Multiple physical and mental health comorbidity in adults with intellectual disabilities: population-based cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Adults with intellectual disabilities have increased early mortality compared with the general population. However, their extent of multimorbidity (two or more additional conditions) compared with the general population is unknown, particularly with regards to physical ill-health, as are associations between comorbidities, neighbourhood deprivation, and age. METHODS: We analysed primary health-care data on 1,424,378 adults registered with 314 representative Scottish practices. Data on intellectual disabilities, 32 physical, and six mental health conditions were extracted. We generated standardised prevalence rates by age-groups, gender, and neighbourhood deprivation, then calculated odds ratio (OR) and 95 % confidence intervals (95 % CI) for adults with intellectual disabilities compared to those without, for the prevalence, and number of condition. RESULTS: Eight thousand fourteen (0.56 %) had intellectual disabilities, of whom only 31.8 % had no other conditions compared to 51.6 % without intellectual disabilities (OR 0.26, 95 % 0.25-0.27). The intellectual disabilities group were significantly more likely to have more conditions, with the biggest difference found for three conditions (10.9 % versus 6.8 %; OR 2.28, 95 % CI 2.10-2.46). Fourteen physical conditions were significantly more prevalent, and four cardiovascular conditions occurred less frequently, as did any cancers, and chronic obstructive pulmonary diseases. Five of the six mental health conditions were significantly more prevalent. For the adults with intellectual disabilities, no gradient was seen in extent of multimorbidity with increasing neighbourhood deprivation; indeed findings were similar in the most affluent and most deprived areas. Co-morbidity increased with age but is highly prevalent at all ages, being similar at age 20-25 to 50-54 year olds in the general population. CONCLUSIONS: Multi-morbidity burden is greater, occurs at much earlier age, and the profile of health conditions differs, for adults with intellectual disabilities compared with the general population. There is no association with neighbourhood deprivation; people with intellectual disabilities need focussed services irrespective of where they live, and at a much earlier age than the general population. They require specific initiatives to reduce inequalities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".