The Effect of Nicotine Dependence on the Risk of Developing Post-traumatic Stress Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions
Bibliographic record
Abstract
INTRODUCTION: It is evident that an association between smoking and post-traumatic stress disorder (PTSD) exists, but research is lacking in establishing the directionality of the relationship. AIMS AND METHODS: We used longitudinal data from waves I (2001-2002) and II (2004-2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Individuals with nicotine dependence (ND) were matched to individuals without ND using propensity score matching to estimate the risk of developing PTSD after trauma. We also matched smokers (with or without ND) to lifetime nonsmokers to estimate their risk of developing PTSD after trauma. Lastly, we conducted a mediation analysis on the effect of ND severity on PTSD symptoms. RESULTS: Individuals with ND (n = 1514) were more likely to develop PTSD (odds ratio: 1.59; 95% confidence interval: 1.09-2.32; p = .017) compared with individuals without ND (n = 6047). Smokers (regardless of ND status) (n = 2335) compared with nonsmokers (n = 5226) had no significant effect on risk of PTSD (p = .26). Withdrawal was found to be a mediator of the effect of ND severity on PTSD symptoms. CONCLUSIONS: ND, but not smoking status, increases a smoker's risk of developing PTSD. This provides information that could aid in preventive strategies for individuals with ND who are exposed to trauma. IMPLICATIONS: This study provides evidence in a national representative sample of adults in the United States that ND may increase one's risk of developing PTSD after exposure to trauma. It also shows the directionality of the association between smoking and PTSD. Lastly, it demonstrates that withdrawal may be the link to the association between ND and PTSD. We hope that with these findings, preventative strategies are put in place for smokers who are dependent and are exposed to trauma, such that they do not develop PTSD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".