Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A COVID-19 Risk Assessment for the US Labor Force.

ABSTRACT: The consequences of COVID-19 infection varies substantially based on individual social risk factors and predisposing health conditions. Understanding this variability may be critical for targeting COVID-19 control measures, resources and policies, including efforts to return people back to the workplace. We compiled individual level data from the National Health Information Survey and Quarterly Census of Earnings and Wages to estimate the number of at-risk workers for each US county and industry, accounting for both social and health risks. Nearly 80% of all workers have at least one health risk and 11% are over 60 with an additional health risk. We document important variation in the at-risk population across states, counties, and industries that could provide a strategic underpinning to a staged return to work.

SUBMITTER: Maher S

PROVIDER: S-EPMC7217089 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A COVID-19 Risk Assessment for the US Labor Force.

Maher Samantha S Hill Alexandra E AE Britton Peter P Fenichel Eli P EP Daszak Peter P Zambrana-Torrelio Carlos C Bayham Jude J

medRxiv : the preprint server for health sciences 20200417

The consequences of COVID-19 infection varies substantially based on individual social risk factors and predisposing health conditions. Understanding this variability may be critical for targeting COVID-19 control measures, resources and policies, including efforts to return people back to the workplace. We compiled individual level data from the National Health Information Survey and Quarterly Census of Earnings and Wages to estimate the number of at-risk workers for each US county and industry ...[more]

PMID: 32511435

Similar Datasets

Labor force participation during COVID-19 and risk of depression: a Danish register study.

Project description:BackgroundCOVID-19 caused economic insecurity for businesses and their employees. Understanding effects of changes in labor force participation on depression risk during economic recession is fundamental for early diagnosis. The study evaluates if changes in labor force participation are associated with depression risk during COVID-19 in Denmark.MethodsA register-based longitudinal study of Danes aged 25-67 years without depression 2 years prior to baseline defined as February 2020. An eight-level categorical variable on stable or changing labor force participation was defined from monthly employment percentage gradients in the Danish Register-based Evaluation and Marginalization Database from February 2020. The cohort was followed until 31 December 2020 for depressions overall and mild-, moderate- and severe depression. Sex-stratified cox regression models with hazard ratios (HR) and 95% confidence intervals (95% CI) were performed accounting for important confounders.ResultsIn total, 1 619 240 (50.3%) men of mean age 45.6 years and 1 598 587 (49.7%) women of mean age 45.9 years were included. Becoming unemployed implied an increased HR of depression in men (HR 2.02; 95% CI 1.94-2.10) and women (2.19; 2.12-2.26) compared to a steady-state full-time employment. Being outside the labor force or employed part-time implied an elevated HR in men (3.02; 2.82-3.23 and 2.41; 2.35-2.48) and women (3.13; 2.30-3.31 and 2.30; 2.26-2.35), respectively, compared to a steady-state full-time employment.ConclusionsChanges in labor force participation were associated with higher risk of depression relative to a steady-state full-time employment particularly among individuals with low labor force participation during COVID-19.

| S-EPMC9897998 | biostudies-literature

Early Signs Indicate That COVID-19 Is Exacerbating Gender Inequality in the Labor Force.

Project description:In this data visualization, the authors examine how the coronavirus disease 2019 (COVID-19) crisis in the United States has affected labor force participation, unemployment, and work hours across gender and parental status. Using data from the Current Population Survey, the authors compare estimates between February and April 2020 to examine the period of time before the COVID-19 outbreak in the United States to the height of the first wave, when stay-at-home orders were issued across the country. The findings illustrate that women, particularly mothers, have employment disproportionately affected by COVID-19. Mothers are more likely than fathers to exit the labor force and become unemployed. Among heterosexual married couples of which both partners work in telecommuting-capable occupations, mothers have scaled back their work hours to a far greater extent than fathers. These patterns suggest that the COVID-19 crisis is already worsening existing gender inequality, with long-term implications for women's employment.

| S-EPMC7399570 | biostudies-literature

Seasonal farm labor and COVID-19 spread.

Project description:The COVID-19 pandemic in 2020 caused unprecedented shocks to agricultural food systems, including increased risk to worker health, labor-related input costs, and production uncertainty. Despite employer precautions, there were numerous worksite outbreaks of COVID-19. This paper examines the relationship between month-to-month variation in historical agricultural employment and changes in the incidence of confirmed COVID-19 cases and deaths within U.S. counties from April to August 2020. The results show that employment of 100 additional workers in fruit, vegetable, and horticultural production was associated with 4.5% more COVID-19 cases within counties or an additional 18.65 COVID-19 cases and 0.34 additional COVID-19 deaths per 100,000 individuals in the county workforce.

| S-EPMC8646853 | biostudies-literature

Toward Combatting COVID-19: A Risk Assessment System.

Project description:The coronavirus disease 2019 (COVID-19) has rapidly become a significant public health emergency all over the world since it was first identified in Wuhan, China, in December 2019. Until today, massive disease-related data have been collected, both manually and through the Internet of Medical Things (IoMT), which can be potentially used to analyze the spread of the disease. On the other hand, with the help of IoMT, the analysis results of the current status of COVID-19 can be delivered to people in real time to enable situational awareness, which may help mitigate the disease spread in communities. However, current accessible data on COVID-19 are mostly at a macrolevel, such as for each state, county, or metropolitan area. For fine-grained areas, such as for each city, community, or geographical coordinate, COVID-19 data are usually not available, which prevents us from obtaining information on the disease spread in closer neighborhoods around us. To address this problem, in this article, we propose a two-level risk assessment system. In particular, we define a "risk index." Then, we develop a risk assessment model, called MK-DNN, by taking advantage of the multikernel density estimation (MKDE) and deep neural network (DNN). We train MK-DNN at the macrolevel (for each metro area), which subsequently enables us to obtain the risk indices at the microlevel (for each geographic coordinate). Moreover, a heuristic validation method is further designed to help validate the obtained microlevel risk indices. Simulations conducted on real-world data demonstrate the accuracy and validity of our proposed risk assessment system.

| S-EPMC8768990 | biostudies-literature

Twin Peaks: Covid-19 and the labor market.

Project description:This paper develops a choice-theoretic equilibrium model of the labor market in the presence of a pandemic. It includes heterogeneity in productivity, age and the ability to work from home. Worker and firm behavior changes in the presence of the virus, which itself has equilibrium consequences for the infection rate. The model is calibrated to the UK and counterfactual lockdown measures are evaluated. We find a different response in both the evolution of the virus and the labor market with different lockdown policies. A laissez-faire approach results in lives lost and acts as negative shock to the economy. A lockdown policy, absent any other intervention, will reduce the lives lost but increase the economic burden. Consistent with recent evidence, we find that the economic costs from lockdown are most felt by those earning the least. Finally, we introduce a job retention scheme as implemented by the UK Government and find that it spreads the economic hardship more equitably.

| S-EPMC8425362 | biostudies-literature

Cohort survey of healthcare workers with COVID-19 infection

Project description: Not available

| S-BSST563 | biostudies-other

Assessment of the platelet transcriptome in patients with COVID-19 versus controls

Project description:To understand how COVID-19 alters the platelet transcriptome.

2021-09-09 | GSE176480 | GEO

Systems biological assessment of immunity to severe and mild COVID-19 infections

Project description:CITE-seq of 7 patients with COVID-19 and 5 healthy controls

2020-08-07 | GSE155673 | GEO

Systems biological assessment of immunity to severe and mild COVID-19 infections

Project description:The recent emergence of COVID-19 presents a major global crisis. Profound knowledge gaps remain about the interaction between the virus and the immune system. Here, we used a systems biology approach to analyze immune responses in 76 COVID-19 patients and 69 age and sex- matched controls, from Hong Kong and Atlanta. Mass cytometry revealed prolonged plasmablast and effector T cell responses, reduced myeloid expression of HLA-DR and inhibition of mTOR signaling in plasmacytoid DCs (pDCs) during infection. Production of pro-inflammatory cytokines plasma levels of inflammatory mediators, including EN-RAGE, TNFSF14, and Oncostatin-M, which correlated with disease severity, and increased bacterial DNA and endotoxin in plasma in and reduced HLA-DR and CD86 but enhanced EN-RAGE expression in myeloid cells in severe transient expression of IFN stimulated genes in moderate infections, consistent with transcriptomic analysis of bulk PBMCs, that correlated with transient and low levels of plasma COVID-19.

2020-07-31 | GSE152418 | GEO

Methodology for risk assessment of COVID-19 pandemic propagation.

Project description:This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used. Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension (i.e., the maximum distance as well as the angle) enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event "COVID-19 infection" was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.

| S-EPMC8220128 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data