DETERMINANTS OF COVID-19 SEVERITY AND OUTCOME AMONG NORTHERN SASKATCHEWAN FIRST NATIONS
Notice bibliographique
Résumé
Background: Severe acute respiratory syndrome due to Coronavirus-2 (SARS-CoV-2) remains a global public health concern. Demographic and medical factors like vaccination status have been reported to influence the disease burden and outcome. Indigenous populations have been reported to be disproportionately affected by COVID -19; however, the impact of COVID-19 on Indigenous people in Canada remains understudied. The objectives of the study are to: 1) describe the characteristics of COVID-19 cases among on-reserve northern Saskatchewan First Nations people for the period March 2020 to December 2022; and 2) determine the association of demographic and medical factors with various indicators of COVID-19 severity and outcomes. Methods: We accessed de-identified data of 8,428 laboratory-confirmed COVID-19 cases during the period March 2020–December 2022. We conducted univariate, bivariate, and multivariate analyses to describe COVID-19 in this population and to determine the of association between various characteristics and COVID-19 severity. Characteristics of interest were demographic, clinical, and vaccine related. Three indicators of severity were included: hospitalization, admittance to an intensive care unit, and death. Results: Even though they account for <5% of the population, northern Saskatchewan First Nations on-reserve reported 5.6% of the total number of COVID-19 cases in the province. Over 90% of cases were under 65 years old. More than 53% of cases had no COVID-19 vaccination history at the time of infection. The most common clinical symptoms reported among COVID-19 patients in the study were cough, fever, loss of taste, and loss of smell. We observed that people 65 years and older were more likely to be hospitalized with severe COVID-19, despite having less than 10% of the infection rate of younger individuals. This finding is similar to other studies in Canada and other parts of the world. More hospitalization and deaths were associated with males than females. Hospitalization, ICU admission, and death were higher among unvaccinated persons when compared to those who were vaccinated. Similarly, hospitalization was higher among individuals who were vaccinated for >12 months before onset of infection. Like in other studies, the presence of symptoms, and co-existing medical conditions were significantly associated with increased odds of hospitalization. The risk of dying from COVID-19 was higher in people >65 years, males, and those with co-existing medical conditions. The risk of dying from COVID-19 iii was lowered following vaccination with two or more doses of COVID-19 vaccine when compared to those who received one dose of the vaccine or those who were not vaccinated. Conclusion: The implication of this study finding is that prioritizing vulnerable populations during COVID-19 and in subsequent public health emergencies and providing them with relevant interventions will reduce the burden of disease among these groups of individuals.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».