Violent crime rates as a proxy for the social determinants of sexually transmissible infection rates: the consistent state-level correlation between violent crime and reported sexually transmissible infections in the United States, 1981–2010
Bibliographic record
Abstract
BACKGROUND: Numerous social determinants of health are associated with violent crime rates and sexually transmissible infection (STI) rates. This report aims to illustrate the potential usefulness of violent crime rates as a proxy for the social determinants of STI rates. METHODS: For each year from 1981 to 2010, we assessed the strength of the association between the violent crime rate and the gonorrhoea (Neisseria gonorrhoeae) rate (number of total reported cases per 100?000) at the state level. Specifically, for each year, we calculated Pearson correlation coefficients (and P-values) between two variables (the violent crime rate and the natural log of the gonorrhoea rate) for all 50 states and Washington, DC. For comparison, we also examined the correlation between gonorrhoea rates, and rates of poverty and unemployment. We repeated the analysis using overall syphilis rates instead of overall gonorrhoea rates. RESULTS: The correlation between gonorrhoea and violent crime was significant at the P<0.001 level for every year from 1981 to 2010. Syphilis rates were also consistently correlated with violent crime rates. In contrast, the P-value for the correlation coefficient exceeded 0.05 in 9 of the 30 years for the association between gonorrhoea and poverty, and in 17 of the 30 years for that between gonorrhoea and unemployment. CONCLUSIONS: Because violent crime is associated with many social determinants of STIs and because it is consistently associated with STI rates, violent crime rates can be a useful proxy for the social determinants of health in statistical analyses of STI rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".