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      Comparative Effectiveness of Embedded Mental Health Services in Pain Management Clinics vs Standard Care

      1 , 2 , 3 , 1 , 1 , 2 , 4 , 3 , 1 , 2
      Pain Medicine
      Oxford University Press (OUP)

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          Abstract

          Objective

          Embedded behavioral medicine services are a common component of multidisciplinary chronic pain treatment programs. However, few studies have studied whether these services are associated with improved treatment outcomes.

          Methods

          Using a retrospective, matched, two-cohort study design, we examined patient-reported outcomes (PROs), including Patient-Reported Outcomes Measurement Information System pain, mental health, and physical function measures, collected at every clinic visit in every patient. Changes from baseline through 12 months were compared in those receiving embedded Behavioral Medicine in addition to usual care to a Standard Care group seen in the same pain practice and weighted via propensity scoring.

          Results

          At baseline, Behavioral Medicine patients had worse scores on most pain, mental health, and physical health measures and were more likely to be female, a member of a racial minority, and have lower socioeconomic status. Regardless of having a worse clinical pain syndrome at baseline, at follow-up both Behavioral Medicine (N = 451) and Standard Care patients (N = 8,383) showed significant and comparable improvements in pain intensity, physical function, depression, and sleep disturbance. Behavioral Medicine patients showed significantly greater improvements in their global impressions of change than the Standard Care patients.

          Conclusions

          Despite worse pain and physical and psychological functioning at baseline, Behavioral Medicine patients showed improvements comparable to patients not receiving these services. Further, Behavioral Medicine patients report higher global impressions of change, indicating that embedded mental health services appear to have the additive value of amplifying the benefits of multimodal pain care.

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          Most cited references64

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              Is Open Access

              Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

              The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Pain Medicine
                Oxford University Press (OUP)
                1526-2375
                1526-4637
                May 2020
                May 01 2020
                November 15 2019
                May 2020
                May 01 2020
                November 15 2019
                : 21
                : 5
                : 978-991
                Affiliations
                [1 ]UPMC Pain Medicine, Pittsburgh, Pennsylvania
                [2 ]Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
                [3 ]Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
                [4 ]Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                Article
                10.1093/pm/pnz294
                12d6ecf4-efe7-4e53-8d29-b97a901d896c
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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