Original Research

Gender Disparities in Income Among Board-Certified Dermatologists

Author and Disclosure Information

Although there is evidence that gender-based disparities exist in salary, academic rank, and other factors in several areas in medicine, limited data exist on differences between male and female dermatologists. Existing studies have focused on academic dermatologists, not including the vast majority of dermatologists who work in solo and group private practices. A cross-sectional self-reported survey eliciting total annual income and other factors was performed in the fall of 2018 in the United States. A total of 397 board-certified dermatologists (MDs/DOs) participated in this study, including 53.63% female and 46.37% male respondents. A statistically significant difference existed within total annual income between male and female dermatologists (P<.0001). Several factors were identified that demonstrated statistically significant differences between male and female dermatologists, including productivity, practice area of focus, type of fellowship training, and faculty rank. However, despite controlling for these variations, gender remained a statistically significant predictor of income on both univariate and multivariate regression analyses (P=.0002/P<.0001), indicating that a gender-based income disparity exists in the field of dermatology that cannot be explained by other factors.

Practice Points

  • In this survey-based cross-sectional study, a statistically significant income disparity between male and female dermatologists was found.
  • Although several differences were identified between male and female dermatologists that contribute to income, gender remained a statistically significant predictor of income, and this disparity could not be explained by other factors.


 

References

Although the number of female graduates from US medical schools has steadily increased,1 several studies since the 1970s indicate that a disparity exists in salary, academic rank, and promotion among female and male physicians across multiple specialties.2-8 Proposed explanations include women working fewer hours, having lower productivity rates, undernegotiating compensation, and underbilling for the same services. However, when controlling for variables such as time, experience, specialty, rank, and research activities, this gap unequivocally persists. There are limited data on this topic in dermatology, a field in which women comprise more than half of the working population.6,7 Most analyses of gender disparities in dermatology are based on data primarily from academic dermatologists, which may not be representative of the larger population of dermatologists.8,9 The purpose of this study is to determine if an income disparity exists between male and female physicians in dermatology, including those in private practice and those who are specialty trained.

Methods

Population—We performed a cross-sectional self-reported survey to examine compensation of male and female board-certified dermatologists (MDs/DOs). Several populations of dermatologists were surveyed in August and September 2018. Approximately 20% of the members of the American Academy of Dermatology were randomly selected and sent a link to the survey. Additionally, a survey link was emailed to members of the Association of Professors of Dermatology, American College of Mohs Surgery, and American Society for Dermatologic Surgery. A link to the survey also was published on “The Board Certified Dermatologists” Facebook group.

Statistical Analysis—Descriptive statistics were used to summarize the distribution of variables overall and within gender (male or female). Not all respondents completed every section, and duplicates and incomplete responses were removed. Variables were compared between genders using t tests (continuous), the Pearson χ2 test (nominal), or the Cochran-Mantel-Haenszel test (ordinal). For categorical variables with small cell counts, an exact χ2 test for small samples was used. For continuous variables, t test P values were calculated using either pooled or Satterthwaithe approximation.

To analyze the effect of different variables on total income using multivariate and univariate linear regression, the income variable was transformed into a continuous variable by using midpoints of the categories. Univariate linear regression was used to assess the effect and significance of each variable on total annual income. Variables that were found to have a P value of less than .05 (α=.05) were deemed as significant predictors of total annual income. These variables were added to a multivariate linear regression model to determine their effect on income when adjusting for other significant (and approaching significance) factors. In addition, variables that were found to have a P value of less than .2 (α=.05) were added to the multivariate linear regression model to assess significance of these specific variables when adjusting for other factors. In this way, we tested and accounted for a multitude of variables as potential sources of confounding.

Results

Demographics—Our survey was emailed to 3079 members of the American Academy of Dermatology, and 277 responses were received. Approximately 144 additional responses were obtained collectively from links sent to the directories of the Association of Professors of Dermatology, American College of Mohs Surgery, and American Society for Dermatologic Surgery and from social media. Of these respondents, 53.65% (213/397) were female and 46.35% (184/397) were male. When stratifying by race/ethnicity, 77.33% identified as White; 13.85% identified as Asian; 6.3% identified as Black or African American, Hispanic/Latino, and Native American; and 2.52% chose not to respond. Although most male and female respondents were White, a significantly higher proportion of female respondents identified as Asian or Black/African American/Hispanic/Latino/Native American (P=.0006). We found that race/ethnicity did not significantly impact income (P=.2736). All US Census regions were represented in this study, and geographic distribution as well as population density of practice location (ie, rural, suburban, urban setting) did not differ significantly between males and females (P=.5982 and P=.1007, respectively) and did not significantly impact income (P=.3225 and P=.10663, respectively).

Total annual income of male and female dermatologists (n=399).

Income—Total annual income was defined as the aggregate sum of all types of financial compensation received in 1 calendar year (eg, salary, bonuses, benefits) and was elicited as an ordinal variable in income brackets of US $100,000. Overall, χ2 analysis showed a statistically significant difference in annual total income between male and female dermatologists (P<.0001), with a higher proportion of males in the highest pay bracket (Figure). Gender remained a statistically significant predictor of income on both univariate and multivariate linear regression analyses (P=.0002 and P<.0001, respectively), indicating that gender has a significant impact on compensation, even after controlling for other variables (eTable). Of note, males in this sample were on average older and in practice longer than females (approximately 6 years, P<.0001). However, when univariate linear regression was performed, both age (P=.8281) and number of years since residency or fellowship completion (P=.8743) were not significant predictors of income.

Practice Type—There were no statistically significant differences between men and women in practice type (P=.1489), including academic/university, hospital based, and solo and group private practice; pay structure (P=.1437), including base salary, collection-based salary, or salary plus incentive; holding a supervisory role (P=.0846); or having ownership of a practice (P=.3565)(eTable). Most respondents were in solo or group private practice (58.2%) and had a component of productivity-based compensation (77.5%). In addition, 62% of private practice dermatologists (133/212) had an ownership interest in their practice. As expected, univariate and multivariate regression analyses showed that practice type, pay structure, supervisory roles, and employee vs ownership roles were significant predictors of income (P<.05)(eTable).

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