Mortality burden due to ambient nitrogen dioxide pollution in China: Application of high-resolution models.

BACKGROUND: A large gap exists between the latest Global Air Quality Guidelines (AQG 2021) and Chinese air quality standards for NO2. Assessing whether and to what extent air quality standards for NO2 should be tightened in China requires a comprehensive understanding of the spatiotemporal characteristics of population exposure to ambient NO2 and related health risks, which have not been studied to date. OBJECTIVE: We predicted ground NO2 concentrations with high resolution in mainland China, explored exposure characteristics to NO2 pollution, and assessed the mortality burden attributable to NO2 exposure. METHODS: Daily NO2 concentrations in 2019 were predicted at 1-km spatial resolution in mainland China using random forest models incorporating multiple predictors. From these high-resolution predictions, we explored the spatiotemporal distribution of NO2, population and area percentages with NO2 exposure exceeding criterion levels, and premature deaths attributable to long- and short-term NO2 exposure in China. RESULTS: The cross-validation R2and root mean squared error of the NO2 predicting model were 0.80 and 7.78 µg/m3, respectively,at the daily level in 2019.The percentage of people (population number) with annual NO2 exposure over 40 µg/m3 in mainland China in 2019 was 10.40 % (145,605,200), and it reached 99.68 % (1,395,569,840) with the AQG guideline value of 10 µg/m3. NO2 levels and population exposure risk were elevated in urban areas than in rural. Long- and short-term exposures to NO2 were associated with 285,036 and 121,263 non-accidental deaths, respectively, in China in 2019. Tightening standards in steps gradually would increase the potential health benefit. CONCLUSION: In China, NO2 pollution is associated with significant mortality burden. Spatial disparities exist in NO2 pollution and exposure risks. China's current air quality standards may no longer objectively reflect the severity of NO2 pollution and exposure risk. Tightening the national standards for NO2 is needed and will lead to significant health benefits.

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study.

BACKGROUND: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5°â€ˆ× 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5°â€ˆ× 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6). INTERPRETATION: Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING: Australian Research Council, Australian National Health & Medical Research Council.

Progression of severity in coronavirus disease 2019 patients before treatment and a self-assessment scale to predict disease severity.

OBJECTIVES: This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings. SETTING AND PARTICIPANTS: Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China. PRIMARY AND SECONDARY OUTCOME MEASURES: We categorized COVID-19 disease severity, based on The Chinese Diagnosis and Treatment Protocol for COVID-19, as "nonsevere" (which grouped asymptomatic, mild, and ordinary disease) versus "severe" (grouping severe and critical illness). RESULTS: Twelve scale items-age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage-were identified and showed good predictive ability (area under the curve = 0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (= 0·71). CONCLUSIONS: Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Our study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance.

City-level greenness exposure is associated with COVID-19 incidence in China.

Accumulating studies have suggested an important role of environmental factors (e.g. air pollutants) on the occurrence and development of coronavirus disease 2019 (COVID-19). Evidence concerning the relationship of greenness on COVID-19 is still limited. This study aimed to assess the association between greenness and COVID-19 incidence in 266 Chinese cities. A total of 12,377 confirmed COVID-19 cases were identified through February 29th, 2020. We used the average normalized difference vegetation index (NDVI) during January and February 2020 from MOD13A2 product, to represent the city-level greenness exposure. A generalized linear mixed-effects model was used to estimate the association between NDVI exposure and COVID-19 incidence using COVID-19 cases as the outcome. We evaluated whether the association was modified by population density, GDP per capita, and urbanization rate, and was mediated by air pollutants. We also performed a series of sensitivity analyses to discuss the robustness of our results. Per 0.1 unit increment in NDVI was negatively associated with COVID-19 incidence (IRR: 0.921, 95% CI: 0.898, 0.944) after adjustment for confounders. Associations with COVID-19 incidence were stronger in cities with lower population density, lower GDP per capita, and lower urbanization rate. We failed to detect any mediation effect of air pollutants on the association between NDVI and COVID-19 incidence. Sensitivity analyses also indicated consistent estimates. In conclusion, our study suggested a beneficial association between city-level greenness and COVID-19 incidence. We could not establish which mechanisms may explain this relationship.

Residential greenness is associated with disease severity among COVID-19 patients aged over 45 years in Wuhan, China.

Evidence regarding environmental factors associated with disease severity of COVID-19 remained scarce. This study aimed to investigate the association of residential greenness exposure with COVID-19 severity applying a retrospective cross-sectional study in Wuhan, China. We included 30,253 COVID-19 cases aged over 45 years from January 1 to February 27, 2020. Residential greenness was quantitatively assessed using normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). A multilevel generalized linear model using Poisson regression was implemented to analyze the association between greenness exposure and disease severity of COVID-19, after adjusting for potential covariates. A linear exposure-response relationship was found between greenness and COVID-19 severity. In the adjusted model, one 0.1 unit increase of NDVI and EVI in the 1000-m buffer radius was significantly associated with a 7.6% (95% confidence interval (CI): 4.0%, 11.1%) and 10.0% (95% CI: 5.1%, 14.7%) reduction of the prevalence of COVID-19 severity, respectively. The effect of residential greenness seemed to be more pronounced among participants with lower population density and economic levels. Air pollutants mediated 0.82~12.08% of the greenness and COVID-19 severity association, particularly to nitrogen dioxide. Sensitivity analyses suggested the robustness of the results. Our findings suggested that residential greenness exposure was beneficial to reduce the prevalence of COVID-19 severity.

Fine particulate matter air pollution and under-5 children mortality in China: A national time-stratified case-crossover study.

BACKGROUND: Under-5 mortality rate is an important indicator in Millennium Development Goals and Sustainable Development Goals. To date, no nationally representative studies have examined the effects of fine particulate matter (PM2.5) air pollution on under-5 mortality. OBJECTIVE: To investigate the association of short-term exposure to PM2.5 with under-5 mortality from total and specific causes in China. METHODS: We used the national Maternal and Child Health Surveillance System to identify under-5 mortality cases during the study period of 2009 to 2019. We adopted a time-stratified case-crossover study design at the individual level to capture the effect of short-term exposure to daily PM2.5 on under-5 mortality, using conditional logistic regression models. RESULTS: A total of 61,464 under-5 mortality cases were included. A 10 µg/m3 increase in concentrations of PM2.5 on lag 0-1 d was significantly associated with a 1.15% (95%confidence interval: 0.65%, 1.65%) increase in under-5 mortality. Mortality from diarrhea, pneumonia, digestive diseases, and preterm birth were significantly associated with exposure to PM2.5. The effect estimates were larger for neonatal mortality (<28 days), female children, and in warm seasons. We observed steeper slopes in lower ranges (<50 µg/m3) of the concentration-response curve between PM2.5 and under-5 mortality, and positive associations remained below the 24-h PM2.5 concentration limit recommended by WHO Air Quality Guidelines and China Air Quality Standards. CONCLUSIONS: This nationwide case-crossover study in China demonstrated that acute exposure to PM2.5 may significantly increase the risk of under-5 mortality, with larger effects for neonates, female children, and during warm seasons. Relevant control strategies are needed to remove this roadblock to achieving under-5 mortality targets in developing countries.

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China.

Lockdown measures are essential to containing the spread of coronavirus disease 2019 (COVID-19), but they will slow down economic growth by reducing industrial and commercial activities. However, the benefits of activity control from containing the pandemic have not been examined and assessed. Here we use daily carbon dioxide (CO2) emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide (NO2) measured by the Ozone Monitoring Instrument (OMI) as an indicator for reduced activities consecutive to a lockdown. We perform a correlation analysis to show that a 1% day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO2 emissions of 0.22% ± 0.02% using statistical data for energy consumption relative to emissions without COVID-19, or 0.20% ± 0.02% using satellite data for atmospheric column NO2. We estimate that swift action in China is effective in limiting the number of COVID-19 cases <100,000 with a reduction in CO2 emissions of up to 23% by the end of February 2020, whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same goal. By analyzing the costs of health care and fatalities, we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic product. Our findings suggest an unprecedentedly high cost of maintaining activities and CO2 emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

Ambient nitrogen dioxide pollution and spreadability of COVID-19 in Chinese cities.

This study aims to explore the relationship between ambient NO2 levels and the transmission ability (basic reproductive number, R0) of COVID-19 in 63 Chinese cities. After adjustment for temperature and relative humidity, R0 was positively associated with NO2 concentration at city level. The temporal analysis within Hubei province indicated that all the 11 Hubei cities (except Xianning City) had significant positive correlations between NO2 concentration (with 12-day time lag) and R0 (r > 0.51, p < 0.005). Since the association between ambient NO2 and R0 indicated NO2 may increase underlying risk of infection in the transmission process of COVID-19. In addition, NO2 is also an indicator of traffic-related air pollution, the association between NO2 and COVID-19's spreadability suggest that reduced population movement may have reduced the spread of the SARS-CoV-2.

Warmer weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 countries.

PURPOSE: To examine the association between meteorological factors (temperature, relative humidity, wind speed, and UV radiation) and transmission capacity of COVID-19. METHODS: We collected daily numbers of COVID-19 cases in 202 locations in 8 countries. We matched meteorological data from the NOAA National Centers for Environmental Information. We used a time-frequency approach to examine the possible association between meteorological conditions and basic reproductive number (R0) of COVID-19. We determined the correlations between meteorological factors and R0 of COVID-19 using multiple linear regression models and meta-analysis. We further validated our results using a susceptible-exposed-infectious-recovered (SEIR) metapopulation model to simulate the changes of daily cases of COVID-19 in China under different temperatures and relative humidity conditions. PRINCIPAL RESULTS: Temperature did not exhibit significant association with R0 of COVID-19 (meta p = 0.446). Also, relative humidity (meta p = 0.215), wind speed (meta p = 0.986), and ultraviolet (UV) radiation (meta p = 0.491) were not significantly associated with R0 either. The SEIR model in China showed that with a wide range of meteorological conditions, the number of COVID-19 confirmed cases would not change substantially. CONCLUSIONS: Meteorological conditions did not have statistically significant associations with the R0 of COVID-19. Warmer weather alone seems unlikely to reduce the COVID-19 transmission.

Temporal association between particulate matter pollution and case fatality rate of COVID-19 in Wuhan.

The coronavirus (COVID-19) epidemic reported for the first time in Wuhan, China at the end of 2019, which has caused 4648 deaths in China as of July 10, 2020. This study explored the temporal correlation between the case fatality rate (CFR) of COVID-19 and particulate matter (PM) in Wuhan. We conducted a time series analysis to examine the temporal day-by-day associations. We observed a higher CFR of COVID-19 with increasing concentrations of inhalable particulate matter (PM) with an aerodynamic diameter of 10 µm or less (PM10) and fine PM with an aerodynamic diameter of 2.5 µm or less (PM2.5) in the temporal scale. This association may affect patients with mild to severe disease progression and affect their prognosis.

Association of particulate matter pollution and case fatality rate of COVID-19 in 49 Chinese cities.

The COVID-19 epidemic, caused by the SARS-CoV-2 virus, has resulted in 3352 deaths in China as of April 12, 2020. This study aimed to investigate the associations between particulate matter (PM) concentrations and the case fatality rate (CFR) of COVID-19 in 49 Chinese cities, including the epicenter of Wuhan. We used the Global Moran's I to analyze spatial distribution and autocorrelation of CFRs, and then we used multivariate linear regression to analyze the associations between PM2.5 and PM10 concentrations and COVID-19 CFR. We found positive associations between PM pollution and COVID-19 CFR in cities both inside and outside Hubei Province. For every 10 µg/m3 increase in PM2.5 and PM10 concentrations, the COVID-19 CFR increased by 0.24% (0.01%-0.48%) and 0.26% (0.00%-0.51%), respectively. PM pollution distribution and its association with COVID-19 CFR suggests that exposure to such may affect COVID-19 prognosis.

Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals.

The ongoing outbreak of coronavirus disease 2019 (COVID-19) has spread rapidly on a global scale. Although it is clear that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted through human respiratory droplets and direct contact, the potential for aerosol transmission is poorly understood1-3. Here we investigated the aerodynamic nature of SARS-CoV-2 by measuring viral RNA in aerosols in different areas of two Wuhan hospitals during the outbreak of COVID-19 in February and March 2020. The concentration of SARS-CoV-2 RNA in aerosols that was detected in isolation wards and ventilated patient rooms was very low, but it was higher in the toilet areas used by the patients. Levels of airborne SARS-CoV-2 RNA in the most public areas was undetectable, except in two areas that were prone to crowding; this increase was possibly due to individuals infected with SARS-CoV-2 in the crowd. We found that some medical staff areas initially had high concentrations of viral RNA with aerosol size distributions that showed peaks in the submicrometre and/or supermicrometre regions; however, these levels were reduced to undetectable levels after implementation of rigorous sanitization procedures. Although we have not established the infectivity of the virus detected in these hospital areas, we propose that SARS-CoV-2 may have the potential to be transmitted through aerosols. Our results indicate that room ventilation, open space, sanitization of protective apparel, and proper use and disinfection of toilet areas can effectively limit the concentration of SARS-CoV-2 RNA in aerosols. Future work should explore the infectivity of aerosolized virus.