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PURPOSE: COVID-19 is a condition that can lead to other chronic conditions. These conditions are frequently diagnosed in the primary care setting. We used a novel primary care registry to quantify the burden of post-COVID conditions among adult patients with a COVID-19 diagnosis across the United States. METHODS: We used the American Family Cohort, a national primary care registry, to identify study patients. After propensity score matching, we assessed the prevalence of 17 condition categories individually and cumulatively, comparing patients having COVID-19 in 2020-2021 with (1) historical control patients having influenza-like illness in 2018 and (2) contemporaneous control patients seen for wellness or preventive visits in 2020-2021. RESULTS: We identified 28,215 patients with a COVID-19 diagnosis and 235,953 historical control patients with influenza-like illness. The COVID-19 group had higher prevalences of breathing difficulties (4.2% vs 1.9%), type 2 diabetes (12.0% vs 10.2%), fatigue (3.9% vs 2.2%), and sleep disturbances (3.5% vs 2.4%). There were no differences, however, in the postdiagnosis monthly trend in cumulative morbidity between the COVID-19 patients (trend = 0.026; 95% CI, 0.025-0.027) and the patients with influenza-like illness (trend = 0.026; 95% CI, 0.023-0.027). Relative to contemporaneous wellness control patients, COVID-19 patients had higher prevalences of breathing difficulties and type 2 diabetes. CONCLUSIONS: Our findings show a moderate burden of post-COVID conditions in primary care, including breathing difficulties, fatigue, and sleep disturbances. Based on clinical registry data, the prevalence of post-COVID conditions in primary care practices is lower than that reported in subspecialty and hospital settings.
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COVID-19 , Gripe Humana , Atención Primaria de Salud , Sistema de Registros , SARS-CoV-2 , Humanos , COVID-19/epidemiología , Masculino , Femenino , Estados Unidos/epidemiología , Atención Primaria de Salud/estadística & datos numéricos , Persona de Mediana Edad , Gripe Humana/epidemiología , Adulto , Anciano , Prevalencia , Enfermedad Crónica/epidemiologíaRESUMEN
The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.
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We report on the experience of small primary care practices participating in a national clinical registry with COVID-19 vaccines and vaccination data. At the end of 2021, 11.2 percent of these practices' 3.9 million patients had records of COVID-19 vaccination; 43.1 percent of clinics had no record of patients' COVID-19 vaccinations, but 93.4 percent of clinics had provided or recorded other routine vaccinations.
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Vacunas contra la COVID-19 , COVID-19 , Humanos , COVID-19/prevención & control , Vacunación , Atención Primaria de SaludRESUMEN
Many stool-based gut microbiome studies have highlighted the importance of the microbiome. However, we hypothesized that stool is a poor proxy for the inner-colonic microbiome and that studying stool samples may be inadequate to capture the true inner-colonic microbiome. To test this hypothesis, we conducted prospective clinical studies with up to 20 patients undergoing an FDA-cleared gravity-fed colonic lavage without oral purgative pre-consumption. The objective of this study was to present the analysis of inner-colonic microbiota obtained non-invasively during the lavage and how these results differ from stool samples. The inner-colonic samples represented the descending, transverse, and ascending colon. All samples were analyzed for 16S rRNA and shotgun metagenomic sequences. The taxonomic, phylogenetic, and biosynthetic gene cluster analyses showed a distinctive biogeographic gradient and revealed differences between the sample types, especially in the proximal colon. The high percentage of unique information found only in the inner-colonic effluent highlights the importance of these samples and likewise the importance of collecting them using a method that can preserve these distinctive signatures. We proposed that these samples are imperative for developing future biomarkers, targeted therapeutics, and personalized medicine.
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BACKGROUND: Little is known about the role of the genitourinary and gastrointestinal microbiota in the pathogenesis of male infertility. OBJECTIVE: To compare the taxonomic and functional profiles of the gut, semen, and urine microbiomes of infertile and fertile men. DESIGN, SETTING, AND PARTICIPANTS: We prospectively enrolled 25 men with primary idiopathic infertility and 12 healthy men with proven paternity, and we collected rectal swabs, semen samples, midstream urine specimens, and experimental controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We performed comprehensive semen analysis, 16S rRNA sequencing for quantitative high-resolution taxonomy, and shotgun metagenomics with a median of 140 million reads per sample for functional metabolic pathway profiling. RESULTS AND LIMITATIONS: We identified a diverse semen microbiome with modest similarity to the urinary microbiome. Infertile men harbored increased seminal α-diversity and distinct ß-diversity, increased seminal Aerococcus, and decreased rectal Anaerococcus. Prevotella abundance was inversely associated with sperm concentration, and Pseudomonas was directly associated with total motile sperm count. Vasectomy appeared to alter the seminal microbiome, suggesting a testicular or epididymal contribution. Anaerobes were highly over-represented in the semen of infertile men with a varicocele, but oxidative stress and leukocytospermia were associated with only subtle differences. Metagenomics data identified significant alterations in the S-adenosyl-L-methionine cycle, which may play a multifaceted role in the pathogenesis of infertility via DNA methylation, oxidative stress, and/or polyamine synthesis. CONCLUSIONS: This pilot study represents the first comprehensive investigation into the microbiome in male infertility. These findings provide the foundation for future investigations to explore causality and identify novel microbiome-based diagnostics and therapeutics for men with this complex and emotionally devastating disease. PATIENT SUMMARY: We explored the resident populations of bacteria living in the gut, semen, and urine of infertile and fertile men. We found several important bacterial and metabolic pathway differences with the potential to aid in diagnosing and treating male infertility in the future.