Mean Corpuscular Volume and Mortality in Incident Hemodialysis Patients.

Background/aimsAnemia is common in patients with advanced chronic kidney disease (CKD). A proportion of patients present with macrocytic anemia, manifested by elevated mean corpuscular volume (MCV), which has been associated with worse outcomes in CKD patients. However, it is unknown whether elevated MCV is associated with higher mortality risk in incident hemodialysis (HD) patients.MethodsThis retrospective observational cohort study examined all-cause, cardiovascular, and infectious mortality associations with both baseline and time-varying MCV in 109,501 incident HD patients using Cox proportional hazards models with 3 levels of hierarchical multivariable adjustment. Odds ratios of high versus low baseline MCV were evaluated using logistic regression.ResultsThe mean age of patients was 65 ± 15 (standard deviation) years and the cohort was 44% female, 58% diabetic, and 31% African American. Higher MCV was associated with older age, female sex, non-Hispanic White race-ethnicity, alcohol consumption, and having a decreased albumin or protein intake. Patients with higher MCV levels (> 98 fL) had a higher all-cause, cardiovascular, and infectious mortality risk in both baseline and time varying models, and across all levels of adjustment. In the fully adjusted models, compared to a reference of MCV 92-< 94 fL, patients with a baseline MCV > 100+ fL had a 28% higher risk of all-cause mortality (hazard ratio [HR] 1.28, 95% CI 1.22-1.34), 27% higher risk of cardiovascular mortality (HR 1.27, 95% CI 1.18-1.36), and 18% higher risk of infectious mortality (HR 1.18, 95% CI 1.02-1.38). Associations of higher MCV with these adverse outcomes persisted across all examined subgroups of clinical characteristics.ConclusionsHigher MCV was associated with higher all-cause, cardiovascular, and infectious mortality in HD patients. Further investigation is necessary to understand the underlying nature of the observed association

Semi-parametric modeling of SARS-CoV-2 transmission in Orange County, California using tests, cases, deaths, and seroprevalence data

Mechanistic modeling of SARS-CoV-2 transmission dynamics and frequently estimating model parameters using streaming surveillance data are important components of the pandemic response toolbox. However, transmission model parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, we propose a Bayesian modeling framework that integrates multiple surveillance data streams. Our model uses both SARS-CoV-2 diagnostics test and mortality time series to estimate our model parameters, while also explicitly integrating seroprevalence data from cross-sectional studies. Importantly, our data generating model for incidence data takes into account changes in the total number of tests performed. We model transmission rate, infection-to-fatality ratio, and a parameter controlling a functional relationship between the true case incidence and the fraction of positive tests as time-varying quantities and estimate changes of these parameters nonparameterically. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March, 2020 and March, 2021 and find that 33-62% of the Orange County residents experienced SARS-CoV-2 infection by the end of February, 2021. Despite this high number of infections, our results show that the abrupt end of the winter surge in January, 2021, was due to both behavioral changes and a high level of accumulated natural immunity.Comment: 37 pages, 16 pages of main text, including 5 figures, 1 tabl

Epidemiology of the early COVID-19 epidemic in Orange County, California: comparison of predictors of test positivity, mortality, and seropositivity

COVID-19 is one of the largest public health emergencies in modern history. Here we present a detailed analysis from a large population center in Southern California (Orange County, population of 3.2 million) to understand heterogeneity in risks of infection, test positivity, and death. We used a combination of datasets, including a population-representative seroprevalence survey, to assess the true burden of disease as well as COVID-19 testing intensity, test positivity, and mortality. In the first month of the local epidemic, case incidence clustered in high income areas. This pattern quickly shifted, with cases next clustering in much higher rates in the north-central area which has a lower socio-economic status. Since April, a concentration of reported cases, test positivity, testing intensity, and seropositivity in a north-central area persisted. At the individual level, several factors (e.g., age, race/ethnicity, zip codes with low educational attainment) strongly affected risk of seropositivity and death

Predictors of Test Positivity, Mortality, and Seropositivity during the Early Coronavirus Disease Epidemic, Orange County, California, USA.

We conducted a detailed analysis of coronavirus disease in a large population center in southern California, USA (Orange County, population 3.2 million), to determine heterogeneity in risks for infection, test positivity, and death. We used a combination of datasets, including a population-representative seroprevalence survey, to assess the actual burden of disease and testing intensity, test positivity, and mortality. In the first month of the local epidemic (March 2020), case incidence clustered in high-income areas. This pattern quickly shifted, and cases next clustered in much higher rates in the north-central area of the county, which has a lower socioeconomic status. Beginning in April 2020, a concentration of reported cases, test positivity, testing intensity, and seropositivity in a north-central area persisted. At the individual level, several factors (e.g., age, race or ethnicity, and ZIP codes with low educational attainment) strongly affected risk for seropositivity and death

Semi-parametric modeling of SARS-CoV-2 transmission in Orange County, California using tests, cases, deaths, and seroprevalence data.

Mechanistic modeling of SARS-CoV-2 transmission dynamics and frequently estimating model parameters using streaming surveillance data are important components of the pandemic response toolbox. However, transmission model parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, we propose a Bayesian modeling framework that integrates multiple surveillance data streams. Our model uses both SARS-CoV-2 diagnostics test and mortality time series to estimate our model parameters, while also explicitly integrating seroprevalence data from cross-sectional studies. Importantly, our data generating model for incidence data takes into account changes in the total number of tests performed. We model transmission rate, infection-to-fatality ratio, and a parameter controlling a functional relationship between the true case incidence and the fraction of positive tests as time-varying quantities and estimate changes of these parameters nonparameterically. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March, 2020 and March, 2021 and find that 33-62% of the Orange County residents experienced SARS-CoV-2 infection by the end of February, 2021. Despite this high number of infections, our results show that the abrupt end of the winter surge in January, 2021, was due to both behavioral changes and a high level of accumulated natural immunity

Enhanced contact investigations for nine early travel-related cases of SARS-CoV-2 in the United States

Coronavirus disease 2019 (COVID-19), the respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China and has since become pandemic. In response to the first cases identified in the United States, close contacts of confirmed COVID-19 cases were investigated to enable early identification and isolation of additional cases and to learn more about risk factors for transmission. Close contacts of nine early travel-related cases in the United States were identified and monitored daily for development of symptoms (active monitoring). Selected close contacts (including those with exposures categorized as higher risk) were targeted for collection of additional exposure information and respiratory samples. Respiratory samples were tested for SARS-CoV-2 by real-time reverse transcription polymerase chain reaction at the Centers for Disease Control and Prevention. Four hundred four close contacts were actively monitored in the jurisdictions that managed the travel-related cases. Three hundred thirty-eight of the 404 close contacts provided at least basic exposure information, of whom 159 close contacts had ≥1 set of respiratory samples collected and tested. Across all actively monitored close contacts, two additional symptomatic COVID-19 cases (i.e., secondary cases) were identified; both secondary cases were in spouses of travel-associated case patients. When considering only household members, all of whom had ≥1 respiratory sample tested for SARS-CoV-2, the secondary attack rate (i.e., the number of secondary cases as a proportion of total close contacts) was 13% (95% CI: 4–38%). The results from these contact tracing investigations suggest that household members, especially significant others, of COVID-19 cases are at highest risk of becoming infected. The importance of personal protective equipment for healthcare workers is also underlined. Isolation of persons with COVID-19, in combination with quarantine of exposed close contacts and practice of everyday preventive behaviors, is important to mitigate spread of COVID-19