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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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01 Jan 2017
TL;DR: The SUMMIT collaborative care intervention as mentioned in this paper was designed to increase the delivery of either a 6-session brief psychotherapy treatment and/or medication-assisted treatment with either sublingual buprenorphine/naloxone for opioid use disorders or long-acting injectable naltrexone for alcohol use disorders.
Abstract: Importance Primary care offers an important and underutilized setting to deliver treatment for opioid and/or alcohol use disorders (OAUD). Collaborative care (CC) is effective but has not been tested for OAUD. Objective To determine whether CC for OAUD improves delivery of evidence-based treatments for OAUD and increases self-reported abstinence compared with usual primary care. Design, Setting, and Participants A randomized clinical trial of 377 primary care patients with OAUD was conducted in 2 clinics in a federally qualified health center. Participants were recruited from June 3, 2014, to January 15, 2016, and followed for 6 months. Interventions Of the 377 participants, 187 were randomized to CC and 190 were randomized to usual care; 77 (20.4%) of the participants were female, of whom 39 (20.9%) were randomized to CC and 38 (20.0%) were randomized to UC. The mean (SD) age of all respondents at baseline was 42 (12.0) years, 41(11.7) years for the CC group, and 43 (12.2) yearsfor the UC group. Collaborative care was a system-level intervention, designed to increase the delivery of either a 6-session brief psychotherapy treatment and/or medication-assisted treatment with either sublingual buprenorphine/naloxone for opioid use disorders or long-acting injectable naltrexone for alcohol use disorders. Usual care participants were told that the clinic provided OAUD treatment and given a number for appointment scheduling and list of community referrals. Main Outcomes and Measures The primary outcomes were use of any evidence-based treatment for OAUD and self-reported abstinence from opioids or alcohol at 6 months. The secondary outcomes included the Healthcare Effectiveness Data and Information Set (HEDIS) initiation and engagement measures, abstinence from other substances, heavy drinking, health-related quality of life, and consequences from OAUD. Results At 6 months, the proportion of participants who received any OAUD treatment was higher in the CC group compared with usual care (73 [39.0%] vs 32 [16.8%]; logistic model adjusted OR, 3.97; 95% CI, 2.32-6.79; P < .001). A higher proportion of CC participants reported abstinence from opioids or alcohol at 6 months (32.8% vs 22.3%); after linear probability model adjustment for covariates (&bgr; = 0.12; 95% CI, 0.01-0.23; P = .03). In secondary analyses, the proportion meeting the HEDIS initiation and engagement measures was also higher among CC participants (initiation, 31.6% vs 13.7%; adjusted OR, 3.54; 95% CI, 2.02-6.20; P < .001; engagement, 15.5% vs 4.2%; adjusted OR, 5.89; 95% CI, 2.43-14.32; P < .001) as was abstinence from opioids, cocaine, methamphetamines, marijuana, and any alcohol (26.3% vs 15.6%; effect estimate, &bgr; = 0.13; 95% CI, 0.03-0.23; P = .01). Conclusions and Relevance Among adults with OAUD in primary care, the SUMMIT collaborative care intervention resulted in significantly more access to treatment and abstinence from alcohol and drugs at 6 months, than usual care. Trial Registration clinicaltrials.gov Identifier: NCT01810159

84 citations

Journal ArticleDOI
TL;DR: It is indicated that having a clinical team dedicated to providing substance use disorder treatment, HIV risk reduction, and case management services integrated into primary care clinics has the potential to greatly enhance the ability to serve a challenging population with unmet treatment needs.
Abstract: The benefits of integrating primary care and substance use disorder treatment are well known, yet true integration is difficult. We developed and evaluated a team-based model of integrated care within the primary care setting for HIV-infected substance users and substance users at risk for contracting HIV. Qualitative data were gathered via focus groups and satisfaction surveys to assess patients' views of the program, evaluate key elements for success, and provide recommendations for other programs. Key themes related to preferences for the convenience and efficiency of integrated care; support for a team-based model of care; a feeling that the program requirements offered needed structure; the importance of counseling and education; and how provision of concrete services improved overall well-being and quality of life. For patients who received buprenorphine/naloxone for opioid dependence, this was viewed as a major benefit. Our results support other studies that theorize integrated care could be of significant value for hard-to-reach populations and indicate that having a clinical team dedicated to providing substance use disorder treatment, HIV risk reduction, and case management services integrated into primary care clinics has the potential to greatly enhance the ability to serve a challenging population with unmet treatment needs.

73 citations

Journal ArticleDOI
01 Oct 2005-AIDS
TL;DR: Quality improvement strategies are attractive vehicles for implementing care integration and measuring its impact and careful assessment of the problem to be solved and the development of targeted strategies will maximize chances of a successful outcome.
Abstract: Patients with chronic viral infections such as HIV/AIDS or hepatitis C often have multiple co-existing problems such as psychiatric and addictive disorders, as well as social problems such as lack of housing, transportation and income that present challenging obstacles to successful management. Because services for these different problems are usually provided by different disciplines in varying locations, fragmentation of care can lead to treatment dropouts, lack of adherence, and poor outcomes. Integration strategies, ranging from simple efforts to improve communication and coordinate care to fully integrated multidisciplinary teams have been used to improve disease management. Although evidence for effectiveness is comprised primarily of observational studies of demonstration programmes, integration may be desirable on a pragmatic basis alone. Quality improvement strategies are attractive vehicles for implementing care integration and measuring its impact. Careful assessment of the problem to be solved and the development of targeted strategies will maximize chances of a successful outcome.

71 citations


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

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Journal ArticleDOI
TL;DR: Analysis of costs as they relate to the financial costs of providing drug abuse treatment in private and public health plans, costs to society relating to drug abuse, and many smaller costing studies of various stakeholders in the health care system indicates that a wide collection of cost findings is available to policy makers.

69 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)
What can you do with a substance abuse certification?

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.