Substance Use Treatment Services in New York (2021–2023): A State Profile Analysis Based on National Survey of Substance Abuse and Mental Health Services (N-SUMHSS) Data
Bibliographic record
Abstract
BACKGROUND: Substance use disorders (SUDs) are a significant public health issue in the U.S., with New York State facing rising treatment demands. The National Survey of Substance Abuse and Mental Health Services (N-SUMHSS) offers valuable data on treatment trends across the state. OBJECTIVE: This study analyzes trends in substance use treatment services in New York from 2021 to 2023 using N-SUMHSS data, focusing on facility capacity, medication-assisted treatment (MAT) utilization, and treatment effectiveness. METHODS: Secondary data from the N-SUMHSS (2021-2023) were analyzed, examining facility types, client numbers, and MAT use (methadone, buprenorphine, naltrexone). Descriptive statistics and trend analysis were conducted to assess changes in treatment services. RESULTS: From 2021 to 2023, the number of facilities remained stable, with slight fluctuations. Private non-profit organizations dominated, comprising 69.7% of facilities in 2022-2023. MAT utilization declined in both opioid treatment programs (OTPs) and non-OTP facilities, with methadone usage remaining prevalent. Clients receiving MAT at OTP facilities decreased from 39,534 (9.3%) in 2021 to 34,186 (7.8%) in 2023, while naltrexone use rose. Outpatient services increased to 547 (72.1%), while residential and hospital inpatient facilities saw slight declines. CONCLUSION: MAT utilization has improved, but challenges persist in addressing the growing demand for residential and detox services. Expanding outpatient and inpatient services, along with greater access to MAT, is crucial to improving treatment in New York.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".