Wildfire-related PM2.5 and health economic loss of mortality in Brazil.

BACKGROUND: Wildfire imposes a high mortality burden on Brazil. However, there is a limited assessment of the health economic losses attributable to wildfire-related fine particulate matter (PM2.5). METHODS: We collected daily time-series data on all-cause, cardiovascular, and respiratory mortality from 510 immediate regions in Brazil during 2000-2016. The chemical transport model GEOS-Chem driven with Global Fire Emissions Database (GFED), in combination with ground monitored data and machine learning was used to estimate wildfire-related PM2.5 data at a resolution of 0.25°â€ˆ× 0.25°. A time-series design was applied in each immediate region to assess the association between economic losses due to mortality and wildfire-related PM2.5 and the estimates were pooled at the national level using a random-effect meta-analysis. We used a meta-regression model to explore the modification effect of GDP and its sectors (agriculture, industry, and service) on economic losses. RESULTS: During 2000-2016, a total of US$81.08 billion economic losses (US$5.07 billion per year) due to mortality were attributable to wildfire-related PM2.5 in Brazil, accounting for 0.68% of economic losses and equivalent to approximately 0.14% of Brazil's GDP. The attributable fraction (AF) of economic losses due to wildfire-related PM2.5 was positively associated with the proportion of GDP from agriculture, while negatively associated with the proportion of GDP from service. CONCLUSION: Substantial economic losses due to mortality were associated with wildfires, which could be influenced by the agriculture and services share of GDP per capita. Our estimates of the economic losses of mortality could be used to determine optimal levels of investment and resources to mitigate the adverse health impacts of wildfires.

Associations of mixture of air pollutants with estimated 10-year atherosclerotic cardiovascular disease risk modified by socio-economic status: The Henan Rural Cohort Study.

BACKGROUND: Low socio-economic status (SES) and exposure to single-air pollutant relate to increased prevalent atherosclerotic cardiovascular diseases (ASCVD), however, interactive effect between SES and exposure to single- or multiple-air pollutants on high 10-year ASCVD risk remains unclear. METHODS: A total of 31,162 individuals were derived from the Henan Rural Cohort Study. Concentrations of air pollutants (particulate matter with an aerodynamic diameter ≤ 1.0 µm (PM1), ≤2.5 µm (PM2.5) or ≤10 µm (PM10), nitrogen dioxide (NO2)) were assessed using a spatiotemporal model based on satellites data. Independent and joint associations of SES, single- and multiple- air pollutants with high 10-year ASCVD risk were evaluated using logistic regression models, quantile g-computation and structural equation models. The interactive effects of SES and exposure to single- or multiple air pollutants on high 10-year ASCVD risk were visualized by using Interaction plots. RESULTS: Exposure to single air pollutant (PM1, PM2.5, PM10 or NO2) related to increased high 10-year ASCVD risk among individuals with low education level or personal average monthly income, compared to the ones with high education level or personal average monthly income. Furthermore, similar results of exposure to mixture of air pollutants with high 10-year ASCVD risk were observed. Positive interactive effects between low SES and exposure to high single air pollutant or the mixture of air pollutants on high 10-year ASCVD risk were observed. CONCLUSION: Positive association of low SES with high 10-year ASCVD risk was amplified by exposure to high levels of single air pollutant or a mixture of air pollutants, implying that individuals with low SES may more susceptible to air pollution-related adverse health effect.

Is long-term exposure to air pollution associated with poor sleep quality in rural China?

BACKGROUND: Poor sleep quality is associated with poor quality of life and may even lead to mental illnesses. Several studies have indicated the association between exposure to air pollution and sleep quality. However, the evidence is very limited in China, especially in rural areas. METHODS: Participants in this study were obtained from the Henan Rural Cohort established during 2015-2017. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) in the baseline survey. Poor sleep quality was defined by the global score of PSQI > 5. Participants' exposures to PM2.5, PM10 (particulate matter with aerodynamic diameters ≤2.5 µm and 10 µm, respectively) and NO2 (nitrogen dioxide) during the three years before the baseline survey were estimated using a satellite-based prediction. The associations between long-term exposure to air pollutants and sleep quality were examined using both the linear regression and logistic regression models. RESULTS: The IQRs (interquartile range) of mean levels of participants' exposures to PM2.5, PM10 and NO2 were 3.3 µg/m3, 8.8 µg/m3, and 4.8 µg/m3, respectively. After adjusted for potential confounders, the global score of PSQI (and 95%CI, 95% confidence intervals) increased by 0.16 (0.04, 0.27), 0.09 (-0.01, 0.19) and 0.14 (0.03, 0.24), associated with per IQR increase in PM2.5, PM10 and NO2, respectively. The odds ratios (and 95%CI) of poor sleep quality associated with per IQR increase in PM2.5, PM10 and NO2 were 1.15 (1.03, 1.29), 1.11 (1.02, 1.21) and 1.14 (1.03, 1.25), respectively. CONCLUSIONS: Long-term exposures to PM2.5, PM10 and NO2 were associated with poor sleep quality in rural China. Improvement of air quality may help to improve sleep quality among rural population of China.

Evidence for Urban-Rural Disparity in Temperature-Mortality Relationships in Zhejiang Province, China.

BACKGROUND: Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban-rural differences in the temperature impacts on health outcomes. OBJECTIVES: We investigated whether temperature-mortality relationships vary between urban and rural counties in China. METHODS: We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban-rural differences were explored using meta-regression with county-level characteristics. RESULTS: Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban-rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 [95% confidence interval (CI): 1.32, 1.62] associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban-rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types. CONCLUSIONS: Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure-response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban-rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban-rural disparity in mortality risks. https://doi.org/10.1289/EHP3556.

Is short-term exposure to ambient fine particles associated with measles incidence in China? A multi-city study.

BACKGROUND: China's rapid economic development has resulted in severe particulate matter (PM) air pollution and the control and prevention of infectious disease is an ongoing priority. This study examined the relationships between short-term exposure to ambient particles with aerodynamic diameter ≤2.5µm (PM2.5) and measles incidence in China. METHODS: Data on daily numbers of new measles cases and concentrations of ambient PM2.5 were collected from 21 cities in China during Oct 2013 and Dec 2014. Poisson regression was used to examine city-specific associations of PM2.5 and measles, with a constrained distributed lag model, after adjusting for seasonality, day of the week, and weather conditions. Then, the effects at the national scale were pooled with a random-effect meta-analysis. RESULTS: A 10µg/m3 increase in PM2.5 at lag 1day, lag 2day and lag 3day was significantly associated with increased measles incidence [relative risk (RR) and 95% confidence interval (CI) were 1.010 (1.003, 1.018), 1.010 (1.003, 1.016) and 1.006 (1.000, 1.012), respectively]. The cumulative relative risk of measles associated with PM2.5 at lag 1-3 days was 1.029 (95% CI: 1.010, 1.048). Stratified analyses by meteorological factors showed that the PM2.5 and measles associations were stronger on days with high temperature, low humidity, and high wind speed. CONCLUSIONS: We provide new evidence that measles incidence is associated with exposure to ambient PM2.5 in China. Effective policies to reduce air pollution may also reduce measles incidence.