COVID-19 mortality among working-age Americans in 46 states, by industry and occupation

IntroductionA small body of epidemiological research suggests that working in an essential sector is a risk factor for SARS-CoV-2 infection or subsequent disease or mortality. However, there is limited evidence to date on the US, or on how the risks associated with essential work differ across demographic subgroups defined by age, sex, and race/ethnicity. MethodsUsing publicly available data from the National Center for Health Statistics on deaths occurring in the US in 2020, we calculated per-capita COVID-19 mortality by industry and occupation. We additionally calculated per-capita COVID-19 mortality by essential industry--essential or not--by age group, sex, and race/ethnicity. ResultsAmong non-military individuals and individuals with a known industry or occupation, there were 48,030 reported COVID-19 deaths, representing 25.1 COVID-19 deaths per 100,000 working-age individuals after age standardization. Per-capita age-standardized COVID-19 mortality was 1.96 times higher among essential workers than among workers in non-essential industries, representing an absolute difference of 14.9 per 100,000. Across industry, per-capita age-standardized COVID-19 mortality was highest in the following industries: accommodation and food services (45.4 per 100,000); transportation and warehousing (43.4); agriculture, forestry, fishing and hunting (42.3); mining (39.6); and construction (38.7). DiscussionGiven that SARS-CoV-2 is an airborne virus, we call for collaborative efforts to ensure that workplace settings are properly ventilated and that workers have access to effective masks. We also urge for paid sick leave, which can help increase vaccine access and minimize disease transmission.

COVID-19 mortality and excess mortality among working-age Californians, by occupational sector: March 2020 through November 2021

BackgroundDuring the first year of the pandemic, essential workers faced higher rates of SARS-CoV-2 infection and COVID-19 mortality than non-essential workers. It is unknown whether disparities in pandemic-related mortality across occupational sectors have continued to occur, amidst SARS-CoV-2 variants and vaccine availability. MethodsWe obtained data on all deaths occurring in the state of California from 2016 through 2021. We restricted our analysis to California residents who were working age (18-65 years at time of death) and died of natural causes. Occupational sector was classified into 9 essential sectors; non-essential; or not in the labor market. We calculated the number of COVID-19 deaths in total and per capita that occurred in each occupational sector. Separately, using autoregressive integrated moving average models, we estimated total, per-capita, and relative excess natural-cause mortality by week between March 1, 2020, and November 30, 2021, stratifying by occupational sector. We additionally stratified analyses of occupational risk into regions with high versus low vaccine uptake, categorizing high-uptake regions as counties where at least 50% of the population completed a vaccination series by August 1, 2021. FindingsFrom March 2020 through November 2021, essential work was associated with higher COVID-19 and excess mortality compared with non-essential work, with the highest per-capita COVID-19 mortality in agriculture (131.8 per 100,000), transportation/logistics (107.1), manufacturing (103.3), and facilities (101.1). Essential workers continued to face higher COVID-19 and excess mortality during the period of widely available vaccines (March through November 2021). Between July and November 2021, emergency workers experienced higher per-capita COVID-19 mortality (113.7) than workers from any other sector. Essential workers faced the highest COVID-19 mortality in counties with low vaccination rates, a difference that was more pronounced during the period of the Delta surge in Summer 2021. InterpretationEssential workers have continued to bear the brunt of high COVID-19 and excess mortality throughout the pandemic, particularly in the agriculture, emergency, manufacturing, facilities, and transportation/logistics sectors. This high death toll has continued during periods of vaccine availability and the delta surge. In an ongoing pandemic without widespread vaccine coverage and anticipated threats of new variants, the US must actively adopt policies to more adequately protect essential workers.

All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021)

Official COVID-19 mortality statistics are strongly influenced by local diagnostic capacity, strength of the healthcare and vital registration systems, and death certification criteria and capacity, often resulting in significant undercounting of COVID-19 attributable deaths. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock - both direct and indirect - of the COVID-19 pandemic. Here, we use data from civil death registers from a convenience sample of 90 municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to weekly data from January 2019 to February 2020, we estimated excess mortality over the course of the pandemic from March 2020 to April 2021. We estimated 21,300 [95% CI: 20,700, 22,000] excess deaths across these municipalities in this period, representing a 44% [95% CI: 43%, 45%] increase over the expected baseline. The sharpest increase in deaths in our sample was observed in late April 2021, with an estimated 678% [95% CI: 649%, 707%] increase in mortality from expected counts. The 40 to 65 age group experienced the highest increase in mortality relative to the other age groups. We found substantial, yet similar, increases in mortality for males and females. Our excess mortality estimate for these 90 municipalities, representing approximately 5% of the states population, exceeds the official COVID-19 death count for the entire state of Gujarat, even before the delta wave of the pandemic in India peaked in May 2021. Prior studies have concluded that true pandemic-related mortality in India greatly exceeds official counts. This study, using data directly from the first point of official death registration data recording, provides incontrovertible evidence of the high excess mortality in Gujarat from March 2020 to April 2021.

Geographically-targeted COVID-19 vaccination is more equitable than age-based thresholds alone

COVID-19 mortality increases dramatically with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts introduce tradeoffs because BIPOC populations are younger than white populations. In analyses of California and Minnesota--demographically divergent states--we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. One-sentence summaryAge-based COVID-19 vaccination prioritizes white people above higher-risk others; geographic prioritization improves equity.

The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok

1Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics.

Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis

BackgroundAirline travel has been significantly reduced during the COVID-19 pandemic due to concern for individual risk of SARS-CoV-2 infection and population-level transmission risk from importation. Routine viral testing strategies for COVID-19 may facilitate safe airline travel through reduction of individual and/or population-level risk, although the effectiveness and optimal design of these "test-and-travel" strategies remain unclear. MethodsWe developed a microsimulation of SARS-CoV-2 transmission in a cohort of airline travelers to evaluate the effectiveness of various testing strategies to reduce individual risk of infection and population-level risk of transmission. We evaluated five testing strategies in asymptomatic passengers: i) anterior nasal polymerase chain reaction (PCR) within 3 days of departure; ii) PCR within 3 days of departure and PCR 5 days after arrival; iii) rapid antigen test on the day of travel (assuming 90% of the sensitivity of PCR during active infection); iv) rapid antigen test on the day of travel and PCR 5 days after arrival; and v) PCR within 3 days of arrival alone. The travel period was defined as three days prior to the day of travel and two weeks following the day of travel, and we assumed passengers followed guidance on mask wearing during this period. The primary study outcome was cumulative number of infectious days in the cohort over the travel period (population-level transmission risk); the secondary outcome was the proportion of infectious persons detected on the day of travel (individual-level risk of infection). Sensitivity analyses were conducted. FindingsAssuming a community SARS-CoV-2 incidence of 50 daily infections, we estimated that in a cohort of 100,000 airline travelers followed over the travel period, there would be a total of 2,796 (95% UI: 2,031, 4,336) infectious days with 229 (95% UI: 170, 336) actively infectious passengers on the day of travel. The pre-travel PCR test (within 3 days prior to departure) reduced the number of infectious days by 35% (95% UI: 27, 42) and identified 88% (95% UI: 76, 94) of the actively infectious travelers on the day of flight; the addition of PCR 5 days after arrival reduced the number of infectious days by 79% (95% UI: 71, 84). The rapid antigen test on the day of travel reduced the number of infectious days by 32% (95% UI: 25, 39) and identified 87% (95% UI: 81, 92) of the actively infectious travelers; the addition of PCR 5 days after arrival reduced the number of infectious days by 70% (95% UI: 65, 75). The post-travel PCR test alone (within 3 days of landing) reduced the number of infectious days by 42% (95% UI: 31, 51). The ratio of true positives to false positives varied with the incidence of infection. The overall study conclusions were robust in sensitivity analysis. InterpretationRoutine asymptomatic testing for COVID-19 prior to travel can be an effective strategy to reduce individual risk of COVID-19 infection during travel, although post-travel testing with abbreviated quarantine is likely needed to reduce population-level transmission due to importation of infection when traveling from a high to low incidence setting.

Implications of the COVID-19 San Francisco Bay Area Shelter-in-Place Announcement: A Cross-Sectional Social Media Survey

BackgroundThe U.S. has experienced an unprecedented number of shelter-in-place orders throughout the COVID-19 pandemic. There is limited empirical research that examines the impact of these orders. We aimed to rapidly ascertain whether social distancing; difficulty with daily activities (obtaining food, essential medications and childcare); and levels of concern regarding COVID-19 changed after the March 16, 2020 announcement of shelter-in-place orders for seven counties in the San Francisco Bay Area. MethodsWe conducted an online, cross-sectional social media survey from March 14 - April 1, 2020. We measured changes in social distancing behavior; experienced difficulties with daily activities (i.e., access to healthcare, childcare, obtaining essential food and medications); and level of concern regarding COVID-19 after the March 16 shelter-in-place announcement in the San Francisco Bay Area and elsewhere in the U.S. ResultsThe percentage of respondents social distancing all of the time increased following the shelter-in-place announcement in the Bay Area (9.2%, 95% CI: 6.6, 11.9) and elsewhere in the U.S. (3.4%, 95% CI: 2.0, 5.0). Respondents also reported increased difficulty with obtaining food, hand sanitizer, and medications, particularly with obtaining food for both respondents from the Bay Area (13.3%, 95% CI: 10.4, 16.3) and elsewhere (8.2%, 95% CI: 6.6, 9.7). We found limited evidence that level of concern regarding the COVID-19 crisis changed following the shelter-in-place announcement. ConclusionThese results capture early changes in attitudes, behaviors, and difficulties. Further research that specifically examines social, economic, and health impacts of COVID-19, especially among vulnerable populations, is urgently needed.

U.S. county-level characteristics to inform equitable COVID-19 response

BackgroundThe spread of Coronavirus Disease 2019 (COVID-19) across the United States confirms that not all Americans are equally at risk of infection, severe disease, or mortality. A range of intersecting biological, demographic, and socioeconomic factors are likely to determine an individuals susceptibility to COVID-19. These factors vary significantly across counties in the United States, and often reflect the structural inequities in our society. Recognizing this vast inter-county variation in risks will be critical to mounting an adequate response strategy. Methods and FindingsUsing publicly available county-specific data we identified key biological, demographic, and socioeconomic factors influencing susceptibility to COVID-19, guided by international experiences and consideration of epidemiological parameters of importance. We created bivariate county-level maps to summarize examples of key relationships across these categories, grouping age and poverty; comorbidities and lack of health insurance; proximity, density and bed capacity; and race and ethnicity, and premature death. We have also made available an interactive online tool that allows public health officials to query risk factors most relevant to their local context. Our data demonstrate significant inter-county variation in key epidemiological risk factors, with a clustering of counties in certain states, which will result in an increased demand on their public health system. While the East and West coast cities are particularly vulnerable owing to their densities (and travel routes), a large number of counties in the Southeastern states have a high proportion of at-risk populations, with high levels of poverty, comorbidities, and premature death at baseline, and low levels of health insurance coverage. The list of variables we have examined is by no means comprehensive, and several of them are interrelated and magnify underlying vulnerabilities. The online tool allows readers to explore additional combinations of risk factors, set categorical thresholds for each covariate, and filter counties above different population thresholds. ConclusionCOVID-19 responses and decision making in the United States remain decentralized. Both the federal and state governments will benefit from recognizing high intra-state, inter-county variation in population risks and response capacity. Many of the factors that are likely to exacerbate the burden of COVID-19 and the demand on healthcare systems are the compounded result of long-standing structural inequalities in US society. Strategies to protect those in the most vulnerable counties will require urgent measures to better support communities attempts at social distancing and to accelerate cooperation across jurisdictions to supply personnel and equipment to counties that will experience high demand.