Air pollution and the pandemic: Long-term PM2.5 exposure and disease severity in COVID-19 patients

Health benefits of air pollution reduction: Evidence from economic slowdown in India

This paper evaluates health benefits associated with the impact of air pollution reduction on infant mortality in India. Leveraging plausibly exogenous geographic variation in air pollution due to the post-2010 economic slowdown-a period largely overlooked in the literature-I find that improvements in air quality resulted in a significant decline in infant mortality, particularly through respiratory diseases and biological pathways such as in utero and post-birth exposure. The associated health benefits correspond to 1338 saved infant lives, translating to monetary gains of $312.5 million. The paper advances our understanding of the link between air pollution and human health in settings with elevated air pollution and suboptimal regulatory frameworks.

The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques

BACKGROUND

Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran. Study Design: An ecological study.

METHODS

In this ecological research, three main ML techniques, including decision trees, random forest, and extreme gradient boosting (XGBoost), have been applied to correlate meteorological parameters and air pollutants with infection transmission, hospitalization, and death due to COVID-19 from 1 October 2020 to 1 March 2022. These parameters and pollutants included particulate matter (PM2), sulfur dioxide (SO2 ), nitrogen dioxide (NO2 ), nitric oxide (NO), ozone (O3 ), carbon monoxide (CO), temperature (T), relative humidity (RH), dew point (DP), air pressure (AP), and wind speed (WS).

RESULTS

Based on the three ML techniques, NO2 (lag 5 day), CO (lag 4), and T (lag 25) were the most important environmental features affecting the spread of COVID-19 infection. In addition, the most important features contributing to hospitalization due to COVID-19 included RH (lag 28), T (lag 11), and O3 (lag 10). After adjusting for the number of infections, the most important features affecting the number of deaths caused by COVID-19 were NO2 (lag 20), O3 (lag 22), and NO (lag 23).

CONCLUSION

Our findings suggested that epidemics caused by COVID-19 and (possibly) similarly viral transmitted infections, including flu, air pollutants, and meteorological parameters, can be used to predict their burden on the community and health system. In addition, meteorological and air quality data should be included in preventive measures.

Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands

Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020-February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals' average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017-2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.

Indirect Effects of PM2.5 Exposure on COVID-19 Mortality in Greater Jakarta, Indonesia: An Ecological Study

Background

Air pollution, including PM2.5, was suggested as one of the primary contributors to COVID-19 fatalities worldwide. Jakarta, the capital city of Indonesia, was recognized as one of the ten most polluted cities globally. Additionally, the incidence of COVID-19 in Jakarta surpasses that of all other provinces in Indonesia. However, no study has investigated the correlation between PM2.5 concentration and COVID-19 fatality in Jakarta.

Objective

To investigate the correlation between short-term and long-term exposure to PM2.5 and COVID-19 mortality in Greater Jakarta area.

Methods

An ecological time-trend study was implemented. The data of PM2.5 ambient concentration obtained from Nafas Indonesia and the National Institute for Aeronautics and Space (LAPAN)/National Research and Innovation Agency (BRIN). The daily COVID-19 death data obtained from the City's Health Office.

Findings

Our study unveiled an intriguing pattern: while short-term exposure to PM2.5 showed a negative correlation with COVID-19 mortality, suggesting it might not be the sole factor in causing fatalities, long-term exposure demonstrated a positive correlation. This suggests that COVID-19 mortality is more strongly influenced by prolonged PM2.5 exposure rather than short-term exposure alone. Specifically, our regression analysis estimate that a 50 µg/m3 increase in long-term average PM2.5 could lead to an 11.9% rise in the COVID-19 mortality rate.

Conclusion

Our research, conducted in one of the most polluted areas worldwide, offers compelling evidence regarding the influence of PM2.5 exposure on COVID-19 mortality rates. It emphasizes the importance of recognizing air pollution as a critical risk factor for the severity of viral respiratory infections.

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter ( PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083-1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Association between long-term PM2.5 exposure and risk of Kawasaki disease in children: A nationwide longitudinal cohort study

BACKGROUND

Based on previous studies suggesting air pollution as a potential risk factor for Kawasaki Disease (KD), we examined the association of long-term exposure to childhood fine particulate matter (PM2.5) with the risk of KD.

METHODS

We used National Health Insurance Service-National Sample Cohort data from 2002 to 2019, which included beneficiaries aged 0 years at enrollment and followed-up until the onset of KD or age 5 years. The onset of KD was defined as the first hospital visit record with a primary diagnostic code of M30.3, based on the 10th revision of the International Classification of Diseases, and with an intravenous immunoglobulin (IVIG) prescription. We assigned PM2.5 concentrations to 226 districts, based on mean annual predictions from a machine learning-based ensemble prediction model. We performed Cox proportional-hazards modeling with time-varying exposures and confounders.

RESULTS

We identified 134,634 individuals aged five or less at enrollment and, of these, 1220 individuals who had a KD onset and an IVIG prescription during study period. The average annual concentration of PM2.5 exposed to the entire cohort was 28.2 μg/m³ (Standard Deviation 2.9). For each 5 μg/m³ increase in annual PM2.5 concentration, the hazard ratio of KD was 1.21 (95% CI 1.05-1.39).

CONCLUSIONS

In this nationwide, population-based, cohort study, long-term childhood exposure to PM2.5 was associated with an increased incidence of KD in children. The study highlights plausible mechanisms for the association between PM2.5 and KD, but further studies are needed to confirm our findings.

Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data

Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00-12:00 on the following day, and less so during 12:00-18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00-12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

Causal relationship between particulate matter 2.5 and infectious diseases: A two-sample Mendelian randomization study

Background

Previous observational studies suggested a correlation between particulate matter 2.5 (PM2.5) and infectious diseases, but causality remained uncertain. This study utilized Mendelian randomization (MR) analysis to investigate causal relationships between PM2.5 concentrations and various infectious diseases (COVID-19 infection, hospitalized COVID-19, very severe COVID-19, urinary tract infection, bacterial pneumonia, and intestinal infection).

Methods

Inverse variance weighted (IVW) was the primary method for evaluating causal associations. For significant causal estimates, multiple sensitivity tests were further performed: (i) three additional MR methods (MR-Egger, weighted median, and maximum likelihood method) for supplementing IVW; (ii) Cochrane's Q test for assessing heterogeneity; (iii) MR-Egger intercept test and MR-PRESSO global test for evaluating horizontal pleiotropy; (iv) leave-one-out sensitivity test for determining the stability.

Results

PM2.5 concentration significantly increased the risk of hospitalized COVID-19 (OR = 1.91, 95 % CI: 1.06-3.45, P = 0.032) and very severe COVID-19 (OR = 3.29, 95 % CI: 1.48-7.35, P = 3.62E-03). However, no causal effect was identified for PM2.5 concentration on other infectious diseases (P > 0.05). Furthermore, various sensitivity tests demonstrated the reliability of significant causal relationships.

Conclusions

Overall, lifetime elevated PM2.5 concentration increases the risk of hospitalized COVID-19 and very severe COVID-19. Therefore, controlling air pollution may help mitigate COVID-19 progression.

Association of air pollution and weather conditions during infection course with COVID-19 case fatality rate in the United Kingdom

Although the relationship between the environmental factors, such as weather conditions and air pollution, and COVID-19 case fatality rate (CFR) has been found, the impacts of these factors to which infected cases are exposed at different infectious stages (e.g., virus exposure time, incubation period, and at or after symptom onset) are still unknown. Understanding this link can help reduce mortality rates. During the first wave of COVID-19 in the United Kingdom (UK), the CFR varied widely between and among the four countries of the UK, allowing such differential impacts to be assessed. We developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to estimate the odds ratio of the weather factors (i.e., temperature, sunlight, relative humidity, and rainfall) and air pollution (i.e., ozone, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]) using data between March 26, 2020 and September 15, 2020 in the UK. After retrospectively time adjusted CFR was estimated using back-projection technique, the stepwise model selection method was used to choose the best model based on Akaike information criteria and the closeness between the predicted and observed values of CFR. The risk of death reached its maximum level when the low temperature (6 °C) occurred 1 day before (OR 1.59; 95% CI 1.52-1.63), prolonged sunlight duration (11-14 h) 3 days after (OR 1.24; 95% CI 1.18-1.30) and increased [Formula: see text] (19 μg/m3) 1 day after the onset of symptom (OR 1.12; 95% CI 1.09-1.16). After reopening, many COVID-19 cases will be identified after their symptoms appear. The findings highlight the importance of designing different preventive measures against severe illness or death considering the time before and after symptom onset.

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.

Impact of Ambient Air Pollution Exposure on Long COVID-19 Symptoms: A Cohort Study within the Saudi Arabian Population

Evidence suggests that air pollution, specifically the particulate matters PM2.5 and PM10, plays a key role in exacerbating the risk of prolonged symptoms following COVID-19 infection.

AIM

This study endeavors to elucidate the potential interaction between chronic air pollution exposure and the manifestation of long COVID symptoms within a cohort based in Makkah, Saudi Arabia.

METHODS

Participants included residents from the Makkah region who had recovered from COVID-19 between 2022 and 2023. A comprehensive questionnaire was utilized to gather detailed demographic data and assess the persistent symptoms seen during the post-COVID period. To gauge the environmental exposure to potential risk factors, air sampling for PM10 and PM2.5 was systematically conducted in various locations in Makkah over a year.

RESULTS

Significant positive associations were found between PM2.5 and PM10 exposure and long COVID. Furthermore, specific symptom analysis revealed a significant association between air pollution and shortness of breath (for PM2.5). Only PM2.5 exposure remained statistically significant (RR = 1.32, 95% CI: 1.05, 1.67). In contrast, the association with PM10 remained on the cusp of significance, with an RR of 1.27 (95% CI: 1.00, 1.61).

CONCLUSION

This study highlights the importance of reducing air pollution levels to mitigate the long-term health consequences of COVID-19.

Long-term exposure to air pollution and risk of SARS-CoV-2 infection and COVID-19 hospitalisation or death: Danish nationwide cohort study

BACKGROUND

Early ecological studies have suggested links between air pollution and risk of coronavirus disease 2019 (COVID-19), but evidence from individual-level cohort studies is still sparse. We examined whether long-term exposure to air pollution is associated with risk of COVID-19 and who is most susceptible.

METHODS

We followed 3 721 810 Danish residents aged ≥30 years on 1 March 2020 in the National COVID-19 Surveillance System until the date of first positive test (incidence), COVID-19 hospitalisation or death until 26 April 2021. We estimated residential annual mean particulate matter with diameter ≤2.5 μm (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) in 2019 by the Danish DEHM/UBM model, and used Cox proportional hazards regression models to estimate the associations of air pollutants with COVID-19 outcomes, adjusting for age, sex, individual- and area-level socioeconomic status, and population density.

RESULTS

138 742 individuals were infected, 11 270 were hospitalised and 2557 died from COVID-19 during 14 months. We detected associations of PM2.5 (per 0.53 μg·m-3) and NO2 (per 3.59 μg·m-3) with COVID-19 incidence (hazard ratio (HR) 1.10 (95% CI 1.05-1.14) and HR 1.18 (95% CI 1.14-1.23), respectively), hospitalisations (HR 1.09 (95% CI 1.01-1.17) and HR 1.19 (95% CI 1.12-1.27), respectively) and death (HR 1.23 (95% CI 1.04-1.44) and HR 1.18 (95% CI 1.03-1.34), respectively), which were strongest in the lowest socioeconomic groups and among patients with chronic respiratory, cardiometabolic and neurodegenerative diseases. We found positive associations with BC and negative associations with O3.

CONCLUSION

Long-term exposure to air pollution may contribute to increased risk of contracting severe acute respiratory syndrome coronavirus 2 infection as well as developing severe COVID-19 disease requiring hospitalisation or resulting in death.

Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic review and meta-analysis

Ecological evidence links ambient particulate matter ≤2.5 mm (PM2.5) and the rate of COVID-19 infections, severity, and deaths. However, such studies are unable to account for individual-level differences in major confounders like socioeconomic status and often rely on imprecise measures of PM2.5. We conducted a systematic review of case-control and cohort studies, which rely on individual-level data, searching Medline, Embase, and the WHO COVID-19 database up to 30 June 2022. Study quality was evaluated using the Newcastle-Ottawa Scale. Results were pooled with a random effects meta-analysis, with Egger's regression, funnel plots, and leave-one-out/trim-and-fill sensitivity analyses to account for publication bias. N = 18 studies met inclusion criteria. A 10 μg/m3 increase in PM2.5 was associated with 66 % (95 % CI: 1.31-2.11) greater odds of COVID-19 infection (N = 7) and 127 % (95 % CI: 1.41-3.66) odds of severe illness (hospitalisation, ICU admission, or requiring respiratory support) (N = 6). Pooled mortality results (N = 5) indicated increased deaths due to PM2.5 but were non-significant (OR 1.40; 0.94 to 2.10). Most studies were rated "good" quality (14/18 studies), though there were numerous methodological issues; few used individual-level data to adjust for socioeconomic status (4/18 studies), instead using area-based indicators (11/18 studies) or no such adjustments (3/18 studies). Most severity (9/10 studies) and mortality studies (5/6 studies) were based on people already diagnosed COVID-19, potentially introducing collider bias. There was evidence of publication bias in studies of infection (p = 0.012) but not severity (p = 0.132) or mortality (p = 0.100). While methodological limits and evidence of bias require cautious interpretation of the findings, we found compelling evidence that PM2.5 increases the risk of COVID-19 infection and severe disease, and weaker evidence of an increase in mortality risk.

Long-term exposure to air pollution and COVID-19 severity: A cohort study in Greater Manchester, United Kingdom

Exposure to outdoor air pollution may affect incidence and severity of coronavirus disease 2019 (COVID-19). In this retrospective cohort based on patient records from the Greater Manchester Care Records, all first COVID-19 cases diagnosed between March 1, 2020 and May 31, 2022 were followed until COVID-19 related hospitalization or death within 28 days. Long-term exposure was estimated using mean annual concentrations of particulate matter with diameter ≤2.5 μm (PM_2.5), ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), ozone (O₃), sulphur dioxide (SO₂) and benzene (C₆H₆) in 2019 using a validated air pollution model developed by the Department for Environment, Food and Rural Affairs (DEFRA). The association of long-term exposure to air pollution with COVID-19 hospitalization and mortality were estimated using multivariate logistic regression models after adjusting for potential individual, temporal and spatial confounders. Significant positive associations were observed between PM_2.5, PM_10, NO_2, SO_2, benzene and COVID-19 hospital admissions with odds ratios (95% Confidence Intervals [CI]) of 1.27 (1.25-1.30), 1.15 (1.13-1.17), 1.12 (1.10-1.14), 1.16 (1.14-1.18), and 1.39 (1.36-1.42), (per interquartile range [IQR]), respectively. Significant positive associations were also observed between PM_2.5, PM₁₀, SO₂, or benzene and COVID-19 mortality with odds ratios (95% CI) of 1.39 (1.31-1.48), 1.23 (1.17-1.30), 1.18 (1.12-1.24), and 1.62 (1.52-1.72), per IQR, respectively. Individuals who were older, overweight or obese, current smokers, or had underlying comorbidities showed greater associations between all pollutants of interest and hospital admission, compared to the corresponding groups. Long-term exposure to air pollution is associated with developing severe COVID-19 after a positive SARS-CoV-2 infection, resulting in hospitalization or death.

Air pollution-associated shifts in the human airway microbiome and exposure-associated molecular events

Publications addressing air pollution-induced human respiratory microbiome shifts are reviewed in this article. The healthy respiratory microbiota is characterized by a low density of bacteria, fungi and viruses with high diversity, and usually consists of Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, Fusobacteria, viruses and fungi. The air's microbiome is highly dependent on air pollution levels and is directly reflected within the human respiratory microbiome. In addition, pollutants indirectly modify the local environment in human respiratory organs by reducing antioxidant capacity, misbalancing proteolysis and modulating inflammation, all of which regulate local microbiomes. Improving air quality leads to more diverse and healthy microbiomes of the local air and, subsequently, residents' airways.

Multilevel Analysis of Racial and Ethnic Disparities in COVID-19 Hospitalization among Children with Allergies

Rationale: Previous studies have identified risk factors for coronavirus disease (COVID-19) hospitalization in children. However, these studies have been limited in their ability to disentangle the contribution of racial disparities, allergic comorbidities, and environmental exposures to the development of severe COVID-19 in at-risk children with allergies. Objectives: To examine racial and ethnic disparities in COVID-19 hospitalization and their links to potentially underlying allergic comorbidities and individual and place-based factors in children with allergies. Methods: This is an electronic health record-based retrospective study of children in 2020. The outcome was COVID-19 hospitalization categorized as no hospital care for patients with asymptomatic/mild illness, short stay for patients admitted and discharged within 24 hours, and prolonged stay for patients requiring additional time to discharge (more than 24 h). Mixed-effects and mediation models were used to determine relationships among independent variables, mediators, and COVID-19 hospitalization. Results: Among the 5,258 children with COVID-19 positive test or diagnosis, 10% required a short stay, and 3.7% required a prolonged stay. Black and Hispanic children had higher odds of longer stays than non-Hispanic White children (both P < 0.001). Children with obesity and eosinophilic esophagitis diagnoses had higher odds of short and prolonged stay (all P < 0.05). Area-level deprivation was associated with short stay (adjusted odds ratio [AOR], 15.49; 95% confidence interval [CI], 5.16-45.47 for every 0.1-unit increase) and prolonged stay (AOR, 11.82; 95% CI, 2.25-62.01 for every 0.1-unit increase). Associations between race/ethnicity and COVID-19 hospitalization were primarily mediated by insurance and area-level deprivation, altogether accounting for 99% of the variation in COVID-19 hospitalization. Conclusions: There were racial and ethnic differences in children with allergies and individual and place-based factors related to COVID-19 hospitalization. Differences were primarily mediated by insurance and area-level deprivation, altogether accounting for 99% of the variation in COVID-19 hospitalization. A better understanding of COVID-related morbidity in children and the link to place-based factors is key to developing prevention strategies capable of equitably improving outcomes.

A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization

Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis

BACKGROUND

The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India.

METHOD

District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), Ozone (O₃), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19.

RESULTS

Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O₃, and NO₂ in India.

CONCLUSIONS

COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Air pollution and respiratory infections: the past, present, and future

Air pollution levels across the globe continue to rise despite government regulations. The increase in global air pollution levels drives detrimental human health effects, including 7 million premature deaths every year. Many of these deaths are attributable to increased incidence of respiratory infections. Considering the COVID-19 pandemic, an unprecedented public health crisis that has claimed the lives of over 6.5 million people globally, respiratory infections as a driver of human mortality is a pressing concern. Therefore, it is more important than ever to understand the relationship between air pollution and respiratory infections so that public health measures can be implemented to ameliorate further morbidity and mortality. This article aims to review the current epidemiologic and basic science research on interactions between air pollution exposure and respiratory infections. The first section will present epidemiologic studies organized by pathogen, followed by a review of basic science research investigating the mechanisms of infection, and then conclude with a discussion of areas that require future investigation.

Severe Acute Respiratory Syndrome and Particulate Matter Exposure: A Systematic Review

BACKGROUND

Particulate matter (PM) exposure is responsible for seven million deaths annually and has been implicated in the pathogenesis of respiratory infections such as severe acute respiratory syndrome (SARS). Understanding modifiable risk factors of high mortality, resource burdensome C19 and exposure risks such as PM is key to mitigating their devastating effects. This systematic review focuses on the literature available, identifying the spatial and temporal variation in the role of quantified PM exposure in SARS disease outcome and planning our future experimental studies.

METHODS

The systematic review utilized keywords adhered to the PRISMA guidelines. We included original human research studies in English.

RESULTS

Initial search yielded N = 906, application of eligibility criteria yielded N = 46. Upon analysis of risk of bias N = 41 demonstrated high risk. Studies found a positive association between elevated PM2.5, PM10 and SARS-related outcomes. A geographic and temporal variation in both PM and C19's role was observed.

CONCLUSION

C19 is a high mortality and resource intensive disease which devastated the globe. PM exposure is also a global health crisis. Our systematic review focuses on the intersection of this impactful disease-exposure dyad and understanding the role of PM is important in the development of interventions to prevent future spread of viral infections.

Relationship between meteorological and environmental factors and acute exacerbation for pediatric bronchial asthma: Comparative study before and after COVID-19 in Suzhou

Objective

Climate and environmental change is a well-known factor causing bronchial asthma in children. After the outbreak of coronavirus disease (COVID-19), climate and environmental changes have occurred. The present study investigated the relationship between climate changes (meteorological and environmental factors) and the number of hospitalizations for pediatric bronchial asthma in Suzhou before and after the COVID-19 pandemic.

Methods

From 2017 to 2021, data on daily inpatients diagnosed with bronchial asthma at Children's Hospital of Soochow University were collected. Suzhou Meteorological and Environmental Protection Bureau provided daily meteorological and environmental data. To assess the relationship between bronchial asthma-related hospitalizations and meteorological and environmental factors, partial correlation and multiple stepwise regression analyses were used. To estimate the effects of meteorological and environmental variables on the development of bronchial asthma in children, the autoregressive integrated moving average (ARIMA) model was used.

Results

After the COVID-19 outbreak, both the rate of acute exacerbation of bronchial asthma and the infection rate of pathogenic respiratory syncytial virus decreased, whereas the proportion of school-aged children and the infection rate of human rhinovirus increased. After the pandemic, the incidence of an acute asthma attack was negatively correlated with monthly mean temperature and positively correlated with PM2.5. Stepwise regression analysis showed that monthly mean temperature and O3 were independent covariates (risk factors) for the rate of acute asthma exacerbations. The ARIMA (1, 0, 0) (0, 0, 0) 12 model can be used to predict temperature changes associated with bronchial asthma.

Conclusion

Meteorological and environmental factors are related to bronchial asthma development in children. The influence of meteorological and environmental factors on bronchial asthma may be helpful in predicting the incidence and attack rates.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

Binary Polyamide-Imide Fibrous Superelastic Aerogels for Fire-Retardant and High-Temperature Air Filtration

Fibrous air filtration materials are highly desirable for particle removal from high-temperature emission sources. However, the existing commercial filter materials suffer from either low filtration efficiency or high pressure drop, due to the difficulty in achieving small fiber diameter and high porosity simultaneously. Herein, we report a facile strategy to fabricate mechanical robust fibrous aerogels by using dual-scale sized PAI/BMI filaments and fibers, which are derived from wet spinning and electrospinning technologies, respectively. The creativity of this design is that PAI/BMI filaments can serve as the enhancing skeleton and PAI/BMI fibers can assemble into high-porosity interconnected networks, enabling the improvement of both mechanical property and air filtration performance. The resultant dual-scale sized PAI/PBMI fibrous aerogels show a compressive stress of 8.36 MPa, a high filtration efficiency of 90.78% (particle diameter of 2.5 μm); for particle diameter over 5 μm, they have 99.99% ultra-high filtration efficiency, a low pressure drop of 20 Pa, and high QF of 0.12 Pa^-1, as well as thermostable and fire-retardant properties (thermal decomposition temperature up to 342.7 °C). The successive fabrication of this material is of great significance for the govern of industrial dust.

Ambient Air Pollutant Exposures and COVID-19 Severity and Mortality in a Cohort of Patients with COVID-19 in Southern California

Rationale: Ecological studies have shown air pollution associations with coronavirus disease (COVID-19) outcomes. However, few cohort studies have been conducted. Objectives: To conduct a cohort study investigating the association between air pollution and COVID-19 severity using individual-level data from the electronic medical record. Methods: This cohort included all individuals who received diagnoses of COVID-19 from Kaiser Permanente Southern California between March 1 and August 31, 2020. One-year and 1-month averaged ambient air pollutant (particulate matter ⩽2.5 μm in aerodynamic diameter [PM_2.5], NO₂, and O₃) exposures before COVID-19 diagnosis were estimated on the basis of residential address history. Outcomes included COVID-19-related hospitalizations, intensive respiratory support (IRS), and ICU admissions within 30 days and mortality within 60 days after COVID-19 diagnosis. Covariates included socioeconomic characteristics and comorbidities. Measurements and Main Results: Among 74,915 individuals (mean age, 42.5 years; 54% women; 66% Hispanic), rates of hospitalization, IRS, ICU admission, and mortality were 6.3%, 2.4%, 1.5%, and 1.5%, respectively. Using multipollutant models adjusted for covariates, 1-year PM_2.5 and 1-month NO₂ average exposures were associated with COVID-19 severity. The odds ratios associated with a 1-SD increase in 1-year PM_2.5 (SD, 1.5 μg/m³) were 1.24 (95% confidence interval [CI], 1.16-1.32) for COVID-19-related hospitalization, 1.33 (95% CI, 1.20-1.47) for IRS, and 1.32 (95% CI, 1.16-1.51) for ICU admission; the corresponding odds ratios associated with 1-month NO₂ (SD, 3.3 ppb) were 1.12 (95% CI, 1.06-1.17) for hospitalization, 1.18 (95% CI, 1.10-1.27) for IRS, and 1.21 (95% CI, 1.11-1.33) for ICU admission. The hazard ratios for mortality were 1.14 (95% CI, 1.02-1.27) for 1-year PM_2.5 and 1.07 (95% CI, 0.98-1.16) for 1-month NO₂. No significant interactions with age, sex or ethnicity were observed. Conclusions: Ambient PM_2.5 and NO₂ exposures may affect COVID-19 severity and mortality.

Initially High Correlation between Air Pollution and COVID-19 Mortality Declined to Zero as the Pandemic Progressed: There Is No Evidence for a Causal Link between Air Pollution and COVID-19 Vulnerability

Wu et al. found a strong positive association between cumulative daily county-level COVID-19 mortality and long-term average PM_2.5 concentrations for data up until September 2020. We replicated the results of Wu et al. and extended the analysis up until May 2022. The association between PM_2.5 concentration and cumulative COVID-19 mortality fell sharply after September 2020. Using the data available from Wu et al.'s "updated_data" branch up until May 2022, we found that the effect of a 1 μg/m³ increase in PM_2.5 was associated with only a +0.603% mortality difference. The 95% CI of this difference was between -0.560% and +1.78%, narrow bounds that include zero, with the upper bound far below the Wu et al. estimate. Short-term trends in the initial spread of COVID-19, not a long-term epidemiologic association, caused an early correlation between air pollution and COVID-19 mortality.

The association of airborne particulate matter and benzo[a]pyrene with the clinical course of COVID-19 in patients hospitalized in Poland

Air pollution can adversely affect the immune response and increase the severity of the viral disease. The present study aimed to explore the relationship between symptomatology, clinical course, and inflammation markers of adult patients with coronavirus disease 2019 (COVID-19) hospitalized in Poland (n = 4432) and air pollution levels, i.e., mean 24 h and max 24 h level of benzo(a)pyrene (B(a)P) and particulate matter <10 μm (PM₁₀) and <2.5 μm (PM_2.5) during a week before their hospitalization. Exposures to PM_2.5 and B(a)P exceeding the limits were associated with higher odds of early respiratory symptoms of COVID-19 and hyperinflammatory state: interleukin-6 > 100 pg/mL, procalcitonin >0.25 ng/mL, and white blood cells count >11 × 10³/mL. Except for the mean 24 h PM₁₀ level, the exceedance of other air pollution parameters was associated with increased odds for oxygen saturation <90%. Exposure to elevated PM_2.5 and B(a)P levels increased the odds of oxygen therapy and death. This study evidences that worse air quality is related to increased severity of COVID-19 and worse outcome in hospitalized patients. Mitigating air pollution shall be an integral part of measures undertaken to decrease the disease burden during a pandemic of viral respiratory illness.

Quantifying the impact of air pollution on Covid-19 hospitalisation and death rates in Scotland

Better understanding the risk factors that exacerbate Covid-19 symptoms and lead to worse health outcomes is vitally important in the public health fight against the virus. One such risk factor that is currently under investigation is air pollution concentrations, with some studies finding statistically significant effects while other studies have found no consistent associations. The aim of this paper is to add to this global evidence base on the potential association between air pollution concentrations and Covid-19 hospitalisations and deaths, by presenting the first study on this topic at the small-area scale in Scotland, United Kingdom. Our study is one of the most comprehensive to date in terms of its temporal coverage, as it includes all hospitalisations and deaths in Scotland between 1st March 2020 and 31st July 2021. We quantify the effects of air pollution on Covid-19 outcomes using a small-area spatial ecological study design, with inference using Bayesian hierarchical models that allow for the residual spatial correlation present in the data. A key advantage of our study is its extensive sensitivity analyses, which examines the robustness of the results to our modelling assumptions. We find clear evidence that PM_2.5 concentrations are associated with hospital admissions, with a 1 μgm−3 increase in concentrations being associated with between a 7.4% and a 9.3% increase in hospitalisations. In addition, we find some evidence that PM_2.5 concentrations are associated with deaths, with a 1 μgm−3 increase in concentrations being associated with between a 2.9% and a 10.3% increase in deaths.

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

Air pollution might affect the clinical course of COVID-19 in pediatric patients

Air pollution, to which children are more susceptible than adults, can promote airway inflammation, potentially exaggerating the effects of respiratory viral infection. This study examined the association between the clinical manifestation of COVID-19 in unvaccinated pediatric patients hospitalized in Poland (n = 766) and levels of particulate matter 2.5 (PM_2.5) and benzo(a)pyrene (B(a)P) within a week before hospitalization. Children aged ≤ 12 years exposed to mean and max 24 h B(a)P levels > 1 ng/m³ revealed higher odds of cough, dyspnea, fever, and increased concentrations of inflammatory markers (C-reactive protein, interleukin-6, procalcitonin, white blood cell count). In older patients (13-17 years), elevated mean 24 h B(a)P levels increased odds of dyspnea, fever, and diarrhea, and higher concentrations of C-reactive protein and procalcitonin. Exposure to max 24 h PM_2.5 levels > 20 µg/m³ was associated with higher odds of cough, increased concentrations of C-reactive protein (group ≤12 years), and increased procalcitonin concentration (groups ≤12 years and 13-17 years). In both age groups, length of stay was extended in patients exposed to elevated levels of max 24 h PM_2.5, mean and max 24 h B(a)P. This study suggests that worse air quality, particularly reflected in increased B(a)P levels, might affect the clinical course of COVID-19 in pediatric patients and adds to the disease burden during a pandemic.

Contemporary Concise Review 2021: COVID-19 and other respiratory infections

Bats are likely the primary source of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2).

Minks are highly susceptible to infection by SARS‐CoV‐2.

Transmission from asymptomatic individuals was estimated to account for over 50% of all transmissions of coronavirus disease 2019 (COVID‐19) cases.

SARS‐CoV‐2 is evolving towards more efficient aerosol transmission.

Remdesivir, baricitinib, tocilizumab and dexamethasone are frequently used for the treatment of patients with respiratory failure due to COVID‐19.

There is a rising incidence of non‐tuberculous Mycobacterium pulmonary disease globally, with a higher prevalence in Asian countries than in the Western world.

Protracted bacterial bronchitis is a common cause of chronic productive cough in childhood.

Re‐emergence of respiratory syncytial virus may occur after the relaxation of infection control measures and the reopening of borders during COVID‐19 pandemic.

Effects of short-term ambient particulate matter exposure on the risk of severe COVID-19

OBJECTIVES

Previous studies have suggested a relationship between outdoor air pollution and the risk of coronavirus disease 2019 (COVID-19). However, there is a lack of data related to the severity of disease, especially in China. This study aimed to explore the association between short-term exposure to outdoor particulate matter (PM) and the risk of severe COVID-19.

METHODS

We recruited patients diagnosed with COVID-19 during a recent large-scale outbreak in eastern China caused by the Delta variant. We collected data on meteorological factors and ambient air pollution during the same time period and in the same region where the cases occurred and applied a generalized additive model (GAM) to analyze the effects of short-term ambient PM exposure on the risk of severe COVID-19.

RESULTS

A total of 476 adult patients with confirmed COVID-19 were recruited, of which 42 (8.82%) had severe disease. With a unit increase in PM10, the risk of severe COVID-19 increased by 81.70% (95% confidence interval [CI]: 35.45, 143.76) at a lag of 0-7 days, 86.04% (95% CI: 38.71, 149.53) at a lag of 0-14 days, 76.26% (95% CI: 33.68, 132.42) at a lag of 0-21 days, and 72.15% (95% CI: 21.02, 144.88) at a lag of 0-28 days. The associations remained significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. With a unit increase in PM2.5, the risk of severe COVID-19 increased by 299.08% (95% CI: 92.94, 725.46) at a lag of 0-7 days, 289.23% (95% CI: 85.62, 716.20) at a lag of 0-14 days, 234.34% (95% CI: 63.81, 582.40) at a lag of 0-21 days, and 204.04% (95% CI: 39.28, 563.71) at a lag of 0-28 days. The associations were still significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models.

CONCLUSIONS

Our results indicated that short-term exposure to outdoor PM was positively related to the risk of severe COVID-19, and that reducing air pollution may contribute to the control of COVID-19.

Climate change and global health: A call to more research and more action

There is increasing understanding, globally, that climate change and increased pollution will have a profound and mostly harmful effect on human health. This review brings together international experts to describe both the direct (such as heat waves) and indirect (such as vector-borne disease incidence) health impacts of climate change. These impacts vary depending on vulnerability (i.e., existing diseases) and the international, economic, political, and environmental context. This unique review also expands on these issues to address a third category of potential longer-term impacts on global health: famine, population dislocation, and environmental justice and education. This scholarly resource explores these issues fully, linking them to global health in urban and rural settings in developed and developing countries. The review finishes with a practical discussion of action that health professionals around the world in our field can yet take.

Ethnicity and Clinical Outcomes in Patients Hospitalized for COVID-19 in Spain: Results from the Multicenter SEMI-COVID-19 Registry

(1) Background: This work aims to analyze clinical outcomes according to ethnic groups in patients hospitalized for COVID-19 in Spain. (2) Methods: This nationwide, retrospective, multicenter, observational study analyzed hospitalized patients with confirmed COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry) from 1 March 2020 to 31 December 2021. Clinical outcomes were assessed according to ethnicity (Latin Americans, Sub-Saharan Africans, Asians, North Africans, Europeans). The outcomes were in-hospital mortality (IHM), intensive care unit (ICU) admission, and the use of invasive mechanical ventilation (IMV). Associations between ethnic groups and clinical outcomes adjusted for patient characteristics and baseline Charlson Comorbidity Index values and wave were evaluated using logistic regression. (3) Results: Of 23,953 patients (median age 69.5 years, 42.9% women), 7.0% were Latin American, 1.2% were North African, 0.5% were Asian, 0.5% were Sub-Saharan African, and 89.7% were European. Ethnic minority patients were significantly younger than European patients (median (IQR) age 49.1 (40.5−58.9) to 57.1 (44.1−67.1) vs. 71.5 (59.5−81.4) years, p < 0.001). The unadjusted IHM was higher in European (21.6%) versus North African (11.4%), Asian (10.9%), Latin American (7.1%), and Sub-Saharan African (3.2%) patients. After further adjustment, the IHM was lower in Sub-Saharan African (OR 0.28 (0.10−0.79), p = 0.017) versus European patients, while ICU admission rates were higher in Latin American and North African versus European patients (OR (95%CI) 1.37 (1.17−1.60), p < 0.001) and (OR (95%CI) 1.74 (1.26−2.41), p < 0.001). Moreover, Latin American patients were 39% more likely than European patients to use IMV (OR (95%CI) 1.43 (1.21−1.71), p < 0.001). (4) Conclusion: The adjusted IHM was similar in all groups except for Sub-Saharan Africans, who had lower IHM. Latin American patients were admitted to the ICU and required IMV more often.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.

Modifiable Risk Factors for SARS-CoV-2

As the COVID-19 pandemic has raged on, considerable research has been performed around the world evaluating the environmental, genetic, lifestyle, and nutritional factors that significantly impact the COVID-19 pandemic. Many studies have now shown that key risk factors for SARS-CoV-2 infection, severity, and even death are modifiable. Patients, whether partially vaccinated, fully vaccinated, or not vaccinated, are expecting their clinicians to provide them with evidence-based guidance and to help them prioritize the factors most important for them. In this editorial we review the current state of the research on modifiable risk factors for SARS-CoV-2 infection, disease severity, and death.