Incidence and mortality for respiratory cancer and traffic-related air pollution in São Paulo, Brazil

Ribeiro, Adeylson Guimarães; Downward, George S.; Freitas, Clarice Umbelino de; Neto, Francisco Chiaravalloti; Cardoso, Maria Regina Alves; Latorre, Maria do Rosário Dias de Oliveira; Hystad, Perry; Vermeulen, Roel; Nardocci, Adelaide Cássia

doi:10.1016/j.envres.2018.12.034

Cited by 54 publications

(21 citation statements)

References 49 publications

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“…It is estimated that air pollution in São Paulo is responsible for thousands of premature deaths annually, largely attributed to respiratory and cardiovascular effects of exposure to air pollution. It is important to note that the regional impacts of poor air quality are not experienced uniformly throughout the city, a result of the local impact of air pollution, as well as inequalities in access to health care and increased rates of respiratory cancer incidence and mortality within the poorest populations [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health

Connerton

Assunção

Miranda

et al. 2020

IJERPH

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The study described in this manuscript analyzed the effects of quarantine and social distancing policies implemented due to the Coronavirus Disease 2019 (COVID-19) pandemic on air pollution levels in four western megacities: São Paulo in Brazil; Paris in France; and Los Angeles and New York in the United States. The study investigated the levels of four air pollutants—Carbon monoxide (CO), Ozone (O3), Fine Particulate (PM2.5) and Nitrogen dioxide (NO2)—during the month of March 2020, compared to 2015–2019, in the urban air of these metropolitan areas, controlling for meteorological variables. Results indicated reductions in the levels of PM2.5, CO and NO2, with reductions of the latter two showing statistical significance. In contrast, tropospheric ozone levels increased, except in Los Angeles. The beneficial health effects of cleaner air might also help prevent deaths caused by the epidemic of COVID-19 in megacities by diminishing pressure on hospitals and health equipment. Future actions for the re-starting of non-essential economic activities in these cities should take into consideration the overall importance of health for the individual, as well as for societies.

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Section: Methodsmentioning

confidence: 99%

Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health

Connerton

Assunção

Miranda

et al. 2020

IJERPH

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“…These studies used two types of data in their studies, i.e., air pollution data and health data. In order to merge these datasets residential address [3], [4], [16]- [26], post/zip code [27]- [30], community/county/block/city [31]- [43], hospital/school address [26], [44]- [46] was used. As discussed in Section I, using these parameters for the association of air pollution and health datasets is inefficient as there is a concentration of AQM stations in urban regions.…”

Section: Background and Motivationmentioning

confidence: 99%

“…To cater to this problem, we propose a spatial feature engineering algorithm, which automatically finds the appropriate AQM station and associate the patient with it. Oxidative stress [26] Blood pressure [55] Breast cancer [56] Prostate cancer [56] Respiratory [30], [34] [41], [42], [ III. METHODOLOGY In this section, we explain the datasets used in this study and the spatial feature engineering algorithm in detail.…”

Section: Background and Motivationmentioning

confidence: 99%

A Spatial Feature Engineering Algorithm for Creating Air Pollution Health Datasets

Usmani¹,

Pillai²,

Hashem³

et al. 2020

Preprint

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Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you.<br><br>Abstract: Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

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“…The present study was carried out in Brazil, the largest country in South America, with a diverse urbanization level and weather conditions across cities, spanning from the tropical zone to the far end of South America. Most air pollution research conducted in Brazil has focused on assessing the daily-term effects of pollutants on mortality [15,16,17] and to a lesser extent in morbidity [18,19,20], but little is known about the health effects or air pollutants at an intermediate time term. There is a vast amount of information available so far regarding short-term and long-term exposure effects, however, the “harvesting” effect of air pollution on health effects, mainly in respiratory disease, have been described since 2000 but have been less frequently assessed in academic literature on air pollution and health effects [21].…”

Section: Introductionmentioning

confidence: 99%

Monthly-Term Associations Between Air Pollutants and Respiratory Morbidity in South Brazil 2013–2016: A Multi-City, Time-Series Analysis

Agudelo-Castañeda

Teixeira

Alves

et al. 2019

IJERPH

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Most air pollution research conducted in Brazil has focused on assessing the daily-term effects of pollutants, but little is known about the health effects of air pollutants at an intermediate time term. The objective of this study was to determine the monthly-term association between air pollution and respiratory morbidity in five cities in South Brazil. An ecological time-series study was performed using the municipality as the unit of observation in five cities in South Brazil (Gravataí, Triunfo, Esteio, Canoas, and Charqueadas) between 2013 and 2016. Data for hospital admissions was obtained from the records of the Hospital Information Service. Air pollution data, including PM10, SO2, CO, NO2, and O3 (µg/m3) were obtained from the environmental government agency in Rio Grande do Sul State. Panel multivariable Poisson regression models were adjusted for monthly counts of respiratory hospitalizations. An increase of 10 μg/m3 in the monthly average concentration of PM10 was associated with an increase of respiratory hospitalizations in all age groups, with the maximum effect on the population aged between 16 and 59 years (IRR: Incidence rate ratio 2.04 (95% CI: Confidence interval = 1.97–2.12)). For NO2 and SO2, stronger intermediate-term effects were found in children aged between 6 and 15 years, while for O3 higher effects were found in children under 1 year. This is the first multi-city study conducted in South Brazil to account for intermediate-term effects of air pollutants on respiratory health.

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Incidence and mortality for respiratory cancer and traffic-related air pollution in São Paulo, Brazil

Cited by 54 publications

References 49 publications

Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health

Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health

A Spatial Feature Engineering Algorithm for Creating Air Pollution Health Datasets

Monthly-Term Associations Between Air Pollutants and Respiratory Morbidity in South Brazil 2013–2016: A Multi-City, Time-Series Analysis

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