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Brief Report| Volume 61, ISSUE 7, P728-732, July 2008

Characteristics of nonparticipants differed based on reason for nonparticipation: a study involving the chronically ill

      Abstract

      Background

      Individuals who do and do not participate in studies often have different characteristics. The profile of nonparticipants may vary by type of nonparticipation. Few cross-sectional or longitudinal studies of chronically ill individuals identify these differences at baseline and of those that do; many do not have information on type of nonparticipation. We compared characteristics of chronically ill individuals to determine if they differ by type of nonparticipation.

      Methods

      Data are from an evaluation of a chronic disease management program conducted in New South Wales, Australia during 2001 and 2002. Characteristics were compared using routinely collected hospital data. Reasons for nonparticipation were categorized as refusal, death, or untraceable.

      Results

      Individuals who refused to participate were older, female, discharged at risk, and have heart failure. Those untraceable were younger, not married, Indigenous, and receiving care during the intervention phase but not recruited. Individuals not participating due to death were older, male, not cohabiting, discharged at risk, and have a diagnosis of cancer or heart failure.

      Conclusion

      The significant differences between untraceable, refused, and deceased individuals should be considered when designing studies and when adjusting for nonparticipation bias.

      Keywords

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      References

        • Chatfield M.
        • Brayne C.
        • Mathews F.
        A systematic literature review of attrition between waves in longitudinal studies in elderly show a consistent pattern in drop-out between differing studies.
        J Clin Epidemiol. 2005; 58: 13-19
        • Deeg D.
        Attrition in longitudinal population studies: does it affect the generalisability of findings? An introduction to the series.
        J Clin Epidemiol. 2002; 55: 213-215
        • Melton L.
        • Dyck P.
        • Karnes J.
        • O'Brien P.
        • Service F.
        Non-response bias in studies of diabetic complications: the Rochester Diabetic Neuropathy Study.
        J Clin Epidemiol. 1993; 46: 341-348
        • Kempen G.
        • van Sonderen E.
        Psychological attributes and changes in disability among low-functioning older persons: does attrition affect outcomes?.
        J Clin Epidemiol. 2002; 55: 224-229
        • Deeg D.
        • van Tillburg W.
        • Smit J.
        • de Leeuw E.
        Attrition in the longitudinal Ageing Study Amsterdam: the effect of differential inclusion in side studies.
        J Clin Epidemiol. 2000; 55: 319-328
        • Ross L.
        • Thompson B.
        • Boesen E.
        • Johansen C.
        In a randomized control trial, missing data led to biased results regarding anxiety.
        J Clin Epidemiol. 2004; 57: 1131-1137
        • Neumark D.
        • Stommel M.
        • Given C.
        • Given B.
        Research design and subject characteristics predicting non-participation in a panel survey of older families with cancer.
        Nurs Res. 2001; 50: 363-368
        • Mathews F.
        • Chatfield M.
        • Freeman C.
        • CMcCracken C.
        • Brayne C.
        Attrition bias in the MRC cognitive function and aging study: an epidemiological investigation.
        BMC Public Health. 2004; 4
        • Ware R.
        • Williams G.
        • Aird R.
        Participants who left a multiple-wave cohort study had similar baseline characteristics to participants who returned.
        Ann Epidemiol. 2006; 16: 820-823
        • Garcia M.
        • Fernandez E.
        • Schiaffino A.
        • Borrell C.
        • Marti M.
        • Borras J.
        Attrition in a population-based cohort eight years after baseline interview: the Cornella Health Survey Follow-up (CHIS.FU) Study.
        Ann Epidemiol. 2005; 15: 98-104
        • Van Beijsterveldt C.
        • van Boxtel M.
        • Bosma H.
        • Houx P.
        • Buntinx F.
        • Jolles J.
        Predictors of attrition in a longitudinal cognitive aging study.
        J Clin Epidemiol. 2002; 55: 216-223
        • Zunzunegui M.
        • Beland F.
        • Guitierrez-Cuadra P.
        Loss to follow-up in a longitudinal study on ageing in Spain.
        J Clin Epidemiol. 1999; 54: 201-510
        • Eagan T.
        • Eide G.
        • Cgulsvik A.
        • Bakke P.
        Non-response in a community cohort study.
        J Clin Epidemiol. 2002; 55: 775-781
        • Criqui M.
        • Barrett-Connor E.
        • Austin M.
        Differences between respondents and non-respondents in a population-based cardiovascular disease study.
        Am J Epidemiol. 1978; 108: 367-372
        • Adams M.
        • Branch L.
        • Henbert L.
        • Cook N.
        • Lane A.
        • Brock D.
        • et al.
        A comparison of elderly participants in a community survey with non-participants.
        Public Health Rep. 1990; 105: 617-622
        • Launer L.
        • Wind A.
        • Deeg D.
        Nonresponse pattern and bias in a community-based cross-sectional study of cognitive functioning among the elderly.
        Am J Epidemiol. 1994; 139: 803-812
        • Norton M.
        • Breitner J.
        • Welsh K.
        • Wyse B.
        Characteristics of non-responders in a community survey of elderly people.
        J Am Geriatr Soc. 1994; 42: 1252-1256
        • Australian Institute of Health and Welfare (AIHW)
        Australia's health 2006.
        in: AIHW AIHW. 2006
        • Wood A.
        • White I.
        • Hillsdon M.
        • Carpenter J.
        Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes.
        Int Epidemiol Assoc. 2005; 34: 89-99
        • Helliwell B.
        • Aylesworth R.
        • McDowell I.
        • Baumgarten M.
        • Sykes E.
        Correlates of non-participation in the Canadian study of health and aging.
        Int Psychogeriatr. 2001; 13: 49-56
        • Slymen D.
        • Drew J.
        • Wright B.
        • Elder J.
        • wiliams S.
        Compliance with a 12-month assessment in an elderly cohort participating in a preventative intervention study: the San Diego Medicare Preventative Health Project.
        Int J Epidemiol. 1992; 21: 701-706
        • Sugisawa H.
        • Kishino H.
        • Sugihara Y.
        • Shibata H.
        Characteristics of dropouts and participants in a twelve-year longitudinal research of Japanese elderly.
        Jap J Public Health. 2000; 47: 337-349
        • Benefante R.
        • Reed D.
        • MacLean C.
        • Kagan A.
        Response bias in the Honolulu Heart Program.
        Am J Epidemiol. 1989; 130: 1088-1100
        • Herbert R.
        • Bravo G.
        • Korner-Bitensky N.
        • Voyer L.
        Refusal and information bias associated with postal questionnaires and face-to-face interviews in very elderly subjects.
        J Clin Epidemiol. 1996; 49: 373-381
        • Sugisawa H.
        • Kishino H.
        • Sugihara Y.
        • Okabayashi H.
        • Shibata H.
        Comparison of characteristics between respondents and non-respondents in the national survey of Japanese elderly using six year follow-up study.
        Jap J Public Health. 1999; 46: 551-562