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Aggregated urine drug test (UDT) data can effectively estimate national drug overdose deaths during data lag periods. This method accurately captured overdose trends, including the rise during the COVID-19 pandemic.

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Area of Science:

  • Public Health
  • Epidemiology
  • Toxicology

Background:

  • Provisional fatal drug overdose estimates in the US have a 6-month lag.
  • Accurate, timely data are crucial for understanding overdose trends, especially during public health crises like the COVID-19 pandemic.
  • Real-time data sources are needed to identify sudden shifts in overdose trajectories.

Purpose of the Study:

  • To assess the utility of aggregated urine drug test (UDT) data for estimating national drug overdose deaths within the 6-month data lag window.
  • To evaluate if UDT data can provide timely insights into overdose mortality trends.

Main Methods:

  • A cross-sectional study utilized over 3.1 million UDT specimens from US healthcare facilities (2015-2025).
  • Monthly UDT positivity rates and drug levels (fentanyl, methamphetamine) were aggregated.
  • Generalized linear models, trained on 4 years of data, used UDT data to estimate overdose deaths for the 6-month lag period, with comparisons to CDC mortality counts.

Main Results:

  • The UDT-based model demonstrated superior accuracy (MAPE 7.1%) compared to a baseline ARIMA model (MAPE 9.0%) in estimating overdose deaths.
  • The model successfully identified the significant increase in overdose deaths at the onset of the COVID-19 pandemic.
  • Over 537,000 overdose deaths were recorded in the US from 2019 to August 2024.

Conclusions:

  • Aggregated urine drug test data show promise for estimating current drug overdose death trends.
  • This approach can provide crucial, up-to-date information to inform public health interventions.
  • Future model enhancements could incorporate additional data sources like drug seizures and syndromic surveillance.