SARS-CoV-2 Omicron subvariant genomic variation associations with immune evasion in Northern California: A retrospective cohort study.

Background: The possibility of association between SARS-CoV-2 genomic variation and immune evasion is not known among persons with Omicron variant SARS-CoV-2 infection. Methods: In a retrospective cohort, using Poisson regression adjusting for sociodemographic variables and month of infection, we examined associations between individual non-lineage defining mutations and SARS-CoV-2 immunity status, defined as a) no prior recorded infection, b) not vaccinated but with at least one prior recorded infection, c) complete primary series vaccination, and/or d) primary series vaccination and ≥ 1 booster. We identified all non-synonymous single nucleotide polymorphisms (SNPs), insertions and deletions in SARS-CoV-2 genomes with ≥5% allelic frequency and population frequency of ≥5% and ≤95%. We also examined correlations between the presence of SNPs with each other, with subvariants, and over time. Results: Seventy-nine mutations met inclusion criteria. Among 15,566 persons infected with Omicron SARS-CoV-2, 1,825 (12%) were unvaccinated with no prior recorded infection, 360 (2%) were unvaccinated with a recorded prior infection, 13,381 (86%) had a complete primary series vaccination, and 9,172 (58%) had at least one booster. After examining correlation between SNPs, 79 individual non-lineage defining mutations were organized into 38 groups. After correction for multiple testing, no individual SNPs or SNP groups were significantly associated with immunity status levels. Conclusions: Genomic variation identified within SARS-CoV-2 Omicron specimens was not significantly associated with immunity status, suggesting that contribution of non-lineage defining SNPs to immune evasion is minimal. Larger-scale surveillance of SARS-CoV-2 genomes linked with clinical data can help provide information to inform future vaccine development.

Impact of COVID-19 on myalgic encephalomyelitis/chronic fatigue syndrome-like illness prevalence: A cross-sectional survey.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) can be triggered by infectious agents including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the impact of the coronavirus disease 2019 (COVID-19) pandemic on ME/CFS prevalence is not well characterized. METHODS: In this population-based cross-sectional study, we enrolled a stratified random sample of 9,825 adult participants in the Kaiser Permanente Northern California (KPNC) integrated health system from July to October 2022 to assess overall ME/CFS-like illness prevalence and the proportion that were identified following COVID-19 illness. We used medical record and survey data to estimate the prevalence of ME/CFS-like illness based on self-reported symptoms congruent with the 2015 Institute of Medicine ME/CFS criteria. History of COVID-19 was based on a positive SARS-CoV-2 nucleic acid amplification test or ICD-10 diagnosis code in the medical record, or self-report of prior COVID-19 on a survey. RESULTS: Of 2,745,374 adults in the eligible population, an estimated 45,892 (95% confidence interval [CI]: 32,869, 58,914) or 1.67% (CI 1.20%, 2.15%) had ME/CFS-like illness. Among those with ME/CFS-like illness, an estimated 14.12% (CI 3.64%, 24.6%) developed the illness after COVID-19. Among persons who had COVID-19, those with ME/CFS-like illness after COVID-19 were more likely to be unvaccinated and to have had COVID-19 before June 1, 2021. All persons with ME/CFS-like illness had significant impairment in physical, mental, emotional, social, and occupational functioning compared to persons without ME/CFS-like illness. CONCLUSIONS: In a large, integrated health system, 1.67% of adults had ME/CFS-like illness and 14.12% of all persons with ME/CFS-like illness developed it after COVID-19. Though COVID-19 did not substantially increase ME/CFS-like illness in the KPNC population during the study time period, ME/CFS-like illness nevertheless affects a notable portion of this population and is consistent with estimates of ME/CFS prevalence in other populations. Additional attention is needed to improve awareness, diagnosis, and treatment of ME/CFS.

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.