Evaluating methods to define place of residence in Canadian administrative data and the impact on observed associations with all-cause mortality in type 2 diabetes
Notice bibliographique
Résumé
An individual’s location of residence may impact health, however, health services and outcomes research generally use a single point in time to define where an individual resides. While this estimate of residence becomes inaccurate when the study subject moves, the impact on observed associations is not known. This study quantifies the impact of different methods to define residence (rural, urban, metropolitan) on the association with all-cause mortality. A diabetes cohort of new metformin users was identified from administrative data in Alberta, Canada between 2008 and 2019. An individual’s residence (rural/urban/metropolitan) was defined from postal codes using 4 different methods: residence defined at 1-year before first metformin (this served as the reference model), comparison 1- stable residence for 3 years before first metformin, comparison 2– residence as time-varying (during the outcome observation window), and comparison 3 - nested case control (residence closest to the index date after identifying cases and controls). Multivariable Cox proportional hazard and logistic regression models were constructed to examine the association between residence definitions and all-cause mortality. We identified 157,146 new metformin users (mean age of 55 years and 57% male) and 8,444 (5%) deaths occurred during the mean follow up of 4.7 (SD 2.3) years. There were few instances of moving after first metformin; 2.6% of individuals moved to a smaller centre (metropolitan to urban or rural, or urban to rural) and 3.1% moved to a larger centre (rural to urban or metropolitan, or urban to metropolitan). The association between rural residence and all-cause mortality was consistent (aHR:1.18; 95%CI:1.12–1.24), regardless of the method used to define residence. The method used to define residence in a population of adults newly treated with metformin for type 2 diabetes has minimal impact on measures of all-cause mortality, possibly due to infrequent migration. The observed association between residence and mortality is compelling but requires further investigation and more robust analysis. There is growing evidence describing the impact of where an individual lives on their health. However, most of these studies identify place of residence at a single point in time and do not consider when a person moves. This could result in misclassification, which could over- or underestimate the influence of residence on health outcomes. In this study, residence was defined with 4 different methods: at 1-year before starting metformin for type 2 diabetes; stable residence for 3-years before starting metformin; accounting for changes in residence after being newly treated with metformin for type 2 diabetes; and near the end of the study at death or the end of follow up. The results of this study describe that individuals rarely move after treatment initiation with metformin for type 2 diabetes and that living in a rural area has a higher risk of death from any cause, further investigation into the latter is required. Individuals rarely move after treatment initiation with metformin for type 2 diabetes. In population-based cohort studies of adults with type 2 diabetes, classifying place of residence at baseline, 1-year prior to the index date is reasonable. The increased mortality of rural residents requires further investigation.
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 | 0,093 | 0,247 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,004 | 0,011 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».