Coexisting illness and heart disease among elderly Medicare managed care enrollees.
Notice bibliographique
Résumé
High rates of comorbidity present a challenge in providing care to elderly Medicare managed care enrollees. Comorbidity or the presence of coexisting illness strongly influences utilization, costs, and outcomes of health care. Ischemic heart disease (IHD) and congestive heart failure (CHF) are leading causes of morbidity and mortality among Medicare beneficiaries. Both have been the targets of successful quality improvement initiatives by CMS (Jencks, Huff, and Cuerdon, 2003). Medicare HEDIS® has targeted improved management of hypertension and diabetes, as well as smoking cessation, all important risk factors for IHD and CHF. The impact of disease management programs on outcomes for these conditions is being evaluated in CMS demonstration projects (Haffer et al., 2003). Additional improvements in quality and outcomes of care for beneficiaries with these conditions may be achieved by improving management of common coexisting illnesses. The large sample size of the Medicare Health Outcomes Survey (HOS) affords an unprecedented opportunity to look at the prevalence and patterns of coexisting illness among enrollees with IHD and CHF. The HOS instrument contains items for assessing physical and mental health status, chronic conditions, clinical symptoms, and demographic information (National Committee for Quality Assurance, 2000). The following figures are based on the responses of 167,854 community-dwelling individuals age 65 or over enrolled in Medicare managed care who participated in the HOS Cohort I Baseline Survey. The sample is 58 percent female and includes 31,315 respondents who report having IHD, and 11,239 respondents who report having CHF. Enrollees with IHD or CHF have lower incomes and lower levels of educational attainment than the overall M+C enrollee population, placing them at increased risk of encountering both financial and non-financial barriers to care. They also report higher levels of comorbidity, and a higher prevalence of common chronic conditions. Nine out of ten enrollees with these conditions report having three or more chronic conditions, and they report having a mean of five chronic conditions. In addition to hypertension and diabetes, risk factors for heart disease, there is a high prevalence of chronic non-fatal disabling conditions that can affect outcomes and compliance with treatments including arthritis, severe low-back pain, urinary incontinence, and sensory impairments. The high prevalence of depressed mood underscores the need to also address mental health issues in these beneficiaries. In addition, the burden of coexisting illness varies by sex, race/ethnicity, and socioeconomic status. Females, African-American, Latino, and socioeco-nomically disadvantaged enrollees report a higher burden of coexisting illness. Future efforts should focus on implementing and evaluating models of care for beneficiaries with heart disease that address the coexisting illnesses present in these patients. Opportunities also exist for prevention. Insights from the HOS survey can inform the development of comprehensive models of care.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».