Elite athletes\u27 perceptions of the effects of illicit drugs use on athletic performance

Objective: To investigate the perceived risks and benefits that elite athletes associate with illicit drugs and their beliefs concerning the effects of recreational drug use on athletic performance.Design: Self-administered survey.Participants: Nine hundred seventy-four elite athletes (mean age, 23 years; range, 18-30 years) were recruited from 8 national sporting organizations in Australia and the Australian Institute of Sport.Interventions: Participants completed a self-administered survey that included questions exploring participants&rsquo; perceptions regarding the effects of illicit drug use on physical performance.Setting: National sporting organization meetings or competitions.Main Outcome Measures: The main outcome measure was risk perception on athletic performance associated with illicit drug use.Results: The majority of athletes believed that illicit drug use would impact negatively on athletic performance. The main perceived effects of illicit drugs on athletic performance were physical and mental functioning. A minority of athletes indicated that drug use would not impact on physical performance when taken during the offseason or in moderation.Conclusions: The main risks perceived in association with illicit drug use were short-term consequences, such as physical and mental functioning, rather than long-term health consequences. The current findings may contribute to the development of harm reduction strategies that communicate drug-related consequences to elite athletes in an appropriate and effective manner.<br /

Geostatistical analysis of Malawi’s changing malaria transmission from 2010 to 2017 [version 1; peer review: awaiting peer review]

Background: The prevalence of malaria infection in time and space provides important information on the likely sub-national epidemiology of malaria burdens and how this has changed following intervention. Model-based geostatitics (MBG) allow national malaria control programmes to leverage multiple data sources to provide predictions of malaria prevalance by district over time. These methods are used to explore the possible changes in malaria prevalance in Malawi from 2010 to 2017. Methods: Plasmodium falciparum parasite prevalence (PfPR) surveys undertaken in Malawi between 2000 and 2017 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2–10 years (PfPR2–10) at 1×1 km spatial resolutions. Parameter estimation was carried out using the Monte Carlo maximum likelihood methods. Population-adjusted prevalence and populations at risk by district were calculated for 2010 and 2017 to inform malaria control program priority setting. Results: 2,237 surveys at 1,834 communities undertaken between 2000 and 2017 were identified, geo-coded and used within the MBG framework to predict district malaria prevalence properties for 2010 and 2017. Nationally, there was a 47.2% reduction in the mean modelled PfPR2-10 from 29.4% (95% confidence interval (CI) 26.6 to 32.3%) in 2010 to 15.2% (95% CI 13.3 to 18.0%) in 2017. Declining prevalence was not equal across the country, 25 of 27 districts showed a significant decline ranging from a 3.3% reduction to 79% reduction. By 2017, 16% of Malawi’s population still lived in areas that support PfPR2-10 ≥ 25%. Conclusions: Malawi has made substantial progress in reducing the prevalence of malaria over the last seven years. However, Malawi remains in meso-endemic malaria transmission risk. To sustain the gains made and continue reducing the transmission further, universal control interventions need to be maintained at a national level