Data Source

In a 2013 study of physician professional satisfaction, we surveyed 656 practicing physicians in 30 practices within each of six states: Colorado, Massachusetts, North Carolina, Texas, Washington, and Wisconsin.14 We consulted with each state’s medical society and created a list of practices for potential inclusion in the study. Practices were selected to achieve diversity in practice size (10 small with < 10 physicians, 11 medium with 10–49 physicians, 9 large with > 49 physicians), specialty (15 multispecialty, 10 primary care, 5 single subspecialty), and ownership model (19 physician owned or physician partnership, 11 hospital or other corporate ownerships). The practice sample was not nationally representative. Most practices agreed to participate and all but one participating practice completed the study. That practice was replaced before data collection with another practice from the same state. We surveyed all physicians within each practice, receiving 452 responses, of which 439 included a response for physician gender (69% response rate overall; 67% response rate for gender).

The physician survey (see Online Appendix 1 for survey instrument) collected self-reported income from the past year as well as gender, specialty, hours worked per week, weeks worked per year, composition of work hours, percent of patient care time spent providing procedures, compensation type, age, years in practice, race, and ethnicity. This study is a secondary analysis of the survey data; the survey was originally intended to measure physician professional satisfaction and related determinants. Analysis of these survey data was approved by the RAND Human Subjects Protection Committee.

Measures

Our outcome was annual income in dollars. Demographic covariates included the following: gender (male/female), specialty group (primary care: family practice, general practice, internal medicine, and pediatrics; obstetrics/gynecology; medical specialties: cardiology, dermatology, gastroenterology, neurology, oncology, and pulmonology; surgical specialties: general surgery, otolaryngology, ophthalmology, orthopedic surgery, and urology; other: emergency medicine, psychiatry and other), race (white [European, Middle Eastern, other], black or African American, American Indian or Alaskan, Native Hawaiian or Pacific Islander, Asian, or other), ethnicity (Hispanic or Latino, or Not Hispanic or Latino), age (grouped as 18–40 years old, 41–50 years old, 51–60 years old, 61–70 years old, or 71 years old or more), and years in practice (5 years or less, 6–10 years, 11–20 years, 21–30 years, or 31 years or more). Work covariates included the following: hours worked per year (in hours), work hour composition (percentage of time spent on patient care, teaching, research, administration, and other), percent of patient care hours spent performing procedures with and without general anesthesia (categorized as 0%, 1–24%, or ≥ 25%), and compensation type (fixed salary, salary adjusted for performance, shift, hourly or other time-based method, or share of practice billing or workload). Practice covariates included the following: practice size (≤ 9 physicians, 10–49 physicians, or ≥ 50 physicians), practice specialty (primary care, single subspecialty, or multispecialty), and practice ownership model (physician-owned or partnership, or hospital or corporate owner). We also included state and practice random effects.

Statistical Analyses

We estimated several multilevel mixed-effects generalized linear models of annual income as a function of different covariates. Model 1 estimated income as a function of physician gender alone; Model 2 additionally adjusted for state and practice random effects and practice covariates; Model 3 additionally adjusted for hours worked per year; Model 4 additionally adjusted for specialty. Model 5 additionally adjusted for work hour composition, and Model 6 additionally adjusted for the percent of patient care hours spent performing procedures with and without general anesthesia, and compensation type. Model 7 additionally adjusted for age, years in practice, race, and ethnicity. We report the difference between male and female incomes in every model to show how the difference was “explained” by additional covariates and how much of the residual difference remained unexplained.

We excluded respondents whose reported work hours exceeded the 95th percentile (3550 h/year) because such extreme values might have been misreported or might be considered “overtime” hours that deserve higher effective hourly wages. In all models, we winsorized income at the 95th percentile ($600,000) to limit the influence of extreme outliers. Winsorization of these outliers can help compare “like to like,” as respondents earning very high incomes may have fundamentally different work or business arrangements compared to all other respondents.15 Non-response can bias estimates of parameters and relationships even among surveys with high response rates.16, 17 Therefore, we constructed non-response weights using data on gender, age, AMA membership, state, individual specialty, practice size, practice specialty, and practice ownership model and used the weights in all models to account for differences between survey responders and non-responders. Multiple imputation by chained equations is the preferred method for addressing missing data in observational studies.18, 19 Here, we used multiple imputation for the following covariates: gender; age; years in practice; race; ethnicity; hours worked per year; percent of hours worked in teaching, research, administration, and other; percent of patient care hours spent performing procedures with and without general anesthesia; and compensation type. State effects were assigned to the state level of the model. Practice random effects and a vector of practice characteristics (practice specialty, size, and ownership model) were assigned to the practice level of the model. All other covariates were assigned to the individual level. Standard errors were clustered within states and practices.