COVID-19 Case Rates Research Articles

Clinical characteristics and antibody response to Omicron variants among solid carcinoma patients in China on the 2022.12–2023.4 wave of the COVID-19 pandemic

BackgroundChina experienced a surge of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variants after adjusting its zero-coronavirus disease 2019 (COVID-19) policy. Although infections with Omicron variants are generally less severe than infections with previous SARS-CoV-2 variants, the clinical characteristics, persistent symptoms, and antibody responses in solid carcinoma patients (SCPs) with COVID-19 during the Omicron wave are unclear.MethodsWe conducted a cross-sectional study in April 2023, recruiting healthy controls (HCs) from the community and SCPs from Zhejiang Provincial People’s Hospital. Serum samples were collected, and a questionnaire was used to assess SARS-CoV-2 infection status, including demographic characteristics, clinical manifestations, and “long COVID” symptoms. Humoral immune responses were analyzed by enzyme-linked immunosorbent assays (ELISAs) targeting immunoglobulin G (IgG) antibodies against the receptor-binding domain (RBD; Omicron BA.4/5) protein and cell culture-based neutralization assays against Omicron variants (BA.4/5, BF.7, XBB.1.5, and EG.5).ResultsIn total, 298 SCPs and 258 HCs were enrolled. Self-reported COVID-19 case rates were significantly lower in SCPs than in HCs (78.5% vs. 93.8%, P<0.001). Common COVID-19 symptoms were similar between the two groups, primarily comprising general (92.6% vs. 84.9%) and respiratory symptoms (51.9% vs. 48.2%) after acute infection. There was no significant difference in persistent symptoms at 1–3 months post-infection (P=0.353); fatigue was the most common symptom (45.0% vs. 44.8%). SCPs exhibited lower anti-RBD-IgG titers compared with HCs (1.061 vs. 1.978, P=0.001). The 50% pseudovirus neutralization titer (pVNT50) values for prevalent Omicron strains (BA.4/5 and BF.7) were lower in SCPs than in HCs (621.0 [288.8, 1333.0] vs. 894.1 [458.5, 1637.0] and 529.6 [215.3, 1264.5] vs. 463.1 [185.2, 914.0], respectively). Levels of antibodies against subsequent variants (XBB.1.5 and EG.5) also were reduced. There were no significant differences among carcinoma types in the levels of antibodies against Omicron variants. However, SCPs who received the SARS-CoV-2 vaccine or had COVID-19 during the Omicron wave displayed higher antibody levels.ConclusionsThis study elucidated the clinical and immunological characteristics of SCPs during the Omicron wave in China after the shift away from a zero-COVID-19 policy. Our findings provide insights regarding factors that influence COVID-19 symptoms and antibody levels in this population.

Assessing the influence of nursing home size and care models on COVID-19 case and mortality rates

IntroductionThis study examines the impact of COVID-19 on older adults in traditional nursing homes, Green Houses (GHs), and Continuing Care Retirement Communities-Nursing Homes (CCRCNHs). MethodsCOVID-19 cases and deaths from CMS and LTCFocus data across 11 states from 6/2020 to 9/2022 were used, and Poisson regressions to compare care settings while adjusting for covariates. ResultsCCRCNH and GH residents were older (84.4 and 83.1 years) than those in traditional NHs (77.5 and 77.6 years, p < 0.0001), with more female and White individuals. COVID-19 risk was higher in CCRCNHs (RR 1.51, 95 % CI 1.10–2.07), large NHs (RR 1.57, 95 % CI 1.14–2.15), and highest in small NHs (RR 1.80, 95 % CI 1.32–2.47). Mortality rates across NH types showed no significant difference. ConclusionThe study shows that smaller, person-centered GHs are more effective in controlling COVID-19 infection, suggesting a need to rethink care models to incorporating GH features for better outcomes.GEORGE2 07-AUG-2024 11:31.

Upstream Oil and Gas Production and Community COVID-19 Case and Mortality Rates in California, USA.

Higher concentrations of ambient air pollutants, including PM2.5 and NO2, and other pollutants have been found near active oil and gas wells and may be associated with adverse COVID-19 outcomes. We assessed whether residential exposure to nearby oil and gas production was associated with higher rates of the respiratory infection COVID-19 and related mortality using a population-based ecological study in California. Using gridded population estimates, we estimated area-level exposure to annual average oil and gas production volume from active wells within 1kilometer (km) of populated areas within census block groups from 2018 to 2020. We geocoded confirmed cases and associated deaths to assess block group case and mortality rates from COVID-19 from February 2020 to January 2021. We fit hierarchical Poisson models with individual and area covariates (e.g., age, sex, socioeconomic disadvantage), and included time and other interactions to assess additional variation (e.g., testing, reporting rates). In the first 4months of the study period (February-May 2020), block groups in the highest tertile of oil and gas production exposure had 34% higher case rates (IRR: 1.34 95% CI: 1.20, 1.49) and 55% higher mortality rates (MRR: 1.52 95%: CI: 1.14, 2.03) than those with no estimated production, after accounting for area-level covariates. Over the entire study period, we observed moderately higher mortality rates in the highest group (MRR: 1.16 95%: CI: 1.01, 1.33) and null associations for case rates.

The Effectiveness of Face Mask Mandates on COVID-19 Transmission in Utah.

Throughout the COVID-19 pandemic, the effectiveness of face mask mandates was intensely debated. The objective of this study was to describe how face mask mandates at the state, county, and local levels differed in their effectiveness in reducing the number of COVID-19 cases in the jurisdiction where the mandate was implemented and throughout Utah. We used publicly available data from the Utah Department of Health and Human Services. We calculated the effectiveness of face mask mandates (EFm) in each local health district after 3 key mandates: the enactment of face mask mandates in Salt Lake and Summit counties (SLSC) on June 28, 2020, and June 27, 2020, respectively; the enactment of a statewide face mask mandate on November 9, 2020; and the lifting of the statewide face mask mandate on April 10, 2021. Most counties in Utah had a reduction in the growth rate of COVID-19 cases after enactment of face mask mandates. We found an average 11.9% reduction in EFm after the introduction of the SLSC face mask mandates, with 8 of 13 local health districts experiencing a reduction, and an average 12.7% reduction after introduction of the statewide mandate, with 12 of 13 local health districts experiencing a reduction. After mandates were lifted, many counties had an increase in EFm. Face mask mandates were an effective way to reduce transmission of COVID-19 in local jurisdictions and in neighboring jurisdictions in Utah. Our evidence supports the use of face mask mandates as a way to prevent disease transmission and be better equipped to respond to future pandemics.

A longitudinal study of hospital workers' mental health from fall 2020 to the end of the COVID-19 pandemic in 2023.

Most longitudinal studies of healthcare workers' mental health during COVID-19 end in 2021. We examined trends in hospital workers eight times, ending in 2023. A cohort of healthcare workers at one organization was surveyed at 3-month intervals until Spring 2022 and re-surveyed in Spring 2023 using validated measures of common mental health problems. Of 538 workers in the original cohort, 289 (54%) completed the eighth survey. Repeated-measures ANOVA revealed significant changes in psychological distress (F = 7.4, P < .001), posttraumatic symptoms (F = 14.1, P < .001), and three dimensions of burnout: emotional exhaustion (F = 5.7, P < .001), depersonalization (F = 2.7, P = .01), and personal accomplishment (F = 2.8, P = .008). Over time, psychological distress and depersonalization increased, posttraumatic symptoms and personal accomplishment decreased, and emotional exhaustion fluctuated significantly without net change. Most measures did not improve significantly in the year prior to the declaration of the pandemic's end. The lack of improvement in psychological distress, emotional exhaustion, depersonalization, and personal accomplishment during the period in which COVID-19 case rates declined and public health measures were relaxed is a concerning indication of the chronicity of the impact of the pandemic on healthcare workers.

Validation of the Parent-report Pandemic Anxiety Scale (PAS-P) in the context of COVID-19

AbstractTo be able to develop effective policy and targeted support for children and young people, it is vital to develop and validate measures that enable us to understand what aspects of pandemics are associated with anxiety and stress across a wide age range. We examined the psychometric properties of the Pandemic Anxiety Scale– Parent-report (PAS-P), which measures levels of child and adolescent pandemic-related anxieties. Factor structure, reliability, and convergent and discriminant validity of the PAS-P was assessed in a convenience sample of parents/carers (N­ = 8410) over at three time points in 2020 when COVID-19 case rates and restrictions varied. Factor structure was identified via two exploratory factor analyses (EFAs; n = 5601 and n = 1005) and then tested using confirmatory factor analysis (CFA; n = 800), measurement invariance tests, and a longitudinal CFA (n = 1651). Two factors structure for disease and consequence anxiety were observed across both EFAs and were found to have good fit in the CFAs. The PAS-P demonstrated good internal consistency and discriminant validity, as well as partial scalar invariance in latent construct measurement across child age, gender, and time. The PAS-P is a robust parent-report measure of two distinct forms of pandemic-related anxiety, suitable for reporting on children and adolescents aged 4-16 years. Although the scale has been validated in the context of the COVID-19 pandemic, it is not limited to this specific pandemic and, with minor wording modifications, may be a reliable tool in other health epidemic contexts.

School Mask Mandates and COVID-19: The Challenge of Using Difference-in-Differences Analysis of Observational Data to Estimate the Effectiveness of a Public Health Intervention.

There are considerable challenges when using difference-in-differences (DiD) analysis of ecological data to estimate the effectiveness of public health interventions in rapidly changing situations. To discuss the shortcomings of DiD methodology for the estimation of the effects of public health interventions using ecological data. As an example, the authors consider an analysis that used DiD methodology and reported a causal reduction in COVID-19 cases due to the maintenance of school mask mandates. They did alternate analyses using various control groups to assess the robustness of the prior analysis. School districts in the greater Boston area and Massachusetts during the 2021-to-2022 academic year. Students and school staff. Changes in COVID-19 case rates in districts that did and did not lift mask mandates. Important potential confounders rendered DiD methodology inappropriate for causal inference, including prior immunity, temporal variation in rates of infection, and changes in testing practices. The racial composition and income of intervention and control groups also differed substantially. Compared with maintaining the mask requirement, dropping the requirement was associated with anywhere from an increase of 5.64 cases (95% CI, 3.00 to 8.29 cases) per 1000 persons to a decrease of 2.74 cases (CI, 0.63 to 4.85 cases) per 1000 persons, depending on choice of control group and whether students or staff were examined. Ecological data were used; detailed data on all potential confounders were unavailable. Alternate analyses yielded estimates consistent with a wide range of both negative and positive associations in COVID-19 case rates after removal of mask mandates. The findings highlight the challenges of using DiD analysis of ecological data to estimate the effectiveness of interventions in divergent intervention and control groups during rapidly changing circumstances. None.

Responding to COVID-19: an exploration of EU country responses and directions for further research.

During COVID-19, scientists advising policymakers were forced to deal with high uncertainty and risks in an environment of unknowns. Evidence on which policies and measures were effective in responding to the pandemic remains underdeveloped to answer the key question 'what worked and why?'. This study aims to provide a basis for studies to go further to answer this critical question, by starting to look efficacy or how countries ensured that health services remained available and what measures were enacted to protect and treat their populations and workers. We applied a three-phase sequential mixed methods design. In phase one, we started with a qualitative content analysis of the EU Country Profile reports to retrieve and analyse data on COVID-19 responses taken by 29 countries in the European region. Phase two is the step of data transformation, converting qualitative data into numerical codes that can be statistically analysed, which are then used in a quantitative cross-national comparative analysis that comprises phase three. The quantifying process resulted in a numerical indicator to measure the 'response efficacy' of the 29 countries, which is used in phase three's association of the response measure with country performance indicators that were derived from European Centre for Disease Control (ECDC) COVID-19 case and death rate data. Through comparing the frequency of COVID-19 measures taken, we found that many countries in the European region undertook similar actions but with differing effects. The cross-national analysis revealed an expected relationship: a lower COVID-19 response efficacy appeared to be related to a higher case and death rates. Still, marked variation for countries with similar response efficacy indicators was found, signalling that the combination and sequence of implementation of COVID-19 responses is possibly just as important as their efficacy in terms of which response measures were implemented. Many European countries employed similar COVID-19 measures but still had a wide variation in their case and death rates. To unravel the question 'what worked and why?', we suggest directions from which more refined research can be designed that will eventually contribute to mitigate the impact of future pandemics and to be better prepared for their economic and human burden.

Distribution Model of Personal Protective Equipment (PPE) Using the Spatial Dominance Test and Decision Tree Algorithm

The COVID-19 case has developed positively, but preventive measures must be taken to anticipate SARS-CoV-2 mutations. Anticipation can include policies, preparing health workers, and providing personal protective equipment. Personal Protective Equipment (PPE) availability is a big challenge in handling pandemics, especially COVID-19. The level of need for PPE in an area depends on the number of COVID-19 cases. This research provides a solution to overcome the availability of PPE by applying the concept of cross-regional collaboration. Areas with low COVID-19 case rates can help areas with high COVID-19 case rates by sending PPE assistance. Implementing the cross-regional collaboration concept is assisted by the spatial dominance test algorithm, namely the spatial skyline query. Spatial Skyline Query works by searching for the most ideal area. The ideal area is an area with low COVID-19 case criteria. The low number of positive cases, death cases, probable cases, and close contact cases supports the low number of COVID-19 cases. Areas with the highest number of recovered cases are also priorities. The SSQ model was developed into two models for searching priority areas for PPE assistants. The first model is Sort Filter Skyline 1 (SFS1), and the second is Sort Filter Skyline 2 (SFS2). SFS1 is a form of SFS algorithm optimization that searches for the best 50% of all regions. SFS2 modifies SFS1 by selecting areas whose distance is &lt;= the average distance of the area to the Health Crisis Centre of the Ministry of Health of the Republic of Indonesia. This research involves searching for priority areas and applying a prediction algorithm to extract knowledge built from the prediction model. The algorithm used is C5.0. The data used to apply the prediction algorithm results from the application of SFS1 and SFS2. The results of testing the prediction model by the C5.0 algorithm produced an accuracy of 77.26% for SFS1 data and 92.01% for SFS2. The average rules resulting from the C5.0 algorithm are three for SFS1 and two for SFS2.

Public health infrastructure and COVID-19 spread: An air transportation network analysis

This study examines the effectiveness of public health infrastructure, specifically hospital and Intensive Care Unit (ICU) capacities, in mitigating the spread of COVID-19 in various US counties. We use the air transportation network to quantify the risk of importing COVID-19-infected passengers to a given county in the United States. Utilizing a dataset that encompasses COVID-19 case rates, healthcare resources, and socioeconomic and transportation-related variables across multiple destination counties, we found that increased availability of hospital and ICU beds is statistically significantly associated with a reduced risk of COVID-19 spread, after controlling for origin county infection rates, travel patterns, and socioeconomic factors. These findings underline the critical role of robust healthcare infrastructure in public health crisis management and suggest targeted policy interventions to bolster healthcare capacity as a viable strategy for mitigating pandemic impacts.

The Processing Spatial Data for Statistical Modeling and Visualization Case study: INLA model for COVID-19 in Alabama, USA

This research emphasizes the visualization of spatial data for statistical modelling and analysis of the relative risk associated with the COVID-19 pandemic in Alabama, USA. We used Bayesian analysis and the Integrated Nested Laplace Approximation (INLA) approach on data ranging from March 11, 2020, to December 31, 2022, which included observed COVID-19 cases, the population for each of the Alabama counties, and a Geographical map of the state. The geographical distribution of COVID-19’s relative risk was determined using various spatial statistical techniques, indicating high-risk locations. The study used Besag-York-Mollié (BYM) models to assess the posterior relative risk of COVID-19, and it found a statistically significant average decrease in COVID-19 case rates across the 67 counties evaluated. These findings have practical implications for evidence-based policymaking in pandemic prevention, mitigation, and preparation.

Knowledge, attitude, and perception of University students toward COVID-19 Pandemic

Background: An increasing rate of COVID-19 cases throughout the world has increased the need to investigate the knowledge, attitude, and perception (KAP) of university students toward the COVID-19 pandemic to prevent its rapid spread as public cooperation and awareness regarding prevention is the only way to deal with this pandemic. Methods: A retrospective cross-sectional study was conducted in Pakistan in April 2020. Data was collected by Google statistics and percent reliability was calculated through SPSS. Results: The number of respondents n=678, out of which 443(65.2%) were female and 235(34.85%) were male. The perception of students regarding this disease is shown in a table in which 33.2% of students consider it very dangerous. 54.2% thought it moderately dangerous. 62.38% of students consider muscle pain and 73.59% of students consider throat pain as a clinical presentation of COVID-19. 80.7% of students thought that exposure to a suspected case of COVID-19 serves as a means of acquiring COVID-19 infection. The most effective measure of prevention comes out to be hand washing and covering face during coughing and sneezing. The main cause of the spread is a lack of public awareness and cooperation. Social media campaigns are an effective means of educating students about quarantine and controlling its rapid spread. Conclusion: Researchers concluded that It’s the need of the hour to increase awareness related to protective hygienic measures, rapid spread, and providing reliable approaches through social media campaigns to manage this pandemic. We believe that students should be involved in educating their surrounding society.

Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction

The COVID-19 pandemic has dramatically transformed human mobility patterns. Therefore, human mobility prediction for the “new normal” is crucial to infrastructure redesign, emergency management, and urban planning post the pandemic. This paper aims to predict people’s number of visits to various locations in New York City using COVID and mobility data in the past two years. To quantitatively model the impact of COVID cases on human mobility patterns and predict mobility patterns across the pandemic period, this paper develops a model CCAAT-GCN ( C ross- and C ontext- A ttention based Spatial-Temporal G raph C onvolutional N etworks). The proposed model is validated using SafeGraph data in New York City from August 2020 to April 2022. A rich set of baselines are performed to demonstrate the performance of our proposed model. Results demonstrate the superior performance of our proposed method. Also, the attention matrix learned by our model exhibits a strong alignment with the COVID-19 situation and the points of interest within the geographic region. This alignment suggests that the model effectively captures the intricate relationships between COVID-19 case rates and human mobility patterns. The developed model and findings can offer insights into the mobility pattern prediction for future disruptive events and pandemics, so as to assist with emergency preparedness for planners, decision-makers and policymakers.

Did the COVID-19 quarantine policies applied in Cochabamba, Bolivia mitigated cases successfully? an interrupted time series analysis

ABSTRACT Background The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies’ impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited. Objective To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia. Methods Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020–2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods. Results The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies. Conclusion This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures’ intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.

California's COVID-19 Vaccine Equity Policy: Cases, Hospitalizations, And Deaths Averted In Affected Communities.

In March2021, California implemented a vaccine equity policy that prioritized COVID-19 vaccine allocation to communities identified as least advantaged by an area-based socioeconomic measure, the Healthy Places Index. We conducted quasi-experimental and counterfactual analyses to estimate the effect of this policy on COVID-19 vaccination, case, hospitalization, and death rates. Among prioritized communities, vaccination rates increased 28.4percent after policy implementation. Furthermore, an estimated 160,892 COVID-19 cases, 10,248 hospitalizations, and 679 deaths in the least-advantaged communities were averted by the policy. Despite these improvements, the share of COVID-19 cases, hospitalizations, and deaths in prioritized communities remained elevated. These estimates were robust in sensitivity analyses that tested exchangeability between prioritized communities and those not prioritized by the policy; model specifications; and potential temporal confounders, including prior infections. Correcting for disparities by strategically allocating limited resources to the least-advantaged or most-affected communities can reduce the impacts of COVID-19 and other diseases but might not eliminate health disparities.

#MaskMadness: The contributions of facemask stigma, republican voting, and COVID-19 case rates to mask-wearing and affective well-being.

#MaskMadness: The contributions of facemask stigma, republican voting, and COVID-19 case rates to mask-wearing and affective well-being.

SARS-CoV-2 seroprevalence in Nova Scotia blood donors.

SARS-CoV-2 seroprevalence monitors cumulative infection rates irrespective of case testing protocols. We aimed to describe Nova Scotia blood donor seroprevalence in relation to public health policy and reported data over the course of the COVID-19 pandemic (May 2020 to August 2022). Monthly random Nova Scotia blood donation samples (24,258 in total) were tested for SARS-CoV-2 infection antibodies (anti-nucleocapsid) from May 2020 to August 2022, and vaccination antibodies (anti-spike) from January 2021 to August 2022. Multivariable logistic regression for infection antibodies and vaccination antibodies separately with month, age, sex, and racialization identified independent predictors. The provincial nucleic acid amplification test (NAAT)-positive case rate over the pandemic was calculated from publicly available data. Anti-N seroprevalence was 3.8% in January 2022, increasing to 50.8% in August 2022. The general population COVID-19 case rate was 3.5% in January 2022, increasing to 12.5% in August 2022. The percentage of NAAT-positive samples in public health laboratories increased from 1% in November 2021 to a peak of 30.7% in April 2022 with decreasing numbers of tests performed. Higher proportions of younger donors as well as Black, Indigenous, and racialized blood donors were more likely to have infection antibodies (p < 0.01). Vaccination antibodies increased to 100% over 2021, initially in older donors (60+ years), and followed by progressively younger age groups. SARS-CoV-2 infection rates were relatively low in Nova Scotia until the more contagious Omicron variant dominated, after which about half of Nova Scotia donors had been infected despite most adults being vaccinated (although severity was much lower in vaccinated individuals). Most COVID-19 cases were detected by NAAT until Omicron arrived. When NAAT testing priorities focused on high-risk individuals, infection rates were better reflected by seroprevalence.

Resilience Within Constraints: An Event Oriented Approach to Crisis Response

Scholars have started unpacking how individuals, organizations, and communities interact to build a shared capacity for resilience. This research, however, has not yet examined how the institutional environment influences local responses to crises. This is an important omission since crises do not occur in a vacuum—decisions of actors, at one level, constrain or catalyze the resilience responses of communities and the individuals and organizations within them. Furthermore, research on resilience often assumes a relatively static nature of crises, without accounting for the need to revise responses over time. Using an original set of high frequency data on COVID-19 rates in the United States, we investigate the relationship between healthcare decision agency at the county level and the incidence of the pandemic as reflected in the number of COVID cases and death by county. Our results provide robust evidence that higher degrees of decision-making autonomy are associated with lower rates of COVID-19 cases and deaths. That is, a positive relationship exists between decision-making autonomy and higher capacities for resilience. We further show that this relationship is strengthened, the larger and more heterogeneous is the mix of healthcare organizations in a county, and the higher is the need for revising responses given the changing nature of the crisis. Our results emphasize the need for a system approach to the study of resilience, and suggest that, by enabling the customization of responses to local needs and resources, decision-making autonomy enhances the shared capacities for resilience.

Effects of Face Mask Mandates on COVID-19 Transmission in 51 Countries: Retrospective Event Study.

The question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing. To add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates. We applied an event study approach to data pooled from four sources: (1) country-level information on self-reported mask use was obtained from the COVID-19 Trends and Impact Survey, (2) data from the Oxford COVID-19 Government Response Tracker provided information on face mask mandates and recommendations and any other nonpharmacological interventions implemented, (3) mobility indicators from Google's Community Mobility Reports were also included, and (4) SARS-CoV-2 reproduction numbers and COVID-19 case growth rates were retrieved from the Our World in Data-COVID-19 data set. Mandates increased mask use by 8.81 percentage points (P=.006) on average, and SARS-CoV-2 reproduction numbers declined on average by -0.31 units (P=.008). Although no significant average effect of mask mandates was observed for growth rates of COVID-19 cases (-0.98 percentage points; P=.56), the results indicate incremental effects on days 26 (-1.76 percentage points; P=.04), 27 (-1.89 percentage points; P=.05), 29 (-1.78 percentage points; P=.04), and 30 (-2.14 percentage points; P=.02) after mandate implementation. For self-reported face mask use and reproduction numbers, incremental effects are seen 6 and 13 days after mandate implementation. Both incremental effects persist for >30 days. Furthermore, mask recommendations increased self-reported mask use on average (5.84 percentage points; P<.001). However, there were no effects of recommendations on SARS-CoV-2 reproduction numbers or COVID-19 case growth rates (-0.06 units; P=.70 and -2.45 percentage points; P=.59). Single incremental effects on self-reported mask use were observed on days 11 (3.96 percentage points; P=.04), 13 (3.77 percentage points; P=.04) and 25 to 27 (4.20 percentage points; P=.048 and 5.91 percentage points; P=.01) after recommendation. Recommendations also affected reproduction numbers on days 0 (-0.07 units; P=.03) and 1 (-0.07 units; P=.03) and between days 21 (-0.09 units; P=.04) and 28 (-0.11 units; P=.05) and case growth rates between days 1 and 4 (-1.60 percentage points; P=.03 and -2.19 percentage points; P=.03) and on day 23 (-2.83 percentage points; P=.05) after publication. Contrary to recommendations, mask mandates can be used as an effective measure to reduce SARS-CoV-2 reproduction numbers. However, mandates alone are not sufficient to reduce growth rates of COVID-19 cases. Our study adds external validity to the existing randomized controlled trials on the effectiveness of face masks to reduce the spread of SARS-CoV-2.

Impact of universal chlorhexidine bathing with or without COVID-19 intensive training on staff and resident COVID-19 case rates in nursing homes.

We evaluated whether universal chlorhexidine bathing (decolonization) with or without COVID-19 intensive training impacted COVID-19 rates in 63 nursing homes (NHs) during the 2020-2021 Fall/Winter surge. Decolonization was associated with a 43% lesser rise in staff case-rates (P < .001) and a 52% lesser rise in resident case-rates (P < .001) versus control.