Cumulative toll of exposure to stressors in Canadians: An allostatic load profile
Bibliographic record
Abstract
BACKGROUND: The cumulative toll of exposure to stressors (psychosocial, chemical, physical) can contribute to disease processes. The concept of allostatic load, essentially the cost of maintaining physiological stability in response to environmental demands, may be useful in assessing broad population health impacts of stressors beyond morbidity and mortality. In the present study, allostatic load scores were generated for Canadians and associations with age, sex, education and household income were examined. DATA AND METHODS: Data from cycles 1, 2, and 3 (2007 to 2013) of the Canadian Health Measures Survey (CHMS) were used to generate a composite index of cumulative health burden (allostatic load score) for adults aged 20 to 79 (n=8,678) based on risk thresholds for nine biological measures: diastolic blood pressure, systolic blood pressure, heart rate, high-density lipoprotein (HDL), total cholesterol, glycated hemoglobin (HBA1c), waist-to-hip ratio, C-reactive protein (CRP), and albumin. Logistic regression models that included age (continuous), sex, education and household income were fit to generate model-adjusted predicted allostatic load scores. RESULTS: The most prevalent individual risk factors were elevated waist-to-hip ratio, elevated CRP, total cholesterol, and low HDL. Allostatic load scores increased with age. Males generally exhibited higher scores than females. Lower educational attainment and lower household income were found to be significantly associated with higher allostatic load scores after taking account of the effects of age and sex. DISCUSSION: Age and socioeconomic gradients are associated with differences in allostatic load scores in the Canadian population. This composite measure of multisystem dysfunction, generated from a nationally representative survey that includes measurement of numerous health-relevant behaviours, biomarkers, and chemical levels, can be used in future to quantify sub-clinical impacts on health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".