Evidence map›Paper›PMID 41492326›Full record

ReviewCureus2026

Fentanyl's Deadly Footprint: A New Framework for Predicting Overdose Hotspots.

Deborah Okunola, Abdulazeez Alabi, Olajide Akinpeloye, Osayimwense Izinyon, Tope Amusa

Abstract readReview
In one paragraph

Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Deborah OkunolaMathematics and Statistics (Biostatistics), Georgia State University, Atlanta, USA.
Abdulazeez AlabiMathematics and Statistics (Biostatistics), Georgia State University, Atlanta, USA.
Olajide AkinpeloyeEpidemiology and Medical Statistics, University of Ibadan, Ibadan, NGA.
Osayimwense IzinyonStatistics, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, USA.
Tope AmusaMathematics and Statistics (Biostatistics), Georgia State University, Atlanta, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Illicitly manufactured fentanyl has become a major driver of fatal drug overdoses in the United States, with annual mortality approaching six figures. Spatial analyses consistently demonstrate that the overdose burden clusters within structurally vulnerable micro-areas rather than distributing uniformly across regions. A narrative review of US studies (2010-2025) synthesized evidence from mortality, emergency department, emergency medical services, wastewater, social media, and related data streams that examined small-area patterns or the prediction of fentanyl or synthetic opioid overdose. Evidence shows a strongly clustered fentanyl burden, with small-area mapping identifying persistent hotspots and Bayesian forecasting and nowcasting models providing short-horizon predictions. Emerging wastewater and social media indicators offer additional early warning capacity, especially where routine surveillance is sparse. However, external validation, calibration reporting, uncertainty characterization, and equity-sensitive performance metrics are uncommon. This review organizes these strands into a three-tier framework--mortality-only, EMS and emergency department, and multi-stream environments--that links mapping, forecasting, and spike detection to operational response while embedding explicit equity and governance safeguards.

Indexed as

fentanyl overdosehotspot predictionpublic health surveillancespatiotemporal analysiswastewater-based epidemiology

Identifiers

PMID41492326
PMCPMC12765505

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.