Changes in the prevalence of chronic disease and the association with disability in the older Dutch population between 1987 and 2001
Bibliographic record
Abstract
BACKGROUND: most studies of older populations in developed countries show a decrease in the prevalence of disabilities, and an increase in chronic diseases over the past decades. Data in the Netherlands, however, mostly show an increase in the prevalence of chronic diseases and mixed results with regard to the prevalence of disability. This study aims at comparing changes in the prevalence, as well as the association between chronic diseases and disability between 1987 and 2001 in the older Dutch population using data representative of the general population. Most studies, so far, have only dealt with self-reported diseases, but in this study, we will use both self-reported and GP-registered diseases. STUDY DESIGN: data from the first (1987) and second (2001) Dutch National Survey of General Practice were used. In 1987, 103 general practices, compared to 104 in 2001, participated. Approximately 5% of the listed persons aged 18 years and over was asked to participate in an extensive health interview survey. An all-age random sample was drawn by the researchers from the patients listed in the participating practices (in 1987 n = 2, 708; in 2001 n = 3, 474). Both surveys are community based, with an age range between 55 and 97 years. Data on chronic diseases were based on GP registries and self-report. RESULTS: the prevalence of disability and of asthma/COPD, cardiac disease, stroke, and osteoarthritis decreased between 1987 and 2001, while the prevalence of diabetes increased. Changes were largely similar for GP-registered and self-reported diseases. Cardiac disease, asthma/COPD, and depression led to less disability, whereas low back pain and osteoarthritis led to more disability. CONCLUSIONS: in general, there were reductions in GP-registered chronic diseases as well as in self-reported diseases and disability. Results suggest that the disabling impact of fatal diseases decreased, while the impact of non-fatal diseases increased.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".