Chronic exposure to ambient air pollution has been related to increased mortality in the general population [1]. After the outbreak of the SARS-CoV-2 pandemic in 2019, there has been a fast proliferation of epidemiological studies linking ambient air pollution to coronavirus disease 2019 (COVID-19) incidence or adverse prognosis [2]. It has been hypothesised that ambient air pollution might increase human vulnerability to viruses by reducing immune defences, promoting a low-level chronic inflammatory state, or leading to chronic diseases [3]. Most studies have applied ecological designs, and failed to account for key individual-level or area-level determinants of COVID-19 spread or severity, such as demographic characteristics of the studied populations, socioeconomic or clinical susceptibility, and area-level proxies of disease spread such as mobility or population density [4].
Short abstract
Long-term exposure to air pollution (PM2.5 and NO2) was associated with COVID-19 mortality, but not with SARS-CoV-2 incidence, in a large observational population-based cohort of >1.5 million subjects in Rome, Italy https://bit.ly/3zZjjSC
To the Editor:
Chronic exposure to ambient air pollution has been related to increased mortality in the general population [1]. After the outbreak of the SARS-CoV-2 pandemic in 2019, there has been a fast proliferation of epidemiological studies linking ambient air pollution to coronavirus disease 2019 (COVID-19) incidence or adverse prognosis [2]. It has been hypothesised that ambient air pollution might increase human vulnerability to viruses by reducing immune defences, promoting a low-level chronic inflammatory state, or leading to chronic diseases [3]. Most studies have applied ecological designs, and failed to account for key individual-level or area-level determinants of COVID-19 spread or severity, such as demographic characteristics of the studied populations, socioeconomic or clinical susceptibility, and area-level proxies of disease spread such as mobility or population density [4].
In this study we aimed to investigate the association between chronic exposure to air pollution and SARS-CoV-2 incidence and COVID-19 mortality, independent from age, sex, individual-level and area-level socioeconomic deprivation, clinical history and neighbourhood characteristics.
All subjects aged ≥30 years resident in Rome, Italy, at 1 January 2020 were followed-up until 15 April 2021 through record linkage between the different administrative archives of the Lazio Region Health Information System: population and mortality registries, 2011 Census data, and COVID-19 surveillance system. The COVID-19 integrated surveillance system collects all the new confirmed SARS-CoV-2 infections reported to the Regional Service for Surveillance of Infectious Diseases throughout the Lazio Region. Each subject entered at baseline and was observed until death, emigration out of the study area or end of follow-up, whichever came first. For each subject, baseline information was available on demographic characteristics (age, sex, marital status, place of birth), socioeconomic indicators (education level, occupational status, census block-level deprivation), clinical history (a list of 67 prevalent conditions based on past 5-year hospitalisations and drug prescriptions), neighbourhood characteristics (housing prices, unemployment rate, education level), and geographical coordinates of the residential address. Three study outcomes were defined: incidence of SARS-CoV-2 infection (newly identified cases based on a positive test through RT-PCR), COVID-19 mortality (deaths within 30 days from infection), and non-COVID-19 mortality (deaths among non-cases, or after 30 days since infection). Annual average concentrations of particulate matter smaller than 2.5 μm (PM2.5) and nitrogen dioxide (NO2) were estimated for 2019 at 1-km2 spatial resolution for the entire Italian territory, using a machine learning spatiotemporal model which incorporated data from existing air quality monitoring networks, satellite images, atmospheric models, land-use and population characteristics [5].
We applied Cox proportional hazard models with adjustment for individual- and area-level covariates. These include: calendar time (as time axis); demographic variables (age in five classes: 30–54, 55–64, 65–74, 75–84, ≥85 years; sex; marital status; place of birth; nationality); socioeconomic indicators (education level, occupational status and census block-level deprivation index); pre-existing chronic conditions (number of any conditions out of a list of 67 diseases, and six specific diseases); and neighbourhood-level characteristics. The latter were adjusted differently for incidence (a strata term for the 155 districts in Rome) or mortality outcomes (housing prices, unemployment rate and % university degree at the district level). We operated this choice because we assumed that factors related to viral spread in the general population (person-to-person contacts, individual mobility, etc.) were better accounted for by assuming different baseline rates for each neighbourhood. Instead, socioeconomic characteristics of the residential neighbourhood could adequately adjust for spatially heterogenous susceptibility of the study population. Next, we added each exposure in turn (PM2.5 or NO2) as a linear term, and expressed all estimates as hazard ratios and 95% confidence intervals, of the study outcomes, per increments in the exposures equal to their interquartile ranges (IQRs).
We conducted a number of additional/sensitivity analyses: we defined three pandemic waves consistent with the viral spread in Rome (February–September 2020, October–December 2020, January–April 2021) and estimated wave-specific effects of air pollutants using time-varying models; we replaced the strata term for districts with population density in the adjustment model for SARS-CoV-2 incidence; we dropped the pre-existing conditions from the adjusted model, under the assumption that these might act as mediators, rather than confounders, of the studied associations; we adjusted for district-level rates of diabetes, COPD and lung cancer as proxies for BMI and smoking; we analysed COVID-19 hospitalisations and intensive care units as alternative outcomes of COVID-19 severity; finally, we estimated the exposure–response functions between the air pollutants and the study outcomes by modelling air pollutants with natural splines.
Descriptive statistics and results are displayed in table 1. We enrolled 1 594 308 subjects, with a mean±sd follow up of 461±48 days. Of these, 79 976 individuals were infected with SARS-CoV-2, 2656 died within 30 days from infection, and 1002 died after 30 days from infection. 31 563 individuals died without ever being diagnosed with COVID-19 during the follow-up. The average air pollution exposures at the baseline were: 14.63 µg·m−3 for PM2.5 (IQR 0.92 µg·m−3) and 31.45 µg·m−3 for NO2 (IQR 9.22 µg·m−3). Infection incidence rates, and hazard ratios from the fully adjusted model, were highest among younger subjects, students or employed people, those with higher socioeconomic deprivation and in neighbourhoods with lowest housing prices. COVID-19 mortality rates and hazard ratios substantially increased with age and number of pre-existing chronic conditions, and were higher among males, subjects with poor education and highest deprivation, or among patients with pre-existing renal failure, heart failure, ischaemic heart disease, COPD, type 2 diabetes or cancer. Similar patterns emerged for non-COVID-19 mortality, although with reduced differentials by sex and socioeconomic deprivation.
TABLE 1.
Population characteristics, study outcomes, and associations between individual covariates, exposure and the study outcomes in the fully adjusted model
| Study population (n=1 594 308) | Incident cases (n=79 976) | COVID-19 deaths (n=2656) | Non-COVID-19 deaths (n=32 565) | |||||||||||
| N | % | Rate per 1000 person-years | HR | 95% CI | Rate per 1000 person-years | HR | 95% CI | Rate per 1000 person-years | HR | 95% CI | ||||
| Demographic variables | ||||||||||||||
| Age (years) | ||||||||||||||
| 30–54 | 699 791 | 43.9 | 47.1 | 1.00 | – | – | 0.09 | 1.00 | – | – | 1.19 | 1.00 | – | – |
| 55–64 | 332 651 | 20.9 | 41.1 | 0.84 | 0.82 | 0.85 | 0.46 | 3.76 | 2.89 | 4.90 | 4.57 | 2.95 | 2.74 | 3.19 |
| 65–74 | 260 430 | 16.3 | 30.2 | 0.62 | 0.60 | 0.64 | 1.55 | 8.93 | 6.93 | 11.53 | 13.42 | 5.74 | 5.33 | 6.18 |
| 75–84 | 208 644 | 13.1 | 28.3 | 0.59 | 0.57 | 0.61 | 3.79 | 16.85 | 12.92 | 21.99 | 38.65 | 11.81 | 10.94 | 12.75 |
| ≥85 | 92 792 | 5.8 | 31.3 | 0.67 | 0.64 | 0.71 | 8.05 | 34.94 | 26.61 | 45.90 | 136.02 | 37.51 | 34.71 | 40.54 |
| Sex | ||||||||||||||
| Male | 715 610 | 44.9 | 42.3 | 1.00 | – | – | 1.76 | 1.00 | – | – | 16.56 | 1.00 | – | – |
| Female | 878 698 | 55.1 | 37.8 | 0.94 | 0.92 | 0.95 | 0.96 | 0.46 | 0.43 | 0.51 | 15.55 | 0.81 | 0.79 | 0.83 |
| Marital status | ||||||||||||||
| Single | 724 491 | 45.4 | 39.6 | 1.00 | – | – | 1.32 | 1.00 | – | – | 16.15 | 1.00 | – | – |
| Married | 682 340 | 42.8 | 40.0 | 1.01 | 0.99 | 1.02 | 1.32 | 1.02 | 0.94 | 1.11 | 15.63 | 1.00 | 0.97 | 1.02 |
| Separated/divorced | 67 373 | 4.2 | 39.3 | 1.00 | 0.96 | 1.03 | 1.30 | 0.95 | 0.79 | 1.16 | 17.22 | 1.05 | 1.00 | 1.11 |
| Widowed | 120 104 | 7.5 | 39.8 | 1.00 | 0.98 | 1.03 | 1.29 | 0.96 | 0.82 | 1.12 | 16.58 | 1.02 | 0.97 | 1.06 |
| Born in Rome | ||||||||||||||
| Yes | 940 690 | 59.0 | 39.7 | 1.00 | – | – | 1.29 | 1.00 | – | – | 15.67 | 1.00 | – | – |
| No | 653 618 | 41.0 | 40.0 | 0.99 | 0.97 | 1.00 | 1.35 | 0.99 | 0.91 | 1.08 | 16.49 | 1.00 | 0.98 | 1.03 |
| Italian nationality | ||||||||||||||
| Yes | 1 380 875 | 86.6 | 39.8 | 1.00 | – | – | 1.31 | 1.00 | – | – | 15.83 | 1.00 | – | – |
| No | 213 433 | 13.4 | 39.8 | 1.00 | 0.98 | 1.02 | 1.37 | 1.04 | 0.92 | 1.18 | 17.18 | 1.01 | 0.98 | 1.05 |
| Socioeconomic variables | ||||||||||||||
| Level of education | ||||||||||||||
| Primary or less | 195 587 | 12.3 | 35.5 | 1.00 | – | – | 4.40 | 1.00 | – | – | 54.72 | 1.00 | – | – |
| Middle school | 350 961 | 22.0 | 42.1 | 0.95 | 0.93 | 0.98 | 1.49 | 0.85 | 0.77 | 0.94 | 17.12 | 0.93 | 0.91 | 0.96 |
| High school | 652 376 | 40.9 | 41.9 | 0.93 | 0.91 | 0.96 | 0.77 | 0.77 | 0.69 | 0.86 | 9.13 | 0.84 | 0.82 | 0.87 |
| University or more | 395 384 | 24.8 | 36.4 | 0.86 | 0.83 | 0.88 | 0.60 | 0.65 | 0.56 | 0.75 | 7.78 | 0.77 | 0.74 | 0.80 |
| Employment status | ||||||||||||||
| Employed | 891 706 | 55.9 | 44.7 | 1.00 | – | – | 0.40 | 1.00 | – | – | 3.62 | 1.00 | – | – |
| Searching for first employment | 18 999 | 1.2 | 39.1 | 0.80 | 0.75 | 0.85 | 0.17 | 0.80 | 0.30 | 2.15 | 2.53 | 1.27 | 0.99 | 1.64 |
| Unemployed | 59 121 | 3.7 | 39.3 | 0.81 | 0.78 | 0.84 | 0.36 | 1.17 | 0.79 | 1.73 | 3.73 | 1.39 | 1.23 | 1.57 |
| Retired | 317 698 | 19.9 | 28.9 | 0.82 | 0.80 | 0.85 | 4.24 | 1.21 | 1.05 | 1.39 | 53.43 | 1.43 | 1.36 | 1.49 |
| Student | 40 097 | 2.5 | 42.7 | 0.94 | 0.90 | 0.98 | 0.10 | 0.86 | 0.35 | 2.09 | 1.10 | 0.77 | 0.59 | 1.00 |
| Housewife | 181 ,316 | 11.4 | 35.6 | 0.87 | 0.84 | 0.89 | 1.24 | 1.09 | 0.92 | 1.30 | 18.05 | 1.21 | 1.15 | 1.28 |
| Other | 85 371 | 5.4 | 35.7 | 0.82 | 0.79 | 0.84 | 1.95 | 1.42 | 1.18 | 1.69 | 24.85 | 1.71 | 1.62 | 1.80 |
| Socioeconomic deprivation (of the census block) | ||||||||||||||
| Low | 338 918 | 21.3 | 34.4 | 1.00 | – | – | 1.10 | 1.00 | – | – | 16.39 | 1.00 | – | – |
| Mid-low | 424 703 | 26.6 | 37.0 | 1.01 | 0.99 | 1.03 | 1.30 | 1.16 | 1.03 | 1.30 | 15.92 | 1.02 | 0.99 | 1.05 |
| Medium | 290 133 | 18.2 | 40.7 | 1.01 | 0.99 | 1.04 | 1.23 | 1.11 | 0.97 | 1.27 | 15.55 | 1.06 | 1.03 | 1.10 |
| Mid-high | 251 250 | 15.8 | 44.7 | 1.05 | 1.02 | 1.08 | 1.38 | 1.22 | 1.06 | 1.41 | 14.87 | 1.06 | 1.02 | 1.10 |
| High | 289 304 | 18.1 | 45.2 | 1.07 | 1.04 | 1.09 | 1.63 | 1.31 | 1.14 | 1.50 | 17.14 | 1.13 | 1.09 | 1.17 |
| Pre-existing chronic conditions # | ||||||||||||||
| Number | ||||||||||||||
| 0 | 763 781 | 47.9 | 41.6 | 1.00 | – | – | 0.24 | 1.00 | – | – | 2.80 | 1.00 | – | – |
| 1 | 348 492 | 21.9 | 39.0 | 1.07 | 1.05 | 1.09 | 1.00 | 1.62 | 1.37 | 1.91 | 11.26 | 1.44 | 1.37 | 1.51 |
| 2 | 209 386 | 13.1 | 37.0 | 1.12 | 1.09 | 1.15 | 1.70 | 1.74 | 1.47 | 2.06 | 22.37 | 1.65 | 1.57 | 1.73 |
| 3 | 121 952 | 7.6 | 35.8 | 1.13 | 1.10 | 1.17 | 2.82 | 2.15 | 1.80 | 2.56 | 34.47 | 1.84 | 1.74 | 1.93 |
| ≥4 | 150 697 | 9.5 | 39.3 | 1.24 | 1.19 | 1.29 | 5.98 | 2.79 | 2.33 | 3.33 | 73.08 | 2.19 | 2.08 | 2.31 |
| Specific conditions | ||||||||||||||
| Cancer | 41 985 | 2.6 | 37.9 | 1.06 | 1.01 | 1.11 | 4.13 | 1.47 | 1.27 | 1.70 | 97.84 | 3.47 | 3.37 | 3.58 |
| Type 2 diabetes | 101 874 | 6.4 | 40.3 | 1.08 | 1.05 | 1.12 | 4.16 | 1.13 | 1.02 | 1.25 | 43.72 | 1.06 | 1.03 | 1.09 |
| Ischaemic heart disease | 65 307 | 4.1 | 40.9 | 1.08 | 1.03 | 1.12 | 6.31 | 1.10 | 0.98 | 1.23 | 70.78 | 1.06 | 1.02 | 1.09 |
| Heart failure | 60 726 | 3.8 | 40.5 | 1.11 | 1.06 | 1.16 | 7.58 | 1.33 | 1.19 | 1.48 | 104.39 | 1.52 | 1.48 | 1.57 |
| COPD | 88 007 | 5.5 | 38.7 | 1.07 | 1.03 | 1.10 | 4.57 | 1.15 | 1.04 | 1.28 | 62.88 | 1.33 | 1.30 | 1.37 |
| Renal failure | 21 850 | 1.4 | 42.2 | 1.14 | 1.07 | 1.21 | 10.27 | 1.65 | 1.44 | 1.89 | 128.68 | 1.55 | 1.49 | 1.61 |
| District-level covariates | ||||||||||||||
| House prices (quintiles) | ||||||||||||||
| 1 | 338 871 | 21.3 | 49.5 | – | – | – | 1.23 | 0.85 | 0.66 | 1.10 | 12.14 | 0.94 | 0.87 | 1.01 |
| 2 | 330 823 | 20.8 | 43.3 | – | – | – | 1.45 | 0.79 | 0.63 | 0.98 | 16.24 | 0.94 | 0.89 | 1.00 |
| 3 | 300 216 | 18.8 | 38.3 | – | – | – | 1.47 | 0.86 | 0.70 | 1.05 | 17.65 | 0.98 | 0.93 | 1.04 |
| 4 | 314 726 | 19.7 | 34.8 | – | – | – | 1.24 | 0.84 | 0.72 | 0.98 | 17.18 | 0.99 | 0.94 | 1.03 |
| 5 | 309 672 | 19.4 | 31.9 | – | – | – | 1.20 | 1.00 | – | – | 17.21 | 1.00 | – | – |
| Unemployment rate¶ (mean, IQR) | 6.5 | 1.8 | – | – | – | – | – | 1.04 | 0.93 | 1.16 | – | 1.02 | 0.99 | 1.05 |
| Per cent with university degree or more¶ (mean, IQR) | 39.3 | 31.3 | – | – | – | – | – | 0.86 | 0.71 | 1.04 | – | 1.05 | 1.00 | 1.11 |
| Exposures ¶ | ||||||||||||||
| PM2.5 (μg·m−3) (mean, IQR) | 14.63 | 0.92 | – | 1.01 | 0.99 | 1.03 | – | 1.08 | 1.03 | 1.13 | – | 1.01 | 1.00 | 1.02 |
| NO2 (μg·m−3) (mean, IQR) | 31.45 | 9.22 | – | 1.00 | 0.98 | 1.02 | – | 1.09 | 1.02 | 1.16 | – | 1.02 | 1.00 | 1.04 |
Rates are computed as ratios between numbers of outcomes and person-years, multiplied by 1000. Hazard ratios are estimated from a Cox proportional hazards model adjusted for calendar time (time axis), age (five classes), sex, marital status (for classes), place of birth, Italian nationality, education level (four classes), employment status (seven classes), census-block-level socioeconomic deprivation index (five classes), number of pre-existing conditions (five classes), presence of six specific pre-existing conditions (cancer, type-2 diabetes, ischaemic heart disease, heart failure, COPD, renal failure), and district-level characteristics. The latter are adjusted with a “strata” term for the 155 Rome districts in incidence analysis, and with three district-level covariates (house prices in five classes, % university degree or more, unemployment rate) in the mortality analyses. PM2.5: particulate matter smaller than 2.5 μm; NO2: nitrogen dioxide; IQR: interquartile range. #: pre-existing chronic conditions include a list of 67 groups of diseases based on past 5-year hospital admissions or drug prescriptions; ¶: associations between continuous covariates (unemployment rate, per cent with university degree or more) and air pollutants with the study outcomes are expressed as hazard ratios (and 95% confidence intervals) per IQR increments. Exposures are modelled one at a time (single-pollutant models).
Table 1 reports the results of the association between PM2.5 and NO2 with the three study outcomes in the fully adjusted model. We found no association between air pollution and SARS-CoV-2 incidence: IQR increments in PM2.5 and NO2 were associated with hazard ratios of 1.01 (95% CI 0.99–1.03) and 1.00 (95% CI 0.98–1.02), respectively. In contrast, we estimated strong associations between the two air pollutants and COVID-19 mortality: IQR increments in PM2.5 and NO2 were associated with hazard ratios of 1.08 (95% CI 1.03–1.13) and 1.09 (95% CI 1.02–1.16). The association between air pollutants and non-COVID-19 mortality was comparatively smaller than the one with COVID-19 mortality: we estimated hazard ratios of 1.01 (95% CI 1.00–1.02) and 1.02 (95% CI 1.00–1.04) per IQR increments in PM2.5 and NO2, respectively. The results of the additional/sensitivity analyses confirm the main findings: associations were unaffected by alternative adjustment models, they did not differ substantially by pandemic wave, and were significant with hospital admissions but not with accesses to intensive care units. Finally, the exposure–response functions were consistent with flat associations between the two air pollutants and SARS-CoV-2 incidence, while associations with mortality outcomes were approximately linear, and much steeper for COVID-19 mortality (data not shown).To date, few studies have investigated the relationship between air pollution and COVID-19-related outcomes in population-based longitudinal studies. Chadeau-Hyam et al. [6] and Elliott et al. [7] linked COVID-19 data and mortality records to the UK Biobank and found no association between residential PM2.5 exposure and either positive testing to SARS-CoV-2 or COVID-19 mortality, after multivariate adjustment for individual and area-level risk factors. Similarly, no association between air pollutants and SARS-CoV-2 positive testing was detected in the COVICAT cohort of Catalonia, Spain, although a statistically significant association was estimated with severe COVID-19 disease among infected patients [8]. No association between PM2.5 or NO2 and mortality was found in a prospective longitudinal study conducted in Ontario, Canada, while significant associations were estimated with hospitalisations and accesses to intensive care units [9]. In contrast, positive associations between PM2.5 exposure and COVID-19 incidence were estimated in northern Italy [10] and southern California [11].
Our estimates of association between air pollutants and COVID-19 mortality are similar to those found in previous large ecological studies [2, 12], and much higher than those usually found with natural-cause mortality in the general population [1, 13]. Several mechanisms have been proposed as responsible for an enhanced severity of COVID-19 in combination with exposure to air pollution. First, air pollution-induced inflammation may amplify inflammation due to COVID-19 and lead to adverse health outcomes, including premature death; second, air pollution may reduce the immune response against the virus by inhibiting phagocytic function of macrophages and decreasing the T-cell response; third, chronic exposure to air pollution may induce endothelial damage and microthrombi, thus increasing the risk of cerebral damage, pulmonary embolism, and cardiac dysfunction among COVID-19 patients [14].
This study has two main limitations. First, our cohort lacks data on relevant individual-level lifestyle characteristics, such as smoking, physical activity and dietary habits, or physiological parameters, such as body mass index and cholesterol levels. While these might confer greater susceptibility to the individuals, it is however not clear to what extent they should correlate with ambient air pollution, once area-specific covariates (e.g. socioeconomic deprivation) are accounted for. Secondly, our COVID-19 surveillance system, especially in the early stages of the pandemic, could only identify a selected sample of all infected individuals, e.g. those with severe symptoms or close contacts of primary cases. The testing policy was broadened to asymptomatic primary contacts and to various screening programmes (e.g. ahead of hospital admission for other causes) only after the first wave, when Italy entered the transition phase and a test–track–trace strategy was adopted. Therefore, our definition of SARS-CoV-2 incidence is only partial. Again, however, there are no a priori reasons to believe that included and excluded cases should be different with regard to air pollutant distributions.
In conclusion, in this large longitudinal study, long-term residential exposure to air pollution was associated with increased mortality among COVID-19 patients, but not with SARS-CoV-2 incidence in the general population. Our study supports the hypothesis that chronic exposure to air pollution might increase human vulnerability to viruses, thus worsening prognosis of COVID-19 cases, while they are unlikely to increase the spread of infection in the general population.
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Footnotes
Authors contribution: F. Nobile, P. Michelozzi and M. Stafoggia conceived and designed the study. G. Cappai, G. Cesaroni, M. Di Martino and C. Sorge collected the data. F. Nobile analysed the data, with input from M. Stafoggia, P. Michelozzi and C. Ancona. P. Michelozzi and C. Ancona helped interpret the results. F. Nobile and M. Stafoggia drafted the manuscript, and M. Davoli, E. Nicastri, E. Girardi, A. Beccacece, P. Scognamiglio and F. Vairo critically revised it for important intellectual content. All authors read and approved the final manuscript.
Conflict of interest: The authors declare that they have no conflict of interest.
Support statement: This study was supported by the Italian Ministry of Health (COVID-2020-12371675) and by line 1 “Ricerca Corrente” on emerging and re-emerging infections. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Funding information for this article has been deposited with the Crossref Funder Registry.
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