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

A Tale of Two Institutions: COVID-19 Positive Rates in Asymptomatic Patients Pre-Screened for Spine Procedures and Surgeries in Los Angeles, California

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

The coronavirus disease (COVID-19), caused by the severe respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented global public health emergency. The aim of the current study was to report on COVID-19 rates in an asymptomatic population prior to undergoing spine procedures or surgeries at two large Los Angeles healthcare systems.

METHODS

Elective spine procedures and surgeries from May 1, 2020 to January 31, 2021 were included. Results from SARS-CoV-2 virus RT-PCR nasopharyngeal testing within 72 hours prior to elective spine procedures were recorded. Los Angeles County COVID-19 rates were calculated using data sets from Los Angeles County Department of Public Health. Chi-squared test and Stata/IC were used for statistical analysis.

RESULTS

A total of 4,062 spine procedures and surgeries were scheduled during this time period. Of these, 4,043 procedures and surgeries were performed, with a total of 19 patients testing positive. Nine positive patients were from UCLA, and 10 from USC. The overall rate of positive tests was low at .47% and reflected similarities with Los Angeles County COVID-19 rates over time.

CONCLUSIONS

The current study shows that pre-procedure COVID-19 testing rates remains very low, and follows similar patterns of community rates. While pre-procedure testing increases the safety of elective procedures, universal COVID-19 pre-screening adds an additional barrier to receiving care for patients and increases cost of delivering care. A combination of pre-screening, pre-procedure self-quarantine, and consideration of overall community COVID-19 positivity rates should be further studied.

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.

Concentric regulatory zones failed to halt surging COVID-19: Brooklyn 2020

Methods

We relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in South Brooklyn in the fall of 2020. The regulatory scheme imposed differential controls on access to eating places, schools, houses of worship, large gatherings and other businesses within the three zones, but without restrictions on mobility.

Results

Within the central red zone, COVID-19 incidence temporarily declined from 131.2 per 100,000 population during the week ending October 3 to 62.5 per 100,000 by the week ending October 31, but then rebounded to 153.6 per 100,000 by the week ending November 28. Within the intermediate orange and peripheral yellow zones combined, incidence steadily rose from 28.8 per 100,000 during the week ending October 3 to 109.9 per 100,000 by the week ending November 28. Data on device visits to pairs of eating establishments straddling the red-orange boundary confirmed compliance with access controls. More general analysis of device movements showed stable patterns of movement between and beyond zones unaffected by the Governor's orders. A geospatial regression model of COVID-19 incidence in relation to device movements across zip code tabulation areas identified a cluster of five high-movement ZCTAs with estimated reproduction number 1.91 (95% confidence interval, 1.27-2.55).

Discussion

In the highly populous area of South Brooklyn, controls on access alone, without restrictions on movement, were inadequate to halt an advancing COVID-19 outbreak.

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.

Inequities in spatial accessibility to COVID-19 testing in 30 large US cities

Testing for SARS-CoV-2 infection has been a key strategy to mitigate and control the COVID-19 pandemic. Wide spatial and racial/ethnic disparities in COVID-19 outcomes have emerged in US cities. Previous research has highlighted the role of unequal access to testing as a potential driver of these disparities. We described inequities in spatial accessibility to COVID-19 testing locations in 30 large US cities. We used location data from Castlight Health Inc corresponding to October 2021. We created an accessibility metric at the level of the census block group (CBG) based on the number of sites per population in a 15-minute walkshed around the centroid of each CBG. We also calculated spatial accessibility using only testing sites without restrictions, i.e., no requirement for an appointment or a physician order prior to testing. We measured the association between the social vulnerability index (SVI) and spatial accessibility using a multilevel negative binomial model with random city intercepts and random SVI slopes. Among the 27,195 CBG analyzed, 53% had at least one testing site within a 15-minute walkshed, and 36% had at least one site without restrictions. On average, a 1-decile increase in the SVI was associated with a 3% (95% Confidence Interval: 2% - 4%) lower accessibility. Spatial inequities were similar across various components of the SVI and for sites with no restrictions. Despite this general pattern, several cities had inverted inequity, i.e., better accessibility in more vulnerable areas, which indicates that some cities may be on the right track when it comes to promoting equity in COVID-19 testing. Testing is a key component of the strategy to mitigate transmission of SARS-CoV-2 and efforts should be made to improve accessibility to testing, particularly as new and more contagious variants become dominant.

Mobility was a significant determinant of reported COVID-19 incidence during the Omicron Surge in the most populous U.S. Counties

BACKGROUND

Significant immune escape by the Omicron variant, along with the emergence of widespread worry fatigue, have called into question the robustness of the previously observed relation between population mobility and COVID-19 incidence.

METHODS

We employed principal component analysis to construct a one-dimensional summary indicator of six Google mobility categories. We related this mobility indicator to case incidence among 111 of the most populous U.S. counties during the Omicron surge from December 2021 through February 2022.

RESULTS

Reported COVID-19 incidence peaked earlier and declined more rapidly among those counties exhibiting more extensive decline in mobility between December 20 and January 3. Based upon a fixed-effects, longitudinal cohort model, we estimated that every 1% decline in mobility between December 20 and January 3 was associated with a 0.63% decline in peak incidence during the week ending January 17 (95% confidence interval, 0.40-0.86%). Based upon a cross-sectional analysis including mean household size and vaccination participation as covariates, we estimated that the same 1% decline in mobility was associated with a 0.36% decline in cumulative reported COVID-19 incidence from January 10 through February 28 (95% CI, 0.18-0.54%).

CONCLUSION

Omicron did not simply sweep through the U.S. population until it ran out of susceptible individuals to infect. To the contrary, a significant fraction managed to avoid infection by engaging in risk-mitigating behaviors. More broadly, the behavioral response to perceived risk should be viewed as an intrinsic component of the natural course of epidemics in humans.

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.

The Impact of Structural Inequities on Older Asian Americans During COVID-19

Structural racism manifests as an historical and continued invisibility of Asian Americans, whose experiences of disparities and diverse needs are omitted in research, data, and policy. During the pandemic, this invisibility intersects with rising anti-Asian violence and other persistent structural inequities that contribute to higher COVID-19 mortality in older Asian Americans compared to non-Hispanic whites. This perspective describes how structural inequities in social determinants of health-namely immigration, language and telehealth access, and economic conditions-lead to increased COVID-19 mortality and barriers to care among older Asian Americans. Specifically, we discuss how the historically racialized immigration system has patterned older Asian immigrant subpopulations into working in frontline essential occupations with high COVID-19 exposure. The threat of "public charge" rule has also prevented Asian immigrants from receiving eligible public assistance including COVID-19 testing and vaccination programs. We highlight the language diversity among older Asian Americans and how language access remains unaddressed in clinical and non-clinical services and creates barriers to routine and COVID-19 related care, particularly in geographic regions with small Asian American populations. We discuss the economic insecurity of older Asian immigrants and how co-residence in multigenerational homes has exposed them to greater risk of coronavirus transmission. Using an intersectionality-informed approach to address structural inequities, we recommend the disaggregation of racial/ethnic data, meaningful inclusion of older Asian Americans in research and policy, and equitable investment in community and multi-sectoral partnerships to improve health and wellbeing of older Asian Americans.

An integrated risk and epidemiological model to estimate risk-stratified COVID-19 outcomes for Los Angeles County: March 1, 2020-March 1, 2021

The objective of this study was to use available data on the prevalence of COVID-19 risk factors in subpopulations and epidemic dynamics at the population level to estimate probabilities of severe illness and the case and infection fatality rates (CFR and IFR) stratified across subgroups representing all combinations of the risk factors age, comorbidities, obesity, and smoking status. We focus on the first year of the epidemic in Los Angeles County (LAC) (March 1, 2020-March 1, 2021), spanning three epidemic waves. A relative risk modeling approach was developed to estimate conditional effects from available marginal data. A dynamic stochastic epidemic model was developed to produce time-varying population estimates of epidemic parameters including the transmission and infection observation rate. The epidemic and risk models were integrated to produce estimates of subpopulation-stratified probabilities of disease progression and CFR and IFR for LAC. The probabilities of disease progression and CFR and IFR were found to vary as extensively between age groups as within age categories combined with the presence of absence of other risk factors, suggesting that it is inappropriate to summarize epidemiological parameters for age categories alone, let alone the entire population. The fine-grained subpopulation-stratified estimates of COVID-19 outcomes produced in this study are useful in understanding disparities in the effect of the epidemic on different groups in LAC, and can inform analyses of targeted subpopulation-level policy interventions.