A Patient-Centered PaTH to Address Diabetes: Protocol for a Study on the Impact of Obesity Counseling
Notice bibliographique
Résumé
BACKGROUND: Overweight and obesity are America's number one health concern. The prevalence of obesity in the United States is greater than 36%, a rate that has doubled since 1970. As the second most preventable cause of death, obesity is a risk factor for diabetes, cardiovascular disease, stroke, and cancer, all major causes of death. Primary care clinics may be an ideal setting for weight control interventions to help manage and prevent diabetes. For this reason, the Centers for Medicare and Medicaid Services (CMS) implemented a health care procedure coding system code for intensive behavioral therapy (IBT) for obesity within primary care in 2012 to facilitate payment for addressing obesity, which was followed by broader coverage by most insurers for IBT for adults in 2013. However, the impact of this coverage on patient-centered outcomes is largely unknown. OBJECTIVE: The overarching goal of this study is to understand the comparative effectiveness of obesity counseling as covered by CMS and other insurers in improving weight loss for adults either with or at increased risk for type 2 diabetes. METHODS: This study leverages the novel infrastructure of the Patient-Centered Outcomes Research Institute-funded PaTH Clinical Data Research Network. The PaTH network is comprised of Geisinger Health System, Johns Hopkins University, Johns Hopkins Health System, Lewis Katz School of Medicine at Temple University, Temple Health System, Penn State College of Medicine, Penn State Milton S Hershey Medical Center, University of Pittsburgh, UPMC and UPMC Health Plan, and the University of Utah. Electronic health record (EHR) data will originate from the 6 PaTH health systems. Specifically, we will (1) evaluate the impact of broader preventive service coverage for obesity screening and counseling on weight loss, diabetes incidence, and diabetes outcomes in patients with diabetes or at increased risk for diabetes (defined by body mass index [BMI] ≥25). We will determine how the annual probability of receiving obesity and/or nutritional counseling changed pre- and postpolicy across all insurers in a cohort of patients with diabetes and at high risk for diabetes. We will (2) compare patient weight loss and diabetes-related outcomes among those who receive obesity screening and counseling with those who do not, following implementation of preventive service coverage. We will examine postpolicy impact of obesity screening and counseling in a cohort of patients with diabetes and at increased risk for diabetes. Specific outcomes to be examined include weight loss, diabetes incidence, and diabetes outcomes. Exploratory outcomes will include patient-reported outcomes. Furthermore, we will determine patient characteristics, including demographics, and practice characteristics, including provider type. RESULTS: Our PCORI-funded study is underway. To date, we have obtained our second data extraction from the PaTH CDRN and are performing data editing and cleaning. Next steps include analysis of early policy change. CONCLUSIONS: Given patients who are overweight are at highest risk for diabetes, improved weight management services could prevent diabetes and its negative health outcomes. Comparing weight and diabetes outcomes in 3 states using EHRs and claims data before and after this policy was implemented using the PaTH Network will allow important insight into policy effectiveness. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12054.
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 | 0,034 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,180 | 0,035 |
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 ».