Race, Gender, and Faculty Retention in Academic Medicine.

Scheuermann TS ，Clark L ，Sultana N ，Machado N ，Shergina E ，Polineni D ，Shih GH ，Simari RD ，Wick JA ，Richter KP ... -  《JAMA Network Open》

What are the Trends in Racial Diversity Among Orthopaedic Applicants, Residents, and Faculty?

Orthopaedic surgery has recruited fewer applicants from underrepresented in medicine (UIM) racial groups than many other specialties, and recent studies have shown that although applicants from UIM racial groups are competitive for orthopaedic surgery, they enter the specialty at lower rates. Although previous studies have measured trends in orthopaedic surgery applicant, resident, or attending diversity in isolation, these populations are interdependent and therefore should be analyzed together. It is unclear how racial diversity among orthopaedic applicants, residents, and faculty has changed over time and how it compares with other surgical and medical specialties. (1) How has the proportion of orthopaedic applicants, residents, and faculty from UIM and White racial groups changed between 2016 and 2020? (2) How does representation of orthopaedic applicants from UIM and White racial groups compare with that of other surgical and medical specialties? (3) How does representation of orthopaedic residents from UIM and White racial groups compare with that of other surgical and medical specialties? (4) How does representation of orthopaedic faculty from UIM and White racial groups compare with that of other surgical and medical specialties? We drew racial representation data for applicants, residents, and faculty between 2016 and 2020. Applicant data on racial groups was obtained for 10 surgical and 13 medical specialties from the Association of American Medical Colleges Electronic Residency Application Services report, which annually publishes demographic data on all medical students applying to residency through Electronic Residency Application Services. Resident data on racial groups were obtained for the same 10 surgical and 13 medical specialties from the Journal of the American Medical Association Graduate Medical Education report, which annually publishes demographic data on residents in residency training programs accredited by the Accreditation Council for Graduate Medical Education. Faculty data on racial groups were obtained for four surgical and 12 medical specialties from the Association of American Medical Colleges Faculty Roster United States Medical School Faculty report, which annually publishes demographic data of active faculty at United States allopathic medical schools. UIM racial groups include American Indian or Alaska Native, Black or African American, Hispanic or Latino, and Native American or Other Pacific Islander. Chi-square tests were performed to compare representation of UIM and White groups among orthopaedic applicants, residents, and faculty between 2016 and 2020. Further, chi-square tests were performed to compare aggregate representation of applicants, residents, and faculty from UIM and White racial groups in orthopaedic surgery to aggregate representation among other surgical and medical specialties with available data. The proportion of orthopaedic applicants from UIM racial groups increased between 2016 to 2020 from 13% (174 of 1309) to 18% (313 of 1699, absolute difference 0.051 [95% CI 0.025 to 0.078]; p < 0.001). The proportion of orthopaedic residents (9.6% [347 of 3617] to 10% [427 of 4242]; p = 0.48) and faculty (4.7% [186 of 3934] to 4.7% [198 of 4234]; p = 0.91) from UIM racial groups did not change from 2016 to 2020. There were more orthopaedic applicants from UIM racial groups (15% [1151 of 7446]) than orthopaedic residents from UIM racial groups (9.8% [1918 of 19,476]; p < 0.001). There were also more orthopaedic residents from UIM groups (9.8% [1918 of 19,476]) than orthopaedic faculty from UIM groups (4.7% [992 of 20,916], absolute difference 0.051 [95% CI 0.046 to 0.056]; p < 0.001). The proportion of orthopaedic applicants from UIM groups (15% [1151 of 7446]) was greater than that of applicants to otolaryngology (14% [446 of 3284], absolute difference 0.019 [95% CI 0.004 to 0.033]; p = 0.01), urology (13% [319 of 2435], absolute difference 0.024 [95% CI 0.007 to 0.039]; p = 0.005), neurology (12% [1519 of 12,862], absolute difference 0.036 [95% CI 0.027 to 0.047]; p < 0.001), pathology (13% [1355 of 10,792], absolute difference 0.029 [95% CI 0.019 to 0.039]; p < 0.001), and diagnostic radiology (14% [1635 of 12,055], absolute difference 0.019 [95% CI 0.009 to 0.029]; p < 0.001), and it was not different from that of applicants to neurosurgery (16% [395 of 2495]; p = 0.66), plastic surgery (15% [346 of 2259]; p = 0.87), interventional radiology (15% [419 of 2868]; p = 0.28), vascular surgery (17% [324 of 1887]; p = 0.07), thoracic surgery (15% [199 of 1294]; p = 0.94), dermatology (15% [901 of 5927]; p = 0.68), internal medicine (15% [18,182 of 124,214]; p = 0.05), pediatrics (16% [5406 of 33,187]; p = 0.08), and radiation oncology (14% [383 of 2744]; p = 0.06). The proportion of orthopaedic residents from UIM groups (9.8% [1918 of 19,476]) was greater than UIM representation among residents in otolaryngology (8.7% [693 of 7968], absolute difference 0.012 [95% CI 0.004 to 0.019]; p = 0.003), interventional radiology (7.4% [51 of 693], absolute difference 0.025 [95% CI 0.002 to 0.043]; p = 0.03), and radiation oncology (7.9% [289 of 3659], absolute difference 0.020 [95% CI 0.009 to 0.029]; p < 0.001), and it was not different from UIM representation among residents in plastic surgery (9.3% [386 of 4129]; p = 0.33), urology (9.7% [670 of 6877]; p = 0.80), dermatology (9.9% [679 of 6879]; p = 0.96), and diagnostic radiology (10% [2215 of 22,076]; p = 0.53). The proportion of orthopaedic faculty from UIM groups (4.7% [992 of 20,916]) was not different from UIM representation among faculty in otolaryngology (4.8% [553 of 11,413]; p = 0.68), neurology (5.0% [1533 of 30,871]; p = 0.25), pathology (4.9% [1129 of 23,206]; p = 0.55), and diagnostic radiology (4.9% [2418 of 49,775]; p = 0.51). Compared with other surgical and medical specialties with available data, orthopaedic surgery had the highest proportion of White applicants (62% [4613 of 7446]), residents (75% [14,571 of 19,476]), and faculty (75% [15,785 of 20,916]). Orthopaedic applicant representation from UIM groups has increased over time and is similar to that of several surgical and medical specialties, suggesting relative success with efforts to recruit more students from UIM groups. However, the proportion of orthopaedic residents and UIM groups has not increased accordingly, and this is not because of a lack of applicants from UIM groups. In addition, UIM representation among orthopaedic faculty has not changed and may be partially explained by the lead time effect, but increased attrition among orthopaedic residents from UIM groups and racial bias likely also play a role. Further interventions and research into the potential difficulties faced by orthopaedic applicants, residents, and faculty from UIM groups are necessary to continue making progress. A diverse physician workforce is better suited to address healthcare disparities and provide culturally competent patient care. Representation of orthopaedic applicants from UIM groups has improved over time, but further research and interventions are necessary to diversify orthopaedic surgery to ultimately provide better care for all orthopaedic patients.

Kalyanasundaram G ，Mener A ，DiCaprio MR 《-》

Disparities in wellbeing in the USA by race and ethnicity, age, sex, and location, 2008-21: an analysis using the Human Development Index.

The Human Development Index (HDI)-a composite metric encompassing a population's life expectancy, education, and income-is used widely for assessing and comparing human development and wellbeing at the country level, but does not account for within-country inequality. In this study of the USA, we aimed to adapt the HDI framework to measure the HDI at an individual level to examine disparities in the distribution of wellbeing by race and ethnicity, sex, age, and geographical location. We used individual-level data on adults aged 25 years and older from the 2008-21 American Community Survey (ACS) Public Use Microdata Sample. We extracted information on race and ethnicity, age, sex, location (Public Use Microdata Areas), educational attainment, and household income and size. We merged these data with estimated life tables by race and ethnicity, sex, age, location (county), and year, generated using Bayesian small-area estimation models applied to death certificate data from the National Vital Statistics System. For each individual in the ACS, we used these combined data to estimate years of education, household consumption, and expected lifespan; converted each of these three features into an index using a percentile score; and calculated the HDI as the geometric mean of these three indices. Finally, we grouped individuals into yearly HDI deciles. Years of education, household consumption, and expected lifespan-and thus the HDI-varied considerably among adults in the USA during the 2008-21 period. For most race and ethnicity and sex groups, the mean HDI increased gradually from 2008 to 2019, then declined in 2020 due to declines in expected lifespan, although there were systematic differences in the distribution of the HDI by race and ethnicity and sex. In the lowest HDI decile, there was over-representation (ie, >10% of the total population of a given race and ethnicity and sex group) of American Indian and Alaska Native (AIAN) males (50% [SE 0·2] in decile, mean annual population in decile 0·37 million [SE 0·002]), Black males (40% [<0·1], 4·67 million [0·006]), AIAN females (23% [0·1], 0·19 million [0·001]), Latino males (21% [<0·1], 3·27 million [0·006]), Black females (14% [<0·1], 1·86 million [0·004]), and Latina females (13% [<0·1], 2·07 million [0·006]). Given differences in total population size, however, White males were the largest population group in the lowest decile (27% [<0·1] of the lowest decile, 5·87 million [0·012]), followed by Black males (22% [<0·1]) and Latino males (15% [<0·1]). There were notable differences in these patterns by age group: for example, for the 25-44 years age group, the lowest HDI decile had even greater over-representation of AIAN males (66% [0·2] in decile, 0·22 million [0·001]) and Black males (46% [<0·1], 2·52 million [0·005]) than the 85 years and older age group (22% [1·1], <0·01 million [<0·001]; and 20% [0·3], 0·03 million [<0·001]). By contrast, the lowest decile had an under-representation of Asian females (2% [<0·1], 0·06 million [<0·001]) in the 25-44 years age group, but an over-representation in the 85 years and older age group (25% [0·3], 0·03 million [<0·001]). The lowest HDI decile for the 25-44 years age group was primarily male (76% [<0·1], 6·44 million [0·009]) whereas for age 85 years and older it was predominantly female (71% [0·1], 0·42 million [0·002]). In the highest HDI decile, shifts in the composition of the population by age were particularly large for White males, who made up 5% (0·1; 0·39 million [0·001]) of this decile in the 25-44 years age group, but 49% (0·2; 0·29 million [0·001]) in the 85 years and older age group. From 2012 to 2021, the proportion of the population living in the lowest HDI decile varied substantially by location, and a disproportionately high share of the population living in locations in much of the southern half of the USA, Appalachia, and Rust Belt states were in the lowest HDI decile. Substantial disparities in wellbeing exist within the USA and are heavily influenced by race and ethnicity (due to racism), sex, age, and geographical location. These disparities are not immutable, but improvement is not a given, and gains can be fleeting in the face of a crisis such as the COVID-19 pandemic. Sustained action to ensure that everyone has meaningful access to a high-quality education, the means to earn a sufficient income, and the opportunity to live a long and healthy life is needed to reduce these disparities and should focus on the populations and locations that are worst off. State of Washington and National Institute on Minority Health and Health Disparities.

Dwyer-Lindgren L ，Kendrick P ，Baumann MM ，Li Z ，Schmidt C ，Sylte DO ，Daoud F ，La Motte-Kerr W ，Aldridge RW ，Bisignano C ，Hay SI ，Mokdad AH ，Murray CJL ... -  《-》

Race and Ethnicity, Gender, and Promotion of Physicians in Academic Medicine.

Clark L ，Shergina E ，Machado N ，Scheuermann TS ，Sultana N ，Polineni D ，Shih GH ，Simari RD ，Wick JA ，Richter KP ... -  《JAMA Network Open》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》