The Canadian Chronic Disease Surveillance System: A Distributed Surveillance Model
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Notice bibliographique
Résumé
ObjectiveTo describe the process, benefits, and challenges of implementinga distributed model for chronic disease surveillance across thirteenCanadian jurisdictions.IntroductionThe Public Health Agency of Canada (PHAC) established theCanadian Chronic Disease Surveillance System (CCDSS) in 2009 tofacilitate national estimates of chronic disease prevalence, incidence,and health outcomes. The CCDSS uses population-based linkedhealth administrative databases from all provinces/territories (P/Ts)and a distributed analytic protocol to produce standardized diseaseestimates.MethodsThe CCDSS is founded on deterministic linkage of threeadministrative health databases in each Canadian P/T: health insuranceregistration files, physician billing claims, and hospital dischargeabstracts. Data on all residents who are eligible for provincial orterritorial health insurance (about 97% of the Canadian population) arecaptured in the health insurance registration files. Thus, the CCDSScoverage is near-universal. Disease case definitions are developed byexpert Working Groups after literature reviews are completed andvalidation studies are undertaken. Feasibility studies are initiatedin selected P/Ts to identify challenges when implementing thedisease case definitions. Analytic code developed by PHAC is thendistributed to all P/Ts. Data quality surveys are routinely conductedto identify database characteristics that may bias disease estimatesover time or across P/Ts or affect implementation of the analytic code.The summary data produced in each P/T are approved by ScientificCommittee and Technical Committee members and then submitted toPHAC for further analysis and reporting.ResultsNational surveillance or feasibility studies are currently ongoing fordiabetes, hypertension, selected mental illnesses, chronic respiratorydiseases, heart disease, neurological conditions, musculoskeletalconditions, and stroke. The advantages of the distributed analyticprotocol are (Figure 1): (a) changes in methodology can be easilymade, and (b) technical expertise to implement the methodology is notrequired in each P/T. Challenges in the use of the distributed analyticprotocol are: (a) heterogeneity in healthcare databases across P/Tsand over time, (b) the requirement that each P/T use the minimum setof data elements common to all jurisdictions when producing diseaseestimates, and (c) balancing disclosure guidelines to ensure dataconfidentiality with comprehensive reporting. Additional challenges,which include incomplete data capture for some databases and poormeasurement validity of disease diagnosis codes for some chronicconditions, must be continually addressed to ensure the scientificrigor of the CCDSS methodology.ConclusionsThe CCDSS distributed analytic protocol offers one model fornational chronic disease surveillance that has been successfullyimplemented and sustained by PHAC and its P/T partners. Manylessons have been learned about national chronic disease surveillanceinvolving jurisdictions that are heterogeneous with respect tohealthcare databases, expertise, and population characteristics.
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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 | 0,013 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,009 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 découle