Measuring the association between marginalization and multimorbidity in Ontario, Canada: A cross-sectional study
Bibliographic record
Abstract
There is growing evidence to suggest that multimorbidity is not only a consequence of aging but also other environmental risk factors such as socio-economic status and social marginalization. In this study, the prevalence of multimorbidity was examined (defined as the simultaneous occurrence of two or more chronic morbidities) by age, gender and the Ontario Marginalization index (material deprivation, residential instability, dependency and ethnic concentration). With a cross-sectional design, 2015 data on 18 morbidities from 12,516,587 residents of the province of Ontario, Canada, were analysed. About 82.1% of the population had one or no chronic conditions, 10.3% were multimorbid with two chronic conditions and 7.6% had three or more chronic conditions. The results showed that the prevalence of multimorbidity is noticeably higher in the most deprived areas compared to least deprived for all age groups. Our findings challenge the notion that multimorbidity is primarily driven by aging. Of the 18% of the total population which were multimorbid, 43% of them were under the age of 65. We noted a substantial excess of multimorbidity in younger and middle-aged adults who were most deprived. In some cases, those in the most deprived areas were showing increased cases of multimorbidity nearly 10 years sooner than those who were least deprived. This study shows that environmental factors such as material deprivation and residential instability are correlated with higher prevalence of multimorbidity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".