Evidence map›Paper›PMID 35954686›Full record

ArticleInternational journal of environmental research and public health2022

Relationship between Doping Prevalence and Socioeconomic Parameters: An Analysis by Sport Categories and World Areas.

José Luís Terreros, Pedro Manonelles, Daniel López-Plaza

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

José Luís TerrerosCELAD, Spanish Commission of Anti-Doping Fight, 29016 Madrid, Spain.
Pedro ManonellesInternational Sports Medicine Chair, Catholic University of San Antonio, 30107 Murcia, Spain.
Daniel López-PlazaInternational Sports Medicine Chair, Catholic University of San Antonio, 30107 Murcia, Spain.ORCID 0000-0003-1832-273X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Socioeconomic differences between countries, including corruption and doping scandals, have increased in the last few decades. The aims of the current investigation were to examine doping prevalence according to world areas and sport groups and its association with socioeconomic factors worldwide. The Anti-Doping Rule Violations (ADRVs) of 160 countries competing at 2016 Olympics were analyzed between 2013 and 2018. In addition, the relationship between doping prevalence and socioeconomic characteristics, including Human Development Index (HDI), Per Capita Income (PCI) and Corruption Index (CI), was investigated. Africa, Asia, and America were revealed to have a significantly lower doping prevalence than Europe and Oceania when observing the sum and the mean ADRV/10,000 inhabitants (p < 0.01). Strong to moderate correlations were identified between Corruption Index and ADRVs and HDI and ADRVs (p < 0.01). However, the number of Olympic athletes was positively associated with the ADRVs and the HDI (r = 0.663 and 0.424, respectively). In the comparison by sport groups, the Independent Recognized Sports (AIMS) showed significantly higher Adverse Analytical Findings (AAF) and ADRVs (p < 0.01) than Olympic and Recognized International Sports (ARISF). In conclusion, the results of the current study reveal doping prevalence differences between world areas and sport categories, identifying associations with socioeconomic characteristics of each country.

Indexed as

Doping in SportsSportsAthletesEuropeHumansPrevalenceSocioeconomic Factorsanti-dopingOlympicsprohibited substancessocioeconomic characteristicssport

Identifiers

PMID35954686
PMCPMC9367925

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.