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: Assessment of SUD service integration in California primary care settings found there was a trend for SUD services to be less integrated with primary care, and Sud services were rated significantly less effective.
Abstract: Each year, nearly 20 million Americans with alcohol or illicit drug dependence do not receive treatment. The Affordable Care Act and parity laws are expected to result in increased access to treatment through integration of substance use disorder (SUD) services with primary care. However, relatively little research exists on the integration of SUD services into primary care settings. Our goal was to assess SUD service integration in California primary care settings and to identify the practice and policy facilitators and barriers encountered by providers who have attempted to integrate these services. Primary survey and qualitative interview data were collected from the population of federally qualified health centers (FQHCs) in five California counties known to be engaged in SUD integration efforts was surveyed. From among the organizations that responded to the survey (78% response rate), four were purposively sampled based on their level of integration. Interviews were conducted with management, staff, and patients (n = 18) from these organizations to collect further qualitative information on the barriers and facilitators of integration. Compared to mental health services, there was a trend for SUD services to be less integrated with primary care, and SUD services were rated significantly less effective. The perceived difference in effectiveness appeared to be due to provider training. Policy suggestions included expanding the SUD workforce that can bill Medicaid, allowing same-day billing of two services, facilitating easier reimbursement for medications, developing the workforce, and increasing community SUD specialty care capacity. Efforts to integrate SUD services with primary care face significant barriers, many of which arise at the policy level and are addressable.
49 citations
Cites background from "Utilization and cost impact of inte..."
...on community-based specialty SUD treatment alone, promises better outcomes for patients [5-11] and can result in reduced overall health care costs ([8,12])....
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...In fact, research and the real-world experience of insurance companies is that treating SUD problems often leads to savings in total medical costs [8,12]....
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TL;DR: In this article, the authors present background information on the economics of addiction health services, reviews recent empirical and methodological contributions, and provides 15 research recommendations for the next wave of research.
45 citations
Cites background from "Utilization and cost impact of inte..."
...Total medical costs, inpatient days, emergency department use, and hospitalization rates all dropped significantly for patients with substance abuse-related medical conditions who received primary care along with the substance abuse intervention (Parthasarathy et al., 2003)....
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...A longer follow-up period could demonstrate whether these patterns of decreased health care use and costs continue over time (Parthasarathy et al., 2003)....
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TL;DR: The reasons for integrating SUD and PC services are laid out and the models used and the experiences of providers as they have begun SUD/PC integration in California are explored.
Abstract: Integrating substance use disorder (SUD) services with primary care (PC) can improve access to SUD services for the 20.9 million Americans who need SUD treatment but do not receive it, and help prevent the onset of SUDs among the 68 million Americans who use psychoactive substances in a risky manner. We lay out the reasons for integrating SUD and PC services and then explore the models used and the experiences of providers as they have begun SUD/PC integration in California.
43 citations
Cites background from "Utilization and cost impact of inte..."
...For individuals with medical problems associated with substance use, integrating primary care services provided by physicians, medical assistants, and nurses with SUD treatment can cut total monthly medical costs in half (Parthasarathy et al. 2003; Weisner et al. 2001)....
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...…substance use disorder (SUD) services with primary care (PC) can reduce levels of substance use (Gryczynski et al. 2011; Madras et al. 2009), leading to improved physical and mental health (Madras et al. 2009) and overall healthcare cost savings (Babor et al. 2007; Parthasarathy et al. 2003, 2001)....
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...…and mental health (Madras et al. 2009), increase the likelihood that patients with HIV will receive antiretroviral therapy (Parry, Blank & Pithey 2007), and decrease hospitalization rates, inpatient days, and emergency department utilization (Parthasarathy et al. 2003; Weisner et al. 2001)....
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TL;DR: Compared with referral care, providing primary care within a VA addiction clinic increased primary care access and initial SUD treatment retention but showed no effect on overall health status or costs.
Abstract: Background: Patients presenting for treatment of substance use disorders (SUDs) often exhibit medical comorbidities that affect functional health status and healthcare costs. Providing primary care within addictions clinics (onsite care) may improve medical and SUD treatment outcomes in this population. Objective: The objective of this study was to compare outcomes among Veterans’ Administration (VA) patients who receive medical care within the SUD clinic and those referred to a general medicine clinic at the same facility. Methods: Veterans entering SUD treatment with a chronic medical condition and no current primary care were randomized to receive primary medical care: 1) onsite in the VA SUD clinic (n = 358), or 2) in the VA general internal medicine clinic (n = 362). Subjects were assessed at baseline and at 3, 6, and 12 months postrandomization. Intention-to-treat analyses used random-effects regression. Measures: Measures included SF-36 Physical and Mental Component Summaries (PCS, MCS), VA service utilization, SUD treatment retention, Addiction Severity Index (ASI) scores, 30-day abstinence, and total VA healthcare costs. Results: Over the study year, patients assigned to onsite care were more likely to attend primary care (adjusted odds ratio [OR] = 2.20; 95% confidence interval [CI] = 1.53–3.15) and to remain engaged in SUD treatment at 3 months (adjusted OR = 1.36; 1.00–1.84). Overall, outcomes on the MCS (but not the PCS) and the ASI improved significantly over time but did not differ by treatment condition. Total VA healthcare costs did not differ reliably across conditions. Conclusions: Compared with referral care, providing primary care within a VA addiction clinic increased primary care access and initial SUD treatment retention but showed no effect on overall health status or costs.
43 citations
Cites background from "Utilization and cost impact of inte..."
...E-mail: Andrew.Saxon@med.va.gov Copyright © 2006 by Lippincott Williams & Wilkins ISSN: 0025-7079/06/4404-0334 Medical Care • Volume 44, Number 4, April 2006334 than those referred to an offsite medical clinic.11 A trial with patients receiving SUD treatment in a large HMO found better alcohol and drug abstinence rates for those randomized to integrated versus independent (standard) primary care among the subset of subjects with substance abuse-related medical conditions (SAMCs).12 Analysis found no differences in medical utilization or cost for the sample as a whole but significant decreases in utilization and cost for the subset of integrated care subjects with SAMCs.13 Finally, a trial involving veterans with serious psychiatric illness treated in a VA mental health clinic demonstrated a significant reduction over time in SF-36 physical component summary scores for subjects randomized to integrated care, no significant change in subjects assigned to referral primary care, and no associated increase in healthcare costs.14 We tested the effectiveness of onsite primary medical care in a diverse population of veterans beginning an episode of SUD treatment....
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...Analysis found no differences in medical utilization or cost for the sample as a whole but significant decreases in utilization and cost for the subset of integrated care subjects with SAMCs.(13) Finally, a trial involving veterans with serious psychiatric illness treated in a VA mental health clinic demonstrated a significant reduction over time in SF-36 physical component summary scores for subjects randomized to integrated care, no significant change in subjects assigned to referral primary care, and no associated increase in healthcare costs....
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TL;DR: A protocol to evaluate the implementation of an E-Health integrated communication technology delivered via mobile phones, called Seva, into primary-care settings and to conduct an intensive, mixed-methods assessment at three diverse Federally Qualified Healthcare Centers in the United States.
Abstract: Healthcare reform in the United States is encouraging Federally Qualified Health Centers and other primary-care practices to integrate treatment for addiction and other behavioral health conditions into their practices. The potential of mobile health technologies to manage addiction and comorbidities such as HIV in these settings is substantial but largely untested. This paper describes a protocol to evaluate the implementation of an E-Health integrated communication technology delivered via mobile phones, called Seva, into primary-care settings. Seva is an evidence-based system of addiction treatment and recovery support for patients and real-time caseload monitoring for clinicians. Our implementation strategy uses three models of organizational change: the Program Planning Model to promote acceptance and sustainability, the NIATx quality improvement model to create a welcoming environment for change, and Rogers’s diffusion of innovations research, which facilitates adaptations of innovations to maximize their adoption potential. We will implement Seva and conduct an intensive, mixed-methods assessment at three diverse Federally Qualified Healthcare Centers in the United States. Our non-concurrent multiple-baseline design includes three periods — pretest (ending in four months of implementation preparation), active Seva implementation, and maintenance — with implementation staggered at six-month intervals across sites. The first site will serve as a pilot clinic. We will track the timing of intervention elements and assess study outcomes within each dimension of the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, including effects on clinicians, patients, and practices. Our mixed-methods approach will include quantitative (e.g., interrupted time-series analysis of treatment attendance, with clinics as the unit of analysis) and qualitative (e.g., staff interviews regarding adaptations to implementation protocol) methods, and assessment of implementation costs. If implementation is successful, the field will have a proven technology that helps Federally Qualified Health Centers and affiliated organizations provide addiction treatment and recovery support, as well as a proven strategy for implementing the technology. Seva also has the potential to improve core elements of addiction treatment, such as referral and treatment processes. A mobile technology for addiction treatment and accompanying implementation model could provide a cost-effective means to improve the lives of patients with drug and alcohol problems. ClinicalTrials.gov (
NCT01963234
).
41 citations
References
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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