Comparisons of multi-morbidity in family practice--issues and biases
Bibliographic record
Abstract
BACKGROUND: As the population ages, practice and policy need to be guided by accurate estimates of chronic disease burden in primary care. OBJECTIVE: To produce a preliminary set of methodological considerations for cross-sectional and retrospective cohort studies of multi-morbidity in primary care using three studies as examples. Prevalence rate results from the three studies were re-estimated using identical age-sex groups. METHODS: We compared the methods and results of three separate studies in primary care: (i) patients in the Saguenay region of Quebec, Canada (2005); (ii) a substudy of the BEACH (Bettering the Evaluation and Care of Health) programme in Australia (2008); and (iii) the DELPHI (Deliver Primary Health Care Information) project in South-western Ontario, Canada (2009). Areas where the methods of multi-morbidity studies may differ were identified. The percentage of patients with two or more chronic conditions was compared by age-sex groups. RESULTS: Multi-morbidity prevalence varied by as much as 61%, where reported prevalence was 95% among females aged 45-64 in the Saguenay study, 46% in the BEACH substudy and 34% in the DELPHI study. Several aspects of the methods and study designs were identified as differing among the studies, including the sampling of frequent attenders, sampling period, source of data, and both the definition and count of chronic conditions. CONCLUSIONS: Understanding the differences among the methods used to produce prevalence data on multi-morbidity in primary care can help explain the varying results. Standardization of methods would allow for more valid inter-study comparisons.
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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 | 0.417 | 0.536 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".