Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States

Long-term PM2.5 exposure might predispose populations to SARS-CoV-2 infection and intervention policies might interrupt SARS-CoV-2 transmission and reduce the risk of COVID-19. We conducted an ecologic study across the United States, using county-level COVID-19 incidence up to 12 September 2020, to represent the first two surges in the U.S., annual average of PM2.5 between 2000 and 2016 and state-level facemask mandates and stay home orders. We fit negative binomial models to assess COVID-19 incidence in association with PM2.5 and policies. Stratified analyses by facemask policy and stay home policy were also performed. Each 1-µg/m3 increase in annual average concentration of PM2.5 exposure was associated with 7.56% (95% CI: 3.76%, 11.49%) increase in COVID-19 risk. Facemask mandates and stay home policies were inversely associated with COVID-19 with adjusted RRs of 0.8466 (95% CI: 0.7598, 0.9432) and 0.9193 (95% CI: 0.8021, 1.0537), respectively. The associations between PM2.5 and COVID-19 were consistent among counties with or without preventive policies. Our study added evidence that long-term PM2.5 exposure increased the risk of COVID-19 during each surge and cumulatively as of 12 September 2020, in the United States. Although both state-level implementation of facemask mandates and stay home orders were effective in preventing the spread of COVID-19, no clear effect modification was observed regarding long-term exposure to PM2.5 on the risk of COVID-19.

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Association between air pollution and COVID-19 mortality and morbidity

Internal and Emergency Medicine ◽

10.1007/s11739-021-02834-5 ◽

2021 ◽

Author(s):

Karolina Semczuk-Kaczmarek ◽

Anna Rys-Czaporowska ◽

Janusz Sierdzinski ◽

Lukasz Dominik Kaczmarek ◽

Filip Marcin Szymanski ◽

...

Keyword(s):

Air Pollution ◽

Respiratory Diseases ◽

Average Concentration ◽

Cumulative Number ◽

Annual Average ◽

Mortality And Morbidity ◽

Increased Risk ◽

Annual Average Concentration ◽

Official Data

AbstractCoronavirus disease (COVID-19) pandemic is affecting the world unevenly. One of the highest numbers of cases were recorded in the most polluted regions worldwide. The risk factors for severe COVID-19 include diabetes, cardiovascular, and respiratory diseases. It has been known that the same disease might be worsened by chronic exposure to air pollution. The study aimed to determine whether long-term average exposure to air pollution is associated with an increased risk of COVID-19 cases and deaths in Poland. The cumulative number of COVID-19 cases and deaths for each voivodeship (the main administrative level of jurisdictions) in Poland were collected from March 4, 2020, to May 15, 2020. Based on the official data published by Chief Inspectorate of Environmental Protection voivodeship-level long-term exposure to main air pollution: PM2.5, PM10, NO2, SO2, O3 (averaged from 2013 to 2018) was established. There were statistically significant correlation between COVID-19 cases (per 100,000 population) and annual average concentration of PM2.5 (R2 = 0.367, p = 0.016), PM10 (R2 = 0.415, p = 0.009), SO2 (R2 = 0.489, p = 0.003), and O3 (R2 = 0.537, p = 0.0018). Moreover, COVID-19 deaths (per 100,000 population) were associated with annual average concentration of PM2.5 (R2 = 0.290, p = 0.038), NO2 (R2 = 0.319, p = 0.028), O3 (R2 = 0.452, p = 0.006). The long-term exposure to air pollution, especially PM2.5, PM10, SO2, NO2, O3 seems to play an essential role in COVID-19 prevalence and mortality. Long-term exposure to air pollution might increase the susceptibility to the infection, exacerbates the severity of SARS-CoV-2 infections, and worsens the patients’ prognosis. The study provides generalized and possible universal trends. Detailed analyzes of the phenomenon dedicated to a given region require taking into account data on comorbidities and socioeconomic variables as well as information about the long-term exposure to air pollution and COVID-19 cases and deaths at smaller administrative level of jurisdictions (community or at least district level).

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Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities

10.1101/2020.05.22.20110460 ◽

2020 ◽

Cited By ~ 3

Author(s):

Bhuma Krishnamachari ◽

Alexander Morris ◽

Diane Zastrow ◽

Andrew Dsida ◽

Brian Harper ◽

...

Keyword(s):

United States ◽

Population Density ◽

Cumulative Incidence ◽

Negative Binomial ◽

State Level ◽

The United States ◽

Reverse Causality ◽

Social Distancing ◽

School Closing ◽

Adjusted Rate

AbstractCOVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: “above median value,” and “median value and below” for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

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Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study

Clinical Infectious Diseases ◽

10.1093/cid/ciaa923 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nadir Yehya ◽

Atheendar Venkataramani ◽

Michael O Harhay

Keyword(s):

United States ◽

Negative Binomial ◽

State Level ◽

Negative Binomial Regression ◽

The United States ◽

School Closure ◽

Sensitivity Analyses ◽

Test Model ◽

School Closures ◽

Social Distancing

ABSTRACT Background Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in Covid-19 has not been demonstrated. In the United States social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were two early statewide interventions. Methods To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55,146 Covid-19 non-survivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. Results At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay, 95% CI 1.00 to 1.09, p=0.040) and later school closure (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.020) or closing schools (aMRR 1.06, 95% CI 1.03 to 1.09, p<0.001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. Conclusions Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

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The Impact of Uncertainty on State-Level Housing Markets of the United States: The Role of Social Cohesion

Sustainability ◽

10.3390/su13063065 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3065

Author(s):

Linyan Dai ◽

Xin Sheng

Keyword(s):

United States ◽

Social Cohesion ◽

Housing Markets ◽

State Level ◽

The United States ◽

Negative Effects ◽

Housing Returns ◽

The Impact

While considering the role of social cohesion, we analyse the impact of uncertainty on housing markets across the 50 states of the United States, plus the District of Columbia, using the local projection method for panel data. We find that both short-term and long-term measurements of macroeconomic and financial uncertainties reduce real housing returns, with the strongest effect originated from the macro-economic uncertainty over the long term. Moreover, the degree of social cohesion does not change the nature of the impact of uncertainty on real housing returns dramatically, but the size of the negative effects is relatively large for states with low social cohesion.

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Toxic Heavy Metals in Sediments of Mahul Creek near Mumbai, India

International Letters of Natural Sciences ◽

10.18052/www.scipress.com/ilns.15.69 ◽

2014 ◽

Vol 15 ◽

pp. 69-78

Author(s):

Pravin U. Singare ◽

M.V.A. Ansari ◽

N.N. Dixit

Keyword(s):

Heavy Metals ◽

Average Concentration ◽

Economic Prosperity ◽

Toxic Heavy Metals ◽

Short Term ◽

Annual Average ◽

Industrial Growth ◽

Annual Average Concentration ◽

One Year

The present study was performed for the period of one year from January 2013 to December 2013 in order to understand the level of toxic heavy metals in the sediments of Mahul Creek near Mumbai. The annual average concentration of heavy metals like Cr, Zn, Cu, Ni, Pb, Cd, As and Hg was found to be 277.5, 121.7, 100.3, 63.8, 21.5, 14.6, 10.4 and 4.9 ppm respectively. It is feared that this heavy metals accumulated in the creek sediments may enter the water thereby creating threat to the biological life of an aquatic ecosystem. The results of present study indicates that the existing situation if mishandled can cause irreparable ecological harm in the long term well masked by short term economic prosperity due to extensive industrial growth

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Magnitude, Trends, and Impacts of Ambient Long-Term Ozone Exposure in the United States from 2000–2015

10.5194/acp-2019-672 ◽

2019 ◽

Author(s):

Karl M. Seltzer ◽

Drew T. Shindell ◽

Prasad Kasibhatla ◽

Christopher S. Malley

Keyword(s):

United States ◽

Human Health ◽

Crop Yields ◽

Relative Yield ◽

The United States ◽

Average Concentration ◽

Ozone Exposure ◽

Health Impacts ◽

Human Health Impacts

Abstract. Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse human-health effects and reduced yields in commercial crops. Ground-level O3 concentrations for assessments are typically predicted using chemical transport models, however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000–2015, and generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we find that two commonly-used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–September average of the daily 1-hour maximum concentration peaked in 2002 at 55.9 ppb and decreased by −0.43 [95 % CI: −0.28, −0.57] ppb/yr between 2000–2015, yielding a ~ 18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8-hour average concentration between 2000–2015, which resulted in a ~ 5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. By contrast, all agriculture-weighted crop-loss metrics featured decreasing trends, leading to reductions in the estimated national relative yield loss ranging from 1.7–1.9 % for maize, 5.1–7.1 % for soybeans, and 2.7 % for wheat. Overall, these results provide a measurement-based estimate of long-term O3 exposure over the United States, quantify the historical magnitude, trends, and impacts of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.

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Geographical Distributions and Spatial Equilibrium in Historical Epidemics of the United States

10.1101/486506 ◽

2018 ◽

Author(s):

Stephen Coleman

Keyword(s):

United States ◽

Geographical Area ◽

State Level ◽

The United States ◽

Spatial Equilibrium ◽

Mathematical Properties ◽

Level Data ◽

Equilibrium Distributions ◽

Long Term Trends

This research examines the geographical distributions of several historical epidemics in the United States and investigates whether they reached a geographical equilibrium, however briefly. An equilibrium distribution over a geographical area, as the end state of a diffusion or spatial contagion process, has definitive mathematical properties. These permit qualitative and quantitative tests that may confirm an equilibrium and identify its characteristics. The analysis uses United States state-level data for several common infectious diseases of the 1950s, and results show geographical equilibrium distributions for several epidemics. These are not predicted by the most commonly used epidemiological models but are consistent with observed geographical disparities in disease prevalence that continued over a number of years in spite of recurrent epidemic cycles and long-term trends.

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Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-1757-2020 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1757-1775 ◽

Cited By ~ 5

Author(s):

Karl M. Seltzer ◽

Drew T. Shindell ◽

Prasad Kasibhatla ◽

Christopher S. Malley

Keyword(s):

United States ◽

Human Health ◽

Crop Yields ◽

Relative Yield ◽

The United States ◽

Average Concentration ◽

Ozone Exposure ◽

Health Impacts ◽

Human Health Impacts

Abstract. Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse human-health effects and reduced yields in commercial crops. Ground-level O3 concentrations for assessments are typically predicted using chemical transport models; however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000 to 2015, and we generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we found that two commonly used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–September average of the daily 1 h maximum concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr−1 between 2000 and 2015, yielding an ∼18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8 h average concentration between 2000 and 2015, which resulted in a ∼5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. Trends of all agriculture-weighted crop-loss metrics indicated yield improvements, with reductions in the estimated national relative yield loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and 2.7 % for wheat. Overall, these results provide a measurement-based estimate of long-term O3 exposure over the United States, quantify the historical trends of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.

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Craft Beer in the United States: History, Numbers, and Geography

Journal of Wine Economics ◽

10.1017/jwe.2015.22 ◽

2015 ◽

Vol 10 (3) ◽

pp. 242-274 ◽

Cited By ~ 62

Author(s):

Kenneth G. Elzinga ◽

Carol Horton Tremblay ◽

Victor J. Tremblay

Keyword(s):

United States ◽

San Francisco ◽

Negative Binomial ◽

State Level ◽

The United States ◽

Wine Industry ◽

Craft Beer ◽

Statistical Measures ◽

History Of ◽

Beer Production

AbstractWe provide a mini-history of the craft beer segment of the U.S. brewing industry with particular emphasis on producer-entrepreneurs but also other pioneers involved in the promotion and marketing of craft beer who made contributions to brewing it. In contrast to the more commodity-like lager beer produced by the macrobrewers in the United States, the output of the craft segment more closely resembles the product differentiation and fragmentation in the wine industry. We develop a database that tracks the rise of craft brewing using various statistical measures of output, number of producers, concentration within the segment, and compares output with that of the macro and import segment of the industry. Integrating our database into Geographic Information Systems software enables us to map the spread of the craft beer segment from its taproot in San Francisco across the United States. Finally, we use regression analysis to explore variables influencing the entrants and craft beer production at the state level from 1980 to 2012. We use Tobit estimation for production and negative binomial estimation for the number of brewers. We also analyze whether strategic effects (e.g., locating near competing beer producers) explain the location choices of craft beer producers. (JEL Classifications: L26, L66, N82, R12)

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Long-term Deadly Impacts of Redlining: An examination of people killed by police in American cities

10.31235/osf.io/fu8rg ◽

2021 ◽

Author(s):

Jeffrey Mitchell ◽

Guilherme Kenji Chihaya

Keyword(s):

Negative Binomial ◽

The United States ◽

Economic Conditions ◽

Structural Racism ◽

Census Tracts ◽

Black Residents ◽

Loan Corporation ◽

American Cities ◽

Binomial Models

How does structural racism influence where people are killed during encounters with police? We analyzed geo-located incidents of fatal encounters with police that occurred between 2000-2020 in Census tracts that received a classification by the Home Owners Loan Corporation (HOLC) during the 1930’s. After adjusting for population, 53 of the 100 most deadly Census tracts analyzed in this study were rated as “D” zones, contemporarily referred to as “redlined” areas. 38 are in “C” zones, 8 are “B” zones and only 1 is an “A” zone. Hierarchical Bayesian Negative Binomial models of all tracts estimate incidents of fatal encounters with police are highest in formerly redlined areas, and are 66% more likely than in zones that received the more favorable “A” rating. Contemporary demographic and economic conditions in Census tracts also predict the incidence of fatal encounters with the police, but the effect of historic HOLC classification remains after taking these factors into account. The estimates of fatal encounters converge across zone classifications only in areas with high proportions of Black residents or residents in in poverty (>60% or >30% respectively). These findings augment the literature on the lasting effect of redlined communities in the United States and provides evidence of structural biases in policing rooted in historical segregation policies.

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