25
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Frequent hospital admission of older people with chronic disease: a cross-sectional survey with telephone follow-up and data linkage

      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

          Background

          The continued increase in hospital admissions is a significant and complex issue facing health services. There is little research exploring patient perspectives or examining individual admissions among patients with frequent admissions for chronic ambulatory care sensitive (ACS) conditions. This paper aims to describe characteristics of older, rural patients frequently admitted with ACS conditions and identify factors associated with their admissions from the patient perspective.

          Methods

          Patients aged 65+ resident in North Coast NSW with three or more admissions for selected ACS chronic conditions within a 12 month period, were invited to participate in a postal survey and follow up telephone call. Survey and telephone data were linked to admission and health service program data. Descriptive statistics were generated for survey respondents; logistic regression models developed to compare characteristics of patients with 3 or with 4+ admissions; and comparisons made between survey respondents and non-respondents.

          Results

          Survey respondents (n=102) had a mean age of 77.1 years (range 66–95 years), and a mean of 4.1 admissions within 12 months; 49% had at least three chronic conditions; the majority had low socioeconomic status; one in five (22%) reported some difficulty affording their medication; and 35% lived alone. The majority reported psychological distress with 31% having moderate or severe psychological distress. While all had a GP, only 38% reported having a written GP care plan. 22% of those who needed regular help with daily tasks did not have a close friend or relative who regularly cared for them. Factors independently associated with more frequent (n=4+) relative to less frequent (n=3) admissions included having congestive heart failure (p=0.003), higher social isolation scores (p=0.040) and higher Charlson Comorbidity Index scores (p=0.049). Most respondents (61%) felt there was nothing that could have avoided their most recent admission, although some potential avoidability of admission was described around medication and health behaviours. Respondents were younger and less sick than non-respondents.

          Conclusions

          This study provides a detailed description of older patients with multiple chronic conditions and a history of frequent admission in rural Australia. Our results suggest that programs targeting medication use, health behaviours and social isolation may help reduce multiple hospital admissions for chronic disease.

          Related collections

          Most cited references31

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

          Nonresponse Rates and Nonresponse Bias in Household Surveys

          R. Groves (2006)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Polypharmacy in the elderly: A literature review

            To review the body of literature addressing polypharmacy in individuals aged 60 years and older to (a) determine primary care providers' definition of polypharmacy, (b) explore how polypharmacy was assessed in primary care, and (c) seek tested interventions that address polypharmacy.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.

              To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of "reference" conditions for which improved management may help to prevent future admissions. Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were "flagged" incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly "flagged" patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a "business case" has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.
                Bookmark

                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2012
                30 October 2012
                : 12
                : 373
                Affiliations
                [1 ]University Centre for Rural Health – University of Sydney, PO Box 3074, Lismore, NSW 2480, Australia
                [2 ]Southern Cross Univeristy, PO Box 157, Lismore, NSW 2480, Australia
                [3 ]North Coast NSW Medicare Local, PO Box 519, Lismore, NSW 2480, Australia
                [4 ]North Coast Area Health Service, Locked Mail Bag 11, Lismore, NSW 2480, Australia
                [5 ]School of Medicine, Griffith University, High St, Southport, Qld 4215, Australia
                Article
                1472-6963-12-373
                10.1186/1472-6963-12-373
                3504579
                23110342
                e61bc2fe-1260-474a-8e37-88b8e8a4958a
                Copyright ©2012 Longman et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 July 2012
                : 22 October 2012
                Categories
                Research Article

                Health & Social care
                frequent admission,chronic disease,social isolation.,avoidable admission,frequent hospitalisation,potentially preventable admission,australia,rural,older people

                Comments

                Comment on this article