The culture of academic medicine may foster mistreatment that disproportionately affects individuals who have been marginalized within a given society (minoritized groups) and compromises workforce ...vitality. Existing research has been limited by a lack of comprehensive, validated measures, low response rates, and narrow samples as well as comparisons limited to the binary gender categories of male or female assigned at birth (cisgender).
To evaluate academic medical culture, faculty mental health, and their relationship.
A total of 830 faculty members in the US received National Institutes of Health career development awards from 2006-2009, remained in academia, and responded to a 2021 survey that had a response rate of 64%. Experiences were compared by gender, race and ethnicity (using the categories of Asian, underrepresented in medicine defined as race and ethnicity other than Asian or non-Hispanic White, and White), and lesbian, gay, bisexual, transgender, queer (LGBTQ+) status. Multivariable models were used to explore associations between experiences of culture (climate, sexual harassment, and cyber incivility) with mental health.
Minoritized identity based on gender, race and ethnicity, and LGBTQ+ status.
Three aspects of culture were measured as the primary outcomes: organizational climate, sexual harassment, and cyber incivility using previously developed instruments. The 5-item Mental Health Inventory (scored from 0 to 100 points with higher values indicating better mental health) was used to evaluate the secondary outcome of mental health.
Of the 830 faculty members, there were 422 men, 385 women, 2 in nonbinary gender category, and 21 who did not identify gender; there were 169 Asian respondents, 66 respondents underrepresented in medicine, 572 White respondents, and 23 respondents who did not report their race and ethnicity; and there were 774 respondents who identified as cisgender and heterosexual, 31 as having LGBTQ+ status, and 25 who did not identify status. Women rated general climate (5-point scale) more negatively than men (mean, 3.68 95% CI, 3.59-3.77 vs 3.96 95% CI, 3.88-4.04, respectively, P < .001). Diversity climate ratings differed significantly by gender (mean, 3.72 95% CI, 3.64-3.80 for women vs 4.16 95% CI, 4.09-4.23 for men, P < .001) and by race and ethnicity (mean, 4.0 95% CI, 3.88-4.12 for Asian respondents, 3.71 95% CI, 3.50-3.92 for respondents underrepresented in medicine, and 3.96 95% CI, 3.90-4.02 for White respondents, P = .04). Women were more likely than men to report experiencing gender harassment (sexist remarks and crude behaviors) (71.9% 95% CI, 67.1%-76.4% vs 44.9% 95% CI, 40.1%-49.8%, respectively, P < .001). Respondents with LGBTQ+ status were more likely to report experiencing sexual harassment than cisgender and heterosexual respondents when using social media professionally (13.3% 95% CI, 1.7%-40.5% vs 2.5% 95% CI, 1.2%-4.6%, respectively, P = .01). Each of the 3 aspects of culture and gender were significantly associated with the secondary outcome of mental health in the multivariable analysis.
High rates of sexual harassment, cyber incivility, and negative organizational climate exist in academic medicine, disproportionately affecting minoritized groups and affecting mental health. Ongoing efforts to transform culture are necessary.
This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models ...of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
Clinical prediction models estimated with health records data may perpetuate inequities.
To evaluate racial/ethnic differences in the performance of statistical models that predict suicide.
In this ...diagnostic/prognostic study, performed from January 1, 2009, to September 30, 2017, with follow-up through December 31, 2017, all outpatient mental health visits to 7 large integrated health care systems by patients 13 years or older were evaluated. Prediction models were estimated using logistic regression with LASSO variable selection and random forest in a training set that contained all visits from a 50% random sample of patients (6 984 184 visits). Performance was evaluated in the remaining 6 996 386 visits, including visits from White (4 031 135 visits), Hispanic (1 664 166 visits), Black (578 508 visits), Asian (313 011 visits), and American Indian/Alaskan Native (48 025 visits) patients and patients without race/ethnicity recorded (274 702 visits). Data analysis was performed from January 1, 2019, to February 1, 2021.
Demographic, diagnosis, prescription, and utilization variables and Patient Health Questionnaire 9 responses.
Suicide death in the 90 days after a visit.
This study included 13 980 570 visits by 1 433 543 patients (64% female; mean SD age, 42 18 years. A total of 768 suicide deaths were observed within 90 days after 3143 visits. Suicide rates were highest for visits by patients with no race/ethnicity recorded (n = 313 visits followed by suicide within 90 days, rate = 5.71 per 10 000 visits), followed by visits by Asian (n = 187 visits followed by suicide within 90 days, rate = 2.99 per 10 000 visits), White (n = 2134 visits followed by suicide within 90 days, rate = 2.65 per 10 000 visits), American Indian/Alaskan Native (n = 21 visits followed by suicide within 90 days, rate = 2.18 per 10 000 visits), Hispanic (n = 392 visits followed by suicide within 90 days, rate = 1.18 per 10 000 visits), and Black (n = 65 visits followed by suicide within 90 days, rate = 0.56 per 10 000 visits) patients. The area under the curve (AUC) and sensitivity of both models were high for White, Hispanic, and Asian patients and poor for Black and American Indian/Alaskan Native patients and patients without race/ethnicity recorded. For example, the AUC for the logistic regression model was 0.828 (95% CI, 0.815-0.840) for White patients compared with 0.640 (95% CI, 0.598-0.681) for patients with unrecorded race/ethnicity and 0.599 (95% CI, 0.513-0.686) for American Indian/Alaskan Native patients. Sensitivity at the 90th percentile was 62.2% (95% CI, 59.2%-65.0%) for White patients compared with 27.5% (95% CI, 21.0%-34.7%) for patients with unrecorded race/ethnicity and 10.0% (95% CI, 0%-23.0%) for Black patients. Results were similar for random forest models, with an AUC of 0.812 (95% CI, 0.800-0.826) for White patients compared with 0.676 (95% CI, 0.638-0.714) for patients with unrecorded race/ethnicity and 0.642 (95% CI, 0.579-0.710) for American Indian/Alaskan Native patients and sensitivities at the 90th percentile of 52.8% (95% CI, 50.0%-55.8%) for White patients, 29.3% (95% CI, 22.8%-36.5%) for patients with unrecorded race/ethnicity, and 6.7% (95% CI, 0%-16.7%) for Black patients.
These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients or those with undrecorded race/ethnicity compared with White, Hispanic, and Asian patients. Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.
To compare racial and ethnic differences between obstetrician-gynecologists (ob-gyns) and other large groups of adult medical specialists who provide the predominant care of women. Whether physician ...diversity influences their practice locations in underserved areas was also sought.
This cross-sectional study reports an analysis of U.S. national data about racial and ethnic characteristics, gender, and specialty (obstetrics and gynecology, general internal medicine, family medicine, emergency medicine) of 190,379 physicians who came from three resources (Association of American Medical Colleges Student Records System, Association of American Medical Colleges Minority Physicians Database, American Medical Association Physician Masterfile). Underserved locations were identified as being rural, having 20% or more of the population living in poverty or being federally designated as areas of professional shortages or underserved populations. Bivariate measures of associations were performed to study the association between physician race and ethnicity and their practice location.
Female physicians in all specialties were more likely than males to be nonwhite, and ob-gyns were most likely to be female (61.9%). Compared with other studied specialists, ob-gyns had the highest proportion of underrepresented minorities (combined, 18.4%), especially black (11.1%) and Hispanic (6.7%) physicians. Underrepresented minority ob-gyns were more likely than white or Asians to practice in federally funded underserved areas or where poverty levels were high. Native Americans, Alaska Natives, and Pacific Islanders were the ob-gyn group with the highest proportion practicing in rural areas.
Compared with other adult medical specialists, ob-gyns have a relatively high proportion of black and Hispanic physicians. A higher proportion of underrepresented minority ob-gyns practiced at medically underserved areas.
Disparities in Access to Oral Health Care Northridge, Mary E; Kumar, Anjali; Kaur, Raghbir
Annual review of public health,
04/2020, Letnik:
41, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In the United States, people are more likely to have poor oral health if they are low-income, uninsured, and or members of racial ethnic minority, immigrant, or rural populations who have suboptimal ...access to quality oral health care. As a result, poor oral health serves as the national symbol of social inequality. There is increasing recognition among those in public health that oral diseases such as dental caries and periodontal disease and general health conditions such as obesity and diabetes are closely linked by sharing common risk factors, including excess sugar consumption and tobacco use, as well as underlying infection and inflammatory pathways. Hence, efforts to integrate oral health and primary health care, incorporate interventions at multiple levels to improve access to and quality of services, and create health care teams that provide patient-centered care in both safety net clinics and community settings may narrow the gaps in access to oral health care across the life course.
Aims Subclassification of large B cell lymphoma (LBCL) is challenging due to the overlap in histopathological, immunophenotypical and genetic data. In particular, the criteria to separate diffuse ...large B cell lymphoma (DLBCL) and high‐grade B cell lymphoma (HGBL) are difficult to apply in practice. The Lunenburg Lymphoma Biomarker Consortium previously reported a cohort of over 5000 LBCL that included fluorescence in‐situ hybridisation (FISH) data. This cohort contained 209 cases with MYC rearrangement that were available for a validation study by a panel of eight expert haematopathologists of how various histopathological features are used. Methods and results Digital whole slide images of haematoxylin and eosin‐stained sections allowed the pathologists to visually score cases independently as well as participate in virtual joint review conferences. Standardised consensus guidelines were formulated for scoring histopathological features and included overall architecture/growth pattern, presence or absence of a starry‐sky pattern, cell size, nuclear pleomorphism, nucleolar prominence and a range of cytological characteristics. Despite the use of consensus guidelines, the results show a high degree of discordance among the eight expert pathologists. Approximately 50% of the cases lacked a majority score, and this discordance spanned all six histopathological features. Moreover, none of the histological variables aided in prediction of MYC single versus double/triple‐hit or immunoglobulin‐partner FISH‐based designations or clinical outcome measures. Conclusions Our findings indicate that there are no specific conventional morphological parameters that help to subclassify MYC ‐rearranged LBCL or select cases for FISH analysis, and that incorporation of FISH data is essential for accurate classification and prognostication.
Assembling a collection of very prominent researchers in the field, the Handbook of Spatial Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this ...maturing area. It takes a unified, integrated approach to the material, providing cross-references among chapters.The handbook begins with a historical intro
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