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One out of every twenty emergency department (ED) visits in the United States is due to a psychiatric issue. Providing a gateway between the community and the mental health system, psychiatry emergency clinicians are responsible for assessing and managing a wide array of clinical presentations and conditions. Among emergency mental health-related visits, substance-related disorders, mood disorders, anxiety disorders, psychosis, and suicide attempts are among the most prevalent presentations. Although urgent conditions are common, increasing numbers of patients who present to the emergency department seek treatment for routine or non-acute psychiatric symptoms. Some patients self-refer to the emergency department, while others may be referred by family, friends, outpatient treatment providers, public agencies, or representatives of the law enforcement system.
Some issues in the application of benefit–cost analysis (BCA) remain contentious. Although a strong conceptual case can be made for taking account of the marginal excess tax burden (METB) in conducting BCAs, it is usually excluded. Although a strong conceptual case can be made that BCA should not include distributional values, some analysts continue to advocate doing so. We discuss the cases for inclusion of the METB and the exclusion of distributional weights from what we refer to as “core” BCA, which we argue should be preserved as a protocol for assessing allocative efficiency. These issues are topical because a recent article in this journal recommends ignoring the METB on the grounds that desirable distributional effects offset its cost. We challenge the logic of this article and explain why it may encourage inefficient policies.
Psychosis is more prevalent among people in prison compared with the community. Early detection is important to optimise health and justice outcomes; for some, this may be the first time they have been clinically assessed.
Determine factors associated with a first diagnosis of psychosis in prison and describe time to diagnosis from entry into prison.
This retrospective cohort study describes individuals identified for the first time with psychosis in New South Wales (NSW) prisons (2006–2012). Logistic regression was used to identify factors associated with a first diagnosis of psychosis. Cox regression was used to describe time to diagnosis from entry into prison.
Of the 38 489 diagnosed with psychosis for the first time, 1.7% (n = 659) occurred in prison. Factors associated with an increased likelihood of being diagnosed in prison (versus community) were: male gender (odds ratio (OR) = 2.27, 95% CI 1.79–2.89), Aboriginality (OR = 1.81, 95% CI 1.49–2.19), older age (OR = 1.70, 95% CI 1.37–2.11 for 25–34 years and OR = 1.63, 95% CI 1.29–2.06 for 35–44 years) and disadvantaged socioeconomic area (OR = 4.41, 95% CI 3.42–5.69). Eight out of ten were diagnosed within 3 months of reception.
Among those diagnosed with psychosis for the first time, only a small number were identified during incarceration with most identified in the first 3 months following imprisonment. This suggests good screening processes are in place in NSW prisons for detecting those with serious mental illness. It is important these individuals receive appropriate care in prison, have the opportunity to have matters reheard and possibly diverted into treatment, and are subsequently connected to community mental health services on release.
Pediatric trauma is one of the leading causes of child mortality and morbidity and is a major challenge for healthcare systems worldwide. Treatment of pediatric trauma requires special attention according to the unique needs of children, especially in children affected by severe trauma who require life-saving treatments. It is essential to examine the preparedness of Emergency Departments (EDs) for admitting and treating pediatric casualties.
To develop a model for admitting and treating pediatric trauma casualties in EDs.
Seventeen health professionals were interviewed using a semi-structured qualitative tool. A quantitative questionnaire was distributed among general and pediatric EDs’ medical and nursing staff. Following the qualitative and quantitative findings, another round of interviews was performed to identify constraints, to construct a “Current Reality Tree,” and develop a model for admission and management of pediatric casualties in EDs. The model was validated by the National Council for Trauma and Emergency Medicine.
Lack of uniformity was found regarding age limit and levels of injury of pediatric patients. Most study participants believe that severe pediatric casualties should be concentrated in designated medical centers and that minor and major pediatric casualties should be treated in pediatric rather that general EDs. Pediatric emergency medicine specialists are preferred as case managers for pediatric casualties. Significant diversity in pediatric-care training was found. Based on qualitative and quantitative findings, a model for the optimal admitting and managing of pediatric casualties was designed.
To provide the best care for pediatric casualties and regulate its key aspects, clear statutory guidelines should be formulated at national and local levels. The model developed in this study considers EDs’ medical teams and policy leaders’ perceptions, and hence its significant contribution. Implementation of the findings and their integration in pediatric trauma care in EDs can significantly improve pediatric emergency medical services.
With significant numbers of individuals in the criminal justice system having mental health problems, court-based diversion programmes and liaison services have been established to address this problem.
To examine the effectiveness of the New South Wales (Australia) court diversion programme in reducing re-offending among those diagnosed with psychosis by comparing the treatment order group with a comparison group who received a punitive sanction.
Those with psychoses were identified from New South Wales Ministry of Health records between 2001 and 2012 and linked to offending records. Cox regression models were used to identify factors associated with re-offending.
A total of 7743 individuals were identified as diagnosed with a psychotic disorder prior to their court finalisation date for their first principal offence. Overall, 26% of the cohort received a treatment order and 74% received a punitive sanction. The re-offending rate in the treatment order group was 12% lower than the punitive sanction group. ‘Acts intended to cause injury’ was the most common type of the first principal offence for the treatment order group compared with the punitive sanction group (48% v. 27%). Drug-related offences were more likely to be punished with a punitive sanction than a treatment order (12% v. 2%).
Among those with a serious mental illness (i.e. psychosis), receiving a treatment order by the court rather than a punitive sanction was associated with reduced risk for subsequent offending. We further examined actual mental health treatment received and found that receiving no treatment following the first offence was associated with an increased risk of re-offending and, so, highlighting the importance of treatment for those with serious mental illness in the criminal justice system.
Little is known about the prevalence of mental health outcomes in UK personnel at the end of the British involvement in the Iraq and Afghanistan conflicts.
We examined the prevalence of mental disorders and alcohol misuse, whether this differed between serving and ex-serving regular personnel and by deployment status.
This is the third phase of a military cohort study (2014–2016; n = 8093). The sample was based on participants from previous phases (2004–2006 and 2007–2009) and a new randomly selected sample of those who had joined the UK armed forces since 2009.
The prevalence was 6.2% for probable post-traumatic stress disorder, 21.9% for common mental disorders and 10.0% for alcohol misuse. Deployment to Iraq or Afghanistan and a combat role during deployment were associated with significantly worse mental health outcomes and alcohol misuse in ex-serving regular personnel but not in currently serving regular personnel.
The findings highlight an increasing prevalence of post-traumatic stress disorder and a lowering prevalence of alcohol misuse compared with our previous findings and stresses the importance of continued surveillance during service and beyond.
Declaration of interest:
All authors are based at King's College London which, for the purpose of this study and other military-related studies, receives funding from the UK Ministry of Defence (MoD). S.A.M.S., M.J., L.H., D.P., S.M. and R.J.R. salaries were totally or partially paid by the UK MoD. The UK MoD provides support to the Academic Department of Military Mental Health, and the salaries of N.J., N.G. and N.T.F. are covered totally or partly by this contribution. D.Mu. is employed by Combat Stress, a national UK charity that provides clinical mental health services to veterans. D.MacM. is the lead consultant for an NHS Veteran Mental Health Service. N.G. is the Royal College of Psychiatrists’ Lead for Military and Veterans’ Health, a trustee of Walking with the Wounded, and an independent director at the Forces in Mind Trust; however, he was not directed by these organisations in any way in relation to his contribution to this paper. N.J. is a full-time member of the armed forces seconded to King's College London. N.T.F. reports grants from the US Department of Defense and the UK MoD, is a trustee (unpaid) of The Warrior Programme and an independent advisor to the Independent Group Advising on the Release of Data (IGARD). S.W. is a trustee (unpaid) of Combat Stress and Honorary Civilian Consultant Advisor in Psychiatry for the British Army (unpaid). S.W. is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response at King's College London in partnership with Public Health England, in collaboration with the University of East Anglia and Newcastle University. The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, the Department of Health, Public Health England or the UK MoD.
This study examined the effectiveness of a formal postdoctoral education program designed to teach skills in clinical and translational science, using scholar publication rates as a measure of research productivity.
Participants included 70 clinical fellows who were admitted to a master’s or certificate training program in clinical and translational science from 1999 to 2015 and 70 matched control peers. The primary outcomes were the number of publications 5 years post-fellowship matriculation and time to publishing 15 peer-reviewed manuscripts post-matriculation.
Clinical and translational science program graduates published significantly more peer-reviewed manuscripts at 5 years post-matriculation (median 8 vs 5, p=0.041) and had a faster time to publication of 15 peer-reviewed manuscripts (matched hazard ratio = 2.91, p=0.002). Additionally, program graduates’ publications yielded a significantly higher average H-index (11 vs. 7, p=0.013).
These findings support the effectiveness of formal training programs in clinical and translational science by increasing academic productivity.
Generally, estimation of changes in social surplus requires knowledge of entire demand and supply schedules. Chapter 4 discusses direct estimation of demand and supply curves, focusing on the demand curve for the purpose of measuring consumer surplus. It assumes that there is a market demand schedule for the good in question, such as garbage collection or gasoline, and we can observe at least one point on this demand curve. In many applications of CBA, however, the markets for certain “goods,” such as human life or pollution, do not exist or are imperfect for reasons discussed in Chapter 3.
In practice in CBA we calculate expected surplus. However, the theoretically correct measure of net benefits is option price (also called certainty equivalent). The difference between expected surplus and option price is option value. It can be thought of as an insurance benefit. Assuming that individuals are risk averse, expected surplus can either underestimate or overestimate option price depending on the sources of risk. For an individual who is risk averse and whose utility function depends only on income, expected surplus will underestimate option price for policies that reduce income risk and overestimate option price for policies that increase income risk. However, the over or underestimation is generally relatively small. Therefore, analysts can use expected surplus without incurring too much bias.
In the Affair of so much Importance to you, wherein you ask my Advice, I cannot for want of sufficient Premises, advise you what to determine, but if you please I will tell you how. When those difficult Cases occur, they are difficult, chiefly because while we have them under Consideration, all the Reasons pro and con are not present to the Mind at the same time; but sometimes one Set present themselves, and at other times another, the first being out of Sight. Hence the various Purposes or Inclinations that alternately prevail, and the Uncertainty that perplexes us.
The prices of most goods and services tend to rise over time, that is, we experience inflation. However, in practice, not all prices (or values) increase at the same rate. Some prices, such as house prices and fuel prices, are often much more volatile than others. For this reason, some countries exclude such volatile items from their basket of goods when computing the CPI. And the prices of some goods and services sometimes go in a different direction to other prices. For example, from December 2010 to December 2016 in the US, the all-items CPI rose about 10 percent; however, the price of houses rose about 21 percent, while the price of gold fell about 15 percent.1
When policies impact goods for which markets do not exist, analysts develop shadow prices for the impacts to monetize them. This chapter assess the advantages and disadvantages of several methods commonly used to find shadow prices. The market analogy method involves using information about the market demand for an analogous good to develop a shadow price for a non-market good. The tradeoff method imputes prices to non-market goods based on how people trade changes in its quantity for other things they value such as time. The intermediate good method involves linking changes in the non-market good to changes in observable measures such as labor market earnings. The asset valuation method looks for changes in the values of assets like stocks or housing that capture changes in non-market goods. The hedonic pricing method employs regression analysis to control for multiple factors that affect asset values and heterogeneity in the demand for characteristics of assets. The travel cost method uses differences in travel time as a proxy for price differences for goods not traded in markets. The defensive expenditures method looks at how changes in non-market goods affect the production functions for market goods.
Cost-effectiveness Analysis (CEA) is often used instead of CBA in areas such as education, health and defense. It is used in situations with two characteristics. First, the policies being evaluated have one major benefit that analysts or clients are unwilling or unable to monetize. Second, the only cost analysts or decision-makers want to consider is the financial cost of the technology (i.e., the policy alternative) incurred by the government agency that will pay for it, such as the public health plan. Cost-utility analysis also relates costs to a single benefit measure, but the benefit measure is a construct made up of several (usually two) benefit categories, reflecting both quantity and quality. For example, in health technology assessment, the benefit measure is usually quality-adjusted life-years (QALY), which combines both the number of additional years of life (mortality) and the quality of life during those years (morbidity).
The broad purpose of Cost-Benefit Analysis (CBA) is to help social decision making and to increase social value or, more technically, to improve allocative efficiency. CBA is a policy assessment method that quantifies in monetary terms the value of all consequences (usually called impacts) of a policy to all members of society. This chapter provides a non-technical overview of the ten steps involved in performing a CBA. It involves issues around standing, prediction of impacts, monetization of impacts, discounting and sensitivity analysis.
It seems only natural to think about the alternative courses of action we face as individuals in terms of their costs and benefits. Is it appropriate to evaluate public policy alternatives in the same way? The CBA of the highway sketched in Chapter 1 identifies some of the practical difficulties analysts typically encounter in measuring costs and benefits. Yet, even if analysts can measure costs and benefits satisfactorily, evaluating alternatives solely in terms of their net benefits may not always be appropriate. An understanding of the conceptual foundations of CBA provides a basis for determining when CBA can be appropriately used as a decision rule, when it can usefully be part of a broader analysis, and when it should be avoided.
In the actual practice of ex anteCBA in circumstances involving significant risks, analysts almost always apply the Kaldor–Hicks criterion to expected net benefits. They typically estimate changes in social surplus conditional on particular contingencies occurring, and then they compute the expected value over the contingencies as explained in Chapter 11. Economists, however, now generally consider option price, the amount that individuals are willing to pay for policies prior to the realization of contingencies, to be the theoretically correct measure of willingness to pay in circumstances of uncertainty or risk.
Chapter 19 examines the role of the distribution of benefits and costs among groups in using CBA for decision making. Although those affected by a policy can potentially be divided into groups along many dimensions— income levels, age, gender, race, ethnicity, location, and so forth—the chapter emphasizes CBAs of policies that have differential effects on groups that differ by income—for example, projects that are located in underdeveloped regions or programs targeted at disadvantaged persons. The chapter first examines the economic rationale for treating dollars received or expended by various income groups differently in CBA. It then considers using distributional weights--numbers such as 1, 2, or 1.5 that are intended to reflect the value placed on each dollar paid out or received by different groups--for doing this in practice. Chapter 19 examines how these distritributional weights might be estimated. For illustrative purposes, distributional weights are applied to a number of previous CBAs welfare-to-work programs. The chapter makes clear that a widely agreed upon set of distributional weights do not yet exist and, therefore, distributional weighting is controversial.