Coexisting illness and heart disease among elderly Medicare managed care enrollees.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".