Methodological limitations in studies assessing the effects of environmental and socioeconomic variables on the spread of COVID-19: a systematic review

Association of inflammation and protein carbamylation in patients with COVID-19

Introduction

Carbamylation involves the non-enzymatic binding of isocyanic acid to the amino groups of proteins, making it associated with many pathological conditions, including inflammation, aging, arteriosclerosis, and renal failure. However, there are no data on protein carbamylation in patients with COVID-19. Our study is the first to evaluate the association between blood inflammation and protein carbamylation in patients who died from COVID-19 compared to COVID-19 survivors.

Methods

The study included 50 patients admitted to Dr. Tytus Chałubiński Specialist Hospital in Radom, Poland. Twenty-five of them were COVID-19 survivors (15 men, 10 women), and 25 were COVID-19 deceased patients (15 men, 10 women). The number of subjects was based on a pilot study assuming a significance level of 0.05 and a test power of 0.8. Plasma/serum samples were assayed for carbamyl-lysine (CBL) and inflammatory biomarkers (CRP, procalcitonin, D-dimer, IL-6, and WBC). The concentration of CBL was measured using an enzyme-linked immunosorbent assay (ELISA). Statistical analysis was performed using the Mann-Whitney U test and Spearman rank correlation. Receiver Operating Characteristic (ROC) analysis was used to assess the diagnostic utility of serum CBL.

Results

Serum CBL levels were significantly higher in patients who died from COVID-19 compared to COVID-19 survivors (p = 0.0011). There was a positive correlation of serum CBL with IL-6, D-dimer, and WBC. Serum CBL levels >101 ng/mL, with moderate sensitivity and specificity, differentiate COVID-19 deceased from recovered patients (area under the curve 0.76).

Discussion

In conclusion, COVID-19 is associated with excessive protein carbamylation. Inflammation may be a source of higher CBL production in COVID-19. A thorough understanding of the consequences of increased protein carbamylation may clarify the consequences of COVID-19 complications.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

IMPACT STATEMENT

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

Pathogenic built environment? Reflections on modeling spatial determinants of health in urban settings considering the example of COVID-19 studies

The triad of host, agent, and environment has become a widely accepted framework for understanding infectious diseases and human health. While modern medicine has traditionally focused on the individual, there is a renewed interest in the role of the environment. Recent studies have shifted from an early-twentieth-century emphasis on individual factors to a broader consideration of contextual factors, including environmental, climatic, and social settings as spatial determinants of health. This shifted focus has been particularly relevant in the context of the COVID-19 pandemic, where the built environment in urban settings is increasingly recognized as a crucial factor influencing disease transmission. However, operationalizing the complexity of associations between the built environment and health for empirical analyses presents significant challenges. This study aims to identify key caveats in the operationalization of spatial determinants of health for empirical analysis and proposes guiding principles for future research. We focus on how the built environment in urban settings was studied in recent literature on COVID-19. Based on a set of criteria, we analyze 23 studies and identify explicit and implicit assumptions regarding the health-related dimensions of the built environment. Our findings highlight the complexities and potential pitfalls, referred to as the 'spatial trap,' in the current approaches to spatial epidemiology concerning COVID-19. We conclude with recommendations and guiding questions for future studies to avoid falsely attributing a built environment impact on health outcomes and to clarify explicit and implicit assumptions regarding the health-related dimensions.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Leveraging new methodologies for public health crisis management

Evidence-based medicine is critical in public health emergencies, offering a framework for decision-making and adaptive healthcare responses. By relying on up-to-date and reliable evidence, EBM enables healthcare systems to respond quickly to evolving crises and ensures efficient resource allocation. This perspective presents the importance of evidence-based medicine in public health emergencies, emphasizing the need for rapid decision-making and preparedness. It identifies challenges from the COVID-19 pandemic, including barriers to evidence synthesis, and explores innovative solutions, including methodological pluralism and systems thinking. The findings highlight that evidence-based medicine improves health care systems' responsiveness to public health crises, supports the efficient resource allocation, and reinforces the need for flexible strategies that adapt to rapidly evolving information. In particular, the practical implications underscore that, in crisis settings, EBM must expand beyond strict evidence hierarchies to include timely, reasonable, and sometimes intuitive expert judgments, ensuring robust and adaptable responses. In conclusion, while EBM enhances healthcare adaptability and decision-making in emergencies, future responses will benefit from incorporating more diverse and flexible approaches to ensure more resilient and effective public health strategies.

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

Systematic review of empiric studies on lockdowns, workplace closures, and other non-pharmaceutical interventions in non-healthcare workplaces during the initial year of the COVID-19 pandemic: benefits and selected unintended consequences

BACKGROUND

We conducted a systematic review aimed to evaluate the effects of non-pharmaceutical interventions within non-healthcare workplaces and community-level workplace closures and lockdowns on COVID-19 morbidity and mortality, selected mental disorders, and employment outcomes in workers or the general population.

METHODS

The inclusion criteria included randomized controlled trials and non-randomized studies of interventions. The exclusion criteria included modeling studies. Electronic searches were conducted using MEDLINE, Embase, and other databases from January 1, 2020, through May 11, 2021. Risk of bias was assessed using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Meta-analysis and sign tests were performed.

RESULTS

A total of 60 observational studies met the inclusion criteria. There were 40 studies on COVID-19 outcomes, 15 on anxiety and depression symptoms, and five on unemployment and labor force participation. There was a paucity of studies on physical distancing, physical barriers, and symptom and temperature screening within workplaces. The sign test indicated that lockdown reduced COVID-19 incidence or case growth rate (23 studies, p < 0.001), reproduction number (11 studies, p < 0.001), and COVID-19 mortality or death growth rate (seven studies, p < 0.05) in the general population. Lockdown did not have any effect on anxiety symptoms (pooled standardized mean difference = -0.02, 95% CI: -0.06, 0.02). Lockdown had a small effect on increasing depression symptoms (pooled standardized mean difference = 0.16, 95% CI: 0.10, 0.21), but publication bias could account for the observed effect. Lockdown increased unemployment (pooled mean difference = 4.48 percentage points, 95% CI: 1.79, 7.17) and decreased labor force participation (pooled mean difference = -2.46 percentage points, 95% CI: -3.16, -1.77). The risk of bias for most of the studies on COVID-19 or employment outcomes was moderate or serious. The risk of bias for the studies on anxiety or depression symptoms was serious or critical.

CONCLUSIONS

Empiric studies indicated that lockdown reduced the impact of COVID-19, but that it had notable unwanted effects. There is a pronounced paucity of studies on the effect of interventions within still-open workplaces. It is important for countries that implement lockdown in future pandemics to consider strategies to mitigate these unintended consequences.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration # CRD42020182660.

Association metrological factors with Covid-19 mortality in Tehran, Iran (2020-2021)

The outbreak of the Coronavirus disease (COVID-19) has raised questions about the potential role of climate and environmental factors in disease transmission. This study examined meteorological and demographic factors to determine their impact on mortality and hospitalization rates in Tehran, Iran from January 1, 2021, to December 31, 2022. Notably, hospitalization cases were positively associated with temperature (P-value: 0.001 in spring, P-value: 0.045 in winter) and pressure (P-value: 0.004 in spring), while being negatively associated with wind speed (P-value: 0.03 in spring, P-value: 0.01 in autumn) and humidity (P-value: 0.001 in autumn) during the spring and autumn seasons. Conversely, mortality was associated with wind speed (P-value: 0.01) and pressure (P-value: 0.02) during winter and spring, respectively. Moreover, temperature was associated with mortality in both spring (P-value: 0.00) and winter (P-value: 0.04). The findings suggest that identifying the environmental factors that contribute to the spread of COVID-19 can help prevent future waves of the pandemic in Tehran.

Exploring the links between indoor air pollutants and health outcomes in South Asian countries: a systematic review

Indoor air pollution (IAP) has adverse effects on the health of people, globally. The objective of this systematic review was to present the range of health problems studied in association with indoor air pollutants in South Asian countries. We searched five databases, including PubMed, Web of Science, Scopus, Google Scholar, and CAB Direct for articles published between the years 2000 and 2020. We retrieved 5,810 articles, out of which we included 90 articles in our review. Among South Asian countries, only five countries have published results related to relationship between indoor air pollutants and adverse health conditions. All studies have shown adversity of indoor air pollutants on human's health. We found indoor solid fuel burning as a key source of indoor air pollution in the included studies, while women and children were most affected by their exposure to solid fuel burning. More than half of the studies accounted particulate matter responsible for indoor air pollution bearing negative health effects. In the included studies, eyes and lungs were the most commonly affected body organs, exhibiting common symptoms like cough, breathing difficulty and wheezing. This might have developed into common conditions like respiratory tract infection, chronic obstructive pulmonary diseases and eye cataract. In addition to promote research in South Asian countries, future research should focus on novel digital ways of capturing effects of indoor air pollutants among vulnerable segments of the population. As a result of this new knowledge, public health agencies should develop and test interventions to reduce people's exposure levels and prevent them to develop adverse health outcomes.

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.

Neighborhood Socioeconomic Characteristics Associated with the COVID-19 Incidence in Elementary School Children: An Ecological Study in Osaka City, Japan

We aimed to determine whether neighborhood socioeconomic characteristics are associated with the coronavirus disease 2019 (COVID-19) incidence in elementary school children and, if so, the associated characteristics. We obtained data on the number of infected children from 282 public elementary schools and the socioeconomic characteristics of each school district in Osaka City, Japan. We examined associations between these variables through negative binomial regression analyses. The proportion of employment in the wholesale and retail trade industry and the college graduation rate were significantly positively and negatively associated, respectively, with the total number of COVID-19-infected children. It was discovered that percentages of employment in the accommodation and food service industries in Wave 2, wholesale and retail trade industries after Wave 3, and healthcare and social assistance industries in Wave 5 were significantly positively associated with the number of infected children; likewise, the college graduation rate in Wave 5 was significantly negatively associated with the number of infected children. Our findings provide insight into the relevant and important areas of focus for public health policymakers and practitioners to ensure reduced disparities in COVID-19 infection rates.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BACKGROUND

COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

METHODS

We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.

RESULTS

Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.

CONCLUSIONS

This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

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

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

COVID-19 and deprivation amplification: An ecological study of geographical inequalities in mortality in England

'Deprivation amplification' is used to understand the relationship between deprivation, scale and COVID-19 mortality rates. We found that more deprived Middle Super Output Areas (MSOAs) in the more deprived northern regions suffered greater COVID-19 mortality rates. Across England, the most deprived 20% of MSOAs had higher mortality than the least deprived (44.1% more COVID-19 deaths/10,000). However, the most deprived MSOAs in the north fared worse than equally deprived areas in the rest of England (14.5% more deaths/10,000, beta = 0.136, p < 0.01). There was also strong evidence of spatial clustering and spill-overs. We discuss these findings in relation to 'deprivation amplification', the 'syndemic pandemic', and the health and place literature.

Potential limitations in systematic review studies assessing the effect of the main intervention for treatment/therapy of COVID-19 patients: An overview

Background

Although several studies have assessed the safety, efficacy, and effectiveness of interventions in treating the COVID-19, many of them have limitations that can have an immense impact on their results. This study aims to assess the potential limitations in systematic reviews (SRs) that evaluate the effect of interventions on the treatment of the COVID-19.

Methods

PubMed, Scopus, and Web of Sciences (WOS) databases were searched from inception to January 1, 2022. All systematic reviews investigated the effectiveness, efficacy, safety, and outcome of the main intervention (Favipiravir, Remdesivir, Hydroxychloroquine, Ivermectin, Lopinavir/Ritonavir, or Tocilizumab) for the treatment of COVID-19 patients and reported the potential limitations of the included studies. We assessed the quality of the included studies using the Quality Assessment Tool (QAT) for review articles. We conducted a content analysis and prepared a narrative summary of the limitations.

Results

Forty-six studies were included in this review. Ninety one percent of the included studies scored as strong quality and the remaining (9%) as moderate quality. Only 29.7% of the included systematic reviews have a registered protocol. 26% of the included studies mentioned a funding statement. The main limitations of the included studies were categorized in 10 domains: sample size, heterogeneity, follow-up, treatment, including studies, design, definitions, synthesis, quality, and search.

Conclusion

Various limitations have been reported in all the included studies. Indeed, the existence of limitations in studies can affect their results, therefore, identifying these limitations can help researchers design better studies. As a result, stronger studies with more reliable results will be reported and disseminated. Further research on COVID-19 SRs is essential to improve research quality and also, efficiency among scientists across the world.

Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

Air quality index variation before and after the onset of COVID-19 pandemic: a comprehensive study on 87 capital, industrial and polluted cities of the world

BACKGROUND

Coronavirus disease 2019 (COVID-19) pandemic provided an opportunity for the environment to reduce ambient pollution despite the economic, social and health disruption to the world. The purpose of this study was to investigate the changes in the air quality indexes (AQI) in industrial, densely populated and capital cities in different countries of the world before and after 2020. In this ecological study, we used AQI obtained from the free available databases such as the World Air Quality Index (WAQI). Bivariate correlation analysis was used to explore the correlations between meteorological and AQI variables. Mean differences (standard deviation: SD) of AQI parameters of different years were tested using paired-sample t-test or Wilcoxon signed-rank test as appropriate. Multivariable linear regression analysis was conducted to recognize meteorological variables affecting the AQI parameters.

RESULTS

AQI-PM2.5, AQI-PM10 and AQI-NO2 changes were significantly higher before and after 2020, simultaneously with COVID-19 restrictions in different cities of the world. The overall changes of AQI-PM2.5, AQI-PM10 and AQI-NO2 in 2020 were - 7.36%, - 17.52% and - 20.54% compared to 2019. On the other hand, these results became reversed in 2021 (+ 4.25%, + 9.08% and + 7.48%). In general, the temperature and relative humidity were inversely correlated with AQI-PM2.5, AQI-PM10 and AQI-NO2. Also, after adjusting for other meteorological factors, the relative humidity was inversely associated with AQI-PM2.5, AQI-PM10 and AQI-NO2 (β = - 1.55, β = - 0.88 and β = - 0.10, P < 0.01, respectively).

CONCLUSIONS

The results indicated that air quality generally improved for all pollutants except carbon monoxide and ozone in 2020; however, changes in 2021 have been reversed, which may be due to the reduction of some countries' restrictions. Although this quality improvement was temporary, it is an important result for planning to control environmental pollutants.