An Examination of Right to Treatment Standards: Mental Health Policy within the Context of the State Hospital System

This paper discusses the use of court-imposed standards for public mental hospitals as a method of improving public mental health services. The standards set out in Wyatt v. Stickney are examined, and the author concludes that if implemented nationally such standards would transform the public hospitals. In addition, implementation would alter the power structure of mental health workers, effect the allocation of state and federal funds, and influence the larger system of mental health services. Socio-economic characteristics of public mental hospital patients, and an assessment of present care in this system are presented as central issues in mental health policy and planning

Modelling low pathogenic avian influenza introduction into the commercial poultry industry

Outbreaks of highly pathogenic avian influenza (HPAI) in commercial poultry flocks are rare but highly disruptive to the industry. There is evidence that low pathogenic avian influenza (LPAI) can transfer from wild birds to domestic flocks, where it may mutate to HPAI, and the industry is concerned that an increasing demand for free-range produce may affect the risk of LPAI and HPAI outbreaks. In this paper we focus on LPAI introduction and establishment, and formulate a branching process model to compare risk between sectors and their contribution to overall industry-level risk. Our aim is to determine how heterogeneity in avian influenza viruses and the distinct population structures of each sector - caged, barn and free-range, meat and layer - interact with a continuous risk of virus introduction to affect outbreak probabilities. We show that free-range access is the most influential driver of LPAI outbreaks, with production cycle length having relatively little effect. We demonstrate that variation in virus transmission rates is particularly important when modelling avian influenza introduction to domestic poultry. Virus-free status is of interest for biosecurity and we distinguish how it differs from the usual probability of extinction, and discuss how production cycle length affects this difference. We also use the nonlinear relationship between shed size and risk to identify conditions for which shed size is most influential.This project wassupported financially by the Poultry CRC

Variation in Specificity of HIV Rapid Diagnostic Tests over Place and Time: An Analysis of Discordancy Data Using a Bayesian Approach

BACKGROUND Recent trends to earlier access to anti-retroviral treatment underline the importance of accurate HIV diagnosis. The WHO HIV testing strategy recommends the use of two or three rapid diagnostic tests (RDTs) combined in an algorithm and assume a population is serologically stable over time. Yet RDTs are prone to cross reactivity which can lead to false positive or discordant results. This paper uses discordancy data from Médecins Sans Frontières (MSF) programmes to test the hypothesis that the specificity of RDTs change over place and time. METHODS Data was drawn from all MSF test centres in 2007-8 using a parallel testing algorithm. A Bayesian approach was used to derive estimates of disease prevalence, and of test sensitivity and specificity using the software WinBUGS. A comparison of models with different levels of complexity was performed to assess the evidence for changes in test characteristics by location and over time. RESULTS 106, 035 individuals were included from 51 centres in 10 countries using 7 different RDTs. Discordancy patterns were found to vary by location and time. Model fit statistics confirmed this, with improved fit to the data when test specificity and sensitivity were allowed to vary by centre and over time. Two examples show evidence of variation in specificity between different testing locations within a single country. Finally, within a single test centre, variation in specificity was seen over time with one test becoming more specific and the other less specific. CONCLUSION This analysis demonstrates the variable specificity of multiple HIV RDTs over geographic location and time. This variability suggests that cross reactivity is occurring and indicates a higher than previously appreciated risk of false positive HIV results using the current WHO testing guidelines. Given the significant consequences of false HIV diagnosis, we suggest that current testing and evaluation strategies be reviewed.The authors have no funding or support to report

African swine fever in wild boar: investigating model assumptions and structure

African swine fever (ASF) is a highly virulent viral disease that affects both domestic pigs and wild boar. Current ASF transmission in Europe is in part driven by wild boar populations, which act as a disease reservoir. Wild boar are abundant throughout Europe and are highly social animals with complex social organisation. Despite the known importance of wild boar in ASF spread and persistence, there remain knowledge gaps surrounding wild boar transmission. To investigate the influence of density-contact functions and wild boar social structure on disease dynamics, we developed a wild boar modelling framework. The framework included an ordinary differential equation model, a homogeneous stochastic model, and various network-based stochastic models that explicitly included wild boar social grouping. We found that power law functions (transmission $\propto$ density$^{0.5}$) and frequency-based density-contact functions were best able to reproduce recent Baltic outbreaks; however, power law function models predicted considerable carcass transmission, while frequency-based models had negligible carcass transmission. Furthermore, increased model heterogeneity caused a decrease in the relative importance of carcass-based transmission. The different dominant transmission pathways predicted by each model type affected the efficacy of potential interventions, which highlights the importance of evaluating model type and structure when modelling systems with uncertainties.Comment: 37 pages. 11 figures in main, 9 figures in appendix. 3 tables in main, 8 tables in appendi

Incorporating population dynamics into household models of infectious disease transmission

Most household models of disease transmission assume static household distributions. Although this is a reasonable simplification for assessing vaccination strategies at a single point in time or over the course of an outbreak, it has considerable drawbacks for assessing long term vaccination policies or for predicting future changes in immunity. We demonstrate that household models that include births, deaths and movement between households can show dramatically different patterns of infection and immunity to static population models. When immunity is assumed to be life-long, the pattern of births by household size is the key driver of infection, suggesting that the influx of susceptibles has most impact on infection risk in the household. In a comparison of 12 countries, we show that both the crude birth rate and the mean household size affect the risk of infection in households

Predicting case numbers during infectious diseaseoutbreaks when some cases are undiagnosed

We describe a method for calculating 95 per cent bounds for the current number of hidden cases and the future number of diagnosed cases during an outbreak of an infectious disease. A Bayesian Markov chain Monte Carlo approach is used to fit a model of infectious disease transmission that takes account of undiagnosed cases. Assessing this method on simulated data, we find that it provides conservative 95 per cent bounds for the number of undiagnosed cases and future case numbers, and that these bounds are robust to modifications in the assumptions generating the simulated data. Moreover, the method provides a good estimate of the initial reproduction number, and the reproduction number in the latter stages of the outbreak. Applying the approach to SARS data from Hong Kong, Singapore, Taiwan and Canada, the bounds on future diagnosed cases are found to be reliable, and the bounds on hidden cases suggests that there were few hidden cases remaining at the end of the outbreaks in each region. We estimate that the initial reproduction numbers lay between 1.5 and 3, and the reproduction numbers in the later stages of the outbreak lay between 0.36 and 0.6

Clostridium difficile classification overestimates hospital acquired infections

BACKGROUND Clostridium difficile infections are common among hospitalised patients, with some infections acquired in hospital and others in the community. International guidelines classify cases as hospital-acquired if symptom onset occurs >2 days after admission. This classification informs surveillance and infection control, but has not been verified by empirical or modelling studies. AIMS To assess current classification of C. difficile acquisition using a simulation model as a gold standard. METHODS We simulated C. difficile transmission in a range of hospital scenarios. We calculated the sensitivity, specificity and precision of classifications that use cut-offs ranging from 0.25 hours to 40 days. We identified the optimal cut-off that correctly estimated the proportion of cases that were hospital acquired and the balanced cut-off that had equal sensitivity and specificity. FINDINGS The recommended two-day cut-off overestimated the incidence of hospital-acquired cases in all scenarios and by >100% in the base scenario. The two-day cut-off had good sensitivity (96%) but poor specificity (48%) and precision (52%) to identify cases acquired during the current hospitalisation. A five-day cut-off was balanced and a six-day cut-off was optimal in the base scenario. The optimal and balanced cut-offs were more than two days for nearly all scenarios considered (ranges four to nine days and two to eight days). CONCLUSIONS Current guidelines for classifying C. difficile infections overestimate the proportion of cases acquired in hospital in all model scenarios. To reduce misclassification bias, an infection should be classified as being acquired prior to admission if symptoms begin within five days of admission

Estimating Risks of Heat Strain by Age and Sex: A Population-Level Simulation Model

Individuals living in hot climates face health risks from hyperthermia due to excessive heat. Heat strain is influenced by weather exposure and by individual characteristics such as age, sex, body size, and occupation. To explore the population-level drivers of heat strain, we developed a simulation model that scales up individual risks of heat storage (estimated using Myrup and Morgan’s man model “MANMO”) to a large population. Using Australian weather data, we identify high-risk weather conditions together with individual characteristics that increase the risk of heat stress under these conditions. The model identifies elevated risks in children and the elderly, with females aged 75 and older those most likely to experience heat strain. Risk of heat strain in males does not increase as rapidly with age, but is greatest on hot days with high solar radiation. Although cloudy days are less dangerous for the wider population, older women still have an elevated risk of heat strain on hot cloudy days or when indoors during high temperatures. Simulation models provide a valuable method for exploring population level risks of heat strain, and a tool for evaluating public health and other government policy interventions

Sequelae of foodborne Illness caused by 5 pathogens, Australia, Circa 2010

In Australia circa 2010, 4.1 million (90% credible interval [CrI] 2.3–6.4 million) episodes of foodborne gastroenteritis occurred, many of which might have resulted in sequelae. We estimated the number of illnesses, hospitalizations, and deaths from Gu