SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics

Time-space characteristics of emergency medical service attendance and layperson naloxone administration during non-fatal opioid overdoses in Rhode Island: A retrospective, event-level analysis

PURPOSE

As the opioid overdose crisis worsens in the United States (U.S.), emerging scholarship has uncovered time-and-place variations in substance use and overdose response efforts in community settings. Building on this work, we characterized spatio-temporal attributes of naloxone administration during non-fatal opioid overdoses attended by laypersons and emergency medical services (EMS) over a three-year period.

METHODS

Leveraging EMS encounter data across Rhode Island between January 2020 and December 2022, we quantified hour-by-hour variations in EMS deployment locations for non-fatal opioid-involved overdoses among adults (aged 18 + years). We used multivariable Poisson regression with robust standard errors to identify spatio-temporal patterns in EMS-attended overdoses by location type and evidence of layperson naloxone administration during these events.

RESULTS

Of the 5377 EMS non-fatal opioid overdose encounters, most occurred in residential housing (61.1 %) and outdoor public spaces (19.3 %). We identified substantial time-space variations in non-fatal overdoses, with EMS deployments to residential housing clustering in non-daylight hours (5:00PM-8:59AM) and to outdoor public spaces in daylight hours (9:00AM-8:59PM). Documented naloxone intervention by laypersons prior to EMS arrival was uncommon (10.6 %) but was most pronounced in overdoses occurring in residential housing and the early afternoon (1:00PM-4:59PM).

CONCLUSIONS

Despite the clustering of non-fatal opioid overdoses in housing environments, we identified substantial within-location variations in overdose-related EMS encounters over time and place.

Social Support and Psychological Capital Mediate the Effect of Personalities on the Mental Health of Professional Staff in China During COVID-19 Pandemic

Objective

COVID-19-related lockdown can lead to mental health problem, which displays heterogeneous between individuals. The aim of this study was to explore the association between mental health, social support and psychological capital state of professional staff with different personalities during the COVID-19 pandemic in China.

Methods

A cross-section study was conducted via online survey using the questionnaires of General Health Questionnaire (GHQ-12), Multidimensional Scale of Perceived Social Support (MSPSS), Psychological Capital Questionnaire (PCQ), Eysenck Personality Questionnaire-Revision Short Scale of China (EPQ-RSC). A total of 626 employees were included. Multiple regression analysis was performed to investigate the association of psychological capital, perceived social support, EPQ-N and EPQ-E and their interactions in general mental health.

Results

About 2.7% of professionals had mental health. The married had a higher mental health score than the single (P<0.05). The regular exercising workers had the lowest mental health score (P<0.05), and higher psychological capital and social support scores than the non-exercising ones (P<0.01). Multivariate analysis showed that the interaction between social support, psychological capital and neuroticism was statistically significant (β=-0.161, P<0.001) in general mental health with neuroticism ranking the top (β=0.352, P<0.001). Mediation analysis showed that social support modified the effect of psychological capital on mental health, accounting for 25.5% of the total effect, and that both social support and psychological capital mediated the effect of neuroticism or extroversion differentially on mental health.

Conclusion

Neuroticism is an influencing factor on mental health of professional staff. Social support and psychological capital played a partial mediating role in the effect of neuroticism or extroversion differentially on mental health in China. The findings suggest that during the COVID-19 pandemic, more social support and psychological capital are needed for the professional individuals with neuroticism to alleviate their stress and improve mental health.

Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium

Introduction

COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension.

Methods

We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis.

Results

Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition.

Conclusion

It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.

Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021

Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump's political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162) and 8.275 cases (95% CI = -1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States

BACKGROUND

As we continue the fourth year of the COVID-19 epidemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade.

METHODS

We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus and account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider three exponentially distributed waning rates, with means of 365 days (1 year), 548 days (1.5 years), and 730 days (2 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome.

RESULTS

As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 81,000-164,600 annual reported deaths, depending on duration of immunity, are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with 52-80% annual vaccination coverage and below 10,000 annual deaths with 59-83% annual vaccination coverage, depending on duration of immunity. Universal treatment reduces hospitalizations by 88.6% and deaths by 93.1% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged.

CONCLUSIONS

Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.

Impact of Early Pandemic SARS-CoV-2 Lineages Replacement with the Variant of Concern P.1 (Gamma) in Western Bahia, Brazil

BACKGROUND

The correct understanding of the epidemiological dynamics of COVID-19, caused by the SARS-CoV-2, is essential for formulating public policies of disease containment.

METHODS

In this study, we constructed a picture of the epidemiological dynamics of COVID-19 in a Brazilian population of almost 17000 patients in 15 months. We specifically studied the fluctuations of COVID-19 cases and deaths due to COVID-19 over time according to host gender, age, viral load, and genetic variants.

RESULTS

As the main results, we observed that the numbers of COVID-19 cases and deaths due to COVID-19 fluctuated over time and that men were the most affected by deaths, as well as those of 60 or more years old. We also observed that individuals between 30- and 44-years old were the most affected by COVID-19 cases. In addition, the viral loads in the patients' nasopharynx were higher in the early symptomatic period. We found that early pandemic SARS-CoV-2 lineages were replaced by the variant of concern (VOC) P.1 (Gamma) in the second half of the study period, which led to a significant increase in the number of deaths.

CONCLUSIONS

The results presented in this study are helpful for future formulations of efficient public policies of COVID-19 containment.

SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021

Importance

In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated.

Objective

To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut.

Design, Setting, and Participants

This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021.

Exposures

COVID-19-positive test result reported to state department of health.

Main Outcomes and Measures

The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated.

Results

At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive.

Conclusions and Relevance

These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.