Air Pollution and SARS
Cui et al. (2003
) conducted an ecological study to determine the association between air pollution and case–fatality rates from SARS (reported deaths/probable cases) in five regions in China with 100 or more cases. Deaths and incident cases were extracted from a publicly available source (Chinese Center for Disease Control and Prevention). The maximum Air Pollution Index (API; Chinese National Environmental Protection Agency), which combined concentrations of inhalable particles with aerodynamic diameter of
, CO, and ozone for each of the five areas, was used. A summary measure of the API was derived for the two time periods April–May 2003 and June 2000–October 2002. The former period was considered representative of “short-term” exposures and corresponded to the time when the majority of cases were diagnosed. In contrast, the latter period was used to represent “longer-term,” average exposure. A total of 349 deaths were reported among 5,327 probable SARS cases in the five study regions. Ordinary linear regression was used to estimate the slope of the linear relationship between the case–fatality percentages for SARS and the API across the five cities, with a slope of 0.001 (no measure of precision was provided) per unit increase in the API. Categorizing the API into three categories (
) yielded for the two highest categories (relative to the lowest) a rate ratio of 2.18 [95% confidence interval (CI): 1.31, 3.65] and 1.84 (95% CI: 1.41, 2.40), respectively, for API during April–May 2003, whereas the corresponding ratios for average API during June 2000–October 2002 were 1.71 (95% CI: 1.34, 3.33) and 2.26 (95% CI: 1.53, 3.35). The authors acknowledged that they did not account for potential confounding factors such as age, sex, sociodemographic status, or regional differences in the quality of care.
Kan et al. (2005
) employed a time series analysis, using generalized additive models (GAMs; Hastie and Tibshirani 1993
), to determine whether daily fluctuations in ambient concentrations of
in Beijing were associated with daily mortality from SARS from 25 April to 31 May 2003 (37 d). The authors indicated that they adjusted for trends for day of observation using splines as well as including a term for day-of-the-week. The authors reported an average of 3.8 SARS deaths per day over a span of 37 d (141 deaths, total). Daily mean ambient pollution concentrations (averaged over 12 fixed-site monitoring stations) were:
. The authors estimated associations with a variety of exposures lagged from 0 to 5 d using log-linear models adjusted for day of the week, daily temperature, dew point, and relative humidity. Relative increases of SARS mortality counts with a
increase in the 5 d moving average of
were 1.06 (95% CI: 1.00, 1.12), 0.74 (95% CI: 0.48, 1.13), and 1.22 (95% CI: 1.01, 1.48), respectively.
A novel cohort design was used to investigate secondary attack rates among individuals who were in contact with 350 probable index cases diagnosed in China between 1 January and 31 May 2003 (Cai et al. 2007
). This study identified health outcomes by using individual-level data and relied on area-wide measures of weather and air pollution. The study first identified 365 probable SARS cases in mainland China. Close contacts were identified using individual-level survey databases of these cases and other contacts of these cases, SARS transmission chains in affected areas, and hospital records. Telephone interviews were used to confirm histories of the close contacts. This process resulted in the identification of 6,727 close contacts for the time periods when the corresponding probable case exhibited symptoms but had not yet been admitted to hospital. Of these close contacts, 135 (2%) were later diagnosed with probable SARS by 31 May 2003. For the primary cases, daily average values for weather and the API for the period between the onset of symptoms and hospital admission were modeled. Logistic regression was used to estimate the associations between the frequency of secondary attacks and these weather and air pollution measures. The API was based on individual maximal pollution index—this was typically particulate matter. The authors considered several daily average weather variables, including temperature, relative humidity, air pressure, wind velocity, and hours of sunshine. The models incorporated both the weather and air pollutant covariables and a binary variable to denote whether the onset date for the primary case was before or after 21 April 2003. This date corresponded to when major intervention measures to control the epidemic were enacted. The multivariable models included terms for the weather variables (described above) and the API. For a unit increase in the API (
, range from 34.3 to 260.3), the unadjusted odds ratio (OR) was 0.99 (95% CI: 0.85, 1.17), and the adjusted OR was 0.88 (95% CI: 0.76, 1.02). Stronger associations were observed with weather variables including daily average temperature, air pressure and relative humidity.
Air Pollution and COVID-19
An ecological study was undertaken with the objective to determine whether average exposure to
was associated with mortality (Ogen 2020
). The study included 4,443 deaths attributed to COVID-19 as of 19 March 2020 in 66 administrative regions in Europe (Italy, Spain, France, and Germany) in relation to tropospheric concentrations of
derived from the Sentinel-5 Precursor satellite (spatial resolution of 5.5 km). These concentrations were averaged over a 2-month period (January–February 2020) before the COVID-19 outbreak in Europe. Ogen showed scatterplots of counts of death against concentrations of
for these 66 data points, and these plots showed an increase in the number of deaths with increasing concentrations of
, but no quantitative measure of association was provided.
In a nonpeer reviewed paper, Travaglio et al. (2020a
) estimated for seven regions of England the correlation between presumed mortality from COVID-19, until April 8, 2020. Their aim was to investigate associations between annual ambient concentrations of air pollution in 2018 and rates of infection and mortality from COVID-19. Data for the daily number of infections for each region were obtained from Public Health England. Similarly, the number of deaths were extracted from national health data and included the number of deaths of patients who died in hospitals who tested positive for COVID-19. The analyses excluded deaths that did not occur in hospitals. Exposure comprised annual average concentrations of
, NO, and ozone, and the English AQI measured at 120 fixed-site monitoring stations during the period 2018–2019. The analysis comprised estimating Spearman’s and Pearson’s correlation coefficients between pollutants and mortality from COVID-19, and these statistics ranged from 0.32 to 0.67. Plots of average concentrations of air pollution against the total number of COVID-19 deaths across the regions were also presented.
A preprint by Yao et al. (2020
) presented a cross-sectional analysis of ecological data to determine whether city-specific measures of
were associated with death rates of COVID-19. They estimated spatial correlations in 49 Chinese cities between case–fatality rates of COVID-19 and concentrations of
on the day of death (time period not specified). Sixteen of these cities were inside the province of Hubei, including Wuhan, the apparent origin of the pandemic, and the remaining 33 cities that were outside of Hubei. They also obtained per capita gross domestic product (GDP), number of hospital beds, and population size for each province, and it appears that values of these were assigned to each city. The results for
were presented as scatterplots, and two ordinary linear regression lines were shown for cities inside and outside Hubei. For
, the correlation coefficient with case-fatality rates of COVID-19, adjusted for temperature, relative humidity, GDP per capita, and hospital beds per capita; for cities outside Hubei was 0.56 and for cities inside of Hubei, excluding Wuhan, it was 0.33.
In another posted study by the World Bank Group, Andrée conducted an ecological analysis of incident COVID-19 cases against annual average concentrations of
across 355 municipalities in the Netherlands (Andrée 2020
). The analyses comprised 4,004 confirmed cases of COVID-19 until 22 March 2020 for which residential addresses were available. For the main analyses, annual spatially interpolated measurements of
for 2017 at a
grid were derived from fixed-site monitors. Andrée (2020
) also modeled a remote sensing measure of
derived using data between 1998 and 2014 at a
resolution. Adjustment was made for a number of area-wide variables (Table 1
). Multiple linear regression using a gaussian error term was used to model COVID-19 cases (per 100,000) and, depending on the covariables included, an increase of
increased the number of cases by between 3.5 and 10.2 cases per 100,000.
A published time series study by Zhu et al. (2020
) made use of daily confirmed cases of COVID-19 in 120 cities in China after the lockdown started (observation period of 23 January to 29 February 2020). The objective of their study was to estimate associations between 1-, 2-, and 3-wk measurements of ambient pollution and confirmed incident cases of COVID-19. Using another GAM framework (Wood 2006
), the authors regressed logarithmically transformed daily counts of cases (average of 12 deaths per day) against daily mean concentrations that were used to create
metrics for 0–7, 0–14, and 0–21 d before death. They also adjusted for mean temperature, relative humidity, air pressure, and wind speed. Count data usually require Poisson or quasi-likelihood models, and often distributed lag nonlinear models are used (Gasparrini et al. 2012
), but the authors used a gaussian error term instead. All of the covariables were modeled as thin plate splines [maximum of 3 degrees of freedom (df)]. Usually a filter is applied to remove any long-term trends in the outcome as well as including day-of-the-week effects (Goldberg et al. 2003
), but in this study only a categorical variable was included for day of study and a first order autoregressive term. Their models also included a fixed-effect term to capture variability by city. The pollutants were modeled as linear terms. The main results across the 120 cities were as follows: for a mean
increase across lags of 0–14 d, the percent change in the number of counts were:
, 2.24% (95% CI: 1.02, 3.46);
, 1.76% (95% CI: 0.89, 2.63);
, 6.94% (95% CI: 2.38, 11.51);
, 4.76% (95% CI: 1.99, 7.52); and
The research that appears to have generated the most attention in the media during the COVID-19 pandemic is an unpublished ecological study that considered as the unit of observation data from a total of 3,080 counties in the United States (Wu et al. 2020
). We found two versions of this manuscript online that used slightly different methods (Table 1
), and in our view, it is important to provide details on both versions, given that the original analyses generated considerable media attention, and the latter version provided a lower risk estimate. The aim of the study was to investigate whether chronic exposure to ambient pollution, over 17 y, was associated with increased risk of COVID-19 mortality. Deaths from COVID-19 were obtained from the Johns Hopkins University Center for Systems Science and Engineering Coronavirus Resource (Xu and Kraemer 2020
). The first report comprised mortality data up to 4 April 2020, and the second incorporated additional data until 22 April 2020. Reported counts of deaths from COVID-19 and total estimates of the population for each county were used. The summary county data provided only aggregated COVID-19 death data and therefore did not allow for these deaths to be tabulated by age group, sex, race, or other sociodemographic characteristics. In the first report, concentrations of
were derived for the period from 2000 to 2016 using an exposure prediction model that conjoins satellite, modeled, and monitored and has a resolution of about
(van Donkelaar et al. 2019
). These values were combined to represent county-level averages. County-specific rates of mortality attributed to COVID-19 were regressed against average county-specific concentrations of
using a zero-inflated Poisson model with a random effect for state. Adjustments included 16 county-level variables and the number of COVID-19 tests performed in each state (Table 1
). In the first version of the paper, a number of counties were excluded because they lacked covariable data or had a small number of identified COVID-19 deaths. Specifically, the main analyses derived risk estimates using 1,783 counties, which represented 90% of all COVID-19 deaths identified in the United States as of 4 April 2020. The authors reported that for an increase of
, the rate ratio for COVID-19 mortality was 1.15 (95% CI: 1.05, 1.25).
An updated version of this paper was posted online on 27 April 2020. These analyses differed in several ways from the earlier version. First, the more recent preprint incorporated additional deaths that occurred up to 22 April 2020. A number of new county-level risk factors were included: days since the first COVID-19 case and days since the issuance of stay-at-home orders. Some minor changes to other county-level factors were made, which included capturing the percentage of the population between the ages of 45–64 and 15–44 and the percent who were obese [from mean body mass index (BMI) used in original analysis]. The authors used a negative binomial mixed model instead of a zero-inflated one. There were 3,087 counties from which data were drawn from. The updated rate ratio for COVID-19 mortality in relation to increase of was 1.08 (95% CI: 1.02, 1.15). The authors also pursued a large number of sensitivity analyses and estimated the minimum magnitude of the association between an unmeasured confounding variable and the outcome and exposure that could entirely explain the observed association (-value). The -value was estimated to be 1.37, and the authors used this value to suggest that it was unlikely that the findings could be explained by unmeasured confounding.