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A systematic review of the effect of retention methods in population-based cohort studies

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BMC Public Health

BioMed Central

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      Abstract

      BackgroundLongitudinal studies are of aetiological and public health relevance but can be undermined by attrition. The aim of this paper was to identify effective retention strategies to increase participation in population-based cohort studies.MethodsSystematic review of the literature to identify prospective population-based cohort studies with health outcomes in which retention strategies had been evaluated.ResultsTwenty-eight studies published up to January 2011 were included. Eleven of which were randomized controlled trials of retention strategies (RCT). Fifty-seven percent of the studies were postal, 21% in-person, 14% telephone and 7% had mixed data collection methods. A total of 45 different retention strategies were used, categorised as 1) incentives, 2) reminder methods, repeat visits or repeat questionnaires, alternative modes of data collection or 3) other methods. Incentives were associated with an increase in retention rates, which improved with greater incentive value. Whether cash was the most effective incentive was not clear from studies that compared cash and gifts of similar value. The average increase in retention rate was 12% for reminder letters, 5% for reminder calls and 12% for repeat questionnaires. Ten studies used alternative data collection methods, mainly as a last resort. All postal studies offered telephone interviews to non-responders, which increased retention rates by 3%. Studies that used face-to-face interviews increased their retention rates by 24% by offering alternative locations and modes of data collection.ConclusionsIncentives boosted retention rates in prospective cohort studies. Other methods appeared to have a beneficial effect but there was a general lack of a systematic approach to their evaluation.

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      Barriers to participation in randomised controlled trials: a systematic review.

      A systematic review of three bibliographic databases from 1986 to 1996 identified 78 papers reporting barriers to recruitment of clinicians and patients to randomised controlled trials. Clinician barriers included: time constraints, lack of staff and training, worry about the impact on the doctor-patient relationship, concern for patients, loss of professional autonomy, difficulty with the consent procedure, lack of rewards and recognition, and an insufficiently interesting question. Patient barriers included: additional demands of the trial, patient preferences, worry caused by uncertainty, and concerns about information and consent. To overcome barriers to clinician recruitment, the trial should address an important research question and the protocol and data collection should be as straightforward as possible. The demands on clinicians and patients should be kept to a minimum. Dedicated research staff may be required to support clinical staff and patients. The recruitment aspects of a randomised controlled trial should be carefully planned and piloted. Further work is needed to quantify the extent of problems associated with clinician and patient participation, and proper evaluation is required of strategies to overcome barriers.
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        Attitudes toward Family Obligations among American Adolescents with Asian, Latin American, and European Backgrounds

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          Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data

          Background Meta-analysis is increasingly used as a key source of evidence synthesis to inform clinical practice. The theory and statistical foundations of meta-analysis continually evolve, providing solutions to many new and challenging problems. In practice, most meta-analyses are performed in general statistical packages or dedicated meta-analysis programs. Results Herein, we introduce Meta-Analyst, a novel, powerful, intuitive, and free meta-analysis program for the meta-analysis of a variety of problems. Meta-Analyst is implemented in C# atop of the Microsoft .NET framework, and features a graphical user interface. The software performs several meta-analysis and meta-regression models for binary and continuous outcomes, as well as analyses for diagnostic and prognostic test studies in the frequentist and Bayesian frameworks. Moreover, Meta-Analyst includes a flexible tool to edit and customize generated meta-analysis graphs (e.g., forest plots) and provides output in many formats (images, Adobe PDF, Microsoft Word-ready RTF). The software architecture employed allows for rapid changes to be made to either the Graphical User Interface (GUI) or to the analytic modules. We verified the numerical precision of Meta-Analyst by comparing its output with that from standard meta-analysis routines in Stata over a large database of 11,803 meta-analyses of binary outcome data, and 6,881 meta-analyses of continuous outcome data from the Cochrane Library of Systematic Reviews. Results from analyses of diagnostic and prognostic test studies have been verified in a limited number of meta-analyses versus MetaDisc and MetaTest. Bayesian statistical analyses use the OpenBUGS calculation engine (and are thus as accurate as the standalone OpenBUGS software). Conclusion We have developed and validated a new program for conducting meta-analyses that combines the advantages of existing software for this task.
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            Author and article information

            Affiliations
            [1 ]MRC/CSO Social and Public Health Sciences Unit, Glasgow, UK
            [2 ]University of Essex, Institute for Social & Economic Research, Colchester, UK
            Contributors
            Journal
            BMC Public Health
            BMC Public Health
            BioMed Central
            1471-2458
            2011
            19 April 2011
            : 11
            : 249
            3103452
            1471-2458-11-249
            21504610
            10.1186/1471-2458-11-249
            Copyright ©2011 Booker 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.

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            Research Article

            Public health

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