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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
Citations
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01 Nov 2008
TL;DR: There is a reasonably strong body of evidence to encourage integrated care, at least for depression, and there is no discernible effect of integration level, processes of care, or combination on patient outcomes for mental health services in primary care settings.
Abstract: Objectives To describe models of integrated care used in the United States, assess how integration of mental health services into primary care settings or primary health care into specialty outpatient settings impacts patient outcomes and describe barriers to sustainable programs, use of health information technology (IT), and reimbursement structures of integrated care programs within the United States. Data sources MEDLINE, CINAHL, Cochrane databases, and PsychINFO databases, the internet, and expert consultants for relevant trials and other literature that does not traditionally appear in peer reviewed journals. Review methods Randomized controlled trials and high quality quasi-experimental design studies were reviewed for integrated care model design components. For trials of mental health services in primary care settings, levels of integration codes were constructed and assigned for provider integration, integrated processes of care, and their interaction. Forest plots of patient symptom severity, treatment response, and remission were constructed to examine associations between level of integration and outcomes. Results Integrated care programs have been tested for depression, anxiety, at-risk alcohol, and ADHD in primary care settings and for alcohol disorders and persons with severe mental illness in specialty care settings. Although most interventions in either setting are effective, there is no discernible effect of integration level, processes of care, or combination, on patient outcomes for mental health services in primary care settings. Organizational and financial barriers persist to successfully implement sustainable integrated care programs. Health IT remains a mostly undocumented but promising tool. No reimbursement system has been subjected to experiment; no evidence exists as to which reimbursement system may most effectively support integrated care. Case studies will add to our understanding of their implementation and sustainability. Conclusions In general, integrated care achieved positive outcomes. However, it is not possible to distinguish the effects of increased attention to mental health problems from the effects of specific strategies, evidenced by the lack of correlation between measures of integration or a systematic approach to care processes and the various outcomes. Efforts to implement integrated care will have to address financial barriers. There is a reasonably strong body of evidence to encourage integrated care, at least for depression. Encouragement can include removing obstacles, creating incentives, or mandating integrated care. Encouragement will likely differ between fee-for-service care and managed care. However, without evidence for a clearly superior model, there is legitimate reason to worry about premature orthodoxy.

420 citations


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Journal ArticleDOI
TL;DR: Significant progress has been made in adapting addiction treatment to respond more fully to the chronic nature of most patients’ problems, and the importance of adjusting treatment funding and organizational structures to better meet the needs of individuals with a chronic disease is addressed.
Abstract: This article reviews progress in adapting addiction treatment to respond more fully to the chronic nature of most patients' problems. After reviewing evidence that the natural history of addiction involves recurrent cycles of relapse and recovery, we discuss emerging approaches to recovery management, including techniques for improving the continuity of care, monitoring during periods of abstinence, and early reintervention; recent developments in the field related to self-management, mutual aid, and other recovery supports; and system-level interventions. We also address the importance of adjusting treatment funding and organizational structures to better meet the needs of individuals with a chronic disease.

327 citations


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

  • ...Godley and colleagues (2002, 2004, 2007) developed a protocol called assertive continuing care (ACC) and showed that it improved participation and recovery indicators....

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Journal ArticleDOI
TL;DR: Whereas the NCADI data base offers with insight into the research and and clinical practice emphasis on special populations, data from the National Drug and Alcohol Treatment Survey (NDATUS) can help to identify both the trends and the current distribution of treatment programs available for special population groups.
Abstract: s into the 14 special population groups shown in Table 14-1. Catalogued materials include research studies, books, newsletter articles, case studies, program descriptions, journal articles, monographs, communications, and so forth. Table 14-1 shows the frequency distribution of materials in each of the specified areas for three distinct time periods: 1973-1982, 1983-1985, and 1986-1987. The table also contains a summary for the total 15-year period, 1973-1987. THE TREATMENT OF SPECIAL POPULATIONS: OVERVIEW AND DEFINITIONS 350 Broadening the Base of Treatment for Alcohol Problems Copyright National Academy of Sciences. All rights reserved. TABLE 14-1 Total Number of Resource Materials on Special Population Groups Included in the National Clearinghouse for Alcohol and Drug Abuse Information (NCADI) Data Base Special Population Group 1973-1982 1983-1985 1986-1987 Total 1973-1987 Youth 722 1,120 511 2,353 College/university students 8 119 134 261 Elderly 205 186 49 440 Alcoholic females 425 347 208 980 Homosexuals 16 14 22 52 Economically disadvantaged 39 66 47 152 Racial and ethnic groups (general) 301 338 165 804 Blacks 103 131 69 303 Hispanics 48 54 42 144 Asians and Pacific Islanders 54 60 30 144 American Indians 117 85 35 237 Religious groups 79 113 51 243 Public inebriates 2 33 46 81 Handicapped/disabled 0 22 23 45 SOURCE: Committee analysis of data from the National Clearinghouse for Alcohol and Drug Abuse Information data base. Over the 15 year period the emphasis in catalogued material has been predominantly on youth, women, and racial and ethnic groups. The increase in abstracted materials for all the special populations in the last five years deserves notice; youth and women are the categories for which the most materials are recorded. There is also a marked increase in attention paid to college students, whereas there seems to be a slight tapering off in attention paid to elderly, youth, and American Indians. It is possible to characterize the literature abstracted in the NCADI data base as containing only a very few controlled trials in which the effectiveness of generic treatment is compared with treatment specifically tailored to the characteristics of the special population under consideration. There is a paucity of adequate studies on treatment outcome for any of the groups identified (Gilbert and Cervantes, 1988; Vannicelli, 1988; Westermeyer, 1988). The comment on treatment outcome made by Braiker (1982) continues to have current general applicability to all special population groups: A review of the general literature on alcoholism treatment effectiveness reveals that most studies either fail to distinguish between outcome rates for men and women alcoholics or exclude the latter group from the study samples altogether. Among those studies that distinguish outcome rate by sex, varying and often conflicting results are reported. (p. 127) Whereas the NCADI data base offers with insight into the research and and clinical practice emphasis on special populations, data from the National Drug and Alcohol Treatment Survey (NDATUS) can help to identify both the trends and the current distribution of treatment programs available for special population groups. These were surveys of alcoholism treatment services provided by all known public and private alcoholism and drug abuse facilities and units in the United States (NIAAA, 1983; Reed and Sanchez, 1986; NIDA/NIAAA 1989) (see Chapter 4 and Chapter 7). Table 14-2 presents data on the number of specialized programs offered by alcoholism treatment units by the year THE TREATMENT OF SPECIAL POPULATIONS: OVERVIEW AND DEFINITIONS 351 Broadening the Base of Treatment for Alcohol Problems Copyright National Academy of Sciences. All rights reserved. of the survey. Youth, women, the elderly, Hispanics, public inebriates, and blacks were the only special population groups included in all three of these surveys; American Indians/Alaskan natives were included in the last two surveys. TABLE 14-2 Specialized Programs Offered by Alcoholism Treatment Units by Survey Yeara Percentage of Total Units Reporting Specialized Program 1982b 1984c 1987d Youth 21 27 31 Elderly 9 9 8 Women 23 22 28 Hispanics 9 9 11 Blacks 8 7 6 American Indians/Alaskan natives —e 5 5 Public inebriates 13 9 7 Other 13 9 15 None 51 46 41 Total units reporting 4,233 6,963 5,791 a Includes both alcoholism-only units and combined alcoholism and drug abuse units. b Data from the 1982 National Drug and Alcoholism Treatment Utilization Survey (NIAAA, 1983). c Data from the 1984 National Alcoholism and Drug Abuse Program Inventory (Reed and Sanchez, 1986). d Data from the 1987 National Drug and Alcoholism Treatment Unit Survey (NIDA/NIAAA, 1989). e Not included in the 1982 survey. The inventory asked respondents to identify whether they offered one or more specialized programs to certain population groups. Judging on the basis of the treatment units reporting, it appears that an increasing percentage of units are offering one or more specialized programs. In 1987 the largest number of specialized programs offered in treatment units were for youth (31 percent), followed closely by those for women (28 percent), with a sharp drop to programs for Hispanics (11 percent) and the elderly (8 percent). Changes in the total number of units reporting and in the number of specialized programs must be interpreted cautiously because there was a more thorough outreach effort in 1984 to locate all units that were either not identified in 1982 or that did not respond; this effort may simply have uncovered existing units that had not responded earlier rather than identifying new units that had only recently been established (cf. Reed and Sanchez, 1986:2). An examination of these two sources—the NCADI database and the NIAAA surveys of treatment units— shows that women and youth are the special population groups that have received the most attention since the early 1970s. What they do not reveal are the most effective ways to meet the needs of individual problem drinkers or how to identify factors germane to a special population that might affect treatment. The overviews are also unable to provide guidance on when treatment should emphasize an individual's special population membership to facilitate a successful outcome. Indeed, if these overviews tell us anything, it is that women and youth appear to be the special population groups that people are most concerned about. Given the historical dilemmas, variations, and inconsistencies in defining which groups should be considered as special populations in the planning, funding and evaluation THE TREATMENT OF SPECIAL POPULATIONS: OVERVIEW AND DEFINITIONS 352 Broadening the Base of Treatment for Alcohol Problems Copyright National Academy of Sciences. All rights reserved. of alcohol problems treatment, Lex (1985:90) has suggested that a special population be defined as any subgroup that is “special in terms of their uniformity on some dimension and their differences from more typical societal patterns and problems.” The committee agrees with this definition. However, the definition does not fully capture the problems encountered in attempting to review existing knowledge on the value of special population programming. This review of the history of attention to special population groups suggests that their definition is often not only in terms of the unique biological and sociocultural characteristics that define a group with similar risk factors and drinking practices but also in terms of the momentary concern regarding access to appropriate services. Interest in each group has waxed and waned. There has been no systematic follow up to determine whether access has been improved or treatment outcome positively affected by these periods of attention. What is challenging, for both researchers and clinicians, is to determine where and how the emphasis on special population membership can best facilitate effective treatment for alcohol problems. Given this background, for the purposes of this report, a special population will be viewed as any subgroup that has been identified by the field as needing a specifically tailored “culturally sensitive” treatment program. The committee has chosen to look at developments and issues for only a few of the commonly identified special population groups and the evolution and effectiveness of treatment programs designed for them as portrayed in the research and clinical literature. It is important to note that these groups are by no means inclusive of all special population groups; rather, they have been selected as representatives of special populations as a whole. Chapter 15 considers these groups on the basis of structural characteristics (i.e., demographic characteristics); Chapter 16, adapts the perspectives of functional characteristics (i.e., circumstantial concerns) as a definitional framework. Chapter 17 presents the committee's conclusions and recommendations on the issue of treatment for alcohol problems among special populations.

272 citations

Journal ArticleDOI
TL;DR: A small but growing body of research suggests that a range of models may hold potential for improving patients' health and health care, at a relatively modest cost.

195 citations

Journal ArticleDOI
TL;DR: A review of the evidence shows that many forms of behavioral health services, particularly when delivered as part of primary medical care, can be central to such an improvement.
Abstract: The health care system in the United States, plagued by spiraling costs, unequal access, and uneven quality, can find its best chance of improving the health of the population through the improvement of behavioral health services. It is in this area that the largest potential payoff in reduction of morbidity and mortality and increased cost-effectiveness of care can be found. A review of the evidence shows that many forms of behavioral health services, particularly when delivered as part of primary medical care, can be central to such an improvement. The evidence supports many but not all behavioral health services when delivered in settings in which people will accept these services under particular administrative and fiscal structures.

169 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.