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Participation Bias and Its Impact on the Assembly of a Genetic Specimen Repository for a Myocardial Infarction Cohort

      OBJECTIVE

      To assess participation bias in the assembly of a specimen repository for genetic studies and to examine the association of participation with outcome within the Olmsted County myocardial infarction (MI) cohort.

      PARTICIPANTS AND METHODS

      From January 1, 1979, to May 31, 2006, 3081 persons had MI in Olmsted County, MN. Face-to-face contact was used to recruit patients who were hospitalized for an acute event. Persons who had had an MI before establishment of this repository were contacted by mail. At initial contact, we sought consent to use blood samples for genetic studies. Persons who refused were contacted by mail and were asked to consent to the use of stored tissue samples. For deceased subjects, stored tissue was collected when available.

      RESULTS

      Of the 3081 persons in the Olmsted County MI cohort, 1994 participated in the study; 1007 (50.5%) blood and 987 (49.5%) tissue specimens were provided. Participants were more likely to be younger men with hypertension, comorbidities, and non-ST-segment elevation MI (all, P<.05). Participants who provided blood specimens were more likely to have non-ST-segment elevation MI and lower Killip class than those who provided tissue. After adjustment for age, sex, hypertension, ST-segment elevation, Killip class, and comorbidities, participation was not associated with outcome. Participants who provided blood specimens were less likely to have heart failure (hazard ratio, 0.49; 95% confidence interval, 0.40-0.59; P<.01) or to die (hazard ratio, 0.16; 95% confidence interval, 0.12-0.21; P<.01) than those who provided tissue.

      CONCLUSIONS

      A variety of sources can be used to assemble community specimen repositories. Baseline characteristics differed between participants and nonparticipants and, among participants, by specimen source. Participants who provided blood specimens had better outcomes than those who provided tissue specimens. No survival advantage was observed for participants after combining blood and tissue specimens.
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      REFERENCES

        • Fletcher RH
        • Fletcher SW
        Clinical Epidemiology: The Essentials. 4th ed. Lippincott, Williams, & Wilkins, Philadelphia, PA2005
        • Morton LM
        • Cahill J
        • Hartge P
        Reporting participation in epidemiologic studies: a survey of practice.
        Am J Epidemiol. 2006 Feb 1; 163 (Epub 2005 Dec 7.): 197-203
        • Gerber Y
        • Jacobsen SJ
        • Killian JM
        • Weston SA
        • Roger VL
        Participation bias assessment in a community-based study of myocardial infarction, 2002-2005.
        Mayo Clin Proc. 2007; 82: 933-938
        • Jacobsen SJ
        • Mahoney DW
        • Redfield MM
        • Bailey KR
        • Burnett Jr, JC
        • Rodeheffer RJ
        Participation bias in a population-based echocardiography study.
        Ann Epidemiol. 2004; 14: 579-584
        • Steinberg K
        • Beck J
        • Nickerson D
        • et al.
        DNA banking for epidemiologic studies: a review of current practices.
        Epidemiology. 2002; 13: 246-254
        • Godard B
        • Schmidtke J
        • Cassiman JJ
        • Ayme S
        Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality issues, ownership, return of benefits: a professional perspective.
        Eur J Hum Genet. 2003; 11: S88-S122
        • Manolio TA
        • Bailey-Wilson JE
        • Collins FS
        Genes, environment and the value of prospective cohort studies.
        Nat Rev Genet. 2006; 7: 812-820
        • Melton III, LJ
        History of the Rochester Epidemiology Project.
        Mayo Clin Proc. 1996; 71: 266-274
        • Roger VL
        • Jacobsen SJ
        • Weston SA
        • et al.
        Trends in the incidence and survival of patients with hospitalized myocardial infarction, Olmsted County, Minnesota, 1979 to 1994.
        Ann Intern Med. 2002; 136: 341-348
        • Roger VL
        • Killian JM
        • Weston SA
        • et al.
        Redefinition of myocardial infarction: prospective evaluation in the community.
        Circulation. 2006 Aug 22; 114 (Epub 2006 Aug 14.): 790-797
        • Kors JA
        • van Herpen G
        • Wu J
        • Zhang Z
        • Prineas RJ
        • van Bemmel JH
        Validation of a new computer program for Minnesota coding.
        J Electrocardiol. 1996; 29: 83-88
        • Luepker RV
        • Apple FS
        • Christenson RH
        • et al.
        Case definitions for acute coronary heart disease in epidemiology and clinical research studies: a statement from the AHA Council on Epidemiology and Prevention; AHA Statistics Committee; World Heart Federation Council on Epidemiology and Prevention; the European Society of Cardiology Working Group on Epidemiology and Prevention; Centers for Disease Control and Prevention; and the National Heart, Lung, and Blood Institute.
        Circulation. 2003 Nov 18; 108 (Epub 2003 Nov 10.): 2543-2549
        • Alpert JS
        • Thygesen K
        • Antman E
        • Bassand JP
        Myocardial infarction redefined—a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction [published correction appears in J Am Coll Cardiol. 2001;37(3):973].
        J Am Coll Cardiol. 2000; 36: 959-969
        • Lloyd-Jones DM
        • Nam BH
        • D'Agostino Sr, RB
        • et al.
        Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring.
        JAMA. 2004; 291: 2204-2211
        • Charlson ME
        • Pompei P
        • Ales KL
        • MacKenzie CR
        A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
        J Chronic Dis. 1987; 40: 373-383
        • Yawn BP
        • Yawn RA
        • Geier GR
        • Xia Z
        • Jacobsen SJ
        The impact of requiring patient authorization for use of data in medical records research.
        J Fam Pract. 1998; 47: 361-365
        • Ho KK
        • Anderson KM
        • Kannel WB
        • Grossman W
        • Levy D
        Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
        Circulation. 1993; 88: 107-115
        • Hellermann JP
        • Goraya TY
        • Jacobsen SJ
        • et al.
        Incidence of heart failure after myocardial infarction: is it changing over time?.
        Am J Epidemiol. 2003; 157: 1101-1107
        • Roger VL
        • Killian J
        • Henkel M
        • et al.
        Coronary disease surveillance in Olmsted County objectives and methodology.
        J Clin Epidemiol. 2002; 55: 593-601
        • Eastwood BJ
        • Gregor RD
        • MacLean DR
        • Wolf HK
        Effects of recruitment strategy on response rates and risk factor profile in two cardiovascular surveys.
        Int J Epidemiol. 1996; 25: 763-769
        • Eaker S
        • Bergstrom R
        • Bergstrom A
        • Adami HO
        • Nyren O
        Response rate to mailed epidemiologic questionnaires: a population-based randomized trial of variations in design and mailing routines.
        Am J Epidemiol. 1998; 147: 74-82
        • Eagan TM
        • Eide GE
        • Gulsvik A
        • Bakke PS
        Nonresponse in a community cohort study: predictors and consequences for exposure-disease associations.
        J Clin Epidemiol. 2002; 55: 775-781
        • Jousilahti P
        • Salomaa V
        • Kuulasmaa K
        • Niemela M
        • Vartiainen E
        Total and cause specific mortality among participants and non-participants of population based health surveys: a comprehensive follow up of 54 372 Finnish men and women.
        J Epidemiol Community Health. 2005; 59: 310-315
        • Shahar E
        • Folsom AR
        • Jackson R
        • Atherosclerosis Risk in Communities (ARIC) Study Investigators
        The effect of nonresponse on prevalence estimates for a referent population: insights from a population-based cohort study.
        Ann Epidemiol. 1996; 6: 498-506
        • Boshuizen HC
        • Viet AL
        • Picavet HS
        • Botterweck A
        • van Loon AJ
        Nonresponse in a survey of cardiovascular risk factors in the Dutch population: determinants and resulting biases.
        Public Health. 2006 Apr; 120 (Epub 2005 Dec 20.): 297-308
        • Lissner L
        • Skoog I
        • Andersson K
        • et al.
        Participation bias in longitudinal studies: experience from the Population Study of Women in Gothenburg, Sweden.
        Scand J Prim Health Care. 2003; 21: 242-247
        • Bergstrand R
        • Vedin A
        • Wilhelmsson C
        • Wilhelmsen L
        Bias due to nonparticipation and heterogenous sub-groups in population surveys.
        J Chronic Dis. 1983; 36: 725-728
        • Benfante R
        • Reed D
        • MacLean C
        • Kagan A
        Response bias in the Honolulu Heart Program.
        Am J Epidemiol. 1989; 130: 1088-1100
        • Begg CB
        • Orlow I
        • Hummer AJ
        • Environment and Melanoma Study Group
        • et al.
        Lifetime risk of melanoma in CDKN2A mutation carriers in a population-based sample.
        J Natl Cancer Inst. 2005; 97: 1507-1515
        • Holland NT
        • Pfleger L
        • Berger E
        • Ho A
        • Bastaki M
        Molecular epidemiology biomarkers—sample collection and processing considerations.
        Toxicol Appl Pharmacol. 2005; 206: 261-268
        • Hartge P
        Raising response rates: getting to yes [editorial].
        Epidemiology. 1999; 10: 105-107
        • Corbie-Smith G
        • Viscoli CM
        • Kernan WN
        • Brass LM
        • Sarrel P
        • Horwitz RI
        Influence of race, clinical, and other socio-demographic features on trial participation.
        J Clin Epidemiol. 2003; 56: 304-309
        • Heilbrun LK
        • Ross PD
        • Wasnich RD
        • Yano K
        • Vogel JM
        Characteristics of respondents and nonrespondents in a prospective study of osteoporosis.
        J Clin Epidemiol. 1991; 44: 233-239
        • Singh M
        • Reeder GS
        • Jacobsen SJ
        • Weston S
        • Killian J
        • Roger VL
        Scores for post-myocardial infarction risk stratification in the community.
        Circulation. 2002; 106: 2309-2314
        • Shen R
        • Fan JB
        • Campbell D
        • et al.
        High-throughput SNP genotyping on universal bead arrays.
        Mutat Res. 2005; 573: 70-82