scholarly journals Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China

2020 ◽  
Vol 17 (15) ◽  
pp. 5453
Author(s):  
Xuyan Wang ◽  
Chuanhua Yu ◽  
Yunquan Zhang ◽  
Fang Shi ◽  
Runtang Meng ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Hospital Admissions ◽  
Age Groups ◽  
Additive Model ◽  
Economic Cost ◽  
Attributable Risk ◽  
Ambient Particulate Matter ◽  
Particulate Pollution ◽  
Hospitalization Costs ◽  
Pm2.5 And Pm10

Although the adverse effects of ambient particulate matter (PM) on cardiovascular disease (CVD) have been previously documented, information about their economic consequence was insufficient. This study aimed to evaluate the attributable risk and economic cost of cardiovascular hospitalizations due to ambient PM. Data of CVD hospitalizations and PM concentrations from 1 January 2015 to 31 December 2017 were collected in Wuhan, China. A generalized additive model was applied to quantify the PM-attributable CVD hospitalizations, and total attributable hospitalization costs were calculated via multiplying the total attributable cases by the case-average hospitalization costs. A total of 45,714 CVD hospitalizations were included in this study. The results showed that a 10 µg/m3 increase in PM2.5 and PM10 concentrations at lag7 day, respectively, contributed to a 1.01% (95% confidence interval: 0.67–1.34) and 0.48% (0.26–0.70) increase in CVD hospitalizations. During the study period, 1487 and 983 CVD hospitalizations were attributable to PM2.5 and PM10, equaling an economic cost of 29.27 and 19.34 million RMB (1 RMB = 0.1424 USD), respectively, and significant differences in PM-attributable hospitalizations and economic burden were found between gender and age groups. Our study added evidence in heavily polluted megacities regarding the increased health risk and economic cost of CVD hospitalizations associated with ambient particulate pollution.

scholarly journals Ambient PM2.5 and PM10 Exposure and Respiratory Disease Hospitalization in Kandy, Sri Lanka

2021 ◽  
Vol 18 (18) ◽  
pp. 9617
Author(s):  
Sajith Priyankara ◽  
Mahesh Senarathna ◽  
Rohan Jayaratne ◽  
Lidia Morawska ◽  
Sachith Abeysundara ◽  
...  
Keyword(s):  
Air Pollution ◽  
Sri Lanka ◽  
Respiratory Disease ◽  
Hospital Admissions ◽  
Additive Model ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Increased Risk ◽  
Pm2.5 And Pm10 ◽  
Pm10 Exposure

Evidence of associations between exposure to ambient air pollution and health outcomes are sparse in the South Asian region due to limited air pollution exposure and quality health data. This study investigated the potential impacts of ambient particulate matter (PM) on respiratory disease hospitalization in Kandy, Sri Lanka for the year 2019. The Generalized Additive Model (GAM) was applied to estimate the short-term effect of ambient PM on respiratory disease hospitalization. As the second analysis, respiratory disease hospitalizations during two distinct air pollution periods were analyzed. Each 10 μg/m3 increase in same-day exposure to PM2.5 and PM10 was associated with an increased risk of respiratory disease hospitalization by 1.95% (0.25, 3.67) and 1.63% (0.16, 3.12), respectively. The effect of PM2.5 or PM10 on asthma hospitalizations were 4.67% (1.23, 8.23) and 4.04% (1.06, 7.11), respectively (p < 0.05). The 65+ years age group had a higher risk associated with PM2.5 and PM10 exposure and hospital admissions for all respiratory diseases on the same day (2.74% and 2.28%, respectively). Compared to the lower ambient air pollution period, higher increased hospital admissions were observed among those aged above 65 years, males, and COPD and pneumonia hospital admissions during the high ambient air pollution period. Active efforts are crucial to improve ambient air quality in this region to reduce the health effects.

scholarly journals Association between COVID-19, mobility and environment in São Paulo, Brazil

2021 ◽  
Author(s):  
Sergio Ibarra-Espinosa ◽  
Edmilson Dias de Freitas ◽  
Karl Ropkins ◽  
Francesca Dominici ◽  
Amanda Rehbein
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Relative Humidity ◽  
Residential Mobility ◽  
Additive Model ◽  
Sao Paulo ◽  
São Paulo ◽  
Ambient Particulate Matter ◽  
Mobility Index ◽  
Small Increments

AbstractBackgroundBrazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to control quarantines and prevent the transmissions of SARS-CoV2.In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, BrazilObjectivesTo estimate the associations between daily residential mobility index (RMI), air pollution, and meteorology, and daily cases and deaths for COVID-19 in São Paulo, Brazil.MethodsWe applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between residential mobility index and cases and deaths due to COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 3-21 days and 2) the association between exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility.ResultsWe found an RMI of 45.28% results in 1,212 cases (95% CI: 1,189 to 1,235) and 44 deaths (95% CI: 40 to 47). Reducing mobility 5% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 μg·m-3 of PM2.5 risk of 1.140 (95% CI: 1.021 to 1.274) for cases and of 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively.DiscussionWe compared our results with observations and literature review, finding well agreement. These results implicate that authorities and policymakers can use such mobility indices as tools to support social distance activities and assess their effectiveness in the coming weeks and months. Small increments of air pollution pose a risk of COVID-19 cases.ConclusionSpatial distancing is a determinant factor to control cases and deaths for COVID-19. Small increments of air pollution result in a high number of COVID-19 cases and deaths. PM2.5 has higher relative risks for COVID-19 than O3.

scholarly journals Temporal variation of ambient particulate matter in Chattogram City, Bangladesh

2020 ◽  
Author(s):  
Ahmad Kamruzzaman Majumder ◽  
Abdullah Al Nayeem ◽  
Md Nasir Ahmmed Patoary ◽  
William S. Carter
Keyword(s):  
Particulate Matter ◽  
Meteorological Parameters ◽  
Clean Air Act ◽  
Quality Standard ◽  
Dhaka City ◽  
Inverse Relation ◽  
Ambient Particulate Matter ◽  
Sustainable Environment ◽  
Clean Air ◽  
Pm2.5 And Pm10

Introduction: Chattogram is known as the Bangladesh’s commercial capital with its diversified industrial areas and seaport. This study aimed to assess the Particulate Matter (PM2.5 and PM10) in relation to meteorological characteris- tics in Chattogram city from 2013-2018. Materials and methods: Monthly PM2.5 and PM10 data were collected from the Continuous Air Monitoring Station (CAMS) in Chattogram City (Agrabad Point) which is operated by the Department of Environment (DoE) of Ban- gladesh under the Clean Air and Sustainable Environment (CASE) project. Results: This Study found the higher concentration of both PM2.5 and PM10 occurred from December to February and it decreases from July-September and begins to increase from the month of October. The PM values seasonally varied being higher during the winter seasons and decreased in rainy seasons. The PM2.5 mass was detected 50% of that of PM10 which is mostly from bio- mass burn and vehicles activities. Meteorological parameters such as rainfall and humidity had strong inverse relation with both PM2.5 and PM10 over the years. Conclusion: The Study found the average annual concentration of PM2.5 was 5-6 times higher and PM10 was 3 times higher than Bangladesh National Am- bient Air Quality Standard (BNAAQS) in Chattogram city over this six year period. It can be concluded that the air pollution in Dhaka city is deteriorating rapidly and it is high time to implement the clean air act urgently to reduce such destruction.

Attributable risk and economic cost of hospital admissions for mental disorders due to PM2.5 in Beijing

2020 ◽  
Vol 718 ◽  
pp. 137274 ◽  
Author(s):  
Ziting Wu ◽  
Xi Chen ◽  
Guoxing Li ◽  
Lin Tian ◽  
Zhan Wang ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Hospital Admissions ◽  
Economic Cost ◽  
Attributable Risk

scholarly journals Reliability Validation of a Low-Cost Particulate Matter IoT Sensor in Indoor and Outdoor Environments Using a Reference Sampler

2019 ◽  
Vol 11 (24) ◽  
pp. 7220 ◽  
Author(s):  
Sergio Trilles ◽  
Ana Belen Vicente ◽  
Pablo Juan ◽  
Francisco Ramos ◽  
Sergi Meseguer ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Linear Model ◽  
Low Cost ◽  
Additive Model ◽  
Quality Data ◽  
Outdoor Environments ◽  
Pm2.5 And Pm10 ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

A suitable and quick determination of air quality allows the population to be alerted with respect to high concentrations of pollutants. Recent advances in computer science have led to the development of a high number of low-cost sensors, improving the spatial and temporal resolution of air quality data while increasing the effectiveness of risk assessment. The main objective of this work is to perform a validation of a particulate matter (PM) sensor (HM-3301) in indoor and outdoor environments to study PM2.5 and PM10 concentrations. To date, this sensor has not been evaluated in real-world situations, and its data quality has not been documented. Here, the HM-3301 sensor is integrated into an Internet of things (IoT) platform to establish a permanent Internet connection. The validation is carried out using a reference sampler (LVS3 of Derenda) according to EN12341:2014. It is focused on statistical insight, and environmental conditions are not considered in this study. The ordinary Linear Model, the Generalized Linear Model, Locally Estimated Scatterplot Smoothing, and the Generalized Additive Model have been proposed to compare and contrast the outcomes. The low-cost sensor is highly correlated with the reference measure ( R 2 greater than 0.70), especially for PM2.5, with a very high accuracy value. In addition, there is a positive relationship between the two measurements, which can be appropriately fitted through the Locally Estimated Scatterplot Smoothing model.

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