Impact of Pandemic-Induced Service Disruptions and Behavioral Changes on Hepatitis C Virus and HIV Transmission Amongst People Who Inject Drugs: A Modeling Study
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic may have disproportionally impacted vulnerable groups such as people who inject drugs (PWID) through reduced health care services as well as social changes from pandemic mitigation measures. Understanding how the COVID-19 pandemic and associated mitigation strategies subsequently changed the trajectory of hepatitis C virus (HCV) and human immunodeficiency virus (HIV) transmission is critical to estimating disease burdens, identifying outbreak risk, and developing informed intervention strategies. METHODS: Using behavioral data from the AIDS Linked to the IntraVenous Experience (ALIVE) study, an ongoing community-based cohort of PWID in Baltimore, United States, and an individual-based network model, we explored the impacts of service disruptions combined with changes in social networks and injecting behaviors of PWID on HCV and HIV transmission. RESULTS: Analyses of ALIVE data showed that during the pandemic, there was an acceleration in injection cessation trajectories overall, but those who continued injecting increased the frequency of injection; at the same time, individual drug-use networks became smaller and the probability of injecting with others decreased. Simulation results demonstrated that HCV and HIV prevalence increased from service disruptions alone, but these effects were mitigated when including observed behavior changes in addition. CONCLUSIONS: Model results combined with rich individual behavioral data indicated that pandemic-induced behavioral changes of PWID that lasted longer than service disruptions could have offset the increasing disease burden caused by disrupted service access during the pandemic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".