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Journal ArticleDOI

Utilization and cost impact of integrating substance abuse treatment and primary care.

01 Mar 2003-Medical Care (Med Care)-Vol. 41, Iss: 3, pp 357-367
TL;DR: The findings for the full sample suggest that integrating substance abuse treatment with primary care, may not be necessary or appropriate for all patients, but it may be beneficial to refer patients with substance abuse related medical conditions to a provider also trained in addiction medicine.
Abstract: Objective. To examine the impact of integrating medical and substance abuse treatment on health care utilization and cost.Research Design. Randomized clinical trial assigning patients to one of two treatment modalities: an Integrated Care model where primary health care is provided along with substa
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Journal ArticleDOI
TL;DR: It is concluded that recovery support services (RSS) should be further assessed for effectiveness and cost-effectiveness, that greater efforts must be made to develop the RSS delivery workforce, and that RSS should capitalize on ongoing efforts to create a comprehensive, integrated and patient-centered health care system.

131 citations

01 Oct 2013
TL;DR: The National Summit on Recovery: Conference Report as discussed by the authors defined 12 guiding principles of recovery and key elements of recovery-oriented systems of care and provided a philosophical and conceptual framework to guide stakeholders in the treatment and recovery field.
Abstract: 1. BackgroundThe concept of recovery lies at the core of the Substance Abuse and Mental Health Services Administration's (SAMHSA's) mission, and fostering the development of recovery- oriented systems of care and services is a Center for Substance Abuse Treatment (CSAT) priority. In support of that commitment, in 2005, SAMHSA's CSAT convened a National Summit on Recovery. Participants at the Summit represented a broad group of stakeholders, policymakers, advocates, consumers, clinicians, and administrators from diverse ethnic and professional backgrounds. Although the substance use problems and disorders treatment and recovery field has discussed and lived recovery for decades, the Summit represented the first broad-based national effort to reach a definition of recovery and a common understanding of the guiding principles of recovery and the elements of recovery-oriented systems of care.Through a multistage process, key stakeholders formulated guiding principles of recovery and key elements of recovery-oriented systems of care. Summit participants then further refined the guiding principles and key elements in response to two questions: 1) What principles of recovery should guide the field in the future? and 2) What ideas could help make the field more recovery oriented?A working definition of recovery, 12 guiding principles of recovery, and 17 elements of recovery-oriented systems of care emerged from the Summit process; these are subsequently defined in this paper and in the National Summit on Recovery: Conference Report. [1] These principles and elements can now provide a philosophical and conceptual framework to guide SAMHSA/CSAT and other stakeholder groups and offer a shared language for dialog.Summit participants agreed on the following working definition of recovery:Recovery from alcohol and drag problems is a process of change through which an individual achieves abstinence and improved health, wellness, and quality of life.The guiding principles that emerged from the Summit are broad and overarching. They are intended to give general direction to SAMHSA/CSAT and other stakeholder groups as the treatment and recovery field moves toward operationalizing recovery-oriented systems of care and developing core measures, promising approaches, and evidence-based practices. The principles also helped Summit participants define the recovery-oriented elements and guided recommendations for the field.Following are the 12 guiding principles identified by participants (defined in this paper):* There are many pathways to recovery.* Recovery is self-directed and empowering.* Recovery involves a personal recognition of the need for change and transformation.* Recovery is holistic.* Recovery has cultural dimensions.* Recovery exists on a continuum of improved health and wellness.* Recovery emerges from hope and gratitude.* Recovery involves a process of healing and self-redefinition.* Recovery involves addressing discrimination and transcending shame and stigma.* Recovery is supported by peers and allies.* Recovery involves (re)joining and (re)building a life in the community.* Recovery is a reality.Participants at the Summit agreed that recovery-oriented systems of care are as complex and dynamic as the process of recovery itself. They are designed to support individuals seeking to overcome substance use problems and disorders across their lifespan. Participants at the Summit declared, "There will be no wrong door to recovery" and also recognized that recovery-oriented systems of care need to provide "genuine, free and independent choice" among an array of treatment and recovery support options. Services should optimally be provided in flexible, unbundled packages that evolve over time to meet the changing needs of recovering individuals. Individuals should also be able to access a comprehensive array of services that are fully coordinated to provide support to individuals throughout their unique journeys to sustained recovery. …

121 citations

Journal ArticleDOI
TL;DR: Family members of patients with AODs have greater health care costs and are more likely to be diagnosed with a number of medical conditions than family members of similar persons without an AOD.
Abstract: BACKGROUND:: Having a family member with substance use problems affects family functioning, which may lead to increased medical problems and increased health care utilization and costs in the other family members. AIM:: We sought to estimate the excess medical costs and prevalence of diagnosed health conditions of family members of persons with an alcohol or drug diagnosis (AOD) compared with the family members of similar persons without an AOD. METHODS:: Using a large health plan's administrative databases, we identified persons who received an AOD between 2001 and 2004 and a similar group of persons with no AOD during that time. Using a hierarchical linear mixed model, we compared the cost and utilization of the family members of the AOD and non-AOD patients in the 2 years prior to the AOD patient's first AOD. Using logistic regression, we determined whether the family members of patients with AODs were more likely than comparison family members to be diagnosed with medical conditions. RESULTS:: Family members of patients with AODs had greater health care costs than comparison family members in the second year before the index date ($490) and in the year before the index date ($433). This was the case for both adult and child family members. They also were more likely to be diagnosed with many medical conditions, especially substance abuse and depression. CONCLUSIONS:: Family members of patients with AODs have greater health care costs and are more likely to be diagnosed with a number of medical conditions than family members of similar persons without an AOD. Language: en

111 citations

Book ChapterDOI
01 Jan 2008
TL;DR: It is found that psychological issues are found to be part of acute medical issues, such as sleeping problems, headache or gastrointestinal problems, as well as complex chronic medical conditions such as diabetes, cardiac conditions or pain.
Abstract: ● Many medical presentations contain significant psychological comorbidity. Strosahl and Robinson point out in Chap. 8 that presentations that are for specific psychological or substance abuse issues are infrequent. More often, psychological issues are found to be part of acute medical issues, such as sleeping problems, headache or gastrointestinal problems, as well as complex chronic medical conditions such as diabetes, cardiac conditions or pain.

109 citations


Cites result from "Utilization and cost impact of inte..."

  • ...03.(92) Combined behavioral interventions for patients with alcohol dependence were demonstrated to have as good outcomes as that with naltrexone and better outcomes than that with acamprosate (Campral)....

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Journal ArticleDOI
TL;DR: Common SUDs, particularly opioid use disorders, are associated with substantial disease burden for privately insured individuals without significant impediments to care, and signals the need to explore the full impact Suds have on the course and outcome of prevalent conditions and initiate enhanced service engagement strategies to improve disease burden.
Abstract: Author(s): Bahorik, Amber L; Satre, Derek D; Kline-Simon, Andrea H; Weisner, Constance M; Campbell, Cynthia I | Abstract: ObjectivesWe examined prevalence of major medical conditions and extent of disease burden among patients with and without substance use disorders (SUDs) in an integrated health care system serving 3.8 million members.MethodsMedical conditions and SUDs were extracted from electronic health records in 2010. Patients with SUDs (n = 45,461; alcohol, amphetamine, barbiturate, cocaine, hallucinogen, and opioid) and demographically matched patients without SUDs (n = 45,461) were compared on the prevalence of 19 major medical conditions. Disease burden was measured as a function of 10-year mortality risk using the Charlson Comorbidity Index. P-values were adjusted using Hochberg's correction for multiple-inference testing within each medical condition category.ResultsThe most frequently diagnosed SUDs in 2010 were alcohol (57.6%), cannabis (14.9%), and opioid (12.9%). Patients with these SUDs had higher prevalence of major medical conditions than non-SUD patients (alcohol use disorders, 85.3% vs 55.3%; cannabis use disorders, 41.9% vs 23.0%; and opioid use disorders, 44.9% vs 26.1%; all P l 0.001). Patients with these SUDs also had higher disease burden than non-SUD patients; patients with opioid use disorders (M = 0.48; SE = 1.46) had particularly high disease burden (M = 0.23; SE = 0.09; P l 0.001).ConclusionsCommon SUDs, particularly opioid use disorders, are associated with substantial disease burden for privately insured individuals without significant impediments to care. This signals the need to explore the full impact SUDs have on the course and outcome of prevalent conditions and initiate enhanced service engagement strategies to improve disease burden.

95 citations

References
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Book
01 Jan 1983
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Abstract: The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log- likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components).

23,215 citations

Journal ArticleDOI
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Abstract: SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the proposed estimators in two simple situations is considered. The approach is closely related to quasi-likelih ood. Some key ironh: Estimating equation; Generalized linear model; Longitudinal data; Quasi-likelihood; Repeated measures.

17,111 citations

Journal ArticleDOI
TL;DR: This is the Ž rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
Abstract: This is the Ž rst book on generalized linear models written by authors not mostly associated with the biological sciences. Subtitled “With Applications in Engineering and the Sciences,” this book’s authors all specialize primarily in engineering statistics. The Ž rst author has produced several recent editions of Walpole, Myers, and Myers (1998), the last reported by Ziegel (1999). The second author has had several editions of Montgomery and Runger (1999), recently reported by Ziegel (2002). All of the authors are renowned experts in modeling. The Ž rst two authors collaborated on a seminal volume in applied modeling (Myers and Montgomery 2002), which had its recent revised edition reported by Ziegel (2002). The last two authors collaborated on the most recent edition of a book on regression analysis (Montgomery, Peck, and Vining (2001), reported by Gray (2002), and the Ž rst author has had multiple editions of his own regression analysis book (Myers 1990), the latest of which was reported by Ziegel (1991). A comparable book with similar objectives and a more speciŽ c focus on logistic regression, Hosmer and Lemeshow (2000), reported by Conklin (2002), presumed a background in regression analysis and began with generalized linear models. The Preface here (p. xi) indicates an identical requirement but nonetheless begins with 100 pages of material on linear and nonlinear regression. Most of this will probably be a review for the readers of the book. Chapter 2, “Linear Regression Model,” begins with 50 pages of familiar material on estimation, inference, and diagnostic checking for multiple regression. The approach is very traditional, including the use of formal hypothesis tests. In industrial settings, use of p values as part of a risk-weighted decision is generally more appropriate. The pedagologic approach includes formulas and demonstrations for computations, although computing by Minitab is eventually illustrated. Less-familiar material on maximum likelihood estimation, scaled residuals, and weighted least squares provides more speciŽ c background for subsequent estimation methods for generalized linear models. This review is not meant to be disparaging. The authors have packed a wealth of useful nuggets for any practitioner in this chapter. It is thoroughly enjoyable to read. Chapter 3, “Nonlinear Regression Models,” is arguably less of a review, because regression analysis courses often give short shrift to nonlinear models. The chapter begins with a great example on the pitfalls of linearizing a nonlinear model for parameter estimation. It continues with the effective balancing of explicit statements concerning the theoretical basis for computations versus the application and demonstration of their use. The details of maximum likelihood estimation are again provided, and weighted and generalized regression estimation are discussed. Chapter 4 is titled “Logistic and Poisson Regression Models.” Logistic regression provides the basic model for generalized linear models. The prior development for weighted regression is used to motivate maximum likelihood estimation for the parameters in the logistic model. The algebraic details are provided. As in the development for linear models, some of the details are pushed into an appendix. In addition to connecting to the foregoing material on regression on several occasions, the authors link their development forward to their following chapter on the entire family of generalized linear models. They discuss score functions, the variance-covariance matrix, Wald inference, likelihood inference, deviance, and overdispersion. Careful explanations are given for the values provided in standard computer software, here PROC LOGISTIC in SAS. The value in having the book begin with familiar regression concepts is clearly realized when the analogies are drawn between overdispersion and nonhomogenous variance, or analysis of deviance and analysis of variance. The authors rely on the similarity of Poisson regression methods to logistic regression methods and mostly present illustrations for Poisson regression. These use PROC GENMOD in SAS. The book does not give any of the SAS code that produces the results. Two of the examples illustrate designed experiments and modeling. They include discussion of subset selection and adjustment for overdispersion. The mathematic level of the presentation is elevated in Chapter 5, “The Family of Generalized Linear Models.” First, the authors unify the two preceding chapters under the exponential distribution. The material on the formal structure for generalized linear models (GLMs), likelihood equations, quasilikelihood, the gamma distribution family, and power functions as links is some of the most advanced material in the book. Most of the computational details are relegated to appendixes. A discussion of residuals returns one to a more practical perspective, and two long examples on gamma distribution applications provide excellent guidance on how to put this material into practice. One example is a contrast to the use of linear regression with a log transformation of the response, and the other is a comparison to the use of a different link function in the previous chapter. Chapter 6 considers generalized estimating equations (GEEs) for longitudinal and analogous studies. The Ž rst half of the chapter presents the methodology, and the second half demonstrates its application through Ž ve different examples. The basis for the general situation is Ž rst established using the case with a normal distribution for the response and an identity link. The importance of the correlation structure is explained, the iterative estimation procedure is shown, and estimation for the scale parameters and the standard errors of the coefŽ cients is discussed. The procedures are then generalized for the exponential family of distributions and quasi-likelihood estimation. Two of the examples are standard repeated-measures illustrations from biostatistical applications, but the last three illustrations are all interesting reworkings of industrial applications. The GEE computations in PROC GENMOD are applied to account for correlations that occur with multiple measurements on the subjects or restrictions to randomizations. The examples show that accounting for correlation structure can result in different conclusions. Chapter 7, “Further Advances and Applications in GLM,” discusses several additional topics. These are experimental designs for GLMs, asymptotic results, analysis of screening experiments, data transformation, modeling for both a process mean and variance, and generalized additive models. The material on experimental designs is more discursive than prescriptive and as a result is also somewhat theoretical. Similar comments apply for the discussion on the quality of the asymptotic results, which wallows a little too much in reports on various simulation studies. The examples on screening and data transformations experiments are again reworkings of analyses of familiar industrial examples and another obvious motivation for the enthusiasm that the authors have developed for using the GLM toolkit. One can hope that subsequent editions will similarly contain new examples that will have caused the authors to expand the material on generalized additive models and other topics in this chapter. Designating myself to review a book that I know I will love to read is one of the rewards of being editor. I read both of the editions of McCullagh and Nelder (1989), which was reviewed by Schuenemeyer (1992). That book was not fun to read. The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.

10,520 citations

Journal ArticleDOI
TL;DR: The clinical and research uses of the ASI over the past 12 years are discussed, emphasizing some special circumstances that affect its administration.

4,045 citations

Trending Questions (2)
How much do substance abuse doctors make?

However, it may be beneficial to refer patients with substance abuse related medical conditions to a provider also trained in addiction medicine.

Which type of facility is best for treating patient suffering with substance abuse?

(Non)findings for the full sample suggest that integrating substance abuse treatment with primary care, may not be necessary or appropriate for all patients.