Short-term effects of individual exposure to PM2.5 on hospital admissions for myocardial infarction and stroke: a population-based case-crossover study in Guangzhou, China.

Some studies have investigated the effects of PM2.5 on cardiovascular diseases based on the population-average exposure data from several monitoring stations. No one has explored the short-term effect of PM2.5 on cardiovascular hospitalizations using individual-level exposure data. We assessed the short-term effects of individual exposure to PM2.5 on hospitalizations for myocardial infarction (MI) and stroke in Guangzhou, China, during 2014-2019. The population-based data on cardio-cerebrovascular events were provided by Guangzhou Center for Disease Control and Prevention. Average annual percent changes (AAPCs) were used to describe trends in the hospitalization rates of MI and stroke. The conditional logistic regression model with a time-stratified case-crossover design was applied to estimate the effects of satellite-retrieved PM2.5 with 1-km resolution as individual-level exposure. Furthermore, we performed stratified analyses by demographic characteristics and season. There were 28,346 cases of MI, 188,611, and 36,850 cases of ischemic stroke (IS) and hemorrhagic stroke (HS), respectively, with an annual average hospitalization rate of 37.2, 247, and 48.4 per 100,000 people. Over the six-year study period, significant increasing trends in the hospitalization rates were observed with AAPCs of 12.3% (95% confidence interval [CI]: 7.24%, 17.6%), 13.1% (95% CI: 9.54%, 16.7%), and 9.57% (95% CI: 6.27%, 13.0%) for MI, IS, and HS, respectively. A 10 µg/m3 increase in PM2.5 was associated with an increase of 1.15% (95% CI: 0.308%, 1.99%) in MI hospitalization and 1.29% (95% CI: 0.882%, 1.70%) in IS hospitalization. A PM2.5-associated reduction of 1.17% (95% CI: 0.298%, 2.03%) was found for HS hospitalization. The impact of PM2.5 was greater in males than in females for MI hospitalization, and greater effects were observed in the elderly (≥ 65 years) and in cold seasons for IS hospitalization. Our study added important evidence on the adverse effect of PM2.5 based on satellite-retrieved individual-level exposure data.

Individual exposure to ambient PM2.5 and hospital admissions for COPD in 110 hospitals: a case-crossover study in Guangzhou, China.

Few studies have evaluated the short-term association between hospital admissions and individual exposure to ambient particulate matter (PM2.5). Particularly, no studies focused on hospital admissions for chronic obstructive pulmonary disease (COPD) at the individual level. We assessed the short-term effects of PM2.5 on hospitalization admissions for COPD in Guangzhou, China, during 2014-2015, based on satellite-derived estimates of ambient PM2.5 concentrations at a 1-km resolution near the residential address as individual-level exposure for each patient. Around 40,002 patients with COPD admitted to 110 hospitals were included in this study. A time-stratified case-crossover design with conditional logistic regression models was applied to assess the effects of PM2.5 based on a 1-km grid data of aerosol optical depth provided by the National Aeronautics and Space Administration on hospital admissions for COPD. Further, we performed stratified analyses by individual demographic characteristics and season of hospital admission. Around 10 µg/m3 increase in individual-level PM2.5 was associated with an increase of 1.6% (95% confidence interval [CI]: 0.6%, 2.7%) in hospitalization for COPD at a lag of 0-5 days. The impact of PM2.5 on hospitalization for COPD was greater significantly in males and patients admitted in summer. Our study strengthened the evidence for the adverse effect of PM2.5 based on satellite-based individual-level exposure data.

Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities.

OBJECTIVE: Ambient particulate pollution, especially PM2.5, has adverse impacts on health and welfare. To manage and control PM2.5 pollution, it is of great importance to determine the factors that affect PM2.5 levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM2.5 in 109 Chinese cities from January 1, 2015 to December 31, 2015. METHODS: To evaluate potential risk factors associated with the spatial and temporal variations in PM2.5 levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM2.5 levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters. RESULTS: Daily concentrations of PM2.5 peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 µg/m3) in the Beijing-Tianjin-Hebei area. The city-level PM2.5 was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO2), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM2.5 was found. The city-level and daily-level of weather conditions within a city were both associated with PM2.5. CONCLUSION: PM2.5 levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM2.5 pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.