Prevalence of self-reported multimorbidity in the general population and in primary care practices: a cross-sectional study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Settings affect estimation of multimorbidity prevalence. Multimorbidity prevalence was reported to be substantially higher among family practice-based patients than in the general population, but prevalence estimates were obtained with different methods and at different time periods. The aim of the present study was to compare estimates of the prevalence of multimorbidity in the general population and in primary care clinical practices, both measured simultaneously and with the same methods. METHODS: Cross-sectional analysis of results from the Program of Research on the Evolution of a Cohort Investigating Health System Effects (PRECISE) in Quebec, Canada. Subjects aged between 25 and 75 years. A randomly-selected cohort in the general population recruited by telephone, and patients recruited in the waiting room of 12 primary care clinics. Prevalence of multimorbidity was estimated using three operational definitions of multimorbidity: (a) two or more chronic conditions (MM 2+); (b) three or more chronic conditions (MM 3+); and (c) disease burden morbidity assessment score of 10 or higher (DBMA 10+). RESULTS: Prevalence in the general population ranged from 59.4 % (with MM2+) to 16.9 %, (with DBMA10+). In primary care practices, prevalence estimates ranged from 69.5 to 29.5 %. Prevalence estimates of multimorbidity were about 10 % higher in primary care clinical practices than in the sample from the general population. The difference was not importantly affected by the use of different operational definitions of multimorbidity. Also, there was a higher burden of disease among patients attending primary care clinics. CONCLUSIONS: The study suggests that the problem of multimorbidity in the two settings is different both quantitatively (a higher proportion of patients with multimorbidity in primary care clinical practices), and qualitatively (a higher disease burden of patients attending primary care clinics). For decision-makers interested in resource allocation, prevalence estimates in samples from primary care practices are more informative than estimates in the general population, but burden of disease should also be considered as it results in more complexity in primary care clinical practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it