Future patterns of health inequalities in the population of England to 2040: a microsimulation study
Bibliographic record
Abstract
Abstract Introduction The existence of wide inequalities in health across England is well-documented. Our research adds to this evidence on inequalities in self-reported health by describing current patterns and projecting future patterns of inequality in diagnosed illness across multiple conditions by deprivation. Methods We used the IMPACTNCD microsimulation model that simulates a close-to-reality synthetic population of adults in England from 2019 to 2040. This model combines individual-level data on demographics, health and mortality from linked administrative data for primary and secondary care with survey responses on individual-level risk factors and epidemiological evidence on the associations between risk factors and chronic illness. This model can be adapted for other countries based on data availability. We used the Cambridge Multimorbidity Score (CMS) as our multimorbidity measure. This assigns a weight to 20 common long-term conditions based on individuals’ healthcare use and their likelihood of death. We further focus on “major illness” which corresponds to a CMS greater than 1.5. Results In preliminary results, we project that health inequalities are not projected to improve between 2019 and 2040. In 2019, the difference in the average time spent without major illness between the most and least deprived 10% of areas in England was 10.4 years. This is projected to remain largely unchanged at 10.7 years (8.8, 11.7). We also find that in 2019, the share of working age people living with major illness in the most deprived 10% of areas in England (14.6%) was more than double the rate seen in the least deprived 10% of areas (6.3%). In 2040, we project these rates to remain largely unchanged at 15.2% (13.0%, 17.6%) and 6.8% (5.4%, 9.1%) respectively. Discussion On current trends, health inequalities are projected to persist into the future. This has significant implications not just for population health but for labour supply and wider economic growth. Key messages • If current trends in risk factors continue into the future, existing wide inequalities in diagnosed illness in England are projected to persist onto 2040. • With significant disparities in major illness among the working-age population, this has implications not just for population health but for labour supply and wider economic growth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".