Estimating multiple morbidity disease burden among older persons: a convergent construct validity study to discriminate among six chronic illness measures, CCHS 2008/09
Bibliographic record
Abstract
BACKGROUND: Since approximately two in three older adults (65+) report having two or more chronic diseases, causes and consequences of multimorbidity among older persons has important personal and societal issues. Indeed, having more than one chronic condition might involve synergetic effects, which can increase impact on disabilities and quality of life of older adults. Moreover, persons with multimorbidity require more health care treatments, implying burden for the person, her/his family and the health care system. METHODS: Using the 2008/09 Canadian Community Health Survey (CCHS), this paper assesses the convergent construct validity of six measures of multimorbidity for persons aged 65 and over. These measures include: 1) Multimorbidity Dichotomized (0, 1+ conditions); 2) Multimorbidity Dichotomized (0/1, 2+); 3) Multimorbidity Additive Scale; 4) Multimorbidity Weighted by the Health Utility (HUI3) Scale; 5) Multimorbidity Weighted by the OARS Activity of Daily Living (ADL) Scale; and 6) Multimorbidity Weighted by HUI3 (using beta coefficients). Convergent construct validity was assessed using correlations and OLS regression coefficients for each of the multimorbidity measures with the following social-psychological and health outcome variables: life satisfaction, perceived health, number of health professional visits, and medication use. RESULTS: Overall, the two dichotomies (scales #1 & #2) showed the weakest construct validity with the health outcome variables. The additive chronic illness scale (#3) and the multimorbidity weighted by ADLs (#5), performed better than the other two weighted scales using (HUI #4 & #6). Measurement errors apparent in the dichotomous multimorbidity measures were amplified for older women, especially for life satisfaction and perceived health, but decreased when using the scales, suggesting stronger validity of scales #3 through #6. CONCLUSIONS: To properly represent multimorbidity, using dichotomous measures should be used with caution. When only prevalence data are available for chronic conditions, such as in the CCHSs or CLSA, an additive multimorbidity scale can better measure total illness burden than simple dichotomous or other discrete measures.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".