Economic conditions and health behaviours during the ‘Great Recession’
Bibliographic record
Abstract
BACKGROUND: The adoption of healthier behaviours has been hypothesised as a mechanism to explain empirical findings of population health improvements during some economic downturns. METHODS: We estimated the effect of the local unemployment rate on health behaviours using pooled annual surveys from the 2003-2010 Behavioral Risk Factor Surveillance Surveys, as well as population-based telephone surveys of the US adult general population. Analyses were based on approximately 1 million respondents aged 25 years or older living in 90 Metropolitan Statistical Areas and Metropolitan Divisions (MMSAs). The primary exposure was the quarterly MMSA-specific unemployment rate. Outcomes included alcohol consumption, smoking status, attempts to quit smoking, body mass index, overweight/obesity and past-month physical activity or exercise. RESULTS: The average unemployment rate across MMSAs increased from a low of 4.5% in 2007 to a high of 9.3% in 2010. In multivariable models accounting for individual-level sociodemographic characteristics and MMSA and quarter fixed effects, a one percentage-point increase in the unemployment rate was associated with 0.15 (95% CI -0.31 to 0.01) fewer drinks consumed in the past month and a 0.14 (95% CI -0.28 to 0.00) percentage-point decrease in the prevalence of past-month heavy drinking; these effects were driven primarily by men. Changes in the unemployment rate were not consistently associated with other health behaviours. Although individual-level unemployment status was associated with higher levels of alcohol consumption, smoking and obesity, the MMSA-level effects of the recession were largely invariant across employment groups. CONCLUSIONS: Our results do not support the hypothesis that health behaviours mediate the effects of local-area economic conditions on mortality.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".