Patterns of polysubstance use disorder among human trafficking survivors: A latent class analysis
Bibliographic record
Abstract
• Survivors of human trafficking are at risk for substance use disorder (SUD). • Survivors of human trafficking (HT) are at risk for substance use disorder (SUD). • Our latent class analysis of SUDs among emergency visits involving HT survivors found three distinct groups. • Classes were distinguished by their predicted probability of alcohol, opioid, cannabis, stimulant, and other SUDs. • Classes in which survivors had greater probability of multiple SUDs had greater likelihood of being hospitalized. Background: Substance use is commonly documented among human trafficking (HT) survivors in emergency department (ED) settings. Multiple substance use disorders (poly-SUD) are associated with poor health and psychosocial outcomes. This study identified latent classes and demographic covariates of HT-related ED visits by the types of SUDs documented in survivors’ medical records. Methods: We used cross-sectional data from the United States 2019–2021 Nationwide Emergency Department Sample, including visits of patients aged 12–64 years with an ICD-10-CM code documenting either sex or labor exploitation (N = 4,212). A bias-adjusted three-step latent class analysis was conducted, with SUDs documented via ICD-10-CM codes included as indicators in the model. Results: The optimal three-class solution had superior fit based on pre-selected indicators, low classification error, and acceptable entropy. The largest class comprised 76.01 % of the sample and showed a lower predicted probability of the SUD classes considered. The second largest class (17.27 %) was characterized by high predicted probability of stimulant use disorder with moderately high predicted probability of opioid use disorder. The smallest class (6.72 %) was characterized by high predicted probability of each SUD considered. Class membership was differentially associated with disposition from the ED, nicotine use disorder, and income. Conclusions: Although most ED visits were classified as having relatively low probability of SUD, nearly one quarter of the sample had high risk of either stimulant use disorder or high poly-SUD. Poly-SUD in HT survivors is associated with increased risk of hospitalization. Findings provide direction for tailoring intervention programs to support SUD recovery among HT survivors.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".