Is anyone truly healthy? Trends in health risk factors prevalence and changes in their associations with all-cause mortality
Bibliographic record
Abstract
OBJECTIVE: The purpose of the study was to determine trends in the prevalence of individual health risk factors across time and to examine if their associations with mortality have changed over time. METHODS: Data from the National Health and Nutrition Examination Surveys (NHANES III- 1988-1994 and NHANES 1999-2014; age ≥20 years) was used to examine differences in the odds ratio (OR) of 5-year mortality risk associated with various common health risk factors over the two survey periods using weighted logistic regression analysis adjusting for age, sex, obesity category and white ethnicity (n = 28,279). RESULTS: Over 97% of individuals had at least one of the 19 risk factors examined with no difference in the prevalence over time (P>0.34). The prevalence of lifestyle, social/mental and physical risk factors (2.2 to 19.1%) increased over time (P<0.0002), while the prevalence of having physiological risk factors decreased by ~6.5% (P<0.0001). Having any lifestyle or social/mental risk factor was significantly associated with a higher 5-year OR for mortality risk in 1999-2014, than 1988-94. In particular, having low education or use of mental health medication were not associated with mortality risk in 1988-94 (P>0.1), but were significantly associated with a higher 5-year OR for mortality in 1999-2014 (P<0.0001). Conversely, physiological risk factors were more weakly related with mortality risk in 1988-1994, than 1999-2014. Having any physical risk factor, and poor self-rated health were similarly related with 5-year mortality risk at both timepoints. CONCLUSION: Health risk factors have both increased and decreased in prevalence over time, along with changes in the association between many of the risk factors and mortality risk. Taken together, these changes complicate interpretation of temporal trends and warrant cautious interpretation of population health patterns based on surveillance data.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".