Background Long COVID patients experience persistent symptoms after acute SARS-CoV-2 infection. Healthcare utilization data could provide critical information on the disease burden of long COVID for service planning, however, not all patients are diagnosed or assigned long COVID diagnostic codes. We developed an algorithm to identify individuals with long COVID using population-level health administrative data from British Columbia (BC), Canada. Methods An elastic net penalized logistic regression model was developed to identify long COVID patients based on demographic characteristics, pre-existing conditions, COVID-19-related data, and all symptoms/conditions recorded 28-183 days after the COVID-19 symptom onset/reported (index) date of known long COVID patients (N = 2,430) and a control group (N = 24,300); selected from all adult COVID-19 cases in BC with an index date on/before October 31, 2021 (N = 168,111). Known long COVID cases were diagnosed in a clinic and/or had the ICD-10-CA code for “Post COVID-19 condition” in their records. Results The algorithm retained known symptoms/conditions associated with long COVID, demonstrating high sensitivity (86%), specificity (86%), and area under the receiver operator curve (93%). It identified 25,220 (18%) long COVID patients among the remaining 141,381 adult COVID-19 cases; over ten times the number of known cases. Known and predicted long COVID patients had comparable demographic and health-related characteristics. Conclusions Our algorithm identified long COVID patients with a high level of accuracy. This large cohort of long COVID patients will serve as a platform for robust assessments on the clinical course of long COVID, and provide much needed concrete information for decision-making.
The SARS‐CoV‐2 variant Omicron emerged in late 2021. In British Columbia (BC), Canada, and globally, three genetically distinct subvariants of Omicron, BA.1, BA.2, and BA.5, emerged and became dominant successively within an 8‐month period. SARS‐CoV‐2 subvariants continue to circulate in the population, acquiring new mutations that have the potential to alter infectivity, immunity, and disease severity. Here, we report a propensity‐matched severity analysis from residents of BC over the course of the Omicron wave, including 39,237 individuals infected with BA.1, BA.2, or BA.5 based on paired high‐quality sequence data and linked to comprehensive clinical outcomes data between December 23, 2021 and August 31, 2022. Relative to BA.1, BA.2 cases were associated with a 15% and 28% lower risk of hospitalization and intensive care unit (ICU) admission (aHRhospital = 1.17; 95% confidence interval [CI] = 1.096–1.252; aHRICU = 1.368; 95% CI = 1.152–1.624), whereas BA.5 infections were associated with an 18% higher risk of hospitalization (aHRhospital = 1.18; 95% CI = 1.133–1.224) after accounting for age, sex, comorbidities, vaccination status, geography, and social determinants of health. Phylogenetic analysis revealed no specific subclades associated with more severe clinical outcomes for any Omicron subvariant. In summary, BA.1, BA.2, and BA.5 subvariants were associated with differences in clinical severity, emphasizing how variant‐specific monitoring programs remain critical components of patient and population‐level public health responses as the pandemic continues.
Background: Polymerase chain reaction (PCR) cycle threshold (Ct) values can be used to estimate the viral burden of Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2) and predict population-level epidemic trends. We investigated the use of machine learning (ML) and epidemic transmission modeling based on Ct value distribution for SARS-CoV-2 incidence prediction during an Omicron-predominant period.Methods: Using simulated data, we developed a ML model to predict the reproductive number based on Ct value distribution, and validated it on out-of-sample province-level data. We also developed an epidemiological model and fitted it to province-level data to accurately predict incidence.Results: Based on simulated data, the ML model predicted the reproductive number with highest performance on out-of-sample province-level data. The epidemiological model was validated on outbreak data, and fitted to province-level data, and accurately predicted incidence.Conclusions: These modeling approaches can complement traditional surveillance, especially when diagnostic testing practices change over time. The models can be tailored to different epidemiological settings and used in real time to guide public health interventions.
Contexte : Le taux d’incidence de la tuberculose chez les Premières Nations du nord de la Saskatchewan vivant dans les réserves est 1,5 fois plus élevé que la moyenne nationale. En décembre 2018, un membre de l’une de ces communautés a été diagnostiqué avec une tuberculose avec un frottis positif 4+, ce qui a déclenché une enquête sur l’éclosion. Objectifs : Décrire la réponse de la santé publique à l’enquête sur l’éclosion de tuberculose et mettre en évidence les facteurs de risque associés à la transmission de la tuberculose dans le nord de la Saskatchewan; et souligner la pertinence de l’outil de recherche des contacts basée sur les réseaux sociaux dans la gestion des éclosions. Méthodes : L’analyse descriptive comprenait les cas de tuberculose active et les cas d’infection tuberculeuse latente (ITL) liés au cas index par une recherche des contacts. Les données ont été recueillies à partir des cas de tuberculose active. Des analyses statistiques ont été effectuées et une analyse des réseaux sociaux a été réalisée en utilisant les lieux de résidence comme points de contact entre les cas. Résultats : Au total, huit cas de tuberculose active et 41 cas d’ITL ont été identifiés dans le cadre de cette éclosion entre décembre 2018 et mai 2019. La moitié des cas (4/8) étaient âgés de 25 à 34 ans, et cinq d’entre eux avaient un frottis négatif. Un tiers des personnes atteintes d’ITL étaient âgées de 15 à 24 ans, et environ la moitié d’entre elles ont obtenu un résultat positif au nouveau test cutané à la tuberculine (TCT). Les facteurs de risque couramment rapportés pour les cas de tuberculose et d’ITL étaient : la consommation d’alcool, le tabagisme, la consommation de marijuana, une infection tuberculeuse antérieure et être en situation d’itinérance. L’analyse des réseaux sociaux a indiqué une relation entre l’augmentation de la centralité du nœud et le fait de devenir un cas actif. Conclusion : La recherche en temps réel de contacts basée sur les réseaux sociaux, utilisée dans le cadre de la recherche active de cas, a été très efficace pour identifier les cas, et le soutien infirmier renforcé, les cliniques mobiles et la radiographie mobile ont bien fonctionné comme moyen de confirmer les cas et de proposer un traitement. Les éclosions de tuberculose dans les communautés des Premières Nations du nord de la Saskatchewan vivant dans les réserves sont favorisées par des facteurs propres à la population. Les efforts visant à mettre en œuvre des interventions adaptées au contexte sont primordiaux pour gérer les éclosions de tuberculose et prévenir leur transmission future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.