Evidence map›Paper›PMID 39313416›Full record

ArticleAddiction (Abingdon, England)2025

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

Thomas Patton, Sharon Trillo-Park, Bethan Swift, Annick Bórquez

Abstract read
In one paragraph

Article in Addiction (Abingdon, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Thomas PattonDivision of Infectious Diseases and Global Public Health, UCSD Department of Medicine, 9500 Gilman Drive, La Jolla, CA, USA.ORCID 0000-0002-5134-3308
Sharon Trillo-ParkDivision of Infectious Diseases and Global Public Health, UCSD Department of Medicine, 9500 Gilman Drive, La Jolla, CA, USA.
Bethan SwiftWellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Annick BórquezDivision of Infectious Diseases and Global Public Health, UCSD Department of Medicine, 9500 Gilman Drive, La Jolla, CA, USA.ORCID 0000-0002-6033-9837

Funding

VirologyP30AI036214 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SUSAN JANET LITTLE · 1994 to 2026
$78.4M
Training Program in Substance Use, HIV and Related InfectionsT32DA023356 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Natasha Martin, Steffanie A Strathdee · 2007 to 2026
$7.8M
Real-time prediction of emerging drug use epidemics in the United StatesDP2DA049295 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BORQUEZ, ANNICK · 2019 to 2019
$2.4M
NIAID NIH HHS P30 AI036214NIDA Avenir Grant DP2DA049295NIDA NIH HHS DP2 DA049295NIDA NIH HHS T32 DA023356NIDA NIH HHS T32DA023356
6 · The paper itself

Abstract

aimsThe 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

participantsTime-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.

findingsBoth 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.

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

Indexed as

AlgorithmsDisease OutbreaksDrug OverdoseEarly DiagnosisCaliforniaFloridaHumansOutbreakOverdoseSurveillance

Identifiers

PMID39313416
PMCPMC11707314

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.