Health Behavior Survey Among People Who Use Opioids: Protocol for Implementing Technology-Based Rapid Response Surveillance in Community Settings
Bibliographic record
Abstract
BACKGROUND: In 2018, 2 million Americans met the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnostic criteria for an opioid use disorder, and 9.9 million Americans had misused prescription pain relievers the previous year. Despite a rapid increase in opioid misuse, opioid use disorders, and overdoses, data are limited on the behavioral and contextual risks as well as the protective factors fueling the opioid epidemic in some hard hit US cities-Atlanta, Los Angeles, and Las Vegas. Opioid use also contributes to the risk of other health problems such as HIV and hepatitis C virus infections or mental health disorders and is linked to behavioral and environmental risks (eg, homelessness, experiences of violence, involvement in the justice system). Knowledge of the relationships between these linked vulnerabilities and how they influence service utilization is critical to effective policy and interventions. OBJECTIVE: This survey explores the relationships between demographic and economic characteristics, behavioral and environmental risk factors, and service utilization of people who use opioids to inform public health practice, policy, and future efforts to mitigate the risks faced by this population experiencing multiple health, social, and economic vulnerabilities. The results of this survey will be used to identify needs and intervention points for people who use drugs currently served by public health organizations. METHODS: We implemented a community-engaged strategy that involved development and execution of a two-stage purposive sampling plan involving selection of partner organizations (syringe exchange programs in urban settings) and recruitment and enrollment of participants aged 18-69 years served by these organizations in Atlanta, Los Angeles, and Las Vegas from 2019 to 2020. The recruited participants completed a survey, including a variety of measures to assess health (physical and mental) and health behaviors such as sexual behavior, vaccine receipt, and HIV/ hepatitis C virus infection testing. Additional items assessed drug use and misuse, syringe exchange and health service utilization, sex exchange, histories of interpersonal violence, and vaccine confidence. RESULTS: This protocol was successfully implemented despite challenges such as real-time technology issues and rapidly finding and surveying a difficult-to-reach population. We sampled 1127 unique participants (248 in Atlanta, 465 in Los Angeles, and 414 in Las Vegas). CONCLUSIONS: The establishment and utilization of strong community partnerships enabled the rapid collection of data from a typically difficult-to-reach population. Local efforts such as these are needed to develop policies and practices that promote harm reduction among people who use opioids. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/25575.
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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 | 0.042 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".