Evidence map›Paper›PMID 42850620›Full record

ArticleCritical care (London, England)2026

Prehospital transport prioritization and time-varying selection bias in traffic-related multi-patient incidents: a nationwide retrospective cohort study.

Atsushi Kubo, Shinobu Tamura, Atsushi Hiraide, Daigo Morioka, Ryu Murakami, Kenko Fukui, Shigeaki Inoue

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Article in Critical care (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Atsushi KuboFaculty of Emergency Medical Science, Meiji University of Integrative Medicine, 2nd Floor, Building 6, Lecturer's Office, 6-1 Hinoya, Honoda, Hiyoshi-cho, Nantan-shi, 629-0392, Kyoto, Japan. a_kubo@meiji-u.ac.jp.ORCID http://orcid.org/0000-0003-0141-1285
Shinobu TamuraFirst Department of Internal Medicine, Wakayama Medical University, Wakayama, Japan.
Atsushi HiraideFaculty of Emergency Medical Science, Meiji University of Integrative Medicine, 2nd Floor, Building 6, Lecturer's Office, 6-1 Hinoya, Honoda, Hiyoshi-cho, Nantan-shi, 629-0392, Kyoto, Japan.
Daigo MoriokaFaculty of Emergency Medical Science, Meiji University of Integrative Medicine, 2nd Floor, Building 6, Lecturer's Office, 6-1 Hinoya, Honoda, Hiyoshi-cho, Nantan-shi, 629-0392, Kyoto, Japan.
Ryu MurakamiFaculty of Risk and Crisis Management, Chiba Institute of Science, Chiba, Japan.
Kenko FukuiFaculty of Emergency Medical Science, Meiji University of Integrative Medicine, 2nd Floor, Building 6, Lecturer's Office, 6-1 Hinoya, Honoda, Hiyoshi-cho, Nantan-shi, 629-0392, Kyoto, Japan.
Shigeaki InoueDepartment of Emergency and Critical Care Medicine, Wakayama Medical University, Wakayama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn prehospital multi-patient traffic incidents, the first ambulance to depart -priority transport - may reflect underlying injury severity rather than a treatment effect, as more severely injured patients are systematically prioritized. Conventional statistical methods often fail to separate this severity-dependent selection from actual outcomes. We applied a "doubly robust" statistical framework (augmented inverse probability weighting, AIPW), which combines a treatment model and an outcome model, to better account for measured differences, reduce selection bias, and examine whether on-scene time modifies this association.

methodsWe conducted a nationwide retrospective cohort study using emergency medical service transport records from the Fire and Disaster Management Agency of Japan (January 2021-December 2023). Of 13,821,224 records, 143,130 patients from traffic-related multi-patient incidents (TMIs; ≥2 patients per incident) with complete data were included. The exposure was priority transport. The primary outcome was physician-assessed death or critical/severe injury at hospital arrival. Marginal adjusted risk differences (RDs) were estimated using AIPW. Effect modification by on-scene time (5-60 min) was assessed by marginal standardization with cluster-bootstrap confidence intervals. Sensitivity analyses included overlap restriction and E-value quantification of unmeasured confounding.

resultsAmong 143,130 patients (median age 41 years; female, 50.57%), 62,769 (43.85%) underwent priority transport. Severe outcomes occurred in 4.35% of priority and 1.87% of non-priority patients. The overall RDs was 2.2% points (95% CI, 2.01-2.36). A significant interaction with on-scene time was observed (p < .001): the marginal RD was peaked at 5 min (6.4% points; 95% CI, 5.6-7.2), declined to a minimum of 1.4% points (95% CI, 1.3-1.6) at approximately 32 min, and increased modestly to 2.4% points (95% CI, 2.0-2.8) at 60 min. Sensitivity analyses confirmed result robustness, yielding an E-value of 3.76 (lower bound, 3.57).

conclusionsThe association between priority transport and severe outcome was modified by on-scene time, with the largest risk difference at the shortest on-scene times. This U-shaped pattern is consistent with preferential selection of more severely injured patients for rapid transport, rather than a direct effect of transport order. Accounting for prehospital triage selection when interpreting transport-outcome associations in critical care research.

Indexed as

Accidents, TrafficEmergency Medical ServicesTransportation of PatientsTriageAdultAgedCohort StudiesFemaleHumansJapanMaleMiddle AgedRetrospective StudiesSelection BiasTime FactorsCausal inferenceDoubly robust estimationMass casualty incidentsPrehospital careSelection biasTransport prioritizationTrauma systemsTriage

Identifiers

PMID42850620
PMCPMC13647583

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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.