The trajectory of substance use disorder among people formerly in foster care: A survival analysis
Bibliographic record
Abstract
BACKGROUND: Substance use disorder (SUD) represents a significant public health challenge, especially among individuals who have faced early life adversities. Foster care aims to provide a supportive environment for children; however, the relationship between a history of foster care and SUD development remains unclear. AIMS: This study aims to examine the likelihood of developing SUD among individuals with a history of foster care, who have used substances during their lifetime, compared to those raised by biological parents. METHOD: Using data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), we analyzed the prevalence of social demographics and clinical variables among individuals who have used alcohol, nicotine, or cannabis. We calculated the probabilities of developing SUD for each factor, including exposure to foster care. Various covariates that could impact SUD occurrence were also assessed. The duration from initial substance use to SUD onset was calculated for both groups. Survival analysis curves were generated for each substance to depict the probability of SUD development over time. RESULTS: Our analysis revealed that foster care may act as a protective factor against Cannabis Use Disorder (CUD), with a hazard ratio of 0.25 (95% CI [0.07, 0.88]). No significant associations were found with Alcohol Use Disorder (AUD) or Nicotine Use Disorder (NUD). Both foster and non-foster care groups exhibited higher probabilities of developing NUD compared to the general population. For cannabis users, the probability of developing CUD stabilizes after approximately 10 years. Family history of SUD and clinical predictors such as mood disorders consistently showed significant associations across all substance groups, highlighting their importance in SUD development. CONCLUSION: Our findings suggest that foster care may offer some protective benefits, particularly against CUD, emphasizing the need for further research into its protective factors and the development of targeted interventions to reduce SUD prevalence in this population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".