Evidence map›Paper›PMID 40604633›Full record

ArticleBMC public health2025

Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.

Amir Hossein Ebrahimi, Ali Asghar Alesheikh, Aynaz Lotfata

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Amir Hossein EbrahimiDepartment of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Valiasr Street, Mirdamad Cross, Tehran, 19967 15433, Iran.
Ali Asghar AlesheikhDepartment of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Valiasr Street, Mirdamad Cross, Tehran, 19967 15433, Iran. alesheikh@kntu.ac.ir.
Aynaz LotfataDepartment of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, there has been a growing body of literature on identifying effective determinants for modeling the spatial variation of overdose rates, addressing this emerging public health concern globally. We compiled a range of widely recognized factors to examine spatial heterogeneity and its associations with overdose mortality using a non-linear geographically weighted random forest approach. The model outperformed conventional ones with (R

Indexed as

Drug OverdoseMachine LearningSpatial AnalysisHumansUnited StatesOverdose mortalitySpatial analysisSpatial machine learning model

Identifiers

PMID40604633
PMCPMC12219971

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.