Compounding risks of chronic health conditions and substance use disorder on healthcare burden in the USA: Analysis of NSDUH data (2021–2023)
Bibliographic record
Abstract
• One-third of adults had chronic conditions; one-fifth visited the ER. • Drug use disorder is a strong predictor of emergency room utilization. • Severe substance use disorder increases emergency room healthcare use. • Black and Native/Alaska Native adults show higher emergency room use. • Mild and moderate drug use disorders raise ER visits across CHC levels. • Mental health conditions substantially increase ER use and substance risks. Emergency room (ER) use reflects acute healthcare burden, but the roles of chronic health conditions (CHCs), substance use disorders (SUDs), drug use disorders (DUDs), and mental health conditions (MHCs) remain underexplored across populations. Using nationally representative survey data (N = 226,838; weighted = 1,243,120,763), we applied survey-weighted logistic regression to examine predictors of ER visits. Covariates included CHCs, SUDs, DUDs, severity levels, MHCs, race/ethnicity, education, employment, residence, and body mass index. Adults with ≥ 1 CHC were more likely to visit the ER (OR = 1.72; 95 % CI: 1.60–1.85). DUD significantly increased ER use (OR = 1.70; 95 % CI: 1.54–1.88), while overall SUD was not significant after adjustment (OR = 1.05; 95 % CI: 0.98–1.12). Severe SUD elevated ER use even without CHCs (OR = 1.89; 95 % CI: 1.67–2.13). African Americans had higher odds of ER visits (OR = 1.28; 95 % CI: 1.21–1.36), and Native American/Alaska Natives were more likely to report DUD (OR = 1.55; 95 % CI: 1.31–1.82). Lower educational attainment (OR = 1.22; 95 % CI: 1.16–1.28) and unemployment (OR = 1.34; 95 % CI: 1.25–1.43) were linked to higher risks. MHCs predicted ER use (OR = 1.63; 95 % CI: 1.53–1.74) and substance-related disorders. CHCs, DUD severity, and MHCs are strong predictors of ER utilization. Disparities among African Americans and Native American/Alaska Natives highlight the need for integrated care addressing chronic illness, behavioral health, and substance use—particularly for socioeconomically and racially marginalized groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".