Predictors of nirmatrelvir-ritonavir receipt among COVID-19 patients in a large US health system.

A clear understanding of real-world uptake of nirmatrelvir-ritonavir for treatment of SARS-CoV-2 can inform treatment allocation strategies and improve interpretation of effectiveness studies. We used data from a large US healthcare system to describe nirmatrelvir-ritonavir dispenses among all SARS-CoV-2 positive patients aged ≥ 12 years meeting recommended National Institutes of Health treatment eligibility criteria for the study period between 1 January and 31 December, 2022. Overall, 10.9% (N = 34,791/319,900) of treatment eligible patients with SARS-CoV-2 infections received nirmatrelvir-ritonavir over the study period. Although uptake of nirmatrelvir-ritonavir increased over time, by the end of 2022, less than a quarter of treatment eligible patients with SARS-CoV-2 infections had received nirmatrelvir-ritonavir. Across patient demographics, treatment was generally consistent with tiered treatment guidelines, with dispenses concentrated among patients aged ≥ 65 years (14,706/63,921; 23.0%), and with multiple comorbidities (10,989/54,431; 20.1%). However, neighborhoods of lower socioeconomic status (upper third of neighborhood deprivation index [NDI]) had between 12% (95% CI: 7-18%) and 28% (25-32%) lower odds of treatment dispense over the time periods studied compared to the lower third of NDI distribution, even after accounting for demographic and clinical characteristics. A limited chart review (N = 40) confirmed that in some cases a decision not to treat was appropriate and aligned with national guidelines to use clinical judgement on a case-by-case basis. There is a need to enhance patient and provider awareness on the availability and benefits of nirmatrelvir-ritonavir for the treatment of COVID-19 illness.

Reactions following Pfizer-BioNTech COVID-19 mRNA vaccination and related healthcare encounters among 7,077 children aged 5-11 years within an integrated healthcare system.

BACKGROUND: Studies combining data from digital surveys and electronic health records (EHR) can be used to conduct comprehensive assessments on COVID-19 vaccine safety. METHODS: We conducted an observational study using data from a digital survey and EHR of children aged 5-11 years vaccinated with Pfizer-BioNTech COVID-19 mRNA vaccine across Kaiser Permanente Southern California during November 4, 2021-February 28, 2022. Parents/guardians who enrolled their children were sent a 14-day survey on reactions. Survey results were combined with EHR, and medical encounters were described for children whose parents or guardians indicated seeking medical care for vaccine-related symptoms. This study describes self-reported reactions (local and systemic) and additional symptoms (chest pain, tachycardia, and pre-syncope). RESULTS: The study recruited 7,077 participants aged 5-11 years who received the Pfizer-BioNTech COVID-19 mRNA vaccine. Of 6,247 participants with survey responses after dose 1, 2,176 (35 %) reported at least one systemic reaction, and 1,076 (32 %) of 3,401 respondents following dose 2 reported at least one systemic reaction. Local reactions were reported less frequently following dose 2 (1,113, 33 %) than dose 1 (3,140, 50 %). The most frequently reported reactions after dose 1 were pain at the injection site (48 %), fatigue (20 %), headache (12 %), myalgia (9 %) and fever (5 %). The most frequently reported symptoms after dose 2 were also pain at the injection site (30 %), fatigue (19 %), headache (13 %), myalgia (10 %) and fever (9 %). Post-vaccination reactions occurred most frequently-one day following vaccination. Chest pain or tachycardia were reported infrequently (1 %). EHR demonstrated that parents rarely sought care for post-vaccination symptoms, and among those seeking care, the most common symptoms documented in EHR were fever and nausea, comprising <0.5 % of children. No encounters were related to myocarditis. CONCLUSION: While post-vaccination reactions to the Pfizer-BioNTech COVID-19 mRNA vaccine were common in children aged 5-11 years, our data showed that in most cases they were transient and did not require medical care.

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System.

BACKGROUND: Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. OBJECTIVE: The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. METHODS: This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2-positive cases were used to validate the algorithm performance. RESULTS: A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). CONCLUSIONS: These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Health Care Utilization in the 6 Months Following SARS-CoV-2 Infection.

Importance: After SARS-CoV-2 infection, many patients present with persistent symptoms for at least 6 months, collectively termed post-COVID conditions (PCC). However, the impact of PCC on health care utilization has not been well described. Objectives: To estimate COVID-19-associated excess health care utilization following acute SARS-CoV-2 infection and describe utilization for select PCCs among patients who had positive SARS-CoV-2 test results (including reverse transcription-polymerase chain reaction and antigen tests) compared with control patients whose results were negative. Design, Setting, and Participants: This matched retrospective cohort study included patients of all ages from 8 large integrated health care systems across the United States who completed a SARS-CoV-2 diagnostic test during March 1 to November 1, 2020. Patients were matched on age, sex, race and ethnicity, site, and date of SARS-CoV-2 test and were followed-up for 6 months. Data were analyzed from March 18, 2021, to June 8, 2022. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: Ratios of rate ratios (RRRs) for COVID-19-associated health care utilization were calculated with a difference-in-difference analysis using Poisson regression models. RRRs were estimated overall, by health care setting, by select population characteristics, and by 44 PCCs. COVID-19-associated excess health care utilization was estimated by health care setting. Results: The final matched cohort included 127 859 patients with test results positive for SARS-CoV-2 and 127 859 patients with test results negative for SARS-CoV-2. The mean (SD) age of the study population was 41.2 (18.6) years, 68 696 patients in each group (53.7%) were female, and each group included 66 211 Hispanic patients (51.8%), 9122 non-Hispanic Asian patients (7.1%), 7983 non-Hispanic Black patients (6.2%), and 34 326 non-Hispanic White patients (26.9%). Overall, SARS-CoV-2 infection was associated with a 4% increase in health care utilization over 6 months (RRR, 1.04 [95% CI, 1.03-1.05]), predominantly for virtual encounters (RRR, 1.14 [95% CI, 1.12-1.16]), followed by emergency department visits (RRR, 1.08 [95% CI, 1.04-1.12]). COVID-19-associated utilization for 18 PCCs remained elevated 6 months from the acute stage of infection, with the largest increase in COVID-19-associated utilization observed for infectious disease sequelae (RRR, 86.00 [95% CI, 5.07-1458.33]), COVID-19 (RRR, 19.47 [95% CI, 10.47-36.22]), alopecia (RRR, 2.52 [95% CI, 2.17-2.92]), bronchitis (RRR, 1.85 [95% CI, 1.62-2.12]), pulmonary embolism or deep vein thrombosis (RRR, 1.74 [95% CI, 1.36-2.23]), and dyspnea (RRR, 1.73 [95% CI, 1.61-1.86]). In total, COVID-19-associated excess health care utilization amounted to an estimated 27 217 additional medical encounters over 6 months (212.9 [95% CI, 146.5-278.4] visits per 1000 patients). Conclusions and Relevance: This cohort study documented an excess health care burden of PCC in the 6 months after the acute stage of infection. As health care systems evolve during a highly dynamic and ongoing global pandemic, these data provide valuable evidence to inform long-term strategic resource allocation for patients previously infected with SARS-CoV-2.

Hospitalization and Emergency Department Encounters for COVID-19 After Paxlovid Treatment - California, December 2021-May 2022.

Nirmatrelvir/ritonavir (Paxlovid) is a combination protease inhibitor that blocks replication of SARS-CoV-2 (the virus that causes COVID-19) and has been shown to reduce the risk for hospitalization and death among patients with mild to moderate COVID-19 who are at risk for progression to severe disease* (1). In December 2021, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for early treatment with Paxlovid among persons with mild to moderate cases of COVID-19 who are at high risk for progression to severe disease (2). FDA and a small number of published case reports have documented recurrence of COVID-19 symptoms or a positive viral test result (COVID-19 rebound) 2-8 days after recovery or a negative SARS-CoV-2 test result among patients treated with Paxlovid (3-7); however, large-scale studies investigating severe illness after Paxlovid treatment are limited. This study used electronic health record (EHR) data from a large integrated health care system in California (Kaiser Permanente Southern California [KPSC]) to describe hospital admissions and emergency department (ED) encounters related to SARS-CoV-2 infections during the 5-15 days after pharmacy dispensation of a 5-day treatment course of Paxlovid. Among 5,287 persons aged ≥12 years who received Paxlovid during December 31, 2021-May 26, 2022, 73% had received ≥3 doses of COVID-19 vaccine†, and 8% were unvaccinated. During the 5-15 days after Paxlovid treatment was dispensed, six hospitalizations and 39 ED encounters considered to be related to SARS-CoV-2 infection were identified, representing <1% of all patients to whom Paxlovid treatment was dispensed during the study period. Among these 45 persons, 21 (47%) were aged ≥65 years, and 35 (78%) had at least one underlying medical condition§ (8). This study found that hospitalization or ED encounters for COVID-19 during the 5-15 days after Paxlovid treatment was dispensed for mild to moderate COVID-19 illness were rarely identified. When administered as an early-stage treatment, Paxlovid might prevent COVID-19-related hospitalization among persons with mild to moderate cases of COVID-19 who are at risk for progression to severe disease.

Distribution of SARS-CoV-2 Variants in a Large Integrated Health Care System - California, March-July 2021.

Data from observational studies demonstrate that variants of SARS-CoV-2, the virus that causes COVID-19, have evolved rapidly across many countries (1,2). The SARS-CoV-2 B.1.617.2 (Delta) variant of concern is more transmissible than previously identified variants,* and as of September 2021, is the predominant variant in the United States.† Studies characterizing the distribution and severity of illness caused by SARS-CoV-2 variants, particularly the Delta variant, are limited in the United States (3), and are subject to limitations related to study setting, specimen collection, study population, or study period (4-7). This study used whole genome sequencing (WGS) data on SARS-CoV-2-positive specimens collected across Kaiser Permanente Southern California (KPSC), a large integrated health care system, to describe the distribution and risk of hospitalization associated with SARS-CoV-2 variants during March 4-July 21, 2021, by patient vaccination status. Among 13,039 SARS-CoV-2-positive specimens identified from KPSC patients during this period, 6,798 (52%) were sequenced and included in this report. Of these, 5,994 (88%) were collected from unvaccinated persons, 648 (10%) from fully vaccinated persons, and 156 (2%) from partially vaccinated persons. Among all sequenced specimens, the weekly percentage of B.1.1.7 (Alpha) variant infections increased from 20% to 67% during March 4-May 19, 2021. During April 15-July 21, 2021, the weekly percentage of Delta variant infections increased from 0% to 95%. During March 4-July 21, 2021, the weekly percentage of variants was similar among fully vaccinated and unvaccinated persons, but the Delta variant was more commonly identified among vaccinated persons then unvaccinated persons overall, relative to other variants. The Delta variant was more prevalent among younger persons, with the highest percentage (55%) identified among persons aged 18-44 years. Infections attributed to the Delta variant were also more commonly identified among non-Hispanic Black persons, relative to other variants. These findings reinforce the importance of continued monitoring of SARS-CoV-2 variants and implementing multiple COVID-19 prevention strategies, particularly during the current period in which Delta is the predominant variant circulating in the United States.

Circulating asymmetric dimethylarginine and cognitive decline: A 4-year follow-up study of the 1936 Aberdeen Birth Cohort.

BACKGROUND: The underlying mechanisms leading to dementia and Alzheimer's disease (AD) are unclear. Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthase, may be associated with cognitive decline, but population-based evidence is lacking. METHODS: Change in cognitive performance was assessed in participants of the Aberdeen Birth Cohort of 1936 using longitudinal Raven's progressive matrices (RPM) between 2000 and 2004. Multiple linear regression was used to estimate the association between ADMA concentrations in 2000 and change in cognitive performance after adjustment for potential confounders. RESULTS: A total of 93 participants had complete information on cognitive performance between 2000 and 2004. Mean plasma ADMA concentrations were approximately 0.4 µmol/L lower in those participants with stable or improved RPM scores over follow-up compared with participants whose cognitive performance worsened. In confounder-adjusted analysis, one SD (0.06 µmol/L) increase in ADMA at 63 years of age was associated with an average reduction in RPM of 1.26 points (95% CI 0.14-2.26) after 4 years. CONCLUSION: Raised plasma ADMA concentrations predicted worsening cognitive performance after approximately 4 years in this cohort of adults in late-middle age. These findings have implications for future research, including presymptomatic diagnosis or novel therapeutic targets for dementia and AD.