Intertwined Epidemics: National Demographic Trends in Hospitalizations for Heroin- and Opioid-Related Overdoses, 1993–2009
Bibliographic record
Abstract
The historical patterns of opiate use show that sources and methods of access greatly influence who is at risk. Today, there is evidence that an enormous increase in the availability of prescription opiates is fuelling a rise in addiction nationally, drawing in new initiates to these drugs and changing the geography of opiate overdoses. Recent efforts at supply-based reductions in prescription opiates may reduce harm, but addicted individuals may switch to other opiates such as heroin. In this analysis, we test the hypothesis that changes in the rates of Prescription Opiate Overdoses (POD) are correlated with changes in the rate of heroin overdoses (HOD). ICD9 codes from the Nationwide Inpatient Sample and population data from the Census were used to estimate overall and demographic specific rates of POD and HOD hospital admissions between 1993 and 2009. Regression models were used to test for linear trends and lagged negative binomial regression models were used to model the interrelationship between POD and HOD hospital admissions. Findings show that whites, women, and middle-aged individuals had the largest increase in POD and HOD rates over the study period and that HOD rates have increased in since 2007. The lagged models show that increases in a hospitals POD predict an increase in the subsequent years HOD admissions by a factor of 1.26 (p<0.001) and that each increase in HOD admissions increase the subsequent years POD by a factor of 1.57 (p<0.001). Our hypothesis of fungibility between prescription opiates and heroin was supported by these analyses. These findings suggest that focusing on supply-based interventions may simply lead to a shift in use to heroin rather minimizing the reduction in harm. The alternative approach of using drug abuse prevention resources on treatment and demand-side reduction is likely to be more productive at reducing opiate abuse related harm.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".