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Who Is Most Burdened in Health Care? An Analysis of Responses to the ICAN Discussion Aid.
Steiger, Kyle G; Boehmer, Kasey R; Klanderman, Molly C; Mookadam, Aamena; Koneru, Sethu Sandeep; Montori, Victor M; Mookadam, Martina.
Afiliación
  • Steiger KG; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
  • Boehmer KR; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
  • Klanderman MC; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
  • Mookadam A; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
  • Koneru SS; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
  • Montori VM; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
  • Mookadam M; From the Mayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Scottsdale (KGS); Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota (KRB, VMM); Division of Health Care Delivery Research, Mayo Clinic, Rochester, Minnesota (KRB); Department of Quantit
J Am Board Fam Med ; 36(2): 277-288, 2023 04 03.
Article en En | MEDLINE | ID: mdl-36948538
ABSTRACT

OBJECTIVE:

To create a model based on patients' characteristics that can predict the number of burdens reported using the ICAN Discussion Aid, to target use of this tool to patients likeliest to benefit. PATIENTS AND

METHODS:

Six hundred thirty-five patients (aged ≥18 years) completed the ICAN Discussion Aid at a Scottsdale, Arizona, family medicine clinic. Patient characteristics were gathered from their health records. Regression trees with Poisson splitting criteria were used to model the data.

RESULTS:

Our model suggests the patients with the most burdens had major depressive disorder, with twice as many overall burdens (personal plus health care burdens) than patients without depression. Patients with depression who were younger than 38 years had the highest number of personal burdens. A body mass index (BMI) of 26 or greater was associated with increased health care burden versus a BMI below 26.

CONCLUSION:

The number of burdens a patient will report on the ICAN Discussion Aid can be approximated based on certain patient characteristics. Adults with major depression, a BMI of 26 or greater, and younger age may have greater reported burdens on ICAN, but this finding needs to be validated in independent samples.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastorno Depresivo Mayor Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adolescent / Adult / Humans Idioma: En Revista: J Am Board Fam Med Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Trastorno Depresivo Mayor Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adolescent / Adult / Humans Idioma: En Revista: J Am Board Fam Med Año: 2023 Tipo del documento: Article
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