The short-term seasonal analyses between atmospheric environment and COVID-19 in epidemic areas of Cities in Australia, South Korea, and Italy

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The impact of the outbreak of COVID-19 on health has been widely concerned. Disease risk assessment, prediction, and early warning have become a significant research field. Previous research suggests that there is a relationship between air quality and the disease. This paper investigated the impact of the atmospheric environment on the basic reproduction number (R$_0$) in Australia, South Korea, and Italy by using atmospheric environment data, confirmed case data, and the distributed lag non-linear model (DLNM) model based on Quasi-Poisson regression. The results show that the air temperature and humidity have lag and persistence on short-term R$_0$, and seasonal factors have an apparent decorating effect on R$_0$. PM$_{10}$ is the primary pollutant that affects the excess morbidity rate. Moreover, O$_3$, PM$_{2.5}$, and SO$_2$ as perturbation factors have an apparent cumulative effect. These results present beneficial knowledge for correlation between environment and COVID-19, which guiding prospective analyses of disease data.

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Background: There is valid evidence that air pollution is associated with respiratory disease. However, few studies have quantified the short-term effects of six air pollutants on influenza-like illness (ILI). This study explores the potential relationship between air pollutants and ILI in Jinan, China. Methods: Daily data on the concentration of particulate matters < 2.5 μm (PM 2.5), particulate matters < 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) and ILI counts from 2016 to 2017 were retrieved. The wavelet coherence analysis and generalized poisson additive regression model were employed to qualify the relationship between air pollutants and ILI risk. The effects of air pollutants on different age groups were investigated. Results: A total of 81,459 ILI counts were collected, and the average concentrations of PM2.5, PM10, O3, CO, SO2 and NO2 were 67.8 μg/m3, 131.76 μg/ m3, 109.85 μg/ m3, 1133 μg/ m3, 33.06 μg/ m3 and 44.38 μg/ m3, respectively. A 10 μg/ m3 increase in concentration of PM2.5, PM10, CO at lag0 and SO2 at lag01, was positively associated with a 1.0137 (95% confidence interval (CI): 1.0083-1.0192), 1.0074 (95% CI: 1.0041-1.0107), 1.0288 (95% CI: 1.0127-1.0451), and 1.0008 (95% CI: 1.0003-1.0012) of the relative risk (RR) of ILI, respectively. While, O3 (lag5) was negatively associated with ILI (RR 0.9863; 95%CI: 0.9787-0.9939), and no significant association was observed with NO2, which can increase the incidence of ILI in the two-pollutant model. A short-term delayed impact of PM2.5, PM10, SO2 at lag02 and CO, O3 at lag05 was also observed. People aged 25-59, 5-14 and 0-4 were found to be significantly susceptible to PM2.5, PM10, CO; and all age groups were significantly susceptible to SO2; People aged ≥60 year, 5-14 and 0-4 were found to be significantly negative associations with O3. Conclusion: Air pollutants, especially PM2.5, PM10, CO and SO2, can increase the risk of ILI in Jinan. The government should create regulatory policies to reduce the level of air pollutants and remind people to practice preventative and control measures to decrease the incidence of ILI on pollution days.
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In this paper I present the genesis of R 0 in demography, ecology and epidemiology, from embryo to its current adult form. I argue on why it has taken so long for the concept to mature in epidemiology when there were ample opportunities for cross-fertilisation from demography and ecology from where it reached adulthood fifty years earlier. Today, R 0 is a more fully developed adult in epidemiology than in demography. In the final section I give an algorithm for its calculation in heterogeneous populations.
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Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure-response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure-response dependencies and delayed effects. This methodology is based on the definition of a 'cross-basis', a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987-2000.
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To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter < or = 10 microns (PM10) contributes to excess mortality in Salt Lake County, Utah. In Cook County, Illinois, we found evidence of a positive PM10 effect in spring and autumn, but not in winter and summer. We conclude that the reported effects of particulates on mortality are unconfirmed.
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There are a number of applied settings where a response is measured repeatedly over time, and the impact of a stimulus at one time is distributed over several subsequent response measures. In the motivating application the stimulus is an air pollutant such as airborne particulate matter and the response is mortality. However, several other variables (e.g. daily temperature) impact the response in a possibly non-linear fashion. To quantify the effect of the stimulus in the presence of covariate data we combine two established regression techniques: generalized additive models and distributed lag models. Generalized additive models extend multiple linear regression by allowing for continuous covariates to be modeled as smooth, but otherwise unspecified, functions. Distributed lag models aim to relate the outcome variable to lagged values of a time-dependent predictor in a parsimonious fashion. The resultant, which we call generalized additive distributed lag models, are seen to effectively quantify the so-called 'mortality displacement effect' in environmental epidemiology, as illustrated through air pollution/mortality data from Milan, Italy.
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Since the intentional dissemination of anthrax through the US postal system in the fall of 2001, there has been increased interest in surveillance for detection of biological terrorism. More generally, this could be described as the detection of incident disease clusters. In addition, the advent of affordable and quick geocoding allows for surveillance on a finer spatial scale than has been possible in the past. Surveillance for incident clusters of disease in both time and space is a relatively undeveloped arena of statistical methodology. Surveillance for bioterrorism detection, in particular, raises unique issues with methodological relevance. For example, the bioterrorism agents of greatest concern cause initial symptoms that may be difficult to distinguish from those of naturally occurring disease. In this paper, the authors propose a general approach to evaluating whether observed counts in relatively small areas are larger than would be expected on the basis of a history of naturally occurring disease. They implement the approach using generalized linear mixed models. The approach is illustrated using data on health-care visits (1996-1999) from a large Massachusetts managed care organization/multispecialty practice group in the context of syndromic surveillance for anthrax. The authors argue that there is great value in using the geographic data.
In 2004, the first American Heart Association scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this association and further elucidated the physiological and molecular mechanisms involved. The main objective of this updated American Heart Association scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clinical implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM <2.5 microm in diameter (PM(2.5)) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; reductions in PM levels are associated with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathological mechanisms have been elucidated that lend biological plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM(2.5) exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Association scientific statement was published. Finally, PM(2.5) exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality.
I used generalized additive models to analyze the time series of daily total nonaccidental deaths and deaths due to vascular disease over the period 1987-1995 in two major metropolitan areas, Cook County, Illinois, and Los Angeles County, California, in the United States. In both counties I had monitoring information on PM(10), CO, SO(2), NO(2), and O(3). In Los Angeles, monitoring information on PM(2.5) was available as well. In addition to full-year analyses, I performed season-specific analyses. I present the results of both single- and multipollutant analyses. Although components of air pollution were associated with total nonaccidental and vascular disease mortality in both counties, the results indicate considerable heterogeneity of these associations in the two locations and also from season to season. In Los Angeles County, the gases, particularly CO and SO(2) but not ozone, were more strongly associated with mortality than was particulate matter, which exhibited only weak and inconsistent associations with both mortality endpoints. Both PM(10) and the gases were associated with total and vascular disease mortality in Cook County. The association of the gases with both mortality endpoints appeared to be stronger and more robust than that of PM(10). Exposure-response analyses using flexible smoothers showed significant departures from linearity, particularly for PM effects.
Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors developed Bayesian semiparametric hierarchical models for estimating time-varying effects of pollution on mortality in multisite time series studies. The methods were applied to the database of the National Morbidity and Mortality Air Pollution Study, which includes data for 100 US cities, for the period 1987-2000. At the national level, a 10-microg/m(3) increase in particulate matter less than 10 microm in aerodynamic diameter at a 1-day lag was associated with 0.15% (95% posterior interval (PI): -0.08, 0.39), 0.14% (95% PI: -0.14, 0.42), 0.36% (95% PI: 0.11, 0.61), and 0.14% (95% PI: -0.06, 0.34) increases in mortality for winter, spring, summer, and fall, respectively. An analysis by geographic region found a strong seasonal pattern in the Northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.
Most toxicological and pharmacological studies are performed in laboratory animals maintained under comfortable environmental conditions. Yet, the exposure to environmental toxicants as well as many drugs can occur under stressful environmental conditions during rest or while exercising. The intake and biological efficacy of many toxicants is exacerbated by exposure to heat stress, which can occur in several ways. The increase in pulmonary ventilation during exposure to hot environments results in an increase in the uptake of airborne toxicants. Furthermore, the transcutaneous absorption of pesticides on the skin as well as drugs delivered by skin patches is increased during heat stress because of the combined elevation in skin blood flow coupled with moist skin from sweat. The thermoregulatory response to toxicant exposure, such as hypothermia in relatively small rodents and fever in humans, also modulates the physiological response to most chemical agents. This paper endeavors to review the issue of environmental heat stress and exercise and how they influence thermoregulatory and related pathophysiological responses to environmental toxicants, as well as exposure to drugs.
Ambient temperature is an important determinant of daily mortality that is of interest both in its own right and as a confounder of other determinants investigated using time-series regressions, in particular, air pollution. The temperature-mortality relationship is often found to be substantially nonlinear and to persist (but change shape) with increasing lag. We review and extend models for such nonlinear multilag forms. Popular models for mortality by temperature at given lag include polynomial and natural cubic spline curves, and the simple but more easily interpreted linear thresholds model, comprising linear relationships for temperatures below and above thresholds and a flat middle section. Most published analyses that have allowed the relationship to persist over multiple lags have done so by assuming that spline or threshold models apply to mean temperature in several lag strata (e.g., lags 0-1, 2-6, and 7-13). However, more flexible models are possible, and a modeling framework using products of basis functions ("cross-basis" functions) suggests a wide range, some used previously and some new. These allow for stepped or smooth changes in the model coefficients as lags increase. Applying a range of models to data from London suggest evidence for relationships up to at least 2 weeks' lag, with smooth models fitting best but lag-stratified threshold models allowing the most direct interpretation. A wide range of multilag nonlinear temperature-mortality relationships can be modeled. More awareness of options should improve investigation of these relationships and help control for confounding by them.
Thermal stress can have a profound impact on the physiological responses that are elicited following environmental toxicant exposure. The efficacy by which toxicants enter the body is directly influenced by thermoregulatory effector responses that are evoked in response to high ambient temperatures. In mammals, the thermoregulatory response to heat stress consists of an increase in skin blood flow and moistening of the skin surface to dissipate core heat to the environment. These physiological responses may exacerbate chemical toxicity due to increased permeability of the skin, which facilitates the cutaneous absorption of many environmental toxicants. The core temperature responses that are elicited in response to high ambient temperatures, toxicant exposure or both can also have a profound impact on the ability of an organism to survive the insult. In small rodents, the thermoregulatory response to thermal stress and many environmental toxicants (such as organophosphate compounds) is often biphasic in nature, consisting initially of a regulated reduction in core temperature (i.e., hypothermia) followed by fever. Hypothermia is an important thermoregulatory survival strategy that is used by small rodents to diminish the effect of severe environmental insults on tissue homeostasis. The protective effect of hypothermia is realized by its effects on chemical toxicity as molecular and cellular processes, such as lipid peroxidation and the formation of reactive oxygen species, are minimized at reduced core temperatures. The beneficial effects of fever are unknown under these conditions. Perspective is provided on the applicability of data obtained in rodent models to the human condition.
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