Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City

Impact of the COVID-19 lockdown on the oral health behaviour of young children

AIM

Children up to the age of 10 are dependant primarily on their caregivers for oral care; COVID-19 lockdowns may have disrupted this care. We therefore assessed whether the COVID-19 lockdown affected routine parental oral care for their children.

METHODS

A short online survey regarding oral health behaviour and changes in the home setting during the COVID-19 lockdown was emailed to parents (n = 782, response 15%) participating in an existing study 'Healthy Teeth All Aboard (HTAA)'.

RESULTS

During the lockdown, 29% of parents reported that they skipped their children's toothbrushing more often than before the lockdown, and 49% of the children were allowed to consume sugary snacks or drinks more often. These behaviours were not significantly associated with socio-economic status, country of birth of the mother, work situation of the parents, school attendance patterns or HTAA participation status.

CONCLUSION

The COVID-19 lockdown affected the daily oral hygiene routines of children, potentially increasing caries risk.

CLINICAL RELEVANCE

Dental professionals should be aware of the potential impacts on oral health in children in the circumstance of a new lockdown. Parents should be made aware of the oral health consequences of a lockdown.

Hospital segregation, critical care strain, and inpatient mortality during the COVID-19 pandemic in New York City

BACKGROUND

Hospital segregation by race, ethnicity, and health insurance coverage is prevalent, with some hospitals providing a disproportionate share of undercompensated care. We assessed whether New York City (NYC) hospitals serving a higher proportion of Medicaid and uninsured patients pre-pandemic experienced greater critical care strain during the first wave of the COVID-19 pandemic, and whether this greater strain was associated with higher rates of in-hospital mortality.

METHODS

In a retrospective analysis of all-payer NYC hospital discharge data, we examined changes in admissions, stratified by use of intensive care unit (ICU), from the baseline period in early 2020 to the first COVID-19 wave across hospital quartiles (265,329 admissions), and crude and risk-adjusted inpatient mortality rates, also stratified by ICU use, in the first COVID wave across hospital quartiles (23,032 inpatient deaths), based on the proportion of Medicaid or uninsured admissions from 2017-2019 (quartile 1 lowest to 4 highest). Logistic regressions were used to assess the cross-sectional association between ICU strain, defined as ICU volume in excess of the baseline average, and patient-level mortality.

RESULTS

ICU admissions in the first COVID-19 wave were 84%, 97%, 108%, and 123% of the baseline levels by hospital quartile 1-4, respectively. The risk-adjusted mortality rates for ICU admissions were 36.4 (CI = 34.7,38.2), 43.6 (CI = 41.5,45.8), 45.9 (CI = 43.8,48.1), and 45.7 (CI = 43.6,48.0) per 100 admissions, and those for non-ICU admissions were 8.6 (CI = 8.3,9.0), 10.9 (CI = 10.6,11.3), 12.6 (CI = 12.1,13.0), and 12.1 (CI = 11.6,12.7) per 100 admissions by hospital quartile 1-4, respectively. Compared with the reference group of 100% or less of the baseline weekly average, ICU admissions on a day for which the ICU volume was 101-150%, 151-200%, and > 200% of the baseline weekly average had odds ratios of 1.17 (95% CI = 1.10, 1.26), 2.63 (95% CI = 2.31, 3.00), and 3.26 (95% CI = 2.82, 3.78) for inpatient mortality, and non-ICU admissions on a day for which the ICU volume was 101-150%, 151-200%, and > 200% of the baseline weekly average had odds ratios of 1.28 (95% CI = 1.22, 1.34), 2.60 (95% CI = 2.40, 2.82), and 3.44 (95% CI = 3.11, 3.63) for inpatient mortality.

CONCLUSIONS

Our findings are consistent with hospital segregation as a potential driver of COVID-related mortality inequities and highlight the need to desegregate health care to address structural racism, advance health equity, and improve pandemic resiliency.

Communication inequalities in the COVID-19 pandemic: socioeconomic differences and preventive behaviors in the United States and South Korea

BACKGROUND

Communication inequalities are important mechanisms linking socioeconomic backgrounds to health outcomes. Guided by the structural influence model of communication, this study examined the intermediate role of health communication in the relationship between education, income, and preventive behavioral intentions during the COVID-19 pandemic in the United States and South Korea.

METHODS

The data were collected through two online surveys conducted by two professional research firms in the US (April 1-3, 2020) and South Korea (April 9-16, 2020). To test the mediating role of health communication, as well as the hypothesized relationships in the proposed model, we performed a path analysis using Mplus 6.1.

RESULTS

In analyzing survey data from 1050 American and 1175 Korean adults, we found that one's socioeconomic positions were associated with their intentions to engage in COVID-19 preventive behaviors through affecting their health communication experiences and then efficacious beliefs. Differences in education and income were associated with willingness to engage in preventive behaviors by constraining health communication among people with low levels of education and income. The findings showed notable differences and some similarities between the US and South Korea. For example, while income was positively associated with health communication in both US and South Korea, education was only significantly related to health communication in US but not in South Korea.

CONCLUSIONS

This study suggests health communication strategies such as choice of communication channels and messages to promote intention for COVID-19 prevention behaviors in particular consideration of individual differences in socioeconomic positions in countries with different cultural features. Pubic policies and health campaigns can utilize the suggestions to promote efficacy and preventive behavioral intention during early pandemics.

Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference

While mobility intervention policies implemented during the early stages of the COVID-19 outbreak had a significant impact on public transit use, few studies have investigated the individual-level responses in metro transit riding behaviors. Using long time-series cellphone big data from frequent metro users in Shenzhen, China, we developed a quasi-experimental interrupted time series (ITS) design to estimate the treatment effects of mobility intervention policies on people's daily shares of metro transit use (SMU). The results indicate that the first-level emergency response (FLR) and the public transit restriction (PTR) policy yielded abrupt drops in SMU of 8.0% and 17.6%, respectively, whereas the return-to-work (RTW) order had an immediate recovery effect of 14.5%. The effect of the FLR is time-decreasing while those effects of the PTR and the RTW are time-increasing. Females and elderly people living in neighborhoods near the city center with low population density and fewer transit stations are more adaptable to policy interventions for reducing SMUs, while the recovery effect of RTW is relatively low for the elderly living in less mixed-use neighborhoods with reduced transit service. These findings can help policymakers design more socially- and spatially-precise and equity mobility intervention policies during a pandemic.

Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11116-023-10392-2.

Addressing racial/ethnic inequities in vaccine hesitancy and uptake: lessons learned from the California alliance against COVID-19

Lack of trust in biomedical research, government, and health care systems, especially among racial/ethnic minorities and under-resourced communities, is a longstanding issue rooted in social injustice. The COVID-19 pandemic has further highlighted existing health and socioeconomic inequities and increased the urgency for solutions to provide access to timely, culturally, and linguistically appropriate evidence-based information about COVID-19; and ultimately to promote vaccine uptake. California's statewide alliance STOP COVID-19 CA (comprising eleven sites), leverages long standing community partnerships to better understand concerns, misinformation, and address racial/ethnic inequities in vaccine hesitancy and uptake. Using data from the California CEAL Communication Working Group, we demonstrate the wide range of strategies, communication methods, languages, and trusted messengers that have been effective in reaching diverse communities across the state. We also showcase challenges and lessons learned, such as the importance of including trusted community partners to share information or provide vaccines. These approaches, rooted in community engagement, are crucial for addressing inequities and responding to future public health emergencies.

Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis

In this paper we investigate the public transport trip frequency variations, as well as the reasons that led to the shift away from public transport means, due to the COVID-19 pandemic. We studied relevant data from the Moovit platform, and we compared operational and trip frequency characteristics of public transport systems before and after the outbreak of the pandemic in 87 cities worldwide. On average, waiting times at public transport stops/stations increased while trip distances decreased, apparently due to the mobility restriction and social distancing measures implemented in 2020. Most of the Moovit users who said that they abandoned public transport in 2020 were found in Italy and Greece. We developed linear regression analysis models to investigate (among the 35 variables examined in the study) the relationship between public transport abandonment rates and socioeconomic factors, quality of service characteristics, and indicators of pandemic's spread. Empirical findings show that public transport dropout rates are positively correlated with the COVID-19 death toll figures, the cleanliness of public transport vehicles and facilities, as well as with the income inequality (GINI) index of the population, and thus reconfirm previous research findings. In addition, the waiting time at stops/stations and the number of transfers required for commute trips appeared to be the most critical public transport trip segments, which significantly determine the discontinuation of public transport use under pandemic circumstances. Our research findings indicate specific aspects of public transport services, which require tailored adjustments in order to recover ridership in the post-pandemic period.

Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region

Harnessing a unique data source - longitudinal travel smartcard data linked to passenger demographics from 2019 to 2022 - we use methods of survival analysis to model the recovery of public transport patronage among 183,891 senior citizens resident in the West Midlands metropolitan region in the United Kingdom. Comparing pre and peri-pandemic patronage, we identify pronounced social and spatial inequalities in the speed of return to public transport. We find that male, younger and non-White passengers are more likely to return to public transport as soon as movement restrictions were lifted, whereas passengers from White ethnic background and affluent areas do not return to public transport within the first year after the outbreak. Pronounced social inequalities persist into the middle of 2021, and only thence they began to attenuate as part of a wider return to public transport among passengers post retirement age. In 2022, 80% of these passengers have returned to public transport but the frequency of use has remained lower than prior to the pandemic. We discuss implications for transport policy and planning.

A Multicenter Study about the Population Treated in the Respiratory Triage Stations Deployed by the Red Cross during the COVID-19 Pandemic

BACKGROUND

Care demand exceeded the availability of human and material resources during the COVID-19 pandemic, which is the reason why triage was fundamental. The objective is to know the clinical and sociodemographic factors of confirmed or suspected COVID-19 cases in triage stations from different Ecuadorian provinces.

METHOD

A multicenter study with a retrospective and descriptive design. The patients included were those who accessed the Respiratory Triage stations deployed by the Ecuadorian Red Cross in eight Ecuadorian provinces during March and April 2021. Triage allows for selecting patients that need urgent treatment and favors efficacy of health resources.

RESULTS

The study population consisted of a total of 21,120 patients, of which 43.1% were men and 56.9% were women, with an age range between 0 and 98 years old. Severity of COVID-19 behaved differently according to gender, with mild symptoms predominating in women and severe or critical symptoms in men. Higher incidence of critical cases was observed in patients over 65 years old. It was observed that overweight predominated in critical, severe, and moderate cases, while the body mass index of patients with mild symptoms was within the normal range.

CONCLUSIONS

The Ecuadorian Red Cross units identified some suspected COVID-19 cases, facilitating their follow-up and isolation. Fever was the most significant early finding.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time

BACKGROUND

Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time.

METHODS

Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality.

RESULTS

Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year.

CONCLUSIONS

Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

Socioeconomic differences in COVID-19 infection, hospitalisation and mortality in urban areas in a region in the South of Europe

BACKGROUND

To analyse differences in confirmed cases, hospitalisations and deaths due to COVID-19 related to census section socioeconomic variables.  METHODS: Ecological study in the 12 largest municipalities in Andalusia (Spain) during the first three epidemic waves of the COVID-19 (02/26/20-03/31/21), covering 2,246 census sections (unit of analysis) and 3,027,000 inhabitants. Incidence was calculated, standardised by age and sex, for infection, hospitalisation and deaths based on average gross income per household (AGI) for the census tracts in each urban area. Association studied using a Poisson Bayesian regression model with random effects for spatial smoothing.

RESULTS

There were 140,743 cases of COVID-19, of which 12,585 were hospitalised and 2,255 died. 95.2% of cases were attributed to the second and third waves, which were jointly analysed. We observed a protective effect of income for infection in 3/12 cities. Almeria had the largest protective effect (smoothed relative risk (SRR) = 0.84 (0.75-0.94 CI 95%). This relationship reappeared with greater magnitude in 10/12 cities for hospitalisation, lowest risk in Algeciras SRR = 0.41 (0.29-0.56). The pattern was repeated for deaths in all urban areas and reached statistical significance in 8 cities. Lowest risk in Dos Hermanas SRR = 0.35 (0.15-0.81).

CONCLUSIONS

Income inequalities by geographical area were found in the incidence of COVID-19. The strengths of the association increased when analysing the severe outcomes of hospitalisations and, above all, deaths.

Spatial modeling of vaccine deserts as barriers to controlling SARS-CoV-2

Background

COVID-19 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We sought to measure if the disparities seen in the geographic distribution of other COVID-19 healthcare resources were also present during the initial rollout of the COVID-19 vaccine.

Methods

Using a comprehensive COVID-19 vaccine database (VaccineFinder), we built an empirically parameterized spatial model of access to essential resources that incorporated vaccine supply, time-willing-to-travel for vaccination, and previous vaccination across the US. We then identified vaccine deserts—US Census tracts with localized, geographic barriers to vaccine-associated herd immunity. We link our model results with Census data and two high-resolution surveys to understand the distribution and determinates of spatially accessibility to the COVID-19 vaccine.

Results

We find that in early 2021, vaccine deserts were home to over 30 million people, >10% of the US population. Vaccine deserts were concentrated in rural locations and communities with a higher percentage of medically vulnerable populations. We also find that in locations of similar urbanicity, early vaccination distribution disadvantaged neighborhoods with more people of color and older aged residents.

Conclusion

Given sufficient vaccine supply, data-driven vaccine distribution to vaccine deserts may improve immunization rates and help control COVID-19.

Racial and ethnic variation in COVID-19 care, treatment, and outcomes: A retrospective cohort study from the MiCOVID-19 registry

BACKGROUND

Racial and ethnic disparities in COVID-19 outcomes exist, but whether in-hospital care explains this difference is not known. We sought to determine racial and ethnic differences in demographics, comorbidities, in-hospital treatments, and in-hospital outcomes of patients hospitalized with COVID-19.

METHODS AND FINDINGS

This was a cohort study using MiCOVID-19, a multi-center, retrospective, collaborative quality improvement registry, which included data on patients hospitalized with COVID-19 across 38 hospitals in the State of Michigan. 2,639 adult patients with COVID-19 hospitalized at a site participating in the MiCOVID-19 Registry were randomly selected. Outcomes included in-hospital mortality, age at death, intensive care unit admission, and need for invasive mechanical ventilation by race and ethnicity. Baseline comorbidities differed by race and ethnicity. In addition, Black patients had higher lactate dehydrogenase, erythrocyte sedimentation rate, C-reactive protein, creatine phosphokinase, and ferritin levels. Black patients were less likely to receive dexamethasone and remdesivir compared with White patients (4.2% vs 14.3% and 2.2% vs. 11.8%, p < 0.001 for each). Black (18.7%) and White (19.6%) patients experienced greater mortality compared with Asian (13.0%) and Latino (5.9%) patients (p < 0.01). The mean age at death was significantly lower by 8 years for Black patients (69.4 ± 13.3 years) compared with White (77.9 ± 12.6), Asian (77.6 ± 6.6), and Latino patients (77.4 ± 15.5) (p < 0.001).

CONCLUSIONS

COVID-19 mortality appears to be driven by both pre-hospitalization clinical and social factors and potentially in-hospital care. Policies aimed at population health and equitable application of evidence-based medical therapy are needed to alleviate the burden of COVID-19.

[Physical inactivity in young people during home confinement due to COVID-19]

Background

Physical inactivity is a highly prevalent condition in the world and has been associated with increased susceptibility to develop comorbidities and present with severe respiratory distress syndrome due to COVID-19.

Objective

To identify the factors present in the family environment and the personal reasons associated with physical inactivity in young people during confinement at home due to COVID-19.

Material and methods

A cross-sectional study analyzed data from 1,326 young people, ages 15 - 18. To collect information, a questionnaire was constructed using the Google Forms tool and distributed through the WhatsApp application and email to collect the information.

Results

The prevalence of physical inactivity was 43.4%. Approximately 24.4% were overweight, and 8.8% were obese. Near 43.0% of young people reported living in an environment with a dysfunctional family. The multivariate logistic regression analysis showed that suffering from obesity, does not have space at home, or devices to exercise and present a change in emotions, are related to the physical inactivity of young people during confinement at home due to the COVID-19 pandemic.

Conclusions

It is important to promote a harmonious environment within the family and the personal development of a healthy lifestyle, during the period of application of the contingency plan due to the presence of a pandemic, in order to maintain a better healthy physical and mental state.

[Epidemiological and clinical characteristics of the COVID-19 epidemic in Mexico: Quintana Roo case]

Objective

Identify risk factors for severe outcome in Mexican patients with COVID-19 in the population of Quintana Roo.

Material and methods

Study of 5,916 who met the criteria for suspected cases of COVID-19, 2,531 confirmed by qrTPCR-Sars-CoV-2 tests, of which 1,486 were positive, among which they were classified as hospitalized (severe COVID-19) and outpatients. Multivariate logistic regression analysis was performed to explore the factors associated with the severity of COVID-19 and death as clinical outcomes. The basic reproduction number (R0) was calculated Statistical analysis) Endorsement of the ethics committee 2301.

Results

SARS-CoV-2 positive patients presented a high prevalence of hypertension 29.1%, diabetes 23.5%, obesity 24%, and 48.5% have at least one chronic disease. There is a high risk of severity for COVID-19 in patients with diabetes OR=3.14, hypertension OR=1.88, obesity OR=1.68, kidney disease OR=3.2, older than 65 years OR=13.6 and men OR=1.7. These factors also increase the risk of death up to 7.7 times. The maximum R0 during the epidemic was 2.4.

Conclusion

Liver and kidney disease, diabetes, hypertension, and obesity are significantly associated with severe COVID-19 and death.

Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) has had severely disruptive impacts on transportation, particularly public transit. To understand metro ridership changes due to the COVID-19 pandemic, this study conducts an in-depth analysis of two Chinese megacities from January 1, 2020, to August 31, 2021. Generalized linear models are used to explore the impact of the COVID-19 pandemic on metro ridership. The dependent variable is the relative change in metro ridership, and the independent variables include COVID-19, socio-economic, and weather variables. The results suggested the following: (1) The COVID-19 pandemic has a significantly negative effect on the relative change in metro ridership, and the number of cumulative confirmed COVID-19 cases within 14 days performs better in regression models, which reflects the existence of the time lag effect of the COVID-19 pandemic. (2) Emergency responses are negatively associated with metro system usage according to severity and duration. (3) The marginal effects of the COVID-19 variables and emergency responses are larger on weekdays than on weekends. (4) The number of imported confirmed COVID-19 cases only significantly affects metro ridership in the weekend and new-normal-phase models for Beijing. In addition, the daily gross domestic product and weather variables are significantly associated with metro ridership. These findings can aid in understanding the usage of metro systems in the outbreak and new-normal phases and provide transit operators with guidance to adjust services.

SARS-CoV-2 Seroprevalence in Those Utilizing Public Transportation or Working in the Transportation Industry: A Rapid Review

Proximity and duration of social contact while working or using public transportation may increase users' risk of SARS-CoV-2 exposure. This review aims to assess evidence of an association between use of public transportation or work in the transportation industry and prevalence of SARS-CoV-2 antibodies as well as to identify factors associated with seropositivity in transit users. A literature search of major databases was conducted from December 2019 to January 2022 using key worlds including "seroprevalence", "SARS-CoV-2", and "public transit". A narrative review of included studies was completed for the following categories: those working in the transportation industry, healthcare workers relying on public transit, and population-based studies. The association between work in the transit industry and seroprevalence varied based on location, demographic characteristics, and test sensitivities. No association was found in healthcare workers. Several population-based studies indicated higher seroprevalence in those using public transit. Overall seroprevalence estimates varied based on geographic location, population demographics, study methodologies, and calendar date of assessment. However, seropositivity was consistently higher in racial minorities and low-income communities.

Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic

COVID-19 has swept the world, and the unprecedented decline in transit ridership has been noticed. However, little attention has been paid to the resilience of the transportation system, particularly in medium-sized cities. Drawing upon a light rail ridership dataset in Salt Lake County from 2017 to 2021, we develop a novel method to measure the vulnerability and resilience of transit ridership using a Bayesian structure time series model. The results show that government policies have a more significant impact than the number of COVID-19 cases on transit ridership. Regarding the built environment, a highly compact urban design might reduce the building coverage ratio and makes transit stations more vulnerable and less resilient. Furthermore, the high rate of minorities is the primary reason for the drops in transit ridership. The findings are valuable for understanding the vulnerability and resilience of transit ridership to pandemics for better coping strategies in the future.

Violence in the Big Apple throughout the COVID-19 pandemic: A borough-specific analysis

In early 2020, the world faced a rapid, life-changing, public health crisis in the form of the Coronavirus (COVID-19) pandemic. The pandemic and its associated social-distancing measures collided with a period of social unrest following the murder of George Floyd at the hands of Minneapolis police and persisted for nearly two years following its emergence. The current study adds to existing research by examining the effect of these events on the incidence of violence (shootings and assaults) in New York City (NYC) over a longer period of time, both in the city as a whole and at the borough-level. To accomplish this, the current study draws from publicly available data using series of analytical techniques to account for underlying trends, seasonality, and temperature while also estimating borough-specific effects. Results indicate that the prevalence of COVID-19 cases, associated social-distancing mandates, and the period of social unrest following Floyd's murder were associated with violence in NYC. Further, findings suggest while a number of the factors explored had consistent effects across each of NYC's five boroughs there was some evidence of heterogeneity. The implications for future research on the COVID-19 pandemic are discussed.

The emission and dynamics of droplets from human expiratory activities and COVID-19 transmission in public transport system: A review

The public transport system, containing a large number of passengers in enclosed and confined spaces, provides suitable conditions for the spread of respiratory diseases. Understanding how diseases are transmitted in public transport environment is of vital importance to public health. However, this is a highly multidisciplinary matter and the related physical processes including the emissions of respiratory droplets, the droplet dynamics and transport pathways, and subsequently, the infection risk in public transport, are poorly understood. To better grasp the complex processes involved, a synthesis of current knowledge is required. Therefore, we conducted a review on the behaviors of respiratory droplets in public transport system, covering a wide scope from the emission profiles of expiratory droplets, the droplet dynamics and transport, to the transmission of COVID-19 in public transport. The literature was searched using related keywords in Web of Science and PubMed and screened for suitability. The droplet size is a key parameter in determining the deposition and evaporation, which together with the exhaled air velocity largely determines the horizontal travel distance. The potential transmission route and transmission rate in public transport as well as the factors influencing the virus-laden droplet behaviors and virus viability (such as ventilation system, wearing personal protective equipment, air temperature and relative humidity) were also discussed. The review also suggests that future studies should address the uncertainties in droplet emission profiles associated with the measurement techniques, and preferably build a database based on a unified testing protocol. Further investigations based on field measurements and modeling studies into the influence of different ventilation systems on the transmission rate in public transport are also needed, which would provide scientific basis for controlling the transmission of diseases.

Association of COVID-19 Case-Fatality Rate With State Health Disparity in the United States

Background

The disproportionate burden of COVID-19 pandemic has become a major concern in the United States (US), but the association between COVID-19 case-fatality rate (CFR) and factors influencing health outcomes at a state level has not been evaluated.

Methods

We calculated COVID-19 CFR for three different waves using COVID Data Tracker from the Centers for Disease Control and Prevention. America's Health Rankings assesses the factors that influence health outcomes to determine state's health rankings. The association between COVID-19 CFR and state health disparities was analyzed by linear regression.

Results

States with better rankings of Physical Environment were associated with lower CFR for the 1st wave (β = 0.06%, R2 = 0.170, P = 0.003). There was a paradoxical association between the 2nd wave CFR and Clinical Care (β = -0.04%, R2 = 0.112, P = 0.017) and Overall health rankings (β = -0.03%, R2 = 0.096, P = 0.029). For the 3rd wave, states with better rankings of Overall health factors (β = 0.01%, R2 = 0.179, P = 0.002), Social & Economic Factors (β = 0.01%, R2 = 0.176, P = 0.002), Behaviors (β = 0.01%, R2 = 0.204, P < 0.001), and Health Outcomes (β = 0.01%, R2 = 0.163, P = 0.004) were associated with lower CFR. COVID-19 vaccination coverage was also associated with state health rankings (at least one dose: β = -0.13%, R2 = 0.305, P < 0.001; fully vaccinated: β = -0.06%, R2 = 0.120, P = 0.014).

Conclusions

These findings suggested targeted public health interventions and mitigation strategies addressing health disparities are essential to improve inequitable outcomes of COVID-19 in the US.

Social distancing and mask-wearing could avoid recurrent stay-at-home restrictions during COVID-19 respiratory pandemic in New York City

Stay-at-home restrictions such as closure of non-essential businesses were effective at reducing SARS-CoV-2 transmission in New York City (NYC) in the spring of 2020. Relaxation of these restrictions was desirable for resuming economic and social activities, but could only occur in conjunction with measures to mitigate the expected resurgence of new infections, in particular social distancing and mask-wearing. We projected the impact of individuals’ adherence to social distancing and mask-wearing on the duration, frequency, and recurrence of stay-at-home restrictions in NYC. We applied a stochastic discrete time-series model to simulate community transmission and household secondary transmission in NYC. The model was calibrated to hospitalizations, ICU admissions, and COVID-attributable deaths over March–July 2020 after accounting for the distribution of age and chronic health conditions in NYC. We projected daily new infections and hospitalizations up to May 31, 2021 under the different levels of adherence to social distancing and mask-wearing after relaxation of stay-at-home restrictions. We assumed that the relaxation of stay-at-home policies would occur in the context of adaptive reopening, where a new hospitalization rate of ≥ 2 per 100,000 residents would trigger reinstatement of stay-at-home restrictions while a new hospitalization rate of ≤ 0.8 per 100,000 residents would trigger relaxation of stay-at-home restrictions. Without social distancing and mask-wearing, simulated relaxation of stay-at-home restrictions led to epidemic resurgence and necessary reinstatement of stay-at-home restrictions within 42 days. NYC would have stayed fully open for 26% of the time until May 31, 2021, alternating reinstatement and relaxation of stay-at-home restrictions in four cycles. At a low (50%) level of adherence to mask-wearing, NYC would have needed to implement stay-at-home restrictions between 8% and 32% of the time depending on individual adherence to social distancing. At moderate to high levels of adherence to mask-wearing without social distancing, NYC would have needed to implement stay-at-home restrictions. In threshold analyses, avoiding reinstatement of stay-at-home restrictions required a minimum of 60% adherence to mask-wearing at 50% adherence to social distancing. With low adherence to mask-wearing and social distancing, reinstatement of stay-at-home restrictions in NYC was inevitable. High levels of adherence to social distancing and mask-wearing could have attributed to avoiding recurrent surges without reinstatement of stay-at-home restrictions.

Divergent effects of social media use on meaning in life via loneliness and existential isolation during the coronavirus pandemic

Stay-at-home orders issued to combat the growing number of infections during the coronavirus pandemic in 2020 had many psychological consequences for people including elevated stress, anxiety, and difficulty maintaining meaning in their lives. The present studies utilized cross-sectional designs and were conducted to better understand how social media usage related to people's subjective isolation (i.e., social loneliness, emotional loneliness, and existential isolation) and meaning in life (MIL) during the early months of the pandemic within the United States. Study 1 found that general social media use indirectly predicted higher MIL via lower existential isolation and social isolation. Study 2 replicated these patterns and found that social media use also predicted lower MIL via higher emotional loneliness, and that the aforementioned effects occurred with active, but not passive, social media use. Findings suggest social media use may be a viable means to validate one's experiences (i.e., reduce existential isolation) during the pandemic but may also lead to intensified feelings concerning missing others (i.e., increased emotional loneliness). This research also helps to identify potential divergent effects of social media on MIL and helps to clarify the relationships among varying types of subjective isolation.

Neighbourhood socio-economic vulnerability and access to COVID-19 healthcare during the first two waves of the pandemic in Geneva, Switzerland: A gender perspective

BACKGROUND

Neighbourhood socio-economic inequities have been shown to affect COVID-19 incidence and mortality, as well as access to tests. This article aimed to study how associations of inequities and COVID-19 outcomes varied between the first two pandemic waves from a gender perspective.

METHODS

We performed an ecological study based on the COVID-19 database of Geneva between Feb 26, 2020, and June 1, 2021. Outcomes were the number of tests per person, the incidence of COVID-19 cases, the incidence of COVID-19 deaths, the positivity rate, and the delay between symptoms and test. Outcomes were described by neighbourhood socio-economic levels and stratified by gender and epidemic waves (first wave, second wave), adjusting for the proportion of inhabitants older than 65 years.

FINDINGS

Low neighbourhood socio-economic levels were associated with a lower number of tests per person (incidence rate ratio [IRR] of 0.88, 0.85 and 0.83 for low, moderate, and highly vulnerable neighbourhood respectively), a higher incidence of COVID-19 cases and of COVID-19 deaths (IRR 2.3 for slightly vulnerable, 1.9 for highly vulnerable). The association between socio-economic inequities and incidence of COVID-19 deaths was mainly present during the first wave of the pandemic, and was stronger amongst women. The increase in COVID-19 cases amongst vulnerable populations appeared mainly during the second wave, and originated from a lower access to tests for men, and a higher number of COVID-19 cases for women.

INTERPRETATION

The COVID-19 pandemic affected people differently depending on their socio-economic level. Because of their employment and higher prevalence of COVID-19 risk factors, people living in neighbourhoods of lower socio-economic levels, especially women, were more exposed to COVID-19 consequences.

FUNDING

This research was supported by the research project SELFISH, financed by the Swiss National Science Foundation, grant number 51NF40-160590 (LIVES centre international research project call).

Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?

The COVID-19 pandemic has induced significant transit ridership losses worldwide. This paper conducts a quantitative analysis to reveal contributing factors to such losses, using data from the Chicago Transit Authority's bus and rail systems before and after the COVID-19 outbreak. It builds a sequential statistical modeling framework that integrates a Bayesian structural time-series model, a dynamics model, and a series of linear regression models, to fit the ridership loss with pandemic evolution and regulatory events, and to quantify how the impacts of those factors depend on socio-demographic characteristics. Results reveal that, for both bus and rail, remote learning/working answers for the majority of ridership loss, and their impacts depend highly on socio-demographic characteristics. Findings from this study cast insights into future evolution of transit ridership as well as recovery campaigns in the post-pandemic era.

Investigating the association between mass transit adoption and COVID-19 infections in US metropolitan areas

Urbanization introduces the threat of increased epidemic disease transmission resulting from crowding on mass transit. The coronavirus disease 2019 (COVID-19) pandemic, which has directly led to over 600,000 deaths in the US as of July 2021, triggered mass social distancing policies to be enacted as a key deterrent of widespread infections. Social distancing can be challenging in confined spaces required for transportation such as mass transit systems. Little is published regarding the degree to which mass transit system adoption effects impacted the rise of the COVID-19 pandemic in urban centers. Taking an ecological approach where areal data are the unit of observation, this national-scale study aims to measure the association between the adoption of mass transit and COVID-19 spread through confirmed cases in US metropolitan areas. National survey-based transit adoption measures are entered in negative binomial regression models to evaluate differences between areas. The model results demonstrate that mass transit adoption in US metropolitan areas was associated with the magnitude of outbreaks. Higher incidence of COVID-19 early in the pandemic was associated with survey results conveying higher transit use. Increasing weekly bus transit usage in metropolitan statistical areas by one scaled unit was associated with a 1.38 [95% CI: (1.25, 1.90)] times increase in incidence rate of COVID-19; a one scaled unit increase in weekly train transit usage was associated with an increase in incidence rate of 1.54 [95% CI: (1.42, 2.07)] times. These conclusions should inform early action practices in urban centers with busy transit systems in the event of future infectious disease outbreaks. Deeper understanding of these observed associations may also benefit modeling efforts by allowing researchers to include mathematical adjustments or better explain caveats to results when communicating with decision makers and the public in the crucial early stages of an epidemic.

Resilience to COVID-19: Socioeconomic Disadvantage Associated With Positive Caregiver-Youth Communication and Youth Preventative Actions

Socioeconomic disadvantage is associated with larger COVID-19 disease burdens and pandemic-related economic impacts. We utilized the longitudinal Adolescent Brain Cognitive Development Study to understand how family- and neighborhood-level socioeconomic disadvantage relate to disease burden, family communication, and preventative responses to the pandemic in over 6,000 youth-caregiver dyads. Data were collected at three timepoints (May-August 2020). Here, we show that both family- and neighborhood-level disadvantage were associated with caregivers' reports of greater family COVID-19 disease burden, less perceived exposure risk, more frequent caregiver-youth conversations about COVID-19 risk/prevention and reassurance, and greater youth preventative behaviors. Families with more socioeconomic disadvantage may be adaptively incorporating more protective strategies to reduce emotional distress and likelihood of COVID-19 infection. The results highlight the importance of caregiver-youth communication and disease-preventative practices for buffering the economic and disease burdens of COVID-19, along with policies and programs that reduce these burdens for families with socioeconomic disadvantage.

Social Determinants of Disease: HIV and COVID-19 Experiences

PURPOSE OF REVIEW

The differential impact of the COVID-19 and HIV pandemics on marginalized communities has renewed calls for more robust and deeper investigation into structural and social causes of health inequities contributing to these infections, including underlying factors related to systematic racism. Using the Social Determinants of Health (SDOH) framework, we analyzed parallel and divergent factors associated with COVID-19 and HIV/AIDS and the prevalence of disparate disease in diverse communities. We utilized PRISMA guidelines to identify relevant literature (N = 210 articles) that resulted in a review of 125 articles included in our synthesis.

RECENT FINDINGS

With racial health inequities as a core contributor to disease vulnerability, we also identified other factors such as economic stability, social and community support, the neighborhood and built environment, healthcare access and quality, and education access and quality as important socioecological considerations toward achieving health equity. Our review identifies structural and systematic factors that drive HIV and COVID-19 transmission. Our review highlights the importance of not solely focusing on biomedical interventions as solutions to ending HIV and COVID-19, but rather call for building a more just public health and social service safety net that meets the needs of people at the intersection of multiple vulnerabilities.

The Unequal Effects of Social Distancing Policy on Subway Ridership during the COVID-19 Pandemic in Seoul, South Korea

In the Republic of Korea, social distancing policies relied on voluntary participation by citizens and exhibited short-term changes. In this situation, the effects of such policies varied depending on each community's capacity to comply. Here, we collected subway ridership data for 294 stations on nine Seoul Metro lines and aggregated the data for each station to the 184 smallest administrative areas. We found that the mean percent change in subway ridership was fitted by an additive model of the log-transformed percent ratio of the restaurant industry (estimated degrees of freedom (EDF) = 3.24, P < 0.001), the Deprivation Index (DI) (EDF = 3.66, P = 0.015), and the proportion of essential workers (β =  - 0.10 (95% confidence interval - 0.15 to - 0.05, P < 0.001). We found a distinct decrease in subway ridership only in the least deprived areas, suggesting that social distancing is costly.

Critical Role of the Subways in the Initial Spread of SARS-CoV-2 in New York City

We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1–2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1–8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1–8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.

Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States

Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19.

A Medical Student-Run Telehealth Primary Care Clinic During the COVID-19 Pandemic: Maintaining Care for the Underserved

BACKGROUND

In this report, we outline our approach to implementing a hybrid in-person and virtual clinic model at a student-run free clinic (SRFC) during the COVID-19 pandemic. Individuals of low socioeconomic status (SES) are at an increased risk for COVID-19 infection and severe clinical outcomes. It is unclear if telehealth is a viable continuity of care enabler for the underserved.

METHODS

The Weill Cornell Community Clinic (WCCC) implemented a novel telehealth clinic model to serve uninsured patients in May 2020. A phone survey of was conducted to assess WCCC patients access to technology needed for telehealth visits (eg, personal computers, smartphones). Patient no-show rates were retrospectively assessed for both in-person (pre-pandemic) and hybrid continuity of care models.

RESULTS

The phone survey found that 90% of WCCC patients had access to technology needed for telehealth visits. In the 8 months following implementation of the hybrid model, telehealth and in-person no-show rates were 11% (14/128) and 15% (10/67) respectively; the combined hybrid no-show rate was 12% (24/195). For comparison, the in-person 2019 no-show rate was 23% (84/367). This study aligns with previous reports that telehealth improves patient attendance.

CONCLUSION

Literature on the transition of SRFCs from in-person to telehealth care delivery models is limited. At the WCCC, the reduction in no-show rates supports the feasibility and benefits of adopting telehealth for the delivery of care to underserved patient populations. We believe the hybrid telehealth model described here is a viable model for other student run free clinics to increase access to care in low SES communities.

Changing Disparities in Coronavirus Disease 2019 (COVID-19) Burden in the Ethnically Homogeneous Population of Hong Kong Through Pandemic Waves: An Observational Study

BACKGROUND

Disparities were marked in previous pandemics, usually with higher attack rates reported for those in lower socioeconomic positions and for ethnic minorities.

METHODS

We examined characteristics of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases in Hong Kong, assessed associations between incidence and population-level characteristics at the level of small geographic areas, and evaluated relations between socioeconomics and work-from-home (WFH) arrangements.

RESULTS

The largest source of COVID-19 importations switched from students studying overseas in the second wave to foreign domestic helpers in the third. The local cases were mostly individuals not in formal employment (retirees and homemakers) and production workers who were unable to WFH. For every 10% increase in the proportion of population employed as executives or professionals in a given geographic region, there was an 84% (95% confidence interval [CI], 1-97%) reduction in the incidence of COVID-19 during the third wave. In contrast, in the first 2 waves, the same was associated with 3.69 times (95% CI, 1.02-13.33) higher incidence. Executives and professionals were more likely to implement WFH and experienced frequent changes in WFH practice compared with production workers.

CONCLUSIONS

Consistent findings on the reversed socioeconomic patterning of COVID-19 burden between infection waves in Hong Kong in both individual- and population-level analyses indicated that risks of infections may be related to occupations involving high exposure frequency and WFH flexibility. Contextual determinants should be taken into account in policy planning aiming at mitigating such disparities.

Socioeconomic Disparities in Severe Acute Respiratory Syndrome Coronavirus 2 Serological Testing and Positivity in New York City

Background

We characterized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody test prevalence and positive test prevalence across New York City (NYC) in order to investigate disparities in testing outcomes by race and socioeconomic status (SES).

Methods

Serologic data were downloaded from the NYC Coronavirus data repository (August 2020–December 2020). Area-level characteristics for NYC neighborhoods were downloaded from United States census data and a socioeconomic vulnerability index was created. Spatial generalized linear mixed models were performed to examine the association between SES and antibody testing and positivity.

Results

The proportion of Hispanic population (posterior median, 0.001 [95% credible interval, 0.0003–0.002]), healthcare workers (0.003 [0.0001–0.006]), essential workers (0.003 [0.001–0.005]), age ≥65 years (0.003 [0.00002–0.006]), and high SES (SES quartile 3 vs 1: 0.034 [0.003–0.062]) were positively associated with antibody tests per 100000 residents. The White proportion (–0.002 [–0.003 to –0.001]), SES index (quartile 3 vs 1, –0.068 [–0.115 to –0.017]; quartile 4 vs 1, –0.077 [–0.134 to –0.018]) and age ≥65 years (–0.005 [–0.009 to –0.002]) were inversely associated with positive test prevalence (%), whereas the Hispanic (0.004 [0.002–0.006]) and essential worker (0.008 [0.003–0.012]) proportions had positive coefficients.

Conclusions

Disparities in serologic testing and seropositivity exist on SES and race/ethnicity across NYC, indicative of excess coronavirus disease burden in vulnerable and marginalized populations.

SARS-CoV-2 Seroprevalence in Germany

BACKGROUND

Until now, information on the spread of SARS-CoV-2 infections in Germany has been based mainly on data from the public health offices. It may be assumed that these data do not include many cases of asymptomatic and mild infection.

METHODS

We determined seroprevalence over the course of the pandemic in a sequential, multilocal seroprevalence study (MuSPAD). Study participants were recruited at random in seven administrative districts (Kreise) in Germany from July 2020 onward; each participant was tested at two different times 3-5 months apart. Test findings on blood samples were used to determine the missed-case rate of reported infections, the infection fatality rate (IFR), and the association between seropositivity and demographic, socio-economic, and health-related factors, as well as to evaluate the self-reported results of PCR and antigenic tests. The registration number of this study is DRKS00022335.

RESULTS

Among non-vaccinated persons, the seroprevalence from July to December 2020 was 1.3-2.8% and rose between February and May 2021 to 4.1-13.1%. In July 2021, 35% of tested persons in Chemnitz were not vaccinated, and the seroprevalence among these persons was 32.4% (07/2021). The surveillance detection ratio (SDR), i.e., the ratio between the true number of infections estimated from seroprevalence and the actual number or reported infections, varied among the districts included in the study from 2.2 to 5.1 up to December 2020 and from 1.3 to 2.9 up to June 2021, and subsequently declined. The IFR was in the range of 0.8% to 2.4% in all regions except Magdeburg, where a value of 0.3% was calculated for November 2020. A lower educational level was associated with a higher seropositivity rate, smoking with a lower seropositivity rate. On average, 1 person was infected for every 8.5 persons in quarantine.

CONCLUSION

Seroprevalence was low after the first wave of the pandemic but rose markedly during the second and third waves. The missed-case rate trended downward over the course of the pandemic.

The impacts of the COVID-19 pandemic on transportation employment: A comparative analysis

The COVID-19 pandemic caused a variety of social, economic, and environmental changes. This paper examines the employment-related impacts of the pandemic on workers in the transportation industry compared to other industries, and within different transportation sectors. We estimated random effects logistic regression models to test the following three hypotheses using the monthly Current Population Survey micro-data. One, the transportation industry experienced a greater incidence of unemployment than other industries. Two, there is heterogeneity in employment impacts within the transportation sector. Three, specific sectors within the transportation industry experienced more employment impacts than other essential industries, as designated by the Centers for Disease Control and Prevention (CDC) Phase 1a vaccination guidelines. Model results highlight that workers in the transportation sector were 20.6% more likely to be unemployed because of the pandemic than workers in non-transportation industries. Model results also indicate large intra-sector heterogeneities in employment impacts within the transportation sector. Taxi and limousine drivers were 28 times more likely to be unemployed compared to essential workers. Scenic and sightseeing transportation workers were 23.8 times more likely to be unemployed compared to essential workers. On the other end of the spectrum, however, postal workers and pipeline workers were 84% and 67% less likely to be unemployed compared to essential workers, respectively. From a policy perspective, these results suggest that attention to several aspects of transportation work is needed in the coming years to prepare for future interruptions to the transportation industry.

Socio-Demographic Health Determinants Are Associated with Poor Prognosis in Spanish Patients Hospitalized with COVID-19

INTRODUCTION

Social vulnerability is a known determinant of health in respiratory diseases. Our aim was to identify whether there are socio-demographic factors among COVID-19 patients hospitalized in Spain and their potential impact on health outcomes during the hospitalization.

METHODS

A multicentric retrospective case series study based on administrative databases that included all COVID-19 cases admitted in 19 Spanish hospitals from 1 March to 15 April 2020. Socio-demographic data were collected. Outcomes were critical care admission and in-hospital mortality.

RESULTS

We included 10,110 COVID-19 patients admitted to 18 Spanish hospitals (median age 68 (IQR 54-80) years old; 44.5% female; 14.8% were not born in Spain). Among these, 779 (7.7%) cases were admitted to critical care units and 1678 (16.6%) patients died during the hospitalization. Age, male gender, being immigrant, and low hospital saturation were independently associated with being admitted to an intensive care unit. Age, male gender, being immigrant, percentile of average per capita income, and hospital experience were independently associated with in-hospital mortality.

CONCLUSIONS

Social determinants such as residence in low-income areas and being born in Latin American countries were associated with increased odds of being admitted to an intensive care unit and of in-hospital mortality. There was considerable variation in outcomes between different Spanish centers.

Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs' visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents' responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

Health Disparities of Coronavirus Disease 2019 in Texas, March-July 2020

OBJECTIVES

Although disparities in coronavirus disease 2019 (COVID-19) prevalence are known, knowledge of the recent surge of COVID-19 in Texas and factors affecting fatality rates is limited. Understanding the health disparities associated with COVID-19 can help healthcare professionals determine the populations that are most in need of COVID-19 preventive care and treatment. The aim of this study was to assess COVID-19-related case and mortality rates.

METHODS

Our cross-sectional analysis used Texas Department of State Health Services COVID-19 case surveillance counts. Case, hospitalization, and mortality counts were obtained from March to July 2020.

RESULTS

From March to July 2020, there were 420,397 COVID-19-related cases and 6954 deaths in Texas. There were 3277 new cases and 104 deaths in March, and 261,876 new cases and 3660 deaths in July. The number of new COVID-19 cases was the highest from March to April (relative risk 1.77, 95% confidence interval [CI] 1.76-1.78). Although the death rate in June was a 30% increase over the rate in May, death rates nearly tripled by the end of July, for a total of 3660 deaths. Of the 3958 deaths, demographic data were available for 753 deaths. Of these, 440 were male, 16 Asian, 95 Black, 221 Hispanic, 325 White, and 96 were "Other" or "Unknown." Males were associated with a slightly higher chance of acquiring COVID-19 than females (odds ratio [OR] 1.11, 95% CI 1.09-1.14) and nearly a 29% higher chance of dying of COVID-19 compared with females (OR 1.29, 95% CI 1.11-1.49). Bivariate analysis revealed that the probability of acquiring COVID-19 was 12% higher in older adults compared with individuals younger than 65 years old (OR 1.12, 95% CI 1.08-1.16), and older adults had an 18.8 times higher risk of death when compared with the rate of younger individuals (OR 18.79, 95% CI 15.93-22.15). Hispanics and Blacks were 70% and 48%, respectively, more likely to contract COVID-19 than Whites. All races had lower significant chance of death when compared with Whites. At the end of July, there was a total of 430,485 Texas COVID-19 cases and 6387 fatalities (8.8% of all cases and 4% of all deaths in the United States.). Case fatality ratios were the highest in older adults. As we continued to observe data, in contrast to previous study time points, we found that Asians and Hispanics had no significant difference in COVID mortality rates and were comparable in terms of mortality odds and death case ratios when compared with Whites.

CONCLUSIONS

This time period represents the highest COVID-19 surge time in Texas. Although our data consist of a short time period of population-level data in an ongoing pandemic and are limited by information reported to the Texas Department of State Health Services, older age, male sex, Hispanics, and Blacks are currently associated with higher infection rates, whereas older age, male sex, and Whites are associated with higher mortality rates. Clinicians and decision makers should be aware of the COVID-19 health disparities and risk factors for mortality to better promote targeted interventions and allocate resources accordingly.

Social inequalities in protective behaviour uptake at the start of the COVID-19 pandemic: results from a national survey

OBJECTIVES

We aimed to assess social patterns of handwashing, social distancing, and working from home at the start of the COVID-19 pandemic in Canada, and determine what proportions of the overall prevalence and social inequalities in handwashing and social distancing are related to inequalities in the opportunity to work from home, to guide pandemic preparedness and response.

METHODS

Using cross-sectional data from the Canadian Perspectives Survey Series, collected between March 29 and April 3, 2020, among Canadian adults (N=4455), we assessed prevalence of not working from home, social distancing in public, or practicing frequent handwashing, according to age, sex, marital status, immigration, education, chronic disease presence, and source of COVID-19 information. Multivariate regression, population attributable fraction estimation, and generalized product mediation analysis were applied.

RESULTS

Absence of frequent handwashing and distancing was more common among those working outside than within the home (prevalence differences of 7% (95% CI: 4, 10) and 7% (95% CI: 3, 10), respectively). Inequalities in handwashing and distancing were observed across education and immigration status. Over 40% of the prevalence of non-uptake of handwashing and distancing was attributable to populations not being able to work from home. If all worked from home, over 40% (95% CI: 8, 70) of education-based inequalities in handwashing and distancing could be eliminated, but differences by immigration status would likely remain.

CONCLUSION

For pandemic response, both workplace safety initiatives and mechanisms to address the inequitable distribution of health risks across socio-economic groups are needed to reduce broader inequalities in transmission risk.

Sociodemographic predictors and transportation patterns of COVID-19 infection and mortality

BACKGROUND

The United States Centers for Disease Control and Prevention (CDC)-sanctioned prevention strategies have included frequent handwashing with soap and water, covering the mouth and nose with a mask when around others, cleaning and disinfecting maintaining a distance of at least 6 feet from others, etc. Although many of these recommendations are based upon observation and past infection control practices, it is important to combine and explore public data sets to identify predictors of infection, morbidity and mortality to develop more finely honed interventions, based on sociodemographic factors.

METHOD

Cross-sectional study of both states in the US and counties in NY state.

RESULTS

Population density was found to be significantly associated with state-level coronavirus infection and mortality rate (b = 0.49, 95% confidence interval (CI): 0.34, 0.64, P < .0001). States that have lower socioeconomic status, lower mean age and denser populations are associated with higher incidence rates. In regard to NY state, counties with a higher percentage of minority residents had higher COVID-19 mortality rates (b = 2.61, 95% CI: 0.36, 4.87, P = 0.023). Larger population cohorts were associated with lower COVID-19 mortality rates after adjusting for other variables in the model (b = -1.39, 95% CI: -2.07, -0.71, P < 0.001). Population density was not significantly associated with COVID-19 mortality rates after adjustment across counties in the NY state. Public ridership was not indicative of cases or mortality across states in the USA; however, it is a significant factor associated with incidence (but not mortality) in NY counties.

CONCLUSION

Population density was the only significant predictor of mortality across states in the USA. Lower mean age, lower median household incomes and more densely populated states were at higher risk of COVID-19 infection. Population density was not found to be a significant independent variable compared to minority status and socioeconomic factors in the New York epicenter. Meanwhile, public ridership was found to be a significant factor associated with incidence in New York counties.

[Social inequalities in the regional spread of SARS-CoV-2 infections]

Hintergrund und Ziel

Ob sozioökonomische Faktoren die Ausbreitung von SARS-CoV‑2 beeinflussen, ist nicht ausreichend beantwortet, da frühere Studien in der Regel kumulative Inzidenzen betrachtet und die zeitliche Entwicklung der Ausbreitung außer Acht gelassen haben. Dieser Beitrag konzentriert sich daher auf die Entwicklung von regionalen Neuinfektionen in Zusammenhang mit sozioökonomischen Faktoren. Ausgehend vom internationalen Forschungsstand präsentieren wir eigene Analysen von Meldedaten aus Deutschland.

Methoden

Diese Studie untersucht regionale Daten gemeldeter COVID-19-Fälle für die 401 Landkreise und kreisfreien Städte (Kreisebene) in Deutschland und vergleicht den zeitlichen Verlauf entlang sozioökonomischer Merkmale der Kreise. Betrachtet werden altersstandardisierte wöchentliche Inzidenzen für den Zeitraum 03.02.2020–28.03.2021. Sozial- und Wirtschaftsindikatoren auf Kreisebene stammen aus der INKAR(Indikatoren und Karten zur Raum- und Stadtentwicklung)-Datenbank (z. B. Einkommen, Beschäftigtenquote, Wohnfläche).

Ergebnisse

Während in der ersten und zu Beginn der zweiten Welle der Pandemie Kreise mit höherem mittleren Haushaltseinkommen höhere Inzidenzen hatten, stiegen sie in Kreisen mit niedrigem Einkommen ab Dezember 2020 deutlich an. Kreise mit einem hohen Anteil an Beschäftigten allgemein und speziell solchen im Produktionssektor hatten gerade in der zweiten und dritten Welle hohe Inzidenzen. Kreise mit einer geringen Wohnfläche je Einwohner hatten ab November 2020 ausgeprägt höhere Inzidenzen.

Schlussfolgerung

Der regionale Verlauf der Pandemie unterscheidet sich nach Sozial- und Wirtschaftsindikatoren. Eine differenzierte Betrachtung dieser Unterschiede könnte Hinweise auf zielgruppenspezifische Schutz- und Teststrategien geben und helfen, soziale Faktoren zu identifizieren, die Infektionen begünstigen.

Zusatzmaterial online

Zusätzliche Informationen sind in der Online-Version dieses Artikels (10.1007/s00103-021-03387-w) enthalten.

Evaluating Effectiveness of Public Health Intervention Strategies for Mitigating COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-white population are at greater risk of increased $R_t$ associated with reopening bars.

Association of Socioeconomic Characteristics With Disparities in COVID-19 Outcomes in Japan

IMPORTANCE

Socioeconomic factors in the disparities in COVID-19 outcomes have been reported in studies from the US and other Western countries. However, no studies have documented national- or subnational-level outcome disparities in Asian countries.

OBJECTIVE

To assess the association between regional COVID-19 outcome disparities and socioeconomic characteristics in Japan.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study collected and analyzed confirmed COVID-19 cases and deaths (through February 13, 2021) as well as population and socioeconomic data in all 47 prefectures in Japan. The data sources were government surveys for which prefecture-level data were available.

EXPOSURES

Prefectural socioeconomic characteristics included mean annual household income, Gini coefficient, proportion of the population receiving public assistance, educational attainment, unemployment rate, employment in industries with frequent close contacts with the public, household crowding, smoking rate, and obesity rate.

MAIN OUTCOMES AND MEASURES

Rate ratios (RRs) of COVID-19 incidence and mortality by prefecture-level socioeconomic characteristics.

RESULTS

All 47 prefectures in Japan (with a total population of 126.2 million) were included in this analysis. A total of 412 126 confirmed COVID-19 cases (326.7 per 100 000 people) and 6910 deaths (5.5 per 100 000 people) were reported as of February 13, 2021. Elevated adjusted incidence and mortality RRs of COVID-19 were observed in prefectures with the lowest household income (incidence RR: 1.45 [95% CI, 1.43-1.48] and mortality RR: 1.81 [95% CI, 1.59-2.07]); highest proportion of the population receiving public assistance (1.55 [95% CI, 1.52-1.58] and 1.51 [95% CI, 1.35-1.69]); highest unemployment rate (1.56 [95% CI, 1.53-1.59] and 1.85 [95% CI, 1.65-2.09]); highest percentage of workers in retail industry (1.36 [95% CI, 1.34-1.38] and 1.45 [95% CI, 1.31-1.61]), transportation and postal industries (1.61 [95% CI, 1.57-1.64] and 2.55 [95% CI, 2.21-2.94]), and restaurant industry (2.61 [95% CI, 2.54-2.68] and 4.17 [95% CI, 3.48-5.03]); most household crowding (1.35 [95% CI, 1.31-1.38] and 1.04 [95% CI, 0.87-1.24]); highest smoking rate (1.63 [95% CI, 1.60-1.66] and 1.54 [95% CI, 1.33-1.78]); and highest obesity rate (0.93 [95% CI, 0.91-0.95] and 1.17 [95% CI, 1.01-1.34]) compared with prefectures with the most social advantages. Among potential mediating variables, higher smoking rate (RR, 1.54; 95% CI, 1.33-1.78) and obesity rate (RR, 1.17; 95% CI, 1.01-1.34) were associated with higher mortality RRs, even after adjusting for prefecture-level covariates and other socioeconomic variables.

CONCLUSIONS AND RELEVANCE

This cross-sectional study found a pattern of socioeconomic disparities in COVID-19 outcomes in Japan that was similar to that observed in the US and Europe. National policy in Japan could consider prioritizing populations in socially disadvantaged regions in the COVID-19 response, such as vaccination planning, to address this pattern.

Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.

Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States

BACKGROUND

The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents' mobility across neighborhoods of different levels of socioeconomic disadvantage.

METHODS

This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.

RESULTS

On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.

CONCLUSIONS

Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

Social inequalities in mobility during and following the COVID-19 associated lockdown of the Madrid metropolitan area in Spain

Spain has been one of the most affected regions by the COVID-19 worldwide, and Madrid its most affected city. In response to this, the Spanish government enacted a strict lockdown in late March 2020, that was gradually eased until June 2020. We explored differentials in mobility by area-level deprivation in the functional area of Madrid, before, during, and after the COVID-19 lockdown. We used cell phone-derived mobility indicators (% of the population leaving their area) from the National Institute of Statistics (INE), and a composite measure of deprivation from the Spanish Society of Epidemiology (SEE). We computed changes in mobility with respect to pre-pandemic levels, and explored spatial patterns and associations with deprivation. We found that levels of mobility before COVID-19 were slightly higher in areas with lower deprivation. The economic hibernation period resulted in very strong declines in mobility, most acutely in low deprivation areas. These differences weakened during the re-opening, and levels of mobility were similar by deprivation once the lockdown was completely lifted. Given the existence of important socioeconomic differentials in COVID-19 exposure, it is key to ensure that these interventions do not widen existing social inequalities.

Neighborhood characteristics associated with COVID-19 burden-the modifying effect of age

BACKGROUND

Neighborhood characteristics have been linked to community incidence of COVID-19, but the modifying effect of age has not been examined.

OBJECTIVE

We adapted a neighborhood-wide analysis study (NWAS) design to systematically examine associations between neighborhood characteristics and COVID-19 incidence among different age groups.

METHODS

The number of daily cumulative cases of COVID-19 by zip code area in Illinois has been made publicly available by the Illinois Department of Public Health. The number of COVID-19 cases was reported for eight age groups (under 20, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+). We reviewed this data published from May 23 through June 17, 2020 with complete data for all eight age groups and linked the data to neighborhood characteristics measured by the American Community Survey (ACS). Geographic age-specific cumulative incidence (cases per 1000 people) of COVID-19 was calculated by dividing the number of daily cumulative cases by the population of the same age group at each zip code area. The association between individual characteristics and COVID-19 incidence was examined using Poisson regression models.

RESULTS

At the zip code level, neighborhood socioeconomic status was a more important risk factor of COVID-19 incidence in children and working-age adults than in seniors. Social demographics and housing conditions were important risk factors of COVID-19 incidence in older age groups. We additionally observed significant associations between transportation-related variables and COVID-19 incidences in multiple age groups.

SIGNIFICANCE

We concluded that age modified the association between neighborhood characteristics and COVID-19 incidence.

Socioeconomic and Racial and/or Ethnic Disparities in Multisystem Inflammatory Syndrome

OBJECTIVES

To characterize the socioeconomic and racial and/or ethnic disparities impacting the diagnosis and outcomes of multisystem inflammatory syndrome in children (MIS-C).

METHODS

This multicenter retrospective case-control study was conducted at 3 academic centers from January 1 to September 1, 2020. Children with MIS-C were compared with 5 control groups: children with coronavirus disease 2019, children evaluated for MIS-C who did not meet case patient criteria, children hospitalized with febrile illness, children with Kawasaki disease, and children in Massachusetts based on US census data. Neighborhood socioeconomic status (SES) and social vulnerability index (SVI) were measured via a census-based scoring system. Multivariable logistic regression was used to examine associations between SES, SVI, race and ethnicity, and MIS-C diagnosis and clinical severity as outcomes.

RESULTS

Among 43 patients with MIS-C, 19 (44%) were Hispanic, 11 (26%) were Black, and 12 (28%) were white; 22 (51%) were in the lowest quartile SES, and 23 (53%) were in the highest quartile SVI. SES and SVI were similar between patients with MIS-C and coronavirus disease 2019. In multivariable analysis, lowest SES quartile (odds ratio 2.2 [95% confidence interval 1.1-4.4]), highest SVI quartile (odds ratio 2.8 [95% confidence interval 1.5-5.1]), and racial and/or ethnic minority background were associated with MIS-C diagnosis. Neither SES, SVI, race, nor ethnicity were associated with disease severity.

CONCLUSIONS

Lower SES or higher SVI, Hispanic ethnicity, and Black race independently increased risk for MIS-C. Additional studies are required to target interventions to improve health equity for children.