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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TL;DR: In this paper, the effects of an organizational readiness and service delivery intervention on increasing the uptake of substance use disorder treatment in primary care and on patient outcomes were evaluated. But, the authors did not consider the effect of SUD treatment on the quality of care.
Abstract: Millions of people who need treatment for substance use disorders (SUD) do not receive it. Evidence-based practices for treating SUD exist, and some are appropriate for delivery outside of specialty care settings. Primary care is an opportune setting in which to deliver SUD treatment because many individuals see their primary care providers at least once a year. Further, the Patient Protection and Affordable Care Act (PPACA) increases coverage for SUD treatment and is increasing the number of individuals seeking primary care services. In this article, we present the protocol for a study testing the effects of an organizational readiness and service delivery intervention on increasing the uptake of SUD treatment in primary care and on patient outcomes.
34 citations
TL;DR: Among MMT patients, receipt of take homes, but not dose of methadone, was associated with decreased hospital admission, suggesting take-home status may reflect not only patients' improved addiction outcomes but also reduced health care utilization.
Abstract: Objectives
Among patients receiving methadone maintenance treatment (MMT) for opioid dependence, receipt of unobserved dosing privileges (take homes) and adequate doses (i.e. ≥ 80mg) are each associated with improved addiction treatment outcomes, but the association with acute care hospitalization is unknown. We studied whether take-home dosing and adequate doses (i.e. ≥ 80 mg) were associated with decreased hospital admission among patients in a MMT program.
32 citations
Cites background from "Utilization and cost impact of inte..."
...Addiction treatment generally is associated with decreased utilization of acute hospital services and, in some cases, lower overall costs (Laine et al., 2001, 2005; Weisner et al., 2001; Parthasarathy et al., 2003; Friedmann et al., 2006; Gourevitch et al., 2007)....
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TL;DR: In this paper, the organizational capacity of community-based primary care clinics to integrate treatment for Opioid and Alcohol Use Disorder (OAUD) was examined. But, the authors focused on three time points: pre-implementation (preparation), early implementation (practice), and full implementation.
29 citations
TL;DR: Six healthcare trends that have important implications for costing methodology are described, including reform in payment mechanisms, the growth of telehealth interventions, and the proliferation of new technology.
Abstract: This paper describes methods of determining costs for economic evaluations of healthcare and considers how cost determination is being affected by recent developments in healthcare. The literature was reviewed to identify the strengths and weaknesses of the four principal methods of cost determination: micro-costing, activity-based costing, charge-based costing, and gross costing. A scoping review was conducted to identify key trends in healthcare delivery and to identify costing issues associated with these changes. Existing guidelines provide information on how to implement various costing methods. Bottom-up costing is needed when accuracy is paramount, but top-down approaches are often the only feasible approach. We describe six healthcare trends that have important implications for costing methodology: (1) reform in payment mechanisms; (2) care delivery in less restrictive settings; (3) the growth of telehealth interventions; (4) the proliferation of new technology; (5) patient privacy concerns; and (6) growing efforts to implement guidelines. Some costs are difficult to measure and have been overlooked. These include physician services for inpatients, facility costs for outpatient services, the cost of developing treatment innovations, patient and caregiver costs, and the indirect costs of organizational interventions. Standardized methods are needed to determine social welfare and productivity costs. In the future, cost determination will be facilitated by technological advances but hindered by the shift to capitated payment, to the provision of care in less restrictive settings, and by heightened concern for medical record privacy.
28 citations
Cites background from "Utilization and cost impact of inte..."
...Reports suggest that many US hospitals use ABC accounting systems, including academic medical centres [16], the Kaiser healthcare system [17, 18] and the US Department of Veterans Affairs (VA) [19]....
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TL;DR: The scope and clinical implications of the public health problem of coexisting substance use and diabetes and opportunities for facilitating integration of preventive services and evidence-based treatments for substance use disorders with diabetes care in community-based medical settings are discussed.
Abstract: Cigarette smoking and alcohol use are prevalent among individuals with diabetes in the US, but little is known about screening and treatment for substance use disorders in the diabetic population. This commentary discusses the scope and clinical implications of the public health problem of coexisting substance use and diabetes, including suggestions for future research. Diabetes is the seventh leading cause of death in the US, and is associated with many severe health complications like cardiovascular disease, stroke, kidney damage, and limb amputations. There are an estimated 24 million adults in the US with type 2 diabetes. Approximately 20% of adults aged 18 years or older with diabetes report current cigarette smoking. The prevalence of current alcohol use in the diabetic population is estimated to be around 50%–60% in epidemiological surveys and treatment-seeking populations. Cigarette smoking is associated with an increased risk of type 2 diabetes in a dose-dependent manner and is an independent modifiable risk factor for development of type 2 diabetes. Diabetic patients with an alcohol or other drug use disorder show a higher rate of adverse health outcomes. For example, these patients experience more frequent and severe health complications as well as an increased risk of hospitalization, and require longer hospital stays. They are also less likely to seek routine care for diabetes or adhere to diabetes treatment than those without an alcohol or other drug use disorder. The Affordable Care Act of 2010 and the Mental Health Parity Act and Addiction Equity Act of 2008 provide opportunities for facilitating integration of preventive services and evidence-based treatments for substance use disorders with diabetes care in community-based medical settings. These laws also offer emerging areas for research.
28 citations
Cites background from "Utilization and cost impact of inte..."
...that in patients with substance use-related physical or psychiatric comorbidities, an integrated care model, in which primary care services were included in a drug abuse treatment program, was more effective than nonintegrated care in reducing hospitalization rates, utilization of emergency department visits, and inpatient care.(37) In primary care settings, substance abuse screening, brief intervention, and referral to treatment (SBIRT) for harmful alcohol use and other drug use disorders has been shown to save health costs....
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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