Pathways to recovery after substance abuse treatment: leaving a life of crime behind

Changes in criminal activity following substance abuse treatment were examined among 941 individuals. The estimated cost to society of crimes committed in the 6 months prior to intake was used to classify participants into three groups: no-, low-, and high-cost. Logistic regression was used to predict criminal activity at 6 and 24 months following intake as a function of (a) intake status, (b) treatment, and (c) outcome status and changes. The three groups varied significantly at intake and what predicted subsequent criminal activity. Treatment effects on criminal activity were mediated by the extent to which treatment reduced substance use. Outcomes were also predicted by other factors (e.g. housing, employment, medical problems, psychological distress, and social support). The results support the need for multidimensional assessments for predicting the risk of illegal activity, the need for reassessments following treatment, and the value of addressing other problems in reducing subsequent criminal activity.     

An eight-year perspective on the relationship between the duration of abstinence and other aspects of recovery

Using data from 1,162 people entering treatment and followed up (> 94%) for 8 years, this article examines the relationship between the duration of abstinence (1 month to 5 or more years) and other aspects of recovery (e.g., health, mental health, coping responses, legal involvement, vocational involvement, housing, peers, social and spiritual support), including the trend and at what point changes occur. It also examines how the duration of abstinence at a given point is related to the odds of sustaining abstinence in the subsequent year. The findings demonstrate the rich patterns of change associated with the course of long-term recovery.     

Social Capital,Organic Agriculture,and Sustainable Livelihood Security: Rethinking Agrarian Change in Mexico*

Abstract This paper explores the relevance of extra local market linkages and local‐level social capital to sustainable livelihood outcomes in two agrarian communities on Mexico's Baja Peninsula. Contextualized by the specificity of Mexico's transition from state‐directed rural development to neoliberally‐guided rural development in the 1990s, findings suggest that market linkages can intersect with pre‐existing social capital to both create new and destroy preexisting social capital, thus shaping the direction of development and inequality outcomes. The nature of a community's social fabric is often a result of long‐standing historical legacies. In the communities presented, the quantity and quality of social capital was intricately connected to their history of state‐sponsored or market agriculture; the nature of local institutions, with particular emphasis on the formation and evolution of the ejido; and the access to and availability of natural resources, namely land and water, which are both intricately connected to market access options. Moving beyond a simple demonstration that social capital matters, this analysis explores the complex and dynamic interaction between local‐level social capital and extralocal market linkages. In doing so, it contributes to the larger debate on how the historical legacy of populist reforms and the social and political institutions created during state populism have nuanced the trajectory of neoliberal development in Mexico.     

Sample size and the probability of a successful trial

This paper describes the distinction between the concept of statistical power and the probability of getting a successful trial. While one can choose a very high statistical power to detect a certain treatment effect, the high statistical power does not necessarily translate to a high success probability if the treatment effect to detect is based on the perceived ability of the drug candidate. The crucial factor hinges on our knowledge of the drug's ability to deliver the effect used to power the study. The paper discusses a framework to calculate the 'average success probability' and demonstrates how uncertainty about the treatment effect could affect the average success probability for a confirmatory trial. It complements an earlier work by O'Hagan et al. (Pharmaceutical Statistics 2005; 4:187-201) published in this journal. Computer codes to calculate the average success probability are included.     

Mapping folk devils old and new through permanent exclusion from London schools

The process of permanent exclusion from school offers a heightened example of the rejections necessary to keep the English neoliberal education treadmill running. This extreme end of education’s disciplinary apparatus illuminates trends less immediately legible across the system, namely how securitization and neoliberal governance heighten inequalities. Unpicking the dynamics at work behind exclusion shows how racialization and marginalization are not reduced but reproduced through this educational format. This paper maps how securitization and neoliberal governance work together through permanent exclusion to reproduce racialized folk devils old and new, drawing on discourses of criminal blackness as well as the radicalized Islamic terrorist. It will also explore how exclusion policy is negotiated and translated into daily practice by exploring parental accounts of their child’s permanent exclusion alongside the narratives of head teachers in London.     

The role of the minimum clinically important difference and its impact on designing a trial

The minimum clinically important difference (MCID) between treatments is recognized as a key concept in the design and interpretation of results from a clinical trial. Yet even assuming such a difference can be derived, it is not necessarily clear how it should be used. In this paper, we consider three possible roles for the MCID. They are: (1) using the MCID to determine the required sample size so that the trial has a pre-specified statistical power to conclude a significant treatment effect when the treatment effect is equal to the MCID; (2) requiring with high probability, the observed treatment effect in a trial, in addition to being statistically significant, to be at least as large as the MCID; (3) demonstrating via hypothesis testing that the effect of the new treatment is at least as large as the MCID. We will examine the implications of the three different possible roles of the MCID on sample size, expectations of a new treatment, and the chance for a successful trial. We also give our opinion on how the MCID should generally be used in the design and interpretation of results from a clinical trial.     

The shrinking or disappearing observed treatment effect

It is frequently noted that an initial clinical trial finding was not reproduced in a later trial. This is often met with some surprise. Yet, there is a relatively straightforward reason partially responsible for this observation. In this article, we examine this reason by first reviewing some findings in a recent publication in the Journal of the American Medical Association. To help explain the non‐negligible chance of failing to reproduce a previous positive finding, we compare a series of trials to successive diagnostic tests used for identifying a condition. To help explain the suspicion that the treatment effect, when observed in a subsequent trial, seems to have decreased in magnitude, we draw a conceptual analogy between phases II–III development stages and interim analyses of a trial with a group sequential design. Both analogies remind us that what we observed in an early trial could be a false positive or a random high. We discuss statistical sources for these occurrences and discuss why it is important for statisticians to take these into consideration when designing and interpreting trial results. Copyright © 2014 John Wiley & Sons, Ltd.     

Why do drug treatment organizations use contingent staffing arrangements? An analysis of market and social influences

Contingent staffing arrangements are defined as conditional and transitory work arrangements. In the drug abuse treatment sector, contingent staffing arrangements have the potential to improve treatment if they are used to increase access to needed services. Alternatively, such arrangements could interfere with the development of consistent, long-term client-staff relationships. Unfortunately, little is known about the consequences of or influences on contingent staff arrangements in this sector. The goal of this study is to examine the conditions under which outpatient substance abuse treatment organizations are more likely to use contingent staffing arrangements. Building on previous research on the social organization of health care structures and practices, we develop a conceptual model based in market economics and institutional perspectives to suggest that treatment organizations choose contingent arrangements in response to market conditions and uncertainty, institutional demands, and client needs. Using data from a nationally representative study conducted in 1988, 1990, and 1995, we find limited evidence that drug treatment units use contingent staff in response to market pressures. Labor market and demand uncertainty, however, are systematically associated with greater use of contingent staff. Study results suggest that expectations and norms from the institutional environment, particularly the organizational context of the treatment unit are strong predictors of the use of contingent staff. By considering both market and social influences of contingent staffing, we contribute to a growing body of research on how markets and institutions interact to influence organizational structures and practices in the health care system.