Multistate survival modelling of multimorbidity and transitions across health needs states and death in an ageing population
Bibliographic record
Abstract
BACKGROUND: Unmet health needs have the potential to capture health inequality. Nevertheless, the course of healthcare needs fulfilment, and the role of multimorbidity in this process remains unclear. This study assessed the bidirectional transitions between met and unmet health needs and the transition to death and examined the effect of multimorbidity on transitions. METHODS: This study was based on the China Health and Retirement Longitudinal Study, a nationally representative survey in 2011-2015 among 18 075 participants aged 45 and above (average age 61.1; SD 9.9). We applied a multistate survival model to estimate the probabilities and the instantaneous risk of state transitions, and Gompertz hazard models were fitted to estimate the total, marginal and state-specific life expectancies (LEs). RESULTS: Living with physical multimorbidity (HR=1.85, 95% CI 1.58 to 2.15) or physical-mental multimorbidity (HR=1.45, 95% CI 1.15 to 1.82) was associated with an increased risk of transitioning into unmet healthcare needs compared with no multimorbidity. Conversely, multimorbidity groups had a decreased risk of transitioning out of unmet needs. Multimorbidity was also associated with shortened total life expectancy (TLEs), and the proportion of marginal LE for having unmet needs was more than two times higher than no multimorbidity. CONCLUSION: Multimorbidity aggravates the risk of transitioning into having unmet healthcare needs in the middle and later life, leading to a notable reduction in TLEs, with longer times spent with unmet needs. Policy inputs on developing integrated person-centred services and specifically scaling up to target the complex health needs of ageing populations need to be in place.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".