The impact of sports mega-events on health and environmental rights in the city of Rio de Janeiro, Brazil

Vilani, Rodrigo Machado; Machado, Carlos José Saldanha

doi:10.1590/0102-311x00047414

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…The subject has also been discussed before and after the London Olympic Games [41][42][43] and the Rio de Janeiro Olympic Games. 44 Results discussed in this work show the importance of using all the criteria pollutants when calculating the AQIs, since the use of a restricted number of compounds may lead to artificially low index values.…”

Section: Resultsmentioning

confidence: 88%

Air Quality Indexes in the City of Rio de Janeiro During the 2016 Olympic and Paralympic Games

Tsuruta¹,

Carvalho²,

Silva³

et al. 2017

J. Braz. Chem. Soc.

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In this work, the air quality indexes (AQIs) determined in Rio de Janeiro from July to September 2016, before and during the Summer Olympic and Paralympic Games, in the monitoring stations operated by the Municipal Secretariat of the Environment, were compiled and analyzed. Only four automatic monitoring stations determined all the Brazilian criteria pollutants (smog, total particulate matter, PM 10 (particulate matter < 10 mm), NO 2 , ozone and SO 2). NO 2 and PM 10 levels were generally lower during the Olympic period because of restrictions on vehicular flux. However, ozone concentrations remained high, due to unfavorable meteorological conditions. The worst conditions were observed in Bangu and Irajá Districts, where ozone concentrations frequently exceeded 160 mg m-3. According to the World Health Organization (WHO), as ozone concentrations increase above this value, health effects became increasingly numerous and more severe. Brazilian air quality allowable standards are clearly higher than WHO guidelines, which, in addition to the combined results of the adopted air quality indexes and the use of a restricted number of criteria pollutants to report the air quality, led to AQIs in the intervals good and moderate, despite concentrations frequently being high in terms of WHO guidelines.

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Section: Resultsmentioning

confidence: 88%

Air Quality Indexes in the City of Rio de Janeiro During the 2016 Olympic and Paralympic Games

Tsuruta¹,

Carvalho²,

Silva³

et al. 2017

J. Braz. Chem. Soc.

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“…However, this raises a question about whether the implementation of concurrent interventions may partly explain our findings. The rollout of the FHS in Rio de Janeiro coincided with implementation of a major violence pacification program and transport investments for the 2014 FIFA World Cup and 2016 Olympics in the city, which contributed to reduced violence, better socioeconomic and living standards, and changes in the urban environment [ 43 – 45 ]. However, these programs were not coordinated and were implemented separately, each in a phased manner, and would not explain the large reductions in mortality across a variety of conditions seen, including CVD.…”

Section: Discussionmentioning

confidence: 99%

Primary healthcare expansion and mortality in Brazil’s urban poor: A cohort analysis of 1.2 million adults

et al. 2020

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Background Expanding delivery of primary healthcare to urban poor populations is a priority in many low- and middle-income countries. This remains a key challenge in Brazil despite expansion of the country’s internationally recognized Family Health Strategy (FHS) over the past two decades. This study evaluates the impact of an ambitious program to rapidly expand FHS coverage in the city of Rio de Janeiro, Brazil, since 2008. Methods and findings A cohort of 1,241,351 low-income adults (observed January 2010–December 2016; total person-years 6,498,607) with linked FHS utilization and mortality records was analyzed using flexible parametric survival models. Time-to-death from all-causes and selected causes were estimated for FHS users and nonusers. Models employed inverse probability treatment weighting and regression adjustment (IPTW-RA). The cohort was 61% female (751,895) and had a mean age of 36 years (standard deviation 16.4). Only 18,721 individuals (1.5%) had higher education, whereas 102,899 (8%) had no formal education. Two thirds of individuals (827,250; 67%) were in receipt of conditional cash transfers ( Bolsa Família ). A total of 34,091 deaths were analyzed, of which 8,765 (26%) were due to cardiovascular disease; 5,777 (17%) were due to neoplasms; 5,683 (17%) were due to external causes; 3,152 (9%) were due to respiratory diseases; and 3,115 (9%) were due to infectious and parasitic diseases. One third of the cohort (467,155; 37.6%) used FHS services. In IPTW-RA survival analysis, an average FHS user had a 44% lower hazard of all-cause mortality (HR: 0.56, 95% CI 0.54–0.59, p < 0.001) and a 5-year risk reduction of 8.3 per 1,000 (95% CI 7.8–8.9, p < 0.001) compared with a non-FHS user. There were greater reductions in the risk of death for FHS users who were black (HR 0.50, 95% CI 0.46–0.54, p < 0.001) or pardo (HR 0.57, 95% CI 0.54–0.60, p < 0.001) compared with white (HR 0.59, 95% CI 0.56–0.63, p < 0.001); had lower educational attainment (HR 0.50, 95% CI 0.46–0.55, p < 0.001) for those with no education compared to no significant association for those with higher education ( p = 0.758); or were in receipt of conditional cash transfers ( Bolsa Família ) (HR 0.51, 95% CI 0.49–0.54, p < 0.001) compared with nonrecipients (HR 0.63, 95% CI 0.60–0.67, p < 0.001). Key limitations in this study are potential unobserved confounding through selection into the program and linkage errors, although analytical approaches have minimized the potential for bias. Conclusions FHS utilization in urban poor populations in Brazil was associated with a lower risk of death, with greater reductions amon...

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“…The rapid development of communication technologies such as machine learning models has made the integration and application of resource information the key to the construction of sports informatization [13][14][15]. The rapid development of science and technology development under machine learning models will promote the application of mobile terminals such as smartphones and tablets in the sports industry [16][17][18]. Machine learning models are a collection of massive data, the volume of which is particularly huge and cannot be processed by conventional software within a certain period of time.…”

Section: Introductionmentioning

confidence: 99%

A strategy for building a smart sports platform based on machine learning models

Gong¹

2023

3C TIC

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With the rapid development of big data technology, it has greatly changed the way people get information, and also improved the speed and quality of information. In this context, smart sports has become a new trend in sports development. This paper creates an intelligent learning environment and builds a smart sports platform through advanced concepts and technical means, which can effectively optimize the integration and sharing of sports resources. Starting from the overall architecture design of smart sports, the key technologies of machine learning model to realize smart sports are sorted out. Through the five basic linking stages with machine learning model as the core, the value innovation path of platform construction structure is analyzed. The current status of sports resources application is studied, and the data mining algorithm is used to calculate the user usage data of the smart sports platform and improve the construction of the smart platform. Through the construction of the smart sports platform, people shift from traditional reading books and watching TV programs to getting information through intelligent mobile terminals, and the proportion of attention to sports information is as high as 58.6%. This shows that by building a smart sports platform, it can provide support and guarantee for the sustainable development of sports.

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The impact of sports mega-events on health and environmental rights in the city of Rio de Janeiro, Brazil

Cited by 5 publications

References 11 publications

Air Quality Indexes in the City of Rio de Janeiro During the 2016 Olympic and Paralympic Games

Air Quality Indexes in the City of Rio de Janeiro During the 2016 Olympic and Paralympic Games

Primary healthcare expansion and mortality in Brazil’s urban poor: A cohort analysis of 1.2 million adults

A strategy for building a smart sports platform based on machine learning models

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