Utilization and cost impact of integrating substance abuse treatment and primary care.
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 Article•
TL;DR: The material presented in this course represents information obtained from the scientific literature, as well as the clinical experiences of the speakers, which might include discussion of investigational agents and/or off-label indications for various agents used in clinical practice.
Abstract: The material presented in this course represents information obtained from the scientific literature, as well as the clinical experiences of the speakers. In some cases, the presentations might include discussion of investigational agents and/or off-label indications for various agents used in clinical practice. Speakers will inform the audience when they are discussing investigational and/ or off-label uses.
5 citations
TL;DR: Identification of a comprehensive model of integrated care is recommended to inform the development and evaluation of future interventions for people with SMI/SUDs.
5 citations
TL;DR: Self-reported costs of people with severe mental illness adequately reflect actual service use as recorded in administrative data, and the increase in health service use after inpatient treatment can be seen as positive, while the pre-inpatient level of care is a potential problem, raising the question whether more or better outpatient care might have prevented hospital admission.
Abstract: Background There is limited evidence on the course of health service costs before and after psychiatric inpatient treatment, which might also be affected by source of cost data. Thus, this study examines: i) differences in health care costs before and after psychiatric inpatient treatment, ii) whether these differences vary by source of cost-data (self-report vs. administrative), and iii) predictors of cost differences over time. Methods Sixty-one psychiatric inpatients gave informed consent to their statutory health insurance company to provide insurance records and completed assessments at admission and 6-month follow-up. These were compared to the self‐reported treatment costs derived from the “Client Socio-demographic and Service Use Inventory” (CSSRI‐EU) for two 6‐month observation periods before and after admission to inpatient treatment to a large psychiatric hospital in rural Bavaria. Costs were divided into subtypes including costs for inpatient and outpatient treatment as well as for medication. Results Sixty-one participants completed both assessments. Over one year, the average patient‐reported total monthly treatment costs increased from € 276.91 to € 517.88 (paired Wilcoxon Z = ‐2.27; P = 0.023). Also all subtypes of treatment costs increased according to both data sources. Predictors of changes in costs were duration of the index admission and marital status. Conclusion Self-reported costs of people with severe mental illness adequately reflect actual service use as recorded in administrative data. The increase in health service use after inpatient treatment can be seen as positive, while the pre-inpatient level of care is a potential problem, raising the question whether more or better outpatient care might have prevented hospital admission. Findings may serve as a basis for future studies aiming at furthering the understanding of what to expect regarding appropriate levels of posthospital care, and what factors may help or inhibit post-discharge treatment engagement. Future research is also needed to examine long-term effects of inpatient psychiatric treatment on outcome and costs.
5 citations
TL;DR: It is suggested that multi-level interventions are required to address the unmet need, including pain management and integrated care, to support PWUD with complex health needs.
Abstract: People who use illicit drugs (PWUD) experience significant barriers to healthcare. However, little is known about levels of attachment to primary care (defined as having a regular family doctor or ...
4 citations
TL;DR: In this paper , the Buprenorphine Nurse Care Manager Initiative (BNCMI) was implemented in New York City, where nurse care managers (NCMs) were employed to provide care and concern for patients' overall well-being in a nonjudgmental manner.
3 citations
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Book•
01 Jan 1983TL;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
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
TL;DR: This is the 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 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 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 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 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 speci 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 speci 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 rst half of the chapter presents the methodology, and the second half demonstrates its application through ve different examples. The basis for the general situation is 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 coef 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
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