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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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TL;DR: The Dual Diagnosis Capability in Health Care Settings (DDCHCS) was found to be feasible and sensitive to detecting variation in integrated behavioral health services capacity, and mental health services were more integrated than substance use.

25 citations


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

  • ...Although more research is needed, findings across several studies support the benefits of integrating behavioral health services (substance use and mental health) with medical care, in terms of patient outcomes and costs (Friedmann, Zhang, Hendrickson, Stein, & Gerstein, 2003; Katon, Russo, Von Korff, et al., 2008; Parthasarathy et al., 2003; Simon et al., 2007; Weisner, Mertens, Parthasarathy, Moore, & Lu, 2001)....

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01 Jan 2004
TL;DR: The aim of this study is to update the review of evidence on the effectiveness and cost-effectiveness of interventions aimed at reducing alcohol misuse, published in 2002, and to identify any new findings in this literature.
Abstract: The aim of this study is to update the review of evidence on the effectiveness and cost-effectiveness of interventions aimed at reducing alcohol misuse, published in 2002, and to identify any new findings in this literature.

23 citations

Journal ArticleDOI
TL;DR: It is shown that it is possible to improve primary care organizations' integrated care capacity as measured by the BHIMC, though it may be difficult or unfeasible for them to provide fully integrated behavioral health services.

23 citations

Journal ArticleDOI
TL;DR: Findings support the feasibility and effectiveness of the LINKAGE intervention in helping patients receiving addiction treatment engage in health care and increase communication with their physicians.
Abstract: Importance Research has shown that higher activation and engagement with health care is associated with better self-management. To our knowledge, the linkage intervention (LINKAGE) is the first to engage patients receiving addiction treatment with health care using the electronic health record and a patient activation approach. Objective To examine the effects of an intervention aiming to link patients receiving addiction treatment with health care. Design, Setting, and Participants A nonrandomized clinical trial evaluating the LINKAGE intervention vs usual care by applying an alternating 3-month off-and-on design over 30 months. Participants were recruited from an outpatient addiction treatment clinic in a large health system between April 7, 2011, and October 2, 2013. Interventions Six group-based, manual-guided sessions on patient engagement in health care and the use of health information technology resources in the electronic health record, as well as facilitated communication with physicians, vs usual care. Main Outcomes and Measures Primary outcomes, measured at 6 months after enrollment, were patient activation (by interview using the Patient Activation Measure), patient engagement in health care (by interview and electronic health record), and alcohol, drug, and depression outcomes (by interview using the Addiction Severity Index for alcohol and drug outcomes and Patient Health Questionnaire (PHQ) for depression). Results A total of 503 patients were recruited and assigned to the LINKAGE (n = 252) or usual care (n = 251) conditions, with no differences in baseline characteristics between conditions. The mean (SD) age of the patients was 42.5 (11.8) years, 31.0% (n = 156) were female, and 455 (90.5%) completed the 6-month interview. Compared with usual care participants, LINKAGE participants showed an increase in the mean number of log-in days (incidence rate ratio, 1.53; 95% CI, 1.19-1.97; P = .001). Similar results were found across types of patient portal use (communicating by email, viewing laboratory test results and information, and obtaining medical advice). LINKAGE participants were more likely to talk with their physicians about addiction problems (odds ratio, 2.30; 95% CI, 1.52-3.49; P Conclusions and Relevance Findings support the feasibility and effectiveness of the LINKAGE intervention in helping patients receiving addiction treatment engage in health care and increase communication with their physicians. The intervention did not affect short-term abstinence or depression outcomes. Understanding if the LINKAGE intervention helps prevent relapse and manage long-term recovery will be important. Trial Registration clinicaltrials.gov Identifier:NCT01621711

22 citations

Journal ArticleDOI
TL;DR: Primary care settings with specialty programs, including community health workers, may provide a venue to screen, assess, and help recently incarcerated women access needed care.
Abstract: Objective This study examined a primary care-based program to address the health needs of women recently released from incarceration by facilitating access to primary medical, mental health, and substance use disorder (SUD) treatment. Study Design Peer community health workers recruited women released from incarceration within the past 9 months into the Women's Initiative Supporting Health Transitions Clinic (WISH-TC). Located within an urban academic medical center, WISH-TC uses cultural, gender, and trauma-specific strategies grounded in the self-determination theory of motivation. Data abstracted from intake forms and medical charts were examined using bivariate and multivariable regression analyses. Results Of the 200 women recruited, 100 attended the program at least once. Most (83.0%) did not have a primary care provider before enrollment. Conditions more prevalent than in the general population included psychiatric disorders (94.0%), substance use (90.0%), intimate partner violence (66.0%), chronic pain (66.0%), and hepatitis C infection (12.0%). Patients received screening and vaccinations (65.9%–87.0%), mental health treatment (91.5%), and SUD treatment (64.0%). Logistic regression revealed that receipt of mental health treatment was associated with number of psychiatric (adjusted odds ratio [AOR], = 4.09; p p = .04), and higher median income (AOR, 1.07; p = .05); African American race predicted lower receipt of SUD treatment (AOR, 0.08; p Conclusions An innovative primary care transitions program successfully helped women recently released from incarceration to receive medical, mental health, and SUD treatment. Primary care settings with specialty programs, including community health workers, may provide a venue to screen, assess, and help recently incarcerated women access needed care.

22 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

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

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