The Impact of Cognitive, Social and Physical Limitations on Income in Community Dwelling Adults With Chronic Medical and Mental Disorders
Bibliographic record
Abstract
INTRODUCTION: As much as 45% of the US population has at least one chronic condition while 21% have multiple chronic diseases. The study examined the impact of cognitive, social and physical limitations on the personal income of U.S. adults with seven chronic diseases. METHODS: A cross-sectional analysis of 19,357 US adults with seven chronic diseases (diabetes, hypertension, heart disease, stroke, depression, emphysema and joint disease) from the 2006 Medical Expenditure Panel Survey (MEPS) was performed. The effect of seven chronic diseases and their associated cognitive, social, and physical limitations on personal income was assessed using a two-stage Heckman model. RESULTS: Depression emerged as the only chronic disease that was independently associated with a significant $1,914 decrease in personal income (95% CI -$2,938--$890). Social and cognitive limitations resulted in $1,944 (95% CI -$3,378--$511) and $3,039 (95% CI -$4,418-$1,659) decreases in personal incomes respectively while physical limitations did not result in a statistically significant reduction. Being Non-Hispanic Black, Hispanic, Other Race, female, never married, married, less than a bachelor's degree, publicly insured, uninsured, or having a health status less than very good were also associated with significant reductions in personal income. CONCLUSIONS: The findings of this study suggest a need to determine the specific limitations associated with common chronic diseases and identify appropriate compensatory strategies to reduce their impact on income.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".