Increased travel times to United States SARS-CoV-2 testing sites: a spatial modeling study

AbstractImportanceAccess to testing is key to a successful response to the COVID-19 pandemic.ObjectiveTo determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time.DesignCross-sectional analysis of SARS-CoV-2 testing sites as of April 7, 2020 in relation to travel time.SettingUnited States COVID-19 pandemic.ParticipantsThe United States, including the 48 contiguous states and the District of Columbia.ExposuresPopulation density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data.Main OutcomeSARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites.Results6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time (β = 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2×10−12), adjusting for population density.Conclusions and RelevanceGeographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may exacerbate health inequalities and bias county-specific transmission estimates. Geographic accessibility should be considered when planning the location of future testing sites and interpreting epidemiological data.Key PointsSARS-CoV-2 testing sites are distributed unevenly in the US geography and population.Median county-level travel time to SARS-CoV-2 testing sites is longer in less densely populated areas, and in areas with a higher percentage of minority or uninsured populations.Improved geographic accessibility to testing sites is imperative to manage the COVID-19 pandemic in the United States.

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On the synthesis of shear-coupled PL waves

Bulletin of the Seismological Society of America ◽

10.1785/bssa0580061849 ◽

1968 ◽

Vol 58 (6) ◽

pp. 1849-1877 ◽

Cited By ~ 1

Author(s):

Ramesh Chander ◽

L. E. Alsop ◽

Jack Oliver

Keyword(s):

United States ◽

Phase Velocity ◽

Travel Time ◽

Current Knowledge ◽

Shear Waves ◽

The United States ◽

Synthetic Seismograms ◽

Travel Times ◽

Precise Knowledge ◽

S Curve

ABSTRACT Using the shear-coupled PL wave hypothesis of Oliver as a basis, a method is developed for computing synthetic long-period seismograms between the onset of the initial S-type body phase and the beginning of surface waves. Comparison of observed and synthetic siesmograms shows that this hypothesis can explain, in considerable detail, most of the waves with periods greater than about 20 sec recorded during this interval. The synthetic seismograms are computed easily on a small digital computer; they resemble the observed seismograms much more closely than the synthetic seismograms obtained through the superposition of normal modes of the Earth that have been reported in the literature. The synthesis of shear-coupled PL waves depends on a precise knowledge of the phase-velocity curve of the PL wave and travel-time curves of shear waves. Hence, in principle, if one of these quantities is well-known the other can be determined by this method. Phase-velocity curves of the PL wave are determined for the Baltic shield, the Russian platform, the Canadian shield, the United States, and the western North-Atlantic ocean, on the assumption that J-B travel-time curves of shear waves apply to these areas. These dispersion curves show the type of variations to be expected on the basis of the current knowledge of the crustal structures in these areas. Examples are presented to show that J-B travel-times of shear waves along paths between Kenai Peninsula, Alaska and Palisades, equatorial mid-Atlantic ridge and Palisades, and Kurile Islands and Uppsala need to be revised. Shear-wave travel-time curves that are not unique for reasons explained in the study but that give synthetic seismograms in agreement with the observed seismograms were obtained. The new S curves are compared with the J-B travel-time curves for S; and they all predict S waves to arrive later than the time given by J-B tables for epicentral distances smaller than about 30°. The new S curve for the Alaska to Palisades path appears to agree with one of the branches of a multi-branched S curve proposed recently by Ibrahim and Nuttli for the ‘average United States’ insofar as travel-times are concerned, but there are some differences in the slopes of the two curves.

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Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study

10.1101/2020.04.05.20054502 ◽

2020 ◽

Cited By ~ 191

Author(s):

Xiao Wu ◽

Rachel C Nethery ◽

M Benjamin Sabath ◽

Danielle Braun ◽

Francesca Dominici

Keyword(s):

United States ◽

Air Pollution ◽

Death Rate ◽

The United States ◽

Sensitivity Analyses ◽

List Type ◽

County Level ◽

Cross Sectional ◽

Increased Risk

AbstractObjectivesUnited States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.DesignA nationwide, cross-sectional study using county-level data.Data sourcesCOVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.Main outcome measuresWe fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.ResultsWe found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses.ConclusionsA small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.Summary BoxWhat is already known on this topicLong-term exposure to PM2.5 is linked to many of the comorbidities that have been associated with poor prognosis and death in COVID-19 patients, including cardiovascular and lung disease.PM2.5 exposure is associated with increased risk of severe outcomes in patients with certain infectious respiratory diseases, including influenza, pneumonia, and SARS.Air pollution exposure is known to cause inflammation and cellular damage, and evidence suggests that it may suppress early immune response to infection.What this study addsThis is the first nationwide study of the relationship between historical exposure to air pollution exposure and COVID-19 death rate, relying on data from more than 3,000 counties in the United States. The results suggest that long-term exposure to PM2.5 is associated with higher COVID-19 mortality rates, after adjustment for a wide range of socioeconomic, demographic, weather, behavioral, epidemic stage, and healthcare-related confounders.This study relies entirely on publicly available data and fully reproducible, public code to facilitate continued investigation of these relationships by the broader scientific community as the COVID-19 outbreak evolves and more data become available.A small increase in long-term PM2.5 exposure was associated with a substantial increase in the county’s COVID-19 mortality rate up to April 22, 2020.

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On the Relationship between Development and Fertility: The Case of the United States

Comparative Population Studies ◽

10.12765/cpos-2015-13 ◽

2015 ◽

Vol 40 (4) ◽

Author(s):

Igor Ryabov

Keyword(s):

United States ◽

Human Development ◽

Negative Relationship ◽

The United States ◽

Median Income ◽

County Level ◽

Cross Sectional ◽

Development Indicators ◽

Rural Counties ◽

The Relationship

The present article addresses the question of whether there is a link between the spatial patterns of human development and period fertility in the United States at the county level. Using cross-sectional analyses of the relationship between Total Fertility Rate (TFR) and an array of human development indicators (pertaining to three components of the Human Development Index (HDI) – wealth, health, and education), this study sheds light on the relationship between fertility and human development. The analyses were conducted separately for urban, suburban and rural counties. According to the multivariate results, a negative association between selected human development indicators and TFR exists in suburban and rural counties, as well as in the United States as a whole. However, this is not the case for urban counties, where the results were inconclusive. Some indicators (e.g., median income per capita) were found to be positively, and some (e.g., the share of adults with at least bachelor’s degree) negatively, associated with TFR in urban counties. All in all, our results provide evidence of a negative relationship between human development indicators and period fertility in the United States at the county level, a finding which is consistent with the basic tenets of classic demographic transition theory.

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The Prevalence of Dysphagia among Adults in the United States

Otolaryngology ◽

10.1177/0194599814549156 ◽

2014 ◽

Vol 151 (5) ◽

pp. 765-769 ◽

Cited By ~ 83

Author(s):

Neil Bhattacharyya

Keyword(s):

United States ◽

Health Care ◽

National Health ◽

Health Care Professionals ◽

Demographic Data ◽

The United States ◽

Cross Sectional ◽

Seek Health Care ◽

Swallowing Problems ◽

Care Survey

Objective To determine the prevalence of dysphagia, reported etiologies, and impact among adults in the United States. Study Design Cross-sectional analysis of a national health care survey. Subjects and Methods The 2012 National Health Interview Survey was analyzed, identifying adult cases reporting a swallowing problem in the preceding 12 months. In addition to demographic data, specific data regarding visits to health care professionals for swallowing problems, diagnoses given, and severity of the swallowing problem were analyzed. The relationship between swallowing problems and lost workdays was assessed. Results An estimated 9.44 ± 0.33 million adults (raw N = 1554; mean age, 52.1 years; 60.2% ± 1.6% female) reported a swallowing problem (4.0% ± 0.1%). Overall, 22.7% ± 1.7% saw a health care professional for their swallowing problem, and 36.9% ± 0.1.7% were given a diagnosis. Women were more likely than men to report a swallowing problem (4.7% ± 0.2% versus 3.3% ± 0.2%, P < .001). Of the patients, 31.7% and 24.8% reported their swallowing problem to be a moderate or a big/very big problem, respectively. Stroke was the most commonly reported etiology (422,000 ± 77,000; 11.2% ± 1.9%), followed by other neurologic cause (269,000 ± 57,000; 7.2% ± 1.5%) and head and neck cancer (185,000 ± 40,000; 4.9% ± 1.1%). The mean number of days affected by the swallowing problem was 139 ± 7. Respondents with a swallowing problem reported 11.6 ± 2.0 lost workdays in the past year versus 3.4 ± 0.1 lost workdays for those without a swallowing problem (contrast, +8.1 lost workdays, P < .001). Conclusion Swallowing problems affect 1 in 25 adults, annually. A relative minority seek health care for their swallowing problem, even though the subjective impact and associated workdays lost with the swallowing problem are significant.

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Rural–Urban Disparities in Obesity Prevalence Among Working Age Adults in the United States: Exploring the Mechanisms

American Journal of Health Promotion ◽

10.1177/0890117116689488 ◽

2017 ◽

Vol 32 (2) ◽

pp. 400-408 ◽

Cited By ~ 14

Author(s):

Ming Wen ◽

Jessie X. Fan ◽

Lori Kowaleski-Jones ◽

Neng Wan

Keyword(s):

United States ◽

Family Income ◽

The United States ◽

Overweight And Obesity ◽

Spatial Distance ◽

Median Income ◽

Cross Sectional ◽

Individual Level ◽

Environmental Features ◽

Working Age

Purpose: Higher prevalence rates of overweight and obesity in rural America have been consistently reported, but sources of these disparities are not well known. This study presented patterns and mechanisms of these disparities among working age Americans. Design: Cross-sectional study. Setting: United States of America. Participants: The study included 10 302 participants of the 2003-2008 National Health and Nutrition Examination Survey (NHANES) who were 20 to 64 years old, not pregnant, and with a body mass index ranging from 18.5 to 60. Measures: Individual-level data were from NHANES including age, gender, race/ethnicity, immigrant status, education, and family income. The outcomes were prevalence of obesity and prevalence of overweight and obesity combined. Neighborhood data were constructed from the 2000 US Census providing tract-level information on family median income and built environmental features and from the 2006 ESRI ArcGIS 9.3 Data DVD providing tract-level park location information. Analysis: Geographic information system (GIS) methods were used to create a measure of spatial distance to local parks capturing park accessibility. Random intercept logistic and ordinal logit regression analyses were performed. Findings: Multivariate regression results showed that the odds of obesity was higher in rural areas compared to urban areas (odds ratio = 1.358, P < .001) net of demographic controls and that this gap was largely attributable to individual educational attainment and neighborhood median household income and neighborhood built environmental features. After controlling for these hypothesized mediators, the elevated odds associated with rural residence was reduced by nearly 94% and rendered statistically insignificant. Conclusions: In this nationally representative cross-sectional sample, rural–urban obesity disparities were large and explained by rural–urban educational differences at the individual level and economic and built environmental differences at the neighborhood level.

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Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

10.1101/2020.05.23.20110189 ◽

2020 ◽

Author(s):

Piyush Mathur ◽

Tavpritesh Sethi ◽

Anya Mathur ◽

Kamal Maheshwari ◽

Jacek B Cywinski ◽

...

Keyword(s):

United States ◽

Machine Learning ◽

Random Forest ◽

Population Density ◽

The United States ◽

Median Income ◽

Relative Importance ◽

Learning Models ◽

Categorical Models ◽

Machine Learning Models

AbstractBackgroundCOVID-19 is now one of the leading causes of mortality amongst adults in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations.ObjectiveThe goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel, and health care characteristics of different states across the United States and COVID-19 mortality.MethodsUsing multiple public data sets, 24 variables linked to COVID-19 disease were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States.ResultsFeature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age,median income, climate and disease mitigation measures on COVID-19 related mortality remained unclear.ConclusionsCOVID-19 policy making will need to take population density, pre-existing medical care and state travel policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.

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Beyond the dichotomy: How ride-hailing competes with and complements public transport

PLoS ONE ◽

10.1371/journal.pone.0262496 ◽

2022 ◽

Vol 17 (1) ◽

pp. e0262496

Author(s):

Oded Cats ◽

Rafal Kucharski ◽

Santosh Rao Danda ◽

Menno Yap

Keyword(s):

United States ◽

Travel Time ◽

Public Transport ◽

The United States ◽

Travel Times ◽

Service Accessibility ◽

Transport Services ◽

Us Cities ◽

Public Transport Services ◽

Two Alternatives

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy—competing with or complementing—is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.

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MO932POPULATION DENSITY AND U.S. KIDNEY TRANSPLANTATION DURING THE FIRST YEAR COVID-19 PANDEMIC

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab110.0011 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Ekamol Tantisattamo ◽

Natnicha Leelaviwat ◽

Natchaya Polpichai ◽

Natsuki Eguchi ◽

Natsumon Udomkittivorakul ◽

...

Keyword(s):

United States ◽

Population Density ◽

Organ Procurement ◽

The United States ◽

Cross Sectional Study ◽

High Rate ◽

High Population ◽

High Population Density ◽

Cross Sectional ◽

Healthcare Resources

Abstract Background and Aims Coronavirus disease 19 (COVID-19) pandemic leads to poorer health outcomes and more utilizing of healthcare resources. Kidney transplant (KT) can lead to worsening transplant outcomes with COVID-19 and trend of KT in the United States decreases. Given a highly contagious disease, high population density may contribute to not only higher rate of the disease, but also lower rate of KT. We aim to examine the association of the number of COVID-19 cases and change in the number of KT with the interaction of population density in the United States. Method A cross-sectional study was conducted by using publicly available data of COVID-19 cases and KT in the United States were retrieved from the Centers of Disease Control and Prevention (CDC) and the Organ Procurement and Transplantation Network/Scientific Registry of Transplant Recipients (OPTN/SRTR), respectively. The association of the cumulative COVID-19 cases of 47 states in the United States where KT occurred between January 1, 20202 and January 6, 2021 with difference in the number of KT between year 2019 and 2020 (ΔKT) was examined by using multiple linear regression. Results During the study period, a total of 20,136,895 COVID-19 cases were detected in the United States and 326,535 patients died. From all 47 states, 23,002 and 20,554 adult KT were performed in 2019 and 2020, respectively. Mean COVID-19 cases and deaths were 428,445±457,344 and 6,948± 6,911, respectively among the 47 states. Mean ΔKT2019 - 2020 were 52± 81. Every 10,000 COVID-19 cases was associated with a decrease in 1.06 KT in year 2020 compared to year 2019 (βcoeff 0.00011, p <0.0001, 95% CI 0.00006, 0.00015). However, after adjusted for the number of KT in 2019, COVID-19 cases (< or ≥ median cases of 317,545), population density (< or ≥ median density of 114 people/mile2), and the interaction term between COVID-19 cases and population density, the states with high rate of COVID-19 (≥317,545 cases/year) and high population density (≥114 people/mile2) had a decrease in 12.4 KT; whereas, there was 4.5 KT decrease in states with low COVID-19 rate and low population density (βcoeff 0.1024705, p 0.000, 95%CI 0.066272, 0.1386691, p interaction -0.686). Conclusion The number of KT in 2020 has decreased independent to the number of 2019 KT and population density. However, a decrease in the number of KT was lower in the states with low COVID-19 rate and low population density compared to those with high COVID-19 rate and high population density. Distribution of healthcare resources and utilization including KT in the states with low COVID-19 cases and low population density may be one of the strategies to continue KT, which is life-saving therapy and better survival benefit compared to being on dialysis in end-stage kidney disease population with a high mortality risk.

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Geographic distribution of family physicians in Japan and the United States: a cross-sectional international comparative study

10.21203/rs.3.rs-61757/v2 ◽

2020 ◽

Author(s):

Yuji Okazaki ◽

Shuhei Yoshida ◽

Saori Kashima ◽

Soichi Koike ◽

Masatoshi Matsumoto

Keyword(s):

United States ◽

Family Medicine ◽

Population Density ◽

Comparative Study ◽

Geographic Distribution ◽

Family Physicians ◽

The United States ◽

Cross Sectional ◽

International Comparative ◽

The U.S

Abstract Background: Family physicians are known to distribute more equally among the population than other physicians. The maturity of family medicine, i.e. the length of its history as a part of healthcare system and the population of qualified family medicine experts, may affect the distribution, but this has not been shown in the literature. This study compares the geographic distribution of family physicians in Japan and the United States (U.S.), both of which are developed countries without a physician allocation system by the public sector, but the two countries differ greatly in the maturity of family medicine as a clinical specialty.Methods: This is a cross-sectional international comparative study using publicly available online database on the number of physicians in Japan (Board-certification Database of Japan Primary Care Association, and Survey of Physicians, Dentists and Pharmacists by Ministry of Health, Labour and Welfare) and the U.S. (Area Resource File by Health Resources and Services Administration). The municipalities in Japan and counties in the U.S. were divided into quintile groups according to population density. The number of family physicians per unit population in each group of areas was calculated. The geographic distribution of all physicians in Japan was simulated assuming that the proportion of family physicians among all physicians in Japan (0.16%) was increased to that in the U.S (11.8%).Results: The distribution of family physicians in Japan noticeably shifted to the areas with the lowest population density. In contrast, family physicians in the U.S. distributed equally across areas. The distribution of physicians with other specialties (general internists, pediatricians, surgeons and obstetricians/gynecologists) shifted heavily to the areas with highest population density in both countries. The simulation analysis showed the geographic maldistribution of all physicians improved substantially if the proportion of family physicians in Japan increases to that in the U.S. Conclusion: The distribution of family physicians is more equal than other medical specialists, and the immaturity of family medicine can even lead to a rural-biased distribution. In a country with emerging family medicine such as Japan, increasing the number of family physicians may effectively mitigate the urban-rural imbalance of physician supply.

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