17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Timing of Home Health Care Initiation and 30-Day Rehospitalizations among Medicare Beneficiaries with Diabetes by Race and Ethnicity

      research-article

      Read this article at

      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

          Older adults with diabetes are at elevated risk of complications following hospitalization. Home health care services mitigate the risk of adverse events and facilitate a safe transition home. In the United States, when home health care services are prescribed, federal guidelines require they begin within two days of hospital discharge. This study examined the association between timing of home health care initiation and 30-day rehospitalization outcomes in a cohort of 786,734 Medicare beneficiaries following a diabetes-related index hospitalization admission during 2015. Of these patients, 26.6% were discharged to home health care. To evaluate the association between timing of home health care initiation and 30-day rehospitalizations, multivariate logistic regression models including patient demographics, clinical and geographic variables, and neighborhood socioeconomic variables were used. Inverse probability-weighted propensity scores were incorporated into the analysis to account for potential confounding between the timing of home health care initiation and the outcome in the cohort. Compared to the patients who received home health care within the recommended first two days, the patients who received delayed services (3–7 days after discharge) had higher odds of rehospitalization (OR, 1.28; 95% CI, 1.25–1.32). Among the patients who received late services (8–14 days after discharge), the odds of rehospitalization were four times greater than among the patients receiving services within two days (OR, 4.12; 95% CI, 3.97–4.28). Timely initiation of home health care following diabetes-related hospitalizations is one strategy to improve outcomes.

          Related collections

          Most cited references60

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

          The central role of the propensity score in observational studies for causal effects

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

            Structural racism and health inequities in the USA: evidence and interventions

            The Lancet, 389(10077), 1453-1463
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

              The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
                Bookmark

                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                25 May 2021
                June 2021
                : 18
                : 11
                : 5623
                Affiliations
                [1 ]College of Nursing, Thomas Jefferson University, Philadelphia, PA 19107, USA; jamie.smith3@ 123456jefferson.edu
                [2 ]School of Nursing, Rutgers, The State University of New Jersey, Newark, NJ 07108, USA; haiqun.lin@ 123456rutgers.edu (H.L.); charlot@ 123456sn.rutgers.edu (C.T.-H.)
                [3 ]School of Public Health, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
                [4 ]Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA; tsuijenn@ 123456usc.edu
                [5 ]Institute for Health, Health Care Policy, and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
                Author notes
                Author information
                https://orcid.org/0000-0003-1621-5711
                https://orcid.org/0000-0001-8168-873X
                https://orcid.org/0000-0002-3349-476X
                Article
                ijerph-18-05623
                10.3390/ijerph18115623
                8197411
                34070282
                f6bc8286-4180-4b82-9681-f948b9511deb
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 04 April 2021
                : 21 May 2021
                Categories
                Article

                Public health
                chronic conditions,diabetes,older adults,race or ethnicity,social determinants of health,inequalities or inequities,policy,health care access,home health care,rehospitalization

                Comments

                Comment on this article