Perioperative Health Care Disparities in the United States: A Systematic Review
Bibliographic record
Abstract
Perioperative health inequities remain a critical issue, contributing to unequal patient outcomes and financial costs despite increasing awareness and efforts to address these disparities. This systematic review evaluated anesthesiology literature from 2010 to 2023 on perioperative health care disparities related to race, ethnicity, gender, and socioeconomic status. The review aimed to identify gaps and propose research and opportunities for intervention. A comprehensive literature search was conducted using PubMed, Embase, Scopus, and Web of Science, with studies included if they focused on perioperative disparities in the United States, were published in anesthesiology journals, and met criteria for methodological rigor. The review was registered with International Prospective Register of Systematic Reviews (PROSPERO); data extraction followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and study quality was assessed with the Newcastle-Ottawa scale. Out of 1050 abstracts screened, 116 articles were reviewed for full text, with 59 studies meeting inclusion criteria. Included studies comprised retrospective cohort studies, cross-sectional analyses, a case-control study, and a randomized controlled trial, covering various surgical procedures and sample sizes from 100 to over 21 million patients. Disparities were noted in peripartum management (n = 14), mortality (n = 12), complications (n = 8), regional anesthesia use (n = 6), and pain management (n = 3), with evidence of poorer outcomes in Black and Hispanic women, older adolescents, and patients who were uninsured or on Medicaid. This review highlights the persistence of significant perioperative disparities and identifies gaps, such as limited exploration of the causes of these disparities, limited examination of disparities during the preoperative and intraoperative period, and few interventions to address these identified disparities. Reducing these disparities requires stakeholder engagement, multifaceted approaches, culturally agile training for health care teams, enhanced decision support tools, and a more diverse health care workforce. Continued research and targeted interventions at individual, community, and societal levels are essential for improving perioperative outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".