BACKGROUND:Air pollution, especially particle pollution, is increasingly recognized as a potential perioperative risk factor, yet modeling environmental exposures in surgical cohorts remains methodologically underdeveloped. We demonstrate a Bayesian hierarchical framework to quantify probabilistic associations between preoperative fine particulate matter (PM2.5) exposure and postoperative complications, highlighting its interpretability and flexibility for clinical environmental epidemiology. METHODS:We conducted a single center, retrospective cohort study using data from 49,615 surgical patients in Utah who underwent elective surgical procedures from 2016 to 2018. Patients' addresses were geocoded and linked to daily Census-tract level PM2.5 estimates. The exposure variable was defined as the maximum PM2.5 concentrations in the 7 days prior to surgery. The binary outcome was a composite of postoperative complications: pneumonia, surgical site infection, urinary tract infection, sepsis, stroke, myocardial infarction, or thromboembolic event. A hierarchical Bayesians regression model with weakly informative priors was used adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities with census tract as a group (random) effect. We present posterior estimates with credible intervals, highlight model transparency and sensitivity, and discuss contrasts with standard frequentist methods. RESULTS:Postoperative complications were associated in a dose-dependent manner with higher concentrations of PM2.5 exposure. We found a relative increase of 8.2% in the odds of complications (OR = 1.082) for every 10.ug/m3 increase in the highest single-day 24-h PM2.5 exposure during the 7 days prior to surgery. For an increase in PM2.5 from 1 to 30 ug/m3, the odds of complication rose to over 27% (95% CI: 4%-55%). The results were robust across prior choices and model specifications. We report full posterior distributions and highlight advantages of Bayesian modeling for uncertainty quantification and clinical interpretability. CONCLUSIONS:This case study demonstrates the application of hierarchical Bayesian modeling to quantify the probabilistic associations between preoperative PM2.5 exposure and postoperative complications, highlighting transparent risk estimation and uncertainty characterization that may inform the design of future multicenter perioperative environmental studies. EDITORIAL COMMENT:Using Bayesian statistical analysis, the authors demonstrate a dose-dependent risk for postoperative complications in patients exposed to air polluted with fine particulate matter with a size of less than 2.5 μm.

Background:Social determinants of health continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the United States, we examined geospatial clustering of perioperative antiemetic disparities. Objective:The study aimed to determine whether disparities in perioperative antiemetic prophylaxis exhibit geographic clustering based on neighborhood-level disadvantage and whether patients from disadvantaged communities are more likely to be undertreated after adjusting for individual postoperative nausea and vomiting risk. Methods:We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital involving 19,477 patients who met the inclusion criteria. We geocoded patient home addresses and combined them with the census block group-level neighborhood disadvantage, a composite index from the National Neighborhood Data Archive. We stratified our patients by antiemetic risk score and calculated the number of antiemetic interventions. We used Poisson spatial scan statistics, implemented in SaTScan (Information Management Services, Inc), to detect geographic clusters of undertreatment. Results:We identified 1 significant cluster (P<.001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk of the whole cluster was 1.44, implying that patients within the cluster were 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below-median antiemetic prophylaxis after controlling for risk. Conclusions:To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk-adjusted antiemetic prophylaxis.

BACKGROUND:Milestones evaluation is mandated by the Accreditation Council for Graduate Medical Education (ACGME) to help training programs measure residents' progress toward competency and identify specific areas for trainee improvement. Data from training programs raised concerns that Milestones ratings may reflect the year of training more than resident progress toward competency. We examined the relationship between residents' Milestones ratings toward the end of residency training and their performance on the American Board of Anesthesiology (ABA) examinations, widely considered as the gold standard of competency. METHODS:We compared Milestones 2.0 ratings and board scores of all anesthesiologists who completed an ACGME-accredited residency program between July 2021 and June 2022 (AY22) and had their first-time ABA ADVANCED Examination (written), Standardized Oral Examination (SOE) and Objective Structured Clinical Examination (OSCE) performance available by 2023. We first assessed the correlation between the average rating achieved across all 23 Milestones during the last 6 months of residency training and the Z-scores of these three examinations among first-time takers. Then, we evaluated the correlations between 9 specific Milestones and their conceptually related domains tested by the ADVANCED, the SOE, and the OSCE; we calculated Pearson and polychoric correlation coefficients for continuous and ordinal data, respectively. RESULTS:All 23 Milestones 2.0 AY22 ratings were available for 1849 Post Graduate Year (PGY)-4, Clinical Anesthesia Year 3 (CA-3) residents. These were matched to 1799 first-time ADVANCED and 1383 first-time SOE and OSCE takers. The average ACGME Milestones ratings across all competencies were significantly correlated with examination Z-scores (all P < .001)-the ADVANCED (r = 0.135 [95% confidence interval {CI}, 0.089-0.180], the SOE (r = 0.117 [0.065-0.169]), and the OSCE (r = 0.112 [0.060-0.164]). For the domain-specific comparisons, scores on the ADVANCED Examination correlated modestly with the Medical Knowledge Milestone domain (r = 0.289, P < .001), but there were no statistically significant associations between SOE task ratings and their related Milestones domains (ρ = 0.027 to 0.091, P = .31 to 0.81). In comparisons of similar domains evaluated by OSCE stations and the Milestones, 2 were statistically significantly correlated with a weak magnitude (Interpretation of Monitors and Echocardiograms [ρ = 0.093, P = .031] and Ethical Issues [ρ = 0.049, P = .003]) while 2 others were not statistically significant (Application of Ultrasonography [ρ = 0.052, P = .775] and Communication with other Professionals [ρ = 0.052, P = .086]). CONCLUSIONS:There was a modest correlation between the last Medical Knowledge Milestone achieved and the ADVANCED Examination. However, the weak correlations between residency Milestones and the SOE or OSCE performance suggest that the Milestones system, as currently implemented by anesthesiology training programs, does not predict certifying examination performance.

Background:While exposure to fine particulate matter air pollution (PM 2.5 ) is known to cause adverse health effects, its impact on postoperative outcomes in US adults remains understudied. Perioperative exposure to PM 2.5 may induce inflammation that interacts insidiously with the surgical stress response, leading to higher postoperative complications. Methods:We conducted a single center, retrospective cohort study using data from 49,615 surgical patients living along Utah's Wasatch Front and who underwent elective surgical procedures at a single academic medical center from 2016-2018. Patients' addresses were geocoded and linked to daily Census-tract level PM 2.5 estimates. We hypothesized that elevated PM 2.5 concentrations in the week prior to surgery would be associated with an increase in a bundle of major postoperative complications. A hierarchical Bayesians regression model was fit adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities. Results:Postoperative complications increased in a dose-dependent manner with higher concentrations of PM 2.5 exposure, with a relative increase of 8% in the odds of complications (OR=1.082) for every 10ug/m 3 increase in the highest single-day 24-hr PM 2.5 exposure during the 7 days prior to surgery. For a 30 fold increase in PM 2.5 (1 ug/m 3 to 30ug/m 3 ) the odds of complication rose to over 27% (95%CI: 4%-55%). The association persisted after controlling for comorbidities and confounders; our inferences were robust to modeling choices and sensitivity analysis. Conclusions:In this large Utah cohort, exposure to elevated PM 2.5 concentrations in the week before surgery was associated with a dose-dependent increase in postoperative complications, suggesting a potential impact of air pollution on surgical outcomes. These findings merit replication in larger datasets to identify populations at risk and define the interaction and impact of different pollutants. PM 2.5 exposure is a potential perioperative risk factor and, given the unmitigated air pollution in urban areas, a global health concern.

Postoperative opioid prescribing has historically lacked information critical to balancing the pain control needs of the individual patient with our professional responsibility to judiciously prescribe these high-risk medications. This data evaluates pain control, satisfaction with pain control, and opioid utilization among patients undergoing isolated mid-urethral sling (MUS) randomized to one of two different opioid prescribing regimens. This study was registered on clinicaltrials.gov (NCT04277975). Women undergoing isolated MUS by a Female Pelvic Medicine and Reconstructive Surgery physician at a Penn State Health hospital from June 1, 2020 to November 22, 2021 were offered enrollment into this prospective, randomized, open-label, non-inferiority clinical trial. Participants gave informed consent and were enrolled by a member of the study team. Allocation was concealed to patient and study personnel until randomization on the day of surgery. Preoperatively, all participants completed baseline demographic and pain surveys including CSI-9, PCS, and Likert pain score (scale 0-10). Participants were randomized to either receive a standard prescription of ten 5 mg tablets oxycodone provided preoperatively (standard) or opioid prescription provided only upon patient request postoperatively (restricted). Randomization was performed by the study team surgeon using the REDCap randomization module on the day of surgery. Following MUS, subjects completed a daily diary for 1 week, i.e., postoperative day (POD) 0 through 7. Within the dairy, subjects provided the following information: average daily pain score, opioid use and amount of opioid utilized, other forms of pain management, satisfaction with pain control, perception of the amount of opioid prescribed, and need for pain management hospital/clinic visits. The online Prescription Drug Monitoring Program (PDMP) was queried for all patients to determine if prescriptions for opioids were filled during the postoperative period. The primary outcome was average postoperative day 1 pain score and an a priori determined margin of non-inferiority was set at 2 points. Secondary outcomes included whether subject filled an opioid prescription (indicated by the online PDMP), opioid use (yes/no), satisfaction with pain control (on a scale of 1= "much worse" to 5= "much better" than expected), and how subjects felt about the amount of opioid prescribed (on a scale of 1="prescribed far more" to 3="prescribed the right amount" to 5="prescribed far less" opioid than needed). 82 participants underwent isolated MUS placement and met inclusion criteria; 40 were randomized to the standard arm and 42 to the restricted group. Within this manuscript, we detail the data obtained from this randomized clinical trial and the methods utilized.

Cognitive task analysis (CTA) methods are traditionally used to conduct small-sample, in-depth studies. In this case study, CTA methods were adapted for a large multi-site study in which 102 anesthesiologists worked through four different high-fidelity simulated high-consequence incidents. Cognitive interviews were used to elicit decision processes following each simulated incident. In this paper, we highlight three practical challenges that arose: (1) standardizing the interview techniques for use across a large, distributed team of diverse backgrounds; (2) developing effective training; and (3) developing a strategy to analyze the resulting large amount of qualitative data. We reflect on how we addressed these challenges by increasing standardization, developing focused training, overcoming social norms that hindered interview effectiveness, and conducting a staged analysis. We share findings from a preliminary analysis that provides early validation of the strategy employed. Analysis of a subset of 64 interview transcripts using a decompositional analysis approach suggests that interviewers successfully elicited descriptions of decision processes that varied due to the different challenges presented by the four simulated incidents. A holistic analysis of the same 64 transcripts revealed individual differences in how anesthesiologists interpreted and managed the same case.

Department of Anesthesiology, University of Utah, Salt Lake City, Utah, [email protected] Department of Anesthesiology, Cornell University, Weill Cornell Medical Center, New York, New York Funding: Work contributing to this publication was supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR004409. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (M.H.A.), and by the Foundation for Anesthesia Education and Research Mentored Research Training Grant ID MRTG-08-15-2021-White (Robert) (R.S.W.).

Editor—Hazardous attitudes contribute to degraded performance in aviation 1 US Department of Transportation-Federal Aviation AdministrationPilot's handbook of aeronautical knowledge, FAA-H-8083-25C. Ch 2: Aeronautical decision-making, p2-1 to 2-32. Oklahoma City, OK, USA. 2023https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/faa-h-8083-25c.pdfDate accessed: September 16, 2023 Google Scholar , 2 Nuñez B. López C. Velazquez J. Mora O.A. Román K. Hazardous attitudes in US part 121 airline accidents. 20th Int Symp Aviat Psychol. 2019; (Available from:): 37-42https://corescholar.libraries.wright.edu/isap_2019/7Date accessed: September 16, 2023 Google Scholar , 3 Hunter D.R. Measurement of hazardous attitudes among pilots. Int J Aviat Psychol. 2005; 15: 23-43 Crossref Scopus (61) Google Scholar , 4 Hunter D.R. Martinussen M. Wiggins M. O'Hare D. Situational and personal characteristics associated with adverse weather encounters by pilots. Accid Anal Prev. 2011; 43: 176-186 Crossref PubMed Scopus (28) Google Scholar and surgery. 5 Kadzielski J. McCormick F. Herndon J.H. Rubash H. Ring D. Surgeons' attitudes are associated with reoperation and readmission rates. Clin Orthop. 2015; 473: 1544-1551 Crossref PubMed Scopus (25) Google Scholar Elevated hazardous attitudes scores appear to predispose to poor decision-making and 'at-risk' behaviours. 4 Hunter D.R. Martinussen M. Wiggins M. O'Hare D. Situational and personal characteristics associated with adverse weather encounters by pilots. Accid Anal Prev. 2011; 43: 176-186 Crossref PubMed Scopus (28) Google Scholar , 5 Kadzielski J. McCormick F. Herndon J.H. Rubash H. Ring D. Surgeons' attitudes are associated with reoperation and readmission rates. Clin Orthop. 2015; 473: 1544-1551 Crossref PubMed Scopus (25) Google Scholar , 6 Saeed N.A. Blakaj A. Kelly J.R. et al. Hazardous attitudes: physician decision making in radiation oncology. Adv Rad Onc. 2022; 7101033 Google Scholar In post-accident analyses, 86% of fatal general aviation accidents involved at least one hazardous attitude. 7 Wetmore M. Lu C.T. The effects of hazardous attitudes on crew resource management skills. Int J Appl Aviat Stud. 2006; 6: 165-182 Google Scholar A key set of hazardous attitudes was identified by the United States Federal Aviation Administration (FAA): anti-authority, impulsivity, invulnerability, resignation, and macho (describing risk tolerance driven by ego and social concern). 1 US Department of Transportation-Federal Aviation AdministrationPilot's handbook of aeronautical knowledge, FAA-H-8083-25C. Ch 2: Aeronautical decision-making, p2-1 to 2-32. Oklahoma City, OK, USA. 2023https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/faa-h-8083-25c.pdfDate accessed: September 16, 2023 Google Scholar Their description of hazardous attitudes, pilot behaviours and mindsets, and potential adverse consequences is summarised in Supplementary Table 1. The domains self-confidence and worry were added to scale versions used in healthcare. 5 Kadzielski J. McCormick F. Herndon J.H. Rubash H. Ring D. Surgeons' attitudes are associated with reoperation and readmission rates. Clin Orthop. 2015; 473: 1544-1551 Crossref PubMed Scopus (25) Google Scholar ,6 Saeed N.A. Blakaj A. Kelly J.R. et al. Hazardous attitudes: physician decision making in radiation oncology. Adv Rad Onc. 2022; 7101033 Google Scholar ,8 Bruinsma W.E. Becker S.J.E. Guitton T.G. Kadzielski J. Ring D. How prevalent are hazardous attitudes among orthopaedic surgeons?. Clin Orthop. 2015; 473: 1582-1589 Crossref PubMed Scopus (28) Google Scholar , 9 Kadzielski J. McCormick F. Zurakowski D. Herndon J.H. Patient safety climate among orthopaedic surgery residents. J Bone Jt Surg. 2011; 93: e62 Crossref PubMed Scopus (8) Google Scholar , 10 Meunier A. Posadzy K. Tinghög G. Aspenberg P. Risk preferences and attitudes to surgery in decision making: a survey of Swedish orthopedic surgeons. Acta Orthop. 2017; 88: 466-471 Crossref PubMed Scopus (12) Google Scholar

Aim: We sought to investigate the impact of social determinants of health on pain clinic attendance. Materials & methods: Retrospective data were collected from the Pain Center at Montefiore Medical Center from 2016 to 2020 and analyzed with multivariable logistic regression. Results: African-Americans were less likely to attend appointments compared with White patients (odds ratio [OR]: 0.73; 95% CI: 0.70-0.77; p < 0.001). Males had decreased attendance compared with females (OR: 0.89; 95% CI: 0.87-0.92; p < 0.001). Compared with Commercial, those with Medicaid (OR: 0.69; 95% CI: 0.66-0.72; p < 0.001) and Medicare (OR: 0.76; 95% CI: 0.73-0.80; p < 0.001) insurance had decreased attendance. Conclusion: Significant disparities exist in pain clinic attendance based upon social determinants of health including race, gender and insurance type.

Effective decision-making in crisis events is challenging due to time pressure, uncertainty, and dynamic decisional environments. We conducted a systematic literature review in PubMed and PsycINFO, identifying 32 empiric research papers that examine how trained professionals make naturalistic decisions under pressure. We used structured qualitative analysis methods to extract key themes. The studies explored different aspects of decision-making across multiple domains. The majority (19) focused on healthcare; military, fire and rescue, oil installation, and aviation domains were also represented. We found appreciable variability in research focus, methodology, and decision-making descriptions. We identified five main themes: (1) decision-making strategy, (2) time pressure, (3) stress, (4) uncertainty, and (5) errors. Recognition-primed decision-making (RPD) strategies were reported in all studies that analyzed this aspect. Analytical strategies were also prominent, appearing more frequently in contexts with less time pressure and explicit training to generate multiple explanations. Practitioner experience, time pressure, stress, and uncertainty were major influencing factors. Professionals must adapt to the time available, types of uncertainty, and individual skills when making decisions in high-risk situations. Improved understanding of these decisional factors can inform evidence-based enhancements to training, technology, and process design.

King Cholera and the first geospatial analysis (GA) by an anesthesiologist King Cholera had wreaked havoc on the health of Londoners for generations, but the outbreak on August 31, 1854 was extreme even by a society accustomed to mass death. Within 3 days, 127 people had died, and within a week, a majority of the population had fled the area. John Snow, considered a pioneer of anesthesia for administering Chloroform to Queen Victoria during the birth of Prince Leopold in 1853, was skeptical of the dominant miasma (airborne) theory of disease spread for Cholera.1 With the help of what would later become known as a Voronoi Diagram,2 Snow was able to visualize geospatial proximity, namely that patients who had consumed water from the Broad Street pump eventually contracted Cholera, while those who consumed water from different sources did not contract the disease. Presented with this map to show clusters of disease, the local authorities disabled the pump (Fig. 1).Figure 1: John Snow Map on mode of communication of cholera. This figure is taken from the map of the book “On the Mode of Communication of Cholera” by John Snow, published in 1854 C.F. Cheffins, Lith, Southampton. Use is in public domain. This is the first example in the literature of a dot map used to display density of cases in a geographic context and is a foundational map of medical geography and epidemiology.We leave it to historians to dispute whether or not the famous map of the outbreak was created until after the outbreak.3 Regardless, the reasoning and approach to the epidemic employed by Snow became foundational work for epidemiology and. GA Snow linked employment with certain companies to worsening of disease transmission, and by identifying geographic clusters of mortality, linked the Cholera outbreak to a polluted water source,4 an approach similar to the focus of this manuscript. The cross-disciplinary expertise, required for GA, common in the days before medical specialization and enabled by the relative paucity of development in various fields compared with today, can be reborn with the advent of Electronic Health Records (EHR) systems that make the tools of medical geography accessible to a wide variety of health care researchers and clinical subspecialties. As the story of John Snow demonstrates, the combination of GA and specialty-based medical knowledge (eg, Anesthesiology and Surgery), can serve as a method to derive insights into socioeconomic and environmental drivers of perioperative care and may lead to the design of appropriate interventions to ameliorate disparities in outcomes. Social context as key driver of health and health care disparities The focus of this manuscript is how context, location, and geography impact health and health care processes.5 Social determinants of health (SDOH) are the fundamental causes of disease, where primary causes manifest eventually as medical conditions. The central SDOH mediate the disease pathway all along; actionable mechanisms of disparity invite targeted countermeasures, once they are clearly identified and exposed. For example, food deserts, the absence of parks, and racial stress all contribute to obesity, a risk factor for obstructive sleep apnea. Night shifts with insufficient rest may lead to a car accident; language barriers and poor health literacy may hinder the provision of regional anesthesia; excessive opioid administration may trigger a respiratory arrest. In a second example, poor vision secondary to a cataract could be corrected with cataract surgery, but lack of social capital and health insurance prevent a surgical intervention. The poor vision compounds the social isolation of the patient, further enhancing cognitive decline and frailty. When a fall eventually leads to a hip fracture and hospital admission, the underlying fundamental causes of disease were poverty, lack of health insurance, social isolation, but the admission diagnosis code will be an unfortunate accident. In Table 1, a fictitious rural patient tells a harrowing story illustrating how geography, socioeconomic status, and ethnicity impact health care access and care, with the SDOHs of health tabulated as key drivers of health care processes and outcomes, with potential remedial action targeting concrete mechanisms. Our fictitious narrative of the tremendous hardships faced by rural, poor, underserved and minoritized populations is corroborated by quantitative and qualitative studies.6–8 An example of how SDOH impacts disease outcomes is in the treatment of melanoma—the skin cancer with the highest mortality in the United States. In fact, the largest Black-White disparities in cancer survival is in melanoma—an absolute survival difference of 22%.9 Retrospective database analysis have shown that older patients, non-White patients and those on Medicare/Medicaid are less likely to be offered a sentinel lymph node biopsy procedure as part of their surgical management.10,11 The sentinel lymph node biopsy is the most important predictor of prognosis for patients with melanoma and facilitates decision about adjuvant treatment as well as intensity of surveillance and therefore omitting it as part of treatment for patients in whom it is indicated is a great disservice. Patients who live in rural communities present with late-stage melanoma compared with their urban counterparts, face significant economic, financial and emotional hardships as care for complex cancers are usually hundreds of miles away from their work and local network of friends.6,12,13 The social and geographic circumstances we are born into, drive perioperative outcomes. Parental income and school district predicts scholastic achievement better than the teacher quality and is a better predictor of longevity than a person’s genetic code. Local pollution may trigger obstructive pulmonary disease.14 Occupational hazards can trigger acute and cause chronic conditions, contingent also on local mitigation strategies and safety precautions taken, which in turn depend on legal and social context. The recent US Supreme Court decision on Roe v Wade, with accompanying in state-level policy responses to the legality of abortion services, underlines the importance of legal and social context for health services offered and the circumstances under which care is offered or denied. Redlining, “cherry picking” and other racist and xenophobic policies illustrate that race and class both matter for access to quality care, and that context impacts the care we receive and the resulting outcomes.15–18 In summary, social circumstances are fundamental causes of some diseases.19 Social circumstances are distal causes of disease in contrast to the more proximal causes traditionally the focus of medical research and teaching. For example, myocardial infarct may be due to atherosclerosis, in turn the result of uncontrolled diabetes mellitus, hypertension, and hyperlipidemia, as the proximal causes. SDOH—the conditions we are born into, learn and live under, work and live in, and age in—are the distal and upstream drivers (through poverty, health literacy, insurance status, income, race/ethnicity) of the proximal causes (diabetes, obesity, hypertension)19 SDOH explains why some children have worse postsurgical outcomes after controlling for comorbidities.20 It also explains why some persons face barriers to access needed chronic pain services,21 the particular need of geriatric communities,22 or why some Black children receive less pain medication for acute appendicitis.23 SDOH facilitates postoperative recovery for some patients but fail to rescue others,24 who fall between the cracks of social networks and medical care. Table 1 - A fictitious narrative illustrating mechanisms of cancer disparities. Social determinant of health Mechanism Countermeasure Lack of transportation, high cost of travel, centralized care Reduced access to quality tertiary care Satellite clinics, public transport, decentralized health care Health literacy, social capital Delayed recognition of signs and symptoms Health curriculum, routine primary care physician visits Trust in health care system, culturally congruent care Compliance with primary prevention and best practices Community nurses, health system confidence building Poor working conditions, inflexible work hours Unable to attend to health and care needs Social policy and workers unions. family and sick leave of absence Poverty, lack of insurance, high health care cost Health care affordability Universal health insurance, living wages, social justice Unsafe working conditions Occupational exposure Occupational safety Environmental hazards Airborne exposure Environmental policy This fictitious narrative illustrates mechanism how social determinants of health (SDOH) can lead to disparities in rural cancer care, and what countermeasure might target such mechanism, [with SDOH in brackets in the text] 6–8:“Traveling back and forth for my cancer treatment is very challenging. [Lack of transportation, high cost of travel, centralized care] My name is Talamo. I live with my wife and two kids and work in one of the mines in Utah, [Unsafe working conditions, Environmental hazards] while my wife looks after the kids.Five years ago, I started having trouble breathing, and I didn’t think much of it until the dizziness began and the headaches began. [Health literacy] I needed to see a doctor, but I don’t get paid if I don’t work. [Inflexible work hours] My wife convinced me to go to a local clinic 40-minute drive away, [Centralized care] but it’s always hard taking off work and even worse getting there. [High cost of travel] Our area has no bus routes, and my car has been faulty for over a week now. [Lack of transportation], Thankfully, my buddy agreed to drive me to the clinic and back since he was off work. [High cost of travel]Three weeks was the earliest appointment time. [Social capital] While there, the doctor took my blood for tests and asked me several questions but couldn’t tell me what was wrong, or at least I did not understand what she was saying. [Health literacy] She spent very little time with me, no wonder she could not figure it out. [Culturally congruent care] I was given pills for the headache and told to get plenty of rest and drink lots of water. My grandmother died after she was seen in the same clinic. I decided not to take the pills. [Trust in healthcare system] The next day, I returned to work; I had no choice. [Inflexible work hours] But the headaches became more frequent and the dizziness almost unbearable. I could go to the ER an hour and a half away, but I can’t afford the bill. [Poverty] I have insurance, but I can’t afford the copay. [Lack of insurance, high healthcare cost] My wife’s grandfather works as a traditional healer in our small town. I saw him and he had a very long talk with me. He advised some life changes and a special tea, which made me feel somewhat better. [Culturally congruent care]Eventually, I went back to the same clinic six months later, and the doctor did a chest x-ray. He found a lump in my lungs but could not explain it precisely. This all did not make sense. [Health literacy] I was told to see a specialist a four hours’ drive from me, [Centralized care] but I don’t know what to do. [Lack of transportation, high cost of travel] I have a wife and kids to take care of. Time passed. My headache has worsened; some days, I could barely get out of bed, and now I can barely see from my right eye. [Health literacy] I woke up one morning coughing hard, struggling to breathe, and later found myself in an emergency room two hours away from our home [Centralized care] and a buddy of mine beside me. I had fainted. The doctor ran some tests and a CT scan of my chest. He also said a lump is growing on my chest, making it hard for me to breathe. He was also concerned about my other symptoms and thought this could be cancer that had spread to my brain, causing headaches, dizziness, and now a loss of vision in my right eye. I wept the entire night.” Dimensions of SDOH We propose to conceptualize SDOH in 3 dimensions, pertaining to (1) the identity (REAL for Race, Ethnicity, Affinity, and Language),25,26 (2) the social standing (SES) and (3) circumstances related to location and geography (GEO).17 Race and class both matter for health care outcomes, and so does geography.27 This article focuses on the third dimension, geography and location, but we need to consider interactions between REAL, SES, and GEO. The different dimension of SDOH (REAL, SES, GEO) can conspire insidiously to compound disparities in access to health, health care processes and outcomes: For example, poverty (SES) compounds barriers to access care in rural areas due to transportation cost.28,29 Provider racism (REAL) against a Black parturient may be accentuated by community health beliefs and health illiteracy (SES).30 Both examples also illustrate how SDOH at the individual person level (Black race, or poverty) may interact with family or community level SDOH (community health beliefs or absence of health services in rural areas).31 This conceptualization can be rendered in a polar diagram with 3 axis, REAL, SES, and GEO (Fig. 2); REAL stands for Race, Ethnicity, Affinity and Language, characteristics pertaining to identity; SES refers to socioeconomic characteristics like wealth, social or legal status, scholastic achievement, income; finally, geospatial pertains to geographic and location characteristics, for example the home, the neighborhood, the built environment, and other location characteristics. The individual is in center of the polar diagram. However, characteristics of an individual’s family, neighborhood, community, county, workplace, the state or nation they live in influence their health, access to care, health care processes and outcomes. These levels are depicted in progressively more peripheral perimeters from the individual. SDOH can be organized according to their axis and their proximity to the individual. For example, a person’s ethnicity is in the center of the polar diagram of SDOH on the REAL axis, and through xenophobia or racisms at the hand of clinicians can lead to barriers to optimal care. A food desert would be a characteristic of the built social environment of the neighborhood GEO axis and a bit further out on the polar diagram.17Figure 2: Polar dimensions of social determinants of health. We organize social determinants of health (SDOH) in this figure by spatial level and in 3 axis. SDOH concern (1) identity: REAL (Race, Ethnicity, Affinity, and Language), (2) socioeconomic status: SES (income, social capital, health literacy, etc.), and (2) the geographic domain: GEO (geographic factors, eg, food desert, availability of public transport, spatial accessibility of medical services). SDOH can act at different spatial levels, pertaining to the individual, their family, community, neighborhood, county, state, and nation. Individual mechanisms can be placed in a polar diagram to illustrate intersectionality and interaction between mechanisms leading to perioperative process and outcome disparities. Pollution is an example for a geographic SDOH acting at the state or community level. The impact of health literacy (SES factor) may span the personal, family and community level. Racism (REAL factor) may act at different spatial scales by different mechanisms: interpersonal racism drives disparities at the person-level, for example, when a clinician neglects a Black patient; structural racism may act at the state or community level, for example, through apartheid. Food desert or poor Public Transport are SDOH somewhere between the GEO and SES axis, acting more at the community than the personal level.The impact geospatial factors on perioperative access and process disparities Perioperative health care disparities can concern access to care, perioperative care processes or subsequent postoperative outcomes. Each approach (focus on access vs. process vs. outcome), has advantages and disadvantages. Below, we pick 2 examples of perioperative process disparities to illustrate the power of GA to explore actionable mechanism with a view to suggesting potential remedial action. The impact of US-census tract level SDOH on equitable antiemetic prophylaxis is the focus of the first case study,32 and the impact of state level policy on access to cataract surgery is illustrated in the second case study. SDOH impact the trajectory of cataract patients. The development of cataracts is driven by exposure to sun and professional exposure. Health literacy, social capital and connections facilitate the recognition of visual problems and a timely diagnosis, but these can be hindered by language barriers. Insurance status will drive access to ophthalmology services and the scheduling of cataract surgery. Lack of transport, social support, and poorly controlled comorbidities dues to lack of primary care can interfere with successful completion of cataract surgery and recovery. The goal is to understand processes leading to disparities with the same granular detail as any cancer pathophysiology. Such granular understanding of cause and effect, mechanism and pathways leading to perioperative disparity, would allow to target and test concrete countermeasures in a framework of continuous quality improvement as already practiced by other specialties.33,34 The added value of anesthesiologists as perioperative physicians would come from improving health equity through original investigations of mechanism of perioperative disparity embedded in continuous quality improvement efforts. GA and the boundaries of geography We begin by explaining GA as defined by the Environmental Systems Research Institute Inc. (ESRI): “The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge.” Spatial analysis extracts or creates new information from spatial data.35GA has been used in a variety of different fields, from urban planning to business development to social sciences and public health. What makes GA distinct from standard statistics is the use of spatial information within a relational database, where statistical analyses are dependent on the use of information that corresponds to a location in space, most usually corresponding to a precise latitude and longitude on planet Earth, and or to the relation of areas in space, notably polygons that represent specific areas such as a Census Tract. This point cannot be emphasized enough to the non-Geography audience. The use of any type of data with a “where” component requires special care and approaches to understand them properly and use them precisely in data analysis and interpretation. This is explained with reference to the case of the water crisis in Flint, Michigan, below. Geocoding and the perils of the Zip Code: The Water Crisis of Flint, Michigan Geocoding is a tool used extensively by Geospatial Information Systems (GIS) practitioners and is defined as the transformation of a textual address field into a latitude and longitude within a geographic information system36 that is a location on the earth’s surface. Geocoding can appear relatively straightforward, but it is important to emphasize that shortcuts in the process and a lack of understanding of GIS themselves can lead to classification bias. Typically, a GIS will use an address locator database to facilitate matching of textual addresses to known places on the earth’s surface. This gives varying levels of accuracy, which within the GIS are well described and tagged in the results of the geocoding. The use of Business Analyst extension from ESRI ArcGIS is one such toolset.37 The first attempt by the GIS is to match the address exactly based on street number, which then if that fails, it attempts to locate the address within a range of street numbers. If that is also not a match, most systems will then default to matching based on the street itself and attempt to find a median location in the street to which it can tag the address. Attempts are also made by the GIS to correct for common spelling errors, which are then given corresponding accuracy scores. Finally, if no matches are found through the above processes, a match based on Zip Code is attempted, with a location being assigned in the Zip Code centroid. Further attempts can also be made at the municipality level, though those are usually of limited utility depending on the intended application of the data. Special attention should be paid to choosing the right geographic projection for the data set, especially when concerning largely spread data sets (eg, multiple states/countries). In addition, matching to Zip Code (postal code) alone can lead to numerous problematic results. This is seen in one popular method of zip-code matching, which is called crosswalk Zip Code matching. This is a process by which a Zip Code from an address file is matched to a “Cross-walk” index file that has associations of Zip Codes with US Census Tracts. This process is problematic for several reasons, which are detailed further below. In summary, the greatest shortcoming is that Zip Codes can often occur across the boundaries of census tracts leading to misclassification bias in the results. Zip Codes have become mainstream within the popular health science media for describing the influence of geography on health of populations. The oft-cited catchphrase that “Zip Code is more important than genetic code”38 has seen a steady rise an adoption, and this has had the unfortunate side effect of leading many researchers without geography training to utilize Zip Codes as a unit of analysis for geocoding of patient data sets. It is the opinion of the authors that this method should only be used as a last resort; we will explain several examples of when this has become problematic. What are Zip Codes? Within the US, Zip Codes are designations of walking paths for delivery of mail created by the US Postal Service in the 1960s.39 During this time in the United States, discrimination by geography was commonplace and often codified, leading to phenomenon such as redlining40 where minorities were denied access to housing in predominately white neighborhoods, among other practices during the age of Jim Crow. The result is an inherent set of bias in the designation of Zip Codes and in the creation of their often-changing boundaries. Instead, Zip Code Tabulation Areas (ZCTAs) are utilized by many geographers to approximate the boundaries of Zip Codes. The issue with ZCTAs is that they also change periodically, as do Zip Codes with changes in populations and new housing developments, and so they can be an unreliable source of boundaries when comparing populations before and after a boundary change.41 The issues with Zip Codes stand in contrast to the design of US Census geographic units. Census Bureau designed their analysis units (Census Block, Census Tract, etc.) with geography in mind from the start and with a basis in the characteristics of the populations in the defined area. Contrast this with Zip Codes which are based on the convenience of mail sorting and delivery. In addition, the Census Bureau has standard processes to evaluate census tracts and other geographies on a regular basis, and utilized a well-grounded, transparent and scientific process.42 Census geographies are also much more amenable to associations based on location. That is, because they are designed to have “like within like,” if one is to geocode an address and it is found within a specific Census Tract, associating population characteristics from Census to individual characteristics for the purposes of a population health study (ie, associating social determinants data with an individual address) is statistically more valid than using Zip Codes. This incongruity is illustrated in Figure 3 below.Figure 3: Comparison of Zip Code and Census tract boundaries. This figure overlays Zip Code Tabulation Area (ZCTA) boundaries on top of Census Tract boundaries. Underlying this is Neighborhood Socioeconomic Disadvantage (NSD) which is detailed later in the text, showing red as most disadvantaged and blue as least disadvantaged. Note the different boundaries, as the ZCTA aggregates and, had that been used as the geography boundary, would have obscured heterogeneity that is evident in the Census Tracts. At the top of the figure, Zip Code 84103 is an example of this, with areas of high social disadvantage mixed together with areas of very low (blue) disadvantage within the same Zip Code.Bronx, a borough of New York City is home to the wealthiest and poorest who live only a few blocks apart. Often, Zip Codes can include both wealthy and disadvantaged neighborhoods within the same ZCTAs, while the US Census will attempt to prevent this type of grouping from occurring. The result of using ZCTA is a further potential for bias. The issue of Zip Codes is most painfully and obviously illustrated by the example of Flint, Michigan. Around 2015 Flint, Michigan began having water quality issues with its municipal tap water. Local physicians began noting elevated blood lead levels among area children, and reported this to the Michigan Department of Health and Human Services.43 The state examined the data and initially declared that there was no statistical association between the new service of Flint River water supply. There was a flaw in the data, however, as the state utilized area Zip Codes to examine the association of blood lead levels. Independent researchers recognized this and published their own study demonstrating that there was in fact an association.44 The independent research team recognized that the state had utilized Zip Codes, and upon examining a map of Flint and its water system the research team understood that the city water system was conterminous with the city boundaries. However, the Zip Codes crossed the City of Flint boundaries and included nearby towns. When the state examined blood lead levels of children by Zip Code, they included approximately 1/3 of addresses that were outside the boundaries of the Flint water system. This resulted in a massive misclassification bias within their data set, and lead them to concluding, erroneously, that there was no statistically significant elevation in blood lead levels. Fortunately, persistent community and medical community action, in collaboration with skilled geographers, were able to demonstrate that the new water system was poisoning the children in the community.43 The erosion of trust in the city and state officials has led residents to maintain a persistent level of suspicion with regards to anything said by local government officials,45 undermining the crucial trust in public health authorities, similar to other trauma experienced by minority communities, for example, the US Public Health Services (USPHS) Syphilis Study at Tuskegee. Zip Codes can have their role in GA, and they are sometimes the only information available to health research teams. However, as the above examples illustrate, they must be utilized with caution and a recognition of their limitations. Leveraging geospatial SDOH to improve perioperative health equity Continuous quality improvement framework Geospatial SDOH can be leveraged to inform public health policy, to improve equitable clinical practice processes for populations at risk, and for health systems science and health care disparity research. Obviously, in the framework of continuous quality improvement, these aforementioned activities are integrated in a process of collecting, analyzing & using data to improve the quality of health services for marginalized populations and ensure more equitable outcomes through process improvement for specific populations at risk, on an ongoing basis.46 Integration of SDOH and clinical data First, we detail how geospatial SDOH can be integrated into electronic health systems and perioperative health registries to leverage the neighborhood level information for health equity research and for equitable clinical care.47 Second, we illustrate with the 2 example use cases the power, promise, and pitfalls of GA for perioperative health systems science.48 Overview of geocoding of SDOH Geocoding SDOH from patient addresses follows this process: Starting from the patient home address at the time of service, we first perform geocoding in order to affix the textual address with a place on Earth corresponding to a specific latitude and longitude. This is then utilized in a GIS software package to match the latitude and longitude to a specific census geographic boundary.42 In terms of the National Neighborhood Data Archive (NaNDA),49 the boundary of choice is the US-Census Tract. The NaNDA database has a variety of socioeconomic (and geographic, such as park access) data sets, all coded at the US-Census Tract and ZCTA level. Matching takes place by the GIS examining each the boundaries of each census tract and determining which tract (polygon, in geographic speak) the geocode falls within. This is how the matching of NaNDA (or any geographic information) takes places within the GIS. As NaNDA data is encoded in Census Tracts (as well as ZCTA), any NaNDA data set can be matched to specific geocoded addresses. This data is then exported from the GIS as a flat file, associating the NaNDA data with each individual patient record and allowing for further statistical analysis. Likewise, data can remain in the GIS for further geostatistical analysis. Recall also, that any set of data that is geographic in nature, or has been coded into a GIS, can be associated with an individual geocode. Once this is complete, one can perform a GA to test hypotheses about social circumstances as key

Background Anesthesiologists’ contribution to perioperative healthcare disparities remains unclear because patient and surgeon preferences can influence care choices. Postoperative nausea and vomiting is a patient- centered outcome measure and a main driver of unplanned admissions. Antiemetic administration is under the sole domain of anesthesiologists. In a U.S. sample, Medicaid insured versus commercially insured patients and those with lower versus higher median income had reduced antiemetic administration, but not all risk factors were controlled for. This study examined whether a patient’s race is associated with perioperative antiemetic administration and hypothesized that Black versus White race is associated with reduced receipt of antiemetics. Methods An analysis was performed of 2004 to 2018 Multicenter Perioperative Outcomes Group data. The primary outcome of interest was administration of either ondansetron or dexamethasone; secondary outcomes were administration of each drug individually or both drugs together. The confounder-adjusted analysis included relevant patient demographics (Apfel postoperative nausea and vomiting risk factors: sex, smoking history, postoperative nausea and vomiting or motion sickness history, and postoperative opioid use; as well as age) and included institutions as random effects. Results The Multicenter Perioperative Outcomes Group data contained 5.1 million anesthetic cases from 39 institutions located in the United States and The Netherlands. Multivariable regression demonstrates that Black patients were less likely to receive antiemetic administration with either ondansetron or dexamethasone than White patients (290,208 of 496,456 [58.5%] vs. 2.24 million of 3.49 million [64.1%]; adjusted odds ratio, 0.82; 95% CI, 0.81 to 0.82; P < 0.001). Black as compared to White patients were less likely to receive any dexamethasone (140,642 of 496,456 [28.3%] vs. 1.29 million of 3.49 million [37.0%]; adjusted odds ratio, 0.78; 95% CI, 0.77 to 0.78; P < 0.001), any ondansetron (262,086 of 496,456 [52.8%] vs. 1.96 million of 3.49 million [56.1%]; adjusted odds ratio, 0.84; 95% CI, 0.84 to 0.85; P < 0.001), and dexamethasone and ondansetron together (112,520 of 496,456 [22.7%] vs. 1.0 million of 3.49 million [28.9%]; adjusted odds ratio, 0.78; 95% CI, 0.77 to 0.79; P < 0.001). Conclusions In a perioperative registry data set, Black versus White patient race was associated with less antiemetic administration, after controlling for all accepted postoperative nausea and vomiting risk factors. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

BACKGROUND:Postoperative opioid prescribing has historically lacked information crucial to balancing the pain control needs of the individual patient with our professional responsibility to judiciously prescribe these high-risk medications. OBJECTIVE:This study aimed to evaluate pain control, satisfaction with pain control, and opioid use among patients undergoing isolated midurethral sling randomized to 1 of 2 different opioid-prescribing regimens. STUDY DESIGN:Patients who underwent isolated midurethral sling placement from June 1, 2020, to November 22, 2021, were offered enrollment into this prospective, randomized, open-label, noninferiority clinical trial. Participants were randomized to receive either a standard prescription of ten 5-mg oxycodone tablets provided preoperatively (standard) or an opioid prescription provided only during patient request postoperatively (restricted). Preoperatively, all participants completed baseline demographic and pain surveys, including the 9-Question Central Sensitization Index, Pain Catastrophizing Scale, and Likert pain score (scale 0-10). The participants completed daily surveys for 1 week after surgery to determine the average daily pain score, number of opioids used, other forms of pain management, satisfaction with pain control, perception of the number of opioids prescribed, and need to return to care for pain management. The online Prescription Drug Monitoring Program was used to determine opioid filling in the postoperative period. The primary outcome was average postoperative day 1 pain score, and an a priori determined margin of noninferiority was set at 2 points. RESULTS:Overall, 82 patients underwent isolated midurethral sling placement and met the inclusion criteria: 40 were randomized to the standard arm, and 42 were randomized to the restricted group. Concerning the primary outcome of average postoperative day 1 pain score, the restricted arm (mean pain score, 3.9±2.4) was noninferior to the standard arm (mean pain score, 3.7±2.7; difference in means, 0.23; 95% confidence interval, -∞ to 1.34). Of note, 23 participants (57.5%) in the standard arm vs 8 participants (19.0%) in the restricted arm filled an opioid prescription (P<.001). Moreover, 18 of 82 participants (22.0%) used opioids during the 7-day postoperative period, with 10 (25.0%) in the standard arm and 8 (19.0%) in the restricted arm using opioids (P=.52). Of participants using opioids, the average number of tablets used was 3.4±2.3, and only 3 participants used ≥5 tablets. On a scale of 1="prescribed far more opioids than needed" to 5="prescribed far less opioids than needed," the means were 1.9±1.0 in the standard arm and 2.7±1.0 in the restricted arm (P<.001). CONCLUSION:Restricted opioid prescription was noninferior to standard opioid prescription in the setting of pain control and satisfaction with pain control after isolated midurethral placement. Participants in the restricted arm filled fewer opioid prescriptions than participants in the standard arm. On average, only 3.4 tablets were used by those that filled prescriptions in both groups. Restrictive opioid-prescribing practices may reduce unused opioids in the community while achieving similar pain control.

Purpose of review Health equity is an important priority for obstetric anesthesia, but describing disparities in perinatal care process and health outcome is insufficient to achieve this goal. Conceptualizing and framing disparity is a prerequisite to pose meaningful research questions. We emphasize the need to hypothesize and test which mechanisms and drivers are instrumental for disparities in perinatal processes and outcomes, in order to target, test and refine effective countermeasures. Recent findings With an emphasis on methodology and measurement, we sketch how health systems and disparity research may advance maternal health equity by narrating, conceptualizing, and investigating social determinants of health as key drivers of perinatal disparity, by identifying the granular mechanism of this disparity, by making the economic case to address them, and by testing specific interventions to advance obstetric health equity. Summary Measuring social determinants of health and meaningful perinatal processes and outcomes precisely and accurately at the individual, family, community/neighborhood level is a prerequisite for healthcare disparity research. A focus on elucidating the precise mechanism driving disparity in processes of obstetric care would inform a more rational effort to promote health equity. Implementation scientists should rigorously investigate in prospective trials, which countermeasures are most efficient and effective in mitigating perinatal outcome disparities.

Barriers to access quality cancer care are among the cardinal reasons for poorer oncologic outcomes for Black, Hispanic, rural, uninsured, and underinsured patients compared with wealthier White urban patients with insurance coverage.Barriers may be geographic, i.e. long travel distances, or social such as the lack of a support system to help the patients navigate the complexities of cancer care, the availability of childcare or means of transportation.In rural Western states, patients must travel hundreds of miles to reach quality cancer care; the exorbitant cost of gasoline and difficult weather conditions further contribute to disparities.Economic barriers conspire with the geographic and social barriers, especially for patients of low socioeconomic status.The coronavirus disease 2019 (COVID-19) pandemic, one of the major catastrophic events of modern history, has not only upended our way of life and routine practice of medicine as we knew it but also laid bare some of the staggering disparities for marginalized, migrant, minoritized patients. 1Social determinants of health-the circumstances we are born into, grow up and learn, receive care, work, worship, and age in-ultimately determine healthcare processes and outcomes.At the height of the pandemic, providers and/or patients were forced to or chose to cancel or reschedule appointments and procedures to minimize their risk of exposure to COVID.The critical need to continue to provide 'elective' patient care in the

Disparities in population health are driven by a dynamic set of factors, known as social determinants of health. Although individuals may not have a direct influence over the upstream social factors (poverty, homelessness, racism) that drive disparities, awareness of the complex social determinants of health at an individual level may facilitate efforts to improve health outcomes. In addition, awareness of how health care, as a component of social determinants of health, is associated with health disparities can also serve as a driving force for change. In this manuscript, we provide working definitions and discuss concrete ways to uncover and mitigate factors that contribute to disparities in health care. We begin at the level of health care systems before focusing on care processes and patient-level factors. Health, health care, and social determinants of health Disparities in health care outcomes are linked to bias on the individual, health system, and societal level.1–4 The United States Department of Health and Human Services defines disparities in population health as a difference that is closely linked to social, economic, and/or environmental disadvantage. Health disparities refer to differences in health outcomes caused by economic, social, and environmental disadvantage.5 Health disparities adversely affect groups who have systematically experienced greater obstacles to health based on their racial or ethnic group; religion; socioeconomic status; gender; age; mental health; cognitive, sensory, or physical disability; sexual orientation or gender identity; geographic location; or other characteristics historically linked to discrimination or exclusion.6Health care disparities refer to differences in “health insurance coverage, access to and use of care, and quality of care” linked to social, economic, racial, and environmental disadvantage (Fig. 1).5Figure 1: Disparities in health versus health care.A social determinants of health model offers a way to consider the role that health care services play since health disparities are caused largely by factors outside of health care (Fig. 2). These include socioeconomic status (education, income, occupation); race and exposure to racisms; living, workplace, and physical (air, water) environments; and culture. However, health care does play a role when considering health service utilization, health care quality, and the upstream factors that influence access, for example allocation of resources and financing care.7Figure 2: Social determinants of health model. Healthy People 2030, US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Available at: https://health.gov/healthypeople/objectives-and-data/social-determinants-health. Accessed April 28, 2021.Health equity The concept of equity is multidimensional and is based on principles of distributive justice. Health equity8,9 refers to the absence of remediable differences in health among population groups defined socially, economically, demographically, or geographically10; “the absence of systemic disparities in health (or its social determinants) between more and less advantaged social groups”7; and where everyone has a fair and just opportunity to be as healthy as possible.11 Admittedly, these definitions leave unanswered questions, including the meaning of fairness and justice; however, an explosion of scholarship has provided further guidance and has extended theories of justice to address health outcomes and its social determinants. Workforce diversity to address health care disparity Addressing health care disparity through workforce initiatives is in part predicated on the concept that a more diverse workforce improves health care outcomes that will translate to improved patient health outcomes. Studies have demonstrated that more diverse workforces have been associated with improved health care outcomes including patient satisfaction, health care delivery, and financial performance.12,13 Creating an infrastructure to support a more diverse health care workforce has been associated with improved access to care for underserved populations and more expansive research agendas.14 To address workforce definitions, we must define under-representation. The Association of American Medical Colleges (AAMC) uses the term Underrepresented in Medicine (URiM) to include “racial and ethnic populations that are underrepresented in the medical profession relative to their numbers in the general population.” The role of graduate medical education programs as contributors to the physician workforce pipeline and mitigate health care disparities has been recognized by the Accreditation Council for Graduate Medical Education (ACGME).15 By increasing diversity in the physician pipeline, this can drive a more balanced workforce that may facilitate inclusion and reduce bias. Although diversity, equity, and inclusion (DEI) efforts extend beyond race/ethnicity and gender, expanded discussions are limited by the available data of other groups. Racial and ethnic disparities in the workforce are particularly evident in academia. For example, those from Black and Hispanic backgrounds make up just 6% of full-time academic physicians despite comprising 31% of the US population.13 This is partly due to leaks along the physician pipeline resulting in smaller reservoirs of potential Black and Hispanic physicians.16 In addition, “taxes” such as the minority tax and gratitude tax have been identified as contributing factors. A minority tax is an umbrella term that encompasses the harassment, bias, discrimination, isolationism, and burdens of representation.17 A gratitude tax occurs when individual achievement is attributed to circumstance and mentors rather than to individual merit.18 In contrast to URiMs, the gender gap has significantly closed along the physician pipeline and women now represent over 50% of medical students in the United States.19 However, this has yet to translate into the upper echelons of medicine, where gaps persist in leadership and among certain specialties. In anesthesiology, 34% of residents and 13% of chairs are women19 and in pain medicine and critical care, only a quarter of trainees are women.20 There are fewer women in anesthesiology leadership positions at the departmental and national levels, and fewer women in national speaking engagements and on editorial boards.21 These gender disparities in anesthesiology have been attributed to leaks along career advancement pipelines from factors that include the influence of careers on childbearing; disproportionate responsibilities related to domestic work; harassment; lack of mentorship; gender-based differences in resources and negotiations; and pervasive implicit biases about women in the workplace, all of which can lead to gaps in career productivity and advancement.22–25 These gaps widen when considering intersectional identities. As an example, Black female physicians experience ageism and sexism variably, but racism persists throughout their career and life spans.26 Mentoring and career development programs can be effective interventions to address leaks in the career pipeline related to faculty retention and promotion.27,28 Finding mentorship can be challenging for women and minorities. Organic mentor-mentee relationships are often informal and are based on shared interests and personal comfort for which shared race, ethnicity, and gender can be strong influencers. Mentoring is best when used in addition to other multifaceted initiatives within a department, including implicit bias education and reducing salary inequality. An academic department witnessed dramatic increases in the number of women promoted to associate professor while reducing gender bias and improving the mentoring experience for both women and men when this combination approach was used.29 Previous work has observed greater challenges when dyads differ by sex and race/ethnicity.30 Social proximity within mentoring relationships, that is overlapping life experiences, may contribute to these challenges in relationship building, which may be overcome by front loading relationship-building efforts to find common ground, develop trust, and establish boundaries. Institutional approaches to improve DEI Structural barriers are thought to be partly responsible for workforce disparity in academic medicine. Tenure and academic promotion systems place value on productivity in early career phases through which peer-reviewed publications, external funding for research, and leadership positions are heavily weighted. However, there are significant funding gaps where racial and ethnic minorities are less likely to receive funding from organizations such as the National Institute of Health.31 In addition, early career is a time where competing demands overlap such as with childbearing and childrearing responsibilities.32 These systems-level metrics for promotion are considered to be structural barriers that prevent academic promotion leading to the “leaky pipeline.” The National Science Foundation is addressing barriers to career development, promotion, and retention through funding for institutional changes,33 and are modifying tenure track timing to better align with work-life balance for young professionals. An awareness of the aforementioned barriers serves as a starting point in circumnavigating these structural challenges. At the institution level, work has been done to drive DEI principles through the formation of pivotal committees such as those responsible for workforce hiring, promotion, and retention. Through intentional formation, these committees may better reflect organizational diversity goals. Specifically, search committees may assess hiring policies and interview techniques to reduce unintended bias. The Department of Medicine at Johns Hopkins instituted several interventions to address academic promotion of women, including shifting the timeline requirements for promotions. After three years, the department witnessed a 66% increase in the proportion of women expecting to remain in academic medicine and a 57% increase among men. In addition, there was a marked increase, from 4 to 26, in the number of female associate professors.29 Institution-level hiring and retention can be reformed so that open positions are advertised through targeted outreach channels that represent under-represented groups such as the National Medical Association and through venues such as national conferences. Before engaging applicants, internal consensus should be established within hiring committees on specific credentials and concrete measures for evaluating applicants.34 Institutions can ensure that their mission statements and public-facing social media make it clear that representation and diversity are prioritized. In addition, informal networks that can have significant influence over organizations can be made more formal so that all members of an organization or department have an opportunity to join and participate such as through steering committees, budget task forces, and planning initiatives. Institution leaders can create accountability for decision makers to demonstrate and adhere to processes that are intentional, inclusive, and effective in overall efforts to mitigate bias and reduce disparities in health care clinicians.35 Measures may include identifying factors that contribute to attrition of providers; improvement in provider satisfaction or morale through periodic climate surveys; tracking trends in offers, recruitment, and retention of under-represented providers; and diversity of committees. In some instances, measures related to structure, process, and outcomes can be tied to leadership evaluation and compensation. Health system opportunities to interrupt bias are expansive. At baseline, a health system’s infrastructure should enable response to data trends that may impact the quality of healthcare for vulnerable populations (racial/ethnic minorities, LGBTQ community, underinsured, urban/rural residents). Although workforce and patient data collection may highlight health care and workforce disparities, comprehensive data collection is a controversial strategy. Data collection, management, and use require careful consideration. Transparency regarding the alignment of data efforts with institutional mission and values is critical, especially when data may compromise the privacy of underrepresented identities. At an institutional and departmental level, understanding the demographic makeup of the workforce and leadership can provide an objective marker for how the health care workforce represents its patient, community, and national population. Data on patient and provider characteristics that shape the health care experience (self-reported race, ethnicity, gender, and language preference) may inform the initiatives, policies, and practices that can address gaps in recruitment, retention, faculty development, and health care utilization. On the individual level, offering training and education that unmasks unconscious bias; mentoring individuals from under-represented groups in anesthesiology (African American/Black, Native American/Alaska Native, Hispanic); and creating a climate of interpersonal inclusion by seeking input and feedback from many sources each provide opportunities that can drive institutional culture. Organization and departmental alignment can facilitate data-driven policies and procedures that amplify DEI principles through recruitment, hiring, mentoring, and inclusive environments throughout the organization. Diversity and inclusion through patient-centered quality improvement Quality improvement (QI) efforts that are aligned with equity principles and supported by policy provide an ideal platform to address actions at the individual level in a systematic and sustainable manner as was seen in the patient safety movement. Clinicians and administrators are better poised to implement patient-centered equitable improvement initiatives, and more recently, these efforts have been supported at a national level by the Institute of Medicine (IOM), who named equity a core domain of health care quality,36 and through federal mandates such as the collection of race and ethnicity data26 to report health care quality performance measures. QI efforts focus on systems of care rather than an individual’s behavior to promote sustainable change. Multitargeted QI efforts are ideal, given that the drivers of healthcare disparities are multifactorial and principles of equity must be emphasized over simple equality. Although QI efforts were founded on the notion of equality, we now know that uniformly equal interventions applied to a heterogeneous population will yield different results for different groups. For example, efforts to increase patient self-efficacy and patient clinician communication using web-based, online platforms fail to recognize differences in accessibility to computers, reliable internet, health literacy, and language barriers. Focusing solely on equal access may inadvertently create differences in access to health information and can worsen patient-provider communication for vulnerable populations. Equitable QI efforts can incorporate social determinants of health (pertinent to the intervention), and implement and evaluate flexible interventions that consider the needs of individuals (patient-centered care) while engaging all stakeholders and assuring accountability. For example, our individual practice as anesthesiologists may be better informed by understanding particular risk-adjusted performance metrics that can self-inform and mitigate unintentional perpetuation of health care disparities. QI efforts that account for process measures such as appropriate and equitable administration of treatments in a systematic manner can help inform clinicians’ behavior at the individual level. For example, Andreae et al found that perioperative patients of lower socioeconomic status (eg, health insurance and median income) were less likely to receive universally available antiemetic prophylaxis despite controlling for risk factors associated with postoperative nausea and vomiting.37 These results point to perioperative disparities for which individual anesthesia clinicians can make actionable change, and offers risk-adjusted performance metrics that could be used to mitigate perioperative healthcare disparities.38 However, QI efforts should incentivize the appropriate behavior as there is the potential for inadvertent negative consequences including public reporting and pay-for-performance programs that can promote avoidance of caring for populations perceived to be at high risk for poor outcomes. Conclusion Disparities in health care contribute to disparities in population health. Greater awareness of contributing factors at the health systems, institution, and individual levels will enable anesthesiologists to contribute meaningfully to mitigation efforts (Table 1). The aim of this review was to provide a baseline understanding of broad concepts and stimulate discussion around strategies to enhance DEI efforts. We acknowledge that priorities may evolve and therefore present DEI efforts as fluid and dynamic, rather than working toward a fixed goal. Similarly, the suggestions placed forth are not prescriptive and should be considered in the institutional, organizational, and cultural context in which each reader is working to influence change. Table 1 - Addressing diversity, equity, and inclusion (DEI) efforts in health care. Goals Improve workforce diversity Understand local workforce demographic data Support formal mentorship and career development programs Advertise open positions through national outreach channels DEI tailored messaging on outward-facing media Fix structural barriers in career advancement Establish formal criteria, vs. informal networks, for evaluating potential hires and promotion criteria Adjust promotion criteria to create flexibility in advancement and promotion timelines Monitor for inequities in salary Mitigate workplace bias Implicit bias training Create voluntary safe spaces for open discussions Drive changes in clinical practice through quality improvement Collect data (climate survey, clinician satisfaction scores, demographic information when appropriate, health care quality performance metrics) Link process to outcome metrics Conflict of interest disclosure The authors declare that they have nothing to disclose.

Patients with mitochondrial disease exhibit disrupted pyruvate oxidation, resulting in intraoperative and perioperative physiologic derangements. Increased enzymatic conversion of pyruvate via lactate dehydrogenase during periods of fasting or stress can lead to metabolic decompensation, with rapid development of fatal lactic acidosis. We describe the intraoperative management and postoperative critical care of a patient with mitochondrial disease who presented for repair of esophageal perforation following repair of a paraesophageal hernia. His surgery was complicated by the development of metabolic crisis and severe lactic acidosis which became resistant to conventional therapy before ultimately resolving with the initiation of venoarterial extracorporeal membrane oxygenation (VA-ECMO).

This article is referred to by:From “Ought” to “Is”: Surfacing Values in Patient and Family Advocacy in Rare Diseases