Real world outcomes of distributing Lucira Check-It® COVID self-tests in Ontario, Canada: the GetaKit COVID study
Notice bibliographique
Résumé
BACKGROUND: In Ontario, Canada we developed and implemented an online screening algorithm for the distribution of HIV self-tests, known as GetaKit. During the COVID pandemic, we adapted the GetaKit algorithm to screen for COVID based on population and infection data and distributed COVID rt-LAMP self-tests (using the Lucira Check-It®) to eligible participants. METHODS: GetaKit/COVID was a prospective observational study that occurred over a 7-month period from September 2021 to April 2022. All potential participants completed an online registration and risk assessment, including demographic information, COVID symptoms and risk factors, and vaccination status. Bivariate comparisons were performed for three outcomes: results reporting status, vaccination status, and COVID diagnosis status. Data were analysed using Chi-Square for categorial covariates and Independent Samples T-Test and Mann-Whitney U test for continuous covariates. Bivariate logistic regression models were applied to examine associations between the covariates and outcomes. RESULTS: During the study period, we distributed 6469 COVID self-tests to 4160 eligible participants; 46% identified as Black, Indigenous or a Person of Colour (BIPOC). Nearly 70% of participants reported their COVID self-test results; 304 of which were positive. Overall, 91% also reported being vaccinated against COVID. Statistical analysis found living with five or fewer people, having tested for COVID previously, and being fully vaccinated were positive factors in results reporting. For COVID vaccination, people from large urban centers, who identified their ethnicity as white, and who reported previous COVID testing were more likely to be fully vaccinated. Finally, being identified as a contact of someone who had tested positive for COVID and the presence of COVID-related symptoms were found to be positive factors in diagnosis. CONCLUSIONS: While most participants who accessed this service were vaccinated against COVID and the majority of diagnoses were identified in participants who had symptoms of, or an exposure to, COVID, our program was able to appropriately link participants to recommended follow-up based on reported risks and results. These findings highlight the utility of online screening algorithms to provide health services, particularly for persons with historical barriers to healthcare access, such as BIPOC or lower-income groups.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».