Method used to provide prevalence estimates
Athletes participating in running or walking races equal to or longer than 800 meters were categorized as endurance athletes, whereas all others were categorized as non-endurance athletes. The altitude of the testing location was identified in 3658 tests. Altitude was also identified from the athletes’ whereabouts as determined in the three-week pre-competition profile of each athlete tested during the 2005 and 2007 World Championships in Athletics (3444 entries). For the purpose of this study only, athletes were roughly categorized into 16 ethnic groups and subsequently categorized into four major sub-classifications (ethnicities and sub-classification code in brackets: Arabic [1], Caucasian [1], Asian [2], Black African [3], Afro-American [3], Chinese [2], Black Caribbean [3], Latino [1], South American [1], African Caribbean [3], Maghreb [1], Pacific [4], African [3], Oceanian [4], Caribbean [1], Native American [1]; sub-classifications: [1]=Caucasian, [2]=Asian, [3]= African, [4]=Oceanian). This categorization in ethnic groups was done independently of the nationality of the athlete.
Blood samples were collected, transported and analyzed following the IAAF Blood Testing Protocol (1). Between 2001 and 2004, approximately 500 pre- or in-competition blood samples were collected every year and analyzed just after collection on a mobile automated analyzer. Between 2005 to 2007, about 1500 in- pre- and out-of-competition samples were collected yearly and analyzed as soon as possible by on-site mobile units if available, or by refrigerated storage and transport to an hematological laboratory located near the collection site (hospital or private or WADA laboratory), in any case within 12 hours from collection. Hemoglobin (HB), hematocrit (HCT) and percent of reticulocytes (RET%) were systematically integrated (3821 samples), and mean corpuscular haemoglobin content (MCHC) and erythropoiesis stimulation index OFF-score (2) calculated. Since 2007, respectively 2009, blood samples were mainly, respectively exclusively, sent to the network of WADA accredited laboratories and protocols for the collection, transport and analysis of blood samples were followed as recommended by the WADA for the ABP (3), with red blood cell count (RBC), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH) and reticulocytes count (RETC) also incorporated (3468 samples). The multi-parametric marker of doping Abnormal Blood Profile Score (ABPS) was calculated from the available blood profile: HB, HCT, MCHC and RET% from 2001 to 2006, and HB, HCT, RBC, MCH, MCV, MCHC and RET% thereafter (4). The marker ABPS is the only universal multiparametric marker of blood doping that discriminates doped from undoped states independently of the administration period of rEPO. Contrarily, the stimulation index OFF-score is less sensitive to the period when the erythropoiesis is stimulated (ON-state). A Bayesian network (BN) was used with heterogeneous factors “gender”, “age”, “exposure to altitude”, “instrument technology” and “ethnicity”, as well as “doping” that mimics doping with low-doses of rEPO (3-5), to generate reference cumulative distribution functions (CDFs) for the marker ABPS (1000 stratified samples). The effects of these factors on the blood variables were modeled with data obtained from two large-scale studies (6,7), with the exception of “exposure to altitude” which was modelled according to the recommendations of the World Health Organization for the detection and classification of anemia (8). A stratified sampling strategy was used in the generation of the reference CDFs. From a Bayesian perspective, the reference CDFs represent the prior predictive CDFs that indicate what the data should look like, given the model, before their acquisition. In detail, for each sub-population, the sampling fraction in each heterogeneous factor was determined over the classes empirically from the information collected during the testing program. For example, to study the data from all female athletes, the repartition in sub-classifications (1: 70%, 2: 8%, 3: 21%, 4: 1%), in altitude (<1000 meters: 95%, 1000-2000 m: 2%, >2000 m: 3%), in age (<19: 6%, 19-24: 33%, >24: 61%), in gender (male: 0%, female: 100%), in instrument technology (Sysmex™: 64%, Bayer-Siemens™: 27%, other: 9%) were entered as hard evidence in the BN and 1000 stratified samples generated. A modal group was referred to as the group composed of Caucasian athletes older than 19 years of age and living at low altitudes measured on a Sysmex instrument. Similarly, in order to avoid any bias caused by missing blood variables pre-2007 in the generation of the reference CDFs, the proportion of ABPS computed from four blood variables (until 2006) and ABPS computed from seven blood variables (from 2007) was chosen as the corresponding proportion obtained of the subgroup to study. For example, for the generation of the CDFs required to study the dataset composed of all 7289 blood samples, 524 (3821/7289=52.4% of 1000 samples) ABPS values were computed from the profile composed of HCT, HB, MCHC and RET%, and the remaining 476 (3468/7289=47.6% of 1000 samples) ABPS values computed from the profile composed of seven variables.
Assuming doping with rEPO microdoses in a modal group, the marker ABPS has a sensitivity high enough (4) to produce meaningful period prevalence estimates when the number of samples is higher than 60, respectively 80, when seven, respectively four, blood variables are used.
References
1 accessed March 18, 2010.
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3 Sottas PE, Robinson N, Saugy M. The athlete's biological passport and indirect markers of blood doping. In: Thieme D, Hemmersbach P, editors. Doping in Sports (Handbook of Experimental Pharmacology, vol 195). Berlin: Springer; 2010.
4 Sottas PE, Robinson N, Giraud S et al. Statistical Classification of Abnormal Blood Profiles in Athletes. Int J Biostatistics 2006; 2:3.
5 Sottas PE, Robinson N, Saugy M, et al. A forensic approach to the interpretation of blood doping markers. Law Prob Risk 2008; 7:191-210.
6 Robinson N, Schattenberg L, Zorzoli M et al. Haematological analysis conducted at the departure of the Tour de France 2001. Int J Sports Med 2005; 26:200-7.
7 Sharpe K, Hopkins W, Emslie KR et al. Development of reference ranges in elite athletes for markers of altered erythropoiesis. Haematologica 2002; 87:1248-57.
8 Word Health Organisation. Iron deficiency anemia: assessment, prevention and control, a guide for programme managers. WHO publications, Geneva, 2001.