DOES THE ASSOCIATION BETWEEN AGE AND MAJOR ILLNESS VARY BY NATIONAL HEALTHCARE QUALITY?
Bibliographic record
Abstract
Abstract Though the risk of chronic disease and disability accelerates once adults are in their 60s, 70s, and 80s, researchers have long suspected that economic, social, and institutional variation — even among high-income Western nations — may powerfully influence the likelihood that people remain healthy at advanced ages. This study builds on comparative research into global aging, by offering a multiple-indicator test of whether national healthcare quality modifies the association between age and major illness. Recent individual-level data on morbidity among respondents aged 50 or older (16 countries; 2014 European Social Survey) are merged with nation-level healthcare indicators. Healthcare quality is assessed using a subjective, evaluation-based approach (based on the 2011 International Social Survey Programme) and an objective, attributable-mortality approach (2010 Healthcare Access and Quality, based on the Global Burden of Disease Study). Lagged nation-level economic and health indicators are controlled to help isolate healthcare effects. Multilevel logistic and linear regression models of any major health condition and morbidity reveal that while older individuals showed approximately a 10% reduction in probability of major illness when residing in countries with higher healthcare quality, associations between age and morbidity indices combining number and severity of illness showed greater modification by healthcare quality, with reductions around 18%. Results across subjective and objective approaches to healthcare quality are strikingly consistent. Taken together, results are suggestive of healthcare’s protective role in reducing age-related illness and disability. Future research should illuminate pathways by which healthcare quality may lead to differences in healthy aging among advanced nations.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".