Exploring the Relationship Between Depression and Adherence in Individuals with Type 2 Diabetes
Notice bibliographique
Résumé
Background: Depression is a well-known risk factor for poor medication adherence in individuals with diabetes; however, this association is based on cross-sectional and cohort studies measuring adherence after depression is diagnosed. Symptoms of depression often progress before medical attention is sought and diagnosis is made by a clinician. Prodromal symptoms of depression could affect medication adherence earlier than currently reported in literature. Additionally, little is known about changes in adherence rates once depression is treated. Given the strong association between depression and poor adherence to antihyperglycemic medications, early recognition and treatment of depression may improve adherence, leading to better glycemic control and prevention of future complications. Objectives: In individuals with diabetes and a new episode of depression, I sought to: 1) determine if symptoms of depression occurring before a diagnosis are associated with poor medication adherence; 2) determine if treatment of depression affects adherence to oral antihyperglycemic medications. Methods: Two retrospective cohort studies following adult new metformin users identified in Alberta Health’s administrative database between 2008 and 2018. Both studies identified a new depressive episode ≥1 year after metformin initiation using a validated case definition for depression. The first study examined adherence patterns in the year before the depression date. People with a new depressive episode were the exposed group and those without depression served as controls. Proportion of Days Covered (PDC) and Group Based Trajectory Modelling (GBTM) were used to examine adherence to oral antihyperglycemic medications one year prior to the depression date. Multivariable logistic regression was used to determine if depression was independently associated with a higher risk of poor adherence antecedent to depression diagnosis. The second study examined association between treatment of a new depressive episode and adherence. The exposure group included those who received at least 2 dispensations of any antidepressant medication within 90 days of depression date while the control group included those with <2 dispensations for any antidepressant medication. PDC was used to calculate adherence to oral antihyperglycemics on days 91-270 from the depression date. Multivariable logistic regression was used to determine if pharmacologic treatment of depression was associated with a lower risk of poor adherence to oral antihyperglycemic medications. Results: 165,056 (77%) new metformin users were identified from 214,762 individuals dispensed an oral antihyperglycemic. A total of 31,513 (19.1%) new metformin users had at least 1 depression-related service record after initiating metformin. Of those, 17,385 (10.5%) had their first depression-related service record at least one year after starting metformin. The mean duration between metformin initiation and a new episode of depression was 3.0 (SD 1.6) years. In the first study, individuals with depression were more likely to have poor adherence to oral antihyperglycemic medications (PDC <0.80) compared to controls (adjusted odds ratio 1.21; 95% CI 1.17, 1.26). Five trajectories were identified: nearly perfect adherence (PDC >0.95 [34.8% of cohort]), discontinued antihyperglycemics (PDC=0 [18.3% of cohort], poor initial adherence (PDC 0.75) that declined either rapidly (9.2% of cohort) or gradually (30.1% of cohort), and poor initial adherence (PDC 0.26) that increased gradually (7.6% of cohort). Individuals with depression were more likely to be in one of the four trajectories of poor adherence compared to controls (adjusted odds ratio 1.24; 95% CI 1.19-1.29). The second study included 7,220 (22.9%) individuals with a new depressive episode who had at least 1 year of data available before their study exit date, no antidepressant dispensations in the previous 6 months and not hospitalized for >50% of the outcome assessment window. A total 1,899 (26.3%) received ≥2 dispensations for antidepressants within 90 days of index date. After adjusting for other comorbidities and characteristics at baseline, individuals treated for depression were associated with a lower, but non-significant likelihood of poor adherence compared to those with no antidepressant treatment (adjusted odds ratio 0.91; 95%CI 0.81,1.02). Conclusion: Individuals with a depressive episode were more likely to have poor adherence in the year preceding diagnosis. Although treatment of a new depressive episode appears to be associated with a lower likelihood of poor adherence, the observed association did not reach statistical significance. These studies suggest depression screening and treatment may improve care for patients living with type 2 diabetes. By following adherence patterns, clinicians may identify individuals with diabetes who are experiencing symptoms of depression earlier and intervene sooner.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».