AUTHOR=Petróczi Andrea , Cruyff Maarten , de Hon Olivier , Sagoe Dominic , Saugy Martial 

TITLE=Hidden figures: Revisiting doping prevalence estimates previously reported for two major international sport events in the context of further empirical evidence and the extant literature

JOURNAL=Frontiers in Sports and Active Living

VOLUME=Volume 4 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2022.1017329

DOI=10.3389/fspor.2022.1017329

ISSN=2624-9367

ABSTRACT=Background: High levels of admitted doping use (43.6% and 57.1%) were reported for two international sport events in 2011. Because these are frequently referenced in evaluating aspects of anti-doping, having high level of confidence in these estimates is paramount.
Objectives: In this study, we present new prevalence estimates from a concurrently administered method, the Single Sample Count (SSC), and critically review the two sets of estimates in the context of other doping prevalence estimates. 
Methods: The survey featuring the SSC model was completed by 1,203 athletes at WAC (65.3% of all participating athletes) and 954 athletes at PAG (28.2% of all participating athletes). At WAC, athletes completed both UQM and SSC surveys in randomised order. At PAG, athletes were randomly allocated to one of the two surveys. Doping was defined as 'having knowingly violated anti-doping regulations by using a prohibited substance or method’.
Results: Estimates with the SSC model for 12-month doping prevalence were 21.2% (95%CI: 9.69–32.7) at WAC and 10.6% (95%CI: 1.76–19.4) at PAG. Estimated herbal, mineral, and/or vitamin supplements use was 8.57% (95%CI: 1.3-16.11) at PAG. Reliability of the estimates were confirmed with re-sampling method (n = 1,000, 80% of the sample). Survey noncompliance (31.90%, 95%CI: 26.28–37.52; p<.0001) was detected in the WAC data but occurred to a lesser degree at PAG (9.85%, 95%CI: 4.01-15.69, p=.0144 and 11.43%, 95%CI: 5.31-11.55, p=.0196, for doping and nutritional supplement use, respectively). A large discrepancy between those previously reported from the UQM and the prevalence rate estimated by the SSC model for the same population is evident. 
Conclusion: Caution in interpreting these estimates as bona fide prevalence rates is warranted. Critical appraisal of the obtained prevalence rates and triangulation with other sources are recommended over ‘the higher rate must be closer to the truth’ heuristics. Noncompliance appears to be the Achilles heel of the indirect estimation models thus it should be routinely tested for and minimized. Further research into cognitive and behaviour aspects, including motivation for honesty, is needed to improve the ecological validity of the estimated prevalence rates.