Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Issue 4 - Impact of air pollution on COVID-19 mortality and morbidity: An epidemiological and mechanistic review

Air pollution is a major global environment and health concern. Recent studies have suggested an association between air pollution and COVID-19 mortality and morbidity. In this context, a close association between increased levels of air pollutants such as particulate matter ≤2.5 to 10 µM, ozone and nitrogen dioxide and SARS-CoV-2 infection, hospital admissions and mortality due to COVID 19 has been reported. Air pollutants can make individuals more susceptible to SARS-CoV-2 infection by inducing the expression of proteins such as angiotensin converting enzyme (ACE)2 and transmembrane protease, serine 2 (TMPRSS2) that are required for viral entry into the host cell, while causing impairment in the host defence system by damaging the epithelial barrier, muco-ciliary clearance, inhibiting the antiviral response and causing immune dysregulation. The aim of this review is to report the epidemiological evidence on impact of air pollutants on COVID 19 in an up-to-date manner, as well as to provide insights on in vivo and in vitro mechanisms.

Age, gender, and race differences in nasal morphology: Linking air conditioning and filtration efficiency to disparities in air pollution health outcomes and COVID-19 mortality

COVID-19 mortality disparities underscore the critical role of environmental factors, age, sex, and racial demographics. This study investigates how individual variations in nasal morphology - specifically its air conditioning (temperature and humidity regulation) and filtration functions - may influence respiratory health and contribute to differential COVID-19 outcomes. Analysis reveals significant differences in nasal structure and function across racial, sex, and age groups, demonstrating associations with disparities in respiratory vulnerability to environmental stressors such as air pollution, infectious aerosols, and climatic conditions. Specifically, wider nasal cavities (more common in certain populations), larger male nasal passages, and age-related changes like mucosal atrophy and increased endonasal volume impair air conditioning and filtration efficiency. These morphological variations influence the nose's protective capacity, which is critical for shielding the middle and lower airways from environmental exposures. Populations with inherently reduced nasal filtration and conditioning efficiency demonstrate higher vulnerability, aligning with U.S. mortality patterns for both COVID-19 and air pollution across demographic groups. This suggests a direct link between nasal anatomy and population-level health disparities. These findings provide novel insights into the role of nasal anatomy in mediating respiratory health disparities by modulating individual responses to environmental exposures, air pollution, and pathogens. They highlight the need to address critical gaps in understanding how airway characteristics influence susceptibility to environmental stressors and to develop targeted interventions aimed at reducing health disparities.

Assessment of air pollution and mortality in Portugal using AirQ+ and the effects of COVID-19 on their relationship

This study uses the World Health Organization's AirQ+ model to assess the relationship between air pollution and mortality in Portugal from 2010 to 2021, focusing on the impact of the COVID-19 pandemic. By integrating AirQ+ with Linear Mixed Models, we analyzed long-term air pollution data and its health effects. Results indicate a significant decrease in [Formula: see text] and [Formula: see text] concentrations in 2020 and 2021 due to COVID-19 restrictions and reduced transportation emissions. Conversely, [Formula: see text] exposure slightly increased. The model estimates over 5000 annual deaths from [Formula: see text] and [Formula: see text] exposure and over 139 annual deaths from [Formula: see text]-related respiratory diseases for 2010-2021. Despite limitations like the need for better assessment of pollutant mixtures and climatic variables, the study shows a decrease in [Formula: see text]-related disease burden during the pandemic. These trends reflect anomalies in mortality and pollution data rather than policy improvements. The study underscores the utility of AirQ+ in guiding public health strategies and tracking progress toward the 2030 Agenda, offering insights into reducing mortality and morbidity through decreased air pollutant exposure and highlighting the need for sustained, multidimensional pollution reduction efforts.

Adding to the Woes: Heterogeneous Effects of Air Pollution on Pandemic Patients

While the direct health impacts of air pollution are widely discussed, its indirect effects, particularly during pandemics, are less explored. Utilizing detailed individual-level data from all designated hospitals in Wuhan during the initial COVID-19 outbreak, we examine the impact of air pollution exposure on treatment costs and health outcomes for COVID-19 patients. Our findings reveal that patients exposed more intensively to air pollution, identified by their residence in downwind areas of high-polluting enterprises, not only had worsened health outcomes but also consumed more medical resources. This increased demand is primarily due to their heightened vulnerability to cardiopulmonary conditions. Using a causal machine learning method called Causal Forests to estimate individual treatment effects, we uncover significant heterogeneity across demographic and socioeconomic characteristics, with older and economically disadvantaged patients showing particular vulnerability. These findings highlight the importance of considering environmental factors in pandemic preparedness and suggest the value of targeted interventions that account for demographic and socioeconomic variations in vulnerability.

Comparison of the rate of healthcare encounters for influenza from source-specific PM2.5 before and after tier 3 vehicle standards in New York state

BACKGROUND

Influenza healthcare encounters in adults associated with specific sources of PM2.5 is an area of active research.

OBJECTIVE

Following 2017 legislation requiring reductions in emissions from light-duty vehicles, we hypothesized a reduced rate of influenza healthcare encounters would be associated with concentrations of PM2.5 from traffic sources in the early implementation period of this regulation (2017-2019).

METHODS

We used the Statewide Planning and Research Cooperative System (SPARCS) to study adult patients hospitalized (N = 5328) or treated in the emergency department (N = 18,247) for influenza in New York State. Using a modified case-crossover design, we estimated the excess rate (ER) of influenza hospitalizations and emergency department visits associated with interquartile range increases in source-specific PM2.5 concentrations (e.g., spark-ignition emissions [GAS], biomass burning [BB], diesel [DIE]) in lag day(s) 0, 0-3 and 0-6. We then evaluated whether ERs differed after Tier 3 implementation (2017-2019) compared to the period prior to implementation (2014-2016).

RESULTS

Each interquartile range increase in DIE in lag days 0-6 was associated with a 21.3% increased rate of influenza hospitalization (95% CI: 6.9, 37.6) in the 2014-2016 period, and a 6.3% decreased rate (95% CI: -12.7, 0.5) in the 2017-2019 period. The GAS/influenza excess rates were larger in the 2017-2019 period than the 2014-2016 period for emergency department visits. We also observed a larger ER associated with increased BB in the 2017-2019 period compared to the 2014-2016 period.

IMPACT STATEMENT

We present an accountability study on the impact of the early implementation period of the Tier 3 vehicle emission standards on the association between specific sources of PM2.5 air pollution on influenza healthcare encounters in New York State. We found that the association between gasoline emissions and influenza healthcare encounters did not lessen in magnitude between periods, possibly because the emissions standards were not yet fully implemented. The reduction in the rates of influenza healthcare encounters associated with diesel emissions may be reflective of past policies to reduce the toxicity of diesel emissions. Accountability studies can help policy makers and environmental scientists better understand the timing of pollution changes and associated health effects.

Independent and combined effects of long-term air pollution exposure and genetic predisposition on COVID-19 severity: A population-based cohort study

The relationships between air pollution, genetic susceptibility, and COVID-19-related outcomes, as well as the potential interplays between air pollution and genetic susceptibility, remain largely unexplored. The Cox proportional hazards model was used to assess associations between long-term exposure to air pollutants and the risk of COVID-19 outcomes (infection, hospitalization, and death) in a COVID-19-naive cohort (n = 458,396). Additionally, associations between air pollutants and the risk of COVID-19 severity (hospitalization and death) were evaluated in a COVID-19 infection cohort (n = 110,216). Furthermore, this study investigated the role of host genetic susceptibility in the relationships between exposure to air pollutants and the development of COVID-19-related outcomes. Long-term exposure to air pollutants was significantly associated with an increased risk of COVID-19-related outcomes in the COVID-19 naive cohort. Similarly, in COVID-19 infection cohort, hazard ratios (HRs) for COVID-19 hospital admission were 1.23 (1.19, 1.27) for PM2.5 and 1.22 (1.17, 1.26) for PM10, whereas HRs for COVID-19 death were 1.28 (1.18, 1.39) for PM2.5 and 1.25 (1.16, 1.36) for PM10. Notably, significant interactions were found between PM2.5/PM10 and genetic susceptibility in COVID-19 death. In COVID-19 infection cohort, participants with both high genetic risk and high air pollutants exposure had 1.86- to 1.97-fold and 1.91- to 2.14-fold higher risk of COVID-19 hospitalization and death compared to those with both low genetic risk and low air pollutants exposure. Exposure to air pollution is significantly associated with an increased burden of severe COVID-19, and air pollution-gene interactions may play a crucial role in the development of COVID-19-related outcomes.

COVID-19 related complications

The COVID-19 pandemic has significantly impacted global healthcare systems, revealed vulnerabilities and prompted a re-evaluation of medical practices. Acute complications from the virus, including cardiovascular and neurological issues, have underscored the necessity for timely medical interventions. Advances in diagnostic methods and personalized therapies have been pivotal in mitigating severe outcomes. Additionally, Long COVID has emerged as a complex challenge, affecting various body systems and leading to respiratory, cardiovascular, neurological, psychological, and musculoskeletal problems. This broad spectrum of complications highlights the importance of multidisciplinary management approaches that prioritize therapy, rehabilitation, and patient-centered care. Vulnerable populations such as paediatric patients, pregnant women, and immunocompromised individuals face unique risks and complications, necessitating continuous monitoring and tailored management strategies to reduce morbidity and mortality associated with COVID-19.

Social infrastructure and the prevalence of deaths of despair: The role of concentrated disadvantage

This study aims to understand the associations between social infrastructure and deaths of despair (DoD), with special attention to the role of concentrated disadvantage. I assembled a county-level dataset in the United States from various sources. A series of multilevel regression results indicate that (1) social infrastructure is associated with DoD; a higher density of public institutions (e.g., libraries) and religious organizations are associated with a lower prevalence of DoD; and (3) the associations between social infrastructure and DoD vary by concentrated disadvantage in that the protective effects of social infrastructure are observed only in less disadvantaged countries.

Creating respiratory pathogen-free environments in healthcare and nursing-care settings: a comprehensive review

Hospital- and nursing-care-acquired infections are a growing problem worldwide, especially during epidemics, posing a significant threat to older adults in geriatric settings. Intense research during the COVID-19 pandemic highlighted the prominent role of aerosol transmission of pathogens. Aerosol particles can easily adsorb different airborne pathogens, carrying them for a long time. Understanding the dynamics of airborne pathogen transmission is essential for controlling the spread of many well-known pathogens, like the influenza virus, and emerging ones like SARS-CoV-2. Particles smaller than 50 to 100 µm remain airborne and significantly contribute to pathogen transmission. This review explores the journey of pathogen-carrying particles from formation in the airways, through airborne travel, to deposition in the lungs. The physicochemical properties of emitted particles depend on health status and emission modes, such as breathing, speaking, singing, coughing, sneezing, playing wind instruments, and medical interventions. After emission, sedimentation and evaporation primarily determine particle fate. Lung deposition of inhaled aerosol particles can be studied through in vivo, in vitro, or in silico methods. We discuss several numerical lung models, such as the Human Respiratory Tract Model, the LUng Dose Evaluation Program software (LUDEP), the Stochastic Lung Model, and the Computational Fluid Dynamics (CFD) techniques, and real-time or post-evaluation methods for detecting and characterizing these particles. Various air purification methods, particularly filtration, are reviewed for their effectiveness in healthcare settings. In the discussion, we analyze how this knowledge can help create environments with reduced PM2.5 and pathogen levels, enhancing safety in healthcare and nursing-care settings. This is particularly crucial for protecting older adults, who are more vulnerable to infections due to weaker immune systems and the higher prevalence of chronic conditions. By implementing effective airborne pathogen control measures, we can significantly improve health outcomes in geriatric settings.

Modification of ZIF-8 Membranes for Gas Separation Using X-ray Radiation

We report an X-ray radiation-induced modification of the structure and gas permeation behavior of ZIF-8 membranes. With 300 min irradiation time, CO2 permeance decreases by only 9 %, while N2 and CH4 permeances reduce by 75 and 65 %, respectively, leading to 3.7- and 2.6-fold enhancements in ideal selectivity for CO2/N2 and CO2/CH4.

Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning

INTRODUCTION

Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as climate change, is an important public health issue, especially with regard to COPD.

METHOD

Using the most recent county-level age-adjusted COPD mortality rates among adults older than 25 years, this study aimed to investigate the spatial trajectory of COPD in the United States between 2001 and 2020. Global Moran's I was used to investigate spatial relationships utilising data from Terra satellite for night-time land surface temperatures (LSTnt), which served as an indicator of warming within the same time period across the United States. The forest-based classification and regression model (FCR) was applied to predict mortality rates.

RESULTS

It was found that COPD mortality over the 20-year period was spatially clustered in certain counties. Moran's I statistic (I=0.18) showed that the COPD mortality rates increased with LSTnt, with the strongest spatial association in the eastern and south-eastern counties. The FCR model was able to predict mortality rates based on LSTnt values in the study area with a R2 value of 0.68.

CONCLUSION

Policymakers in the United States could use the findings of this study to develop long-term spatial and health-related strategies to reduce the vulnerability to global warming of patients with acute respiratory symptoms.

Environmental, climatic, socio-economic factors and non-pharmacological interventions: A comprehensive four-domain risk assessment of COVID-19 hospitalization and death in Northern Italy

INTRODUCTION

Up to now, studies on environmental, climatic, socio-economic factors, and non-pharmacological interventions (NPI) show diverse associations, often contrasting, with COVID-19 spread or severity. Most studies used large-scale, aggregated data, with limited adjustment for individual factors, most of them focused on viral spread than severe outcomes. Moreover, evidence simultaneously evaluating variables belonging to different exposure domains is scarce, and none analysing their collective impact on an individual level.

METHODS

Our population-based retrospective cohort study aimed to assess the comprehensive role played by exposure variables belonging to four different domains, environmental, climatic, socio-economic, and non-pharmacological interventions (NPI), on individual COVID-19-related risk of hospitalization and death, analysing data from all patients (no. 68472) tested positive to a SARS-CoV-2 swab in Modena Province (Northern Italy) between February 2020 and August 2021. Using adjusted Cox proportional hazard models, we estimated the risk of severe COVID-19 outcomes, investigating dose-response relationships through restricted cubic spline modelling for hazard ratios.

RESULTS

Several significant associations emerged: long-term exposure to air pollutants (NO2, PM10, PM2.5) was linked to hospitalization risk in a complex way and showed an increased risk for death; while humidity was inversely associated; temperature showed a U-shaped risk; wind speed showed a linear association with both outcomes. Precipitation increased hospitalization risk but decreased mortality. Socio-economic and NPI indices showed clear linear associations, respectively negative and positive, with both outcomes.

CONCLUSIONS

Our findings offer insights for evidence-based policy decisions, improving precision healthcare practices, and safeguarding public health in future pandemics. Refinement of pandemic response plans by healthcare authorities could benefit significantly.

Urbanization and Infectious Disease

The United Nations currently estimates that over half of the global population has lived in cities since 2017 and that this proportion is continuing to grow, particularly in the Global South. While urbanization is not new, increased population density combined with accelerating rates of (re)emerging and noncommunicable diseases as well as growing economic disparities has created new challenges to human health and well-being. Here, I examine peri-urban communities, peripheral settlements on the edges of urban areas populated by rural people, and argue that these areas are often overlooked, despite becoming increasingly common. Thus, human biologists should move beyond categorizing these spaces as transitional. Using unplanned, peri-urban communities around Lima, Peru as a case study, I detail the complexity of political ecological factors that impact infectious disease risk and rates in peri-urban communities. Using disease mechanisms, I demonstrate the importance of a biocultural approach and a political ecology perspective when investigating infectious disease. I highlight how human biologists and anthropologists are uniquely positioned to explore the heterogeneity of infectious disease patterns and pathways in an increasingly urbanized world.

Ultralow-resistance and self-sterilization biodegradable nanofibrous membranes for efficient PM0.3 removal and machine learning-assisted health management

The development of multifunctional nanofibrous membranes (NFMs) that enable anti-viral protection during air purification and respiratory disease diagnosis for health management is of increasing importance. Herein, we unraveled a heterostructure-enhanced electro-induced stereocomplexation (HEIS) strategy to fabrication of poly(lactic acid) (PLA) NFMs enabling a combination of efficient PM removal, respiratory monitoring and self-sterilization. The strategy involved an electro-induced stereocomplexation (EIS) approach to trigger the generation of hydrogen bonds between enantiomeric poly(L-lactic acid) (PLLA) and poly(D-lactic acid) (PDLA) chains, promoting CO dipole alignment and molecular polarization during electrospinning. This was further enhanced by incorporation of Ag-doped TiO2 (Ag-TIO) nanodielectrics to promote the electroactivity and surface activity, conferring profound refinement of PLA nanofibers (from 460 nm to an ultralow level of 168 nm) and high porosities of over 91 %. Arising from the sustainable generation of plentiful charges based on triboelectric nanogenerator (TENG) mechanisms, the electroactive PLA NFMs exhibited remarkable triboelectric properties even in high-humidity environments (80 %RH), excellent PM0.3 filtration efficiency with an ultralow pressure drop (93.1 %, 31.8 Pa, 32 L/min), and 100 % antimicrobial efficiency against both E. coli and S. aureus. Moreover, a deep-learning algorithm based on convolutional neural network (CNN) was proposed to recognize various respiratory patterns. The proposed strategy confers the biodegradable NFMs an unusual combination of ultralow-resistance air purification and machine learning-assisted health management, signifying promising prospects in environmental protection and personal healthcare.

An Information-Theoretic Method for Identifying Effective Treatments and Policies at the Beginning of a Pandemic

Identifying effective treatments and policies early in a pandemic is challenging because only limited and noisy data are available and biological processes are unknown or uncertain. Consequently, classical statistical procedures may not work or require strong structural assumptions. We present an information-theoretic approach that can overcome these problems and identify effective treatments and policies. The efficacy of this approach is illustrated using a study conducted at the beginning of the COVID-19 pandemic. We applied this approach with and without prior information to the limited international data available in the second month (24 April 2020) of the COVID-19 pandemic. To check if our results were plausible, we conducted a second statistical analysis using an international sample with millions of observations available at the end of the pandemic's pre-vaccination period (mid-December 2020). Even with limited data, the information-theoretic estimates from the original study performed well in identifying influential factors and helped explain why death rates varied across nations. Later experiments and statistical analyses based on more recent, richer data confirm that these factors contribute to survival. Overall, the proposed information-theoretic statistical technique is a robust method that can overcome the challenges of under-identified estimation problems in the early stages of medical emergencies. It can easily incorporate prior information from theory, logic, or previously observed emergencies.

Assessment of the air pollutants on automobiles emission dispersion: the effects of barrier height and the road surface temperature

In the paper, a mathematical model was constructed that describes the specifications of the wind flow and the dispersion of pollutants, taking into account the variable temperature on the roadway surface, which varies depending on the time for some quarter of the city of Almaty. The impact of the traffic tidal flow was studied based on the data of measuring passing vehicles as a source of pollution by the CFD and on the spatial distribution of pollutants for various types of pollution. A test problem was performed to validate the numerical algorithm and the mathematical model. From the obtained numerical solutions, it was determined that the existing barriers along the road have a positive effect on pedestrian zones regardless of the type of pollution. It was also found that, taking into account the variable temperature on the carriageway, the presence of barriers with a height of 4 m shows favorable behavior on the adjacent areas, in which the average concentration value drops by 6.4 times for the pedestrian zone, which is located on the left side and 2.9 times for the pedestrian zone, which is on the right side compared to the option without a barrier.

Indirect and direct effects of nighttime light on COVID-19 mortality using satellite image mapping approach

The COVID-19 pandemic has highlighted the importance of understanding environmental factors in disease transmission. This study aims to explore the spatial association between nighttime light (NTL) from satellite imagery and COVID-19 mortality. It particularly examines how NTL serves as a pragmatic proxy to estimate human interaction in illuminated nocturnal area, thereby impacting viral transmission dynamics to neighboring areas, which is defined as spillover effect. Analyzing 43,199 COVID-19 deaths from national mortality data during January 2020 and October 2022, satellite-derived NTL data, and various environmental and socio-demographic covariates, we employed the Spatial Durbin Error Model to estimate the direct and indirect effect of NTL on COVID-19 mortality. Higher NTL was initially directly linked to increased COVID-19 mortality but this association diminished over time. The spillover effect also changed: during the early 3rd wave (December 2020 - February 2021), a unit (nanoWatts/sr/cm2) increase in NTL led to a 7.9% increase in neighboring area mortality (p = 0.013). In contrast, in the later 7th wave (July - September 2022), dominated by Omicron, a unit increase in NTL resulted in an 8.9% decrease in mortality in neighboring areas (p = 0.029). The shift from a positive to a negative spillover effect indicates a change in infection dynamics during the pandemic. The study provided a novel approach to assess nighttime human activity and its influence on disease transmission, offering insights for public health strategies utilizing satellite imagery, particularly when direct data collection is impractical while the collection from space is readily available.

Spatiotemporally Detailed Quantification of Air Quality Benefits of Emissions Reductions-Part I: Benefit-per-Ton Estimates for Canada and the U.S

The U.S. EPA's Community Multiscale Air Quality (CMAQ)-adjoint model is used to map monetized health benefits (defined here as benefits of reduced mortality from chronic PM2.5 exposure) in the form of benefits per ton (of emissions reduced) for the U.S. and Canada for NOx, SO2, ammonia, and primary PM2.5 emissions. The adjoint model provides benefits per ton (BPTs) that are location-specific and applicable to various sectors. BPTs show significant variability across locations, such that only 20% of primary PM2.5 emissions in each country makes up more than half of its burden. The greatest benefits in terms of BPTs are for primary PM2.5 reductions, followed by ammonia. Seasonal differences in benefits vary by pollutant: while PM2.5 benefits remain high across seasons, BPTs for reducing ammonia are much higher in the winter due to the increased ammonium nitrate formation efficiency. Based on our location-specific BPTs, we estimate a total of 91,000 U.S. premature mortalities attributable to natural and anthropogenic emissions.

County-level associations between drinking water PFAS contamination and COVID-19 mortality in the United States

BACKGROUND

Epidemiologic and animal studies both support relationships between exposures to per- and polyfluoroalkyl substances (PFAS) and harmful effects on the immune system. Accordingly, PFAS have been identified as potential environmental risk factors for adverse COVID-19 outcomes.

OBJECTIVE

Here, we examine associations between PFAS contamination of U.S. community water systems (CWS) and county-level COVID-19 mortality records. Our analyses leverage two datasets: one at the subnational scale (5371 CWS serving 621 counties) and one at the national scale (4798 CWS serving 1677 counties). The subnational monitoring dataset was obtained from statewide drinking monitoring of PFAS (2016-2020) and the national monitoring dataset was obtained from a survey of unregulated contaminants (2013-2015).

METHODS

We conducted parallel analyses using multilevel quasi-Poisson regressions to estimate cumulative incidence ratios for the association between county-level measures of PFAS drinking water contamination and COVID-19 mortality prior to vaccination onset (Jan-Dec 2020). In the primary analyses, these regressions were adjusted for several county-level sociodemographic factors, days after the first reported case in the county, and total hospital beds.

RESULTS

In the subnational analysis, detection of at least one PFAS over 5 ng/L was associated with 12% higher [95% CI: 4%, 19%] COVID-19 mortality. In the national analysis, detection of at least one PFAS above the reporting limits (20-90 ng/L) was associated with 13% higher [95% CI: 8%, 19%] COVID-19 mortality.

IMPACT STATEMENT

Our findings provide evidence for an association between area-level drinking water PFAS contamination and higher COVID-19 mortality in the United States. These findings reinforce the importance of ongoing state and federal monitoring efforts supporting the U.S. Environmental Protection Agency's 2024 drinking water regulations for PFAS. More broadly, this example suggests that drinking water quality could play a role in infectious disease severity. Future research would benefit from study designs that combine area-level exposure measures with individual-level outcome data.

Spatial nonstationarity and the role of environmental metal exposures on COVID-19 mortality in New Mexico

Worldwide, the COVID-19 pandemic has been influenced by a combination of environmental and sociodemographic drivers. To date, population studies have overwhelmingly focused on the impact of societal factors. In New Mexico, the rate of COVID-19 infection and mortality varied significantly among the state's geographically dispersed, and racially and ethnically diverse populations who are exposed to unique environmental contaminants related to resource extraction industries (e.g. fracking, mining, oil and gas exploration). By looking at local patterns of COVID-19 disease severity, we sought to uncover the spatially varying factors underlying the pandemic. We further explored the compounding role of potential long-term exposures to various environmental contaminants on COVID-19 mortality prior to widespread applications of vaccinations. To illustrate the spatial heterogeneity of these complex associations, we leveraged multiple modeling approaches to account for spatial non-stationarity in model terms. Multiscale geographically weighted regression (MGWR) results indicate that increased potential exposure to fugitive mine waste is significantly associated with COVID-19 mortality in areas of the state where socioeconomically disadvantaged populations were among the hardest hit in the early months of the pandemic. This relationship is paradoxically reversed in global models, which fail to account for spatial relationships between variables. This work contributes both to environmental health sciences and the growing body of literature exploring the implications of spatial nonstationarity in health research.

Air quality in a small city: criteria pollutants, volatile organic compounds, metals, and microbes

This work presents a year-long integral study of air quality parameters in Ciudad Real, a small city in the center of Spain, and its influence on the nearby national park, Las Tablas de Daimiel. The study covers meteorological parameters and criteria pollutants such as O3, NO, NO2, SO2, and PM10. Additionally, for each month, a 1-week campaign was performed sampling air in sorbent tubes with 8-h time resolution to analyze anthropogenic volatile organic compounds and the effects of seasons, daytime, and working-weekend days. During these campaigns, 24-h PM2.5 samples were also collected to measure the load of bacteria and fungi, as well as the trace concentrations of elements.The city and the national park NOx profiles showed that emissions from the town had a non-perceivable effect on the protected area. PM10 levels in Ciudad Real were influenced by Saharan intrusions, as was the national park; however, Ciudad Real had a higher contribution from anthropogenic sources. Ozone levels were lower in the city during the cold season due to the higher concentration of NOx and have not changed significantly in the last decade.The VOCs with higher average concentrations were toluene, m,p-xylene, benzene, methylene chloride, and o-xylene, with traffic being the main source of these pollutants in the city. For benzene and carbon tetrachloride levels, weak carcinogenic risks were estimated. In PM2.5, the most abundant metals were Na, Zn, Mg, Ca, Al, Fe, and K. The carcinogenic and non-carcinogenic risks estimated from the levels of the studied metals were negligible. Bacterial and fungal counts positively correlated with the concentration of PM2.5. Microbial community composition showed seasonal variability, with the dominance of human pathogenic bacteria which correlated with certain pollutants such as SO2. Bacillus and Cutibacterium were the most abundant genera.

Diesel exhaust particle inhalation in conjunction with high-fat diet consumption alters the expression of pulmonary SARS-COV-2 infection pathways, which is mitigated by probiotic treatment in C57BL/6 male mice

BACKGROUND

Both exposure to air pollutants and obesity are associated with increased incidence and severity of COVID-19 infection; however, the mechanistic pathways involved are not well-characterized. After being primed by the transmembrane protease serine 2 (TMPRSS2) or furin protease, SARS-CoV-2 uses the angiotensin-converting enzyme (ACE)-2 receptor to enter respiratory epithelial cells. The androgen receptor (AR) is known to regulate both TMPRSS2 and ACE2 expression, and neuropilin-1 (NRP1) is a proposed coreceptor for SARS-CoV-2; thus, altered expression of these factors may promote susceptibility to infection. As such, this study investigated the hypothesis that inhalational exposure to traffic-generated particulate matter (diesel exhaust particulate; DEP) increases the expression of those pathways that mediate SARS-CoV-2 infection and susceptibility, which is exacerbated by the consumption of a high-fat (HF) diet.

METHODS

Four- to six-week-old male C57BL/6 mice fed either regular chow or a HF diet (HF, 45% kcal from fat) were randomly assigned to be exposed via oropharyngeal aspiration to 35 µg DEP suspended in 35 µl 0.9% sterile saline or sterile saline only (control) twice a week for 30 days. Furthermore, as previous studies have shown that probiotic treatment can protect against exposure-related inflammatory outcomes in the lungs, a subset of study animals fed a HF diet were concurrently treated with 0.3 g/day Winclove Ecologic® Barrier probiotics in their drinking water throughout the study.

RESULTS

Our results revealed that the expression of ACE2 protein increased with DEP exposure and that TMPRSS2, AR, NRP1, and furin protein expression increased with DEP exposure in conjunction with a HF diet. These DEP ± HF diet-mediated increases in expression were mitigated with probiotic treatment.

CONCLUSION

These findings suggest that inhalational exposure to air pollutants in conjunction with the consumption of a HF diet contributes to a more susceptible lung environment to SARS-CoV-2 infection and that probiotic treatment could be beneficial as a preventative measure.

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data

BACKGROUND

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted.

OBJECTIVE

This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach.

METHODS

In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance.

RESULTS

Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure.

CONCLUSIONS

The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

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.

Air Pollution in Relation to COVID-19 Morbidity and Mortality: A Large Population-Based Cohort Study in Catalonia, Spain (COVAIR-CAT)

INTRODUCTION

Evidence from epidemiological studies based on individual-level data indicates that air pollution may be associated with coronavirus disease 2019 (COVID-19) severity. We aimed to test whether (1) long-term exposure to air pollution is associated with COVID-19-related hospital admission or mortality in the general population; (2) short-term exposure to air pollution is associated with COVID-19-related hospital admission following COVID-19 diagnosis; (3) there are vulnerable population subgroups; and (4) the influence of long-term air pollution exposure on COVID-19-related hospital admissions differed from that for other respiratory infections.

METHODS

We constructed a cohort covering nearly the full population of Catalonia through registry linkage, with follow- up from January 1, 2015, to December 31, 2020. Exposures at residential addresses were estimated using newly developed spatiotemporal models of nitrogen dioxide (NO23), particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5), particulate matter ≤10 μm in aerodynamic diameter (PM10), and maximum 8-hr-average ozone (O3) at a spatial resolution of 250 m for the period 2018-2020.

RESULTS

The general population cohort included 4,660,502 individuals; in 2020 there were 340,608 COVID-19 diagnoses, 47,174 COVID-19-related hospital admissions, and 10,001 COVID-19 deaths. Mean (standard deviation) annual exposures were 26.2 (10.3) μg/m3 for NO2, 13.8 (2.2) μg/m3 for PM2.5, and 91.6 (8.2) μg/m3 for O3. In Aim 1, an increase of 16.1 μg/m3 NO2 was associated with a 25% (95% confidence interval [CI]: 22%-29%) increase in hospitalizations and an 18% (10%-27%) increase in deaths. In Aim 2, cumulative air pollution exposure over the previous 7 days was positively associated with COVID-19-related hospital admission in the second pandemic wave (June 20 to December 31, 2020). Associations of exposure were driven by exposure on the day of the hospital admission (lag0). Associations between short-term exposure to air pollution and COVID-19-related hospital admission were similar in all population subgroups. In Aim 3, individuals with lower individual- and area-level socioeconomic status (SES) were identified as particularly vulnerable to the effects of long-term exposure to NO2 and PM2.5 on COVID-19-related hospital admission. In Aim 4, long-term exposure to air pollution was associated with hospital admission for influenza and pneumonia: (6%; 95% CI: 2-11 per 16.4-μg/m3 NO2 and 5%; 1-8 per 2.6-μg/m3 PM2.5) as well as for all lower respiratory infections (LRIs) (18%; 14-22 per 16.4-μg/m3 NO2 and 14%; 11-17 per 2.6-μg/m3 PM2.5) before the COVID-19 pandemic. Associations for COVID-19-related hospital admission were larger than those for influenza or pneumonia for NO2, PM2.5, and O3 when adjusted for NO2.

CONCLUSIONS

Linkage across several registries allowed the construction of a large population-based cohort, tracking COVID-19 cases from primary care and testing data to hospital admissions, and death. Long- and short-term exposure to ambient air pollution were positively associated with severe COVID-19 events. The effects of long-term air pollution exposure on COVID-19 severity were greater among those with lower individual- and area-level SES.

Natural and socio-environmental factors in the transmission of COVID-19: a comprehensive analysis of epidemiology and mechanisms

PURPOSE OF REVIEW

There are significant differences in the transmission rate and mortality rate of COVID-19 under environmental conditions such as seasons and climates. However, the impact of environmental factors on the role of the COVID-19 pandemic and the transmission mechanism of the SARS-CoV-2 is unclear. Therefore, a comprehensive understanding of the impact of environmental factors on COVID-19 can provide innovative insights for global epidemic prevention and control policies and COVID-19 related research. This review summarizes the evidence of the impact of different natural and social environmental factors on the transmission of COVID-19 through a comprehensive analysis of epidemiology and mechanism research. This will provide innovative inspiration for global epidemic prevention and control policies and provide reference for similar infectious diseases that may emerge in the future.

RECENT FINDINGS

Evidence reveals mechanisms by which natural environmental factors influence the transmission of COVID-19, including (i) virus survival and transport, (ii) immune system damage, (iii) inflammation, oxidative stress, and cell death, and (iiii) increasing risk of complications. All of these measures appear to be effective in controlling the spread or mortality of COVID-19: (1) reducing air pollution levels, (2) rational use of ozone disinfection and medical ozone therapy, (3) rational exposure to sunlight, (4) scientific ventilation and maintenance of indoor temperature and humidity, (5) control of population density, and (6) control of population movement. Our review indicates that with the continuous mutation of SARS-CoV-2, high temperature, high humidity, low air pollution levels, and low population density more likely to slow down the spread of the virus.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

Effective Treatment of COVID-19 Infection with Repurposed Drugs: Case Reports

The COVID-19 pandemic response has been hindered by the absence of an efficient antiviral therapy for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The reason why the previous preventative approach to COVID-19 solely through vaccines has failed could be a lack of understanding of how quickly the SARS-CoV-2 virus evolves. Given the absence of specific treatments for the virus, efforts have been underway to explore treatment options. Drug repurposing involves identifying new therapeutic uses for approved drugs, proving to be a time-saving strategy with minimal risk of failure. In this study, we report the successful use of a multidrug approach in patients with COVID-19. Successful administration of multidrug therapy, such as combinations of hydroxychloroquine and azithromycin, doxycycline and ivermectin, or ivermectin, doxycycline, and azithromycin, has been reported. Multidrug therapy is effective because of the differing mechanisms of action of these drugs, and it may also mitigate the emergence of drug-resistant SARS-CoV-2 strains. The medicines were lopinavir/ritonavir (Kaletra), bamlanivimab (monoclonal antibody), glycopyrrolate-formoterol (Bevespi), ciclesonide (Alvesco), famotidine (Pepcid), and diphenhydramine (Benadryl).

Respiratory Diseases Associated With Wildfire Exposure in Outdoor Workers

Wildfires, including forest fires, bushfires, and landscape fires, have become increasingly prevalent, fueled by climate change and environmental factors and posing significant challenges to both ecosystems and public health. This review article examines the relationship between wildfires and respiratory diseases in outdoor workers, with a main focus on airway disease. In addition to the expected effects of direct thermal respiratory injuries and possible carbon monoxide poisoning, there are associations between wildfires and upper and lower respiratory effects, including infections as well as exacerbations of asthma and chronic obstructive pulmonary disease. A few studies have also shown an increased risk of new-onset asthma among wildfire firefighters. Outdoor workers are likely to have greater exposure to wildfire smoke with associated increased risks of adverse effects. As wildfires become increasingly prevalent globally, it is crucial to understand the various dimensions of this association. Furthermore, this review addresses preventive measures and potential interventions to alleviate the airway burden on individuals during and after work with wildfires events.

Correlation between Exposure to UFP and ACE/ACE2 Pathway: Looking for Possible Involvement in COVID-19 Pandemic

The overlap between the geographic distribution of COVID-19 outbreaks and pollution levels confirmed a correlation between exposure to atmospheric particulate matter (PM) and the SARS-CoV-2 pandemic. The RAS system is essential in the pathogenesis of inflammatory diseases caused by pollution: the ACE/AngII/AT1 axis activates a pro-inflammatory pathway, which is counteracted by the ACE2/Ang(1-7)/MAS axis, which activates an anti-inflammatory and protective pathway. However, ACE2 is also known to act as a receptor through which SARS-CoV-2 enters host cells to replicate. Furthermore, in vivo systems have demonstrated that exposure to PM increases ACE2 expression. In this study, the effects of acute and sub-acute exposure to ultrafine particles (UFP), originating from different anthropogenic sources (DEP and BB), on the levels of ACE2, ACE, COX-2, HO-1, and iNOS in the lungs and other organs implicated in the pathogenesis of COVID-19 were analyzed in the in vivo BALB/c male mice model. Exposure to UFP alters the levels of ACE2 and/or ACE in all examined organs, and exposure to sub-acute DEP also results in the release of s-ACE2. Furthermore, as evidenced in this and our previous works, COX-2, HO-1, and iNOS levels also demonstrated organ-specific alterations. These proteins play a pivotal role in the UFP-induced inflammatory and oxidative stress responses, and their dysregulation is linked to the development of severe symptoms in individuals infected with SARS-CoV-2, suggesting a heightened vulnerability or a more severe clinical course of the disease. UFP and SARS-CoV-2 share common pathways; therefore, in a "risk stratification" concept, daily exposure to air pollution may significantly increase the likelihood of developing a severe form of COVID-19, explaining, at least in part, the greater lethality of the virus observed in highly polluted areas.

Advancing health equity in the aftermath of COVID-19: Confronting intensifying racial disparities

The COVID-19 pandemic has exposed and exacerbated the persistent racial and ethnic health disparities in the United States. The pandemic has also had profound spillover effects on other aspects of health and wellbeing, such as mental health, chronic diseases, education, and income, for marginalized groups. In this article, we provide a thorough analysis of the pandemic's impact on racial and ethnic health disproportionalities, highlighting the multifaceted and interrelated factors that contribute to these inequities. We also argue for a renewed focus on health equity in healthcare policy and practice, emphasizing the need for systemic changes that address both the immediate and long-term consequences of these imbalances. We propose a framework for achieving health equity that involves creating equitable systems, care, and outcomes for all individuals, regardless of their race or ethnicity.

Short-term effects of PM2.5 components on the respiratory infectious disease: a global perspective

Although previous research has reached agreement on the significant impact of particulate matter (PM2.5) on respiratory infectious diseases, PM2.5 acts as an aggregation of miscellaneous pollutants and the individual effect of each component has not been examined. Here, we investigate the effects of PM2.5 components, including black carbon (BC), organic carbon (OC), sulfate ion (SO4), dust, and sea salt (SS), on the morbidity and mortality of the recent respiratory disease, i.e. COVID-19. The daily data of 236 countries and provinces/states (e.g., in the United States and China) worldwide during 2020-2022 are utilized. To derive the pollutant-specific causal effects, optimal instrumental variables for each pollutant are selected from a large set of atmospheric variables. We find that one µg/m3 increase in OC increases the number of cases and death by about 3% to 6% from the mean worldwide during a lag of one day up to three days. Our findings remain consistent and robust when we change control variables such as the flight index and weather proxies, and also when applying a sine transformation to the positivity and death rate. When analyzing health effects among different areas, we find stronger impact in China, for its higher local OC concentration, as opposed to the impact in the United States. Health benefits from PM2.5 pollution reduction are comparatively high for developed regions, yet decreases in cases and deaths number are rather overt in less developing regions. Our research provides inspiration and reference for dealing with other respiratory diseases in the post-pandemic era.

Exploring the association between ambient air pollution and COVID-19 risk: A comprehensive meta-analysis with meta-regression modelling

Introduction

Air pollution is speculated to increase the risk of Coronavirus disease-2019 (COVID-19). Nevertheless, the results remain inconsistent and inconclusive. This study aimed to explore the association between ambient air pollution (AAP) and COVID-19 risks using a meta-analysis with meta-regression modelling.

Methods

The inclusion criteria were: original studies quantifying the association using effect sizes and 95 % confidence intervals (CIs); time-series, cohort, ecological or case-crossover peer-reviewed studies in English. Exclusion criteria encompassed non-original studies, animal studies, and data with common errors. PubMed, Web of Science, Embase and Google Scholar electronic databases were systemically searched for eligible literature, up to 31, March 2023. The risk of bias (ROB) was assessed following the Agency for Healthcare Research and Quality parameters. A random-effects model was used to calculate pooled risk ratios (RRs) and their 95 % CIs.

Results

A total of 58 studies, between 2020 and 2023, met the inclusion criteria. The global representation was skewed, with major contributions from the USA (24.1 %) and China (22.4 %). The distribution included studies on short-term (43.1 %) and long-term (56.9 %) air pollution exposure. Ecological studies constituted 51.7 %, time-series-27.6 %, cohorts-17.2 %, and case crossover-3.4 %. ROB assessment showed low (86.2 %) and moderate (13.8 %) risk. The COVID-19 incidences increased with a 10 μg/m3 increase in PM2.5 [RR = 4.9045; 95 % CI (4.1548-5.7895)], PM10 [RR = 2.9427: (2.2290-3.8850)], NO2 [RR = 3.2750: (3.1420-3.4136)], SO2 [RR = 3.3400: (2.7931-3.9940)], CO [RR = 2.6244: (2.5208-2.7322)] and O3 [RR = 2.4008: (2.1859-2.6368)] concentrations. A 10 μg/m3 increase in concentrations of PM2.5 [RR = 3.0418: (2.7344-3.3838)], PM10 [RR = 2.6202: (2.1602-3.1781)], NO2 [RR = 3.2226: (2.1411-4.8504)], CO [RR = 1.8021 (0.8045-4.0370)] and O3 [RR = 2.3270 (1.5906-3.4045)] was significantly associated with COVID-19 mortality. Stratified analysis showed that study design, exposure period, and country influenced exposure-response associations. Meta-regression model indicated significant predictors for air pollution-COVID-19 incidence associations.

Conclusion

The study, while robust, lacks causality demonstration and focuses only on the USA and China, limiting its generalizability. Regardless, the study provides a strong evidence base for air pollution-COVID-19-risks associations, offering valuable insights for intervention measures for COVID-19.

The landscape of the COVID-19 pandemic in Poland emerging from epidemiological and genomic data

The COVID-19 pandemic has profoundly affected all aspects of our lives. Through real-time monitoring and rapid vaccine implementation, we succeeded in suppressing the spread of the disease and mitigating its consequences. Finally, conclusions can be summarized and drawn. Here, we use the example of Poland, which was seriously affected by the pandemic. Compared to other countries, Poland has not achieved impressive results in either testing or vaccination, which may explain its high mortality (case fatality rate, CFR 1.94%). Through retrospective analysis of data collected by the COVID-19 Data Portal Poland, we found significant regional differences in the number of tests performed, number of cases detected, number of COVID-19-related deaths, and vaccination rates. The Masovian, Greater Poland, and Pomeranian voivodeships, the country's leaders in vaccination, reported high case numbers but low death rates. In contrast, the voivodeships in the eastern and southern parts of Poland (Subcarpathian, Podlaskie, Lublin, Opole), which documented low vaccination levels and low case numbers, had higher COVID-19-related mortality rates. The strong negative correlation between the CFR and the percentage of the population that was vaccinated in Poland supports the validity of vaccination. To gain insight into virus evolution, we sequenced more than 500 genomes and analyzed nearly 80 thousand SARS-CoV-2 genome sequences deposited in GISAID by Polish diagnostic centers. We showed that the SARS-CoV-2 variant distribution over time in Poland reflected that in Europe. Haplotype network analysis allowed us to follow the virus transmission routes and identify potential superspreaders in each pandemic wave.

Social competition drives collective action to reduce informal waste burning in Uganda

Improving urban air quality is a pressing challenge in the Global South. A key source of air pollution is the informal burning of household waste. Reducing informal burning requires governments to develop formal systems for waste disposal and for residents to adopt new disposal behaviors. Using a randomized experiment, we show that social competitions between pairs of neighborhoods in Nansana municipality, Uganda, galvanized leadership and inspired collective action to reduce informal burning. All 44 neighborhoods in the study received a public health campaign, while 22 treated neighborhoods were paired and competed to reduce waste burning over an 8-mo period. Treated neighborhoods showed a 24 percent reduction (95% CI: 11 to 35 percent) in waste burning relative to control neighborhoods at the end of the competition period. There is no evidence that treated neighborhoods experienced a rebound in waste burning several months after the competitions. Community leaders reported greater effort in coordinating residents and more pride in their neighborhood when assigned to the competition treatment. These results suggest that creating focal points for leadership and collective action can be an effective and low-cost strategy to address policy problems that require broad participation and costly behavior change.

Deciphering the link between healthcare expenditure, corruption, and COVID-19 mortality

This paper analyzes the determinants of COVID-19 mortality across over 140 countries in 2020, with a focus on healthcare expenditure and corruption. It finds a positive association between COVID-19 deaths and aging populations, obesity rates, and healthcare expenditure while noting a negative association with rural residency and corruption perception. The study further reveals that mortality is positively associated with aging populations in high-income countries and positively associated with obesity in upper-middle to high-income countries. Mortality is positively associated with healthcare expenditure, which likely reflects a country's preparedness and ability to better track, document, and report COVID-19 deaths. On the other hand, mortality is negatively associated with corruption perception in upper-middle-income countries. Further analyses based on 2021 data reveal COVID-19 deaths are positively associated with the proportion of the population aged 65 and older in low to lower-middle-income countries, with obesity in high-income countries, and with tobacco use across most countries. Interestingly, there is no evidence linking COVID-19 deaths to healthcare expenditure and corruption perception, suggesting a post-2020 convergence in preparedness likely due to proactive pandemic responses, which might have also mitigated corruption's impact. Policy recommendations are proposed to aid the elderly, address obesity, and combat tobacco use.

Comparative Analysis of Epidemiological Outcome of Incidence, Mortality and Lethality by COVID-19 between the States of Espírito Santo and Minas Gerais, Brazil

At the beginning of December 2019, a new type of coronavirus emerged, SARS-CoV-2. This virus causes COVID-19, a highly contagious disease that can initially present asymptomatically and can also lead to death. Our ecological study goal was to evaluate the incidence, mortality, and lethality rates for COVID-19 between the states of Espírito Santo and Minas Gerais, with time series analysis using secondary and public databases on COVID-19 from January 2020 to December 2022. Prais-Winsten linear regression was used for trend analyses. In 2020, the rate in Espírito Santo was 2.19 times greater than in Minas Gerais. This trend continued in 2021, with Espírito Santo's rate being 1.29 times greater. In 2022, Espírito Santo's rate remained 2.65 times higher than Minas Gerais. Furthermore, Espírito Santo had the highest mortality, with the exception of 2021. In turn, Minas Gerais had the highest fatality rate throughout the analyzed pandemic period. The state of Espírito Santo had a higher incidence of COVID-19, as well as higher mortality when compared to the state of Minas Gerais. Furthermore, both states showed similar trends for mortality, lethality, and incidence in the years 2020 and 2021.

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.

Quantitative evaluation of the impact of indoor relative humidity on deposition of aerosols generated during tooth grinding in a real-world clinical setting

OBJECTIVES

Exposure to aerosol particles generated from tooth grinding has a negative impact on the health of dental personnel. The aim of this study was to quantitatively analyze the impact of indoor relative humidity (IRH) on the deposition of these suspended particles in a well-controlled dental environment.

MATERIALS AND METHODS

In this study, a humidity control system was employed to effectively regulate and maintain indoor relative humidity (IRH). A novel computer-assisted numerical control system was developed to pre-treat the molar specimens, and accurately simulate clinical tooth grinding procedures. Each procedure was performed in triplicate, with an online real-time particle counter (ORPC; TR-8301, TongrenCo.) measuring aerosol production. All testing devices were controlled remotely. The data obtained were statistically analyzed using descriptive statistics and non-parametric tests (Kruskal-Wallis/ Dunn's post hoc test with Bonferroni correction, p < 0.05).

RESULTS

The findings showed that with increasing IRH, the maximum peak concentration of aerosol particles decreased by 397% from 6.51 × 107 particles/m3 at 30% to 1.64 × 107 particles/m3 at 80%. The Kruskal-Wallis test results indicated a statistically significant effect of IRH on the aerosol increment (p < 0.05).

CONCLUSIONS

Increasing the IRH level can effectively promote the deposition of aerosol particles, with a return to baseline within 15 min after reaching 60% or above.

CLINICAL RELEVANCE

Our study suggested that maintaining IRH above 70% during the cleaning process, allowing natural recovery to ambient humidity levels within 15 min after cleaning, and taking basic precautions, may lead to an adequate reduction in the possible health risks of aerosol contamination.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Associations of ambient ozone exposure and CD4+ T cell levels with mortality among people living with HIV: An eight-year longitudinal study

There has been a consistent upward trend in ground-level ozone (O3) concentration in China. People living with HIV (PLWH) may be more vulnerable to the health impacts of O3 exposure due to their immunosuppressed state. This study aims to investigate the association between ambient O3 exposure and mortality among PLWH, as well as the potential exacerbating effects of a decreased CD4+ T cell level. Daily maximum 8-hour O3 concentrations were assigned to 7270 PLWH at a county level in Guangxi, China. Every 10-unit increase in ambient O3 concentration was associated with a significant rise in all-cause mortality ranging from 7.3 % to 28.7 % and a significant rise in AIDS-related mortality ranging from 8.4 % to 14.5 %. When PLWH had a higher CD4+ count (≥350 cells/μL), elevated O3 concentration was associated with increased blood CD4+ count at lag0 [percent change with 95 % confidence interval, 0.20(0.00, 0.40)], lag1 [0.26(0.06, 0.47)], and lag2 [0.23(0.03, 0.44)]; however, an opposite association was observed when CD4+ count was <350 cells/μL for half-year average [-2.45(-4.71, -0.14)] and yearly average [-3.42(-5.51, -1.29)] of O3 exposure. The association of O3 exposure with all-cause and AIDS-related mortality was more prominent among those with higher CD4+ count. Exploratory analysis revealed possible associations between O3 exposure and respiratory infections and clinical symptoms. These findings suggest potential synergistic effects between a compromised immune status and elevated O3 exposure levels on mortality risk among PLWH. Ambient O3 exposure should be considered as an emerging mortality risk factor for PLWH in the era of antiretroviral therapy, requiring further attention from researchers and healthcare professionals.

Efficacy of treating bacterial bioaerosols with weakly acidic hypochlorous water: A simulation chamber study

The COVID-19 pandemic highlighted the dangers of airborne transmission and the risks of pathogen-containing small airborne droplet inhalation as an infection route. As a pathogen control, Weakly Acidic Hypochlorous Water (WAHW) is used for surface disinfection. However, there are limited assessments of air disinfection by WAHW against airborne pathogens like bioaerosols. This was an empirical study evaluating the disinfection efficacy of WAHW in an atmospheric simulation chamber system against four selected model bacteria. The strains tested included Staphylococcus aureus (SA), Escherichia coli (EC), Pseudomonas aeruginosa (PA), and Pseudomonas aeruginosa (PAO1). Each bacterial solution was nebulized into the chamber system as the initial step, and bioaerosol was collected into the liquid medium by a bio-sampler for colony forming units (CFU) determination. Secondly, the nebulized bacterial bioaerosol was exposed to nebulized double distilled water (DDW) as the control and nebulized 150 ppm of WAHW as the experimental groups. After the 3 and 30-min reaction periods, the aerosol mixture inside the chamber was sampled in liquid media and then cultured on agar plates with different dilution factors to determine the CFU. Survival rates were calculated by a pre-exposed CFU value as a reference point. The use of WAHW decreased bacterial survival rates to 1.65-30.15% compared to the DDW control. PAO1 showed the highest survival rates and stability at 3 min was higher than 30 min in all experiments. Statistical analysis indicated that bacteria survival rates were significantly reduced compared to the controls. This work verifies the bactericidal effects against Gram-positive/negative bioaerosols of WAHW treatment. As WAHW contains chlorine in the acid solution, residual chlorine air concentration is a concern and the disinfection effect at different concentrations also requires investigation. Future studies should identify optimal times to minimize the treated time range and require measurements in a real environment.

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

BACKGROUND

Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales.

OBJECTIVE

To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches.

METHODS

We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches.

RESULTS

We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models.

SIGNIFICANCE

The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study

BACKGROUND

A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19.

METHODS

The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days.

RESULTS

Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant.

CONCLUSIONS

Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Vitamin D Supplementation and Prior Oral Poliovirus Vaccination Decrease Odds of COVID-19 Outcomes among Adults Recently Inoculated with Inactivated Poliovirus Vaccine

BACKGROUND

Structural and functional commonalities between poliovirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggest that poliovirus inoculation may induce antibodies that mitigate the coronavirus disease (COVID-19). No known studies have evaluated COVID-19 risk factors in adults recently vaccinated against poliovirus.

STUDY OBJECTIVE

Among adults with no history of COVID-19 infection or vaccination, who recently received an inactivated poliovirus vaccine (IPV), we sought to determine which biological factors and social determinants of health (SDOH) may be associated with (1) testing positive for SARS-CoV-2, (2) experiencing COVID-19 symptoms, and (3) a longer duration of COVID-19 symptoms.

METHODS

The influence of biological factors and SDOH on SARS-CoV-2 infection and COVID-19 symptoms were evaluated among 282 adults recently inoculated with IPV. Participant-reported surveys were analyzed over 12 months post-enrollment. Bivariate and multivariate linear and logistic regression models identified associations between variables and COVID-19 outcomes.

RESULTS

Adjusting for COVID-19 vaccinations, variants, and other SDOH, secondary analyses revealed that underlying conditions, employment, vitamin D, education, and the oral poliovirus vaccination (OPV) were associated with COVID-19 outcomes. The odds of testing positive for SARS-CoV-2 and experiencing symptoms were significantly reduced among participants who took vitamin D (OR 0.12 and OR 0.09, respectively). Unemployed or part-time working participants were 72% less likely to test positive compared with full-time workers. No prior dose of OPV was one of the strongest predictors of SARS-CoV-2 infection (OR 4.36) and COVID-19 symptoms (OR 6.95).

CONCLUSIONS

Findings suggest that prophylactic measures and mucosal immunity may mitigate the risk and severity of COVID-19 outcomes. Larger-scale studies may inform future policies.

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.

Spatialized PM2.5 during COVID-19 pandemic in Brazil's most populous southern city: implications for post-pandemic era

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions-Europe, the United States, and China-correlating generalized pollution levels with health-related scopes. In this study, our objective is to investigate the localized connection between exposure to air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly reveal a significant positive correlation (with r-values reaching up to 0.36, p-value < 0.01) between regions characterized by higher levels of pollution, particularly during the winter months (with r-values peaking at 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indices. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

Socioeconomic and Health Determinants of the Prevalence of COVID-19 in a Population of Children with Respiratory Diseases and Symptoms

BACKGROUND

Most epidemiological studies indicate that bronchial asthma is not a risk factor for COVID-19, but previous analyses have not additionally focused on the socioeconomic determinants of SARS-CoV-2 infection in children with asthma, bronchitis, and respiratory symptoms.

AIMS

This research aimed to investigate the correlation between the socioeconomic status of families and the prevalence of respiratory conditions such as asthma, bronchitis, and respiratory symptoms in children, in addition to exploring their association with the prevalence of COVID-19. The study involved a cross-sectional epidemiological investigation conducted in 2022, encompassing 2454 students from elementary schools in Poland. The parents of the students completed a questionnaire modeled after the International Study on Asthma and Allergies in Childhood (ISAAC). Socioeconomic status (SES) indicators were determined based on parental education, self-reported economic status, and housing conditions. To assess the impact of social factors and health on the occurrence of COVID-19, odds ratios (ORs) were calculated. The findings revealed several COVID-19 risk factors, including higher maternal (OR 2.2; 95%CI: 1.3-3.0) and paternal education (OR 1.9; 95%CI: 1.3-2.4), urban residence (OR 1.7; 95%CI: 1.3-2.1), the presence of mold in residences (OR 1.7; 95%CI: 1.0-2.3), bronchitis (OR 1.5; 95%CI: 1.2-2.0), and chronic cough (OR 1.8; 95%CI: 1.3-2.4). Further analysis, stratifying children based on their baseline health status (i.e., presence or absence of asthma, bronchitis, and chronic cough), indicated that higher parental education increased the risk of COVID-19 solely for children without pre-existing conditions. The occurrence of SARS-CoV-2 infections was found to be notably associated with mold exposure in children who did not have bronchial asthma. Rigorous multivariate analyses substantiated the collective impact of factors such as residential environment, the existence of mold and moisture, and a history of bronchitis. This study's conclusions highlight a higher frequency of SARS-CoV-2 infections in cases where bronchitis had been diagnosed previously and chronic cough was prevalent. Interestingly, the initially hypothesized higher prevalence of COVID-19 among children with bronchial asthma did not receive confirmation in our findings. This study highlights the importance of urban residence, exposure to mold or dampness, and higher parental education in the incidence of COVID-19. Higher parental education was a significant factor in increasing the risk of COVID-19 among children without bronchitis, chronic cough, and asthma.