| Literature DB >> 36186614 |
Allison R Larson1, Meridith J Englander2, Quentin R Youmans3, Monica Verduzco-Gutierrez4, Fatima Cody Stanford5, Sheritta A Strong6, Howard Y Liu6, Julie K Silver7.
Abstract
Purpose: This report investigated physician compensation studies by gender, race, and ethnicity.Entities:
Keywords: ethnicity; gender; physician compensation; physician salary
Year: 2022 PMID: 36186614 PMCID: PMC9518798 DOI: 10.1089/heq.2021.0098
Source DB: PubMed Journal: Health Equity ISSN: 2473-1242
Physician Compensation Studies and Analysis by Gender, Race, and Ethnicity
| Study references First author name (year) | No. of participants | Population studied | Variables in multivariable analysis | Gender disparities studied/found | Race disparities studied/found | Ethnicity disparities studied/found | |
|---|---|---|---|---|---|---|---|
| Multivariable analysis including race/ethnicity/gender (intersection) | Hayes (2020)[ | 2,845 | Physicians at one academic institution (Mayo Clinic) with three locations | Gender, race/ethnicity, specialty, leadership position, full-time equivalent status, experience, age, work location, licensure, other compensable activities | Y/N | Y/N | Y/N |
| Lo Sasso (2020)[ | 16,047 | New York State physicians entering first year of attending-level patient care practice | Specialty training, number of job offers, sex, age, gender, race/ethnicity, citizenship, education and training, educational debt, principal practice setting, location type, obligation to health professional shortage area, weekly patient care hours | Y/Y | N/N | N/N | |
| Langer (2019)[ | 41,396 | Physicians in clinical practice who participated in the Community Tracking Survey | Gender, age, degree, training, work hours, weeks worked, revenue sources, practice ownership status, geographic region, metropolitan statistical area category, race, ethnicity | Y/Y | Y/Y | Y/N | |
| Pallant (2019)[ | 149 | Program director members of the Association of Pediatric Program Directors | Gender, race/ethnicity, age, academic rank, clinical appointment, number of raises, tenure track, years in program director role, number of noncombined residents in program | Y/Y | Y/N | Y/N | |
| Apaydin (2018)[ | 439 | Physicians from 30 diverse practices within six states | Hours worked, composition of work hours, percent procedural time, specialty, compensation type, age, years in practice, gender, race, ethnicity, state and practice random effects | Y/Y | N/N | N/N | |
| Read (2018)[ | 374 | Members (nonstudent) of the Internal Medicine Insider Research Panel within the American College of Physicians | Bivariate analysis performed comparing salary by gender and one other factor: specialty, employment status, age, race, primary professional setting, primary professional activity, marital status, spousal employment status, parental status | Y/Y | Y/N | N/N | |
| Madsen (2017)[ | 1,371 | Full-time faculty members in U.S. academic emergency departments via the 2015 Academy of Administrators in Academic Emergency Medicine Salary Survey | Race/ethnicity, region, rank, years of experience, clinical hours, core faculty status, administrative roles, board certification, fellowship training, gender | Y/Y | Y/N | Y/N | |
| Freund (2016)[ | 490 | Sample of academic medical faculty from 24 U.S. medical schools | Race/ethnicity (combined category), gender, years since first academic appointment, retention in academic career, academic rank, departmental affiliation, percent effort in various areas, marital status, parental status, any leave or part-time status in the years between surveys | Y/Y | Y/N | Y/N | |
| Ly (2016)[ | 61,327 from ACS survey | 2000–2013 ACS | ACS: age, sex, race, weekly hours worked, state of residence, time period | Y/Y | Y/Y | N/N | |
| Jagsi (2013)[ | 1,012 | Recipients of NIH mentored career development awards | Gender, age, race, marital status, parental status, additional doctoral degree, academic rank, years on faculty, specialty, institution type, region, institution NIH funding rank, K award type, K award funding institute, K award year, work hours, research time | Y/Y | Y/N | N/N | |
| Seabury (2013)[ | 7,653 | 1987–2010 March Current Population Survey | Hours worked, age, sex, race, state | Y/Y | N/N | N/N | |
| Analysis by race/gender (separate) | Rosenthal (2017)[ | 157 | Members of the Academy of Psychosomatic Medicine | Multivariable analysis not performed | Y/Y | Y/N | N/N |
| Analysis by race and/or ethnicity only (not gender) | Marcelin (2019)[ | 2,075 | Members of the Infectious Diseases Society of North America | Practice type, race, ethnicity | N/N | Y/Y | Y/N |
| Kaplan (2018)[ | 604 | Sample of academic medical faculty from 24 U.S. medical schools | Race/ethnicity, setting, rank, effort distribution in teaching, clinical and research activities | N/N | Y/N | Y/N | |
| Lin (2016)[ | 26 in 2004, 38 in 2009, 54 in 2014 | Faculty at one academic (Johns Hopkins) otolaryngology program | Multivariable analysis not performed | N/N | Y/N | Y/N | |
| Analysis by gender only (not race or ethnicity) | Cheng (2020)[ | 72 | Members of the American Medical Informatics Association | Multivariable analysis not performed on the physician subset | Y/Y | N/N | N/N |
| Gambhir (2021)[ | 170 | Surgeons within a large multi-institutional health care system (University of California) | Academic rank, surgical subspecialty, gender | Y/Y | N/N | N/N | |
| Pelley (2020)[ | Number not given | Data derived from Doximity 2015 average salary numbers by specialty | Specialty, gender | Y/Y | N/N | N/N | |
| Sangji (2020)[ | 461 | Trauma surgeons, members of The Eastern Association for the Surgery of Trauma | Gender and age or practice type (analyzed separately) | Y/Y | N/N | N/N | |
| Shah (2020)[ | 366 | Neurocritical care physicians, members of the Neurocritical Care Society | Multivariable analysis not performed | Y/Y | N/N | N/N | |
| Winkelman (2020)[ | 85 | Urogynecologists employed at public universities with publicly available salary data | Academic rank, leadership roles, years since residency, gender | Y/Y | N/N | N/N | |
| Dermody (2019)[ | 260 | Otolaryngologists employed at Veterans Affairs Medical Centers with level 1 complexity | Number of years since graduation, h-index, gender, geographic location, faculty rank | Y/N | N/N | N/N | |
| Horowitz (2019)[ | 366 | Neonatologists, members of the American Academy of Pediatrics Section on Neonatal-Perinatal Medicine | Gender, geographic region, work with physician assistants, in-house call, years postfellowship, administrative time, daily rounding on critical care patients, clinical time, medical education time, work with neonatal hospitalists, eligibility for annual bonus, large central metropolitan county, academic institution | Y/Y | N/N | N/N | |
| Wiler (2019)[ | 7,102 | Physicians belonging to academic emergency medicine departments | Gender, academic rank, geographic region, type of hospital, years at faculty appointment, year of survey | Y/Y | N/N | N/N | |
| Burns (2018)[ | 97 | Tenure-track faculty on one academic pathology department (Johns Hopkins) | Type of appointment, academic rank, years at rank, gender | Y/N | N/N | N/N | |
| Hoops (2018)[ | 86 | Surgeons at a single academic institution (Oregon Health & Science University) | Rank, fiscal year, gender | Y/Y | N/N | N/N | |
| Morris (2018)[ | 44 | Surgeons at a single academic medical institution (University of Alabama at Birmingham) | Multivariable analysis not performed | Y/Y | N/N | N/N | |
| Trotman (2018)[ | 2504 | Members of the Infectious Diseases Society of America | Employment affiliation or facility type, age, gender | Y/Y | N/N | N/N | |
| Kapoor (2017)[ | 573 | Academic radiologists at 24 public medical schools | Sex, age, faculty rank, years since residency, clinical trial involvement, NIH funding, total Medicare payments, scientific publications, clinical volume, graduation from a top-20 medical school | Y/N | N/N | N/N | |
| Nguyen Le (2017)[ | 29,856 in 1990 | Physicians from the Integrated Public Use Microdata Series 1990 and 2000 and 2007–2011 ACS (data combined) | Sex, age, race/ethnicity, marital status, number of children, hours worked per week, weeks worked per year, business ownership status | Y/Y | N/N | N/N | |
| Jagsi (2016)[ | 2,679 | Cardiologists from 161 practices | Age range, gender, race/ethnicity, subspecialty, job characteristics including full-time, work RVUs and new patient office visits, patient care breakdown, geographic region, practice composition and other practice factors, practice compensation model | Y/Y | N/N | N/N | |
| Jena (2016)[ | 10,241 | Academic physicians at 24 public medical schools | Age, sex, experience, specialty, years since residency, faculty rank, NIH funding, clinical trial participation, publication count, medical school attended (top 20 vs. not), Medicare payments, geographic region | Y/Y | N/N | N/N | |
| Ritter (2016)[ | 1878 | Infectious disease physicians, members of the Infectious Diseases Society of America | Practice type, gender, age | Y/Y | N/N | N/N | |
| Manahan (2015)[ | 843 | Breast surgeons, members of the American Society of Breast Surgeons | Gender, ownership, years of practice, practice type, fellowship training, geographic location, urbanicity, breast surgery case volume and proportion of practice. | Y/Y | N/N | N/N | |
| Spencer (2016)[ | 848 | Urologists, members of the American Urologic Association | Age, gender, work hours, call frequency, practice setting and type, fellowship training, Advance Practice Provider employment | Y/Y | N/N | N/N | |
| Weaver (2015)[ | 776 | Hospitalists who responded to the 2009–2010 Hospital Medicine Physician Worklife Survey | Gender, leadership role, prioritizes substantial pay, pediatric specialty, practice model, practice region, FTE, days per month of clinical work, daily billable encounters | Y/Y | N/N | N/N | |
| Willett (2015)[ | 241 | Internal Medicine program directors, members of the Association of Program Directors in Internal Medicine | Academic rank, career in general internal medicine, age, gender | Y/Y | N/N | N/N | |
| Henderson (2014)[ | 433 | Faculty members within four neurological specialties within one health care system (the University of California) | Institution, academic rank, chair status, specialty, Scopus publication count, Scopus h-index | Y/Y | N/N | N/N | |
| Neither gender nor race/ethnicity analysis performed | Mead (2020)[ | 1,970 | Physicians practicing general orthopedics and seven orthopedic subspecialties who participated in the American Medical Group Association compensation survey | Multivariable analysis not performed—compensation compared against hours worked per week | N/N | N/N | N/N |
| Ringel (2019)[ | 358 | Endocrinologists, survey of departments via the Association of Endocrine Chiefs and Directors within the Endocrine Society | Multivariable analysis not performed—compensation compared by academic rank, academic track, leadership position (presented separately) | N/N | N/N | N/N | |
| Chunn (2020)[ | 4,830 | Cardiologists in the MedAxiom Annual Survey 2010–2014 | Age category, clinical productivity, ownership model, year of survey, compensation method, subspecialty, employment status, days worked, geographic area | N/N | N/N | N/N | |
| Eltorai (2018)[ | Not given | Mean data from 37 specialties, data from the American Medical Colleges Careers in Medicine website | Specialty, hours worked | N/N | N/N | N/N | |
| Mrak (2018)[ | 168 | Academic pathologists from 43 departments, survey sent through the Association of Pathology Chairs | Terminal degree(s) with academic rank presented separately from subspecialty with work RVUs | N/N | N/N | N/N | |
| Prakash (2017)[ | Not given | Vascular surgeons whose salary data were contained in the Association of American Medical Colleges and Medical Group Management Association databases | Academic vs. private practice, time | N/N | N/N | N/N | |
| Fijalkowski (2013)[ | 433 | Academic physicians in four specialties in the University of California system | Specialty, institution, ranking, sex, number of publications, h-index | N/N | N/N | N/N | |
| Slakey (2013)[ | 72 | U.S. surgery department chairs | Multivariable analysis not performed—Compensation compared by age, additional degree, specialty, location, contract, tenure, clinical hours, program director status, fellowship training separately | N/N | N/N | N/N |
ACS, American Community Survey; FTE, full-time equivalent; HSC, Health System Change; NIH, National Institutes of Health; RVUs, relative value units.
FIG. 1.Of the 47 data sets, 26 analyzed by gender only, 3 by ethnicity and race, 5 by gender and race, and 5 by gender, race, and ethnicity. Eight analyzed none of these.
Gender and Racial Breakdown Within Physician Compensation Studies
| Study references First author name (year) | No. of participants | Women, | American Indian or Alaskan Native, | Asian, | Black or African American, | Native Hawaiian or Pacific Islander, | Two or more races, | White, | Unknown race or other, | URM[ | Other non-URM[ | Hispanic, |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Hayes (2020)[ | 2,845 | 861 | 11 | 469 | 57 | 22 | 2,120[ | 3 | 163 | |||
| Lo Sasso (2020)[ | 16,047 | 7,005 | ∼5,182[ | ∼1,103[ | ∼7,466[ | ∼1,278 | ∼1,199 | |||||
| Langer (2019) | 41,396 | ∼8,859 | ∼166 | ∼5,299 | ∼1,532 | ∼33,241 | ∼1,366 | ∼2,111 | ||||
| Pallant (2019) | 149 | 82 | 17 | 7 | 115[ | 6 | ||||||
| Apaydin (2018)[ | 439 | 176 | 3 | 59 | 9 | 2 | 345 | 4 | 15 | |||
| Read (2018) | 374 | 120 | 125 | |||||||||
| Madsen (2017)[ | 1,371 | 447 | 98 | 54 | 1,066[ | 153 | 40 | |||||
| Freund (2016)[ | 490 | 239 | 429[ | |||||||||
| Ly (2016)[ | 61,327 from ACS survey | 16,416 | 2,950[ | 58,377[ | ||||||||
| Jagsi (2013)[ | 1,275 | 419 | 250 | 26 | 688 | 48 | ||||||
| Seabury (2013) | 6,258 | 1,964 | ||||||||||
| Rosenthal (2017) | 157 | |||||||||||
| Marcelin (2019)[ | 2,075 | 333 | 75 | 1,401 | 85 | 181 | ||||||
| Kaplan (2018) | 604 | 309 | 529[ | 47 | 28 | |||||||
| Lin (2016) | 26 in 2004, 38 in 2009, 54 in 2014 | 2 in 2004, 11 in 2009, 15 in 2014 | Multivariable analysis not performed | 2 in 2004, 4 in 2014 | 22 in 2004, 47 in 2014 | |||||||
| Cheng (2020) | 72 | 35 | ||||||||||
| Gambhir (2020) | 170 | 50 | ||||||||||
| Pelley (2020) | Number not given | |||||||||||
| Sangji (2020) | 461 | 105 | 0 | 29 | 20 | 10 | 383 | 12 | 7 | |||
| Shah (2020) | 366 | 129 | 5 | 93 | 10 | 197 | 32 | 29 | ||||
| Winkelman (2020) | 89 | 53 | ||||||||||
| Dermody (2019)[ | 260 | 63 | ||||||||||
| Horowitz (2019) | 366 | 168 | 59 | 15 | 252 | 12 | 19 | |||||
| Wiler (2019) | 7,102 | 2,412 | ∼284 | ∼283 | ∼5,912 | |||||||
| Burns (2018) | 97 | 37 | ||||||||||
| Hoops (2018) | 86 | 24 | ||||||||||
| Morris (2018) | 44 | 11 | ||||||||||
| Trotman (2018) | 2,504 | ∼1,002 | ∼351 | ∼75 | ∼1,502 | ∼200 | ||||||
| Kapoor (2017) | 573 | 171 | ||||||||||
| Nguyen Le (2017) | 29,856 in 1990 | 6,210 in 1990 | ∼922 in 1990 | ∼25,439 in 1990 | ∼3,466 in 1990 | |||||||
| Jagsi (2016) | 2,679 | 229 | 1 | 75 | 31 | 4 | 1,036 | 40 | 73 | |||
| Jena (2016)[ | 10,241 | 3,549 | ||||||||||
| Ritter (2016) | 1,878 | ∼751 | ||||||||||
| Manahan (2015) | 843 | 542 | ||||||||||
| Spencer (2015) | 848 | 73 | ||||||||||
| Weaver (2015) | 776 | 263 | ||||||||||
| Willett (2015) | 241 | 72 | ||||||||||
| Henderson (2014) | 433 | 98 | ||||||||||
| Mead (2020) | 1,958 | |||||||||||
| Ringel (2019) | 358 | |||||||||||
| Chunn (2018) | 4,830 | |||||||||||
| Eltorai (2018) | ||||||||||||
| Mrak (2018) | 168 | |||||||||||
| Prakash (2017) | Not given | |||||||||||
| Fijalkowski (2013) | 433 | |||||||||||
| Slakey (2013) | 72 |
Category specifically indicated as non-Hispanic.
URM, underrepresented minority.
FIG. 2.A total of 88.9% of the studies that analyzed by gender found disparities in compensation. A total of 30.7% of the studies that analyzed by race and 0% of the studies that analyzed by ethnicity found disparities.