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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".