Medical ethics: four principles plus attention to scope.

The "four principles plus scope" approach provides a simple, accessible, and culturally neutral approach to thinking about ethical issues in health care. The approach, developed in the United States, is based on four common, basic prima facie moral commitments--respect for autonomy, beneficence, nonmaleficence, and justice--plus concern for their scope of application. It offers a common, basic moral analytical framework and a common, basic moral language. Although they do not provide ordered rules, these principles can help doctors and other health care workers to make decisions when reflecting on moral issues that arise at work.     

The case for common reference intervals

The current paradigm for pathology reference intervals is for each laboratory to determine its own interval for use with each test offered by the laboratory. It is our contention that this approach does not best serve the medical community, especially at a time when electronic databases of health information are being expanded and integrated. We also believe that this approach is not performed well in many laboratories and is excessively expensive in practice. In contrast, we believe that the preferable option is to develop and apply common reference intervals throughout Australia and New Zealand, together with common reporting formats and assay standardisation wherever this is possible.

We are aware that these are neither trivial nor simple issues, however we believe that failure to achieve this goal where technically possible will be a failure of the pathology profession to meet the challenges of the modern health community.

     

Malign Neglect: Assessing Older Women’s Health Care Experiences in Prison

The problem of providing mandated medical care has become commonplace as correctional systems in the United States struggle to manage unprecedented increases in its aging prison population. This study explores older incarcerated women’s perceptions of prison health care policies and their day-to-day survival experiences. Aggregate data obtained from a sample of 327 older women (mean age?=?56) residing in prison facilities in five Southern states were used to identify a baseline of health conditions and needs for this vulnerable group. With an average of 4.2 chronic health conditions, frequently histories of victimization, and high rates of mental health issues, the women’s experiences of negotiating health care was particularly challenging. By incorporating the voices of older women, we expose the contradictions, dilemmas, and obstacles they experience in their attempts to obtain health care. It is clear from the personal accounts shared that, despite court mandates, penal harm practices such as delaying or denying medical treatment as well as occasional staff indifferences are common in women’s prisons. With older women having the greatest need for health care, an age- and gender-sensitive approach is recommended.     

A phenomenological account of users' experiences of assertive community treatment

Assertive community treatment (ACT) is a widely propagated team approach to community mental health care that 'assertively' engages a subgroup of individuals with severe mental illness who continuously disengage from mental health services. It involves a number of interested parties – including clients, carers, clinicians and managers. Each operates according to perceived ethical principles related to their values, mores and principles. ACT condenses a dilemma that is common in psychiatry. ACT proffers social control whilst simultaneously holding therapeutic aspiration. The clients' perspective of this dilemma was studied in interviews with 12 clients using the 'grounded theory' approach. Results suggest that clients' disengagement is as much a historical and cultural phenomenon as a result of lack of insight. Many clients had experienced rejection of early help–seeking behaviour and all had been subject to coercive interventions. These coercive interventions were experienced as an attack on identity. All felt that their voice had not been listened to in previous interactions with psychiatric services. Consequentially the clients had an increased level of arousal around issues of power, which needs to be incorporated when examining the ethics of community psychiatry. Traditional notions of the difference between persuasion and coercion – for example – may need to be adapted for this client group. Results are compared with the provider perspective. We conclude that the perspectives differ on two key dimensions. Such an empirical approach to examining psychiatric ethics may ensure that we incorporate the subjectivities of various interested parties in the clinical decision–making process.     

Survey and interzoo studies used to address husbandry problems in some zoo vertebrates

In addition to genetic and demographic considerations, SSP coordinators have been asked to systematically address husbandry issues. Three approaches to the study of captive management issues are typically used: (1) large numbers of individuals are housed at a single institution maintained in a situation that facilitates systematic evaluation of the captive environment, (2) an investigator travels to many institutions to gather data on a single taxon, or (3) an investigator surveys existing information by assembling data from a large number of individuals at a variety of institutions. Each approach has both advantages and disadvantages in terms of feasibility and the type of results obtained. The use of surveys to obtain information about husbandry parameters from a large number of animals maintained at a variety of zoos is quite possibly the most common approach used among zoo managers. Zoo husbandry surveys are typically developed to address issues problematic to a particular species, including reproductive failure and health issues. Unfortunately, surveys appear to be an often misused research tool among zoo professionals. Surveys can be improved by working with psychologists or sociologists at local universities, by narrowing the focus of the survey's purpose, and by carefully constructing each question. © 1994 Wiley-Liss, Inc.     

Monitoring and surveillance for rare health-related events: a review from the veterinary perspective

Disease monitoring and surveillance systems (MOSSs) have become one of the major components of veterinary activity. Such systems are used to assess the existing levels of prevalence, the effectiveness of control programmes and, after disease eradication, to document the continued absence of disease from a given region or zone. With decreasing disease or infection prevalence, traditional approaches become less reliable and increasingly costly. The objective of this work was to summarize and discuss methodological issues related to veterinary (animal health) MOSSs. There are considerable inconsistencies in the use of the terms 'monitoring' and 'surveillance'. Passive as well as active MOSS have their disadvantages when used for rare health-related events such as emerging and re-emerging diseases. There is a need for evaluation and improvement of these approaches. Integrated systems that call for the use of several parallel surveillance activities seem to be the favoured approach, and analytical methods to combine MOSS data from various sources into a population prevalence, or probability of disease freedom, are under development. The health and safety of the animal and human generations depends on our continuous ability to detect, monitor and control newly emerging or re-emerging livestock diseases and zoonoses rapidly. Uniform surveillance definitions, sound scientifically based approaches that use the resources and data available, and a pool of researchers and veterinary public health officials with sufficient training in epidemiology, are critically important to handle this challenging task.     

Common Bayesian Models for Common Cognitive Issues

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.     

Reducing the costs of work-related musculoskeletal disorders: targeting strategies to chronic disability cases.

Musculoskeletal disorders impose a significant direct cost burden on health care systems in the US and Canada and account for even greater indirect losses of productivity. The overall prevalence of musculoskeletal disorders is high, but a disproportionate share of costs is associated with a small number of cases with chronic pain. This is especially true for cases of occupational back pain, the single most common and costly musculoskeletal disorder in the workplace. A number of studies identify workplace characteristics associated with prolonged disability among cases of work-related back pain. These characteristics include: failure to receive job accommodations, receipt of disability benefit payments, and employment in high-risk industries or jobs that require heavy lifting. Research on the predictors of high-cost cases is limited, however, because of the lack of high-quality data and the need for a multidisciplinary approach. A new study, the Arizona State University Healthy Back Study, addresses some of these issues and promises new insights into effective strategies to reduce the proportion of high-cost claims.     

Ethics and health systems research in 'post'-conflict situations

Although considerable attention has been given to ethical issues related to clinical research in developing countries, in particular related to HIV therapy, there has been limited focus on health systems research, despite its increasing importance in the light of current trends in development assistance. This paper examines ethical issues related to health systems research in 'post'-conflict situations, addressing both generic issues for developing countries and those issues specific to 'post'-conflict societies, citing examples from the author's Cambodian experience. It argues that the destruction of health infrastructure results in a loss of structures and processes that would otherwise protect prospective research subjects who are part of vulnerable populations. It identifies the growth of health systems research as part of a trend towards sectoral and programmatic development assistance, the emergence of 'knowledge generation' as a form of research linked to development, and the potential for conflict where multilateral and bilateral donors are both primary funders and users of health systems research. It also examines the position of the health system researcher in relation to the sponsors of this research, and the health system being analysed.     

LINKING INTERNATIONAL RESEARCH TO GLOBAL HEALTH EQUITY: THE LIMITED CONTRIBUTION OF BIOETHICS

Health research has been identified as a vehicle for advancing global justice in health. However, in bioethics, issues of global justice are mainly discussed within an ongoing debate on the conditions under which international clinical research is permissible. As a result, current ethical guidance predominantly links one type of international research (biomedical) to advancing one aspect of health equity (access to new treatments). International guidelines largely fail to connect international research to promoting broader aspects of health equity – namely, healthier social environments and stronger health systems. Bioethical frameworks such as the human development approach do consider how international clinical research is connected to the social determinants of health but, again, do so to address the question of when international clinical research is permissible. It is suggested that the narrow focus of this debate is shaped by high‐income countries' economic strategies. The article further argues that the debate's focus obscures a stronger imperative to consider how other types of international research might advance justice in global health. Bioethics should consider the need for non‐clinical health research and its contribution to advancing global justice.     

Valuing nature in context: the contribution of common-good approaches

We draw on a number of empirical studies undertaken in the UK to show how residents and farmers come to contest scientific approaches to valuing nature as the basis for adjudicating conflicts over protected natural areas. The findings of these studies suggest that a widening of the knowledge base on which the goals and practices of nature conservation are founded, and a more deliberative process of decision making about what nature is important locally, is required if effective conservation partnerships are to be sustained. We offer a common good approach to valuing nature as a means of addressing this problem. A common good approach is based on ethical and moral concerns about nature and expresses these values through a social and political process of consensus building. We illustrate how this common good approach can be used to prioritise issues in a Local Environment Agency Plan. When linked with a method of Stakeholder Decision Analysis this common good approach is capable of building coalitions and a measure of consensus between different interests. It achieves this through a transparent and deliberate process of debate and systematic analysis of values that makes explicit the foundation of different knowledge claims about nature.     

Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods

Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications.     

Mental competence and elderly care

Competence or decision-making capacity are concepts, which are far from clear-cut. The normative consequences of an incapacity-judgment in elderly care require a balanced approach, which takes into consideration the risk of misuse in the context of the justification of interventions that are applied without consent of the patient. In this article a number of central issues in the debate on decision-making capacity are discussed. The different criteria, which are proposed to assess capacity, are discussed as well as the development of instruments in order to objectify capacity. It is concluded that the common approach towards decision-making capacity, which is strongly cognitive-oriented, is inadequate. A number of relevant aspects remain underexposed within this approach. A broader approach is explored, which takes into account the role of emotions, identity, and dialogue and deliberation. The different approaches are illustrated by way of a case example.     

Prospects and pitfalls in whole genome association studies

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype-phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.     

Petri net modeling of high-order genetic systems using grammatical evolution

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.     

The Problem of Thresholding in Small-World Network Analysis

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues.     

Steady state analysis of influx and transbilayer distribution of ergosterol in the yeast plasma membrane

Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models. We describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental models that correspond with applications to pandemic influenza, Ebola, and Zika. Overall, parameter identifiability issues are more likely to arise with more complex models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R0 can still be estimated with precision and accuracy. Because public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.     

Assessing canopy health of native eucalypt forests

Summary   The assessment of forest health is an essential part of the monitoring of ecological sustainability in managed native forests. In Australia, unfortunately, very limited quantitative information on forest health is actually obtained for management and reporting purposes. In this article, we summarize current approaches used in Australia to assess native forest health and some recent developments in the application of remotely acquired digital imagery for classifying canopy health. In a recent study examining Bell miner associated dieback (BMAD), high-resolution airborne imagery was successfully manipulated to present severity categories for BMAD affected canopy. The potential of remotely sensed imagery lies not in map production but in the statistical modelling capacity of this spatial information, particularly when added to climatic and terrain-based spatial data sets. There are several statistical approaches to modelling these spatial datasets and in this article, we discuss our approach to producing a preliminary BMAD model. The importance of ground-based assessments is also emphasized and we recommend tree crown condition as a key health attribute for the spatial modelling of forests. Although significant progress has been made in the application of remote sensing technologies, the structural complexity of native forests means that there are still technical issues that require resolving before this approach becomes operationally routine.     

Methodological Aspects of Shift-Work Research

A major issue in shift-work research is to understand the possible ways in which shift work can impact performance and health. Nearly all body functions, from those of the cellular level to those of the entire body, are circadian rhythmic. Disturbances of these rhythms as well as the social consequences of odd work hours are of importance for the health and well-being of shift workers. This article reviews a number of common methodological issues which are of relevance to epidemiological studies in this area of research. It discusses conceptual problems regarding the use of the term “shift work,” and it underscores the need to develop models that explain the mechanisms of disease in shift workers.     

Data linkage using probabilistic decision rules: a primer

Electronic data linkage is increasingly being used by researchers and health professionals in the birth defects field as a tool for enhancing both research and service/care. However, in many cases, a common pre-existing ID number does not exist across different datasets, and common identifiers, such as names or dates of birth, which could be used to match records, may be known to contain errors or even legitimate differences over time. In such situations, probabilistic matching, which does not require that all identifying fields exactly agree in order for one to conclude that two records belong to the same individual, can be a valuable tool for improving data linkage. However, probabilistic matching is computationally complex and demanding, and not well understood by many who may wish to apply it in their work. Therefore, the purpose of this article is to provide an overview of one approach to probabilistic matching, including the step-by-step procedures involved in the calculation of indices corresponding to the likelihood that two records are a correct match. In addition, the use of multiple iterative protocols, in which several different matching strategies are used in order to maximize the number of linked records, is discussed. Finally, issues related to deduplication and verification of internal-consistency in the linked data set are also reviewed.