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.

Background/aims

Anemia 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.

Methods

This 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.

Results

The 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.

Conclusions

Higher 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.

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.

Mechanistic models fit to streaming surveillance data are critical to understanding the transmission dynamics of an outbreak as it unfolds in real-time. 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, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data, as well as seroprevalence data from cross-sectional studies, and tested the importance of individual data streams for both inference and forecasting. Importantly, our model for incidence data accounts for changes in the total number of tests performed. We model the 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 nonparametrically. We compare our base model against modified versions which do not use diagnostics test counts or seroprevalence data to demonstrate the utility of including these often unused data streams. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March 2020 and February 2021 and find that 32--72\% of the Orange County residents experienced SARS-CoV-2 infection by mid-January, 2021. Despite this high number of infections, our results suggest 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.