Healthy Volunteer Effect and Cardiovascular Risk
Abstract
To the Editor: Most cardiovascular risk prediction functions are developed using data from cohort studies. Invariably, a proportion of invitees will not participate in such studies because relatively good health status is required for a person to agree to undergo the examinations. This implies that persons enrolled in a study requiring active participation are healthier than those who declined to participate. It is thus unclear whether the cardiovascular risk distributions among study participants adequately reflect the risk distribution of the source population. We aimed to quantify the consequences of this “healthy volunteer effect.” Within the Rotterdam Study,1 a prospective population-based cohort, we investigated the association between participation in the third examination (1997–1999), all-cause mortality,2 and coronary risk (Framingham point score; assessed at enrollment 1990–1993)3 (see eAppendix, https://links.lww.com/EDE/A779 for details). Of 5423 eligible invitees (mean age 73.5 years; 39% men), 87% participated, of whom 76% visited the research center (eTable 1, https://links.lww.com/EDE/A779). Nonparticipants had lost interest (50%), had physical complaints (34%), or considered themselves too old to participate (12%; mean age 86.9 years). Persons who were elderly, women, less educated, and with higher levels of specific cardiovascular risk factors were less likely to participate (eTable 1). Nonparticipation was strongly associated with mortality (hazard ratio [HR] = 1.71 [95% confidence interval (CI) = 1.56–1.88]). This was most pronounced shortly after invitation (0–3 months, HR = 4.85 [2.43–9.71]), with a diminishing healthy volunteer effect during follow-up (test for trend, P< 0.001) (Table). Every percentage-point increase in coronary risk yielded an approximately 3% lower probability of participating (eTable 2, https://links.lww.com/EDE/A779). Those categorized as “high risk” were least likely to participate (odds ratio = 0.56 [95% CI = 0.45–0.71]; eTable 2). There was a slightly lower proportion of high-risk persons among the examined participants compared with all invitees (23% vs. 24%) (eTable 3, https://links.lww.com/EDE/A779).TABLE: Risk of Mortality Associated with Nonparticipation in the Third Examination of the Rotterdam Study, by Time Since Invitation (1997–1v999)More than 5 decades ago, investigators from the Framingham Study observed higher mortality rates in those who refused to participate.4 They speculated that mortality in participants and nonparticipants might converge later during follow-up. We indeed noticed declining differences in mortality rates at long-term follow-up. The large difference shortly after invitation is presumably attributable to clinical or subclinical disease that makes invitees less likely to volunteer.5 Residual differences at long-term follow-up could reflect a health-care-aversive attitude or a lower awareness of health in general.6 However, long-term benefits of participation in population-based research cannot be ruled out because most studies (including the Rotterdam Study) disclose results from measurements (eg, blood pressure and cholesterol levels) and incidental findings (eg, aneurysms and indolent cancers) to the participants.7 Our focus on distributions of absolute cardiovascular risk is new information. This is important in light of the recent shift in cardiovascular epidemiologic research toward evaluating new risk markers on the basis of their clinical utility in risk prediction (ie, whether the predicted risks with addition of a new marker change sufficiently to alter recommended therapy) rather than the marker’s association with cardiovascular disease expressed as an odds ratio or HR. In contrast to the study of associations, the risk distribution in the study population affects the magnitude of measures of risk reclassification, such as net reclassification improvement.8 Given that most people have a low predicted cardiovascular risk (eTable 3), underrepresentation of persons at higher cardiovascular risk will usually mean that smaller proportions of persons are reclassified, resulting in lower estimates of net reclassification improvement8; therefore, the contribution of emerging risk markers could be underestimated. We observed a small underrepresentation of persons at high cardiovascular risk (1.2% less at high risk) (eTable 3); thus, results from analyses on absolute cardiovascular risk are not likely to be severely affected. However, future simulation studies could quantify the degree of bias on measures of risk reclassification. Maarten J. G. Leening Jan Heeringa Department of Epidemiology Erasmus MC–University Medical Center Rotterdam Rotterdam, The Netherlands Jaap W. Deckers Department of Cardiology Erasmus MC–University Medical Center Rotterdam Rotterdam, The Netherlands Oscar H. Franco Albert Hofman Jacqueline C. M. Witteman Bruno H. Ch. Stricker Department of Epidemiology Erasmus MC–University Medical Center Rotterdam Rot
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Healthy volunteer bias might have moderated the effects of exercise on the outcomes.
“The healthy volunteer bias might have moderated the effects of exercise on the outcomes.19”
From Effect of exercise training for five years on all cause mortality in older adults—the Generation 100 study: randomised controlled trial (2020) · cites this paper as [19]
Cites
Leening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J., & Steyerberg, E. W. (2014) · Annals of Internal Medicine · doi:10.7326/m13-1522 · Cited by 594 · see citing works
Self-citationThe Rotterdam Study: 2014 objectives and design update
Albert Hofman, D., Murad, S. D., van Duijn, C. M., Franco, O. H., Goedegebure, A., Ikram, M. A., Klaver, C. C. W., Nijsten, T. E. C., Peeters, R. P., Stricker, B. H. C., et al. (2013) · European Journal of Epidemiology · doi:10.1007/s10654-013-9866-z · Cited by 330 · see citing works
Self-citationMethods of data collection and definitions of cardiac outcomes in the Rotterdam Study
Leening, M. J. G., Kavousi, M., Heeringa, J., van Rooij, F. J. A., Heemst, J. V., Deckers, J. W., Mattace-Raso, F. U. S., Ziere, G. J., Albert Hofman, D., Stricker, B. H. C., et al. (2012) · European Journal of Epidemiology · doi:10.1007/s10654-012-9668-8 · Cited by 245 · see citing works
Self-citationGordon, T., Moore, F. E., Shurtleff, D., & Dawber, T. R. (1959) · Journal of Chronic Diseases · doi:10.1016/0021-9681(59)90002-5 · Cited by 177 · see citing works
Healthy volunteer effect in a cohort study: Temporal resolution in the adventist health study
Lindsted, K. D., Fraser, G. E., Steinkohl, M., & Beeson, W. L. (1996) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(96)00009-1 · Cited by 141 · see citing works
Shahar, E. (1996) · Annals of Epidemiology · doi:10.1016/s1047-2797(96)00104-4 · Cited by 126 · see citing works
RESPONSE BIAS IN THE HONOLULU HEART PROGRAM
Benfante, R., REED, D. M., MacLEAN, C., & Kagan, A. (1989) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a115436 · Cited by 85 · see citing works
Koller, M., Leening, M. J. G., Wolbers, M., Steyerberg, E. W., Hunink, M. G. M., Schoop, R., Albert Hofman, D., Bucher, H. C., Psaty, B. M., Lloyd‐Jones, D. M., et al. (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-157-6-201209180-00002 · Cited by 74 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Ligthart, S., van Herpt, T. T. W., Leening, M. J. G., Kavousi, M., Hofman, A., Stricker, B. H. C., van Hoek, M., Sijbrands, E. J. G., Franco, O. H., & Dehghan, A. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00362-9 · Cited by 267 · see citing works
Self-citationStensvold, D., Viken, H., Steinshamn, S. L., Dalen, H., Støylen, A., Loennechen, J. P., Reitlo, L. S., Zisko, N., Bækkerud, F. H., Tari, A. R., et al. (2020) · BMJ · doi:10.1136/bmj.m3485 · Cited by 154 · see citing works
Statin Trials, Cardiovascular Events, and Coronary Artery Calcification
Mortensen, M. B., Falk, E., Li, D., Nasir, K., Blaha, M. J., Sandfort, V., Rodriguez, C. J., Ouyang, P., & Budoff, M. (2018) · JACC: Cardiovascular Imaging · doi:10.1016/j.jcmg.2017.01.029 · Cited by 88 · see citing works
“This proportion would most likely be even higher in a real-world population(26).”
Background
- Authors
- Leening, M. J. G., Heeringa, J., Deckers, J. W., Franco, O. H., Hofman, A., Witteman, J. C. M., & Stricker, B. H. C.
- Affiliations
- Erasmus MC (NL)
- Venue
- Epidemiology
- Published
- 2014-05
- Volume
- 25
- Issue
- 3
- Pages
- 470-471
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2055530317
- Cited by
- 41
- References
- 8
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 3 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 2
- FWCI
- 4.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Keywords
- cardiovascular risk, research participation, non-participation, all-cause mortality, coronary heart disease risk, Framingham risk score, Rotterdam Study, population-based cohort, selection bias, risk prediction, older adults, socioeconomic factors, physical complaints, loss to follow-up, mortality risk stratification, cardiovascular education
- MeSH terms
- Aged, Aged, 80 and over, Cardiovascular Diseases, Cause of Death, Coronary Disease, Female, Humans, Male, Middle Aged, Prognosis, Prospective Studies, Cohort Studies, Incidence, Survival Rate, Confidence Intervals, Proportional Hazards Models, Age Distribution, Sex Distribution, Risk Assessment, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review