Early detection and prediction of non-fatal drug-related incidents and fatal overdose outbreaks using the Farrington algorithm.

Addiction

Division of Infectious Diseases and Global Public Health, UCSD Department of Medicine, 9500 Gilman Drive, La Jolla, CA, USA.

Published: September 2024

AI Article Synopsis

  • The study aimed to evaluate the effectiveness of using time-series analyses to monitor both fatal and non-fatal drug overdoses as a way to spot emerging drug threats and detect fatal overdose outbreaks early.
  • Researchers analyzed county-level data from California and Florida from 2015 to 2021, using the Farrington algorithm to identify unusual increases in overdose counts alongside a standard method for comparison.
  • Findings showed that while both methods generated similar alerts for non-fatal overdoses, the benchmark method identified more alerts for fatal overdoses, with the sensitivity of detecting ongoing fatal overdose outbreaks being 66% at the county level and around 77-81% at the regional level.

Article Abstract

Aims: The aim of this study was to assess the validity of undertaking time-series analyses on both fatal and non-fatal drug overdose outcomes for the surveillance of emerging drug threats, and to determine the validity of analyzing non-fatal indicators to support the early detection of fatal overdose outbreaks.

Design, Setting And Participants: Time-series analyses using county-level data containing fatal overdoses and non-fatal overdose counts were collected at monthly intervals between 2015 and 2021 in California and Florida, USA. To analyze these data, we used the Farrington algorithm (FA), a method used to detect aberrations in time-series data such that an abnormal increase in counts relative to previous observations would result in an alert. The FA's performance was compared with a bench-mark approach, using the standard deviation as an aberration detection threshold. We evaluated whether monthly alerts in non-fatal overdose can aid in identifying fatal drug overdose outbreaks, defined as a statistically significant increase in the 6-month overdose death rate. We also conducted analyses across regions, i.e. clusters of counties.

Measurements: Measurements were taken during emergency department and emergency medical service visits.

Findings: Both methods yielded a similar proportion of alerts across scenarios for non-fatal overdoses, while the bench-mark method yielded more alerts for fatal overdoses. For both methods, the correlations between surveillance evaluations were relatively poor in the detection of aberrations (typically < 35%) but were high between evaluations yielding no alerts (typically > 75%). For ongoing fatal overdose outbreaks, a strategy based on the detection of alerts at the county level from either method yielded a sensitivity of 66% for both California and Florida. At the regional level, the equivalent analyses had sensitivities of 81% for California and 77% for Florida.

Conclusion: Aberration detection methods can support the early detection of fatal drug overdose outbreaks, particularly when methodologies are applied in combination rather than individual methods separately.

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Source
http://dx.doi.org/10.1111/add.16674DOI Listing

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