Environmental Factors Influencing COVID-19 Incidence and Severity

Confounding amplifies the effect of environmental factors on COVID-19

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Identification of environmental, socioeconomic, water, sanitation, and hygiene (WaSH) factors associated with COVID-19 incidence in the Philippines: A nationwide modelling study

Despite the implementation of non-pharmaceutical interventions, the threat of coronavirus disease 2019 (COVID-19) remains significant on a global scale. Identifying external factors contributing to its spread is crucial, especially given the World Health Organization's recommendation emphasizing access to water, sanitation, and hygiene as essential in curbing COVID-19. There is a notable discrepancy in access to sanitation facilities, particularly evident in low- and middle-income countries. However, there is a lack of quantitative assessments regarding these factors. This study examines various environmental, socioeconomic, water, sanitation, and hygiene factors and their associations with COVID-19 incidence. All regions in the Philippines were categorized into clusters based on socioeconomic factors. A conceptual structural equation model (SEM) was developed using domain knowledge. The best-fitting SEM for each cluster was determined, and associations between factors and COVID-19 incidence were estimated. The correlation analysis revealed that rainfall, minimum temperature, and relative humidity were positively correlated with weekly COVID-19 incidence in urban regions. Maximum temperature, mean temperature, wind speed, and wind direction were negatively correlated with weekly COVID-19 incidence in rural regions, with time lags of 0, 3, and 7 weeks. In urban regions (Cluster 1), factors such as urbanization rate (1.00), area (-0.93), and population (0.54) were found to be associated with weekly COVID-19 incidence. Conversely, in rural regions (Cluster 2), factors including area (0.17), basic sanitation (0.84), and wind direction (0.83) showed associations with weekly COVID-19 incidence. These factors were causally associated with a latent variable reflecting the hidden confounders associated with COVID-19 incidence. It is important to note that sanitation factors were associated only in rural regions. Improving access to sanitation facilities in rural regions of the Philippines is imperative to effectively mitigate disease transmission in future pandemics. Identification of the causal effect of unobserved confounders with COVID-19 incidence is recommended for future research.

Does behavior mediate the effect of weather on SARS-CoV-2 transmission? evidence from cell-phone data

There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting. We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021. We also evaluated the evidence for seasonal effect modification by comparing the results of all-season (using season as a covariate) to season-stratified models. Four weather conditions were associated with both time spent indoors away-from-home and 12-day lagged COVID-19 hospital admissions in one or more season: high minimum temperature (all-season), low maximum temperature (spring), low minimum absolute humidity (winter), and high solar radiation (all-season & winter). In our mediation analyses, we found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics, as well as other seasonal respiratory pathogens.

Residential greenspace and COVID-19 Severity: A cohort study of 313,657 individuals in Greater Manchester, United Kingdom

BACKGROUND

Greenspaces contribute positively to mental and physical well-being, promote social cohesion, and alleviate environmental stressors, such as air pollution. Ecological studies suggest that greenspace may affect incidence and severity of Coronavirus Disease 2019 (COVID-19).

OBJECTIVE

This study examines the association between residential greenspace and COVID-19 related hospitalization and death.

METHOD

In this retrospective cohort based on patient records from the Greater Manchester Care Records, all first COVID-19 cases diagnosed between March 1, 2020, and May 31, 2022 were followed until COVID-19 related hospitalization or death within 28 days. Residential greenspace availability was assessed using the Normalized Difference Vegetation Index per lower super output area in Greater Manchester. The association of greenspace with COVID-19 hospitalization and mortality were estimated using multivariate logistic regression models after adjusting for potential individual, temporal, and spatial confounders. We explored potential effect modifications of the associations with greenspace and COVID-19 severity by age, sex, body mass index, smoking, deprivation, and certain comorbidities. Combined effects of greenspace and air pollution (NO2 and PM2.5) were investigated by mutually adjusting pairs with correlation coefficients ≤ 0·7.

RESULTS

Significant negative associations were observed between greenspace availability and COVID-19 hospitalization and mortality with odds ratios [OR] (95 % Confidence Intervals [CI]) of 0·96 (0·94-0·97) and 0·84 (0·80-0·88) (per interquartile range [IQR]), respectively. These were significantly modified by deprivation (P-value for interaction < 0.05), showing that those most deprived obtained largest benefits from greenspace. Inclusion of NO2 and PM2.5 diminished associations to null for COVID-19 hospitalization, but only reduced them slightly for mortality, where inverse associations remained.

CONCLUSION

In the Greater Manchester area, residential greenspace is associated with reduced risk of hospitalization or death in individuals with COVID-19, with deprived groups obtaining the greatest benefits. Associations were strongest for COVID-19 mortality, which were robust to inclusion of air pollutants in the models.

Impact of heavy metals exposure on herpes simplex virus type I infection: A population-based cross-sectional study

This study aims to investigate the significant relationship between serum heavy metals (lead [Pb], cadmium [Cd], mercury [Hg]) and the risk of herpes simplex virus type 1 (HSV-1) infection. Data were derived from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States from 2007 to 2016. This nationally representative survey, conducted by the National Center for Health Statistics, assessed the health status of participants through interviews, physical examinations, and laboratory tests. After excluding participants lacking serum Pb, Cd, and Hg data, as well as those missing HSV-1 testing data and pregnant women, the analysis included 13 772 participants, among whom 3363 were adolescents. A survey-weighted multivariate logistic regression model was used to evaluate the association between heavy metal exposure and the risk of HSV-1 infection, and to explore the dose-response relationship between them. In adults and adolescents, serum concentrations of Pb and Cd were higher in those infected with HSV-1 than in those not infected. However, an increase in serum Hg concentration was observed only in infected adolescents. After adjusting for potential confounders, elevated serum Pb and Cd concentrations in adults were associated with an increased risk of HSV-1 infection. Higher serum Pb and Cd concentrations were associated with an increased risk of HSV-2 infection, irrespective of HSV-1 infection status. In adults, serum concentrations of Pb and Hg showed an approximately linear relationship with HSV-1 infection risk (p for nonlinearity > 0.05), whereas the dose-response relationship between serum Cd concentration and HSV-1 infection was nonlinear (p for nonlinearity = 0.004). In adolescents, serum concentrations of heavy metals (Pb, Cd, Hg) showed an approximately linear relationship with HSV-1 infection (p for nonlinearity > 0.05). Furthermore, the study examined the relationship between serum heavy metal levels and the risk of HSV-1 infection across different genders, races, income levels, weight statuses, and immune statuses. In conclusion, there is a significant association between serum heavy metal concentrations and HSV-1 infection, which warrants further investigation into the causal relationship between them.

Planetary health and its relevance in the modern era: A topical review

Planetary health is a relatively new concept that has gained traction in recent years due to the urgent need to address the health of our planet and its inhabitants. It refers to the interdependent health of both humans and the environment, recognizing that the two are inseparable and that the health of one is intricately linked to the health of the other. This article aims to advocate changes in how health care for both the environment and humans is envisaged, and aligned with sustainable development goals using ethically sound, solution-oriented, and practical approaches to education. Rapid industrialization, urbanization, and population growth led to environmental degradation and climate change in this era. These factors have profound implications for human health, with the World Health Organization estimating that 23% of global deaths are linked to environmental factors. Climate change and extreme weather events are exacerbating existing health problems. Air pollution, water pollution, and toxic chemicals are additional environmental factors that add to it and lead to health issues, including non-communicable diseases and death. A collaborative and interdisciplinary approach is needed to address planetary health challenges, including working across sectors and investing in research to understand better the complex interactions between human health and the environment. By promoting sustainable development and protecting the planet's health and inhabitants, we can ensure a healthy future for generations.

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.

Exposure to urban particulate matter alters responses of olfactory mucosal cells to SARS-CoV-2 infection

Respiratory viruses have a significant impact on health, as highlighted by the COVID-19 pandemic. Exposure to air pollution can contribute to viral susceptibility and be associated with severe outcomes, as suggested by recent epidemiological studies. Furthermore, exposure to particulate matter (PM), an important constituent of air pollution, is linked to adverse effects on the brain, including cognitive decline and Alzheimer's disease (AD). The olfactory mucosa (OM), a tissue located at the rooftop of the nasal cavity, is directly exposed to inhaled air and in direct contact with the brain. Increasing evidence of OM dysfunction related to neuropathogenesis and viral infection demonstrates the importance of elucidating the interplay between viruses and air pollutants at the OM. This study examined the effects of subacute exposure to urban PM 0.2 and PM 10-2.5 on SARS-CoV-2 infection using primary human OM cells obtained from cognitively healthy individuals and individuals diagnosed with AD. OM cells were exposed to PM and subsequently infected with the SARS-CoV-2 virus in the presence of pollutants. SARS-CoV-2 entry receptors and replication, toxicological endpoints, cytokine release, oxidative stress markers, and amyloid beta levels were measured. Exposure to PM did not enhance the expression of viral entry receptors or cellular viral load in human OM cells. However, PM-exposed and SARS-CoV-2-infected cells showed alterations in cellular and immune responses when compared to cells infected only with the virus or pollutants. These changes are highly pronounced in AD OM cells. These results suggest that exposure of human OM cells to PM does not increase susceptibility to SARS-CoV-2 infection in vitro, but it can alter cellular immune responses to the virus, particularly in AD. Understanding the interplay of air pollutants and COVID-19 can provide important insight for the development of public health policies and interventions to reduce the negative influences of air pollution exposure.

Global respiratory health priorities at the beginning of the 21st century

Respiratory health has become a prevailing priority amid the diverse global health challenges that the 21st century brings, due to its substantial impact on individuals and communities on a global scale. Due to rapid advances in medicine, emerging knowledge gaps appear along with new challenges and ethical considerations. While breakthroughs in medical science can bring about encouraging possibilities for better treatments and interventions, they also lead to unanswered questions and areas where further research is warranted. A PubMed search on the topic "global respiratory health priorities" between the years 2000 and 2023 was conducted, which returned 236 articles. Of these, 55 were relevant and selected for inclusion in this article. The selection process took into account literature reviews, opinions from expert groups and careful analysis of existing gaps and challenges within the field; our selection encompasses specific infectious and noninfectious respiratory conditions in both adults and children. The global respiratory health priorities identified were selected on the basis that they have been recognised as critical areas of investigation and potential advancement and they span across clinical, translational, epidemiological and population health domains. Implementing these priorities will require a commitment to fostering collaboration and knowledge-sharing among experts in different fields with the ultimate aim to improve respiratory health outcomes for individuals and communities alike.

The impact of ambient temperature and air pollution on SARS-CoV2 infection and Post COVID-19 condition in Belgium (2021-2022)

INTRODUCTION

The associations between non-optimal ambient temperature, air pollution and SARS-CoV-2 infection and post COVID-19 condition (PCC) remain constrained in current understanding. We conducted a retrospective analysis to explore how ambient temperature affected SARS-CoV-2 infection in individuals who later developed PCC compared to those who did not. We investigated if these associations were modified by air pollution.

METHODS

We conducted a bidirectional time-stratified case-crossover study among individuals who tested positive for SARS-CoV-2 between May 2021 and June 2022. We included 6302 infections, with 2850 PCC cases. We used conditional logistic regression and distributed lag non-linear models to obtain odds ratios (OR) and 95% confidence intervals (CI) for non-optimal temperatures relative to the period median temperature (10.6 °C) on lags 0 to 5. For effect modification, daily average PM2.5 concentrations were categorized using the period median concentration (8.8 μg/m3). Z-tests were used to compare the results by PCC status and PM2.5.

RESULTS

Non-optimal cold temperatures increased the cumulative odds of infection (OR = 1.93; 95%CI:1.67-2.23, OR = 3.53; 95%CI:2.72-4.58, for moderate and extreme cold, respectively), with the strongest associations observed for non-PCC cases. Non-optimal heat temperatures decreased the odds of infection except for moderate heat among PCC cases (OR = 1.32; 95%CI:0.89-1.96). When PM2.5 was >8.8 μg/m3, the associations with cold were stronger, and moderate heat doubled the odds of infection with later development of PCC (OR = 2.18; 95%CI:1.01-4.69). When PM2.5 was ≤8.8 μg/m3, exposure to non-optimal temperatures reduced the odds of infection.

CONCLUSION

Exposure to cold increases SARS-CoV2 risk, especially on days with moderate to high air pollution. Heated temperatures and moderate to high air pollution during infection may cause PCC. These findings stress the need for mitigation and adaptation strategies for climate change to reduce increasing trends in the frequency of weather extremes that have consequences on air pollution concentrations.

Does behavior mediate the effect of weather on SARS-CoV-2 transmission? Evidence from cell-phone data

Background

There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting.

Objectives

We aim to evaluate the extent to which weather impacts COVID-19 via time spent away-from-home in indoor spaces, as compared to a direct effect of weather on COVID-19 hospitalization, independent of mobility.

Methods

We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021.

Results

We found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Sensitivity analyses evaluating time at home as a mediator were consistent with these conclusions.

Discussion

Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics and other seasonal respiratory pathogens.

Assessing the impact of climatic variability on acute respiratory diseases across diverse climatic zones in South Africa

Acute respiratory diseases are a significant public health concern in South Africa, with climatic variables such as temperature and rainfall being key influencers. This study investigates the associations between these variables and the prevalence of acute respiratory diseases in Johannesburg, Cape Town, and Gqeberha (Port Elizabeth), representing distinct climatic zones. Spearman's correlation analyses showed negative correlations in Johannesburg for respiratory disease claims with maximum temperature (r = -0.12, p < 0.0001) and mean temperature (r = -0.13, p < 0.0001), and a negative correlation with daily rainfall (r = -0.12, p < 0.0001). Cape Town demonstrated a negative correlation with maximum temperature (r = -0.18, p < 0.0001) and a positive correlation with rainfall (r = 0.08, p < 0.0001). Utilizing Distributed Lag Non-linear Models (DLNM), the study revealed that in Johannesburg, the relative risk (RR) of respiratory claims increases notably at temperatures below 12 °C, and again at a Tmax between 16 and 23 °C. The risk escalates further at >30 °C, although with a considerable error margin. For Cape Town, a stable level of moderate RR is seen from Tmax 15-24 °C, with a significant increase in RR and error margin above 30 °C. In Gqeberha, the DLNM results are less definitive, reflecting the city's moderate climate and year-round rainfall. The RR of acute respiratory diseases did not show clear patterns with temperature changes, with increasing error margins outside the 22 °C threshold. These findings emphasize the imperative for region-specific public health strategies that account for the complex, non-linear influences of climate on respiratory health. This detailed understanding of the climate-health nexus provides a robust basis for enhancing public health interventions and future research directed at reducing the impacts of climate factors.

Liver injury in COVID-19: an insight into pathobiology and roles of risk factors

COVID-19 is a complex disease that can lead to fatal respiratory failure with extrapulmonary complications, either as a direct result of viral invasion in multiple organs or secondary to oxygen supply shortage. Liver is susceptible to many viral pathogens, and due to its versatile functions in the body, it is of great interest to determine how hepatocytes may interact with SARS-CoV-2 in COVID-19 patients. Liver injury is a major cause of death, and SARS-CoV-2 is suspected to contribute significantly to hepatopathy. Owing to the lack of knowledge in this field, further research is required to address these ambiguities. Therefore, we aimed to provide a comprehensive insight into host-virus interactions, underlying mechanisms, and associated risk factors by collecting results from epidemiological analyses and relevant laboratory experiments. Backed by an avalanche of recent studies, our findings support that liver injury is a sequela of severe COVID-19, and certain pre-existing liver conditions can also intensify the morbidity of SARS-CoV-2 infection in synergy. Notably, age, sex, lifestyle, dietary habits, coinfection, and particular drug regimens play a decisive role in the final outcome and prognosis as well. Taken together, our goal was to unravel these complexities concerning the development of novel diagnostic, prophylactic, and therapeutic approaches with a focus on prioritizing high-risk groups.

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.

In Vitro Profiling of Toxicity Effects of Different Environmental Factors on Skin Cells

The skin is constantly exposed to a variety of environmental threats. Therefore, the influence of environmental factors on skin damage has always been a matter of concern. This study aimed to investigate the cytotoxic effects of different environmental factors, including cooking oil fumes (COFs), haze (PM2.5), and cigarette smoke (CS), on epidermal HaCaT cells and dermal fibroblast (FB) cells. Cell viability, intracellular reactive oxygen species (ROS) generation, inflammatory cytokine levels, and collagen mRNA expression were used as toxicity endpoints. Additionally, the effects of ozone (O3) on cell viability and release of inflammatory cytokines in 3D epidermal cells were also examined. The results showed that the organic extracts of CS, COFs, and PM2.5 significantly inhibited the viability of HaCaT and FB cells at higher exposure concentrations. These extracts also increased intracellular ROS levels in FB cells. Furthermore, they significantly promoted the release of inflammatory cytokines, such as IL-1α and TNF-α, in HaCaT cells and down-regulated the mRNA expression of collagen I, III, IV, and VII in FB cells. Comparatively, SC organic extracts exhibited stronger cytotoxicity to skin cells compared to PM2.5 and COFs. Additionally, O3 at all test concentrations significantly inhibited the viability of 3D epidermal cells in a concentration-dependent manner and markedly increased the levels of TNF-α and IL-1α in 3D epidermal cells. These findings emphasize the potential cytotoxicity of COFs, PM2.5, CS, and O3 to skin cells, which may lead to skin damage; therefore, we should pay attention to these environmental factors and take appropriate measures to protect the skin from their harmful effects.

TYK2, IFITM3, IFNAR2 and OAS3 single-nucleotide polymorphisms among severe COVID-19 ICU patients in Morocco

OBJECTIVES

This study aimed to explore the potential correlation between specific single nucleotide polymorphisms (TYK2, IFITM3, IFNAR2, and OAS3 variants) and the severity of COVID-19 in Moroccan patients.

METHODS

A genetic analysis was conducted on 109 patients with PCR-confirmed SARS-CoV-2 infection in Morocco. Among these patients, 46% were hospitalized in the intensive care unit, while 59% were not hospitalized. Importantly, all patients lacked known risk factors associated with COVID-19 severity. Genotyping was performed to identify variations in TYK2 rs74956615, IFITM3 rs12252, IFNAR2 rs2236757, and OAS3 rs10735079. Statistical analysis was applied using codominant, dominant and recessive logistic regression models to assess correlations with COVID-19 severity.

RESULTS

Our findings revealed no significant correlation between TYK2 rs74956615, IFITM3 rs12252, IFNAR2 rs2236757, and OAS3 rs10735079 with COVID-19 severity in Moroccan patients, as indicated in logistic regression models (p > .05). Interestingly, these results may offer insights into the mitigated impact of the COVID-19 pandemic and the reduced severity observed in SARS-CoV-2 infected patients in Morocco. Age, however, exhibited a significant correlation with severity (p < .001), with a trend towards increased likelihood of ICU admission with advancing age. Additionally, In the severe group, a higher proportion of patients were females (54%), indicating a statistically significant correlation with disease severity (p = .04). Nevertheless, female ICU patients aged above 60 years accounted for 37%, compared to 17% for males.

CONCLUSION

This study underscores the absence of a genetic association between the selected polymorphisms and COVID-19 severity in Moroccan patients. Advanced age emerges as the primary factor influencing the severity of COVID-19 patients without comorbidities. We recommend setting the threshold for advanced age at 60 years as a risk factor for severe forms of COVID-19.

Exposure to outdoor particulate matter and risk of respiratory diseases: a systematic review and meta-analysis

According to epidemiological studies, particulate matter (PM) is an important air pollutant that poses a significant threat to human health. The relationship between particulate matter and respiratory diseases has been the subject of numerous studies, but these studies have produced inconsistent findings. The purpose of this systematic review was to examine the connection between outdoor particulate matter (PM2.5 and PM10) exposure and respiratory disorders (COPD, lung cancer, LRIs, and COVID-19). For this purpose, we conducted a literature search between 2012 and 2022 in PubMed, Web of Science, and Scopus. Out of the 58 studies that were part of the systematic review, meta-analyses were conducted on 53 of them. A random effect model was applied separately for each category of study design to assess the pooled association between exposure to PM2.5 and PM10 and respiratory diseases. Based on time-series and cohort studies, which are the priorities of the strength of evidence, a significant relationship between the risk of respiratory diseases (COPD, lung cancer, and COVID-19) was observed (COPD: pooled HR = 1.032, 95% CI: 1.004-1.061; lung cancer: pooled HR = 1.017, 95% CI: 1.015-1.020; and COVID-19: pooled RR = 1.004, 95% CI: 1.002-1.006 per 1 μg/m3 increase in PM2.5). Also, a significant relationship was observed between PM10 and respiratory diseases (COPD, LRIs, and COVID-19) based on time-series and cohort studies. Although the number of studies in this field is limited, which requires more investigations, it can be concluded that outdoor particulate matter can increase the risk of respiratory diseases.

Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy

Background

Numerous studies have demonstrated that fine particulate matter (PM2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM2.5 and an emerging infectious disease like COVID-19.

Methods

The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity.

Results

In all states, a positive association between PM2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 μg/m3 increase in PM2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate.

Conclusion

Short-term exposure to PM2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.

Seasonality of COVID-19 incidence in the United States

Background

The surges of Coronavirus Disease 2019 (COVID-19) appeared to follow a repeating pattern of COVID-19 outbreaks regardless of social distancing, mask mandates, and vaccination campaigns.

Objectives

This study aimed to investigate the seasonality of COVID-19 incidence in the United States of America (USA), and to delineate the dominant frequencies of the periodic patterns of the disease.

Methods

We characterized periodicity in COVID-19 incidences over the first three full seasonal years (March 2020 to March 2023) of the COVID-19 pandemic in the USA. We utilized a spectral analysis approach to find the naturally occurring dominant frequencies of oscillation in the incidence data using a Fast Fourier Transform (FFT) algorithm.

Results

Our study revealed four dominant peaks in the periodogram: the two most dominant peaks show a period of oscillation of 366 days and 146.4 days, while two smaller peaks indicate periods of 183 days and 122 days. The period of 366 days indicates that there is a single COVID-19 outbreak that occurs approximately once every year, which correlates with the dominant outbreak in the early/mid-winter months. The period of 146.4 days indicates approximately 3 peaks per year and matches well with each of the 3 annual outbreaks per year.

Conclusion

Our study revealed the predictable seasonality of COVID-19 outbreaks, which will guide public health preventative efforts to control future outbreaks. However, the methods used in this study cannot predict the amplitudes of the incidences in each outbreak: a multifactorial problem that involves complex environmental, social, and viral strain variables.

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems.

Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm

BACKGROUND

Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses.

METHODS

We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance.

RESULTS

Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC.

CONCLUSIONS

We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.

Prepandemic personal concentrations of per- and polyfluoroalkyl substances (PFAS) and other pollutants: Specific and combined effects on the incidence of COVID-19 disease and SARS-CoV-2 infection

OBJECTIVE

To investigate the specific and combined effects of personal concentrations of some per- and polyfluoroalkyl substances (PFAS), other persistent organic pollutants (POPs), and chemical elements -measured in individuals' blood several years before the pandemic- on the development of SARS-CoV-2 infection and COVID-19 disease in the general population.

METHODS

We conducted a prospective cohort study in 240 individuals from the general population of Barcelona. PFAS, other POPs, and chemical elements were measured in plasma, serum, and whole blood samples, respectively, collected in 2016-2017. PFAS were analyzed by liquid chromatography-triple quadrupole mass spectrometry. SARS-CoV-2 infection was detected by rRT-PCR in nasopharyngeal swabs and/or antibody serology in blood samples collected in 2020-2021.

RESULTS

No individual PFAS nor their mixtures were significantly associated with SARS-CoV-2 seropositivity or COVID-19 disease. Previously identified mixtures of POPs and elements (Porta et al., 2023) remained significantly associated with seropositivity and COVID-19 when adjusted for PFAS (all OR > 4 or p < 0.05). Nine chemicals comprised mixtures associated with COVID-19: thallium, ruthenium, lead, benzo[b]fluoranthene, DDD, other DDT-related compounds, manganese, tantalum, and aluminium. And nine chemicals comprised the mixtures more consistently associated with SARS-CoV-2 seropositivity: thallium, ruthenium, lead, benzo[b]fluoranthene, DDD, gold, and (protectively) selenium, indium, and iron.

CONCLUSIONS

The PFAS studied were not associated with SARS-CoV-2 seropositivity or COVID-19. The results confirm the associations between personal blood concentrations of some POPs and chemical elements and the risk of COVID-19 and SARS-CoV-2 infection in what remains the only prospective and population-based cohort study on the topic. Mixtures of POPs and chemical elements may contribute to explain the heterogeneity in the risks of SARS-CoV-2 infection and COVID-19 in the general population.

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan

This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.

Exploring the interplay between climate, population immunity and SARS-CoV-2 transmission dynamics in Mediterranean countries

The relationship between SARS-CoV-2 transmission and environmental factors has been analyzed in numerous studies since the outbreak of the pandemic, resulting in heterogeneous results and conclusions. This may be due to differences in methodology, considered variables, confounding factors, studied periods and/or lack of adequate data. Furthermore, previous works have reported that the lack of population immunity is the fundamental driver in transmission dynamics and can mask the potential impact of environmental variables. In this study, we aimed to investigate the association between climate variables and COVID-19 transmission considering the influence of population immunity. We analyzed two different periods characterized by the absence of vaccination (low population immunity) and a high degree of vaccination (high level of population immunity), respectively. Although this study has some limitations, such us the restriction to a specific climatic zone and the omission of other environmental factors, our results indicate that transmission of SARS-CoV-2 may increase independently of temperature and specific humidity in periods with low levels of population immunity while a negative association is found under conditions with higher levels of population immunity in the analyzed regions.

Using a time-varying LCAR-based strategy to investigate the spatiotemporal pattern of association between short-term exposure to particulate matter and COVID-19 incidence: a case study in the continental USA

Numerous studies have demonstrated that short-term exposure to particulate matter less than 10 μm (PM10) is positively associated with the COVID-19 incidence. However, no study has investigated the spatiotemporal pattern in this association, which plays important roles in identifying high-susceptibility regions and stages of epidemic. In this work, taking the 49 native states in America as an example, we used an advanced strategy to investigate this issue. First, time-series generalized additive model (GAM) were independently constructed to obtain the state-specific associations between short-term exposure to PM10 and the daily COVID-19 cases from 1 April 2020 to 31 December 2021. Then, a Leroux-prior-based conditional autoregression (LCAR) was used to spatially smoothen the associations. Third, the temporal variation of association and the reasons underlying the spatiotemporal heterogeneity were investigated by incorporating the time-varying GAM into LCAR. Results showed that PM10 was adversely associated with COVID-19 incidence in all the states. On average, a 10 μg/m3 increase of PM10 was associated with a 7.38% (95% CI 5.20-9.64%) increase in COVID-19 cases. A substantial spatial heterogeneity was observed, with strong associations in the middle and northeastern regions and weak associations in the western regions. The temporal trend of association presented a U shape, with the strongest association in the end of 2021. The vaccination rate was examined as a significant effect modifier. Our study provided the first evidence about the spatiotemporal pattern in PM10-COVID-19 associations and suggested that air pollution deserves more attention in the post-pandemic era and in the middle and northeastern regions in America for COVID-19 control and prevention.

Intronic Variants of the Angiotensin-Converting Enzyme 2 Gene Modulate Plasma ACE2 Levels and Possibly Confer Protection against Severe COVID-19

Membrane-bound angiotensin-converting enzyme 2 (ACE2) receptor acts as the entry point for the novel coronavirus, SARS-CoV-2. Polymorphisms in the ACE2 gene may alter viral binding, regulate the expression of ACE2, and thus, affect disease severity. In this study, 68 COVID-19 patients with varying degrees of severity and 40 healthy controls were enrolled. The genetic landscape of the ACE2 gene was explored by whole exome sequencing of 29 individuals, and specific regions of ACE2 were analyzed for the rest of the participants via PCR, followed by barcode-tagged sequencing. The mean soluble ACE2 level in the plasma of healthy controls and patients did not vary significantly but was higher in the patient group (3.77 ± 1.55 ng/mL vs. 3.94 ± 1.42 ng/mL). Analysis of exon 1, exon 2, and exon 8 of the ACE2 gene revealed that these regions are highly conserved in our population. Investigation of exon 11 and its flanking intronic region revealed that deletions in a stretch of 18T nucleotides in the noncoding region significantly decrease ACE2 levels in plasma, as individuals harboring wild-type variants had higher plasma ACE2 levels compared to those harboring T1del, T2del, and T3del variants. However, the intronic variants were not found to be significantly associated with disease severity.

Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in December 2019 in Wuhan city, in the Hubei province of China. Since then, it has spread practically all over the world, disrupting many human activities. In temperate climates overwhelming evidence indicates that its incidence increases significantly during the cold season. Italy was one of the first nations, in which COVID-19 reached epidemic proportions, already at the beginning of 2020. There is therefore enough data to perform a systematic investigation of the correlation between the spread of the virus and the environmental conditions. The objective of this study is the investigation of the relationship between the virus diffusion and the weather, including temperature, wind, humidity and air quality, before the rollout of any vaccine and including rapid variation of the pollutants (not only their long term effects as reported in the literature). Regarding them methodology, given the complexity of the problem and the sparse data, robust statistical tools based on ranking (Spearman and Kendall correlation coefficients) and innovative dynamical system analysis techniques (recurrence plots) have been deployed to disentangle the different influences. In terms of results, the evidence indicates that, even if temperature plays a fundamental role, the morbidity of COVID-19 depends also on other factors. At the aggregate level of major cities, air pollution and the environmental quantities affecting it, particularly the wind intensity, have no negligible effect. This evidence should motivate a rethinking of the public policies related to the containment of this type of airborne infectious diseases, particularly information gathering and traffic management.

The effects of COVID-19 on African American communities in Baltimore's health enterprise zones: a mixed-methods examination

BACKGROUND

The CoVID-19 pandemic underscored effects of community resources on the built environment, health and health outcomes. The purpose of this study was to conduct community-engaged research and examine aspects of health, and access to healthcare from the voices of community members, as a foundation for improving health equity through the built environment.

METHODS

This study utilized a convergent mixed methods design that included surveys and semi-structured interviews conducted from July 2021 to August 2022 to examine the impact of limited community resources, such as community health clinics on participants during the CoVID-19 pandemic. A convenient sample of 345 male and female African American participants represented five zip codes (21215, 21216, 21217, 21223, and 21229) in with the highest impact from CoVID 19, in Baltimore, Maryland. Quantitative and qualitative data were integrated to describe how the two types supported one another in health, healthcare and healthcare access.

RESULTS

More than half of all participants reported satisfaction with overall health, quality of healthcare provided and access to health care services. However, results indicated extreme differences in factors related to health and wellness after, as comparted to before the onset of the pandemic, Semi-structured interviews, expanded on overall community health, highlighting that overall satisfaction with health does not equal satisfaction with health-related resources and suggested participants felt frustrated and left out of much-needed community health resources to improve health and mental health services for all ages, nutrition services and community activities that make communities thrive. Data integration provided a more realistic view of what participants really experience, due to the expanded analysis of semi-structured interviews, and indicated quantitative and qualitative data did not always support each other.

CONCLUSIONS

Future research to improve the built environment, and to address historic health inequities, will require ongoing community engagement to better understand community needs. This study results encourage ongoing research to expand resources for community-engaged research and interventions. Researchers must remain cognoscente of changing needs, and persistent disparities that can only be addressed if policies, supported by these results, are introduced to make equitable investments to forge an environment where healthy communities thrive.

Nonlinear and Multidelayed Effects of Meteorological Drivers on Human Respiratory Syncytial Virus Infection in Japan

In this study, we aimed to characterize the nonlinear and multidelayed effects of multiple meteorological drivers on human respiratory syncytial virus (HRSV) infection epidemics in Japan. The prefecture-specific weekly time-series of the number of newly confirmed HRSV infection cases and multiple meteorological variables were collected for 47 Japanese prefectures from 1 January 2014 to 31 December 2019. We combined standard time-series generalized linear models with distributed lag nonlinear models to determine the exposure-lag-response association between the incidence relative risks (IRRs) of HRSV infection and its meteorological drivers. Pooling the 2-week cumulative estimates showed that overall high ambient temperatures (22.7 °C at the 75th percentile compared to 16.3 °C) and high relative humidity (76.4% at the 75th percentile compared to 70.4%) were associated with higher HRSV infection incidence (IRR for ambient temperature 1.068, 95% confidence interval [CI], 1.056-1.079; IRR for relative humidity 1.045, 95% CI, 1.032-1.059). Precipitation revealed a positive association trend, and for wind speed, clear evidence of a negative association was found. Our findings provide a basic picture of the seasonality of HRSV transmission and its nonlinear association with multiple meteorological drivers in the pre-HRSV-vaccination and pre-coronavirus disease 2019 (COVID-19) era in Japan.

Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States

Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.

Comparison of Soft Indicator and Poisson Kriging for the Noise-Filtering and Downscaling of Areal Data: Application to Daily COVID-19 Incidence Rates

This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020-2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero.

Genome-Wide Mining of Selaginella moellendorffii for Hevein-like Lectins and Their Potential Molecular Mimicry with SARS-CoV-2 Spike Glycoprotein

Multidisciplinary research efforts on potential COVID-19 vaccine and therapeutic candidates have increased since the pandemic outbreak of SARS-CoV-2 in 2019. This search has become imperative due to the increasing emergences and limited widely available medicines. The presence of bioactive anti-SARS-CoV-2 molecules was examined from various plant sources. Among them is a group of proteins called lectins that can bind carbohydrate moieties. In this article, we present ten novel, chitin-specific Hevein-like lectins that were derived from Selaginella moellendorffii v1.0's genome. The capacity of these lectin homologs to bind with the spike protein of SARS-CoV-2 was examined. Using the HDOCK server, 3D-modeled Hevein-domains were docked to the spike protein's receptor binding domain (RBD). The Smo446851, Smo125663, and Smo99732 interacted with Asn343-located complex N-glycan and RBD residues, respectively, with binding free energies of -17.5, -13.0, and -26.5 Kcal/mol. The molecular dynamics simulation using Desmond and the normal-state analyses via torsional coordinate association for the Smo99732-RBD complex using iMODS is characterized by overall higher stability and minimum deformity than the other lectin complexes. The three lectins interacting with carbohydrates were docked against five individual mutations that frequently occur in major SARS-CoV-2 variants. These were in the spike protein's receptor-binding motif (RBM), while Smo125663 and Smo99732 only interacted with the spike glycoprotein in a protein-protein manner. The precursors for the Hevein-like homologs underwent additional characterization, and their expressional profile in different tissues was studied. These in silico findings offered potential lectin candidates targeting key N-glycan sites crucial to the virus's virulence and infection.

Oral Probenecid for Nonhospitalized Adults with Symptomatic Mild-to-Moderate COVID-19

Probenecid is an orally bioavailable, uricosuric agent that was first approved in 1951 for the treatment of gout, but was later found to have potent, broad-spectrum antiviral activity against several respiratory viruses including SARS-CoV-2. We conducted a phase 2 randomized, placebo-controlled, single-blind, dose-range finding study in non-hospitalized patients with symptomatic, mild-to-moderate COVID-19. Patients were randomly assigned in a 1:1:1 ratio to receive either 500 mg of probenecid, 1000 mg of probenecid, or a matching placebo every 12 h for five days. The patients' COVID-19 viral load hospitalization, or death from any cause through day 28, as well as safety, were evaluated. COVID-19-related symptoms were assessed at baseline, and on days 3, 5, 10, 15, and 28. The primary endpoints of the study were time to first negative SARS-CoV-2 viral test (or viral clearance) and the proportion of patients that were symptom-free at day 5. A total of 75 patients were randomized, with 25 patients in each group. All of the patients completed the study as planned with no hospitalizations or deaths being reported. The median time to viral clearance was significantly shorter for the probenecid 1000 mg group than for placebo (7 days vs. 11 days, respectively; p < 0.0001), and for the probenecid 500 mg group versus placebo (9 days vs. 11 days, respectively; p < 0.0001). In addition, the median time to viral clearance was significantly shorter for the probenecid 1000 mg group than for the probenecid 500 mg group (7 days vs. 9 days, respectively; p < 0.0001). All patients reported at least one COVID-19-related symptom on days 3 and 5; however, on day 10, a significantly greater proportion of patients receiving probenecid 1000 mg reported the complete resolution of symptoms versus placebo (68% vs. 20%, respectively; p = 0.0006), as well as for those receiving probenecid 500 mg versus placebo (56% vs. 20%, respectively, p = 0.0087). The incidence of adverse events during treatment was similar across all groups for any adverse event, and was 12%. All events were mild with no serious adverse events reported and no discontinuations due to an adverse event. The treatment of patients with symptomatic, mild-to-moderate COVID-19 with probenecid resulted in a significant, dose-dependent decrease in the time to viral clearance and a significantly higher proportion of patients reporting complete symptom resolution by day 10. (Supported by TrippBio; ClinicalTrials.gov number, NCT05442983 and Clinical Trials Registry India number CTRI/2022/07/043726).

Household conditions, COVID-19, and equity: Insight from two nationally representative surveys

Background

With people across the United States spending increased time at home since the emergence of COVID-19, housing characteristics may have an even greater impact on health. Therefore, we assessed associations between household conditions and COVID-19 experiences.

Methods

We used data from two nationally representative surveys: the Tufts Equity Study (TES; n = 1449 in 2021; n = 1831 in 2022) and the Household Pulse Survey (HPS; n = 147,380 in 2021; n = 62,826 in 2022). In the TES, housing conditions were characterized by heating/cooling methods; smoking inside the home; visible water damage/mold; age of housing unit; and self-reported concern about various environmental factors. In TES and HPS, household size was assessed. Accounting for sampling weights, we examined associations between each housing exposure and COVID-19 outcomes (diagnosis, vaccination) using separate logistic regression models with covariates selected based on an evidence-based directed acyclic graph.

Results

Having had COVID-19 was more likely among people who reported poor physical housing condition (odds ratio [OR] = 2.32; 95% confidence interval [CI] = 1.17-4.59; 2021), visible water damage or mold/musty smells (OR = 1.50; 95% CI = 1.10-2.03; 2022), and larger household size (5+ versus 1-2 people; OR = 1.53, 95% CI = 1.34-1.75, HPS 2022). COVID-19 vaccination was less likely among participants who reported smoke exposure inside the home (OR = 0.53; 95% CI = 0.31-0.90; 2022), poor water quality (OR = 0.42; 95% CI = 0.21-0.85; 2021), noise from industrial activity/construction (OR = 0.44; 95% CI = 0.19-0.99; 2022), and larger household size (OR = 0.57; 95% CI = 0.46-0.71; HPS 2022). Vaccination was also positively associated with poor indoor air quality (OR = 1.96; 95% CI = 1.02-3.72; 2022) and poor physical housing condition (OR = 2.27; 95% CI = 1.01-5.13; 2022). Certain heating/cooling sources were associated with COVID-19 outcomes.

Conclusions

Our study found poor housing conditions associated with increased COVID-19 burden, which may be driven by systemic disparities in housing, healthcare, and financial access to resources during the COVID-19 pandemic.

Air pollution and COVID-19 severity

Air pollution may elevate the risk of infection and severity (e.g. intensive care unit (ICU) admission and death) from coronavirus disease 2019 (COVID-19) by modifying host susceptibility to infection and disease severity, and by elevating the risk of comorbidities [1]. An emerging body of toxicological and experimental evidence has provided insights on how acute [2] and chronic [3] air pollution exposure could play a role in COVID-19 infection and severity. There is also substantial epidemiological evidence on the effects of long-term air pollution exposure in the incidence and mortality of COVID-19 [1]. However, most epidemiological data is based on ecological studies (susceptible to group-level and cross-level confounding), passive and not active case ascertainment and outcome characterisation (susceptible to case-ascertainment bias), and poor assessment of air pollution exposure (a potential for a misclassification bias).

Air pollution abatement would be a very effective way to protect our population from the impact of COVID-19 https://bit.ly/43d9A75

Pre-admission ambient air pollution and blood soot particles predict hospitalisation outcomes in COVID-19 patients

BACKGROUND

Air pollution exposure is one of the major risk factors for aggravation of respiratory diseases. We investigated whether exposure to air pollution and accumulated black carbon (BC) particles in blood were associated with coronavirus disease 2019 (COVID-19) disease severity, including the risk for intensive care unit (ICU) admission and duration of hospitalisation.

METHODS

From May 2020 until March 2021, 328 hospitalised COVID-19 patients (29% at intensive care) were recruited from two hospitals in Belgium. Daily exposure levels (from 2016 to 2019) for particulate matter with aerodynamic diameter <2.5 µm and <10 µm (PM2.5 and PM10, respectively), nitrogen dioxide (NO2) and BC were modelled using a high-resolution spatiotemporal model. Blood BC particles (internal exposure to nano-sized particles) were quantified using pulsed laser illumination. Primary clinical parameters and outcomes included duration of hospitalisation and risk of ICU admission.

RESULTS

Independent of potential confounders, an interquartile range (IQR) increase in exposure in the week before admission was associated with increased duration of hospitalisation (PM2.5 +4.13 (95% CI 0.74-7.53) days, PM10 +4.04 (95% CI 1.24-6.83) days and NO2 +4.54 (95% CI 1.53-7.54) days); similar effects were observed for long-term NO2 and BC exposure on hospitalisation duration. These effect sizes for an IQR increase in air pollution on hospitalisation duration were equivalent to the effect of a 10-year increase in age on hospitalisation duration. Furthermore, for an IQR higher blood BC load, the OR for ICU admission was 1.33 (95% CI 1.07-1.65).

CONCLUSIONS

In hospitalised COVID-19 patients, higher pre-admission ambient air pollution and blood BC levels predicted adverse outcomes. Our findings imply that air pollution exposure influences COVID-19 severity and therefore the burden on medical care systems during the COVID-19 pandemic.

A global perspective on the intrinsic dimensionality of COVID-19 data

We develop a novel global perspective of the complexity of the relationships between three COVID-19 datasets, the standardised per-capita growth rate of COVID-19 cases and deaths, and the Oxford Coronavirus Government Response Tracker COVID-19 Stringency Index (CSI) which is a measure describing a country's stringency of lockdown policies. We use a state-of-the-art heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. Our findings suggest that these highly popular COVID-19 statistics may project onto two low-dimensional manifolds without significant information loss, suggesting that COVID-19 data dynamics are generated from a latent mechanism characterised by a few important variables. The low dimensionality imply a strong dependency among the standardised growth rates of cases and deaths per capita and the CSI for countries over 2020-2021. Importantly, we identify spatial autocorrelation in the intrinsic dimension distribution worldwide. The results show how high-income countries are more prone to lie on low-dimensional manifolds, likely arising from aging populations, comorbidities, and increased per capita mortality burden from COVID-19. Finally, the temporal stratification of the dataset allows the examination of the intrinsic dimension at a more granular level throughout the pandemic.

There is a need for more precise models to assess the determinants of health crises like COVID-19

The COVID-19 pandemic has had a significant impact on global mortality. While the causal relationship between SARS-CoV-2 and the anomalous increase in deaths is established, more precise and complex models are needed to determine the exact weight of epidemiological factors involved. Indeed, COVID-19 behavior is influenced by a wide range of variables, including demographic characteristics, population habits and behavior, healthcare performance, and environmental and seasonal risk factors. The bidirectional causality between impacted and impacting aspects, as well as confounding variables, complicates efforts to draw clear, generalizable conclusions regarding the effectiveness and cost-benefit ratio of non-pharmaceutical health countermeasures. Thus, it is imperative that the scientific community and health authorities worldwide develop comprehensive models not only for the current pandemic but also for future health crises. These models should be implemented locally to account for micro-differences in epidemiological characteristics that may have relevant effects. It is important to note that the lack of a universal model does not imply that local decisions have been unjustified, and the request to decrease scientific uncertainty does not mean denying the evidence of the effectiveness of the countermeasures adopted. Therefore, this paper must not be exploited to denigrate either the scientific community or the health authorities.

Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR)

During the period in which the Omicron coronavirus variant was rapidly spreading, the impact of the institutional-social-ecological dimensions on the case-fatality rate was rarely afforded attention. By adopting the diagnostic social-ecological system (SES) framework, the present paper aims to identify the impact of institutional-social-ecological factors on the case-fatality rate of COVID-19 in 134 countries and regions and test their spatial heterogeneity. Using statistical data from the Our World In Data website, the present study collected the cumulative case-fatality rate from 9 November 2021 to 23 June 2022, along with 11 country-level institutional-social-ecological factors. By comparing the goodness of fit of the multiple linear regression model and the multiscale geographically weighted regression (MGWR) model, the study demonstrated that the effects of SES factors exhibit significant spatial heterogeneity in relation to the case-fatality rate of COVID-19. After substituting the data into the MGWR model, six SES factors were identified with an R square of 0.470 based on the ascending effect size: COVID-19 vaccination policy, age dependency ratio, press freedom, gross domestic product (GDP), COVID-19 testing policy, and population density. The GWR model was used to test and confirm the robustness of the research results. Based on the analysis results, it is suggested that the world needs to meet four conditions to restore normal economic activity in the wake of the COVID-19 pandemic: (i) Countries should increase their COVID-19 vaccination coverage and maximize COVID-19 testing expansion. (ii) Countries should increase public health facilities available to provide COVID-19 treatment and subsidize the medical costs of COVID-19 patients. (iii) Countries should strictly review COVID-19 news reports and actively publicize COVID-19 pandemic prevention knowledge to the public through a range of media. (iv) Countries should adopt an internationalist spirit of cooperation and help each other to navigate the COVID-19 pandemic. The study further tests the applicability of the SES framework to the field of COVID-19 prevention and control based on the existing research, offering novel policy insights to cope with the COVID-19 pandemic that coexists with long-term human production and life for a long time.

COVID-19, post-acute COVID-19 syndrome (PACS, "long COVID") and post-COVID-19 vaccination syndrome (PCVS, "post-COVIDvac-syndrome"): Similarities and differences

Worldwide there have been over 760 million confirmed coronavirus disease 2019 (COVID-19) cases, and over 13 billion COVID-19 vaccine doses have been administered as of April 2023, according to the World Health Organization. An infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to an acute disease, i.e. COVID-19, but also to a post-acute COVID-19 syndrome (PACS, "long COVID"). Currently, the side effects of COVID-19 vaccines are increasingly being noted and studied. Here, we summarise the currently available indications and discuss our conclusions that (i) these side effects have specific similarities and differences to acute COVID-19 and PACS, that (ii) a new term should be used to refer to these side effects (post-COVID-19 vaccination syndrome, PCVS, colloquially "post-COVIDvac-syndrome"), and that (iii) there is a need to distinguish between acute COVID-19 vaccination syndrome (ACVS) and post-acute COVID-19 vaccination syndrome (PACVS) - in analogy to acute COVID-19 and PACS ("long COVID"). Moreover, we address mixed forms of disease caused by natural SARS-CoV-2 infection and COVID-19 vaccination. We explain why it is important for medical diagnosis, care and research to use the new terms (PCVS, ACVS and PACVS) in order to avoid confusion and misinterpretation of the underlying causes of disease and to enable optimal medical therapy. We do not recommend to use the term "Post-Vac-Syndrome" as it is imprecise. The article also serves to address the current problem of "medical gaslighting" in relation to PACS and PCVS by raising awareness among the medical professionals and supplying appropriate terminology for disease.

Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM_2.5, O₃, SO₂, NO₂, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM_2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO₂ and SO₂ were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.

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

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

Individual blood concentrations of persistent organic pollutants and chemical elements, and COVID-19: A prospective cohort study in Barcelona

BACKGROUND

There is wide, largely unexplained heterogeneity in immunological and clinical responses to SARS-CoV-2 infection. Numerous environmental chemicals, such as persistent organic pollutants (POPs) and chemical elements (including some metals, essential trace elements, rare earth elements, and minority elements), are immunomodulatory and cause a range of adverse clinical events. There are no prospective studies on the effects of such substances on the incidence of SARS-CoV-2 infection and COVID-19.

OBJECTIVE

To investigate the influence of blood concentrations of POPs and elements measured several years before the pandemic on the development of SARS-CoV-2 infection and COVID-19 in individuals from the general population.

METHODS

We conducted a prospective cohort study in 154 individuals from the general population of Barcelona. POPs and elements were measured in blood samples collected in 2016-2017. SARS-CoV-2 infection was detected by rRT-PCR in nasopharyngeal swabs and/or by antibody serology using eighteen isotype-antigen combinations measured in blood samples collected in 2020-2021. We analyzed the associations between concentrations of the contaminants and SARS-CoV-2 infection and development of COVID-19, taking into account personal habits and living conditions during the pandemic.

RESULTS

Several historically prevalent POPs, as well as arsenic, cadmium, mercury, and zinc, were not associated with COVID-19, nor with SARS-CoV-2 infection. However, DDE (adjusted OR = 5.0 [95% CI: 1.2-21]), lead (3.9 [1.0-15]), thallium (3.4 [1.0-11]), and ruthenium (5.0 [1.8-14]) were associated with COVID-19, as were tantalum, benzo(b)fluoranthene, DDD, and manganese. Thallium (3.8 [1.6-8.9]), and ruthenium (2.9 [1.3-6.7]) were associated with SARS-CoV-2 infection, and so were lead, gold, and (protectively) iron and selenium. We identified mixtures of up to five substances from several chemical groups, with all substances independently associated to the outcomes.

CONCLUSIONS

Our results provide the first prospective and population-based evidence of an association between individual concentrations of some contaminants and COVID-19 and SARS-CoV-2 infection. POPs and elements may contribute to explain the heterogeneity in the development of SARS-CoV-2 infection and COVID-19 in the general population. If the associations are confirmed as causal, means are available to mitigate the corresponding risks.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis

BACKGROUND AND AIM

The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally.

METHODS

We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone.

RESULTS

We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries.

CONCLUSION

Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California

BACKGROUND

Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models.

METHODS

We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM_2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM_0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients.

FINDINGS

We found significant associations between COVID-19 death and several air pollution exposures, including: PM_2.5 mass, PM_0.1 mass, PM_2.5 nitrates, PM_2.5 elemental carbon, PM_2.5 on-road diesel, and PM_2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM_2.5 mass and PM_2.5 nitrate to HR ∼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects.

INTERPRETATION

Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

Exposure to a mixture of per-and polyfluoroalkyl substances modulates pulmonary expression of ACE2 and circulating hormones and cytokines

Genetic and environmental factors impact on the interindividual variability of susceptibility to communicable and non-communicable diseases. A class of ubiquitous chemicals, Per- and polyfluoroalkyl substances (PFAS) have been linked in epidemiological studies to immunosuppression and increased susceptibility to viral infections, but possible mechanisms are not well elucidated. To begin to gain insight into the role of PFAS in susceptibility to one such viral infection, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), male and female C57BL/6 J mice were exposed to control water or a mixture of 5 PFAS (PFOS, PFOA, PFNA, PFHxS, Genx) for 12 weeks and lungs were isolated for examination of expression of SARS-CoV-2-related receptors Angiotensin-Converting Enzyme 2 (ACE2) and others. Secondary analyses included circulating hormones and cytokines which have been shown to directly or indirectly impact on ACE2 expression and severity of viral infections. Changes in mRNA and protein expression were analyzed by RT-qPCR and western blotting and circulating hormones and cytokines were determined by ELISA and MESO QuickPlex. The PFAS mixture decreased Ace2 mRNA 2.5-fold in male mice (p < 0.0001), with no significant change observed in females. In addition, TMPRSS2, ANPEP, ENPEP and DPP4 (other genes implicated in COVID-19 infection) were modulated due to PFAS. Plasma testosterone, but not estrogen were strikingly decreased due to PFAS which corresponded to PFAS-mediated repression of 4 representative pulmonary AR target genes; hemoglobin, beta adult major chain (Hbb-b1), Ferrochelatase (Fech), Collagen Type XIV Alpha 1 Chain (Col14a1), 5'-Aminolevulinate Synthase 2 (Alas2). Finally, PFAS modulated circulating pro and anti-inflammatory mediators including IFN-γ (downregulated 3.0-fold in females; p = 0.0301, 2.1-fold in males; p = 0.0418) and IL-6 (upregulated 5.6-fold in males; p = 0.030, no change in females). In conclusion, our data indicate long term exposure to a PFAS mixture impacts mechanisms related to expression of ACE2 in the lung. This work provides a mechanistic rationale for important future studies of PFAS exposure and subsequent viral infection.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.