A Stakeholder-Engaged Economic Evaluation of Site-Specific Wastewater-Based Surveillance for COVID-19 Outbreak Control in Long-Term Care Facilities
Notice bibliographique
Résumé
Wastewater-based surveillance (WBS) gained prominence during the coronavirus disease 2019 (COVID-19) pandemic as a non-invasive infectious disease monitoring tool. Long-term care facilities (LTCF) have been disproportionately impacted during the pandemic. Effective surveillance methods are needed to provide early warnings of new infections to protect both residents and staff. This study aims to analyze the cost-effectiveness of incorporating site-specific WBS in monitoring COVID-19 in LTCFs. This thesis leveraged longitudinal data based on nine facilities in the city of Edmonton, Alberta, Canada participating in the WBS program between January 2021 and May 2023. The study comprises four chapters. The first study (Chapter 2) focuses on exploring stakeholders’ opinions in applying site-specific WBS in LTCFs and engaging them to develop the economic evaluation plan. Their feedback guided the cost-effectiveness analysis by proposing WBS-based actions and refining key assumptions, perspectives, and outcome measures, thereby enhancing the relevance of the evaluation. The second study (Chapter 3) involves a statistical analysis of wastewater data. Constrained distributed lag models were used to estimate the lead time of WBS to detect the existence of COVID-19 infections before clinical diagnosis and the accuracy of wastewater data in predicting mass testing outcomes. Results demonstrated that wastewater detection preceded clinical cases by up to five days, with a three-day lead time being the most common. Additionally, wastewater data predicted mass testing outcomes with 80% accuracy for negative results and 60% for positive results, better predicting resident cases than staff cases (74% vs. 33%, p = 0.02). No significant associations were found between predictive performance and WBS operational factors. The third study (Chapter 4) developed a Susceptible-Infected-Case-Recovered (SICR) model to compare WBS-informed interventions with the Standard of Care across pandemic phases between March 2020 to February 2023. The model estimated that WBS-informed prevalence testing in the early pandemic phase averted 122 cases (95% credible interval (C.I.): 103–143) across four outbreaks, while WBS-informed symptom screening in the endemic transition phase averted 132 cases (95% C.I.: 106-152) across 17 outbreaks. These results highlight the potential of WBS in outbreak control, particularly when vaccination or specific treatment is unavailable. The last study (Chapter 5) integrated the case-averted estimates into a cost-effectiveness analysis. WBS implementation cost at nine LTCFs over three years was $1.6 million, in 2024 Canadian dollars. WBS is shown to be the most cost-effective during the early pandemic, with an expected incremental cost-effectiveness ratio (ICER) of $2,065 per quality-adjusted life year (QALY). Across all three years, the expected ICER was $47,263/QALY, which is within the $50,000/QALY threshold. Sensitivity analyses revealed that implementation costs, the characteristics of the virus, and healthcare system responses significantly influenced cost-effectiveness. Overall, this thesis demonstrates the potential cost-effectiveness of WBS in preventing COVID-19 outbreaks in LTCFs. This thesis will inform various stakeholders about the value of WBS and provide insights into its future development and application for other infectious diseases.
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 | 0,030 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».