Examining the Use of Telehealth During the COVID-19 Pandemic Among Patients With Type 2 Diabetes at a Federally Qualified Health Center
Notice bibliographique
Résumé
Background The COVID-19 pandemic necessitated an expedited shift toward remote health care delivery (eg, telehealth). Prior research has shown individuals from underserved communities may face greater challenges accessing telehealth services, which could exacerbate existing disparities in chronic conditions, including type 2 diabetes (T2D). As patient engagement in telehealth care is likely to persist indefinitely, it is critical to determine whether certain patients may face greater challenges in accessing remote care so that appropriate accommodations can be made. Objective This study aimed to examine factors associated with the use of telehealth during the COVID-19 pandemic among adults with T2D at a large federally qualified health center in Southern California. Methods Electronic health records (EHR) from all T2D-related medical visits completed between July 2019 and July 2021 were obtained. The following variables were extracted from the EHR: modality of visit (in person vs telehealth), patient gender (male, female, nonbinary, or transgender), age, race or ethnicity (non-Hispanic White, Hispanic, Black, Asian, Middle Eastern or Arab, Asian-Pacific Islander, Native American or Alaskan, or multiracial), and income level (below or at vs above the poverty threshold). Patients were trichotomized based on whether they completed at least one telehealth visit following the start of the pandemic, if they completed all visits in person, or if they completed no visits. Chi-square analysis and t tests were conducted to examine univariate group differences. Multinomial logistic regression was conducted to examine associations between telehealth use and patient sociodemographics. Results Participants included 14,989 patients with T2D (51.7% female, 48.1% male, and 0.2% transgender or nonbinary; 83.7% below or at the poverty threshold). Over half (59.0%) of patients completed at least one T2D-related telehealth visit, 27.6% completed only in-person visits, and 13.4% complete no visits after the start of the pandemic. Compared to male (54.9%) and transgender or nonbinary patients (52.8%), significantly more females used telehealth (62.8%; χ2=100.89, P<.001). Significant differences also emerged between racial and ethnic groups, with the highest engagement among Middle Eastern or Arab (66.8%) and Hispanic patients (60.7%) and the lowest among Asian-Pacific Islander (50.0%) and Native American or Alaskan patients (52.2%; χ2=72.33, P<.001). Multinomial regression analysis revealed that women (odds ratio [OR] 1.29, 95% CI 1.17-1.42), Hispanic patients (OR 1.56, 95% CI 1.06-2.30), and Arab patients (OR 2.22, 95% CI 1.32-3.76) were more likely to complete telehealth visits rather than no visits than male patients and those of all other racial and ethnic groups. Similarly, women (OR 1.42, 95% CI 1.33-1.54) and Arab patients (OR 1.62, 95% CI 1.08-2.43) were more likely to complete telehealth than in-person visits. No significant differences by age or income were identified. Conclusions While many patients accessed telehealth during the pandemic, observed differences by sociodemographic characteristics suggest that some patients may require additional support when accessing remote health care. Future research should explore additional factors that could impact telehealth access within underserved communities (eg, internet or broadband access, language concordance, and technology literacy) so that tailored strategies can be developed to facilitate equitable access to care. Conflicts of Interest None declared.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».