Abstract
Purpose
Advancing equity, diversity, and inclusion in the physician workforce is essential to providing high-quality and culturally responsive patient care and has been shown to improve patient outcomes. To better characterize equity in the field of radiation oncology, we sought to describe the current academic radiation oncology workforce, including any contemporary differences in compensation and rank by gender and race/ethnicity.
Methods and Materials
We conducted a retrospective cohort study using data from the Society of Chairs of Academic Radiation Oncology Programs (SCAROP) 2018 Financial Survey. Multivariable logistic regression models were used to identify factors associated with associate or full professor rank. Compensation was compared by gender and race/ethnicity overall and stratified by rank and was further analyzed using multivariable linear regression models.
Results
Of the 858 academic radiation oncologists from 63 departments in the United States in the sample, 33.2% were female, 65.2% were White, 27.2% were Asian, and 7.6% were underrepresented in medicine (URiM). There were 44.0% assistant professors, 32.0% associate professors, and 22.8% full professors. Multivariable logistic regression analysis for factors associated with associate or full professor rank did not reveal statistically significant associations between gender or race/ethnicity with academic rank (odds ratio [OR], 0.86; 95% confidence interval [CI], 0.56-1.32; P = .48 for gender; OR, 0.81; 95% CI, 0.5-1.30; P = .37 for Asian vs White; and OR, 0.69; 95% CI, 0.31-1.55; P = .37 for URiM vs White), but CIs were wide due to sample size, and point estimates were <1. Similarly, multivariable linear regression analysis modeling the log relative total compensation did not detect statistically significant differences between radiation oncologists by gender (–1.7%; 95% CI, –6.8% to 3.4%; P = .51 for female vs male) or race/ethnicity (–1.6%; 95% CI, –7.3% to 4.0%; P = .57 for Asian vs White and –3.0%; 95% CI, –12.1% to 6.0%; P = .51 for URiM vs White).
Conclusions
The low numbers of women and faculty with URiM race/ethnicity in this radiation oncology faculty sample limits the ability to compare career trajectory and compensation by those characteristics. Given that point estimates were <1, our findings do not contradict larger multispecialty studies that suggest an ongoing need to monitor equity.
Introduction
Advancing equity, diversity, and inclusion in the physician workforce is essential to providing high-quality and culturally responsive patient care and has been shown to improve patient outcomes.1, 2, 3, 4, 5 It is also a vital component in ensuring effective recruitment, engagement, and retention of students, residents, staff, and faculty. Despite a call from our licensing and governing bodies to cultivate a diverse and inclusive workforce reflective of the communities we serve, disparities remain in compensation and academic promotion for women and individuals from groups that are underrepresented in medicine (URiM) across specialties.4, 5, 6, 7, 8, 9, 10
Radiation oncology is one of the least-diverse fields in terms of representation of women and URiM individuals compared with other specialties.11, 12, 13 Within radiation oncology, gender is associated with many aspects of a physician's career, including academic advancement, scholarly activity, funding for research, academic position, Medicare reimbursement, representation in leadership positions, and disease site(s) treated.14, 15, 16, 17, 18, 19, 20 Analogous disparities exist within the radiation oncology workforce by race. For example, the leaders of our field are mostly White men, with only 5 of the 110 Society of Chairs of Academic Radiation Oncology Programs (SCAROP) member being URiM in 2022 (Emily Wilson, personal communication, June 6, 2022). Studies of multispecialty samples have suggested ongoing disparities in compensation by both gender and race. Addressing the underlying causes of gender- and race-based income differences should be a priority, particularly as we recruit more women and URiM physicians into our workforce, and given the potential for ongoing perpetuation of social injustice by reducing the power and wealth accumulation of marginalized groups that propagates effects to subsequent generations.
SCAROP conducts a survey every 2 to 3 years that collects information regarding compensation, as well as physician and practice characteristics of radiation oncology departments, providing a comprehensive overview of the academic radiation oncology workforce. Given a growing interest in better understanding and addressing inequities within the field of radiation oncology, we sought to describe the current academic radiation oncology workforce, and assess any contemporary differences in compensation and rank by gender and race/ethnicity.
Methods and Materials
We conducted a retrospective cohort study using data from the SCAROP 2018 Financial Survey. This study was approved by the University of California, Los Angeles Institutional Review Board.
Survey description
The survey included data on 858 academic radiation oncologists from 63 departments in the United States that had membership in SCAROP (n = 108; survey response rate of 58.3%). Representatives from each department were asked to provide deidentified information regarding their individual faculty members. Potential participating departments were identified by their chair's membership in SCAROP and were invited to participate in completion of the SCAROP 2018 Financial Survey via e‐mail with a unique link for each department. The survey was administered via the Qualtrics (Qualtrics, Provo, UT) portal, and launched on January 2, 2019. Five reminder e-mails were sent to department chairs through a 10-week field period, which closed on March 17, 2019. Financial information was to be reported for the most recent fiscal year.
Variables
The 2018 SCAROP survey included items related to department characteristics, physician demographics, and income. Departmental variables included region of country (West, Midwest, South, Northeast), institutional funding (public vs private), and size (whether the department had 50 or more faculty members). Physician-specific variables included position (department chair vs division/section chief vs residency program director vs other faculty), full-time classification (yes vs no vs missing), physician–scientist classification (yes vs no vs missing), years of experience (0-4 years vs 5-9 years vs 10-14 years vs 15-19 years, vs 20+ years vs missing), years in the department (0-4 years vs 5-9 years vs 10-14 years vs 15-19 years, vs 20+ years vs missing), board certification status (yes vs no vs missing), gender (male vs female vs missing), race/ethnicity (White vs Asian vs URiM vs missing), site/primary location of practice (main campus vs satellite campus), academic rank (instructor vs assistant professor vs associate professor vs professor vs missing/not applicable), tenure track (yes vs no vs missing/not applicable), and formal education level (MD, DO with a PhD vs MD, DO with a master's degree, vs MD, DO vs other vs missing), and total compensation. Physicians with the following backgrounds were classified as URiM: Hispanic, Latino, or Spanish origin; Black or African American; American Indian or Alaskan Native; Native Hawaiian or other Pacific Islander; or multiple selections with at least 1 background category classified as URiM. The number of physicians in each of these categories were pooled into the single URiM category for the main analyses as numbers were not sufficiently high to meaningfully analyze separately. Total compensation was defined as base compensation plus incentive bonus during the most recent fiscal year.
Data analysis
For 2018 survey data on physicians, descriptive statistics such as frequency and percentage were summarized and presented in tables for variables related to department characteristics, physician demographics, both overall (Table 1), by gender (Table E1), and by race/ethnicity (Table E2). χ2 tests were used to assess univariate association of these categorical variables and academic rank with gender and race/ethnicity. Multivariable logistic regression models were used to identify factors associated with associate or full professor rank. A full model was first fitted with main effects, as well as all the first-order pairwise interaction terms related to gender and race/ethnicity, respectively. Backward stepwise elimination of nonsignificant (threshold: P < .05) variables was performed to reach a final model. Odds ratios (ORs), the corresponding 95% confidence intervals (CIs), and P values were reported (Table 2).
Table 1.
2018 survey data for academic radiation oncologists, descriptive statistics (categorical variables; N = 858)
| Variable | Level | No. (%) |
|---|---|---|
| Organizational region AAMC | Western region | 156 (18.5) |
| Midwestern region | 161 (19.1) | |
| Southern region | 287 (34.1) | |
| Northeastern region | 238 (28.3) | |
| Missing | 16 | |
| Which of the following best describes your institution? | Publicly funded | 480 (55.9) |
| Privately funded | 378 (44.1) | |
| Do you have more than 50 faculty members? | Yes | 350 (40.8) |
| No | 508 (59.2) | |
| Occupation | Physician department chair | 55 (6.4) |
| Physician division/section chief | 93 (10.8) | |
| Physician residency program director | 50 (5.8) | |
| Physician faculty | 660 (76.9) | |
| Full-time classification | Yes | 787 (93.5) |
| No | 55 (6.5) | |
| Missing | 16 | |
| Physician–scientist | Yes | 137 (18.1) |
| No | 620 (81.9) | |
| Missing | 101 | |
| Years of experience categories | 0-4 | 166 (20.7) |
| 5-9 | 176 (22.0) | |
| 10-14 | 138 (17.2) | |
| 15-19 | 80 (10.0) | |
| 20+ | 241 (30.1) | |
| Missing | 57 | |
| Years in department categories | 0-4 | 299 (37.1) |
| 5-9 | 211 (26.2) | |
| 10-14 | 135 (16.8) | |
| 15-19 | 54 (6.7) | |
| 20+ | 106 (13.2) | |
| Missing | 53 | |
| Board certified | Yes | 807 (98.2) |
| No | 15 (1.8) | |
| Missing | 36 | |
| Gender | Male | 544 (66.8) |
| Female | 270 (33.2) | |
| Missing | 44 | |
| Race (aggregated URiM) | White | 508 (64.4) |
| Asian | 212 (26.9) | |
| URiM | 59 (7.5) | |
| Other and multiracial | 10 (1.3) | |
| Missing | 69 | |
| Race (disaggregated) | American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander | 5 (0.6) |
| Asian | 212 (26.9) | |
| Black or African American | 40 (5.1) | |
| Hispanic, Latino, or Spanish origin | 14 (1.8) | |
| Middle Eastern or North African | 31 (3.9) | |
| White excluding Middle Eastern or North African | 477 (60.5) | |
| Other | 10 (1.3) | |
| Missing | 69 | |
| Race (disaggregated) by academic rank | Instructor or assistant professor | 371 (45.2) |
| American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander | 3 (0.8) | |
| Asian | 96 (25.9) | |
| Black or African American | 17 (4.6) | |
| Hispanic, Latino, or Spanish origin | 5 (1.4) | |
| Middle Eastern or North African | 16 (4.3) | |
| White excluding Middle Eastern or North African | 196 (52.8) | |
| Other race | 5 (1.4) | |
| Missing | 33 (8.9) | |
| Associate professor | 258 (32.0) | |
| American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander | 2 (0.8) | |
| Asian | 68 (26) | |
| Black or African American | 10 (3.8) | |
| Hispanic, Latino, or Spanish origin | 6 (2.3) | |
| Middle Eastern or North African | 8 (3.1) | |
| White excluding Middle Eastern or North African | 143 (54.6) | |
| Other race | 4 (1.5) | |
| Missing | 21 (8) | |
| Professor | 187 (22.8) | |
| American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander | 0 (0) | |
| Asian | 40 (21.4) | |
| Black or African American | 10 (5.4) | |
| Hispanic, Latino, or Spanish origin | 3 (1.6) | |
| Middle Eastern or North African | 4 (2.1) | |
| White excluding Middle Eastern or North African | 116 (62) | |
| Other race | 1 (0.5) | |
| Missing | 13 (7) | |
| Site/primary location | Main campus | 593 (69.4) |
| Satellite campus | 261 (30.6) | |
| Missing | 4 | |
| Tenure track | Yes | 288 (37.0) |
| No | 490 (63.0) | |
| Missing | 80 | |
| Formal education level | MD, DO with a PhD | 175 (22.0) |
| MD, DO with a master's degree | 56 (7.0) | |
| MD, DO | 562 (70.6) | |
| Other | 3 (0.4) | |
| Missing | 62 |
Abbreviation: AAMC = Association of American Medical Colleges.
Table 2.
Multivariable logistic regression analysis (factors associated with associate or full professor rank for academic radiation oncologists)
| Effects | OR | Lower limit of 95% CI | Upper limit of 95% CI | P value |
|---|---|---|---|---|
| Gender: female vs male | 0.86 | 0.56 | 1.32 | .4844 |
| Race/ethnicity | .5107 | |||
| Asian vs White | 0.81 | 0.50 | 1.30 | .3740 |
| URiM vs White | 0.69 | 0.31 | 1.55 | .3690 |
| Geographic region | .0004 | |||
| Midwestern region vs Western region | 0.27 | 0.14 | 0.52 | <.0001 |
| Northeastern region vs Western region | 0.51 | 0.27 | 0.96 | .0368 |
| Southern region vs Western region | 0.74 | 0.42 | 1.33 | .3170 |
| Departmental size >50 faculty members: no vs yes | 0.60 | 0.38 | 0.93 | .0237 |
| Site/practice location: main campus vs satellite campus | 2.36 | 1.50 | 3.70 | .0002 |
| Years of experience | <.0001 | |||
| 0-4 vs 5-9 | 0.12 | 0.05 | 0.29 | <.0001 |
| 10-14 vs 5-9 | 2.95 | 1.57 | 5.53 | .0008 |
| 15-19 vs 5-9 | 4.33 | 1.89 | 9.93 | .0005 |
| 20+ vs 5-9 | 4.38 | 2.24 | 8.58 | <.0001 |
| Years in department | <.0001 | |||
| 0-4 vs 5-9 | 0.41 | 0.23 | 0.71 | .0015 |
| 10-14 vs 5-9 | 1.45 | 0.73 | 2.85 | .2879 |
| 15-19 vs 5-9 | 2.67 | 0.87 | 8.18 | .0850 |
| 20+ vs 5-9 | 5.00 | 1.66 | 15.02 | .0041 |
Abbreviations: CI = confidence interval; OR = odds ratio; URiM = underrepresented in medicine.
This is the final model. Because race/ethnicity and gender are the variable of interest, they are retained in the final model regardless of statistical significance. We have explored the first-order interaction terms of all other covariates with gender and race/ethnicity. None of the first-order interaction terms are significant. Only significant covariates are included in the final model. P values in boldface are statistically significant.
The natural log relative total compensation was calculated by dividing the total compensation by the minimum compensation based on gender, race, geographic region, and academic rank and then calculating the natural log. This allows for simple interpretation of differences × 100 as percentage increases or decreases when differences are close to 0. For differences close to ±1, the differences are a slight underestimation of the true percentage changes. For example, log relative means of 0.88 for Asian professors versus 0.80 for Middle Eastern or North African professors indicates that the mean compensation for an Asian professor in our data set is about 8% {=[(0.88 – 0.80) × 100]%} greater for Asian professors compared with Middle Eastern or North African professors. Log total compensation was stratified by gender/rank (Table E3A) and gender/region/rank (Table E3B). Mean, standard deviation, median, and interquartile range of the log total compensation relative to the reference subgroup are reported. Comparisons of total compensation between men and women were conducted via F-tests within a 2-way factorial analysis of variance framework (gender by rank) and P values were reported. Similar analyses were performed for total compensation by race/ethnicity/rank (Table E4A), and race/ethnicity/region/rank (Table E4B). Since aggregation can limit the ability to measure the effects of the most severe forms of racism, we performed additional analyses examining differences in total compensation by ungrouped race/ethnicity and rank (Tables 3, 4, and E5).
Table 3.
Multivariable linear regression analysis modeling log relative total compensation as outcome variable, gender, and ungrouped race/ethnicity (excluding academic rank as variable)
| Parameter | Estimate* | 95% CI | P value |
|---|---|---|---|
| Gender (female vs male) | –0.018 | (–0.069 to 0.034) | .4977 |
| Race/ethnicity (reference: White excluding Middle Eastern or North African) | .9878 | ||
| American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander | 0.01 | (–0.256 to 0.277) | .9396 |
| Asian | –0.015 | (–0.072 to 0.041) | .594 |
| Black or African American | –0.033 | (–0.148 to 0.081) | .5669 |
| Hispanic, Latino, or Spanish origin | –0.035 | (–0.202 to 0.132) | .6787 |
| Middle Eastern or North African | 0.021 | (–0.111 to 0.153) | .7542 |
| Other race | –0.048 | (–0.315 to 0.22) | .7263 |
| Geographic region (reference: Western region) | .0745 | ||
| Midwestern region | –0.083 | (–0.162 to –0.004) | .04 |
| Northeastern region | –0.019 | (–0.1 to 0.062) | .6424 |
| Southern region | 0.003 | (–0.073 to 0.078) | .9449 |
| Institutional funding (private vs public) | 0.072 | (0.014-0.129) | .0146 |
| Departmental size ≥50 faculty (no vs yes) | –0.06 | (–0.113 to –0.008) | .0242 |
| Full-time classification (no vs yes) | –0.598 | (–0.7 to –0.497) | <.0001 |
| Site/primary location of practice (main vs satellite) | 0.072 | (0.018-0.127) | .0093 |
| Tenure track (no vs yes) | –0.079 | (–0.135 to –0.024) | .0051 |
| Physician–scientist classification (no vs yes) | –0.015 | (–0.085 to 0.056) | .6833 |
| Years of experience (reference: 5-9 y) | <.0001 | ||
| 0-4 | –0.009 | (–0.097 to 0.079) | .8413 |
| 10-14 | 0.127 | (0.041-0.213) | .0037 |
| 15-19 | 0.111 | (0-0.221) | .0491 |
| 20+ | 0.28 | (0.191-0.368) | <.0001 |
| Years in the department (reference: 5-9 y) | .0021 | ||
| 0-4 | –0.137 | (–0.215 to –0.059) | .0006 |
| 10-14 | –0.004 | (–0.094 to 0.085) | .922 |
| 15-19 | 0.046 | (–0.074 to 0.166) | .4499 |
| 20+ | –0.007 | (–0.11 to 0.095) | .8913 |
| Board certification status (no vs yes) | –0.247 | (–0.422 to –0.072) | .0058 |
| Formal education level (reference: MD, DO with a PhD) | .0058 | ||
| MD, DO | 0.092 | (0.03-0.153) | .0037 |
| MD, DO with a master's degree | 0.059 | (–0.043 to 0.161) | .2583 |
Abbreviation: CI = confidence interval.
Point estimates × 100 approximates the percent difference in total compensation. For example, radiation oncologists at the main campus earn ∼7.2% more than those at a satellite location.
Table 4.
Multivariable linear regression analysis modeling log relative total compensation as outcome variable, gender, and ungrouped race/ethnicity (including academic rank as variable)
| Parameter | Estimate* | 95% CI | P value |
|---|---|---|---|
| Gender (female vs male) | –0.003 | (–0.051 to 0.046) | .9166 |
| Race/ethnicity (reference: White excluding Middle Eastern or North African) | .9165 | ||
| American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander | 0.037 | (–0.213 to 0.287) | .771 |
| Asian | –0.027 | (–0.081 to 0.026) | .3185 |
| Black or African American | –0.024 | (–0.131 to 0.083) | .6635 |
| Hispanic, Latino, or Spanish origin | –0.026 | (–0.182 to 0.131) | .7475 |
| Middle Eastern or North African | 0.046 | (–0.079 to 0.17) | .4719 |
| Other race | –0.030 | (–0.281 to 0.221) | .8154 |
| Geographic region (reference: Western region) | .4367 | ||
| Midwestern region | –0.050 | (–0.125 to 0.025) | .1944 |
| Northeastern region | –0.002 | (–0.079 to 0.075) | .9618 |
| Southern region | –0.003 | (–0.074 to 0.068) | .9291 |
| Institutional funding (private vs public) | 0.048 | (–0.006 to 0.103) | .0818 |
| Departmental size ≥50 faculty (no vs yes) | –0.062 | (–0.112 to –0.011) | .017 |
| Full-time classification (no vs yes) | –0.565 | (–0.66 to –0.47) | <.0001 |
| Site/primary location of practice (main vs satellite) | 0.029 | (–0.024 to 0.082) | .2824 |
| Tenure track (no vs yes) | –0.060 | (–0.113 to –0.008) | .0247 |
| Physician–scientist classification (no vs yes) | 0.015 | (–0.052 to 0.082) | .6587 |
| Years of experience (reference: 5-9 y) | .0237 | ||
| 0-4 | 0.042 | (–0.043 to 0.128) | .3296 |
| 10-14 | 0.095 | (0.014-0.177) | .0227 |
| 15-19 | 0.043 | (–0.063 to 0.148) | .4267 |
| 20+ | 0.140 | (0.048-0.233) | .003 |
| Years in the department (reference: 5-9 y) | .0054 | ||
| 0-4 | –0.130 | (–0.204 to –0.056) | .0006 |
| 10-14 | –0.026 | (–0.11 to 0.058) | .5478 |
| 15-19 | –0.001 | (–0.116 to 0.115) | .9921 |
| 20+ | –0.092 | (–0.193 to 0.009) | .074 |
| Board certification status (no vs yes) | –0.095 | (–0.27 to 0.081) | .2904 |
| Formal education level (reference: MD, DO with a PhD) | .0305 | ||
| MD, DO | 0.075 | (0.017-0.134) | .0121 |
| MD, DO with a master's degree | 0.021 | (–0.076 to 0.117) | .678 |
| Academic ranking (reference: professor) | <.0001 | ||
| Assistant professor | –0.319 | (–0.406 to –0.232) | <.0001 |
| Associate professor | –0.197 | (–0.276 to –0.118) | <.0001 |
| Instructor | –0.577 | (–0.806 to –0.348) | <.0001 |
Abbreviation: CI = confidence interval.
Point estimates × 100 approximates the percent difference in total compensation. For example, radiation oncologists at the main campus earn ∼2.9% more than those at a satellite location.
To evaluate percent change in total compensation between genders and race/ethnicity, log relative compensation was modeled as a response variable in a multivariable linear regression model in which gender, race/ethnicity, geographic region, departmental size, institutional funding source, full-time classification, site/primary location of practice, tenure track, physician–scientist classification, years of experience, years in the department, board certification status, and formal education level were included as predictors (Table 5). A similar multivariable linear regression model including academic rank as an additional and possibly mediating independent variable was performed to evaluate percent change in total compensation between genders and race/ethnicity (Table 6). These multivariable linear regression models were repeated for ungrouped races/ethnicities (Tables 3 and 4). Results are summarized using point estimates, 95% CIs, and P values.
Table 5.
Multivariable linear regression analysis modeling log relative total compensation as outcome variable, gender, and grouped race/ethnicity (excluding academic rank as variable)
| Parameter | Estimate* | 95% CI | P value |
|---|---|---|---|
| Gender (female vs male) | –0.017 | (–0.068 to 0.034) | .5115 |
| Race/ethnicity (reference: White) | .7238 | ||
| Asian | –0.016 | (–0.073 to 0.04) | .5675 |
| URiM | –0.03 | (–0.121 to 0.06) | .5124 |
| Geographic region (reference: Western region) | .0818 | ||
| Midwestern region | –0.083 | (–0.162 to –0.003) | .0409 |
| Northeastern region | –0.02 | (–0.101 to 0.061) | .6289 |
| Southern region | 0.001 | (–0.075 to 0.077) | .9804 |
| Institutional funding (private vs public) | 0.071 | (0.014-0.129) | .0149 |
| Departmental size ≥50 faculty (no vs yes) | –0.061 | (–0.113 to –0.008) | .0229 |
| Full-time classification (no vs yes) | –0.598 | (–0.699 to –0.497) | <.0001 |
| Site/primary location of practice (main vs satellite) | 0.072 | (0.018-0.127) | .0093 |
| Tenure track (no vs yes) | –0.083 | (–0.139 to –0.027) | .0039 |
| Physician–scientist classification (no vs yes) | –0.013 | (–0.083 to 0.057) | .7141 |
| Years of experience (reference: 5-9 y) | <.0001 | ||
| 0-4 | –0.008 | (–0.097 to 0.081) | .8522 |
| 10-14 | 0.129 | (0.043-0.215) | .0033 |
| 15-19 | 0.113 | (0.002-0.223) | .0453 |
| 20+ | 0.28 | (0.191-0.368) | <.0001 |
| Years in the department (reference: 5-9 y) | .0019 | ||
| 0-4 | –0.139 | (–0.216 to –0.062) | .0004 |
| 10-14 | –0.007 | (–0.096 to 0.082) | .8793 |
| 15-19 | 0.043 | (–0.077 to 0.163) | .4815 |
| 20+ | –0.012 | (–0.115 to 0.09) | .8117 |
| Board certification status (no vs yes) | –0.26 | (–0.442 to –0.078) | .0052 |
| Formal education level (reference: MD, DO with a PhD) | .0124 | ||
| MD, DO | 0.093 | (0.032-0.155) | .0031 |
| MD, DO with a master's degree | 0.059 | (–0.044 to 0.163) | .2603 |
Abbreviations: CI = confidence interval; URiM = underrepresented in medicine.
Point estimates × 100 approximates the percent difference in total compensation. For example, radiation oncologists at the main campus earn ∼7.2% more than those at a satellite location.
Table 6.
Multivariable linear regression analysis modeling log relative total compensation as outcome variable, gender, and grouped race/ethnicity (including academic rank as variable)
| Parameter | Estimate* | 95% CI | P value |
|---|---|---|---|
| Gender (female vs male) | –0.000 | (–0.05 to 0.047) | .938 |
| Race/ethnicity (reference: White) | .5313 | ||
| Asian | –0.030 | (–0.083 to 0.024) | .276 |
| URiM | –0.020 | (–0.106 to 0.065) | .641 |
| Geographic region (reference: Western region) | .4637 | ||
| Midwestern region | –0.050 | (–0.125 to 0.026) | .199 |
| Northeastern region | –0.000 | (–0.079 to 0.074) | .947 |
| Southern region | –0.010 | (–0.076 to 0.066) | .883 |
| Institutional funding (private vs public) | 0.048 | (–0.007 to 0.102) | .087 |
| Departmental size ≥50 faculty (no vs yes) | –0.060 | (–0.112 to –0.011) | .018 |
| Full-time classification (no vs yes) | –0.570 | (–0.661 to –0.471) | <.0001 |
| Site/primary location of practice (main vs satellite) | 0.029 | (–0.024 to 0.081) | .289 |
| Tenure track (no vs yes) | –0.060 | (–0.114 to –0.008) | .024 |
| Physician–scientist classification (no vs yes) | 0.016 | (–0.05 to 0.083) | .635 |
| Years of experience (reference: 5-9 y) | .0257 | ||
| 0-4 | 0.042 | (–0.044 to 0.128) | .337 |
| 10-14 | 0.095 | (0.013-0.177) | .023 |
| 15-19 | 0.046 | (–0.059 to 0.152) | .39 |
| 20+ | 0.140 | (0.048-0.233) | .003 |
| Years in the department (reference: 5-9 y) | .0043 | ||
| 0-4 | –0.130 | (–0.206 to –0.059) | <.0001 |
| 10-14 | –0.030 | (–0.111 to 0.057) | .529 |
| 15-19 | –0.000 | (–0.118 to 0.112) | .96 |
| 20+ | –0.100 | (–0.196 to 0.006) | .066 |
| Board certification status (no vs yes) | –0.090 | (–0.276 to 0.092) | .327 |
| Formal education level (reference: MD, DO with a PhD) | .0315 | ||
| MD, DO | 0.076 | (0.017-0.135) | .011 |
| MD, DO with a master's degree | 0.025 | (–0.072 to 0.123) | .61 |
| Academic rank (reference: professor) | <.0001 | ||
| Assistant professor | –0.320 | (–0.405 to –0.23) | <.0001 |
| Associate professor | –0.200 | (–0.276 to –0.118) | <.0001 |
| Instructor | –0.570 | (–0.794 to –0.338) | <.0001 |
Abbreviations: CI = confidence interval; URiM = underrepresented in medicine.
Point estimates × 100 approximates the percent difference in total compensation. For example, radiation oncologists at the main campus earn ∼2.9% more than those at a satellite location.
To compare statistically detectable differences with meaningful differences in associate/full professor representation as well as total compensation between White and URiM radiation oncologists, power calculations (80% power) for minimum detectable effect sizes were performed. Power calculations included group sample sizes of 508 White and 59 URiM radiation oncologists and 2-sided t tests allowing for unequal variance with a significance level (alpha) of 0.05. t Tests provide a conservative estimate for minimum detectable effect size in multivariable regression. Power calculations were repeated for American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander (n = 5), Black or African American (n = 40), and Hispanic, Latino, or Spanish origin (n = 14) versus White excluding Middle Eastern or North African (MENA) radiation oncologists (n = 455). For the power calculation of associate/full professor representation, a multivariable logistic regression modeling the binary outcome associate/full professor (yes or no) was performed with 0.540 baseline probability of associate or full professor representation. An adjustment was made because multiple regression of the race variable on the other independent variables in the logistic regression obtained an R-squared of 0.04. All statistical analyses were carried out using statistical software SAS, version 9.4, and R, version 4.0.0.21,22
Results
The departmental and demographic characteristics associated with all identified radiation oncologists (n = 858) are shown in Table 1. The survey included radiation oncologists from 63 departments; 270 (33.2%) physicians were female, 477 (60.5%) were White, 212 (27.2%) were Asian, and 59 (7.6%) were from URiM backgrounds. There were 361 (44.0%) assistant professors, 262 (32.0%) associate professors, and 187 (22.8%) full professors. The largest proportion of radiation oncologists in the sample practiced in the South (n = 287; 34.1%), followed by the Northeast (n = 238; 28.3%), the Midwest (n = 161; 19.1%), and the West (n = 156; 18.5%). Most radiation oncologists in the sample practiced at a publicly funded institution (n = 480; 55.9%) and in departments with 50 or fewer faculty members (n = 508; 59.2%).
On univariate analyses by gender, women were less likely to work full-time (91.0% women vs 95.1% men; P = .02) and be physician–scientists (13.8% women vs 19.8% men; P = .05) compared with men (Table E1). Compared with men, women were more likely to practice in a department with more than 50 faculty members (44.4% women vs 37.3% men; P = .05). Academic rank by gender was not significantly different (P = .16). On univariate analyses by race/ethnicity, the proportion of White, Asian, and URiM radiation oncologists varied significantly based on geographic region (P < .001), type of institutional funding (P = .03), tenure track position (P < .001), physician–scientist classification (P = .02), years of practice experience (P < .001), and years in the department (P = .03) but not academic rank (P = .32) (Table E2). The largest proportion of URiM radiation oncologists (n = 29; 50.9%) practiced in the South, followed by the West (n = 12; 21.1%), Midwest (n = 10; 17.5%), and the Northeast (n = 6; 10.5%). In contrast, the highest proportion of Asian radiation oncologists practiced in the Northeast (n = 64; 30.6%), followed by the West (n = 60; 28.7%), the South (n = 57; 27.3%) and the Midwest (n = 28; 13.4%). With respect to type of institutional funding, 72.9% of URiM radiation oncologists practiced in a publicly funded institution versus 57.1% of Asian and 54.9% of White radiation oncologists. The minority held tenure track positions: 46.0% of URiM, 27.3% of Asian and 43.3% of White radiation oncologists. A smaller proportion of URiM radiation oncologists were classified as physician–scientists (10.7%), compared with Asian (23.7%) and White (16.2%) radiation oncologists.
Multivariable logistic regression analysis for factors associated with associate or full professor rank showed that gender and race/ethnicity were not significantly associated with academic rank (Table 2; OR, 0.86; 95% CI, 0.56-1.32; P = .48 for female vs male; OR, 0.81; 95% CI, 0.5-1.30; P = .37 for Asian vs White; and OR, 0.69; 95% CI, 0.31-1.55; P = .37 for URiM vs White). Of note, these analyses had 80% power to detect a minimum effect size of OR = 0.40 for associate or full professors when comparing URiM versus White radiation oncologists. Radiation oncologists practicing in the Midwest (OR, 0.27; 95% CI, 0.14-0.52 vs West; P < .001) or Northeast (OR, 0.51; 95% CI, 0.27-0.96 vs West; P = .04) or at an institution with 50 or fewer faculty members (OR, 0.60; 95% CI, 0.38-0.93; P = .02) were less likely to be associate or full professors. Radiation oncologists practicing at the main campus location (OR, 2.36; 95% CI, 1.50-3.70; P = .002) with more years of practice experience (OR, 0.12; 95% CI, 0.05-0.29; 0-4 vs 5-9 years; P < .001 and OR, 4.38; 95% CI, 2.24-8.58; 20+ vs 5-9 years; P ≤ .001) and more years in the department (OR, 0.41; 95% CI, 0.23-0.71; 0-4 vs 5-9 years; P = .002 and OR, 5.00; 95% CI, 1.66-15.02; 20+ vs 5-9 years; P = .0041) were significantly more likely to be associate or full professors.
Overall, there were no statistically significant differences in log relative mean total compensation between men and women across different academic ranks (Table E3A) on univariate analyses. However, we observed statistically significant differences among male and female associate professors in the West (15% greater compensation; P = .036) and assistant professors in the Northeast (13% greater compensation; P = .032) (Table E3B). However, once adjusted for predictors of total compensation such as geographic region, departmental size, institutional funding source, full-time classification, site/primary location of practice, tenure track, physician–scientist classification, years of experience, years in the department, board certification status, and formal education level, there were no statistically significant differences across genders (Table 5: –1.7%; 95% CI, –6.8% to 3.4%; P = .51 and Table 6, including academic rank as an additional and possibly mediating independent variable: –0.0%; 95% CI, –5.0% to 4.7%; P = .938 for female vs male, respectively).
Overall, there were no significant differences in total compensation between White, Asian, and URiM instructors, assistant professors, associate professors, or full professors on univariate analyses (Table E4A). In the South, URiM associate professors had significantly greater mean salaries compared with Asian (16.0% greater compensation) and White (13.0% greater compensation) associate professors (overall P = .02) (Table E4B). There were no significant differences in total compensation by ungrouped race/ethnicity and academic rank (Table E5, P values >.05 across all academic ranks). In a multivariable linear regression analysis modeling the log relative total compensation as the outcome variable, there were no statistically significant differences between radiation oncologists by race/ethnicity (Table 5: –1.6%; 95% CI, –7.3% to 4.0%; P = .57 for Asian vs White and –3.0%; 95% CI, –12.1% to 6.0%; P = .51 for URiM vs White; and Table 6, including academic rank as an additional and possibly mediating independent variable: –3.0%; 95% CI, –8.3% to 2.4%; P = .28 for Asian vs White and –2.0%; 95% CI, –10.6% to 6.5%; P = .64 for URiM vs White) for ungrouped race/ethnicity (Table 3: 1.0%; 95% CI, –25.6% to 27.7%; P = .94 for American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander vs White excluding MENA, –3.3%; 95% CI, –14.8% to 8.1%; P = .57 for Black or African American vs White excluding MENA, –3.5%; 95% CI, –20.2% to 13.2%; P = .75 for Hispanic, Latino, or Spanish origin vs White excluding MENA; Table 4, including academic rank as an additional and possibly mediating independent variable: 3.7%; 95% CI, –21.3% to 28.7%; P = .77 for American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander vs White excluding MENA, –2.4%; 95% CI, –13.1% to 8.3%; P = .66 for Black or African American vs White excluding MENA, –2.6%; 95% CI, –18.2% to 13.1%; P = .75 for Hispanic, Latino, or Spanish origin vs White excluding MENA). Our study had 80% power to detect a minimum 10% difference in total compensation between URiM and White radiation oncologists. In addition, our study had 80% power to detect a 20% difference for American Indian, Alaskan Native, Native Hawaiian or other Pacific Islander versus White excluding MENA radiation oncologists; a 30% difference for Hispanic, Latino, or Spanish origin radiation oncologists versus White excluding MENA radiation oncologists; and a 10% difference for Black or African American versus White excluding MENA radiation oncologists.
Discussion
To better understand how gender and racial equity may relate to professional advancement and financial compensation within the field of medicine more generally and the specialty of radiation oncology in particular, we sought to describe contemporary differences in academic achievement and compensation by race/ethnicity and gender in this specialty group. In the current study of 858 academic radiation oncologists from 63 departments in the United States, we observed that women were underrepresented in the academic radiation oncology workforce (constituting only 33.2% of our sample vs half of the total US population and half of all current medical students).23,24 Similarly, individuals from URiM backgrounds were markedly underrepresented, constituting only 7.6% of this sample, compared with approximately 34% of the general population in the United States.24 Unfortunately, the small size of the specialty in combination with this underrepresentation of women and those of URiM race/ethnicity constrains the power of this study to detect only large differences between groups who differ based on those characteristics. We did observe important differences in practice type and location by radiation oncologist race/ethnicity. Specifically, URiM radiation oncologists were more likely to practice in the South and at publicly funded universities, and only a small proportion of URiM radiation oncologists were classified as physician–scientists, compared with Asian or White radiation oncologists.
Although previous reports have documented substantial underrepresentation of women in the most senior positions of radiation oncology, with disproportionately fewer female chairs, American Society for Radiation Oncology (ASTRO) board members and presidents, or gold medal recipients, gender was not predictive of greater academic rank in our analysis.16,25,26 Given that this study demonstrates that a third of all full professors in our field are women, similar to the junior ranks, the pool of talent from which to draw future leaders should be sufficient to permit diversification of gender at the most senior levels. In contrast, it was concerning to observe that both junior and senior positions hover at approximately one third, when one half of the medical student body identify as women. This indicates that the field of radiation oncology needs to not only ensure that female full professors are able to achieve senior leadership positions and recognition but also to prioritize recruiting from the full pool of diverse and talented medical students who would continue to enrich our specialty.19,27
The 2017 ASTRO Radiation Oncology Workforce Study demonstrated that URiM individuals comprised only a small minority of the radiation oncology physician workforce (2.2% Black or African American, 2.3% Hispanic, Latino, or Spanish origin, 0.2% American Indian or Alaskan Native, 0.0% Native Hawaiian or other Pacific Islander, 1.1% other, and 4.0% more than 1 race/ethnicity selected), and only 5 of the 110 SCAROP members are URiM (Emily Wilson, personal communication, June 6, 2022).28 A recent cross-sectional study suggested that the representation of women has increased among academic radiation oncology faculty over time, but URiM representation continued to lag.29 With our sample of 63 responding institutions, we did not detect that race/ethnicity was significantly associated with associate or full professor rank, but CIs were wide due to limited sample size, and point estimates were <1, suggesting that a difference may indeed exist that our study could not detect. A previous study of 128 academic medical centers reported that promotion rates for Black and Hispanic faculty were lower than those for White faculty for both assistant to associate professor and from associate to full professor.6 Another study reported that even after adjusting for cohort (representing faculty who attained their rank during 5 different periods from 1980 to 1989), gender, tenure status, degree, department, medical school type, and National Institutes of Health award status, URiM faculty were still less likely to be promoted compared with White physicians.10 Given the OR of 0.69 (P = .37) for URiM versus White radiation oncologists at the associate or full professor rank in the multivariable analysis, our study might not have had sufficient statistical power to detect a true and meaningful underlying disparity.
Overall, there were no significant differences in total compensation between male and female radiation oncologists by academic rank. However, male associate professors made a significantly greater salary (15%) than female associate professors in the West, and male assistant professors made a significantly greater salary (13%) than female assistant professors in the Northeast. After adjusting for other potentially mechanistic variables in a multivariable regression model, total compensation did not differ significantly between men and women. Although our analysis did not find statistically significant compensation gaps between male and female academic radiation oncologists, reasons for compensation disparities by gender may include gender differences in negotiation as well as conscious and unconscious biases. Studies have shown that women are less likely to initiate negotiations and negotiated lower salaries compared with their male counterparts.30, 31, 32 In addition, women who negotiate are often perceived as too demanding and less likeable. These perceptions can, in turn, lead to hesitancy on the part of women to negotiate for higher salaries while ironically also impeding their access to other resources necessary to achieve the success that leads to pay raises.33, 34, 35 Unconscious biases regarding the value of women's contributions to the workforce could also result in compensation inequalities.36 We must therefore remain vigilant to avoid differences in total compensation due to biases and differences in negotiation behaviors.
Overall, there were no significant differences in total compensation between White, Asian, and URiM academic radiation oncologists. Although power is limited due to small numbers, additional analyses designed to measure the effects of the most severe forms of racism by ungrouping race/ethnicity also did not detect any significant differences in total compensation (Tables 3, 4, and E5). Nevertheless, it is important to interpret these findings in the context of the broader literature on compensation disparities in medicine. In a 2017 Medscape compensation survey, White physicians reported earning approximately $303,000 yearly, followed by Asian ($283,000), Hispanic or Latino ($271,000), and Black ($262,000) physicians.37 A study on primary care physicians reported that Black male physicians had lower yearly incomes compared with their White male counterparts after adjusting for differences in work effort, physician characteristics, and practice characteristics.38 The most recent Association of American Medical Colleges report showed that across all faculty ranks, except those in clinical science departments/specialties with MD-PhD degrees, White faculty had a greater median compensation than faculty of any other race/ethnicity.39 Although it is reassuring that we did not find the large differences of the magnitude that this small sample was powered to detect, these findings should not be taken as evidence that there are no racial or ethnic differences in compensation in our field.
We acknowledge that our study has several limitations. These observational data represent the snapshot of academic departments during 2018, and examining these same trends at multiple time points would be useful to add more context to our findings, particularly because gaps may widen or narrow with time, especially given the disruptions that have occurred since the outbreak of the COVID-19 pandemic. In addition, the 2018 SCAROP survey data are provided by individual departments and may be imperfect. Although the 58.3% response rate may influence results and limit statistical power, this data set remains the best-available information to date on the topic. It is important to note that socially meaningful differences may not be statistically significant in a study of this size. Finally, we studied only academic departments, and our findings cannot be generalized to understand whether disparities might also exist in nonacademic practice.
Conclusion
In summary, our study showed that women and individuals from URiM backgrounds were dramatically underrepresented in the field of radiation oncology but were not disproportionately underrepresented at any particular rank. Although male associate professors made a significantly greater salary (15%) than female associate professors in the West and male assistant professors made a significantly greater salary (13%) than female assistant professors in the Northeast on univariate analyses, there were no significant compensation gaps between genders when adjusted for other relevant variables. Our study affirms that female and URiM representation in the radiation oncology workforce continues to be low and does not reflect the diversity of our patient population. Diversity challenges assumptions, broadens perspectives, and enhances cultural humility.40,41 Thus, ongoing efforts to improve gender and URiM representation in our field should continue to be a specialty-wide area of focus and resource allocation. Importantly, the ASTRO Board of Directors identified Diversity and Inclusion as 1 of 5 core values in its Strategic Plan in 2017, and the Committee on Health Equity, Diversity and Inclusion has been elevated to become a full ASTRO Council with Board representation. The vision of the Committee on Health Equity, Diversity and Inclusion is to advance a culture of inclusive excellence that will foster a diverse workforce and improve health equity in radiation oncology. Future work should continue to ensure that there is active promotion of women and URiM in radiation oncology to ensure continuing excellence in our field and the delivery of culturally compassionate care for our patients.
Footnotes
Sources of support: This study was supported with Society of Chairs of Academic Radiation Oncology Programs (SCAROP) and departmental resources.
Disclosures: Dr Raldow reports ViewRay (research, consulting), Joseph Drown Foundation (research), and Intelligent Automation, Inc (research). Dr Bonner reports Bristol Meyers (royalties/licenses, honoraria, travel), Cel-Sci (consulting, honoraria), Eli Lilly (royalties/licenses, honoraria, travel), ICON plc (consulting), and Merck Serono (royalties/licenses, consulting, honoraria, travel). Dr Liu reports Canadian Institute of Health Research (research), Canadian Medical Protective Association (expert testimony), and Canadian Institutes of Cancer Research (leadership role). Dr Metz reports Varian (honoraria) and IBA (honoraria). Dr Movsas reports Varian (research, travel), Philips (research), and ViewRay (research). Dr Potters reports Smarter Radiation Oncology Health, LLC (leadership) and RO-ILS (participant). Dr Steinberg reports ViewRay (consulting), Matthew Habercorn Attorney (expert testimony), Wiggins, Sewell Ogletree (expert testimony), Manatt, Phelps Phillips (expert testimony), and Schmid Voiles (expert testimony). Dr Jagsi reports Equity Quotient (Advisory board), Greenwall Foundation (research, honoraria, travel), Doris Duke Charitable Foundation (research, honoraria, travel), Komen Foundation (research), Blue Cross Blue Shield of Michigan (research), Genentech (research), Sherinian and Hasso (expert witness), Dressman Benzinger LaVelle (expert testimony), and Kleinbard LLC (expert testimony). No other disclosures were reported.
Supplementary material associated with this article can be found in the online version at doi:10.1016/j.adro.2023.101210.
Appendix. Supplementary materials
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