Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults

Psychosocial and health stressors during the COVID-19 pandemic and their association with sleep quality

OBJECTIVE

We investigated how psychosocial and health stressors and related cognitive-affective factors were differentially associated with sleep quality during the early months of the COVID-19 pandemic.

METHODS AND MEASURES

Adults living in Florida (n = 2,152) completed a Qualtrics survey in April-May 2020 (Wave 1). Participants (n = 831) were reassessed one month later (Wave 2; May-June 2020). At Wave 1, participants reported their level of physical contact with someone they care about, presence of a pre-existing chronic disease, employment status, loneliness, health worry, and financial distress. At Wave 2, participants rated their quality of sleep and insomnia symptoms.

RESULTS

Loneliness, but not health worry or financial distress, directly predicted worse sleep quality. Lack of physical contact was indirectly associated with worse sleep quality via greater levels of loneliness. Further, results showed the presence of a pre-existing chronic disease was associated with both greater health worry and worse sleep quality.

CONCLUSION

Loneliness was the sole cognitive-affective predictor of worse sleep quality when controlling for other psychosocial factors. As expected, adults living with a chronic disease reported impaired sleep quality. Understanding the processes influencing sleep quality during a significant time of stress is important for identifying risk factors, informing treatment, and improving sleep health beyond the pandemic.

Association between Cardiovascular Risk and Coronavirus Disease 2019: Findings from 2021 National Health Interview Survey

PURPOSE

To examine the association between pre-existing cardiovascular disorders and the risk of coronavirus disease 2019 (COVID-19) among community-dwelling adults in the United States (US).

METHODS

We analyzed data from the 2021 National Health Interview Survey (NHIS), encompassing 28,848 nationally representative participants aged ≥18. We examined the association by two age groups, younger adults (aged 18-59) and older adults (aged>60). Weighted analyses were conducted to consider the complex sampling design used in the NHIS.

RESULTS

Our results show that 13.9% of younger and 8.2% of older adults were infected with coronavirus, corresponding to a nationwide estimate of 23,701,358 COVID-19 cases in younger adults and 6,310,206 in older adults in 2021. Subjects who lived in the South region of the US had the highest COVID-19 rate (13.4%), followed by the Midwest (12.6%), West (10.9%), and Northeast (10.4%). Pre-existing cardiovascular risk factors (overweight, obesity, hypertension, and diabetes) were significantly associated with increased risk for COVID-19 infection in younger and older adults. Pre-existing cardiovascular diseases (angina, heart attack, and coronary heart disease) were significantly associated with COVID-19 in older adults but not significantly in younger adults. Significant dose-response relationships existed between the increased number of pre-existing cardiovascular risk factors and COVID-19 infection, with the strongest association in non-Hispanic (NH) Black and Hispanic ethnicities compared to NH White.

CONCLUSION

Pre-existing cardiovascular disorders are significantly associated with the risk of COVID-19 infection. The magnitudes of this risk association are stronger among the minority populations than NH White. Further studies are needed to determine the long-term impact of COVID-19 infection and its relationship to pre-existing cardiovascular disorders.

Intergenerational coresidence and the Covid-19 pandemic in the United States

This paper investigates the relation between intergenerational coresidence and mortality from Covid-19 in 2020. Using a cross-section of U.S. counties, we show that this association is positive, sizeable, significant, and robust to the inclusion of several demographic and socio-economic controls. Furthermore, using evidence from past, pre-pandemic years, we argue that this positive, sizeable and significant association is somewhat specific to the Covid-19 pandemic.

Close encounters during a pandemic: Social habits and inter-generational links in the first two waves of COVID-19

Social habits are ingrained in a community and affect human behaviour. Have they played any role in the spread of the pandemic? We use high-frequency data for 220 regions in 15 European countries from March to December 2020 to compare the association between social contacts outside the family and within inter-generational families, on the one hand, and cases and excess mortality on the other. We find that a standard deviation increase in the percentage of people having daily face-to-face contacts outside the household is associated with 5 new daily cases and 2.6 additional weekly deaths, while the incidence of inter-generational households exhibits a less robust association with both COVID-19 transmission and mortality. We compare results across the first and the second wave of pandemic and show that differences are related to the average age of the most affected groups. Our findings are robust to the inclusion of a number of controls, fixed effects, the chosen sample of countries, and the estimation method. We argue that type and frequency of social interactions are interweaved with a region culture and habits and are informative on the potential transmission of contagion and on its lethality.

Impact of vaccine hesitancy on secondary COVID-19 outbreaks in the US: an age-structured SIR model

Background

The COVID-19 outbreak has become the worst pandemic in at least a century. To fight this disease, a global effort led to the development of several vaccines at an unprecedented rate. There have been, however, several logistic issues with its deployment, from their production and transport, to the hesitancy of the population to be vaccinated. For different reasons, an important amount of individuals is reluctant to get the vaccine, something that hinders our ability to control and—eventually—eradicate the disease.

Materials and methods

Our aim is to explore the impact of vaccine hesitancy when highly transmissible SARS-CoV-2 variants of concern spread through a partially vaccinated population. To do so, we use age-stratified data from surveys on vaccination acceptance, together with age-contact matrices to inform an age-structured SIR model set in the US.

Results

Our results show that per every one percent decrease in vaccine hesitancy up to 45 deaths per million inhabitants could be averted. A closer inspection of the stratified infection rates also reveals the important role played by the youngest groups. The model captures the general trends of the Delta wave spreading in the US (July-October 2021) with a correlation coefficient of $$\rho =0.79$$ ρ = 0.79 .

Conclusions

Our results shed light on the role that hesitancy plays on COVID-19 mortality and highlight the importance of increasing vaccine uptake in the population, specially among the eldest age groups.

Timely epidemic monitoring in the presence of reporting delays: anticipating the COVID-19 surge in New York City, September 2020

Background

During a fast-moving epidemic, timely monitoring of case counts and other key indicators of disease spread is critical to an effective public policy response.

Methods

We describe a nonparametric statistical method, originally applied to the reporting of AIDS cases in the 1980s, to estimate the distribution of reporting delays of confirmed COVID-19 cases in New York City during the late summer and early fall of 2020.

Results

During August 15–September 26, the estimated mean delay in reporting was 3.3 days, with 87% of cases reported by 5 days from diagnosis. Relying upon the estimated reporting-delay distribution, we projected COVID-19 incidence during the most recent 3 weeks as if each case had instead been reported on the same day that the underlying diagnostic test had been performed. Applying our delay-corrected estimates to case counts reported as of September 26, we projected a surge in new diagnoses that had already occurred but had yet to be reported. Our projections were consistent with counts of confirmed cases subsequently reported by November 7.

Conclusion

The projected estimate of recently diagnosed cases could have had an impact on timely policy decisions to tighten social distancing measures. While the recent advent of widespread rapid antigen testing has changed the diagnostic testing landscape considerably, delays in public reporting of SARS-CoV-2 case counts remain an important barrier to effective public health policy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13286-7.

Geospatial Analysis of a COVID-19 Outbreak at the University of Wisconsin - Madison: Potential Role of a Cluster of Local Bars

We combined smartphone mobility data with census track-based reports of positive case counts to study a coronavirus disease 2019 (COVID-19) outbreak at the University of Wisconsin–Madison campus, where nearly 3000 students had become infected by the end of September 2020. We identified a cluster of twenty bars located at the epicentre of the outbreak, in close proximity to campus residence halls. Smartphones originating from the two hardest-hit residence halls (Sellery-Witte), where about one in five students were infected, were 2.95 times more likely to visit the 20-bar cluster than smartphones originating in two more distant, less affected residence halls (Ogg-Smith). By contrast, smartphones from Sellery-Witte were only 1.55 times more likely than those from Ogg-Smith to visit a group of 68 restaurants in the same area [rate ratio 1.91, 95% confidence interval (CI) 1.29–2.85, P < 0.001]. We also determined the per-capita rates of visitation to the 20-bar cluster and to the 68-restaurant comparison group by smartphones originating in each of 21 census tracts in the university area. In a multivariate instrumental variables regression, the visitation rate to the bar cluster was a significant determinant of the per-capita incidence of positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests in each census tract (elasticity 0.88, 95% CI 0.08–1.68, P = 0.032), while the restaurant visitation rate showed no such relationship. The potential super-spreader effects of clusters or networks of places, rather than individual sites, require further attention.

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.

Rapid Review of Social Contact Patterns During the COVID-19 Pandemic

BACKGROUND

Physical distancing measures aim to reduce person-to-person contact, a key driver of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. In response to unprecedented restrictions on human contact during the coronavirus disease 2019 (COVID-19) pandemic, studies measured social contact patterns under the implementation of physical distancing measures. This rapid review synthesizes empirical data on the changing social contact patterns during the COVID-19 pandemic.

METHOD

We conducted a systematic review using PubMed, Medline, Embase, and Google Scholar following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We descriptively compared the distribution of contacts observed during the pandemic to pre-COVID data across countries to explore changes in contact patterns during physical distancing measures.

RESULTS

We identified 12 studies reporting social contact patterns during the COVID-19 pandemic. Eight studies were conducted in European countries and eleven collected data during the initial mitigation period in the spring of 2020 marked by government-declared lockdowns. Some studies collected additional data after relaxation of initial mitigation. Most study settings reported a mean of between 2 and 5 contacts per person per day, a substantial reduction compared to pre-COVID rates, which ranged from 7 to 26 contacts per day. This reduction was pronounced for contacts outside of the home. Consequently, levels of assortative mixing by age substantially declined. After relaxation of initial mitigation, mean contact rates increased but did not return to pre-COVID levels. Increases in contacts post-relaxation were driven by working-age adults.

CONCLUSION

Information on changes in contact patterns during physical distancing measures can guide more realistic representations of contact patterns in mathematical models for SARS-CoV-2 transmission.

Unequal consequences of Covid 19: representative evidence from six countries

Covid-19 and the measures taken to contain it have led to unprecedented constraints on work and leisure activities, across the world. This paper uses nationally representative surveys to document how people of different ages and incomes have been affected in the early phase of the pandemic. The data was collected in six countries (China, South Korea, Japan, Italy, UK, and US) in the third week of April 2020. First, we document changes in job circumstances and social activities. Second, we document self-reported negative and positive consequences of the crisis on well-being. We find that young people have experienced more drastic changes to their life and have been most affected economically and psychologically. There is less of a systematic pattern across income groups. While lower income groups have been more affected economically, higher income groups have experienced more changes in their social life and spending. A large fraction of people of low and high income groups report negative effects on well-being.

Los Angeles County SARS-CoV-2 Epidemic: Critical Role of Multi-generational Intra-household Transmission

We observed wide variation in the incidence of confirmed COVID-19 cases in 300 communities making up Los Angeles County, the largest county by population in the United States. The surge in incidence from October 19, 2020 to January 10, 2021, accounting for two-thirds of all confirmed cases since the start of the epidemic, was concentrated in communities with a high prevalence of multi-generational households. This indicator of household structure was a more important predictor of the surge in incidence than the prevalence of households with low income or with at least one high-risk worker. Based upon a spatial adaptation of the standard SIR model, the cumulative incidence of COVID-19, adjusted for underascertainment of both asymptomatic and symptomatic cases, ranged from under 10% in low multi-generational communities to over 30% in high multi-generational communities.

Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

Are COVID fatalities in the US higher than in the EU, and if so, why?

The COVID crisis has severely hit both the United States and Europe. We construct comparable measures of the death toll of the COVID crisis suffered by US states and 35 European countries: cumulative fatalities attributed to COVID at 100 days since the pandemic's onset in a particular nation/state. When taking account of demographic, economic, and political factors (but not health-policy related factors) we find that, controlling for population size, cumulative deaths are between 100 and 130% higher in a US state than in a European country. We no longer find a US/EUROPE gap in fatalities from COVID after taking account of how each nation/state implemented social distance measures. This suggests that various types of social distance measures such as school closings and lockdowns, and how soon they were implemented, help explain the US/EUROPE gap in cumulative deaths measured 100 days after the pandemic's onset in a state or country.

The many ways COVID-19 affects households: consumption, time, and health outcomes

Many of the direct and indirect effects of the COVID-19 pandemic are mediated mainly through the economics of the household. The Review of Economics of the Household has published 17 papers on interactions between COVID-19, ensuing policy responses, and the economics of the household. Five were published in the December 2020 issue, six in the March issue, and six in this June 2021 issue. Here I give an overview of 10 of these articles. The general conclusion is that consumption, time allocations, and health outcomes have all been significantly affected by the pandemic and ensuing policy responses, but the responses have been rather heterogenous across regions, individuals, and outcomes.

Clinical characteristics and risk factors for severe COVID-19 infections in Malaysia: A nationwide observational study

BACKGROUND

COVID-19 emerged as a major public health outbreak in late 2019. Malaysia reported its first imported case on 25th January 2020, and adopted a policy of extensive contact tracing and hospitalising of all cases. We describe the clinical characteristics of COVID-19 cases nationwide and determine the risk factors associated with disease severity.

METHOD

Clinical records of all RT-PCR confirmed COVID-19 cases aged ≥12 years admitted to 18 designated hospitals in Malaysia between 1st February and 30th May 2020 with complete outcomes were retrieved. Epidemiological history, co-morbidities, clinical features, investigations, management and complications were captured using REDCap database. Variables were compared between mild and severe diseases. Univariate and multivariate regression were used to identify determinants for disease severity.

FINDINGS

The sample comprised of 5889 cases (median age 34 years, male 71.7%). Majority were mild (92%), and 3.3% required intensive care, with 80% admitted within the first five days. Older age (≥51 years), underlying chronic kidney disease and chronic pulmonary disease, fever, cough, diarrhoea, breathlessness, tachypnoea, abnormal chest radiographs and high serum CRP (≥5 mg/dL) on admission were significant determinants for severity (p<0.05). The case fatality rate was 1.2%, and the three commonest complications were liver injuries (6.7%), kidney injuries (4%), and acute respiratory distress syndrome (2.3%).

INTERPRETATIONS

Lower case fatality rate was possibly contributed by young cases with mild diseases and early hospitalisation. Abnormal chest radiographic findings in elderly with tachypnoea require close monitoring in the first five days to detect early deterioration.