Association between environmental factors and COVID-19 in Shanghai, China

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Investigating the cumulative lag effects of environmental exposure under urban differences on COVID-19

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.

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.

Assessment of oral emergency services during COVID-19: a retrospective study of 14,885 cases in Shanghai

BACKGROUND

To assess the impact of coronavirus disease-2019 (COVID-19) in its outbreak stage (Spring Festival in 2020) on oral emergency services.

METHODS

Oral emergency cases in Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, during the Spring Festival after the outbreak of the COVID-19 epidemic in 2020 were collected and compared with those in 2018 and 2019. Electronic medical records including the visited department, age, sex, time, date, region, and diagnosis were collected and analyzed. The results were statistically analyzed using Pearson's Chi-square test and one-way analysis of variance (ANOVA).

RESULTS

Compared with that in 2018 and 2019, the total number of patients decreased during the Spring Festival in 2020 (p < 0.001), but the proportions of patients visiting Oral Surgery and Oral, Head, and Neck Oncology Emergency departments increased. The average age of patients increased, and the number of night visits decreased. Toothache diseases involving endodontic and periodontal diseases increased, while the proportion of maxillofacial trauma decreased. The wasn't a linear association between diagnosis or genders (P > 0.001) across years. However, a linear-by-linear association between age groups and years, visited departments and years were observed (P < 0.001).

CONCLUSIONS

The study revealed that the transmission of COVID-19 affected the patient population and structure of disease types and oral services in 2020 during the Spring Festival, compared with those in the previous two years. The visits to oral emergency departments and the proportions of patients who were children and adolescents reduced; meanwhile, the percentage of the elderly people increased during the outbreak of COVID-19. The clear trend of age groups and visiting divisions could be used as a marker to reflect the severity of the COVID-19 pandemic. These results may serve as a reference for dental practitioners involved in oral emergency services and to allocate the limited emergency health resources.

Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases

Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.

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

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

A review of the impact of environmental factors and pollutants on covid-19 transmission

The coronavirus disease (COVID-19) caused an unprecedented loss of life with colossal social and economic fallout over 237 countries and territories worldwide. Environmental conditions played a significant role in spreading the virus. Despite the availability of literature, the consecutive waves of COVID-19 in all geographical conditions create the necessity of reviewing the impact of environmental factors on it. This study synthesized and reviewed the findings of 110 previously published articles on meteorological factors and COVID-19 transmission. This study aimed to identify the diversified impacts of meteorological factors on the spread of infection and suggests future research. Temperature, rainfall, air quality, sunshine, wind speed, air pollution, and humidity were found as investigated frequently. Correlation and regression analysis have been widely used in previous studies. Most of the literature showed that temperature and humidity have a favorable relationship with the spread of COVID-19. On the other hand, 20 articles stated no relationship with humidity, and nine were revealed the negative effect of temperature. The daily number of COVID-19 confirmed cases increased by 4.86% for every 1 °C increase in temperature. Sunlight was also found as a significant factor in 10 studies. Moreover, increasing COVID-19 incidence appeared to be associated with increased air pollution, particularly PM10, PM2.5, and O₃ concentrations. Studies also indicated a negative relation between the air quality index and the COVID-19 cases. This review determined environmental variables' complex and contradictory effects on COVID-19 transmission. Hence it becomes essential to include environmental parameters into epidemiological models and controlled laboratory experiments to draw more precious results.

Ambient air pollution and COVID-19 incidence during four 2020-2021 case surges

BACKGROUND

Air pollution exposure may make people more vulnerable to COVID-19 infection. However, previous studies in this area mostly focused on infection before May 2020 and long-term exposure.

OBJECTIVE

To assess both long-term and short-term exposure to air pollution and COVID-19 incidence across four case surges from 03/1/2020 to 02/28/2021.

METHODS

The cohort included 4.6 million members from a large integrated health care system in southern California with comprehensive electronic medical records (EMR). COVID-19 cases were identified from EMR. Incidence of COVID-19 was computed at the census tract-level among members. Prior 1-month and 1-year averaged air pollutant levels (PM_2.5, NO₂, and O₃) at the census tract-level were estimated based on hourly and daily air quality data. Data analyses were conducted by each wave: 3/1/2020-5/31/2020, 6/1/202-9/30/2020, 10/1/2020-12/31/2020, and 1/1/2021-2/28/2021 and pooled across waves using meta-analysis. Generalized linear mixed effects models with Poisson distribution and spatial autocorrelation were used with adjustment for meteorological factors and census tract-level social and health characteristics. Results were expressed as relative risk (RR) per 1 standard deviation.

RESULTS

The cohort included 446,440 COVID-19 cases covering 4609 census tracts. The pooled RRs (95% CI) of COVID-19 incidence associated with 1-year exposures to PM_2.5, NO₂, and O₃ were 1.11 (1.04, 1.18) per 2.3 μg/m³,1.09 (1.02, 1.17) per 3.2 ppb, and 1.06 (1.00, 1.12) per 5.5 ppb respectively. The corresponding RRs (95% CI) associated with prior 1-month exposures were 1.11 (1.03, 1.20) per 5.2 μg/m³ for PM_2.5, 1.09 (1.01, 1.17) per 6.0 ppb for NO₂ and 0.96 (0.85, 1.08) per 12.0 ppb for O₃.

CONCLUSION

Long-term PM_2.5 and NO₂ exposures were associated with increased risk of COVID-19 incidence across all case surges before February 2021. Short-term PM_2.5 and NO₂ exposures were also associated. Our findings suggest that air pollution may play a role in increasing the risk of COVID-19 infection.

Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies

BACKGROUND AND AIMS

The coronavirus disease 2019 (COVID-19) pandemic is severely threatening and challenging public health worldwide. Epidemiological studies focused on the influence of outdoor air pollution (AP) on COVID-19 risk have produced inconsistent conclusions. We aimed to quantitatively explore this association using a meta-analysis.

METHODS

We searched for studies related to outdoor AP and COVID-19 risk in the Embase, PubMed, and Web of Science databases. No language restriction was utilized. The search date entries were up to August 13, 2021. Pooled estimates and 95% confidence intervals (CIs) were obtained with random-/fixed-effects models. PROSPERO registration number: CRD42021244656.

RESULTS

A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 μg/m³ increase in nitrogen dioxide (NO₂; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 μm (PM_2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO₂; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 μg/m³ increase in nitrogen dioxide (NO₂; effect size = 1.034, 95% CI 1.006-1.063), PM_2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 μg/m³ increase NO₂ (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 μm (PM₁₀; effect size = 1.005, 95% CI 1.003-1.008), PM_2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO₂ (effect size = 1.015, 95% CI 1.007-1.023).

CONCLUSIONS

Outdoor air pollutants are detrimental factors to COVID-19 outcomes. Measurements beneficial to reducing pollutant levels might also reduce the burden of the pandemic.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.