Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide

Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador

The relationship between COVID-19 infections and environmental contaminants provides insight into how environmental factors can influence the spread of infectious diseases. By integrating epidemiological and environmental variables into a mathematical framework, the interaction between virus spread and the environment can be determined. The aim of this study was to evaluate the impact of atmospheric contaminants on the increase in COVID-19 infections in the city of Quito through the application of statistical tests. The data on infections and deaths allowed to identify the periods of greatest contagion and their relationship with the contaminants O3, SO2, CO, PM2.5, and PM10. A validated database was used, and statistical analysis was applied through five models based on simple linear regression. The models showed a significant relationship between SO2 and the increase in infections. In addition, a moderate correlation was shown with PM2.5, O3, and CO, and a low relationship was shown for PM10. These findings highlight the importance of having policies that guarantee air quality as a key factor in maintaining people's health and preventing the proliferation of viral and infectious diseases.

Evolving patterns of COVID-19 mortality in US counties: A longitudinal study of healthcare, socioeconomic, and vaccination associations

The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM10 and SO2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg-Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (N = 181), sinusitis (N = 83), and upper respiratory infections (N = 194). The study also found demographic differences, with females and people aged 18 to 65 years having greater admission rates. The performance of the ANN model, measured using R2 values, demonstrated a high level of predictive accuracy. Specifically, the R2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Impact of short-term ambient air pollution exposure on the risk of severe COVID-19

Ecological studies suggested a link between air pollution and severe COVID-19 outcomes, while studies accounting for individual-level characteristics are limited. In the present study, we aimed to investigate the impact of short-term ambient air pollution exposure on disease severity among a cohort of 569 laboratory confirmed COVID-19 patients admitted to designated hospitals in Zhejiang province, China, from January 17 to March 3, 2020, and elucidate the possible biological processes involved using transcriptomics. Compared with mild cases, severe cases had higher proportion of medical conditions as well as unfavorable results in most of the laboratory tests, and manifested higher air pollution exposure levels. Higher exposure to air pollutants was associated with increased risk of severe COVID-19 with odds ratio (OR) of 1.89 (95% confidence interval (CI): 1.01, 3.53), 2.35 (95% CI: 1.20, 4.61), 2.87 (95% CI: 1.68, 4.91), and 2.01 (95% CI: 1.10, 3.69) for PM2.5, PM10, NO2 and CO, respectively. OR for NO2 remained significant in two-pollutant models after adjusting for other pollutants. Transcriptional analysis showed 884 differentially expressed genes which mainly were enriched in virus clearance related biological processes between patients with high and low NO2 exposure levels, indicating that compromised immune response might be a potential underlying mechanistic pathway. These findings highlight the impact of short-term air pollution exposure, particularly for NO2, on COVID-19 severity, and emphasize the significance in mitigating the COVID-19 burden of commitments to improve air quality.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Factors Associated with SARS-CoV-2 Infection Evaluated by Antibody Response in a Sample of Workers from the Emilia-Romagna Region, Northern Italy

Factors associated with SARS-CoV-2 infection risk are still debated. This case-control study aims to investigate the possible relationship between SARS-CoV-2 infection, evaluated through antibody response, and the main sociodemographic, occupational, clinical-anamnestic, and biochemical factors in a population of Modena province (Northern Italy), mainly workers. Both workers who voluntarily joined the screening campaign proposed by companies and self-referred individuals who underwent serological testing were enrolled. Subjects with antibody positivity were recruited as cases (n = 166) and subjects tested negative (n = 239) as controls. A questionnaire on sociodemographic, occupational, and clinical data was administered through telephone interviews. Serum zinc/iron/copper/chromium/nickel, vitamins D/B12, folates, triglycerides, and LDL/HDL/total cholesterol were measured. Cases lived more often in urban areas (61.8% vs. 57%). Cases and controls did not differ significantly by working macrocategories, but the percentage of workers in the ceramic sector was higher among cases. Low adherence to preventive measures in the workplace was more frequent among seropositives. Folate concentration was significantly lower among cases. Therefore, adequate folate levels, living in rural areas, and good adherence to preventive strategies seem protective against infection. Workers in the ceramic sector seem to be at greater risk; specific factors involved are not defined, but preventive interventions are needed.

The Impact of the COVID-19 Pandemic and Socioeconomic Deprivation on Admissions to the Emergency Department for Psychiatric Illness: An Observational Study in a Province of Southern Italy

The restriction measures adopted to limit population movement in order to contain the COVID-19 pandemic contributed to a global public health system crisis. This retrospective study aimed at identifying changes in psychiatric admissions to Accident and Emergency Departments (A&Es) in a province in southern Italy during the first two years of the pandemic and was characterized by two different restriction levels (phases 2 and 3) compared to the pre-pandemic period (phase 1). We also investigated the role of socioeconomic deprivation (DI) on psychiatric admissions. The total number of patients admitted to the A&Es was 291,310. The incidence of admission for a psychiatric disorder (IPd) was 4.9 per 1000 admissions, with a significant younger median age of 42 [IQR 33–56] compared to non-psychiatric patients (54 [35–73]). The type of admission and type of discharge were factors related to the psychiatric admission to A&E, and their relationship was modified by the pandemic. In the first year of the pandemic, patients with psychomotor agitation increased compared to the pre-pandemic period (72.5% vs. 62.3%). In the period preceding the spread of SARS-CoV-2, the IPd was equal to 3.33 ± 0.19; after the pandemic started, there was an increase in the IPd: 4.74 ± 0.32 for phase 2 and 3.68 ± 0.25 for phase 3. The IPd was higher for psychiatric admissions from areas with a very low DI compared to areas with a low DI; however, during phase 2, this difference was reduced. In conclusion, an increase in admissions for psychiatric disease was observed during the initial spread of SARS-CoV-2. Patients who lived in the most deprived municipalities generally came to the A&Es less than others, probably because the patients and their families had less awareness of their mental health. Therefore, public health policies to address these issues are needed to reduce the pandemic’s impact on these conditions.

A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization

Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m² showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Substantial impact of mobility restrictions on reducing COVID-19 incidence in Italy in 2020

BACKGROUND

Italy was the first country after China to be severely affected by the COVID-19 pandemic, in early 2020. The country responded swiftly to the outbreak with a nationwide two-step lockdown, the first one light and the second one tight. By analyzing 2020 national mobile phone movements, we assessed how lockdown compliance influenced its efficacy.

METHODS

We measured individual mobility during the first epidemic wave with mobile phone movements tracked through carrier networks, and related this mobility to daily new SARS-CoV-2 infections, hospital admissions, intensive care admissions and deaths attributed to COVID-19, taking into account reason for travel (work-related or not) and the means of transport.

RESULTS

The tight lockdown resulted in an 82% reduction in mobility for the entire country and was effective in swiftly curbing the outbreak as indicated by a shorter time-to-peak of all health outcomes, particularly for provinces with the highest mobility reductions and the most intense COVID-19 spread. Reduction of work-related mobility was accompanied by a nearly linear benefit in outbreak containment; work-unrelated movements had a similar effect only for restrictions exceeding 50%. Reduction in mobility by car and by airplane was nearly linearly associated with a decrease in most COVID-19 health outcomes, while for train travel reductions exceeding 55% had no additional beneficial effects. The absence of viral variants and vaccine availability during the study period eliminated confounding from these two sources.

CONCLUSIONS

Adherence to the COVID-19 tight lockdown during the first wave in Italy was high and effective in curtailing the outbreak. Any work-related mobility reduction was effective, but only high reductions in work-unrelated mobility restrictions were effective. For train travel, there was a threshold above which no further benefit occurred. These findings could be particular to the spread of SARS-CoV-2, but might also apply to other communicable infections with comparable transmission dynamics.

Health impact assessment and evaluation of economic costs attributed to PM2.5 air pollution using BenMAP-CE

Air pollution is considered the most prominent public health. Economically, air pollution imposes additional costs on governments. This study aimed to quantify health effects and associated economic values of reducing PM_2.5 air pollution using BenMAP-CE in Qom in 2019. The air quality data were acquired from Qom Province Environmental Protection Agency, and the population data were collected from Qom Province Management and Planning Organization website. The number of deaths due to Stroke, Chronic Obstructive Pulmonary Disease, Lung Cancer, and Ischemic Heart Disease attributable to PM_2.5 were estimated using BenMAP-CE based on two control scenarios, 2.4 and 10 μg/m³, known as scenarios I and II, respectively. The associated economic effect of premature deaths was assessed by value of a statistical life (VSL) approach. The annual average of PM_2.5 concentration was found to be 16.32 μg/m³ (SD: 9.93). A total of 4694.5 and 2475.94 premature deaths in scenarios I and II were found to be attributable to PM_2.5 in overall, respectively. The total associated cost was calculated to be 855.91 and 451.40 million USD in scenarios I and II, respectively. The total years of life lost due to PM_2.5 exposure in 2019 was 158,657.06 and 78,351.51 in scenarios I and II, respectively. The results of both health and economic assessment indicate the importance of solving the air pollution problem in Qom, as well as other big cities in Iran. The elimination of limitations, such as insufficient local data, should be regarded in future studies.

NO2 pollution over selected cities in the Po Valley in 2018-2021 and its possible effects on boosting COVID-19 deaths

This work analyzes nitrogen dioxide (NO₂) pollution over a set of cities in the Po Valley in northern Italy, using satellite and in situ observations. The cities include Milan, Bergamo, and Brescia, the first area of the COVID-19 outbreak and diffusion in Italy, with a higher mortality rate than in other parts of Italy and Europe. The analysis was performed for three years, from May 2018 to April 2021, including the period of first-wave diffusion of COVID-19 over the Po Valley, that is, January 2020–April 2020. The study aimed at giving a more general picture of the NO₂ temporal and spatial variation, possibly due to the lockdown adopted for the pandemic crisis containment and other factors, such as the meteorological conditions and the seasonal cycle. We have mainly investigated two effects: first, the correlation of NO₂ pollution with atmospheric parameters such as air and dew point temperature, and second the possible correlation between air quality and COVID-19 deaths, which could explain the high mortality rate. We have found a good relationship between air quality and temperature. In light of this relationship, we can conclude that the air quality improvement in March 2020 was primarily because of the lockdown adopted to prevent and limit virus diffusion. We also report a good correlation between NO₂ pollution and COVID-19 deaths, which is not seen when considering a reference city in the South of Italy. The critical factor in explaining the difference is the persistence of air pollution in the Po Valley in wintertime. We found that NO₂ pollution shows a seasonal cycle, yielding a non-causal correlation with the COVID-19 deaths. However, causality comes in once we read the correlation in the context of current and recent epidemiological evidence and leads us to conclude that air pollution may have acted as a significant risk factor in boosting COVID-19 fatalities.

Effects of environmental parameters and their interactions on the spreading of SARS-CoV-2 in North Italy under different social restrictions. A new approach based on multivariate analysis

In 2020 North Italy suffered the SARS-CoV-2-related pandemic with a high number of deaths and hospitalization. The effect of atmospheric parameters on the amount of hospital admissions (temperature, solar radiation, particulate matter, relative humidity and wind speed) is studied through about 8 months (May-December). Two periods are considered depending on different conditions: a) low incidence of COVID-19 and very few regulations concerning personal mobility and protection ("free/summer period"); b) increasing incidence of disease, social restrictions and use of personal protections ("confined/autumn period"). The "hospitalized people in medical area wards/100000 residents" was used as a reliable measure of COVID-19 spreading and load on the sanitary system. We developed a chemometric approach (multiple linear regression analysis) using the daily incidence of hospitalizations as a function of the single independent variables and of their products (interactions). Eight administrative domains were considered (altogether 26 million inhabitants) to account for relatively homogeneous territorial and social conditions. The obtained models very significantly match the daily variation of hospitalizations, during the two periods. Under the confined/autumn period, the effect of non-pharmacologic measures (social distances, personal protection, etc.) possibly attenuates the virus diffusion despite environmental factors. On the contrary, in the free/summer conditions the effects of atmospheric parameters are very significant through all the areas. Particulate matter matches the growth of hospitalizations in areas with low chronic particulate pollution. Fewer hospitalizations strongly correspond to higher temperature and solar radiation. Relative humidity plays the same role, but with a lesser extent. The interaction between solar radiation and high temperature is also highly significant and represents surprising evidence. The solar radiation alone and combined with high temperature exert an anti-SARS-CoV-2 effect, via both the direct inactivation of virions and the stimulation of vitamin D synthesis, improving immune system function.

A review on the biological, epidemiological, and statistical relevance of COVID-19 paired with air pollution

This narrative review paper is aimed to critically evaluate recent studies of the associations between air pollution and the outcomes in the COVID-19 pandemic. The main air pollutants we have considered are carbon monoxide (CO), nitrogen dioxide (NO₂), ground-level ozone (O₃), particulate matter (PM_2.5 and PM₁₀), and sulfur dioxide (SO₂). We, specifically, evaluated the influences of these pollutants, both individually and collaboratively, across various geographic areas and exposure windows. We further reviewed the proposed biological mechanisms underlying the association between air pollution and COVID-19. Ultimately, we aim to inform policy and public health practice regarding the implications of COVID-19 and air pollution.

Nitrogen dioxide pollution increases vulnerability to COVID-19 through altered immune function

Previous ecological studies suggest the existence of possible interplays between the exposure to air pollutants and SARS-CoV-2 infection. Confirmations at individual level, however, are lacking. To explore the relationships between previous exposure to particulate matter < 10 μm (PM₁₀) and nitrogen dioxide (NO₂), the clinical outcome following hospital admittance, and lymphocyte subsets in COVID-19 patients with pneumonia. In 147 geocoded patients, we assessed the individual exposure to PM₁₀ and NO₂ in the 2 weeks before hospital admittance. We divided subjects according to the clinical outcome (i.e., discharge at home vs in-hospital death), and explored the lymphocyte-related immune function as an index possibly affecting individual vulnerability to the infection. As compared with discharged subjects, patients who underwent in-hospital death presented neutrophilia, lymphopenia, lower number of T CD45, CD3, CD4, CD16/56 + CD3 + , and B CD19 + cells, and higher previous exposure to NO₂, but not PM₁₀. Age and previous NO₂ exposure were independent predictors for mortality. NO₂ concentrations were also negatively related with the number of CD45, CD3, and CD4 cells. Previous NO₂ exposure is a co-factor independently affecting the mortality risk in infected individuals, through negative immune effects. Lymphopenia and altered lymphocyte subsets might precede viral infection due to nonmodifiable (i.e., age) and external (i.e., air pollution) factors. Thus, decreasing the burden of air pollutants should be a valuable primary prevention measure to reduce individual susceptibility to SARS-CoV-2 infection and mortality.

Air pollutant spatiotemporal evolution characteristics and effects on human health in North China

North China, the political, economic, and cultural center of China, has been greatly harmed by frequent air pollution incidents. Therefore, it is vital to study air pollution characteristics and clarify their impact on human health. In this study, we first analyzed the spatiotemporal variations of air pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂, and O₃) in North China from 2016 to 2019. Then, the air quality index (AQI), aggregate air quality index (AAQI), and health risk based air quality index (HAQI) were used to assess health risks. Based on these, the AirQ_2.2.3 model was used to quantify health effects. The results showed that the major pollutant in the cities surrounding Beijing was PM_2.5, while PM₁₀ dominated in distant cities. Annual concentrations decreased (except for O₃), which is related to governmental emission reduction policies. However, O₃ concentrations increased owing to the complex precursor emissions. The AQI underestimated air pollution, while the AAQI and HAQI were accurate; the latter indicated that 55% of the study region population was exposed to polluted air. The AirQ_2.2.3 model quantified the total mortality proportions attributable to PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃, which were 1.87%, 3.12%, 1.11%, 1.40%, 4.19%, and 2.52%, respectively. In high concentrations, PM₁₀ and PM_2.5 pose significant health risks. The health effects of SO₂, NO₂, CO, and O₃ at lower concentrations were more obvious, indicating that the expected mortality rate due to low concentrations of some pollutants was much higher than that due to high concentrations of other pollutants.

Transmission in home environment associated with the second wave of COVID-19 pandemic in India

India has suffered from the second wave of COVID-19 pandemic since March 2021. This wave of the outbreak has been more serious than the first wave pandemic in 2020, which suggests that some new transmission characteristics may exist. COVID-19 is transmitted through droplets, aerosols, and contact with infected surfaces. Air pollutants are also considered to be associated with COVID-19 transmission. However, the roles of indoor transmission in the COVID-19 pandemic and the effects of these factors in indoor environments are still poorly understood. Our study focused on reveal the role of indoor transmission in the second wave of COVID-19 pandemic in India. Our results indicated that human mobility in the home environment had the highest relative influence on COVID-19 daily growth rate in the country. The COVID-19 daily growth rate was significantly positively correlated with the residential percent rate in most state-level areas in India. A significant positive nonlinear relationship was found when the residential percent ratio ranged from 100 to 120%. Further, epidemic dynamics modelling indicated that a higher proportion of indoor transmission in the home environment was able to intensify the severity of the second wave of COVID-19 pandemic in India. Our findings suggested that more attention should be paid to the indoor transmission in home environment. The public health strategies to reduce indoor transmission such as ventilation and centralized isolation will be beneficial to the prevention and control of COVID-19.

Long-term exposure to fine particulate matter air pollution: An ecological study of its effect on COVID-19 cases and fatality in Germany

BACKGROUND

COVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health.

RESEARCH QUESTION

This paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany.

STUDY DESIGN

Data from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included.

MAIN FINDINGS

An increase in the average long-term air pollution of 1 μg/m3 particulate matter PM2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM2.5 and PM10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants).

CONCLUSION

Long-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity.

The association between air pollutants and hippocampal volume from magnetic resonance imaging: A systematic review and meta-analysis

Growing epidemiological evidence suggests that air pollution may increase the risk of cognitive decline and neurodegenerative disease. A hallmark of neurodegeneration and an important diagnostic biomarker is volume reduction of a key brain structure, the hippocampus. We aimed to investigate the possibility that outdoor air nitrogen dioxide (NO₂) and particulate matter with diameter ≤2.5 μm (PM_2.5) and ≤10 μm (PM₁₀) adversely affect hippocampal volume, through a meta-analysis. We considered studies that assessed the relation between outdoor air pollution and hippocampal volume by structural magnetic resonance imaging in adults and children, searching in Pubmed and Scopus databases from inception through July 13, 2021. For inclusion, studies had to report the correlation coefficient along with its standard error or 95% confidence interval (CI) between air pollutant exposure and hippocampal volume, to use standard space for neuroimages, and to consider at least age, sex and intracranial volume as covariates or effect modifiers. We meta-analyzed the data with a random-effects model, considering separately adult and child populations. We retrieved four eligible studies in adults and two in children. In adults, the pooled summary β regression coefficients of the association of PM_2.5, PM₁₀ and NO₂ with hippocampal volume showed respectively a stronger association (summary β -7.59, 95% CI -14.08 to -1.11), a weaker association (summary β -2.02, 95% CI -4.50 to 0.47), and no association (summary β -0.44, 95% CI -1.27 to 0.40). The two studies available for children, both carried out in preadolescents, did not show an association between PM_2.5 and hippocampal volume. The inverse association between PM_2.5 and hippocampal volume in adults appeared to be stronger at higher mean PM_2.5 levels. Our results suggest that outdoor PM_2.5 and less strongly PM₁₀ could adversely affect hippocampal volume in adults, a phenomenon that may explain why air pollution has been related to memory loss, cognitive decline, and dementia.

Evaluating Potential Respiratory Benefits of Forest-Based Experiences: A Regional Scale Approach

Background: Several studies have suggested the possibility of obtaining specific respiratory benefits by experiencing forests and other natural resources. Despite this, forests have never been considered according to such potential. This study aims to compare municipalities by considering the absence/presence of tree species generating ‘above threshold’ potential respiratory benefits. Methods: The autonomous Region of Friuli Venezia Giulia in Italy has been assumed as a research area. The natural resource based view (NRBV), postulating the strategic role played by natural resources in achieving both above-average (thus ‘valuable’) and ‘concentrated’ (thus ‘rare’ among competitors) performance, has been adopted. The literature reviews dealing with potential respiratory benefits of biogenic organic compounds (BVOCs) emitted by trees, published within the ‘forest therapy’ research field, have been adopted. Three analysis models rating tree species by their potential respiratory benefits in ‘holistic-general’ (P1), ‘particular’ (P2), and ‘dynamic” terms (P3) have been outlined. The resulting overall potentials of tree species have been assessed by adopting the well-rooted Hollerith distance (HD) model. Tree species have been rated “1” when they satisfy one or more of 58 potential respiratory benefits. Municipalities have been ranked by considering the surface area covered by forest types whose dominant tree species achieve above-average potential respiratory benefits. QGIS software has been adopted to geographically reference the results obtained. Results: (P1) Valuable municipalities include those covered by both coniferous and deciduous forests; (P2–3) Municipalities achieving the highest potential respiratory benefits, in both particular and dynamic terms, have been mapped. Discussion: Forest-based initiatives that are running in the preselected municipalities can be both further improved and diversified in a targeted way. Conclusions: Despite some limitations mostly embedded in the concept of ‘model’, this study allows scholars to reduce uncertainties when locating municipalities in which to conduct local-scale experiments.

Seroprevalence of anti-SARS-CoV-2 antibodies in the Northern Italy population before the COVID-19 second wave

OBJECTIVES

The COVID-19 pandemic is due to SARS-CoV-2 coronavirus infections. It swept across the world in the spring of 2020, and so far it has caused a huge number of hospitalizations and deaths. In the present study, the authors investigated serum anti-SARS-CoV-2 antibody prevalence in the period of June 1-September 25, 2020, in 7561 subjects in Modena, Northern Italy.

MATERIAL AND METHODS

The study population included 5454 workers referred to testing by their companies, and 2107 residents in the Modena area who accessed testing through self-referral.

RESULTS

The authors found the overall seroprevalence to be 4.7% (95% confidence interval [CI] 4.2-5.2%), which was higher in women (5.4%, 95% CI: 4.5-6.2%) than in men (4.3%, 95% CI: 3.7-4.9%), and in the oldest age groups (7.3%, 95% CI: 5.2-9.3% for persons aged 60-69 years, and 11.8%, 95% CI: 8.6-15.1%, for persons aged ≥70 years). Among the occupational categories, the highest seroprevalence was found in healthcare workers (8.8%, 95% CI: 7.0-10.5%), dealers and vehicle repairers (5.2%, 95% CI: 2.9-7.6%), and workers in the sports sector (4.0%, 95% CI: 1.8-6.1%), while there was little or no such evidence for those employed in sectors such as transport and storage, accommodation and restaurant services, and the school system.

CONCLUSIONS

These results have allowed, for the first time, to assess population seroprevalence in this area of Italy severely hit by the epidemic, while at the same time identifying the subgroups at a higher risk of exposure to SARS-CoV-2. Int J Occup Med Environ Health. 2022;35(1):63-74.

Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis

This study employs a spatial autoregressive probit-log linear (SAP-Log) hurdle model to investigate the influencing factors on the probability of death and case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) at the city level in China. The results demonstrate that the probability of death from COVID-19 and the CFR level are 2 different processes with different determinants. The number of confirmed cases and the number of doctors are closely associated with the death probability and CFR, and there exist differences in the CFR and its determinants between cities within Hubei Province and outside Hubei Province. The spatial probit model also presents positive spatial autocorrelation in death probabilities. It is worth noting that the medical resource sharing among cities and enjoyment of free medical treatment services of citizens makes China different from other countries. This study contributes to the growing literature on determinants of CFR with COVID-19 and has significant practical implications.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

Association between exposure to air pollutants and hospitalization for SARS-Cov-2: an ecological time-series study

BACKGROUND

Exposure to air pollutants and illness by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection can cause serious pulmonary impairment.

OBJECTIVE

To identify a possible association between exposure to air pollutants and hospitalizations due to SARS-Cov-2.

DESIGN AND SETTING

Ecological time-series study carried out in Taubaté, Tremembé, and Pindamonhangaba in 2020 and 2021.

METHODS

Study with Sars-Cov-2 hospitalizations with information on hospitalization date, sex and age of the subjects, duration of hospitalization, type of discharge, and costs of these hospitalizations. Statistical analysis was performed through a negative binomial regression, with data on pollutant concentrations, temperature, air relative humidity, and hospitalization date. Coefficients obtained by the analysis were transformed into relative risk for hospitalization, which estimated hospitalizations excess according to an increase in pollutant concentrations.

RESULTS

There were 1,300 hospitalizations and 368 deaths, with a predominance of men (61.7%). These data represent an incidence rate of 250.4 per 100,000 inhabitants and 28.4% hospital lethality. Significant exposure (P value < 0.05) occurred seven days before hospital admission (lag 7) for nitrogen dioxide (NO2) (relative risk, RR = 1.0124) and two days before hospital admission for PM2.5 (RR = 1.0216). A 10 μg/m3 in NO2 concentration would decrease by 320 hospitalizations and » US $ 240,000 in costs; a 5 μg/m3 in PM2.5 concentration would decrease by 278 hospitalizations and » US $ 190,000 in costs.

CONCLUSION

An association between exposure to air pollutants and hospital admission due to Sars-Cov-2 was observed with excess hospitalization and costs for the Brazilian public health system.

The link between COVID-19 mortality and PM2.5 emissions in rural and medium-size municipalities considering population density, dust events, and wind speed

One contemporary issue is how environmental pollution and climate can affect the dissemination and severity of COVID-19 in humans. We documented the first case of association between particulate matter ≤2.5 μm (PM_2.5) and COVID-19 mortality rates that involved rural and medium-sized municipalities in northwestern Mexico, where direct air quality monitoring is absent. Alternatively, anthropogenic PM_2.5 emissions were used to estimate the PM_2.5 exposure in each municipality using two scenarios: 1) considering the fraction derived from combustion of vehicle fuel; and 2) the one derived from modeled anthropogenic sources. This study provides insights to better understand and face future pandemics by examining the relation between PM_2.5 pollution and COVID-19 mortality considering the population density and the wind speed. The main findings are: (i) municipalities with high PM_2.5 emissions and high population density have a higher COVID-19 mortality rate; (ii) the exceptionally high COVID-19 mortality rates of the rural municipalities could be associated to dust events, which are common in these regions where soils without vegetation are dominant; and (iii) the influence of wind speed on COVID-19 mortality rate was evidenced only in municipalities with <100 inhabitants per km². These results confirm the suggestion that high levels of air pollutants associated with high population density and an elevated frequency of dust events may promote an extended prevalence and severity of viral particles in the polluted air of urban, suburban, and rural communities. This supports an additional means of dissemination of the coronavirus SARS-CoV-2, in addition to the direct human-to-human transmission.

Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences

In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM2.5, PM10, O3, NO2, SO2 and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM2.5 and PM10 as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.

Luftverschmutzung als wichtiger Kofaktor bei COVID-19-Sterbefällen

Hintergrund

Die Sterblichkeit bei COVID-19 ist in Anwesenheit kardiopulmonaler Komorbiditäten erhöht. Luftverschmutzung ist ebenfalls mit einer erhöhten Sterblichkeit assoziiert, v. a. vermittelt durch kardiopulmonale Erkrankungen. Beobachtungen zu Beginn der COVID-19-Pandemie zeigten, dass die Sterblichkeit bei COVID-19 v. a. in Regionen mit stärkerer Luftverschmutzung erhöht ist. Ungeklärt ist der Einfluss von Luftverschmutzung für den Krankheitsverlauf bei COVID-19.

Methode

Es wurde eine selektive Literaturrecherche von Studien bis Anfang April 2021 in PubMed zum Zusammenhang von Luftverschmutzung und der COVID-19-Mortalität mit den Suchbegriffen „air pollution AND/OR COVID-19/coronavirus/SARS-CoV‑2 AND/OR mortality“ durchgeführt.

Ergebnisse

Aktuelle Untersuchungen belegen, dass etwa 15 % der weltweiten COVID-19-Todesfälle auf Luftverschmutzung zurückzuführen sind. Der Anteil der luftverschmutzungsbedingten COVID-19-Todesfälle in Europa liegt bei 19 %, in Nordamerika bei 17 % und in Ostasien bei 27 %. Diese Beteiligung der Luftverschmutzung an COVID-19-Todesfällen wurde mittlerweile ebenfalls durch verschiedene Studien aus den USA, Italien und England bestätigt. Luftverschmutzung und COVID-19 führen zu ähnlichen Schäden für das kardiopulmonale System, die möglicherweise den Zusammenhang zwischen Luftverschmutzung und erhöhter COVID-19-Mortalität erklären.

Schlussfolgerung

Der hier gezeigte Umweltaspekt der COVID-19-Pandemie verlangt danach, dass man verstärkt nach wirksamen Maßnahmen zur Reduzierung anthropogener Emissionen, die sowohl Luftverschmutzung als auch den Klimawandel verursachen, streben sollte.

The association between first and second wave COVID-19 mortality in Italy

Background

The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks.

Methods

We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24–June 30, 2020) and to the second wave (September 1–December 31, 2020), using cubic spline regression.

Results

For provinces with the lowest crude mortality rate in the first wave (February–June), i.e. < 22 cases/100,000/month, mortality in the second wave (September–December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period.

Conclusions

These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.

Exposure to air pollution and COVID-19 severity: A review of current insights, management, and challenges

Several epidemiological studies have suggested a link between air pollution and respiratory tract infections. The outbreak of coronavirus disease 2019 (COVID-19) poses a great threat to public health worldwide. However, some parts of the globe have been worse affected in terms of prevalence and deaths than others. The causes and conditions of such variations have yet to be explored. Although some studies indicated a possible correlation between air pollution and COVID-19 severity, there is yet insufficient data for a meaningful answer. This review summarizes the impact of air pollution on COVID-19 infections and severity and discusses the possible management strategies and challenges involved. The available literature investigating the correlation between air pollution and COVID-19 infections and mortality are included in the review. The studies reviewed here suggest that exposure to air pollution, particularly to PM_2.5 and NO₂ , is positively correlated with COVID-19 infections and mortality. Some data indicate that air pollution can play an important role in the airborne transmission of SARS-CoV-2. A high percentage of COVID-19 incidences has been reported in the most polluted areas, where patients needed hospital admission. The available data also show that both short-term and long-term air pollution may enhance COVID-19 severity. However, most of the studies that showed a link between air pollution and COVID-19 infections and mortality did not consider potential confounders during the correlation analysis. Therefore, more specific studies need to be performed focusing on some additional confounders such as individual age, population density, and pre-existing comorbidities to determine the impact of air pollution on COVID-19 infections and deaths. Integr Environ Assess Manag 2021;17:1114-1122. © 2021 SETAC.

Resilient Built Environment: Critical Review of the Strategies Released by the Sustainability Rating Systems in Response to the COVID-19 Pandemic

Since the COVID-19 outbreak, buildings have been viewed as a facilitator of disease spread, where the three main transmission routes (contact, droplets, aerosols) are more likely to happen. However, with proper policies and measures, buildings can be better prepared for re-occupancy and beyond. This study reviews the strategies developed by several Sustainability Rating Systems (SRS, namely WELL, Fitwel and LEED) to respond to any infectious disease and ensure that building occupants protect and maintain their health. The best practices, that are similar between each SRS, highlight that the overall sustainability of the spaces increases if they are resilient. Results indicate that SRS promote a weak sustainability approach since they accept that economic development can reduce natural capitals. SRS are also characterized by an aggregated level of assessment of different criteria that does not allow to map different choices. However, the decomposition of the concept of sustainability in its three bottom lines (i.e., environmental, social and economic) shows that preventive strategies are likely to be systematically adopted as the state-of-the-art. Finally, even if the latest research points out the airborne transmission as the major infection route, the SRS lack analytical measures to address issues such as social distancing.

Transportation, the pathogen vector to rule them all: Evidence from the recent coronavirus pandemic

Introduction

It is common knowledge that mobility refers to a distinct vector for pathogens, but the importance of prevention and the infusion of public health practices within transportation systems is not manifest. Replication studies of this effect are important because transportation remains veiled in modern societies, since its demand is not direct, but derived.

Methods

Variables mirroring transportation and logistics' systems intensity (trade data, the logistics performance index, and investment in transportation) are cross-tabulated with epidemiological data from the recent coronavirus pandemic. As the samples of the data pertain to a dependent commonality, the statistical hypothesis test applicable is McNemar's test. In addition, the statistical power of the test(s) is calculated as a marker of methodological validity and reliability. To further strengthen the analytical methodology, a plethora of descriptive statistics have been calculated and multiple correspondence analysis (MCA) has been conducted.

Results

This work confirms that the domain of transportation bears a strong association with not only mortality of a disease, but its recovery rates as well. All crosstabs provide statistically significant results and the statistical power calculated is very high, signifying the appropriateness of the methodology and the very low probability of Type II error. The MCA results are significant, as well.

Conclusions

The impact, or even the presence of transportation is veiled, as transportation comprises of derived demand dynamics. As such, its activities and even the prerequisites for its efficient operations many times go unnoticed. This work replicates a known effect, that mobility exacerbates the presence of a pathogen. The significance of this research lies on the fact that distinct indicators that reflect transportation and logistics are (though a robust calculatory methodology) statistically associated with epidemiological data.

COVID-19 and environmental concerns: A rapid review

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

Comparison of positive SARS-CoV-2 incidence rate with environmental and socioeconomic factors in northern Illinois

Early studies showed positive associations fine particulate matter (PM2.5), course particulate matter PM10, nitrogen dioxide (NO2) and Ozone (O3) concentrations with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed cases in the United States. One study showed that a1 μg/m3 increase in PM2.5 is associated with an 8% increase in the COVID-19 death rate. Specifically, Chicago and surrounding suburbs have been labeled hot spots in the United States and correlation with air pollutants concentration will help identify specific communities most at risk. A number of studies have identified demographic variables associated with increased positive SARS-CoV-2 and the importance of air quality and socioeconomic factors must be further understood for more targeted public health responses. The results of this analysis noted positive relationships between zip code SARS-CoV-2 incidence rate and environmental and demographic EJ indicators. Evaluation of race and SARS-CoV-2 incidence rate at the zip code level found positive moderate correlations for ethnic minority individuals.

Frequency of Anti-SARS-CoV-2 Antibodies in Various Occupational Sectors in an Industrialized Area of Northern Italy from May to October 2020

The results of a voluntary screening campaign for the presence of anti-SARS-CoV-2 serum antibodies are presented, performed on workers in the highly industrialized province of Modena in northern Italy in the period 18 May-5 October 2020. The employment activities of the subjects that tested positive for anti-SARS-CoV-2 IgM and/or IgG antibodies were determined and classified using the International Standard Industrial Classification of All Economic Activities (ISIC). The distribution across different sectors was compared to the proportion of workers employed in the same sectors in the province of Modena as a whole. Workers with anti-SARS-CoV-2 serum antibodies were mainly employed in manufacturing (60%), trade (12%), transportation (9%), scientific and technical activities (5%), and arts, entertainment and recreation activities (4.5%). Within the manufacturing sector, a cluster of workers with positive serological tests was observed in the meat processing sector, confirming recent data showing a possible increased risk of SARS-CoV-2 infection in these workers.

Perspective of the Relationship between the Susceptibility to Initial SARS-CoV-2 Infectivity and Optimal Nasal Conditioning of Inhaled Air

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as with the influenza virus, has been shown to spread more rapidly during winter. Severe coronavirus disease 2019 (COVID-19), which can follow SARS-CoV-2 infection, disproportionately affects older persons and males as well as people living in temperate zone countries with a tropical ancestry. Recent evidence on the importance of adequately warming and humidifying (conditioning) inhaled air in the nasal cavity for reducing SARS-CoV-2 infectivity in the upper respiratory tract (URT) is discussed, with particular reference to: (i) the relevance of air-borne SARS-CoV-2 transmission, (ii) the nasal epithelium as the initial site of SARS-CoV-2 infection, (iii) the roles of type 1 and 3 interferons for preventing viral infection of URT epithelial cells, (iv) weaker innate immune responses to respiratory viral infections in URT epithelial cells at suboptimal temperature and humidity, and (v) early innate immune responses in the URT for limiting and eliminating SARS-CoV-2 infections. The available data are consistent with optimal nasal air conditioning reducing SARS-CoV-2 infectivity of the URT and, as a consequence, severe COVID-19. Further studies on SARS-CoV-2 infection rates and viral loads in the nasal cavity and nasopharynx in relation to inhaled air temperature, humidity, age, gender, and genetic background are needed in this context. Face masks used for reducing air-borne virus transmission can also promote better nasal air conditioning in cold weather. Masks can, thereby, minimise SARS-CoV-2 infectivity and are particularly relevant for protecting more vulnerable persons from severe COVID-19.

SARS-CoV-2 infection incidence during the first and second COVID-19 waves in Italy

We assessed the relation between COVID-19 waves in Italy, which was severely affected during the pandemic. We evaluated the hypothesis that a larger impact from the first wave (February-May 2020) predicts a smaller peak during the second wave (September-October 2020), in the absence of local changes in public health interventions and area-specific differences in time trends of environmental parameters. Based on publicly available data on province-specific SARS-CoV-2 infections and both crude and multivariable cubic spline regression models, we found that for provinces with the lowest incidence rates in the first wave, the incidence in the second wave increased roughly in proportion with the incidence in the first wave until an incidence of about 500-600 cases/100,000 in the first wave. Above that value, provinces with higher incidences in the first wave experienced lower incidences in the second wave. It appears that a comparatively high cumulative incidence of infection, even if far below theoretical thresholds required for herd immunity, may provide noticeable protection during the second wave. We speculate that, if real, the mechanism for this pattern could be depletion of most susceptible individuals and of superspreaders in the first wave. A population learning effect regarding cautious behavior could have also contributed. Since no area-specific variation of the national policy against the SARS-CoV-2 outbreak was allowed until early November 2020, neither individual behaviors nor established or purported environmental risk factors of COVID-19, such as air pollution and meteorological factors, are likely to have confounded the inverse trends we observed in infection incidence over time.

An Italian individual-level data study investigating on the association between air pollution exposure and Covid-19 severity in primary-care setting

BACKGROUND

Several studies have been focusing on the potential role of atmospheric pollutants in the diffusion and impact on health of Covid-19. This study's objective was to estimate the association between ≤10 μm diameter particulate matter (PM10) exposure and the likelihood of experiencing pneumonia due to Covid-19 using individual-level data in Italy.

METHODS

Information on Covid-19 patients was retrieved from the Italian IQVIA® Longitudinal Patient Database (LPD), a computerized network of general practitioners (GPs) including anonymous data on patients' consultations and treatments. All patients with a Covid-19 diagnosis during March 18th, 2020 - June 30th, 2020 were included in the study. The date of first Covid-19 registration was the starting point of the 3-month follow-up (Index Date). Patients were classified based on Covid-19-related pneumonia registrations on the Index date and/or during follow-up presence/absence. Each patient was assigned individual exposure by calculating average PM10 during the 30-day period preceding the Index Date, and according to GP's office province. A multiple generalized linear mixed model, mixed-effects logistic regression, was used to assess the association between PM10 exposure tertiles and the likelihood of experiencing pneumonia.

RESULTS

Among 6483 Covid-19 patients included, 1079 (16.6%) had a diagnosis of pneumonia. Pneumonia patients were older, more frequently men, more health-impaired, and had a higher individual-level exposure to PM10 during the month preceding Covid-19 diagnosis. The mixed-effects model showed that patients whose PM10 exposure level fell in the second tertile had a 30% higher likelihood of having pneumonia than that of first tertile patients, and the risk for those who were in the third tertile was almost doubled.

CONCLUSION

The consistent findings toward a positive association between PM10 levels and the likelihood of experiencing pneumonia due to Covid-19 make the implementation of new strategies to reduce air pollution more and more urgent.

Back to basics in COVID-19: Antigens and antibodies-Completing the puzzle

The outbreak of the coronavirus disease 2019 (COVID-19) has gathered 1 year of scientific/clinical information. This informational asset should be thoroughly and wisely used in the coming year colliding in a global task force to control this infection. Epidemiology of this infection shows that the available estimates of SARS-CoV-2 infection prevalence largely depended on the availability of molecular testing and the extent of tested population. Within molecular diagnosis, the viability and infectiousness of the virus in the tested samples should be further investigated. Moreover, SARS-CoV-2 has a genetic normal evolution that is a dynamic process. The immune system participates to the counterattack of the viral infection by pathogen elimination, cellular homoeostasis, tissue repair and generation of memory cells that would be reactivated upon a second encounter with the same virus. In all these stages, we still have knowledge to be gathered regarding antibody persistence, protective effects and immunological memory. Moreover, information regarding the intense pro-inflammatory action in severe cases still lacks and this is important in stratifying patients for difficult to treat cases. Without being exhaustive, the review will cover these important issues to be acknowledged to further advance in the battle against the current pandemia.

Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors

Investigating the spatial distribution characteristics of the coronavirus disease 2019 (COVID-19) and exploring the influence of environmental factors that drive it is the basis for formulating rational and efficient prevention and control countermeasures. Therefore, this study aims to analyze the spatial distribution characteristics of COVID-19 pandemic in Beijing and its relationship with the environmental factors. Based on the incidences of new local COVID-19 cases in Beijing from June 11 to July 5, the spatial clustering characteristics of the COVID-19 pandemic in Beijing was investigated using spatial autocorrelation analysis. The relation between COVID-19 cases and environmental factors was assessed using the Spearman correlation analysis. Finally, geographically weighted regression (GWR) was applied to explore the influence of environmental factors on the spatial distribution of COVID-19 cases. The results showed that the development of COVID-19 pandemic in Beijing from June 11 to July 5 could be divided into two stages. The first stage was the outward expansion from June 11 to June 21, and the second stage (from June 22 to July 5) was the growth of the transmission in areas with existing previous cases. In addition, there was a ring of low value clusters around the Xinfadi market. This area was the key area for prevention and control. Population density and distance to Xinfadi market were the most critical factors that explained the pandemic development. The findings of this study can provide useful information for the global fighting against COVID-19.

How Do Inflammatory Mediators, Immune Response and Air Pollution Contribute to COVID-19 Disease Severity? A Lesson to Learn

Inflammatory and immune processes are defensive mechanisms that aim to remove harmful agents. As a response to infections, inflammation and immune response contribute to the pathophysiological mechanisms of diseases. Coronavirus disease 2019 (COVID-19), whose underlying mechanisms remain not fully elucidated, has posed new challenges for the knowledge of pathophysiology. Chiefly, the inflammatory process and immune response appear to be unique features of COVID-19 that result in developing a hyper-inflammatory syndrome, and air pollution, the world's largest health risk factor, may partly explain the behaviour and fate of COVID-19. Understanding the mechanisms involved in the progression of COVID-19 is of fundamental importance in order to avoid the late stage of the disease, associated with a poor prognosis. Here, the role of the inflammatory and immune mediators in COVID-19 pathophysiology is discussed.

Risk of Amyotrophic Lateral Sclerosis and Exposure to Particulate Matter from Vehicular Traffic: A Case-Control Study

(1) Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown etiology. Some occupational and environmental risk factors have been suggested, including long-term air pollutant exposure. We carried out a pilot case-control study in order to evaluate ALS risk due to particulate matter with a diameter of ≤10 µm (PM₁₀) as a proxy of vehicular traffic exposure. (2) Methods: We recruited ALS patients and controls referred to the Modena Neurology ALS Care Center between 1994 and 2015. Using a geographical information system, we modeled PM₁₀ concentrations due to traffic emissions at the geocoded residence address at the date of case diagnosis. We computed the odds ratio (OR) and 95% confidence interval (CI) of ALS according to increasing PM₁₀ exposure, using an unconditional logistic regression model adjusted for age and sex. (3) Results: For the 132 study participants (52 cases and 80 controls), the average of annual median and maximum PM₁₀ concentrations were 5.2 and 38.6 µg/m³, respectively. Using fixed cutpoints at 5, 10, and 20 of the annual median PM₁₀ levels, and compared with exposure <5 µg/m³, we found no excess ALS risk at 5-10 µg/m³ (OR 0.87, 95% CI 0.39-1.96), 10-20 µg/m³ (0.94, 95% CI 0.24-3.70), and ≥20 µg/m³ (0.87, 95% CI 0.05-15.01). Based on maximum PM₁₀ concentrations, we found a statistically unstable excess ALS risk for subjects exposed at 10-20 µg/m³ (OR 4.27, 95% CI 0.69-26.51) compared with those exposed <10 µg/m³. However, risk decreased at 20-50 µg/m³ (OR 1.49, 95% CI 0.39-5.75) and ≥50 µg/m³ (1.16, 95% CI 0.28-4.82). ALS risk in increasing tertiles of exposure showed a similar null association, while comparison between the highest and the three lowest quartiles lumped together showed little evidence for an excess risk at PM₁₀ concentrations (OR 1.13, 95% CI 0.50-2.55). After restricting the analysis to subjects with stable residence, we found substantially similar results. (4) Conclusions: In this pilot study, we found limited evidence of an increased ALS risk due to long-term exposure at high PM₁₀ concentration, though the high statistical imprecision of the risk estimates, due to the small sample size, particularly in some exposure categories, limited our capacity to detect small increases in risk, and further larger studies are needed to assess this relation.