Urban-rural differences in the age of US physicians.

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Published in final edited form as: J Rural Health. 2025 Jun;41(3):e70054. doi: 10.1111/jrh.70054 Urban–rural differences in the age of US physicians Ryan J Crowley 1 , Jag S Lally 2 , David M Kline 3 , Amanda M Bunting 1 Author information Copyright and License information 1 Department of Population Health, New York University Grossman School of Medicine, New York City, New York, USA 2 Department of Dermatology, University of Rochester Medical Center, Rochester, New York, USA 3 Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA # Ryan J. Crowley and Jag S. Lally contributed equally to this work. ✉ Correspondence: Ryan J. Crowley, Department of Population Health, New York University Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, USA. ryan.crowley@nyulangone.org PMC Copyright notice PMCID: PMC12912190  NIHMSID: NIHMS2138142  PMID: 40734197 The publisher's version of this article is available at J Rural Health Abstract Purpose: To assess county-level and specialty-level age differences between urban and rural physicians. Methods: We linked the 2008–2021 Medicare Data on Provider Practice and Specialty (MD-PPAS) dataset with the 2024 Doctors and Clinicians national downloadable file. We assessed specialty-level differences in the age of rural versus urban physicians using Rural–Urban Continuum Codes (RUCC) with four groups: urban (RUCC 1–3), large rural (RUCC 4–5), small rural (RUCC 6–7), and isolated rural (RUCC 8–9). We analyzed the relationship between rurality and physician age using choropleth graphs, spatial clustering, and univariable regression. Findings: Our final cohort comprised 571,886 physicians. The mean ages of physicians were higher in rural counties (large rural: 53.1 years; small rural: 53.3 years; isolated rural: 53.5 years) than urban counties (52.5 years; p value <0.001). Some specialties including medical oncology, palliative care, and thoracic surgery showed particularly large age differences with older physicians in more rural areas. There were clusters of older physicians in the South and clusters of younger physicians in the Mountain West and Midwest. Rurality was strongly associated with clusters of older physicians (odds ratio [OR]: 3.8; 95% confidence interval [CI], 2.6–5.5), and the percentage of households with broadband internet subscription was strongly associated with clusters of younger physicians (OR: 2.6; 95% CI, 2.2–3.0). Conclusions: Rural physicians were older than urban physicians with certain specialties and regions demonstrating large age disparities. The aging of rural physicians could worsen existing urban–rural health care disparities. Initiatives focusing on recruiting and retaining rural physicians should target specific regions and specialties to ameliorate these inequities. Keywords: access to care, demography, geography, health disparities, physician supply INTRODUCTION The United States (US) health care landscape is marked by disparities that affect the equitable provision of health care services. The imbalance between rural and urban health care is one such disparity. Approximately 50 million Americans reside in rural regions, and individuals in these areas disproportionately lack access to health services. 1 , 2 Central to the rural–urban health divide is the maldistribution of physicians across the United States. Physicians are more likely to practice in urban locations due to a variety of factors including market forces, perceptions of lifestyle, location of upbringing, and type of practice. 3 – 5 The limited access to physicians in rural areas exacerbates disparities through reduced access to clinical care, leading to worse patient outcomes. 6 , 7 There is mounting concern regarding worsening rural physician workforce shortages. Medical students from rural locations are the most likely to return to practice in rural areas, yet there has been a substantial decline in medical students from rural areas since 2002. 8 This is compounded by the higher turnover for physicians practicing in rural areas 9 and an increasing number of physicians in rural areas who are nearing retirement age. 10 Previous explorations of workforce demographics have demonstrated that the number of rural physicians is likely to decrease substantially over the coming years. 10 A better understanding of the demographic trends of rural physicians is needed to inform policies that ensure equitable access to care for individuals in rural areas. Recent work on the physician demographic landscape in the United States has demonstrated significant disparities in the physician workforce between urban and rural areas, with rural areas having disproportionately fewer physicians per capita and those physicians being, on average, older than their urban counterparts. 10 , 11 These differences underscore the potential for the exacerbation of physician shortages in rural communities as the current cohort approaches retirement. 10 However, studies that assess these demographic trends thus far have lacked specificity by focusing solely on one specialty 12 – 14 or grouping together physicians collectively without differentiating by specialty. 10 These approaches fail to capture critical differences in specialty-specific trends, such as the dynamics of primary care, medical subspecialties, and surgical specialties. Subspecialty differences are particularly important since rural residents experience higher death rates from cancer, stroke, and heart disease than their urban counterparts. 15 , 16 We expand upon previous research by analyzing physician ages on a per-specialty and per-county basis. Addressing these health care gaps through county-level and specialty-focused research is essential to help foster data-informed strategies aimed at mitigating local rural health care inequalities. Thus, our primary objective was to assess the ages of physicians in the United States in urban versus rural areas on an overall-physician and per-specialty level with county-level granularity. Guided by previous literature, we expected to find an aging rural physician population, contributing to a growing rural physician workforce shortage, but we had no preidentified hypotheses regarding specialty areas. Additionally, we sought to visualize the relationship between physician age and rurality on a per-county level to identify clusters of older physicians. METHODS Data resources Due to the diversity of physician demographic data offered, relative ease of access, and accurate geographic data, 9 , 17 – 19 the Medicare Data on Provider Practice and Specialty (MD-PPAS) 20 and Doctors and Clinicians national downloadable file 21 were the primary datasets for these analyses. The MD-PPAS dataset was created by CMS in 2008 and has been updated yearly with data available through 2021. The Doctors and Clinicians national downloadable file contains information from 2017 to 2025. We used the version from November 1st, 2024. Linkage between these datasets was performed using the National Provider Identifier (NPI), a unique identifier for physicians submitting bills to Medicare. 22 Both datasets include physicians and other health care professionals if they have a valid NPI and submitted at least one Medicare fee-for-service (FFS) physician claim in a calendar year. The MD-PPAS data include information such as birth date and specialty of physicians. 18 , 19 Access to the MD-PPAS dataset was granted following an application process through the Research Data Assistance Center. 23 The Doctors and Clinicians national downloadable file contains information including specialty of physicians and ZIP code location data. For the regression analysis, input variables were obtained from the US Census. 24 Variables Physician ages were calculated based on the physician’s birth date in MD-PPAS as of November 6th, 2024. All other variables came from the Doctors and Clinicians national downloadable file. To capture age data on as many physicians as possible (i.e., some physicians may be present in only certain years), MD-PPAS data were combined from four time points (2008, 2013, 2017, and 2021). Physicians were excluded from the analysis if they were not present in both datasets ( n = 1,219,208), were not physicians ( n = 457,061), had missing age data ( n = 52), their ZIP code data could not be mapped to RUCCs using the crosswalk file ( n = 219), or they were pediatricians ( n = 4483). Pediatricians were excluded to avoid a biased sample because most pediatricians do not submit Medicare claims. We analyzed data using both specific specialties (e.g., radiation oncology) and broad specialties (e.g., primary care). We followed the broad specialty definitions as per the MD-PPAS dataset, except for general practitioners, which we removed from the primary care broad specialty and into their own broad category due to this specialty’s heterogeneity. 25 Details on these specifications can be found in Supporting Information Table 1 . We utilized Rural–Urban Continuum Codes (RUCC) as our main rurality metric. RUCC, developed by the US Department of Agriculture (USDA), provides a detailed classification of rurality by bucketing counties into nine levels based on population size, urbanization, and proximity to metropolitan areas. 26 We designated RUCC 1–3 as urban and split rural into three population bands for our primary analysis: RUCC 4–5 (large rural counties), RUCC 6–7 (small rural counties), and RUCC 8–9 (isolated rural counties). 27 , 28 We refer collectively to RUCC 4–9 counties as rural. 29 – 31 ZIP codes were standardized to five-digits. To identify the rurality of a physician, we first mapped the physician’s ZIP code to the corresponding county code using the United States Department of Housing and Urban Development (HUD)-United States Postal Service (USPS) ZIP crosswalk file from second quarter 2024. 32 We then mapped the county code to RUCC using the 2023 RUCC codes from the USDA. Physicians who submitted claims from ZIP codes present in multiple counties with different RUCCs were categorized in the most rural group and only mapped in the most rural county. Analytic plan For continuous variables, differences in population means between urban and rural physicians were assessed using analysis of variance (ANOVA) and paired t -tests for each rural category versus the urban category. For categorical variables, the chi-squared test was used. All significance tests were performed with a p value cut-off of 0.05. We identified the 10 specialties with the greatest, statistically significant difference in mean age between urban and rural areas. A global Moran’s I was calculated for physician age to assess if the distribution of physician age differed spatially. A local join count statistic for rurality was calculated to determine if rural areas were clustered. Local Moran’s I was used to identify whether counties and their geographical neighbors were statistically significantly different from the national average for physician age. This clustering technique classified counties into four statistically significant groups: high–high, low–low, low–high, and high–low. The first label represented whether a particular county had a higher or lower physician age than the national average. The second label represented whether the neighbors of a particular county had a higher or lower physician age than the national average. Thus, high–high classifications indicated local counties and surrounding regions with older physicians than average, and low–low classifications indicated local counties and surrounding regions with younger physicians than average. The libpysal Python package was used for computation of spatial analysis statistics and cluster identification. 33 We also performed univariable logistic regression analyses to identify features predictive of these clusters of high and low physician ages with the approach informed by previous research. 34 – 36 Analyses were performed using American Community Survey census data 5-year estimates from 2023. Continuous variables were standardized by calculating a z -score. The dichotomous outcome categories predicted were: (1) high–high clusters versus all other clusters, and (2) low–low clusters versus all other clusters. Because the research question of interest was an exploratory analysis of associations and high degrees of multicollinearity within the variables existed, a multivariable logistic regression analysis was not pursued. These analyses were coded using the Python statsmodels package and odds ratios (ORs) with 95% confidence interval (CI) are shown. 37 Sensitivity analyses were conducted (1) to consider an alternative age metric of years since medical school graduation, (2) percentage of physicians older than 65, and (3) an alternative operationalization of rurality to consider rural areas adjacent to metro areas (RUCC 4/6/8) and rural areas not adjacent to metro areas (RUCC 5/7/9). RESULTS The final cohort contained 571,886 physicians. Demographic characteristics of the final cohort stratified by rurality are shown in Table 1 . The mean ages of physicians in large rural counties (53.1 years), small rural counties (53.3 years), isolated rural counties (53.5 years), and urban counties (52.5 years) were statistically different (ANOVA p value <0.001; all paired t -tests p value <0.001). There were substantially more physicians practicing in urban areas (84.7%) than in large rural counties (4.3%), small rural counties (5.1%), and isolated rural counties (5.9%). Physicians were more likely to be female in urban areas (35.2%) than in large rural counties (27.6%), small rural counties (25.8%), and isolated rural counties (24.2%). Physicians in more rural areas were older than urban physicians in all broad specialties except for psychiatry. TABLE 1. Cohort demographics stratified by urban versus rural physicians. Characteristic Total dataset Urban Large rural Small rural Isolated rural p value * Sample size (N) 571,886 484,323 24,579 29,031 33,953 Mean age (SD) 52.6(11.6) 52.5(11.6) 53.1(11.5) 53.3(11.4) 53.5(11.4) <0.001 Percent >65 years 16.8% 16.6% 18.0% 17.8% 18.1% <0.001 Mean years since graduation (SD) a 24.7(11.9) 24.6(12.0) 24.8(11.8) 25.0(11.7) 25.1(11.7) <0.001 Gender b <0.001 Male 379,145 314,055 17,802 21,538 25,750 Female 192,738 170,265 6777 7493 8203 Broad specialty <0.001 Primary care 164,598 136,049 7623 9440 11,486 Mean age (SD) 52.6(11.7) 52.4(11.7) 53.2(11.9) 53.2(11.8) 53.5(11.9) <0.001 Hospital-based 142,772 119,257 6505 7734 9276 Mean age (SD) 50.5 (10.9) 50.3 (10.9) 51.2(10.8) 51.6(10.9) 52.1(10.9) <0.001 Medical specialty 118,291 103,439 4359 4841 5652 Mean age (SD) 53.6(11.6) 53.5(11.7) 54.3(11.3) 54.3(11.2) 54.4(10.9) <0.001 Surgical specialty 94,177 80,017 4142 4751 5267 Mean age (SD) 53.5(11.4) 53.3(11.5) 53.9(11.2) 54.3(11.0) 54.1(11.1) <0.001 Ob/Gyn 27,926 24,538 1100 1183 1105 Mean age (SD) 52.4(11.2) 52.2(11.2) 53.1(11.2) 53.2(11.0) 54.0(11.1) <0.001 Psychiatry 20,316 17,884 689 842 901 Mean age (SD) 56.3(13.1) 56.3(13.2) 55.6(13.0) 56.7(12.6) 56.0(12.0) 0.43 General practice 3806 3139 161 240 266 Mean age (SD) 61.2(12.8) 60.8 (12.9) 62.9(12.9) 62.8(12.2) 63.3(12.5) <0.001 Open in a new tab * p values are from one-way analysis of variance (ANOVA; continuous) or chi-square (categorical). a Years sincemedical school graduation, 379 providers had unknown graduation year. b Three providers had unknown gender. To understand the relationship between age differences and urbanicity on a more granular level, we also analyzed the differences in mean physician age between rural and urban areas on a per-specialty basis. Further, we identified the 10 specialties that exhibited the greatest statistically significant discrepancy in mean physician age between urban and rural areas, as shown in Figure 1A – C . Data on all specialties can be found in Supporting Information Tables 2 – 4 . FIGURE 1. Open in a new tab Specialities with the largest statistically significant difference in urban–rural physician age. The specialty with the largest statistically significant age difference between large rural counties and urban counties was neuropsychiatry (16.5 years; p value 0.01), between small rural counties and urban counties was hospice/palliative care (5.0 years; p value 0.01), and between isolated rural counties and urban counties was sleep medicine (6.2 years; p value <0.001). Five specialties were in the top 10 largest age differences across all three rural RUCC bands: medical oncology, general practice, anesthesiology, radiation oncology and thoracic surgery. To better understand the nuanced regional differences in the relationship between rurality and physician age, results were assessed on a per-county level. The minimum mean physician age for a county was 34.6 years, the maximum mean physician age was 81.6 years, and the median (interquartile range [IQR]) physician age was 53.5 years (51.9–55.4). Additional choropleths can be found in Supporting Information Figures 1 – 4 . The Moran’s I globally for physician age was significant ( p value <0.001) with a z -score that was positive ( z = 25.2), and the join count statistics for rurality were significant ( p = 0.001), indicating that the spatial distribution of physician ages and rurality was more clustered than would be expected if the underlying spatial processes were random. Results from a local Moran’s I clustering to identify local regions of high and low physician age are shown below in Figure 2 highlighting the high–high clusters and low–low clusters, stratified by rurality. FIGURE 2. Open in a new tab Choropleth graph of physician age clusters stratified by rurality. Figure 2 elucidates a variety of clusters of high and low physician ages. These include clusters of high physician age in Wyoming, North Dakota, Texas, Alabama, Mississippi, and Georgia, and clusters of low physician age in Washington, Utah, Colorado, Kansas, and Illinois. Of the 198 counties in clusters of high physician age, 26 (13.1%) were urban. Of the 357 counties in clusters of low physician age, 173 (48.5%) were urban. Some regions, such as Connecticut, were unable to be mapped for visualization due to discrepancies in the ZIP codes and county codes between the dataset used for map creation and the Doctors and Clinicians national downloadable file. In total, there were 61 counties (2.0%) that were unable to be effectively mapped. Results from the univariate regression analysis are shown for the top 10 highest ORs and top 10 lowest ORs for high–high clusters in Figure 3 and for low–low clusters in Figure 4 . Only variables that achieved statistical significance are shown. The full list of included variables and associated ORs can be found in Supporting Information Tables 5 – 7 . FIGURE 3. Open in a new tab Regression analysis of high physician age clusters. CI, confidence interval; OR, odds ratio. FIGURE 4. Open in a new tab Regression analysis of low physician age clusters. CI, confidence interval; OR, odds ratio. In Figure 3 , rurality was the county-level variable most strongly associated with increased probability of high physician age clusters (OR: 3.8; 95% CI, 2.6–5.5). The average gross rent was most strongly associated with decreased probability of high physician age clusters (OR: 0.01; 95% CI, 0.0–0.1). In Figure 4 , the percentage of households with a broadband internet subscription was most strongly associated with increased probability of low physician age clusters (OR: 2.6; 95% CI, 2.2–3.0). The percentage of families living below the federal poverty level (FPL) was most strongly associated with decreased probability of low physician age clusters (OR: 0.3; 95% CI, 0.3–0.4). Results from sensitivity analyses largely reinforce the findings above without significant differences and can be found in Supporting Information Table 8 and Supporting Information Figures 5 – 11 . DISCUSSION We found that physicians in more rural counties were, on average, older than physicians in urban counties. These results were consistent across the three types of rural counties assessed. This discrepancy was also observed across many specialties. Notably, five specialties: medical oncology, general practice, anesthesia, radiation oncology and thoracic surgery exhibited large rural–urban age gaps across all three type of rural counties. The specialties with the largest age discrepancies included many surgical subspecialties and a variety of medical subspecialties. This finding indicates that individuals in rural areas may find it increasingly difficult to find subspecialty care as these physicians continue to age and retire. However, there were also age gaps in primary care fields, indicating a systemic distribution problem that could affect health care at large. Notably, given the large size of physicians included in the current study, even small differences in age can be statistically significant. However, it is important to note that an age difference of a couple of years is clinically significant, especially if this gap were to widen over time. We also identified that physicians in more rural areas were more likely to be male and more likely to practice primary care rather than medical subspecialties. This information provides a more nuanced understanding of the differences in the type of care available in urban versus rural regions. Spatial analysis techniques identified clusters of older physicians, predominantly in rural areas in southern states such as Mississippi, Alabama, and Georgia. These regions are already burdened with high rates of chronic diseases, limited health care infrastructure, and socioeconomic challenges, 38 and an aging physician workforce could compound the impact. In contrast, clusters of younger physicians were predominantly found in western and midwestern states such as Washington, Utah, Colorado, Kansas, and Illinois. These counties with clusters of younger physicians were evenly distributed between rural and urban counties. Regression analysis identified several variables associated with regions where mean physician age differed from the average. More rural counties were significantly more likely to be represented in the clusters of high physician age, further validating that rurality is an important factor affecting the geographic dispersion of physicians. Additionally, counties with low gross rent, which is strongly associated with rurality, 39 were significantly more likely to be parts of clusters of high physician age. In contrast, variables associated with a county’s higher socioeconomic status (e.g., percent of households with a computer, median household income, percent of adults with a bachelor’s degree) were strongly associated with clusters of young physicians. Further delineation of the drivers of differences in physician ages is important to inform data-driven strategies for recruitment and retention of younger physicians, especially in regions where older physician clusters coincide with increased disease burden. This study offers novel insights into workforce demographic shifts regarding the aging of rural and urban physicians by integrating multiple data sources and utilizing both descriptive and spatial methods. However, our approach is not without limitations. First, our datasets were derived from Medicare claims, which represent most US physicians but exclude physicians who do not care for Medicare patients. A recent report found that only 1.1% of physicians have formally opted out of Medicare, with the highest opt-out rates for psychiatry (7.7%) and plastic surgery (4.2%). 40 Thus, this analysis likely captures most physicians in the United States, though there may be more physicians that informally opt-out, and the results for specialties such as psychiatry and plastic surgery should be interpreted cautiously. Further, our demographic data came from the MD-PPAS 2021 dataset, and trends may have shifted since the data collection. This was the most updated demographic data available at the time the data were requested as this dataset lags by a couple of years. This study also did not address other critical health care professionals, and further explorations of the geographic distribution of physician assistants, nurse practitioners, nurses, and physical therapists could provide important additional nuance. Finally, there was some missing data for physician age, some physicians were unable to be linked between datasets, and some ZIP codes were not mappable to county codes. Together, this missing data may decrease the generalizability of these findings. However, our final cohort comprises 571,886 physicians, which is a sizeable portion of the physician workforce. Future work should strive to improve data availability and linkage to provide the most accurate data for future workforce analyses intended to inform national policy. For modeling limitations, this analysis was limited as it only assessed a singular time point, did not explore other demographic characteristics such as gender and race, and did not forecast future trends, all of which are important avenues for future work. The current findings have direct public health implications. Many incentives and programs address rural physician shortages, 41 , 42 and the states such as Wyoming and Alabama and specialties such as medical oncology and hospice/palliative care highlighted by this analysis should be considered as the focus of such programs. Further, identifying and adapting approaches from successful outlier rural regions with many young physicians such as Kansas and Washington could offer additional insights for policymakers and health care organizations. A variety of established programs to support disadvantaged regions exist including the Medicare Health Professional Shortage Area Physician Bonus program 43 and loan repayment assistance through the National Health Service Corps (NHSC). 44 Other supplemental interventions deserving further exploration to address these disparities include rural training pipelines, such as rural-focused residency programs 45 , 46 and expanding telemedicine efforts to bridge gaps in subspecialty care. 47 , 48 Incentive structures for these programs should target specialties and states with the largest rural–urban age gaps, particularly in identified high-need areas, and data-driven, evidence-based interventions should be the foundation for addressing urban–rural health care disparities. Supplementary Material supplemental material NIHMS2138142-supplement-supplemental_material.docx (3.5MB, docx) SUPPORTING INFORMATION Additional supporting information can be found online in the Supporting Information section at the end of this article. ACKNOWLEDGMENTS We would like to thank Research Data Assistance Center (ResDAC) and the Centers for Medicare and Medicaid Services (CMS) for access to these datasets. Support was provided by an NYU Clinical & Translational Science Institute grant UL1 TR001445 from the National Center for Advancing Translational Sciences, National Institutes of Health. Amanda M. Bunting was supported by K01DA053435, National Institute on Drug Abuse, National Institutes of Health. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the views of the funding agencies. Footnotes CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. REFERENCES 1. 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