6
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Continuity of care with physicians and risk of subsequent hospitalization and end-stage renal disease in newly diagnosed type 2 diabetes mellitus patients

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          Effective management for type 2 diabetes mellitus (DM) can slow the progression of kidney outcomes and reduce hospital admissions. Better continuity of care (COC) was found to improve patients’ adherence and self-management. This study examined the associations between COC, hospitalization, and end-stage renal disease (ESRD) in DM patients.

          Patients and methods

          In the cohort study, data from 1996 to 2012 were retrieved from the Longitudinal Health Insurance Database, using inverse probability weighted analysis. A total of 26,063 patients with newly diagnosed type 2 DM who had been treated with antihyperglycemic agents were included. COC is to assess the extent to which a DM patient visited the same physician during the study period. This study categorized COC into 3 groups – low, intermediate, and high, – according to the distribution of scores in our sample.

          Results

          The number of ESRD patients in the high, intermediate, and low COC groups were 92 (22.33%), 130 (31.55%), and 190 (46.12%), respectively, and the mean follow-up periods for the 3 groups were 7.13, 7.12, and 7.27 years, respectively. After using inverse probability weighting, the intermediate and low COC groups were significantly associated with an increased risk of ESRD compared with the high COC group (adjusted hazard ratio (aHR) 1.36 [95% CI, 1.03–1.80] and aHR 1.76 [95% CI, 1.35–2.30], respectively). The intermediate and low COC groups were also significantly associated with the subsequent hospitalization compared with the high COC group (aHR 1.15 [95% CI, 0.99–1.33] and aHR 1.72 [95% CI, 1.50–1.97], respectively).

          Conclusion

          COC is related to ESRD onset and subsequent hospitalization among patients with DM. This study suggested that when DM patients keep visiting the same physician for managing their diseases, the progression of renal disease can be prevented.

          Related collections

          Most cited references 32

          • Record: found
          • Abstract: found
          • Article: not found

          A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study.

          The propensity score--the probability of exposure to a specific treatment conditional on observed variables--is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated subjects. There is an ongoing controversy in the literature as to which variables to include in the propensity score model. Some advocate including those variables that predict treatment assignment, while others suggest including all variables potentially related to the outcome, and still others advocate including only variables that are associated with both treatment and outcome. We provide a case study of the association between drug exposure and mortality to show that including a variable that is related to treatment, but not outcome, does not improve balance and reduces the number of matched pairs available for analysis. In order to investigate this issue more comprehensively, we conducted a series of Monte Carlo simulations of the performance of propensity score models that contained variables related to treatment allocation, or variables that were confounders for the treatment-outcome pair, or variables related to outcome or all variables related to either outcome or treatment or neither. We compared the use of these different propensity scores models in matching and stratification in terms of the extent to which they balanced variables. We demonstrated that all propensity scores models balanced measured confounders between treated and untreated subjects in a propensity-score matched sample. However, including only the true confounders or the variables predictive of the outcome in the propensity score model resulted in a substantially larger number of matched pairs than did using the treatment-allocation model. Stratifying on the quintiles of any propensity score model resulted in residual imbalance between treated and untreated subjects in the upper and lower quintiles. Greater balance between treated and untreated subjects was obtained after matching on the propensity score than after stratifying on the quintiles of the propensity score. When a confounding variable was omitted from any of the propensity score models, then matching or stratifying on the propensity score resulted in residual imbalance in prognostically important variables between treated and untreated subjects. We considered four propensity score models for estimating treatment effects: the model that included only true confounders; the model that included all variables associated with the outcome; the model that included all measured variables; and the model that included all variables associated with treatment selection. Reduction in bias when estimating a null treatment effect was equivalent for all four propensity score models when propensity score matching was used. Reduction in bias was marginally greater for the first two propensity score models than for the last two propensity score models when stratification on the quintiles of the propensity score model was employed. Furthermore, omitting a confounding variable from the propensity score model resulted in biased estimation of the treatment effect. Finally, the mean squared error for estimating a null treatment effect was lower when either of the first two propensity scores was used compared to when either of the last two propensity score models was used. Copyright 2006 John Wiley & Sons, Ltd.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Intensive glucose control improves kidney outcomes in patients with type 2 diabetes.

              The effect of intensive glucose control on major kidney outcomes in type 2 diabetes remains unclear. To study this, the ADVANCE trial randomly assigned 11,140 participants to an intensive glucose-lowering strategy (hemoglobin A1c target 6.5% or less) or standard glucose control. Treatment effects on end-stage renal disease ((ESRD), requirement for dialysis or renal transplantation), total kidney events, renal death, doubling of creatinine to above 200 μmol/l, new-onset macroalbuminuria or microalbuminuria, and progression or regression of albuminuria, were then assessed. After a median of 5 years, the mean hemoglobin A1c level was 6.5% in the intensive group, and 7.3% in the standard group. Intensive glucose control significantly reduced the risk of ESRD by 65% (20 compared to 7 events), microalbuminuria by 9% (1298 compared to 1410 patients), and macroalbuminuria by 30% (162 compared to 231 patients). The progression of albuminuria was significantly reduced by 10% and its regression significantly increased by 15%. The results were almost identical in analyses taking account of potential competing risks. The number of participants needed to treat over 5 years to prevent one ESRD event ranged from 410 in the overall study to 41 participants with macroalbuminuria at baseline. Thus, improved glucose control will improve major kidney outcomes in patients with type 2 diabetes.
                Bookmark

                Author and article information

                Journal
                Ther Clin Risk Manag
                Ther Clin Risk Manag
                Therapeutics and Clinical Risk Management
                Therapeutics and Clinical Risk Management
                Dove Medical Press
                1176-6336
                1178-203X
                2018
                13 March 2018
                : 14
                : 511-521
                Affiliations
                [1 ]School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
                [2 ]School of Health Care Administration, Taipei Medical University, Taipei, Taiwan
                [3 ]Graduate Institute of Clinical Medicine, Taipei Medical University, Taipei, Taiwan
                Author notes
                Correspondence: Hung-Yi Chiou, School of Public Health, College of Public Health, Taipei Medical University, Number 250, Wu-Hsing Street, Taipei 110, Taiwan, Tel +886 2 2736 1661 ext 6512, Email hychiou@ 123456tmu.edu.tw
                Article
                tcrm-14-511
                10.2147/TCRM.S150638
                5856058
                © 2018 Chang et al. This work is published and licensed by Dove Medical Press Limited

                The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

                Categories
                Original Research

                Medicine

                continuity of care, end-stage renal disease, hospitalization, diabetes mellitus

                Comments

                Comment on this article