Severe Acute Respiratory Syndrome Coronavirus 2 Delta Variant Genomic Variation Associated With Breakthrough Infection in Northern California: A Retrospective Cohort Study.

BACKGROUND: The association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic variation and breakthrough infection is not well defined among persons with Delta variant SARS-CoV-2 infection. METHODS: In a retrospective cohort, we assessed whether individual nonlineage defining mutations and overall genomic variation (including low-frequency alleles) were associated with breakthrough infection, defined as SARS-CoV-2 infection after coronavirus disease 2019 primary vaccine series. We identified all nonsynonymous single-nucleotide polymorphisms, insertions, and deletions in SARS-CoV-2 genomes with ≥5% allelic frequency and population frequency of ≥5% and ≤95%. Using Poisson regression, we assessed the association with breakthrough infection for each individual mutation and a viral genomic risk score. RESULTS: Thirty-six mutations met our inclusion criteria. Among 12 744 persons infected with Delta variant SARS-CoV-2, 5949 (47%) were vaccinated and 6795 (53%) were unvaccinated. Viruses with a viral genomic risk score in the highest quintile were 9% more likely to be associated with breakthrough infection than viruses in the lowest quintile, but including the risk score improved overall predictive model performance (measured by C statistic) by only +0.0006. CONCLUSIONS: Genomic variation within SARS-CoV-2 Delta variant was weakly associated with breakthrough infection, but several potential nonlineage defining mutations were identified that might contribute to immune evasion by SARS-CoV-2.

Development and validation of a prediction algorithm to identify birth in countries with high tuberculosis incidence in two large California health systems.

OBJECTIVE: Though targeted testing for latent tuberculosis infection ("LTBI") for persons born in countries with high tuberculosis incidence ("HTBIC") is recommended in health care settings, this information is not routinely recorded in the electronic health record ("EHR"). We develop and validate a prediction model for birth in a HTBIC using EHR data. MATERIALS AND METHODS: In a cohort of patients within Kaiser Permanente Southern California ("KPSC") and Kaiser Permanent Northern California ("KPNC") between January 1, 2008 and December 31, 2019, KPSC was used as the development dataset and KPNC was used for external validation using logistic regression. Model performance was evaluated using area under the receiver operator curve ("AUCROC") and area under the precision and recall curve ("AUPRC"). We explored various cut-points to improve screening for LTBI. RESULTS: KPSC had 73% and KPNC had 54% of patients missing country-of-birth information in the EHR, leaving 2,036,400 and 2,880,570 patients with EHR-documented country-of-birth at KPSC and KPNC, respectively. The final model had an AUCROC of 0.85 and 0.87 on internal and external validation datasets, respectively. It had an AUPRC of 0.69 and 0.64 (compared to a baseline HTBIC-birth prevalence of 0.24 at KPSC and 0.19 at KPNC) on internal and external validation datasets, respectively. The cut-points explored resulted in a number needed to screen from 7.1-8.5 persons/positive LTBI diagnosis, compared to 4.2 and 16.8 persons/positive LTBI diagnosis from EHR-documented birth in a HTBIC and current screening criteria, respectively. DISCUSSION: Using logistic regression with EHR data, we developed a simple yet useful model to predict birth in a HTBIC which decreased the number needed to screen compared to current LTBI screening criteria. CONCLUSION: Our model improves the ability to screen for LTBI in health care settings based on birth in a HTBIC.

Risk of severe clinical outcomes among persons with SARS-CoV-2 infection with differing levels of vaccination during widespread Omicron (B.1.1.529) and Delta (B.1.617.2) variant circulation in Northern California: A retrospective cohort study.

Background: The incidence of and risk factors for severe clinical outcomes with the Omicron (B.1.1.529) SARS-CoV-2 variant have not been well-defined. Methods: We conducted a retrospective cohort study to assess risks of severe clinical outcomes within 21 days after SARS-CoV-2 diagnosis in a large, diverse, integrated health system. Findings: Among 118,078 persons with incident SARS-CoV-2 infection, 48,101 (41%) were during the Omicron period and 69,977 (59%) during the Delta (B.1.617.2) period. Cumulative incidence of any hospitalization (2.4% versus 7.8%; adjusted hazard ratio [aHR] 0.55; 95% confidence interval [CI] (0.51-0.59), with low-flow oxygen support (1.6% versus 6.4%; aHR 0.46; CI 0.43-0.50), with high-flow oxygen support (0.6% versus 2.8%; aHR 0.47; CI 0.41-0.54), with invasive mechanical ventilation (0.1% versus 0.7%; aHR 0.43; CI 0.33-0.56), and death (0.2% versus 0.7%; aHR 0.54; CI 0.42-0.70) were lower in the Omicron than the Delta period. The risk of hospitalization was higher among unvaccinated persons (aHR 8.34; CI 7.25-9.60) and those who completed a primary COVID-19 vaccination series (aHR 1.72; CI 1.49-1.97) compared with those who completed a primary vaccination series and an additional dose. The strongest risk factors for all severe clinical outcomes were older age, higher body mass index and select comorbidities. Interpretation: Persons with SARS-CoV-2 infection were significantly less likely to develop severe clinical outcomes during the Omicron period compared with the Delta period. COVID-19 primary vaccination and additional doses were associated with reduced risk of severe clinical outcomes among those with SARS-CoV-2 infection. Funding: National Cancer Institute and The Permanente Medical Group.