Gender and racial/ethnic disparities in academic oncology leadership.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11009-11009
Author(s):  
Gavin Jones ◽  
Natasha Dhawan ◽  
Akansha Chowdhary ◽  
Trevor Joseph Royce ◽  
Kirtesh R. Patel ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Department Chairs ◽  
White Male ◽  
Ethnic Disparities ◽  
White Female ◽  
Leadership Positions ◽  
Goodness Of Fit Test ◽  
Chi Squared ◽  
Academic Faculty ◽  
Racial Ethnic

11009 Background: Gender & racial/ethnic leadership disparities have been independently identified in academic hematology/oncology (HO) and radiation oncology (RO). Here, we evaluate gender and racial/ethnic intersectionality from the trainee to the leadership level. Methods: All ACGME accredited HO and RO training program websites were queried to identify constituent trainees, academic faculty, program directors (PD) and department chairs (DC), with a leadership position defined as PD or DC. Individual gender & race/ethnicity was determined using externally validated software tools (Gender-API, NamSor, & Onolytics), publicly available descriptors, and image review. We grouped individuals into 6 categories: White Male (WM), White Female (WF), Asian Male (AM), Asian Female (AF), Underrepresented Groups in Medicine (as defined by AAMC) Male (URMM) and Female (URMF). The chi-squared goodness-of-fit test was applied to examine if deviations exist between the observed vs. expected proportions of gender/race dyads in trainees, PD, and DC compared to academic faculty. Results: We identified 7,722 individuals from 2019-2020: 1,759 trainees (HO=1525; RO=234), 5,726 faculty (HO=4834; RO=892), 242 PD (HO=149; RO=93) and 237 DC (HO=144; RO=93). Leadership positions were most often comprised by WM (52.6%), and least often comprised by URMF (2.9%). Combined HO/RO analysis revealed significant differences in the observed representation of trainees & DC vs expected levels based on total faculty, respectively: WM (33.7% & 60.3% vs. 42.3%), WF (19.2% & 13.9% vs. 22.3%), AM (20.75% & 16.9% vs. 16.4%), AF (17.9% & 2.5% vs. 12.7%), URMM (4.09% & 5.5% vs. 3.5%) and URMF (4.3% & 0.8% vs. 2.8%), p<0.01. No differences were seen between PD vs total faculty. On subset analysis, there were significant differences observed in HO programs at the trainee, PD and DC levels compared to total faculty, whereas significant differences in RO programs were seen only at the DC level [Table]. Conclusions: Gender & racial/ethnic disparity is present in academic oncology. Specifically, women of all races/ethnicities are proportionally underrepresented in DC positions in HO and RO programs. These data can serve as a benchmark to raise awareness and monitor progress towards a more balanced workforce in oncology.[Table: see text]

Reversing the Friday peak in metastatic cord compression referrals: Not as simple as previously thought?

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14050-e14050
Author(s):  
Olusola Michael Adeleke ◽  
Rubyyat A Hakim ◽  
Laurence Dean ◽  
Huma Zahid ◽  
Rongyu Lin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Single Point ◽  
District General Hospital ◽  
Cord Compression ◽  
Radiology Reporting ◽  
Goodness Of Fit Test ◽  
Tertiary Centre ◽  
Chi Squared ◽  
Early Case ◽  
The Uk

e14050 Background: Historically, metastatic spinal cord compression (MSCC) referrals trend towards a Friday peak in incidence (Koiter E, Radioth Onc 2013). However, data from a single, tertiary centre in the UK showed a reversal in the Friday peak (Adeleke S, Annals of Oncology 2020). This was attributed to early case referrals and quicker treatment decisions. In this new study, we explored whether a similar pattern was apparent in multiple district general hospital (DGH) settings and attempt to identify underlying causes. DGHs manage a larger proportion of cancer patients in the UK. Methods: 1,069 patients between 1 Jan 2015 and 31 Dec 2020 were identified across 4 hospitals in Kent, UK with a population of 1.6 million people. 220, 181, 182, 159, 134 and 193 MSCC patients were identified annually (2015-2020). Commonest cancers were prostate (24.1%), lung (19.3%) and breast (12.3%). Thoracic and lumbar regions constituted 80% of MSCC sites. Kruskal Wallis was used to compare differences in referrals across weekdays. Data was then dichotomised to Fridays only vs. other days of the week combined, as previously reported (De Bono B, Acta Neurochir 2019). Chi squared was used to compare frequency of referrals between the two groups. Chi squared goodness of fit test was conducted to detect if Friday reflected the day with highest referrals across the week. Results: Across the region, 2015 saw the highest number of Friday referrals relative to other days, p= 0.002. Friday referrals continued to drop, year on year, until 2018 with a corresponding increase in mid-week referrals. After 2018, there was a return in trend to a further Friday peak across the region, though p= 0.836. On an individual hospital basis, the persistent Friday peak in the region was driven by two hospitals. Having a 7-day acute oncology service (AOS), 7-day radiology reporting and single referral point of contact in the department, were factors identified that kept the referrals across the week uniform. On another note, a substantial shift towards a single 8Gy fraction vs. 20Gy in 5 fractions was observed across the region. This change coincided with SCORAD III data (Hoskin P, ASCO 2017) and demonstrates adherence to evidence-based practice in the region. Conclusions: This large multi-centre retrospective study shows a differential referral pattern in the region, with hospitals with 7-day AOS/Radiology reporting and single point of referral (e.g, similar to MSCC coordinator role) having a quicker treatment turnaround and uniform referrals across the week. The MSCC coordinator has been shown to streamline service, ensure timely decision-making and improved survival outcomes (Richards L, Spine J 2017). The role is recommended by NICE UK. DGHs should consider appointing an MSCC coordinator when designing/auditing their service. The shift towards single 8Gy fraction can provide a ‘one-stop’ service where patients are scanned, planned and treated on the same day.

Pearson’s chi-squared statistics: approximation theory and beyond

Biometrika ◽  
2019 ◽  
Vol 106 (3) ◽  
pp. 716-723
Author(s):  
Mengyu Xu ◽  
Danna Zhang ◽  
Wei Biao Wu
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Central Limit ◽  
Approximation Theory ◽  
Quadratic Forms ◽  
Degrees Of Freedom ◽  
Goodness Of Fit ◽  
High Dimensional ◽  
Goodness Of Fit Test ◽  
Chi Squared

Summary We establish an approximation theory for Pearson’s chi-squared statistics in situations where the number of cells is large, by using a high-dimensional central limit theorem for quadratic forms of random vectors. Our high-dimensional central limit theorem is proved under Lyapunov-type conditions that involve a delicate interplay between the dimension, the sample size, and the moment conditions. We propose a modified chi-squared statistic and introduce an adjusted degrees of freedom. A simulation study shows that the modified statistic outperforms Pearson’s chi-squared statistic in terms of both size accuracy and power. Our procedure is applied to the construction of a goodness-of-fit test for Rutherford’s alpha-particle data.

A Bayesian Chi-Squared Goodness-of-Fit Test for Censored Data Models

Biometrics ◽  
2009 ◽  
Vol 66 (2) ◽  
pp. 426-434 ◽  
Author(s):  
Jing Cao ◽  
Ann Moosman ◽  
Valen E. Johnson
Keyword(s):  
Censored Data ◽  
Goodness Of Fit ◽  
Data Models ◽  
Goodness Of Fit Test ◽  
Chi Squared
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