PREDICTORS OF SEVERITY AND DURATION OF INFECTIOUS DISEASE OUTBREAKS IN LONG-TERM CARE FACILITIES IN THE SASKATOON HEALTH REGION
Notice bibliographique
Résumé
Introduction Infectious disease outbreaks in long-term care facilities (LTCFs) pose a significant threat to the health and well-being of residents. While prevention of all outbreaks is not realistic, minimising the severity and duration of outbreaks can be achieved through the prompt implementation of infection control policies, outbreak reporting to Public Health, and outbreak management. In this study, we investigated variables associated with outbreak severity and duration. Objective The objective was to determine if reporting time, the days between illness onset date and date of outbreak reporting to Public Health Services, is a significant predictor of infectious disease severity and duration, after controlling for LTCF location, outbreak type, year and staff to resident ratio. Methods The Public Health Observatory of the Saskatoon Health Region provided LTCF infectious disease outbreak data from 2005 to 2011. A total of 130 outbreaks were eligible for analysis, but only 60 outbreaks were assessed because they had complete data. Generalised linear models were used to test associations of reporting time with outbreak duration and attack rate. These data were modelled using normal and negative binomial distributions, respectively. Outbreak duration was the time, in days, between the index case and the outbreak conclusion. Attack rate was modelled as the number of residents ill, with the number of susceptible residents as the model offset. Model covariates included LTCF location (urban, rural), outbreak type (respiratory, gastrointestinal), year and the LTCF staff to resident ratio. Results Sixty outbreaks occurring in 17 facilities were analysed. Outbreak duration ranged from 4 to 32 days, with a mean of 16.2 days and a median of 16.5 days. The attack rate ranged from 0.0% to 68.7%, with a mean of 21.0% and a median of 16.3%. Reporting time ranged from 0 to 21 days, with mean and median values of 3.6 and 3.0 days, respectively. Reporting time was significantly associated with outbreak duration (0.8±0.2, p=0.0002), while LTCF location, outbreak type, year and staff to resident ratio were not significant (p>0.05). Reporting time was not significantly associated with attack rate (p=0.38). However, urban facilities had lower attack rates (relative rate (RR)=0.6, 95% CI 0.5 to 0.8), and gastrointestinal outbreaks had higher attack rates than respiratory outbreaks (RR=2.2, 95% CI 1.7 to 2.8). Year and staff to resident ratio were not significantly associated with attack rate (p>0.05). Conclusions Our results indicate that the time to report an outbreak is associated with the outbreak duration, and for each day the outbreak is not reported the duration increases by approximately one day. We did not find evidence that reporting time is associated with the attack rate. However, LTCF location and the type of outbreak were associated with attack rate. Attack rates were lower for urban than rural LTCFs, and higher for gastrointestinal than respiratory outbreaks. The amount of missing data was substantial; future analyses will consider imputation methods that will result in unbiased estimates of model associations.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».