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      Processes and outcomes of diabetes mellitus care by different types of team primary care models

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          Abstract

          Background

          Team care improves processes and outcomes of care, especially for patients with complex medical conditions that require coordination of care. This study aimed to compare the processes and outcomes of care provided to older patients with diabetes by primary care teams comprised of only primary care physicians (PCPs) versus team care that included nurse practitioners (NPs) or physician assistants (PAs).

          Methods

          We studied 3,524 primary care practices identified via social network analysis and 306,741 patients ≥66 years old diagnosed with diabetes in or before 2015 in Medicare data. Guideline-recommended diabetes care included eye examination, hemoglobin A1c test, and nephropathy monitoring. High-risk medications were based on recommendations from the American Geriatrics Society Beers Criteria for Potentially Inappropriate Medication Use in Older Adults. Preventable hospitalizations were defined as hospitalizations for a potentially preventable condition.

          Results

          Compared with patients in the PCP only teams, patients in the team care practices with NPs or PAs received more guideline-recommended diabetes care (annual eye exam: adjusted odds ratio (aOR): 1.04 (95% CI: 1.00–1.08), 1.08 (95% CI: 1.03–1.13), and 1.10 (95% CI: 1.05–1.15), and HbA1C test: aOR: 1.11 (95% CI: 1.04–1.18), 1.11 (95% CI: 1.02–1.20), and 1.15 (95% CI: 1.06–1.25) for PCP/NP, PCP/NP/PA, and PCP/PA teams). Patients in the PCP/NP and the PCP/PA teams had a slightly higher likelihood of being prescribed high-risk medications (aOR: 1.03 (95% CI: 1.00–1.07), and 1.06 (95% CI: 1.02–1.11), respectively). The likelihood of preventable hospitalizations was similar among patients cared for by various types of practices.

          Conclusion

          The team care practices with NPs or PAs were associated with better adherence to clinical practice guideline recommendations for diabetes compared to PCP only practices. Both practices had similar outcomes. Further efforts are needed to explore new and cost-effective team-based care delivery models that improve process, outcomes, and continuity of care, as well as patient care experiences.

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          Most cited references29

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          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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            Modularity and community structure in networks

            M. Newman (2006)
            Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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              American Geriatrics Society 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults

              (2019)
              The American Geriatrics Society (AGS) Beers Criteria® (AGS Beers Criteria®) for Potentially Inappropriate Medication (PIM) Use in Older Adults are widely used by clinicians, educators, researchers, healthcare administrators, and regulators. Since 2011, the AGS has been the steward of the criteria and has produced updates on a 3-year cycle. The AGS Beers Criteria® is an explicit list of PIMs that are typically best avoided by older adults in most circumstances or under specific situations, such as in certain diseases or conditions. For the 2019 update, an interdisciplinary expert panel reviewed the evidence published since the last update (2015) to determine if new criteria should be added or if existing criteria should be removed or undergo changes to their recommendation, rationale, level of evidence, or strength of recommendation. J Am Geriatr Soc 67:674-694, 2019.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Formal analysis
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 November 2020
                2020
                : 15
                : 11
                : e0241516
                Affiliations
                [1 ] Department of Obstetrics & Gynecology, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                [2 ] Center for Interdisciplinary Research in Women’s Health, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                [3 ] Department of Preventive Medicine and Population Health, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                [4 ] Department of Internal Medicine, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                [5 ] Sealy Center on Aging, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                [6 ] School of Nursing, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                [7 ] Institute for Translational Science, The University of Texas Medical Branch at Galveston, Galveston, Texas, United States of America
                University of South Carolina College of Pharmacy, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-1927-0927
                Article
                PONE-D-20-19006
                10.1371/journal.pone.0241516
                7644045
                33152002
                d83c285b-57b9-4e79-8e49-8c16ac947854
                © 2020 Guo et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 June 2020
                : 15 October 2020
                Page count
                Figures: 0, Tables: 4, Pages: 12
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000133, Agency for Healthcare Research and Quality;
                Award ID: R01-HS020642
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: K07-CA222343
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: P30‐AG024832
                This study was supported by grants R01-HS020642 from the Agency for Healthcare Research and Quality (AHRQ), P30-AG024832, and UL1TR001439 from the National Institutes of Health (NIH). Dr. Guo is currently supported by the National Cancer Institute of the NIH under Award Number K07CA222343. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or AHRQ. The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication.
                Categories
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                Endocrine Disorders
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                Custom metadata
                Data cannot be shared publicly because of the restriction from the Centers for Medicare & Medicaid Services (CMS). We have data use agreement with CMS (DUA RSCH-2017-50274) in analyzing Medicare data at the CMS Virtual Research Data Center (VRDC). We only allow downloading aggregated reports from VRDC. For analytical data of individual beneficiaries, investigators need to sign a data reuse agreement following the procedure specified by Research Data Assistance Center (ResDAC). Please refer to research identifiable data at https://www.resdac.org/.

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