Assessing the transmissibility of epidemics involving epidemic zoning.

BACKGROUND: Epidemic zoning is an important option in a series of measures for the prevention and control of infectious diseases. We aim to accurately assess the disease transmission process by considering the epidemic zoning, and we take two epidemics with distinct outbreak sizes as an example, i.e., the Xi'an epidemic in late 2021 and the Shanghai epidemic in early 2022. METHODS: For the two epidemics, the total cases were clearly distinguished by their reporting zone and the Bernoulli counting process was used to describe whether one infected case in society would be reported in control zones or not. Assuming the imperfect or perfect isolation policy in control zones, the transmission processes are respectively simulated by the adjusted renewal equation with case importation, which can be derived on the basis of the Bellman-Harris branching theory. The likelihood function containing unknown parameters is then constructed by assuming the daily number of new cases reported in control zones follows a Poisson distribution. All the unknown parameters were obtained by the maximum likelihood estimation. RESULTS: For both epidemics, the internal infections characterized by subcritical transmission within the control zones were verified, and the median control reproduction numbers were estimated as 0.403 (95% confidence interval (CI): 0.352, 0.459) in Xi'an epidemic and 0.727 (95% CI: 0.724, 0.730) in Shanghai epidemic, respectively. In addition, although the detection rate of social cases quickly increased to 100% during the decline period of daily new cases until the end of the epidemic, the detection rate in Xi'an was significantly higher than that in Shanghai in the previous period. CONCLUSIONS: The comparative analysis of the two epidemics with different consequences highlights the role of the higher detection rate of social cases since the beginning of the epidemic and the reduced transmission risk in control zones throughout the outbreak. Strengthening the detection of social infection and strictly implementing the isolation policy are of great significance to avoid a larger-scale epidemic.

The WHO estimates of excess mortality associated with the COVID-19 pandemic.

The World Health Organization has a mandate to compile and disseminate statistics on mortality, and we have been tracking the progression of the COVID-19 pandemic since the beginning of 20201. Reported statistics on COVID-19 mortality are problematic for many countries owing to variations in testing access, differential diagnostic capacity and inconsistent certification of COVID-19 as cause of death. Beyond what is directly attributable to it, the pandemic has caused extensive collateral damage that has led to losses of lives and livelihoods. Here we report a comprehensive and consistent measurement of the impact of the COVID-19 pandemic by estimating excess deaths, by month, for 2020 and 2021. We predict the pandemic period all-cause deaths in locations lacking complete reported data using an overdispersed Poisson count framework that applies Bayesian inference techniques to quantify uncertainty. We estimate 14.83 million excess deaths globally, 2.74 times more deaths than the 5.42 million reported as due to COVID-19 for the period. There are wide variations in the excess death estimates across the six World Health Organization regions. We describe the data and methods used to generate these estimates and highlight the need for better reporting where gaps persist. We discuss various summary measures, and the hazards of ranking countries' epidemic responses.

Effect of ambient air pollutants and meteorological variables on COVID-19 incidence.

OBJECTIVE: To determine whether ambient air pollutants and meteorological variables are associated with daily COVID-19 incidence. DESIGN: A retrospective cohort from January 25 to February 29, 2020. SETTING: Cities of Wuhan, Xiaogan, and Huanggang, China. PATIENTS: The COVID-19 cases detected each day. METHODS: We collected daily data of COVID-19 incidence, 8 ambient air pollutants (particulate matter of ≤2.5 µm [PM2.5], particulate matter ≤10 µm [PM10], sulfur dioxide [SO2], carbon monoxide [CO], nitrogen dioxide [NO2], and maximum 8-h moving average concentrations for ozone [O3-8h]) and 3 meteorological variables (temperature, relative humidity, and wind) in China's 3 worst COVID-19-stricken cities during the study period. The multivariate Poisson regression was performed to understand their correlation. RESULTS: Daily COVID-19 incidence was positively associated with PM2.5 and humidity in all cities. Specifically, the relative risk (RR) of PM2.5 for daily COVID-19 incidences were 1.036 (95% confidence interval [CI], 1.032-1.039) in Wuhan, 1.059 (95% CI, 1.046-1.072) in Xiaogan, and 1.144 (95% CI, 1.12-1.169) in Huanggang. The RR of humidity for daily COVID-19 incidence was consistently lower than that of PM2.5, and this difference ranged from 0.027 to 0.111. Moreover, PM10 and temperature also exhibited a notable correlation with daily COVID-19 incidence, but in a negative pattern The RR of PM10 for daily COVID-19 incidence ranged from 0.915 (95% CI, 0.896-0.934) to 0.961 (95% CI, 0.95-0.972, while that of temperature ranged from 0.738 (95% CI, 0.717-0.759) to 0.969 (95% CI, 0.966-0.973). CONCLUSIONS: Our data show that PM2.5 and humidity are substantially associated with an increased risk of COVID-19 and that PM10 and temperature are substantially associated with a decreased risk of COVID-19.

Stochastic epidemiological model: Simulations of the SARS-CoV-2 spreading in Mexico.

In this paper we model the spreading of the SARS-CoV-2 in Mexico by introducing a new stochastic approximation constructed from first principles, where the number of new infected individuals caused by a single infectious individual per unit time (a day), is a random variable of a time-dependent Poisson distribution. The model, structured on the basis of a Latent-Infectious-(Recovered or Deceased) (LI(RD)) compartmental approximation together with a modulation of the mean number of new infections (the Poisson parameters), provides a good tool to study theoretical and real scenarios.

A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal.

As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases and deaths in the 33 boroughs of Montreal are modelled through a bivariate hierarchical Bayesian model using Poisson distributions. The Poisson means are decomposed in the log scale as the sums of fixed effects and latent effects. The areal median age, the educational level, and the number of beds in long-term care homes are included in the fixed effects. To explore the correlation between cases and deaths inside and across areas, three different bivariate models are considered for the latent effects, namely an independent one, a conditional autoregressive model, and one that allows for both spatially structured and unstructured sources of variability. As the inclusion of spatial effects change some of the fixed effects, we extend the Spatial+ approach to a Bayesian areal set up to investigate the presence of spatial confounding. We find that the model which includes independent latent effects across boroughs performs the best among the ones considered, there appears to be spatial confounding with the diploma and median age variables, and the correlation between the cases and deaths across and within boroughs is always negative.

Re-analyzing the SARS-CoV-2 series using an extended integer-valued time series models: A situational assessment of the COVID-19 in Mauritius.

This paper proposes some high-ordered integer-valued auto-regressive time series process of order p (INAR(p)) with Zero-Inflated and Poisson-mixtures innovation distributions, wherein the predictor functions in these mentioned distributions allow for covariate specification, in particular, time-dependent covariates. The proposed time series structures are tested suitable to model the SARs-CoV-2 series in Mauritius which demonstrates excess zeros and hence significant over-dispersion with non-stationary trend. In addition, the INAR models allow the assessment of possible causes of COVID-19 in Mauritius. The results illustrate that the event of Vaccination and COVID-19 Stringency index are the most influential factors that can reduce the locally acquired COVID-19 cases and ultimately, the associated death cases. Moreover, the INAR(7) with Zero-inflated Negative Binomial innovations provides the best fitting and reliable Root Mean Square Errors, based on some short term forecasts. Undeniably, these information will hugely be useful to Mauritian authorities for implementation of comprehensive policies.

Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19.

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.

COVID-19 Vaccination Associated With Reduced Postoperative SARS-CoV-2 Infection and Morbidity.

OBJECTIVE: The purpose of this study was to determine the effect of COVID-19 vaccination on postoperative mortality, pulmonary and thrombotic complications, readmissions and hospital lengths of stay among patients undergoing surgery in the United States. BACKGROUND: While vaccination prevents COVID-19, little is known about its impact on postoperative complications. METHODS: This is a nationwide observational cohort study of all 1,255 Veterans Affairs facilities nationwide. We compared patients undergoing surgery at least 2 weeks after their second dose of the Pfizer BioNTech or Moderna vaccines, to contemporary propensity score matched controls. Primary endpoints were 30-day mortality and postoperative COVID-19 infection. Secondary endpoints were pulmonary or thrombotic complications, readmissions, and hospital lengths of stay. RESULTS: 30,681 patients met inclusion criteria. After matching, there were 3,104 in the vaccination group (1,903 received the Pfizer BioNTech, and 1,201 received the Moderna vaccine) and 7,438 controls. Full COVID-19 vaccination was associated with lower rates of postoperative 30-day COVID-19 infection (Incidence Rate Ratio and 95% confidence intervals, 0.09 [0.01,0.44]), pulmonary complications (0.54 [0.39, 0.72]), thrombotic complications (0.68 [0.46, 0.99]) and decreased hospital lengths of stay (0.78 [0.69, 0.89]). Complications were also low in vaccinated patients who tested COVID-19 positive before surgery but events were too few to detect a significant difference compared to controls. CONCLUSION: COVID-19 vaccination is associated with lower rates of postoperative morbidity. The benefit is most pronounced among individuals who have never had a COVID-19 infection before surgery.

Tuberculosis trend among native and foreign-born people over a 17 year period (2004-2020) in a large province in Northern Italy.

Tuberculosis (TB) incidence should decline by 20% in the Europe in 2015-2020, in line with End-TB milestones. We retrospectively evaluated TB notifications in the province of Brescia from 2004 to 2020. Cases were classified per patient origin and entitlement to Health Assistance for foreign born people: Italians (ITA), Foreigners permanently entitled (PEF) or Temporarily Entitled (TEF) to Health Regional Assistance. Poisson regression analysis was performed to assess associations between incidence and age, sex, continent of origin and year of notification. Overall 2279 TB cases were notified: 1290 (56.6%) in PEF, 700 (30.7%) in ITA and 289 (12.7%) in TEF. Notifications declined from 15.2/100,000 in 2004 to 6.9/100,000 in 2020 (54.6% reduction, temporary increase in 2013-2018 for TEF). Age (Incidence Risk Ratio, IRR, 1.02, 1.019-1.024 95%CI), sex (IRR 1.22, 1.12-1.34 95%CI), and continent of origin were positively associated with notifications (IRR 34.8, 30.8-39.2 95%CI for Asiatic, and IRR 20.6, 18.1-23.4 95%CI for African origin), p < 0.001. Notification decline was sharper in 2020, especially among TEF. End-TB milestone for 2020 was reached, but foreigners continue to represent a high risk group for the disease. Discontinuation of services due to the COVID-19 pandemic was associated with a sharp decrease in TB notification in 2020.

Impact of the SARS-CoV-2 pandemic and first lockdown on pregnancy monitoring in France: the COVIMATER cross-sectional study.

BACKGROUND: In the context of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, consultations and pregnancy monitoring examinations had to be reorganised urgently. In addition, women themselves may have postponed or cancelled their medical monitoring for organisational reasons, for fear of contracting the disease caused by SARS-CoV-2 (COVID-19) or for other reasons of their own. Delayed care can have deleterious consequences for both the mother and the child. Our objective was therefore to study the impact of the SARS-CoV-2 pandemic and the first lockdown in France on voluntary changes by pregnant women in the medical monitoring of their pregnancy and the associated factors. METHODS: A cross-sectional study was conducted in July 2020 using a web-questionnaire completed by 500 adult (> 18 years old) pregnant women during the first French lockdown (March-May 2020). A robust variance Poisson regression model was used to estimate adjusted prevalence ratios (aPRs). RESULTS: Almost one women of five (23.4%) reported having voluntarily postponed or foregone at least one consultation or pregnancy check-up during the lockdown. Women who were professionally inactive (aPR = 1.98, CI95%[1.24-3.16]), who had experienced serious disputes or violence during the lockdown (1.47, [1.00-2.16]), who felt they received little or no support (1.71, [1.07-2.71]), and those who changed health professionals during the lockdown (1.57, [1.04-2.36]) were all more likely to have voluntarily changed their pregnancy monitoring. Higher level of worry about the pandemic was associated with a lower probability of voluntarily changing pregnancy monitoring (0.66, [0.46-0.96]). CONCLUSIONS: Our results can guide prevention and support policies for pregnant women in the current and future pandemics.

Type I multivariate zero-inflated COM-Poisson regression model.

In this paper, we present the Type I multivariate zero-inflated Conway-Maxwell-Poisson distribution, whose development is based on the extension of the Type I multivariate zero-inflated Poisson distribution. We developed important properties of the distribution and present a regression model. The AIC and BIC criteria are used to select the best fitted model. Two real data sets have been used to illustrate the proposed model. Moreover, we conclude by stating that the Type I multivariate zero-inflated Conway-Maxwell-Poisson distribution produces a better fitted model for multivariate count data with excess of zeros.

Referrals for physical therapy for osteoarthritis during the COVID-19 pandemic: A retrospective analysis.

BACKGROUND: Osteoarthritis (OA) is a leading cause of musculoskeletal pain and disability among Americans. Physical therapy (PT) is recommended per the 2019 ACR /Arthritis Foundation Guideline for Treatment of OA of the Hand, Hip, and Knee. During COVID-19, access to healthcare has been altered in a variety of clinical settings, with the pandemic creating delays in healthcare, with an unknown impact on access to PT care for OA. OBJECTIVES: We sought to determine whether referrals to PT for OA were reduced in 2020 during the COVID-19 pandemic compared to 2019. METHODS: A retrospective analysis was done of 3586 PT referrals placed by the University of California, Davis for 206 OA ICD-10 codes from January to November 2019 and from January to November 2020. The numbers of PT referrals per month of each year were compared using both descriptive statistics and Poisson Regression analysis. RESULTS: A total of 1972 PT referrals for OA were placed from January to November 2019. Only 1614 referrals for OA were placed from January to November 2020, representing a significant decrease (p = 0.001). Month-by-month analysis of 2020 compared to 2019 revealed statistically significant drops in PT referrals for OA in April (p = 0.001), May (p = 0.001), and August (p = 0.001). CONCLUSIONS: These findings reveal a significant reduction in the number of referrals for PT for OA placed in 2020 during the first year of the COVID-19 pandemic. These reductions were particularly evident in the months following state-mandated actions and closures. Factors associated with this outcome may include decreased access to primary care providers, perceptions of PT availability by health care providers, decreased mobility limiting access to both clinic and PT appointments, and/or willingness to engage in PT by patients during the pandemic.

Waning Immunity after the BNT162b2 Vaccine in Israel.

BACKGROUND: In December 2020, Israel began a mass vaccination campaign against coronavirus disease 2019 (Covid-19) by administering the BNT162b2 vaccine, which led to a sharp curtailing of the outbreak. After a period with almost no cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, a resurgent Covid-19 outbreak began in mid-June 2021. Possible reasons for the resurgence were reduced vaccine effectiveness against the delta (B.1.617.2) variant and waning immunity. The extent of waning immunity of the vaccine against the delta variant in Israel is unclear. METHODS: We used data on confirmed infection and severe disease collected from an Israeli national database for the period of July 11 to 31, 2021, for all Israeli residents who had been fully vaccinated before June 2021. We used a Poisson regression model to compare rates of confirmed SARS-CoV-2 infection and severe Covid-19 among persons vaccinated during different time periods, with stratification according to age group and with adjustment for possible confounding factors. RESULTS: Among persons 60 years of age or older, the rate of infection in the July 11-31 period was higher among persons who became fully vaccinated in January 2021 (when they were first eligible) than among those fully vaccinated 2 months later, in March (rate ratio, 1.6; 95% confidence interval [CI], 1.3 to 2.0). Among persons 40 to 59 years of age, the rate ratio for infection among those fully vaccinated in February (when they were first eligible), as compared with 2 months later, in April, was 1.7 (95% CI, 1.4 to 2.1). Among persons 16 to 39 years of age, the rate ratio for infection among those fully vaccinated in March (when they were first eligible), as compared with 2 months later, in May, was 1.6 (95% CI, 1.3 to 2.0). The rate ratio for severe disease among persons fully vaccinated in the month when they were first eligible, as compared with those fully vaccinated in March, was 1.8 (95% CI, 1.1 to 2.9) among persons 60 years of age or older and 2.2 (95% CI, 0.6 to 7.7) among those 40 to 59 years of age; owing to small numbers, the rate ratio could not be calculated among persons 16 to 39 years of age. CONCLUSIONS: These findings indicate that immunity against the delta variant of SARS-CoV-2 waned in all age groups a few months after receipt of the second dose of vaccine.

Two-dose COVID-19 vaccination and possible arthritis flare among patients with rheumatoid arthritis in Hong Kong.

OBJECTIVES: To investigate the relationship between COVID-19 full vaccination (two completed doses) and possible arthritis flare. METHODS: Patients with rheumatoid arthritis (RA) were identified from population-based electronic medical records with vaccination linkage and categorised into BNT162b2 (mRNA vaccine), CoronaVac (inactive virus vaccine) and non-vaccinated groups. The risk of possible arthritis flare after vaccination was compared using a propensity-weighted cohort study design. We defined possible arthritis flare as hospitalisation and outpatient consultation related to RA or reactive arthritis, based on diagnosis records during the episode. Weekly prescriptions of rheumatic drugs since the launch of COVID-19 vaccination programme were compared to complement the findings from a diagnosis-based analysis. RESULTS: Among 5493 patients with RA (BNT162b2: 653; CoronaVac: 671; non-vaccinated: 4169), propensity-scored weighted Poisson regression showed no significant association between arthritis flare and COVID-19 vaccination ((BNT162b2: adjusted incidence rate ratio 0.86, 95% Confidence Interval 0.73 to 1.01); CoronaVac: 0.87 (0.74 to 1.02)). The distribution of weekly rheumatic drug prescriptions showed no significant differences among the three groups since the launch of the mass vaccination programme (all p values >0.1 from Kruskal-Wallis test). CONCLUSIONS: Current evidence does not support that full vaccination of mRNA or inactivated virus COVID-19 vaccines is associated with possible arthritis flare.

Gun violence incidence during the COVID-19 pandemic is higher than before the pandemic in the United States.

During the coronavirus disease 2019 (COVID-19) pandemic, gun violence (GV) in the United States (U.S.) was postulated to increase strain on already taxed healthcare resources, such as blood products, intensive care beds, personal protective equipment, and even hospital staff. This report aims to estimate the relative risk of GV in the U.S. during the pandemic compared to before the pandemic. Daily police reports corresponding to gun-related injuries and deaths in the 50 states and the District of Columbia from February 1st, 2019, to March 31st, 2021 were obtained from the GV Archive. Generalized linear mixed-effects models in the form of Poisson regression analysis were utilized to estimate the state-specific rates of GV. Nationally, GV rates were 30% higher between March 01, 2020, and March 31, 2021 (during the pandemic), compared to the same period in 2019 (before the pandemic) [intensity ratio (IR) = 1.30; 95% CI 1.29, 1.32; p < 0.0001]. The risk of GV was significantly higher in 28 states and significantly lower in only one state. National and state-specific rates of GV were higher during the COVID-19 pandemic compared to the same timeframe 1 year prior. State-specific steps to mitigate violence, or at a minimum adequately prepare for its toll during the COVID-19 pandemic, should be taken.

Sex-differences in COVID-19 associated excess mortality is not exceptional for the COVID-19 pandemic.

Europe experienced excess mortality from February through June, 2020 due to the COVID-19 pandemic, with more COVID-19-associated deaths in males compared to females. However, a difference in excess mortality among females compared to among males may be a more general phenomenon, and should be investigated in none-COVID-19 situations as well. Based on death counts from Eurostat, separate excess mortalities were estimated for each of the sexes using the EuroMOMO model. Sex-differential excess mortality were expressed as differences in excess mortality incidence rates between the sexes. A general relation between sex-differential and overall excess mortality both during the COVID-19 pandemic and in preceding seasons were investigated. Data from 27 European countries were included, covering the seasons 2016/17 to 2019/20. In periods with increased excess mortality, excess was consistently highest among males. From February through May 2020 male excess mortality was 52.7 (95% PI: 56.29; 49.05) deaths per 100,000 person years higher than for females. Increased male excess mortality compared to female was also observed in the seasons 2016/17 to 2018/19. We found a linear relation between sex-differences in excess mortality and overall excess mortality, i.e., 40 additional deaths among males per 100 excess deaths per 100,000 population. This corresponds to an overall female/male mortality incidence ratio of 0.7. In situations with overall excess mortality, excess mortality increases more for males than females. We suggest that the sex-differences observed during the COVID-19 pandemic reflects a general sex-disparity in excess mortality.

Importance of untested infectious individuals for interventions to suppress COVID-19.

The impact of the extent of testing infectious individuals on suppression of COVID-19 is illustrated from the early stages of outbreaks in Germany, the Hubei province of China, Italy, Spain and the UK. The predicted percentage of untested infected individuals depends on the specific outbreak but we found that they typically represent 60-80% of all infected individuals during the early stages of the outbreaks. We propose that reducing the underlying transmission from untested cases is crucial to suppress the virus. This can be achieved through enhanced testing in combination with social distancing and other interventions that reduce transmission such as wearing face masks. Once transmission from silent carriers is kept under control by these means, the virus could have been fully suppressed through fast isolation and contact tracing of tested cases.

Social status and novelty drove the spread of online information during the early stages of COVID-19.

Access to online information has been crucial throughout the COVID-19 pandemic. We analyzed more than eight million randomly selected Twitter posts from the first wave of the pandemic to study the role of the author's social status (Health Expert or Influencer) and the informational novelty of the tweet in the diffusion of several key types of information. Our results show that health-related information and political discourse propagated faster than personal narratives, economy-related or travel-related news. Content novelty further accelerated the spread of these discussion themes. People trusted health experts on health-related knowledge, especially when it was novel, while influencers were more effective at propagating political discourse. Finally, we observed a U-shaped relationship between the informational novelty and the number of retweets. Tweets with average novelty spread the least. Tweets with high novelty propagated the most, primarily when they discussed political, health, or personal information, perhaps owing to the immediacy to mobilize this information. On the other hand, economic and travel-related information spread most when it was less novel, and people resisted sharing such information before it was duly verified.

Evaluation of Racial, Ethnic, and Socioeconomic Disparities in Initiation of Kidney Failure Treatment During the First 4 Months of the COVID-19 Pandemic.

Importance: Persons with kidney failure require treatment (ie, dialysis or transplantation) for survival. The burden of the COVID-19 pandemic and pandemic-related disruptions in care have disproportionately affected racial and ethnic minority and socially disadvantaged populations, raising the importance of understanding disparities in treatment initiation for kidney failure during the pandemic. Objective: To examine changes in the number and demographic characteristics of patients initiating treatment for incident kidney failure following the COVID-19 pandemic by race and ethnicity, county-level COVID-19 mortality rate, and neighborhood-level social disadvantage. Design, Setting, and Participants: This cross-sectional time-trend study used data from US patients who developed kidney failure between January 1, 2018, and June 30, 2020. Data were analyzed between January and July 2021. Exposures: COVID-19 pandemic. Main Outcomes and Measures: Number of patients initiating treatment for incident kidney failure and mean estimated glomerular filtration rate (eGFR) at treatment initiation. Results: The study population included 127 149 patients with incident kidney failure between January 1, 2018, and June 30, 2020 (mean [SD] age, 62.8 [15.3] years; 53 021 [41.7%] female, 32 932 [25.9%] non-Hispanic Black, and 19 835 [15.6%] Hispanic/Latino patients). Compared with the pre-COVID-19 period, in the first 4 months of the pandemic (ie, March 1 through June 30, 2020), there were significant decreases in the proportion of patients with incident kidney failure receiving preemptive transplantation (1805 [2.1%] pre-COVID-19 vs 551 [1.4%] during COVID-19; P < .001) and initiating hemodialysis treatment with an arteriovenous fistula (2430 [15.8%] pre-COVID-19 vs 914 [13.4%] during COVID-19; P < .001). The mean (SD) eGFR at initiation declined from 9.6 (5.0) mL/min/1.73 m2 to 9.5 (4.9) mL/min/1.73 m2 during the pandemic (P < .001). In stratified analyses by race/ethnicity, these declines were exclusively observed among non-Hispanic Black patients (mean [SD] eGFR: 8.4 [4.6] mL/min/1.73 m2 pre-COVID-19 vs 8.1 [4.5] mL/min/1.73 m2 during COVID-19; P < .001). There were significant declines in eGFR at initiation for patients residing in counties in the highest quintile of COVID-19 mortality rates (9.5 [5.0] mL/min/1.73 m2 pre-COVID-19 vs 9.2 [5.0] mL/min/1.73 m2 during COVID-19; P < .001), but not for patients residing in other counties. The number of patients initiating treatment for incident kidney failure was approximately 30% lower than projected in April 2020. Conclusions and Relevance: In this cross-sectional study of US adults, the COVID-19 pandemic was associated with a substantially lower number of patients initiating treatment for incident kidney failure and treatment initiation at lower levels of kidney function during the first 4 months, particularly for Black patients and people living in counties with high COVID-19 mortality rates.