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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".