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Cost-Related Suboptimal Insulin Use

2022· article· en· W7055350780 sur OpenAlexaboutno aff

Notice bibliographique

RevueDigital Commons - East Tennessee State University (East Tennessee State University) · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueLaser Design and Applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInsulinQuarter (Canadian coin)Diabetes mellitusType 2 diabetesPrimary carePandemicNew england
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Insulin is a necessary, life-changing medication for patients living with Type 1 or Type 2 Diabetes Mellitus. Yet, a recent study at an endocrinology clinic in New England indicated that one quarter of patients experienced cost-related suboptimal insulin use. A new study based out of a primary care clinic in the Appalachian region looked at the prevalence of cost-related suboptimal insulin use within this region. The hypothesis is cost-related insulin suboptimal use is higher in the Appalachian region than as reported in the New England area. Surveys were administered to patients who were 18 years of age or older, diagnosed with Type 1 or Type 2 Diabetes Mellitus, and who had been prescribed insulin in the last 12 months. The survey instrument used was adopted and modified from the previous New England study. The survey instrument included 28 items and was administered in person prior to the start of the COVID-19 pandemic from July 2019 to April 2020. Following the start of the COVID-19 pandemic, in person recruitment was suspended. Beginning November 2020, a revised telephonic recruitment began and continued through December 2021. Interested participants were mailed the survey and consent form along with a postage paid return envelope. After the COVID-19 outbreak, the original survey instrument was revised to include 12 additional items designed to measure the impact of COVID-19 on the participant’s diabetes management and on insulin utilization. The primary outcome was cost-related underuse of insulin within the past year. This was measured by a positive response in the questionnaire to at least 1 of 6 questions: did you… (1) use less insulin than prescribed, (2) try to stretch out your insulin, (3) take smaller doses of insulin than prescribed, (4) stop using insulin, (5) not fill an insulin prescription, or (6) not start insulin… because of cost? Descriptive analysis was conducted using SPSS software. The East Tennessee State University Institutional Review Board approved the study protocol. Ninety respondents completed the survey. The average age of respondents was 68 years. The majority were diagnosed with type 2 diabetes (83%), Caucasian race (99%), male (59%), retired or disabled (76%), and had Medicare Part D prescription benefits (63%). The average monthly out-of-pocket cost for insulin was $84.10 (range $0-$566). For the primary outcome, results indicate 44.4% of participants in the Appalachian Mountain community experience cost-related suboptimal therapy. Forty participants completed the revised survey measuring the impact of COVID-19 on their diabetes self-management. From this group, 85% of participants reported their income and job did not change during the pandemic. However, increased dosing of insulin (30%) and increased insulin cost (27.5%) was reported. Respondents also reported increased stress (57.5%), worsened diet (25%) and worsened exercise (40%) as a result of the pandemic. Overall, a higher proportion of people with diabetes in the Appalachian region reported cost-related suboptimal insulin use compared to a previous study. The COVID-19 pandemic also has reportedly contributed to increased insulin requirements in one-third of the surveyed participants.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,825
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0010,000
Communication savante0,0000,003
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,172
Écart entre enseignants0,153 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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