The Epidemiology of COVID-19 by Race/Ethnicity in Oklahoma City-County, Oklahoma (12 March 2020-31 May 2021).

We aimed to better understand the racially-/ethnically-specific COVID-19-related outcomes, with respect to time, to respond more effectively to emerging variants. Surveillance data from Oklahoma City-County (12 March 2020-31 May 2021) were used to summarize COVID-19 cases, hospitalizations, deaths, and COVID-19 vaccination status by racial/ethnic group and ZIP code. We estimated racially-/ethnically-specific daily hospitalization rates, the proportion of cases hospitalized, and disease odds ratios (OR) adjusting for sex, age, and the presence of at least one comorbidity. Hot spot analysis was performed using normalized values of cases, hospitalizations, and deaths generated from incidence rates per 100,000 population. During the study period, there were 103,030 confirmed cases, 3457 COVID-19-related hospitalizations, and 1500 COVID-19-related deaths. The daily 7-day average hospitalization rate for Hispanics peaked earlier than other groups and reached a maximum (3.0/100,000) in July 2020. The proportion of cases hospitalized by race/ethnicity was 6.09% among non-Hispanic Blacks, 5.48% among non-Hispanic Whites, 3.66% among Hispanics, 3.43% among American Indians, and 2.87% among Asian/Pacific Islanders. COVID-19 hot spots were identified in ZIP codes with minority communities. The Hispanic population experienced the first surge in COVID-19 cases and hospitalizations, while non-Hispanic Blacks ultimately bore the highest burden of COVID-19-related hospitalizations and deaths.

Characterization of persons with reported SARS-CoV-2 infection in the Oklahoma City tri-county area: Evidence from the first 12 months of transmission.

BACKGROUND: To describe characteristics, hospitalization, and death for reported cases of SARS-CoV-2 infection in the Oklahoma City tri-county area. METHODS: We extracted notified cases of SARS-CoV-2 infection for our study area and used descriptive statistics and modeling to examine case characteristics and calculate the odds of hospitalization and death in relation to a range of explanatory variables. RESULTS: Between March 12th, 2020 and February 28th, 2021, 124,925 cases of SARS-CoV-2 infection were reported from the study region. Being male, White or Black/African American, aged 50 years or older, presenting with apnea, cough, and shortness of breath, and having diabetes was associated with increased odds of hospitalization. The odds of dying were significantly associated with being Black/African American, presenting with cough and fever, having kidney disease and diabetes and being aged 70 years or older. CONCLUSIONS: The first cohort analysis of SARS-CoV-2 positive individuals in the Oklahoma City tri-county area confirms comorbidities and age as important predictors of COVID-19 hospitalization or death. As a novel aspect, we show that early symptoms of breathing difficulties in particular are associated with hospitalization and death. Initial case assessment and SARS-CoV-2 guidelines should continue to focus on age, comorbidities, and early symptoms.

Integrating adverse outcome pathways (AOPs) and high throughput in vitro assays for better risk evaluations, a study with drug-induced liver injury (DILI).

The emergence of high throughput in vitro assays has the potential to significantly improve toxicological evaluations and lead to more efficient, accurate, and less animal-intensive testing. However, directly using all available in vitro assays in a model is usually impractical and inefficient. On the other hand, mechanistic knowledge has always been critical for toxicological evaluations and should not be ignored even with the increasing availability of data. In this paper, we illus­trate a promising approach to integrating mechanistic knowledge with multiple data sources for in vitro assays, using drug-induced liver injury (DILI) as an example. The adverse outcome pathway (AOP) framework was used as a source for mechanistic knowledge and as a selection tool for high throughput predictors. Our results confirm the value of AOPs as a knowledge source for understanding adverse events and show that the majority of drugs classified as most-DILI-concern were mapped to AOPs related to liver toxicity found in AOPwiki. AOPs were also used effectively to select a subset of assays from the Tox21 and L1000 projects as the predictors in predictive modeling of DILI risk. Together with previously published drug properties for daily dose, lipophilicity, and reactive metabolite formation, these assay endpoints were used to build a penalized logistic regression model for assessing DILI risk. This model obtained an accuracy of 0.91, thus confirming the potential power of integrating mechanistic knowledge with high throughput assays for toxicological evalu­ations. The results also provide a roadmap for data integration that could be used for other adverse effects.