Intelligence‐based doping control planning improves testing effectiveness: Perspectives from a national anti‐doping organisation

Abstract

Anti-doping organisations are mandated to provide a comprehensive anti-doping programme, which aims to detect, deter and prevent doping in sport. Direct detection of prohibited substances and methods by collection of biological samples from athletes makes up about half of the global anti-doping budgets but has in the last decade been under critical scrutiny for its lack of efficiency. To ensure optimum detection and deterrence of testing and prevention efforts, a better understanding of doping practices and comparison of different doping test strategies are needed. This study evaluates 17 years of doping test statistics and Anti-Doping Rule Violations (ADRVs) from the perspectives of a national anti-doping organisation. A total of 48 709 samples (2865 ± 220 annually) were collected by Anti-Doping Norway in the period 2003-2019, which resulted in total 216 ADRVs (12.7 ± 3.7 annually), providing an average sample-to-ADRV rate of 0.44% when including urine, blood and ABP samples. Most athletes who committed ADRVs were men participating in ball and team or strength sports at a national or recreational level. Few ADRVs were recorded among elite athletes and in most high-risk sports, despite these groups being subjected to the highest number of doping tests. The number of annual ADRVs did not correlate with the corresponding number of collected doping samples. However, systematic use of intelligence in the test planning process was associated with an increase in ADRVs.Anti-doping organisations would benefit from improving their target testing capability and to take an intelligence-led approach in planning doping tests.

The purpose and effectiveness of doping testing in sport

“…The importance of information-based testing and the use of forensic methods and intelligence ( 28 , 29 , 31 ), as well as cross sectional cooperation ( 32 ) to uncover both analytical and non-analytical rule violations has been increasingly promoted in the last decade. ADOs should therefore invest in human resources which may increase their capability and capacity to gather and use intelligence in test planning and set up a system that allows for the collection and processing of information on possible rule violations.…”

Trends in dietary supplement use among athletes selected for doping controls

“…The most frequent use of mixed products was found in powerlifting, cheerleading, american football and weightlifting. Interestingly, these sports are frequently viewed as high risk disciplines and rank among the top sports with the most global ( 37 ) and national ADRVs ( 19 ).…”

“…The following information from each DCF was manually registered into an electronic database using Microsoft Excel: Year of the doping control, gender, age group (<20; 20–24; 25–29; 30–34; 35–39; ≥40), athlete level {national level athlete [as defined by ADNO ( 20 )]; recreational athlete}, sport discipline, type of test (in-competition (samples collected upon conclusion of competition); out-of-competition) and nationality. Sport disciplines were categorized into the following groups based on physiological characteristics and the risk of doping: Aiming sports, ball and team sports, combat sports, gymnastic sports, muscular endurance sports, other sports, strength and power sports and VO2 max endurance sports ( 19 , 21 ).…”

“…Anti-Doping Norway (ADNO) annually collects ~2,800 doping samples divided into 2,000 doping tests of athletes participating in sports organized under the Norwegian Olympic and Paralympic Committee and Confederation of Sports ( 19 ). During the doping test, each athlete is required to fill out a doping control form (DCF) including demographic information, athlete level, sport discipline and information about any DS and/or pharmaceutical drug used within the preceding seven days.…”

Annual banned‐substance review 16th edition—Analytical approaches in human sports drug testing 2022/2023

“…Minimizing or even preventing doping in sport has been shown to represent a massive undertaking, which necessitates a holistic approach, including (among other critical aspects) the continuous improvement of analytical approaches that allow for the detection of prohibited substances and methods of doping using athletes' doping control samples 1 . While historically anti‐doping measures have predominantly relied on analytical chemistry, the consideration and implementation of other angles in support of an optimized anti‐doping effort have received growing attention, ranging from enhanced and intelligence‐based athlete identification, 2 sample collection and testing plans 3 to the exploitation of concepts applied in safety science research 4 . Here, an in‐depth understanding of adverse incident causation (i.e., an adverse analytical finding [AAF] and anti‐doping rule violation [ADRV]) and means to prevent future occurrences has been sought, conceptualized either in the light of average sample‐to‐ADRV rates or in a Systems Theoretic Accident Model and ProcesseS (STAMPS) context, designed to assist in identifying interacting systemic conditions creating the undesirable outcome of an ADRV.…”