significant, we could end with a model that predicted well within the cube portion of the design space but that would be unreliable in the balance of the total area of investigation. The paper discusses just such a design, one that disguised the real need for a quadratic term.     

SEMI-MECHANISTIC MODELLING IN NONLINEAR REGRESSION: A CASE STUDY

This paper discusses the use of highly parameterized semi‐mechanistic nonlinear models with particular reference to the PARJIB crop response model of Reid (2002) [Yield response to nutrient supply across a wide range of conditions 1. Model derivation. Field Crops Research 77, 161–171]. Compared to empirical linear approaches, such models promise improved generality of application but present considerable challenges for estimation. Some success has been achieved with a fitting approach that uses a Levenberg–Marquardt algorithm starting from initial values determined by a genetic algorithm. Attention must be paid, however, to correlations between parameter estimates and an approach is described to identify these based on large simulated datasets. This work illustrates the value for the scientist in exploring the correlation structure in mechanistic or semi‐mechanistic models. Such information might be used to reappraise the structure of the model itself, especially if the experimental evidence is not strong enough to allow estimation of a parameter free of assumptions about the values of others. Thus statistical modelling and analysis can complement mechanistic studies, making more explicit what is known and what is not known about the processes being modelled and guiding further research.