Recent estimates suggest that the United States will have a projected total physician shortage of between 54,100 and 139,000 physicians by 2033, in large part due to the accelerating population growth of older adults. 1 Physician shortages are already particularly pronounced in rural and remote communities across the United States. 2,3 Addressing this challenge will require not only investments in health care systems given limited resources but also new strategies to attract physicians to practice in communities characterized by relatively low levels of income and pronounced health disparities. 4,5
Previous studies have revealed that investing in residency programs in underserved communities can attract physicians to practice in those communities. 6 However, studies have also shown that more than 60% of medical students end up practicing in their birth state. 7 The number of medical school applicants and matriculants from states or counties where physicians are most needed has been a periodic source of concern. 8,9 This concern has reemerged in recent years, with high tuition being identified as a significant barrier to application for low-income students. 10–12 Understanding the geographic distribution of medical school applicants and matriculants over time may provide some insights into disparities in access to physicians and how to reduce these differences. 13–17
Medical school tuition has been growing rapidly over the past 20 years and, as such, there is likely a stronger correlation between family income and application rates now than 2 decades ago 18 as well as increased differences in the socioeconomic characteristics of college students who apply to medical school by their geographic location over this same time period.
We studied trends in the rates of application and matriculation to MD-granting medical schools across U.S. counties from 2001 through 2015. We also analyzed the association between county median family income, proximity to a medical school, and application and matriculation rates per 100,000 residents in each county. 19–22 We hypothesized that county median family income was positively associated with higher rates of medical school application and matriculation, as high-income communities have more resources, better access to high-quality public school systems, and are more likely to be in proximity to medical schools, all of which aid students pursuing a medical education.
We obtained the data for this analysis from the Association of American Medical Colleges’ (AAMC’s) Student Records System, which includes characteristics (i.e., age, gender, and Federal Information Processing Standards code for county of legal residence) of all applicants and matriculants from the entering classes at the MD-granting medical schools in the United States, including those in Washington, DC and Puerto Rico. The AAMC provided data aggregated at the county level and over 5-year intervals covering the 2001–2015 period to protect the privacy of applicants. We also obtained county-level population and median family income data from the U.S. Census Bureau and the American Community Survey 23 and matched these data using the Federal Information Processing Standards codes for all 3,220 U.S. counties in the U.S. Census Bureau’s Geodatabase files. 24 AAMC members were the 145 accredited MD-granting medical schools in the United States and Puerto Rico at the end of 2015. We geocoded the addresses of these medical schools, which we obtained from the AAMC, into the format of latitude and longitude. Our study was deemed exempt from review by the Institutional Review Board of the University of Georgia.
In our analysis, applicants refer to students who applied to at least one U.S. MD-granting medical school through either the American Medical College Application Service or the Texas Health Education Service. 25 Matriculants refer to students who applied to begin at a U.S. MD-granting medical school in a specific academic year and enrolled in that academic year. 25 Applicant acceptance ratio refers to the ratio of the number of applicants to the number of matriculants. 26 The application and matriculation rates were defined as the number of applicants and matriculants per 100,000 residents at the county level, respectively.
We estimated the average annual county population for each 5-year interval and for the total 15-year time period, using the county population each year from 2001 to 2015. Counties were grouped into 4 categories according to the interquartile range of the county median family income 27 (i.e., low-income [$0–$44,454], middle-low-income [$44,455–$51,113], middle-income [$51,114–$58,970], and high-income [≥ $58,971]). All of the aforementioned county-level data sources were merged into one database.
We described the trends in application and matriculation rates among students who applied to MD-granting medical schools in the United States from 2001 to 2015 and compared those rates across age, gender, and county income categories. We performed chi-square tests to assess the trends and the association of county median family income with application and matriculation rates. All statistical analyses were performed using R version 3.6. 28
To explore the geographic density and clustering of applicants and matriculants at the county level, we coded each county as a point feature. We imported the latitude and longitude data and the application and matriculation rates of all counties (n = 3,220) into ArcGIS 10.7 (ESRI, Redlands, California). 29,30 In addition, we used kernel density estimation to explore the geographic patterns in the data and to identify hot spot areas with high application and/or matriculation rates. Hot spots are marked with a darker color on the maps in Figures 1 and 2. 31,32
The relationship between proximity to a medical school and application rates in a given county could be due to the spatial clustering effect of high-income counties where medical schools tend to be located; as such, we used the same method to explore the geographic patterns of counties with different family income levels. By comparing the distribution of the counties with different application rates and different family income levels, we explored if geographic clustering of high-income counties exists, which would lead to the aforementioned effects. We estimated the Pearson correlation coefficient to measure the strength of the association between the Euclidean distance (length of a line segment between 2 points) to a medical school and county-level application and matriculation rates.
A total of 581,833 students applied to MD-granting medical schools between 2001 and 2015; 262,730 (45.2%) of these applicants matriculated to medical school over the 15-year time period (see Table 1). There were 220,837 applicants in 2011–2015, a 31.4% increase from 2001 to 2005. There were 95,404 matriculants in 2011–2015, a 17.9% increase from 2001 to 2005. Overall, the numbers of applicants and matriculants per 100,000 persons were 63.7 and 28.8, respectively.
The numbers of applicants per 100,000 persons during 2001–2005, 2006–2010, and 2011–2015 were 57.2, 62.7, and 69.0, respectively. The numbers of matriculants per 100,000 persons during these same time periods were 27.5, 28.1, and 29.8, respectively. The applicant acceptance ratios during these intervals were 2.1, 2.2, and 2.3, respectively. The percentage of applicants who applied to medical school twice was 23.6%, and the percentage who applied more than twice was 3.8%.
From 2001 to 2015, the percentages of female applicants and matriculants were 48.0% and 47.9%, respectively, and the percentages of applicants and matriculants under the age of 24 were 70.1% and 76.8%, respectively (see Table 2). The percentages of female applicants and matriculants and applicants and matriculants under 24 years old declined slightly during the 2001–2015 time period.
From 2001 to 2005, the median numbers of applicants per 100,000 persons in the low-income, middle-low-income, middle-income, and high-income counties were 21.2, 23.0, 30.2, and 39.7, respectively (see Table 3). The number of applicants per 100,000 persons in the high-income counties was 1.9 times (39.7/21.2) that of the low-income counties. Also, between 2006 and 2010, the median numbers of applicants per 100,000 persons in the same categories of counties were 18.8, 24.4, 31.0, and 45.0, respectively. The number of applicants per 100,000 persons in the high-income counties was 2.4 times (45.0/18.8) that of the low-income counties. Similarly, between 2011 and 2015, the median numbers of applicants per 100,000 persons in the 4 income category counties were 17.7, 23.4, 31.7, and 49.6, respectively. The number of applicants per 100,000 persons in the high-income counties was 2.8 times (49.6/17.7) that of the low-income counties.
Chi-square tests showed that the ratio of the application rate in high-income counties to that in low-income counties during the 2001–2005, 2006–2010, and 2011–2015 intervals significantly increased (P < .001). The number and percentage of counties without any applicants during the 3 time periods were 353 (11.0%), 358 (11.1%), and 403 (12.5%), respectively, with the majority of these counties (60%–70%) being low-income or middle-low-income.
In 2001–2005, the number of matriculants per 100,000 persons in the high-income counties was 2.2 times (18.6/8.5) that of the low-income counties. The corresponding ratio increased to 2.5 times (19.9/8.1) in 2006–2010. It then further increased to 3.2 times (21.3/6.7) in 2011–2015, revealing a growing gap in the number of matriculants per 100,000 persons between high-income and low-income counties (P < .001). The number and percentage of counties without any matriculants during 2001–2005, 2006–2010, and 2011–2015 were 614 (19.1%), 637 (19.8%), and 667 (20.7%), respectively, and most were low-income or middle-low-income counties.
Figures 1 and 2 show the geographic distribution of application and matriculation rates by time period. The hot spots included New York City, New York, and the surrounding regions in the Northeastern United States; Washington, DC; Atlanta, Georgia, and the surrounding regions in the Southern United States; Frankfort, Kentucky; Sioux Falls, South Dakota, and the surrounding regions in the Midwestern United States; and Los Angeles, California, and the surrounding regions in the Western United States.
Although the distribution of application rates varied by time, the rates exhibited common features in all 3 time periods. While the application rates in the hot spot regions increased, the rates in all other regions did not change significantly during the 15-year time period, except for an increase in the application rate in the West North Central states during 2006–2010. It appears that certain areas, such as the rural regions of the West and Southwest, saw no or few applicants or matriculants over time, while there was an increasing concentration of applicants in higher-income regions over time. Furthermore, the geographic distribution of the hot spots for application rates and for matriculation rates was similar, with a greater number of matriculants in the regions with a greater number of applicants.
We also compared the geographic distribution of medical schools and that of the counties with different application and/or matriculation rates. We found that geographic proximity to a medical school was associated with higher county application and matriculation rates (Pearson correlation coefficient: r = −0.088, P < .001).
Our study documented trends in application and matriculation rates to MD-granting medical schools in the United States by county from 2001 to 2015. Both rates increased steadily during this time period. However, differences between high-income and low-income counties also widened.
The number of medical school applicants has been fluctuating since 1974. Between 1974 and 2000, that number reached its lowest level in 1988 33 and then rose to its highest level in 1996. 34 From 1996 to 2001, the number of medical school applicants declined before starting to increase again. 35 Between 2001 and 2015, the average annual growth rate of applicants was 3.1%, higher than that of 2.7% between 2003 and 2004, the years closest to those before our study period with available data. 36 This increase is partially attributed to the addition of new medical schools. 37,38 Between 2001 and 2015, 21 new accredited medical schools opened largely as a response to concerns about a future physician shortage. 39,40
During the same period, the applicant acceptance ratio gradually increased to 2.3 between 2011 and 2015; this ratio was much higher than that of 1.7 in 1989. 41 The greater increase in the number of applicants than in the number of matriculants might explain why this ratio increased only slowly and why it was more difficult to be admitted to a medical school later than earlier in our study period.
The percentage of female applicants and matriculants decreased slightly during the study period. However, that percentage reached a historic high from 2001 to 2015 compared with what it was in the 1980s (48.0% vs 30.0%). 33,42 The distribution of applicants and matriculants by age did not change dramatically during our study period. Despite a slight decrease in the percentage of applicants ages 21–24 from 2001 to 2015 compared with the percentage in the 1990s (69.0% vs 59.0%), 41 our data showed that the percentage of matriculants in this age group was greater than that of applicants in this group in the same period.
The hot spot areas we identified showed substantial differences in application and matriculation rates across counties. 43 We found that geographic proximity to a medical school was associated with a higher rate of medical school application and matriculation at the county level. Two reasons may explain this finding. First, college students who live close to a medical school may be more likely to appreciate the value and promise of pursuing a medical degree due to their affiliation or contacts with the medical school, such as family connections, summer internship programs, or personal visits. 44–46 These channels also allow students to get information about scholarships, loan repayment programs, or other training fellowships that may lower the cost of attending medical school. 44,47 Second, college and high school students who study science or related subjects and live close to a medical school may learn from local physicians who can be role models for their careers. 48,49
Another interesting finding concerns the growing gap in medical school applicants and matriculants from low-income and high-income counties from 2001 to 2015. The rising cost of attending medical school might have deterred many students from low-income counties from even applying. At the same time, this growing difference may be related to the increasing concentration of medical schools in high-income areas 39 or to more competitive application processes, both of which might disproportionately affect the enrollment of students from low-income areas. As the literature indicates, students from lower-resourced families and regions have fewer educational opportunities (e.g., limited access to qualified public schools or local colleges), which likely hinders their pursuing higher education. 50,51 Even when they show the potential to be successful in medical education, financial constraints significantly affect their decisions to attend medical school. 52 Black and Hispanic communities are disproportionately affected by these socioeconomic factors. 10
Recently, several leading medical schools have begun offering need-based free tuition to newly enrolled students, which might alter the trends we identified in our study. 8 However, it is unlikely that a free tuition model will be widely adopted in the United States, given the high cost of medical education. 53 Without addressing existing disparities in socioeconomic status and access to public school systems by geography, physician shortages and the lack of access to physicians in rural and remote regions will persist. Simply increasing the number of medical school applicants and matriculants may not eliminate geographic differences and gaps in the physician supply in the short term.
Our study has several limitations. First, to protect confidentiality, annual individual-level data were not available. All data were aggregated and organized into 3 time intervals. Thus, we were not able to conduct formal trend tests. Also, the latest aggregate data covering 2016 to 2020 had not been released at the time of our analysis, so our study explored trends before 2016. Second, data on other characteristics such as race/ethnicity were not available, which meant we could not conduct further, highly relevant subanalyses of application and matriculation rates. Third, individual socioeconomic status was not available, so we used county median family income as a proxy measure; as such, the socioeconomic disparities in application rates we calculated might be underestimated. 10,54 Finally, physician shortages might be partially addressed by international medical graduates, Doctor of Osteopathic Medicine program graduates, nurse practitioners, and physician assistants, all programs that could be more accessible to a broader set of applicants. Future studies should examine trends in these programs that may affect geographic differences in the supply of health care professionals.
Our study showed that the number of applicants and matriculants to MD-granting medical schools in the United States increased steadily between 2001 and 2015. Meanwhile, the gap in the number of applicants and matriculants from low-income and high-income counties grew during this time period. Our findings provide an important perspective on the geographic distribution of applicants and matriculants, including high application and matriculation rates in counties that were close to medical schools. By quantifying the geographic variation in medical school applicants and matriculants, we can better understand the mechanisms and factors that drive geographic differences in physician access and the associated health disparities across the United States.
The authors wish to thank the Association of American Medical Colleges for preparing and sharing the data used in this study.
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