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      The Contribution of Social Networks to the Health and Self-Management of Patients with Long-Term Conditions: A Longitudinal Study

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          Abstract

          Evidence for the effectiveness of patient education programmes in changing individual self-management behaviour is equivocal. More distal elements of personal social relationships and the availability of social capital at the community level may be key to the mobilisation of resources needed for long-term condition self-management to be effective.

          Aim

          To determine how the social networks of people with long-term conditions (diabetes and heart disease) are associated with health-related outcomes and changes in outcomes over time.

          Methods

          Patients with chronic heart disease (CHD) or diabetes (n = 300) randomly selected from the disease registers of 19 GP practices in the North West of England. Data on personal social networks collected using a postal questionnaire, alongside face-to-face interviewing. Follow-up at 12 months via postal questionnaire using a self-report grid for network members identified at baseline.

          Analysis

          Multiple regression analysis of relationships between health status, self-management and health-economics outcomes, and characteristics of patients' social networks.

          Results

          Findings indicated that: (1) social involvement with a wider variety of people and groups supports personal self-management and physical and mental well-being; (2) support work undertaken by personal networks expands in accordance with health needs helping people to cope with their condition; (3) network support substitutes for formal care and can produce substantial saving in traditional health service utilisation costs. Health service costs were significantly (p<0.01) reduced for patients receiving greater levels of illness work through their networks.

          Conclusions

          Support for self-management which achieves desirable policy outcomes should be construed less as an individualised set of actions and behaviour and more as a social network phenomenon. This study shows the need for a greater focus on harnessing and sustaining the capacity of networks and the importance of social involvement with community groups and resources for producing a more desirable and cost-effective way of supporting long term illness management.

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          Most cited references23

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          The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown?

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            The estimation of a preference-based measure of health from the SF-12.

            The SF-12 is a multidimensional generic measure of health-related quality of life. It has become widely used in clinical trials and routine outcome assessment because of its brevity and psychometric performance, but it cannot be used in economic evaluation in its current form. We sought to derive a preference-based measure of health from the SF-12 for use in economic evaluation and to compare it with the original SF-36 preference-based index. The SF-12 was revised into a 6-dimensional health state classification (SF-6D [SF-12]) based on an item selection process designed to ensure the minimum loss of descriptive information. A sample of 241 states defined by the SF-6D (of 7500) have been valued by a representative sample of 611 members of the UK general population using the standard gamble (SG) technique. Models are estimated of the relationship between the SF-6D (SF-12) and SG values and evaluated in terms of their coefficients, overall fit, and the ability to predict SG values for all health states. The models have produced significant coefficients for levels of the SF-6D (SF-12), which are robust across model specification. The coefficients are similar to those of the SF-36 version and achieve similar levels of fit. There are concerns with some inconsistent estimates and these have been merged to produce the final recommended model. As for the SF-36 model, there is evidence of over prediction of the value of the poorest health states. The SF-12 index provides a useful tool for researchers and policy makers wishing to assess the cost-effectiveness of interventions.
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              Analysis of serial measurements in medical research.

              In medical research data are often collected serially on subjects. The statistical analysis of such data is often inadequate in two ways: it may fail to settle clinically relevant questions and it may be statistically invalid. A commonly used method which compares groups at a series of time points, possibly with t tests, is flawed on both counts. There may, however, be a remedy, which takes the form of a two stage method that uses summary measures. In the first stage a suitable summary of the response in an individual, such as a rate of change or an area under a curve, is identified and calculated for each subject. In the second stage these summary measures are analysed by simple statistical techniques as though they were raw data. The method is statistically valid and likely to be more relevant to the study questions. If this method is borne in mind when the experiment is being planned it should promote studies with enough subjects and sufficient observations at critical times to enable useful conclusions to be drawn. Use of summary measures to analyse serial measurements, though not new, is potentially a useful and simple tool in medical research.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                2 June 2014
                : 9
                : 6
                : e98340
                Affiliations
                [1 ]National Institute for Health Research Collaboration for Leadership in Applied Health Research (NIHR CLAHRC) Greater Manchester, Centre for Primary Care, Institute of Population Health, University of Manchester, Manchester, United Kingdom
                [2 ]National Institute for Health Research Collaboration for Leadership in Applied Health Research (NIHR CLAHRC) Wessex, Faculty of Health Sciences, University of Southampton, Southampton, United Kingdom
                [3 ]School of Nursing, Midwifery and Social Work, University of Manchester, Manchester, United Kingdom
                [4 ]Centre for Health Economics and NIHR Research Design Service for Yorkshire and the Humber, University of York, York, United Kingdom
                University of St Andrews, United Kingdom
                Author notes

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

                Conceived and designed the experiments: DR CB IV AK GR AR. Performed the experiments: CB IV AK HB. Analyzed the data: DR HB GR. Wrote the paper: DR CB IV AK AR HB GR. Interpretation of findings: DR CB IV HB AK GR AR.

                Article
                PONE-D-13-49129
                10.1371/journal.pone.0098340
                4041782
                24887107
                74577bb1-04da-44b5-96b8-f2f43b127296
                Copyright @ 2014

                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
                : 21 November 2013
                : 1 May 2014
                Page count
                Pages: 12
                Funding
                This research was funded by the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for Greater Manchester. CLAHRC Greater Manchester is a partnership between the Greater Manchester NHS Trusts and the University of Manchester and is part of the National Institute for Health Research. http://clahrc-gm.nihr.ac.uk/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Cardiology
                Epidemiology
                Cardiovascular Disease Epidemiology
                Health Care
                Elderly Care
                Health Care Policy
                Health Economics
                Health Education and Awareness
                Primary Care
                Socioeconomic Aspects of Health
                Public and Occupational Health
                Behavioral and Social Aspects of Health
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Research and Analysis Methods
                Research Design
                Clinical Research Design
                Survey Research
                Social Sciences
                Economics
                Economic Analysis
                Cost-Effectiveness Analysis

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                Uncategorized

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