Impact of altitude on COVID-19 infection and death in the United States: A modeling and observational study

Background COVID-19, the disease caused by SARS-CoV-2, has caused a pandemic, sparing few regions. However, limited reports suggest differing infection and death rates across geographic areas including populations that reside at higher elevations (HE). We aimed to determine if COVID-19 infection, death, and case mortality rates differed in higher versus low elevation (LE) U.S. counties. Methods Using publicly available geographic and COVID-19 data, we calculated per capita infection and death rates and case mortality in population density matched HE and LE U.S. counties. We also performed population-scale regression analysis to investigate the association between county elevation and COVID-19 infection rates. Findings Population density matching of LA (< 914m, n = 58) and HE (>2133m, n = 58) counties yielded significantly lower COVID-19 cases at HE versus LE (615 versus 905, p = 0.034). HE per capita deaths were significantly lower than LE (9.4 versus 19.5, p = 0.017). However, case mortality did not differ between HE and LE (1.78% versus 1.46%, p = 0.27). Regression analysis, adjusted for relevant covariates, demonstrated decreased COVID-19 infection rates by 12.82%, 12.01%, and 11.72% per 495m of county centroid elevation, for cases recorded over the previous 30, 90, and 120 days, respectively. Conclusions This population-adjusted, controlled analysis suggests that higher elevation attenuates infection and death. Ongoing work from our group aims to identify the environmental, biological, and social factors of residence at HE that impact infection, transmission, and pathogenesis of COVID-19 in an effort to harness these mechanisms for future public health and/or treatment interventions.

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COVID-19 Mortality Rate and Its Incidence in Latin America: Dependence on Demographic and Economic Variables

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18136900 ◽

2021 ◽

Vol 18 (13) ◽

pp. 6900

Author(s):

Javier Cifuentes-Faura

Keyword(s):

Latin America ◽

Mortality Rate ◽

Population Density ◽

Life Expectancy ◽

Latin American ◽

Positive Influence ◽

Death Rates ◽

Per Capita ◽

Latin American Countries ◽

Quality Infrastructure

The pandemic caused by COVID-19 has left millions infected and dead around the world, with Latin America being one of the most affected areas. In this work, we have sought to determine, by means of a multiple regression analysis and a study of correlations, the influence of population density, life expectancy, and proportion of the population in vulnerable employment, together with GDP per capita, on the mortality rate due to COVID-19 in Latin American countries. The results indicated that countries with higher population density had lower numbers of deaths. Population in vulnerable employment and GDP showed a positive influence, while life expectancy did not appear to significantly affect the number of COVID-19 deaths. In addition, the influence of these variables on the number of confirmed cases of COVID-19 was analyzed. It can be concluded that the lack of resources can be a major burden for the vulnerable population in combating COVID-19 and that population density can ensure better designed institutions and quality infrastructure to achieve social distancing and, together with effective measures, lower death rates.

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694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽

10.1093/sleep/zsab072.692 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A271-A271

Author(s):

Azizi Seixas ◽

Nicholas Pantaleo ◽

Samrachana Adhikari ◽

Michael Grandner ◽

Giardin Jean-Louis

Keyword(s):

Regression Models ◽

Negative Binomial ◽

The United States ◽

Poor Sleep ◽

County Level ◽

Infection Rates ◽

Insufficient Sleep ◽

Death Rates ◽

Rural Counties ◽

The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

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SARS-COV-2 Vaccines: What Indicators are Associated with the Worldwide Distribution of the First Doses

INQUIRY The Journal of Health Care Organization Provision and Financing ◽

10.1177/00469580211060184 ◽

2021 ◽

Vol 58 ◽

pp. 004695802110601

Author(s):

Marcos Felipe Falcão Sobral ◽

Brigitte Renata Bezerra de Oliveira ◽

Ana Iza Gomes da Penha Sobral ◽

Marcelo Luiz Monteiro Marinho ◽

Gisleia Benini Duarte ◽

...

Keyword(s):

Population Density ◽

Life Expectancy ◽

Human Development ◽

Pearson Correlation ◽

Correlation Coefficients ◽

The United States ◽

Gdp Per Capita ◽

Reproduction Rate ◽

Worldwide Distribution ◽

Per Capita

The present study aimed to identify the factors associated with the distribution of the first doses of the COVID-19 vaccine. In this study, we used 9 variables: human development index (HDI), gross domestic product (GDP per capita), Gini index, population density, extreme poverty, life expectancy, COVID cases, COVID deaths, and reproduction rate. The time period was until February 1, 2021. The variable of interest was the sum of the days after the vaccine arrived in the countries. Pearson’s correlation coefficients were calculated, and t-test was performed between the groups that received and did not receive the immunizer, and finally, a stepwise linear regression model was used. 58 (30.4%) of the 191 countries received the SARS-CoV-2 vaccine. The countries that received the most doses were the United States, China, the United Kingdom, and Israel. Vaccine access in days showed a positive Pearson correlation HDI, GDP, life expectancy, COVID-19 cases, deaths, and reproduction rate. Human development level, COVID-19 deaths, GDP per capita, and population density are able to explain almost 50% of the speed of access to immunizers. Countries with higher HDI and per capita income obtained priority access.

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Incidence of SARs-CoV-2 (Dikos-Ntsaaigii-19) Among Navajo Nation Children

10.31219/osf.io/dm9su ◽

2021 ◽

Author(s):

Joseph Angel De Soto ◽

Babatunde Ojo

Keyword(s):

Infection Rate ◽

Death Rate ◽

Signal Transduction Pathway ◽

The United States ◽

Vaccination Rate ◽

Transduction Pathway ◽

Bureau Of Indian Affairs ◽

Infection Rates ◽

Navajo Nation ◽

Death Rates

On March 17, 2020 the SARs-CoV-2 virus was first reported on the Navajo Reservation. Today, the Navajo Nation has a 147% higher infection rate and a 450% higher death rate than the national average. Despite this tragedy, a glaring question remains, what is happening among the Navajo children. The study found that Navajo children had an infection rate 220% higher than the general population and a death rate from COVID 1,400% greater than non-Navajo in the United States. This occurs even though of Navajo children having a much higher vaccination rate of 68% compared to about 25% of children Nationwide. The introduction of SARs-CoV variants such as the alpha and omicron variants did not seem to play a role in these findings. The higher infection rates suggest a genetic predisposition among the Navajo to SARs-CoV-2 via the ACE-2 receptor and signal transduction pathway while the increased death rates may also suggest inferior care provided by the Bureau of Indian Affairs Hospitals.

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Confirmation of Tabachnick and Klugman's Hypothesis That the Death Instinct is Constant

Perceptual and Motor Skills ◽

10.1177/00315125920740s301 ◽

1992 ◽

Vol 74 (3_suppl) ◽

pp. 1065-1066

Author(s):

Patrick R. Saucer

Keyword(s):

United States ◽

Liver Disease ◽

Chronic Liver Disease ◽

Death Rate ◽

The United States ◽

Chronic Liver ◽

Death Instinct ◽

Death Rates ◽

Per Capita

In reporting the accident death rate and the chronic liver disease death rate for 1980, the Bureau of the Census divided the United States into nine areas. To test Tabachnick and Klugman's hypothesis that the amount of death instinct per capita remains constant across regions, the 1980 death rates for accidents and chronic liver disease were correlated. Contrary to earlier studies, the present study gave support for Tabachnick and Klugman's hypothesis.

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Inverse correlation between average monthly high temperatures and COVID-19-related death rates in different geographical areas.

10.21203/rs.3.rs-29039/v2 ◽

2020 ◽

Author(s):

Francesca Benedetti ◽

Maria Pachetti ◽

Bruna Marini ◽

Rudy Ippodrino ◽

Robert C. Gallo ◽

...

Keyword(s):

Los Angeles ◽

Population Density ◽

High Temperatures ◽

Death Rate ◽

Inverse Correlation ◽

The United States ◽

Western World ◽

Significant Inverse Correlation ◽

Death Rates ◽

Wide Range

Abstract Background With the aim of providing a dynamic evaluation of the effects of basic environmental parameters on COVID-19-related death rate, we assessed the correlation between average monthly high temperatures and population density, with death/rate (monthly number of deaths/1M people) for the months of March (start of the analysis and beginning of local epidemic in most of the Western World, except in Italy where it started in February) and April 2020 (continuation of the epidemic). Different geographical areas of the Northern Hemisphere in the United States and in Europe were selected in order to provide a wide range among the different parameters. The death rates were gathered from an available dataset. As a further control, we also included latitude, as a proxy for temperature.Methods Utilizing a publicly available dataset, we retrieved data for the months of March and April 2020 for 25 areas in Europe and in the US. We computed the monthly number of deaths/1M people of confirmed COVID-19 cases and calculated the average monthly high temperatures and population density for all these areas. We determined the correlation between number of deaths/1M people and the average monthly high temperatures, the latitude and the population density. Results We divided our analysis in two parts: analysis of the correlation among the different variables in the month of March and subsequent analysis in the month of April. The differences were then evaluated. In the month of March there was no statistical correlation between average monthly high temperatures of the considered geographical areas and number of deaths/1M people. However, a statistically significant inverse correlation became significant in the month of April between average monthly high temperatures (p=0.0043) and latitude (p=0.0253) with number of deaths/1M people. We also observed a statistically significant correlation between population density and number of deaths/1M people both in the month of March (p=0.0297) and in the month of April (p=0.0116), when three areas extremely populated (NYC, Los Angeles and Washington DC) were included in the calculation. Once these three areas were removed, the correlation was not statistically significant (p=0.1695 in the month of March, and p=0.7076 in the month of April). Conclusions The number of COVID-19-related deaths/1M people was essentially the same during the month of March for all the geographical areas considered, indicating essentially that the infection was circulating quite uniformly except for Lombardy, Italy, where it started earlier. Lockdown measures were implemented between the end of March and beginning of April, except for Italy which started March 9th. We observed a strong, statistically significant inverse correlation between average monthly high temperatures with the number of deaths/1M people. We confirmed the data by analyzing the correlation with the latitude, which can be considered a proxy for high temperature. Previous studies indicated a negative effect of high climate temperatures on Sars-COV-2 spreading. Our data indicate that social distancing measure are more successful in the presence of higher average monthly temperatures in reducing COVID-19-related death rate, and a high level of population density seems to negatively impact the effect of lockdown measures.

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AN ANALYSIS OF THE RELATIONSHIP BETWEEN NEW FIRM FORMATION AND ECONOMIC DEVELOPMENT IN THE NORTHEAST REGION OF THE UNITED STATES

Journal of Developmental Entrepreneurship ◽

10.1142/s1084946711001859 ◽

2011 ◽

Vol 16 (03) ◽

pp. 289-306 ◽

Cited By ~ 3

Author(s):

SAIMA BASHIR ◽

TESFA GEBREMEDHIN

Keyword(s):

United States ◽

Economic Development ◽

Population Density ◽

Capita Income ◽

The United States ◽

Firm Formation ◽

Northeast Region ◽

New Firm Formation ◽

New Firm ◽

Per Capita

The overall objective of this study is to provide policy makers identifies and estimates the role and impacts of new firm formation in the Northeast region of the United States. The empirical model of this study is derived from the three-equation simultaneous model of Deller et al. (2001). In this study, Three-Stage Least Squares (3SLS) method is used to estimate the simultaneous equations model. The research findings indicate that population density and per capita income have a positive link with new firm formation. Higher population density and per capita income encourage entrepreneurs to start new firms in the region. This leads to an increase of new jobs, which is a positive contribution to economic development in the Northeast region.

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COVID-19 Attributed Cases and Deaths are Statistically Higher in States and Counties with 5th Generation Millimeter Wave Wireless Telecommunications in the United States.

Medical Research Archives ◽

10.18103/mra.v9i4.2371 ◽

2021 ◽

Vol 9 (4) ◽

Author(s):

angela tsiang ◽

Magda Havas

Keyword(s):

Oxidative Stress ◽

Immune Response ◽

Population Density ◽

Millimeter Wave ◽

Blood Chemistry ◽

The United States ◽

Death Rates ◽

Wireless Telecommunications ◽

Impaired Immune Response ◽

Neurological Response

COVID-19-attributed case and death rates for the U.S.A. were analyzed through May 2020 in three ways – for all 50 states, the country’s largest counties, and the largest counties in California – and found to be statistically significantly higher for states and counties with compared to those without 5G millimeter wave (mmW) technology. 5G mmW index was a statistically significant factor for the higher case and rates in all three analyses, while population density, air quality and latitude were significant for only one or two of the analyses. For state averages, cases per million were 79% higher (p = 0.012), deaths per million were 94% higher (p = 0.049), cases per test were 68% higher (p = 0.003) and deaths per test were 81% higher (p = 0.025) for states with vs. without mmW. For county averages, cases per million were 87% higher (p = 0.005) and deaths per million were 165% higher (p = 0.012) for counties with vs. without mmW. While higher population density contributed to the higher mean case and death rates in the mmW states and counties, exposure to mmW had about the same impact as higher density of mmW states on mean case and death rates and about three times as much impact as higher density for mmW counties on mean case and death rates. Based on multiple linear regression, if there was no mmW exposure, case and death rates would be 18-30% lower for 5G mmW states and 39-57% lower for 5G mmW counties. This assessment clearly shows exposure to 5G mmW technology is statistically significantly associated with higher COVID-19 case and death rates in the U.S.A. The mechanism–should this be a causal relationship–may relate to changes in blood chemistry, oxidative stress, an impaired immune response, an altered cardiovascular and/or neurological response.

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Culture, COVID-19, and Collectivism: A Paradox of American Exceptionalism?

10.31234/osf.io/hqcs6 ◽

2020 ◽

Author(s):

Gregory D. Webster ◽

Jennifer Lee Howell ◽

Joy Ellen Losee ◽

Elizabeth Mahar ◽

Val Wongsomboon

Keyword(s):

Spatial Dependence ◽

State Level ◽

American Exceptionalism ◽

White Population ◽

Death Rates ◽

Per Capita Gdp ◽

Country Level ◽

Per Capita ◽

The U.S ◽

Relevant Covariates

We examined archival data from 98 countries (Study 1) and the 48 contiguous United States (Study 2) on country/state-level collectivism, COVID-19 case/death rates, relevant covariates (per-capita GDP, population density, spatial dependence), and in the U.S., percent of non-Whites. In Study 1, country-level collectivism negatively related to both cases (r = -.28) and deaths (r = -.40) in simple regressions; however, after controlling for covariates, the former became non-significant (rp = -.07), but the later remained significant (rp = -.20). In Study 2, state-level collectivism positively related to both cases (r = .56) and deaths (r = .41) in simple regressions, and these relationships persisted after controlling for all covariates except race, where a state’s non-White population dominated all other predictors of COVID-19 cases (rp = .35) and deaths (rp = .31). We discuss the strong link between race and collectivism in U.S. culture, and its implications for understanding COVID-19 responses.

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Inverse correlation between average monthly high temperatures and COVID-19-related death rates in different geographical areas.

10.21203/rs.3.rs-29039/v1 ◽

2020 ◽

Author(s):

Francesca Benedetti ◽

Maria Pachetti ◽

Bruna Marini ◽

Rudy Ippodrino ◽

Robert C. Gallo ◽

...

Keyword(s):

Los Angeles ◽

Population Density ◽

High Temperatures ◽

Death Rate ◽

Inverse Correlation ◽

The United States ◽

Western World ◽

Significant Inverse Correlation ◽

Death Rates ◽

Wide Range

Abstract Background With the aim of providing a dynamic evaluation of the effects of basic environmental parameters on COVID-19-related death rate, we assessed the correlation between average monthly high temperatures and population density, with death/rate (monthly number of deaths/1M people) for the months of March (start of the analysis and beginning of local epidemic in most of the Western World, except in Italy where it started in February) and April 2020 (continuation of the epidemic). Different geographical areas of the Northern Hemisphere in the United States and in Europe were selected in order to provide a wide range among the different parameters. The death rates were gathered from an available dataset. As a further control, we also included latitude, as a proxy for temperature. Methods Utilizing a publicly available dataset, we retrieved data for the months of March and April 2020 for 25 areas in Europe and in the US. We computed the monthly number of deaths/1M people of confirmed COVID-19 cases and calculated the average monthly high temperatures and population density for all these areas. We determined the correlation between number of deaths/1M people and the average monthly high temperatures, the latitude and the population density. Results We divided our analysis in two parts: analysis of the correlation among the different variables in the month of March and subsequent analysis in the month of April. The differences were then evaluated). In the month of March there was no statistical correlation between average monthly high temperatures of the considered geographical areas and number of deaths/1M people. However, a statistically significant inverse correlation became significative in the month of April between average monthly high temperatures (p=0.0104) and latitude (p=0.0119) with number of deaths/1M people. We also observed a statistically significative correlation between population density and number of deaths/1M people only in the month of April, when three areas extremely populated (NYC, Los Angeles and Washington DC) were included in the calculation. Once these three areas were removed, the correlation was not statistically significant (p=0.682). Conclusions The number of COVID-19-related deaths/1M people was essentially the same during the month of March for all the geographical areas considered, indicating essentially that the infection was circulating quite uniformly except for Lombardy, Italy, where it started earlier. Lockdown measures were implemented between the end of March and beginning of April, except for Italy which started March 9 th . We observed a strong, statistically significant inverse correlation between average monthly high temperatures with the number of deaths/1M people. We confirmed the data by analyzing the correlation with the latitude, which can be considered a proxy for high temperature. Previous studies indicated a negative effect of high climate temperatures on Sars-COV-2 spreading. Our data indicate that social distancing measure are more successful in the presence of higher daily average temperatures in reducing COVID-19-related death rate, and a high level of population density seems to negatively impact the effect of lockdown measures.

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