Investigating use of diagnostic codes for post-COVID- 19 condition in Ontario health administrative data
Notice bibliographique
Résumé
BACKGROUND: In January 2023 the Ontario Health Insurance Plan (OHIP) introduced a diagnostic code for post-COVID-19 condition (PCC). We used this code to estimate the incidence rate of PCC, to compare demographic and clinical characteristics of individuals who received a PCC code to those who didn't, and to investigate healthcare utilization of individuals who received a PCC code. METHODS: We conducted a retrospective cohort study using health administrative data from Ontario, Canada (population approximately 15 million). Individuals who had received a PCC diagnostic code between January 2023 and January 2024 were identified using OHIP, a physician billing database. For the entire population of Ontario, crude incidence rates of PCC were computed and patient characteristics (including age, sex, geographic location, comorbidities, and marginalization index) were collected and compared between individuals who had received a PCC code and those who hadn't using logistic regression models. Healthcare utilization rates for people who received a PCC code were compared pre-pandemic (January 1st, 2017 to March 31st, 2020), pre-PCC (April 1st, 2020 to 24-weeks pre-PCC diagnostic code), and post-PCC (24-weeks pre-PCC diagnostic code to study end). RESULTS: A PCC code was received by 7,343 individuals. Median age was 62, and 60% were female. When compared to the entire population of Ontario, female sex, older adults, Northern Ontario residents, and comorbid individuals had greater odds of receiving a PCC code. People who were visible minorities, immigrants, and had less access to material resources had lower odds of receiving a PCC code. Healthcare utilization rates, pre-pandemic, pre-PCC, and post-PCC were 14.59 (CI 13.63-15.61), 27.43 (CI 25.01-30.27), and 100.61 (CI 93.39-107.73) encounters per person-year respectively. CONCLUSION: The number of cases captured was lower than what Health Canada estimates would indicate and it is likely that the code is underrepresenting PCC in Ontario. The substantial increases in healthcare utilization suggests the code is capturing severe PCC cases. The characteristics of the cohort were similar to what has been described in peer-reviewed literature, suggesting that the patients in this cohort have PCC. This code could offer a promising way to study a large diverse population of people with PCC.
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 | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».