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Morbidity and Length of Stay After Injury Among People Experiencing Homelessness in North America

2024· article· en· W4392242102 on OpenAlexaff
Casey M. Silver, Arielle Thomas, Susheel Reddy, Shelbie D. Kirkendoll, Avery B. Nathens, Nabil Issa, Purvi P. Patel, Rebecca E. Plevin, Hemal K. Kanzaria, Anne M. Stey

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteUniversity of California, San FranciscoNational Cancer InstituteAgency for Healthcare Research and QualityNational Institutes of HealthEastern Association for the Surgery of TraumaNorthwestern University
KeywordsMedicineInjury Severity ScoreIntensive care unitGlasgow Coma ScaleComorbidityModerationPopulationResidenceRetrospective cohort studyDemographyCohortInjury preventionEmergency medicinePoison controlGerontologyPsychiatryInternal medicinePsychologyEnvironmental health

Abstract

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Importance: Traumatic injury is a leading cause of hospitalization among people experiencing homelessness. However, hospital course among this population is unknown. Objective: To evaluate whether homelessness was associated with increased morbidity and length of stay (LOS) after hospitalization for traumatic injury and whether associations between homelessness and LOS were moderated by age and/or Injury Severity Score (ISS). Design, Setting, and Participants: This retrospective cohort study of the American College of Surgeons Trauma Quality Programs (TQP) included patients 18 years or older who were hospitalized after an injury and discharged alive from 787 hospitals in North America from January 1, 2017, to December 31, 2018. People experiencing homelessness were propensity matched to housed patients for hospital, sex, insurance type, comorbidity, injury mechanism type, injury body region, and Glasgow Coma Scale score. Data were analyzed from February 1, 2022, to May 31, 2023. Exposures: People experiencing homelessness were identified using the TQP's alternate home residence variable. Main Outcomes and Measures: Morbidity, hemorrhage control surgery, and intensive care unit (ICU) admission were assessed. Associations between homelessness and LOS (in days) were tested with hierarchical multivariable negative bionomial regression. Moderation effects of age and ISS on the association between homelessness and LOS were evaluated with interaction terms. Results: Of 1 441 982 patients (mean [SD] age, 55.1 [21.1] years; (822 491 [57.0%] men, 619 337 [43.0%] women, and 154 [0.01%] missing), 9065 (0.6%) were people experiencing homelessness. Unmatched people experiencing homelessness demonstrated higher rates of morbidity (221 [2.4%] vs 25 134 [1.8%]; P < .001), hemorrhage control surgery (289 [3.2%] vs 20 331 [1.4%]; P < .001), and ICU admission (2353 [26.0%] vs 307 714 [21.5%]; P < .001) compared with housed patients. The matched cohort comprised 8665 pairs at 378 hospitals. Differences in rates of morbidity, hemorrhage control surgery, and ICU admission between people experiencing homelessness and matched housed patients were not statistically significant. The median unadjusted LOS was 5 (IQR, 3-10) days among people experiencing homelessness and 4 (IQR, 2-8) days among matched housed patients (P < .001). People experiencing homelessness experienced a 22.1% longer adjusted LOS (incident rate ratio [IRR], 1.22 [95% CI, 1.19-1.25]). The greatest increase in adjusted LOS was observed among people experiencing homelessness who were 65 years or older (IRR, 1.42 [95% CI, 1.32-1.54]). People experiencing homelessness with minor injury (ISS, 1-8) had the greatest relative increase in adjusted LOS (IRR, 1.30 [95% CI, 1.25-1.35]) compared with people experiencing homelessness with severe injury (ISS ≥16; IRR, 1.14 [95% CI, 1.09-1.20]). Conclusions and Relevance: The findings of this cohort study suggest that challenges in providing safe discharge to people experiencing homelessness after injury may lead to prolonged LOS. These findings underscore the need to reduce disparities in trauma outcomes and improve hospital resource use among people experiencing homelessness.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.373
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2024
Admission routes1
Has abstractyes

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