Geographic Mobility of Urology Graduates From Residency to First Practice in the United States

Abstract

Background

Geographical mobility of Urologists plays a crucial role in shaping regional healthcare systems; however prior research often overlooks mobility patterns of Urology trainees. In comparing distances traveled during career transitions and initial practice locations in relation to residency training sites, we hope to address gaps in understanding how training environments and social factors influence professional trajectories and discrepancies in care.

Methods

Data on U.S. Urology Residency Graduates (UGs) from 2020 to 2023 was obtained from ACGME-accredited residency programs in 2025. Authors utilized a website analysis to collect location and training data from January 2023 through September 2023. First attending positions were identified by students through residency program alumni pages, Google/LinkedIn, and when available, linked institutional physician profiles. Primary outcomes were regional retention rates, travel distances, and socioeconomic drivers (analyzed by multivariate regression) during transitions from medical school to residency (T1), residency to fellowship (T2), and residency to the first attending position (T3). Retention rates were stratified by NIH funding status (top 50 NIH-funded schools vs. non-top 50).

Results

A total of 659 Urology graduates (UGs) were examined. The Northeast retained the most UGs during T1 but retained the lowest UGs during T2 and T3. The West retained the highest UGs during T2 and T3 and consistently retained over half of their UGs during each transition. More UGs from T50NIH travelled over 500 miles (39%) than NonT50NIH (36%) during T1. Additionally, UGs from T50NIH institutions demonstrated higher T3 mobility. UGs took attending offers in areas with predominantly Caucasian populations, lower minority populations, less physicians, and more rurality compared to their training locations.

Conclusions

The heightened T2 mobility may be due to specialized training at select institutions, while lower T3 mobility highlights shifting priorities as UGs transition to long-term career establishment. Higher T3 mobility from T50NIH institutions suggest graduates from research intensive institutions were more willing to relocate farther distances for their first attending positions. Increased rurality during T3 was demonstrated compared to T1 and T2, which are centered around urban training environments. The findings highlight the importance of strategically aligning urology workforce distribution with regional healthcare needs.

Keywords

urology residency fellowship geographic mobility healthcare accessibility training location urology graduates

Introduction

The geographical mobility of medical professionals, particularly those specializing in urology, plays a crucial role in shaping regional healthcare systems. Prior research extensively explores the distribution and eventual practice locations of healthcare professionals but has often overlooked the specific mobility patterns of Urology residents throughout their training. One study highlighted that geography is a key consideration for Urology residents when selecting away rotations, underscoring the importance of location in their training decisions.¹ Additionally, research analyzing migration patterns of dermatologists found that the location of high school and medical school were closely related to residents’ ultimate employment locations, suggesting early geographical ties influence long-term placement.² Despite these insights, studies focusing on the transitions between different training stages and how these movements affect long-term regional retention of urology specialists are sparse.

Geographic mobility across urology training stages has not been well characterized, particularly across sequential transitions. These patterns describe how movement varies across training stages and regions. Increasing urologist density in underserved areas has been shown to decrease prostate cancer mortality, underscoring the critical role of geographic distribution in healthcare outcomes.³ Despite these benefits, disparities in access to urologic care persist, with less than 13% of the US population unable to obtain urologic care within 30 minutes.⁴ This research is vital for developing strategies to balance the distribution of urological care, particularly in underserved areas. Pittman et al, analyzed urologist availability in the United States from 2000 to 2018, revealing a decline in local availability despite increasing urologists with key predictors of availability including metropolitan status and historical urologist presence.⁵ However, existing literature has not extensively explored the transitions between different training stages or how these movements affect long-term regional retention.

Rather than focusing only on ultimate practice location, examining when geographic movement occurs during training may better inform workforce planning. Residency represents the final stage of training completed by all urology graduates and therefore serves as a common reference point for evaluating mobility. In contrast, fellowship training is subspecialty-specific, shorter in duration, and not uniformly pursued. Recent analyses of AUA fellowship match data suggest that only approximately one-third of graduating U.S. and Canadian urology residents apply to AUA-maintained fellowships each year.⁶ For these reasons, anchoring analyses to residency allows for a more consistent assessment of mobility patterns across the full cohort.

The primary purpose of this study was to characterize geographic mobility across sequential stages of urology training and early practice, with residency serving as the central reference point. Specifically, we examined transitions from medical school to residency, residency to fellowship, and residency to the first attending position to evaluate regional retention, travel distances, and associated community characteristics. By examining transitions sequentially, this study describes when geographic redistribution occurs across the training pipeline where geographic redistribution occurs, which may help inform future workforce planning efforts. We hypothesized that mobility patterns would differ across these transitions, and that graduates from top NIH-funded medical schools would demonstrate distinct mobility profiles compared with those from non–top NIH-funded institutions.

Methods

Cohort Study Design

This study utilized a retrospective cohort design to analyze data on U.S. Urology Residency Graduates (UGs) from 2020 to 2023. This retrospective cohort study was designed and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guideline (Supplemental: Cohort Checklist).⁷

Data was obtained from publicly accessible websites of ACGME-accredited Urology residency programs. Programs that did not report graduate information for these years were excluded. We selected publicly available sources to enable reproducibility across programs; however, we recognize that this approach may incompletely capture the full graduating cohort. Eligible programs provided information on graduates’ medical degrees (MD/DO), medical school names, and the residency program’s location and region. Additional details, including fellowship training and the graduates’ initial attending positions, were recorded. Furthermore, UGs first attending positions were identified through residency program alumni pages, Google/LinkedIn, and when available, linked institutional physician profiles. For international medical graduates (IMG), graduate medical school country was not consistently available across program websites for IMGs; therefore, IMG status was not analyzed in our study.

Geographic Decisions and Rationale

US Census Regions (Geographic Retention Analysis)

Geographic regions were classified using the four main U.S. Census regions: Midwest, Northeast, South, and West. This regional framework was selected to provide a standardized national structure for comparing retention patterns across broad U.S. geographic areas. Regional classification was used primarily to evaluate retention across major training transitions (T1–T3).

County Level Characteristics (Descriptive Socioeconomic/Population Health Statistics)

County-level socioeconomic and population health characteristics were obtained using Federal Information Processing Standard (FIPS) codes and linked to each training or practice location. County-level data were used to characterize the local healthcare and demographic context of graduate movement, including rurality, population composition, and healthcare access indicators. These variables were intended to provide descriptive context rather than to define urological supply or demand at the regional level.

Distance Calculations (Mobility Measurement)

Geographic distance between training stages was calculated as straight-line (great-circle) distance between institutional locations. Training site addresses were geocoded and converted to latitude and longitude coordinates, and distances were computed using the spherical law of cosines. Exact addresses were utilized to precisely measure the geographic training distance in each transition (T1, T2, and T3).

Key Outcome Measures

The primary outcomes were regional retention rates and travel distances during three career transitions: medical school to residency (T1), residency to fellowship (T2), and residency to the first attending position (T3). An analysis of travel distance from fellowship to first attending position was not performed, as the study was designed to examine residency training as the primary anchor point for geographic mobility. Residency is the final stage of training shared by all UGs. Fellowship training is subspecialty-specific, shorter in duration, and not universal, which limits its comparability as a reference point across the cohort. Regional retention rates were calculated as the proportion of graduates who remained within the same geographic region during each transition. To evaluate the influence of NIH funding on mobility patterns, medical schools were categorized based on NIH funding status, distinguishing the top 50 NIH-funded schools from all others. Travel distances were additionally categorized using a straight-line 500-mile threshold to distinguish shorter-range moves from substantial geographic relocation. This cutoff has been used to reflect a meaningful distance in the U.S. context, where moves of this magnitude often involve relocation across multiple states.

Socioeconomic and Population Health Statistics

Descriptive statistics were used to summarize the population characteristics of the counties where UGs originated and relocated. Data sources included the American Community Survey (2019-2022), Behavioral Risk Factor Surveillance System (2021), Bureau of Labor Statistics (2022), and Census Population Estimates (2022). Variables examined included population size (per 100,000 residents), age distribution (percentage under 18 and over 65), racial demographics, gender distribution, median household income (per $1000), uninsured rates (under age 65), commuting distances, high school graduation rates, and population-to-primary care provider ratios. Health indicators such as smoking prevalence, diabetes rates, alcohol consumption, and HIV prevalence were also included. Population characteristics were mapped by FIPS code to respective counties between UG transitions. Delta values were calculated to compare variables between counties. This analysis highlighted patterns in physician movement towards or away from areas with greater healthcare needs, particularly underserved regions. Furthermore, these descriptive statistics were reported to contextualize local health and socioeconomic characteristics between UG transition locations and were not intended to imply a causal relationship between UG movement and county health outcomes.

An independent review was conducted to select socioeconomic variables for further exploratory analysis, aiming to evaluate how these socioeconomic and demographic factors for each county influenced travel distances across the three career transitions. Such variables included rates of uninsured individuals, median household income, county population, and age distribution.

Statistical Analyses

Regional retention across transitions was assessed using chi-square tests of independence comparing region of origin and region of destination. Welch’s two-sample t-tests were performed to compare distances between graduates from top 50 NIH-funded schools and those from other institutions. Given the retrospective nature of the study, a post hoc minimum detectable effect analysis was conducted for the comparison of travel distance between T50NIH and NONT50NIH graduates at T3. With the observed sample sizes (n = 106 and n = 210, respectively) and assuming a conservative standard deviation of 800 miles to account for the right-skewed distribution of travel distances, the study had approximately 80% power to detect a between-group difference of 200 miles at an alpha level of 0.05.

A multivariate regression model was employed to assess how socioeconomic and demographic factors, including rates of uninsured individuals, median household income, county population, and age distribution, affected travel distances during the three career transitions. Additionally, quantile regression was applied to examine how these variables influenced travel distances across different points in the distribution (10th, 50th, and 90th percentiles). This approach provided insight into factors affecting shorter (10th percentile) and longer (90th percentile) moves. Quantile regression was chosen due to the heteroscedasticity in the dataset, as confirmed by Breusch-Pagan and White’s tests, which indicated non-constant variance unsuitable for Ordinary Least Squares (OLS) regression.

Analyses were conducted using RStudio and Microsoft Excel, with results reported at a 95% confidence level and a significance threshold of p < 0.05. IRB approval was not required as data was publicly available.

Results

Descriptive Statistics & Regional Retention

A total of 659 UGs across 150 programs from 2020 to 2023 were included in the study, as shown in Table 1. UGs were divided into cohorts based on whether they attended top 50 NIH funded medical schools (T50NIH) or not (NonT50NIH). UGs from T50NIH programs had higher rates of fellowship attendance compared to NonT50NIH UGs (61% vs 46%, respectively). Therefore, higher rates of employment directly after residency were seen for NonT50NIH UGs compared to T50NIH UGs (54% vs 39%, respectively). Urology programs were grouped into one of five geographical regions across the United States as depicted in Figure 1.

Table 1.

Cohort Totals of 2019-2022 Urology Graduates in Each Training Phase

Cohort	Urology residency	Attended fellowship	Employment directly after residency
T50NIH	270	164 (61%)	106 (39%)
NonT50NIH	389	179 (46%)	210 (54%)
Whole Sample	659	343 (52%)	316 (48%)

Figure 1.

Geographical breakdown of US regions by state

Tables 2 , Tables 3, and Table 4 analyze UG movement from medical school to residency (T1), residency to fellowship (T2), and residency to employment as an attending physician (T3), respectively. Chi square test of independence analysis confirmed a significant relationship between the region of origin and the region of destination for UGs during T1 (p-value < 0.0001***), T2 (p-value < 0.0001***), and T3 (p-value < 0.0001***). Descriptively, though the Northeast had the highest retention of UGs during T1 (68.4%), it had the lowest retention among all regions during T2 and T3 (33.6% and 44.7%, respectively). Descriptively, among all regions, the West had the highest retention of UGs during T2 and T3 (64.0% and 74.1%, respectively) and was the only region to retain over half of their UGs during each transition.

Table 2.

Regional Retention of UGs During T1

Residency region
		Midwest	Northeast	South	West
Medical School Region	Midwest	50.00%	25.29%	13.79%	10.92%
	Northeast	8.67%	68.37%	13.78%	9.18%
	South	16.39%	19.67%	54.92%	9.02%
	West	13.33%	11.11%	17.78%	57.78%

Bolded numerical values marked to denote retention rates within the same region (ex. Midwest to Midwest retention).

Table 3.

Regional Retention of UGs During T2

Fellowship region
		Midwest	Northeast	South	West
Residency Region	Midwest	35.21%	12.68%	33.80%	18.31%
	Northeast	16.41%	33.59%	30.47%	19.53%
	South	22.09%	22.09%	37.21%	18.60%
	West	12.07%	6.90%	17.24%	63.79%

Bolded numerical values marked to denote retention rates within the same region (ex. Midwest to Midwest retention).

Table 4.

Regional Retention of UGs During T3

Region of attending position
		Midwest	Northeast	South	West
Residency Region	Midwest	50.63%	6.33%	27.85%	15.19%
	Northeast	10.68%	44.66%	26.21%	18.45%
	South	6.54%	7.48%	70.09%	15.89%
	West	7.41%	0.00%	18.52%	74.07%

Bolded numerical values marked to denote retention rates within the same region (ex. Midwest to Midwest retention).

Descriptive Travel Distances

For the whole sample (WS), UGs showed higher rates of travelling over 500 miles away to seek training during T1 (37%), T2 (56%), and T3 (43%), while less UGs chose to train at the same institution during T1 (16%), T2 (10%), and T3 (3%). For T1, the rate of UGs traveling over 500 miles for training was slightly higher for T50NIH (39%) vs NonT50NIH (36%). However, the percentage of NonT50NIH UGs traveling 201-500 miles away was higher than T50NIH UGs (28% vs 21%, respectively). T2 displayed the highest rate of UGs traveling over 500 miles away for training compared to T1 and T3 regardless of cohort. T3 showed the lowest rates of UGs retaining at the same institution compared to T1 and T2 regardless of cohort. T50NIH UGs had higher rates of relocating over 500 miles away compared to NonT50NIH UGs (48% vs 40%, respectively).

Cohort Distance Analysis

Distance traveled by UGs was separated by cohort and analyzed during each transition as shown in Figure 2. A Welch 2 sample t-test was utilized to find significance between distance traveled. During T1, there was no significant distance (p-value = 0.26) traveled between T50NIH UGs (583 miles) and NonT50NIH UGs (524 miles). No significant distance was observed for T2 while comparing cohorts. However, T50NIH UGs on average traveled less than their NonT50NIH UG counterparts during T2 (794 miles vs 844 miles, respectively; p-value = 0.55). There was a significant distance observed during T3 between T50NIH and nonT50NIH UGs (733 miles and 525 miles, respectively, p-value = 0.019*) per Figure 2.

Figure 2.

Distances traveled by UGs during each transition phase categorized by cohort or whole sample. Ns = no significance. *p-value <0.05. **p-value <=0.01. ***p-value <=0.001. ****p-value <=0.0001

Distance traveled by UGs was also compared between transitions after separating by cohort as shown in Figure 3. ANOVA analysis was utilized to determine significant differences between each transition stage. T50NIH UGs traveled significantly more on average during T2 (794 miles) than T1 (583 miles, p-value = 0.006**). NonT50NIH UGs traveled significantly more as well during T2 (844 miles) than T1 (524 miles, p-value < 0.0001****), but also significantly more during T2 (844 miles) than T3 (525 miles) per Figure 3 (p-value < 0.0001****).

Figure 3.

Distances traveled by UGs categorized by cohort or whole sample and comparing between transition stages. Ns = no significance. *p-value <0.05. **p-value <=0.01. ***p-value <=0.001. ****p-value <=0.0001

Demographic Descriptive Data

Population health statistics based on geographic distribution were studied according to UG training locations. The descriptive data is shown in Table 5. UGs in the study sample were employed at their first job in areas with a higher average ratio of population to PCPs, White population, and rurality compared to where they seek training during medical school, residency, and fellowship. UGs in the sample also started work in areas with lower average HIV prevalence, total population, distance commuted, Black, Asian, and Hispanic populations compared to where they trained per Table 5.

Table 5.

Population Descriptive Health Statistics by UG Training Area. Values are provided as Averages. Based on Statistics provided by the County Health Rankings Database

	Medical school (N = 659)	Residency (N = 659)	Fellowship (N = 343)	First job (N = 316)
Ratio of population to primary care physicians^b	1.089	1.050	1.050	1.337
High school completiona	88.962	89.097	88.264	90.376
Smokinga	15.319	14.997	13.538	14.724
Obesitya	32.107	31.980	29.367	32.790
Unemploymenta	3.860	3.833	3.750	3.448
Excessive drinkinga	18.655	18.225	18.312	17.893
Diabetes prevalencea	10.759	10.470	10.254	9.870
HIV prevalence^c	762.000	669.862	748.587	388.100
Populationa^d	13.920	16.752	25.037	10.667
Below 18 years of agea	20.599	20.708	20.221	21.586
65 and Oldera	15.514	15.945	15.473	16.598
Blacka	23.734	20.537	18.250	14.001
American Indian or Alaska Nativea	0.926	0.901	0.936	1.102
Asiana	7.032	7.420	10.511	6.531
Native Hawaiian or Other Pacific Islandera	0.163	0.185	0.242	0.268
Hispanica	18.111	18.818	23.424	17.340
Whitea	48.498	50.613	45.222	59.065
Femalea	51.263	51.160	50.964	50.478
Ruralitya	5.229	5.804	3.312	13.465
Median Household Income^e	73.438	76.810	83.100	80.462
Uninsureda	9.696	9.273	9.646	10.048
Long Distance Commutea	37.493	37.886	42.469	35.218
Attended Some Collegea	72.240	72.159	73.707	70.102

^aEach value is calculated as percentages except for ratio of population to primary care physicians, HIV prevalence population, and median household income.

^bRatio of population to primary care physician (PCP) is scaled down by 100 (i.e. 0.300 means 30-to-1 population-to-PCP increase in ratio).

^cHIV prevalence is the prevalence per 100,000 people.

^dPopulation difference is scaled down by 100,000.

^eMedian household income difference is scaled down by 1000.

Population health statistics based on geographic distribution were studied according to UG transition stages and provided in Table 6. Values are provided as deltas between transition stages, where positive values indicate an increase in the corresponding health demographic data, and negative values are vice versa.

Table 6.

Population Trends During Each Transition. Based on Statistics Provided by the County Health Rankings Database

	T1 delta^a (n = 659)	T2 delta (n = 343)	T3 delta (n = 316)
Ratio of population to primary care physicians^b	-0.040 ± 0.383	0.010 ± 0.427	0.276 ± 0.787
High school completion	0.135 ± 4.416	-0.515 ± 5.213	0.932 ± 4.525
Smoking	-0.322 ± 3.628	-1.010 ± 4.179	-0.763 ± 3.770
Obesity	-0.127 ± 5.675	-1.135 ± 6.590	-0.802 ± 5.690
Unemployment	-0.027 ± 1.095	-0.174 ± 1.158	-0.286 ± 1.046
Excessive drinking	-0.430 ± 3.151	-0.209 ± 2.828	-0.008 ± 2.832
Diabetes prevalence	-0.289 ± 2.120	0.001 ± 2.272	-0.837 ± 2.113
HIV prevalence^c	-91.827 ± 645.141	6.512 ± 727.252	-205.205 ± 478.403
Population^d	2.832 ± 22.716	5.039 ± 30.885	-2.539 ± 17.920
Below 18 years of age	0.108 ± 3.479	0.176 ± 3.981	0.154 ± 3.402
65 and Older	0.431 ± 3.071	-0.642 ± 3.375	0.837 ± 3.792
Non-Hispanic Black	-3.197 ± 18.059	-1.126 ± 17.290	-7.803 ± 17.461
American Indian or Alaska Native	-0.025 ± 1.148	0.051 ± 0.712	0.185 ± 1.234
Asian	0.388 ± 6.369	1.841 ± 8.250	0.476 ± 6.259
Native Hawaiian or Other Pacific Islander	0.022 ± 0.307	0.042 ± 0.327	0.098 ± 0.704
Hispanic	0.707 ± 15.791	3.693 ± 17.647	-0.482 ± 15.856
White	2.115 ± 19.866	-4.465 ± 20.924	7.440 ± 21.983
Female	-0.103 ± 1.210	-0.182 ± 1.339	-0.697 ± 1.343
Rurality	0.575 ± 13.710	-1.566 ± 12.326	6.648 ± 18.858
Median Household Income^e	3.372 ± 21.707	3.043 ± 25.183	7.198 ± 22.678
Uninsured	-0.423 ± 5.489	0.857 ± 7.146	0.247 ± 5.684
Long Distance Commute	0.393 ± 14.954	1.636 ± 15.647	0.550 ± 12.645
Attended Some College	-0.081 ± 8.945	0.509 ± 10.094	-0.922 ± 9.694

^aDelta represents the average change in values for each characteristic (final value subtracted by initial value). Each change is calculated as percentages except for ratio of population to primary care physicians, HIV prevalence population, and median household income.

^bRatio of population to primary care physician is scaled down by 100 (i.e. 0.010 means 1-to-1 population-to-PCP increase in ratio).

^cHIV prevalence is the difference in incidences per 100,000 people.

^dPopulation difference is scaled down by 100,000.

^eMedian household income difference is scaled down by 1000.

Regression Analysis

Utilizing US Census data, population health statistics were compared during transitions to establish statistically significant differences between UG training sites. This analysis was done using quantile regression to separate these effects between groups that travelled short distances (QR10%), moderate distances (QR50%), and longer distances (QR90%) during their transition period (Tables 7-9). Beta values indicate the average mileage change for every increase of 1000 in a given characteristic.

Table 7.

Regression Analysis for T1 Grouped by Shorter (QR10%), Average (QR50%), and Longer (QR90%) Travel Distances to Residency

Characteristic	OLS				QR10%			QR50%			QR90%
Characteristic	Beta	95% CI ^a	p-value	VIF ^a	Beta	95% CI ^a	p-value	Beta	95% CI ^a	p-value	Beta	95% CI ^a	p-value
Uninsured Individuals	13	-0.88, 27	0.066	2.1	-0.08	-0.36, 0.19	0.5	25	12, 39	<0.001	-1.4	-42, 39	>0.9
Rurality	7.0	0.76, 13	0.028	1.5	0.54	-1.5, 2.6	0.6	3.6	-2.2, 9.3	0.2	25	6.1, 45	0.010
NIHT50	-5.5	-115, 104	>0.9	1.2	0.00	-1.6, 1.6	>0.9	60	-15, 134	0.12	-32	-353, 288	0.8
Individuals Who Attended Some College	7.8	-1.5, 17	0.10	2.1	0.01	-0.19, 0.21	>0.9	10	4.4, 16	<0.001	21	-8.6, 51	0.2
Long Distance Commute	0.09	-4.4, 4.6	>0.9	1.9	0.03	-0.05, 0.12	0.4	-3.9	-7.4, -0.52	0.024	18	5.2, 31	0.006
Median Household Income	3.4	-0.32, 7.1	0.074	1.8	-0.01	-0.07, 0.06	0.9	-0.01	-3.0, 3.0	>0.9	8.6	-0.27, 17	0.057
Total County Population	0.27	-3.6, 4.1	0.9	1.6	0.01	-0.12, 0.15	0.8	-0.24	-5.5, 5.0	>0.9	1.9	-7.8, 12	0.7
65 and Older	-4.3	-30, 21	0.7	1.5	-0.20	-0.58, 0.18	0.3	12	-8.0, 32	0.2	-35	-108, 38	0.3

^aCI = Confidence Interval, VIF = Variance Inflation Factor.

R² = 0.031; Adjusted R² = 0.019; p-value= 0.008; Log-likelihood= -5,179; F-Statistic= 2.62; No. Obs. = 659.

Bolded numerical values denote statistically significant p-values, p < 0.05.

Table 8.

Regression Analysis for T2 Grouped by Shorter (QR10%), Average (QR50%), and Longer (QR90%) Travel Distances to Fellowship

Characteristic	OLS				QR10%			QR50%			QR90%
Characteristic	Beta	95% CI ^a	p-value	VIF ^a	Beta	95% CI ^a	p-value	Beta	95% CI ^a	p-value	Beta	95% CI ^a	p-value
University Based Residency	-82	-332, 168	0.5	1.3	-5.7	-22, 11	0.5	-31	-374, 312	0.9	24	-192, 241	0.8
Residency Graduation After COVID Onset	116	-52, 284	0.2	1.0	0.00	-15, 15	>0.9	82	-90, 254	0.3	95	-242, 431	0.6
Rurality	1.1	-8.9, 11	0.8	1.8	2.2	-0.51, 4.9	0.11	2.6	-4.6, 9.9	0.5	-4.7	-16, 6.8	0.4
NIHT50	-54	-226, 118	0.5	1.1	-2.2	-15, 10	0.7	-94	-276, 89	0.3	72	-137, 280	0.5
Uninsured Adults	1.6	-23, 26	>0.9	2.4	0.22	-4.3, 4.7	>0.9	31	5.8, 57	0.016	-53	-94, -11	0.014
Unemployment Level	-3.4	-205, 198	>0.9	4.8	-3.1	-65, 59	>0.9	57	-95, 209	0.5	122	-166, 410	0.4
Individuals Who Attended Some College	-3.3	-21, 14	0.7	2.6	0.94	-5.5, 7.4	0.8	-3.4	-20, 13	0.7	-10	-33, 13	0.4
Long Distance Commute	5.4	-6.5, 17	0.4	3.4	-0.30	-3.0, 2.4	0.8	0.06	-10, 10	>0.9	7.7	-11, 26	0.4
Median Household Income	1.3	-6.8, 9.4	0.8	4.1	-0.34	-3.7, 3.0	0.8	3.3	-4.7, 11	0.4	7.6	-4.1, 19	0.2
Total County Population	-2.5	-7.4, 2.3	0.3	2.1	0.07	-0.91, 1.0	0.9	-7.1	-15, 1.2	0.094	-2.0	-6.5, 2.5	0.4
65 and Older	-24	-67, 19	0.3	2.1	0.43	-9.9, 11	>0.9	-8.9	-53, 36	0.7	-52	-114, 9.9	0.10

^aCI = Confidence Interval, VIF = Variance Inflation Factor.

R² = 0.020; Adjusted R² = -0.012; p-value= 0.8; Log-likelihood= -2,774; F-Statistic= 0.623; No. Obs. = 344.

Bolded numerical values denote statistically significant p-values, p < 0.05.

Table 9.

Regression Analysis for T3 Grouped by Shorter (QR10%), Average (QR50%), and Longer (QR90%) Travel Distances to First Attending Job after Residency

Characteristic	OLS				QR10%			QR50%			QR90%
Characteristic	Beta	95% CI ^a	p-value	VIF¹	Beta	95% CI ^a	p-value	Beta	95% CI ^a	p-value	Beta	95% CI ^a	p-value
University Based Residency	-137	-355, 80	0.2	1.1	-16	-41, 9.1	0.2	6.4	-161, 174	>0.9	-254	-630, 122	0.2
Residency Graduation After COVID Onset	21	-131, 173	0.8	1.0	-11	-20, -0.88	0.033	29	-100, 157	0.7	99	-228, 426	0.6
Rurality	-2.7	-12, 6.3	0.6	1.7	0.87	-0.12, 1.9	0.085	-2.9	-11, 5.3	0.5	-19	-37, -0.11	0.049
NIHT50	188	19, 357	0.029	1.1	0.62	-16, 17	>0.9	156	-60, 373	0.2	362	44, 680	0.026
Uninsured Adults	-11	-34, 13	0.4	2.5	-0.10	-1.4, 1.3	0.9	-3.1	-30, 24	0.8	-39	-93, 16	0.2
Unemployment Level	-127	-292, 37	0.13	3.3	1.3	-7.6, 10	0.8	-157	-271, -44	0.007	-247	-699, 205	0.3
Individuals Who Attended Some College	-7.0	-23, 9.2	0.4	2.0	-0.19	-1.1, 0.73	0.7	-8.1	-27, 11	0.4	-7.4	-48, 33	0.7
Long Distance Commute	14	4.0, 25	0.007	2.8	0.14	-0.38, 0.65	0.6	18	8.0, 27	<0.001	19	-11, 48	0.2
Median Household Income	-5.2	-13, 2.2	0.2	2.8	-0.14	-0.54, 0.26	0.5	-6.6	-14, 0.60	0.072	-2.3	-22, 17	0.8
Total County Population	0.35	-5.5, 6.2	>0.9	1.9	-0.25	-0.81, 0.30	0.4	-4.3	-13, 4.9	0.4	6.7	-4.3, 18	0.2
65 and Older	17	-26, 61	0.4	2.2	-0.90	-3.1, 1.3	0.4	2.7	-38, 43	0.9	59	-45, 162	0.3

^aCI = Confidence Interval, VIF = Variance Inflation Factor.

R² = 0.059; Adjusted R² = 0.025; p-value= 0.065; Log-likelihood= -2,494; F-Statistic= 1.73; No. Obs. = 315. Bolded numerical values denote statistically significant p-values, p < 0.05.

During T1, total county population had no significance on distance traveled by UGs. In addition, UGs from T50NIH programs had no significant distance traveled compared to their NonT50NIH counterparts when other characteristics were included in the regression model. Rurality led to increased travel distances for those in QR90% (Beta: 25, p-value: 0.010). Percent of uninsured individuals and college attendance at QR50% led to increased travel distances, with betas of 25 (p-value <0.001) and 10 (p-value <0.001), respectively (Table 7).

During T2, we continue to see that training at a T50NIH medical school and county population had no significant effect on distance travelled by UGs when accounting for other characteristics in the regression. UGs that graduated residency after the COVID onset had no significant effect on the distance traveled to their fellowship positions. An increase in the percent of uninsured individuals did have a significant effect on distance travelled, with UGs in QR50% travelling greater distances (Beta: 31, p-value: 0.016) and UGs in QR90% travelling shorter distances (Beta: -53, p-value: 0.014) during T2 (Table 8).

During T3, the county population continued to have no significant effect on travel distances in the regression. However, NIHT50 UGs at QR90% did see an increase in distance traveled to their first job (Beta: 362, p-value: 0.026). Furthermore, UGs at travel distances at QR10% tended to travel less to their first job if they had graduated from residency after the onset of COVID (Beta: -11, p-value: 0.033). UGs traveled less when accounting for rurality at QR90% (Beta: -19, p-value: 0.049) and when accounting for unemployment level at QR50% (Beta: -157, p-value: 0.007) per Table 9.

Discussion

The analysis of the geographic mobility of UGs across the United States highlights critical trends in professional trajectories with implications for healthcare delivery. Distinct patterns emerged in regional retention, distances traveled, and the demographic shifts accompanying these transitions for the included 659 UGs from 2020 to 2023. These findings provide a foundation for addressing disparities in the distribution of urologists and the systemic challenges affecting their placement.

Regional Retention

Of the regions, the Northeast possessed the highest retention rate during T1 but was the lowest during T2 and T3. While UGs may hope to match near their medical schools for residency, the higher cost of living in many Northeastern areas, such as New York or Massachusetts, and the urban-centric environment may dissuade residents from remaining in the region, particularly for full-time employment as attendings.^8-10 The West retained over half of its UGs in each transition and boasted the highest retention among the regions during T2 and T3. This may be due, in part, to the geographic vastness of the Western region as trainees stay within the region despite travelling significant distances, but aspects such as climate and culture may also contribute to this trend.^11-13 Furthermore, there was a significant relationship between region of origin and destination indicating that UGs do have a preference on training/practice location based on where they are from or trained previously.

Travel Distances

Travel distances during each transition phase reflect evolving priorities and needs within urology training and practice. The highest mobility occurred during T2, with 56% of UGs traveling over 500 miles, compared to 43% during T3. This heightened mobility in T2 underscores the critical role that fellowship training plays in shaping future career trajectory. Unlike general residency training, urology fellowships often focus on highly specialized areas such as oncology, reconstructive surgery, endourology, or pediatrics. The concentration of fellowship programs in a limited number of institutions, often located in major metropolitan areas or academic centers, may compel UGs to travel long distances to access these opportunities.

Moreover, fellowship programs are disproportionately located in NIH top-tier institutions or urban regions with robust research facilities and advanced surgical technologies, which attract UGs seeking cutting-edge training.¹⁴ For example, urology fellowships in robotic surgery or urological cancers are primarily offered at exclusive centers, which are often geographically distant from where residents complete their training.^15,16 This geographic distribution reflects a systemic centralization of resources, creating an environment where mobility is not just advantageous but necessary for those seeking to advance in competitive subspecialties. The shorter duration of urology fellowship training compared with residency may partly explain the greater geographic flexibility observed during T2, as even the longest fellowship pathways, including minimally invasive urology, urologic oncology, and pediatric urology, are typically completed within two years.

In contrast, the relatively lower mobility in T3 highlights a shift in priorities as UGs transition from training to long-term career establishment. At this stage, personal and professional stability often takes precedence. Urologists are more likely to consider factors such as proximity to family, cost of living, and practice setting when deciding on their first job.¹⁷ Additionally, the opportunity to join established practices or hospital systems within regions where they trained may offer continuity and support during early career phases.

The significant travel distances during T2 suggest a concentration of expertise and resources in urology subspecialties, which may inadvertently exacerbate disparities in access to specialized urologic care. Patients in rural or underserved regions are less likely to benefit from these advanced training programs, as the expertise often remains localized in urban academic centers. Conversely, reduced mobility in T3 can enhance workforce stability and foster long-term physician-patient relationships in the regions where UGs establish their practices.

Interestingly, the data revealed that UGs from T50NIH institutions demonstrated higher mobility in T3 compared to NonT50NIH graduates. While T50NIH UGs traveled an average of 733 miles, NonT50NIH UGs traveled 525 miles. This trend suggests that graduates from research intensive institutions may be more willing or able to relocate farther distances for their first positions as attending physicians.

Rural and Urban

Across the transitions from T1, T2, and T3, the data reveals important trends in the distribution of UGs between rural and urban areas. Notably, rurality increases during T3, with 13.47% of UGs practicing in rural settings during their first attending position. This represents a meaningful shift compared to earlier transitions, as T1 and T2 are predominantly centered around urban or suburban training environments, where residency and fellowship programs are typically concentrated. However, despite the increase in T3, the proportion of urologists in rural areas remains disproportionately low relative to the healthcare needs of these populations.

Rural regions continue to face significant challenges in accessing specialized urological care, even with the observed rise in T3. These areas often report higher prevalence rates of chronic conditions, such as chronic kidney disease, kidney stones, and undiagnosed urological cancers, due to limited access to preventive and diagnostic services.^18-20 Furthermore, the population-to-primary care physician ratio is consistently higher in rural areas, reflecting a broader healthcare gap that exacerbates delays in urological referrals and treatment.^21,22

The professional and systemic factors that draw UGs to rural areas in T3, such as financial incentives (e.g., higher salaries, loan repayment programs, and signing bonuses), the opportunity to perform a broader range of procedures due to fewer specialists, and the new developments in telemedicine underscore the potential for growth in rural healthcare settings.^23-25 However, these measures remain insufficient to adequately address the persistent shortage of urologists in these regions. Exposure to rural areas to rural and underserved practice increases likelihood of future practice in such settings, warranting greater incentivization of diverse clinical training.²⁶ While some UGs may be drawn to rural practice for its professional diversity, others may avoid it due to perceived isolation, limited professional development opportunities, and fewer resources for advanced care.¹³

Regression Analysis and Influencing Factors

The regression analysis provides key insights into the factors associated with UG mobility across different career transitions. By analyzing travel distances within different percentiles (10th, 50th, and 90th), we can better understand how various factors shape mobility patterns for those who move shorter or longer distances. This approach ensures that comparisons remain meaningful, as those moving within the 10th percentile face very different circumstances from those in the 90th percentile.

During the T1 transition, rurality played a significant role, particularly for those who moved the farthest (90th percentile). Graduates from more rural medical school regions tended to move significantly farther for residency than their peers (β = 25, p = 0.010), suggesting that these areas may have fewer local training opportunities, requiring students to relocate for residency. Additionally, graduates from regions with a higher percentage of uninsured individuals traveled farther during T1 (β = 25, p < 0.001, at the 50th percentile), indicating that training in medically underserved areas may limit local residency placement options. The delta values further reinforce this pattern, showing that rurality slightly increased in T1 (+0.575), suggesting that some UGs moved to slightly more rural regions for residency, but not significantly so.

The T2 transition involved the highest mobility, with 56% of UGs traveling over 500 miles. Unlike T1, county population size and attending a T50NIH had no significant effect on travel distance. However, the percentage of uninsured individuals in the residency region did have an impact, with UGs in the 50th percentile traveling farther as the uninsured population increased (β = 31, p = 0.016), whereas those in the 90th percentile actually traveled shorter distances as the uninsured rate increased (β = -53, p = 0.014). This suggests that mid-range movers may have sought fellowships in better-funded regions, while those already accustomed to long-distance moves may have relied on existing networks rather than moving even farther. The delta values from Table 6 show that rurality slightly decreased in T2 (-1.566), reinforcing the idea that fellowships are largely concentrated in urban or well-resourced areas, drawing residents away from more rural training locations.

The T3 transition revealed distinct differences in mobility patterns. Graduates from T50NIH institutions in the 90th percentile moved significantly farther for their first job compared to Non-T50NIH graduates (β = 362, p = 0.026). This suggests that graduates from research intensive institutions may have more national networking opportunities or greater career flexibility, allowing them to pursue jobs in more competitive or high-paying locations. Economic factors also influenced job-seeking behavior. Graduates who completed residency after the onset of COVID-19 traveled shorter distances in the 10th percentile (β = -11, p = 0.033), suggesting that pandemic-related uncertainties may have encouraged physicians to prioritize stability and regional job opportunities. Additionally, in areas with higher unemployment rates, UGs in the 50th percentile traveled shorter distances for their first job (β = -157, p = 0.007), implying that weaker job markets may have discouraged long-distance relocation.

Despite these variations, an important trend emerged in T3: an increase in rurality. The delta values show that UGs were more likely to take jobs in rural areas during T3 than in previous transitions (+6.648). This shift may reflect financial incentives, loan repayment programs, or the increased demand for specialists in underserved areas. However, despite this increase, rural areas remain disproportionately underserved in terms of urological care.²⁵

These findings highlight several key structural challenges in workforce distribution. First, rural and underserved areas continue to lose physicians at each transition stage, especially during T2, when residents often leave these areas to train in more urban, well-resourced fellowship programs. Expanding residency and fellowship opportunities in rural hospitals and underserved regions could help address this issue. Second, the centralization of fellowship training programs forces many UGs to move long distances, often concentrating expertise in major academic centers and limiting specialist access for rural populations. Policymakers should consider strategies to decentralize training opportunities, particularly in high-demand subspecialties like urologic oncology and minimally invasive surgery.²⁷ Factors such as program reputation and clinical volume have been demonstrated to influence fellowship ranking, while match satisfaction may be driven by location, faculty, family considerations, and program reputation.²⁸ Finally, economic factors play a crucial role in mobility decisions. The impact of unemployment rates and post-COVID job-seeking trends suggests that financial security and job availability strongly influence early career decisions. Expanding financial incentives, such as rural loan forgiveness programs or competitive salaries, could help encourage UGs to establish long-term careers in regions with greater healthcare needs.²⁹

Limitations

This study relied on publicly available data, which may not comprehensively capture factors influencing mobility decisions, such as personal preferences, familial obligations, or institutional policies. Due to the availability of published trainee information, data collection was isolated to 2020 to 2023 due to limited information prior to 2020. Data collection was completed in 2023, preventing analysis of more recent trends. By utilizing data from publicly accessible websites of ACGME-accredited Urology residency programs, this study is limited in its coverage of urology trainees, as some programs within this time frame did not publish trainee data in a comprehensive manner; however, data for 150 programs was obtained covering the majority of ACGME-accredited programs. Because the cohort was derived from publicly available residency program websites, incomplete program reporting and variable webpage maintenance may introduce coverage error and limit generalizability. The analytic sample should therefore be interpreted as a descriptive cohort of identifiable graduates rather than a complete national census. Studies using linked ACGME and regulatory datasets would be valuable to validate these findings; however, such datasets can be difficult to access. By utilizing publicly available data, our study inherently depended on program staff updating and maintaining trainee biographies, notably with practice sites after graduation, as programs did not provide multiple practice locations for physicians who had multicenter practice. Moreover, by utilizing this data source, biographical variables such as trainee hometown were often unavailable. While data regarding other transitions were readily available, the transition from fellowship to attending position was limited due to the relatively low number of urology fellows, and this transition was not evaluated in our study. Year of graduation was not included as a dependent variable considered in movement of residents, and longitudinal geographic trends were not included in this study. In addition, many programs retain IMGs as residents for training, and such trainees influence overall retention data and movement trends although to a lesser degree than domestically educated trainees. However, data regarding home country and training location for IMGs was not available consistently across publicly available sources. As such, retention and movement data for IMGs were not included as part of our analysis.

While we analyzed regional transitions, nuances within subregions or specific urban versus rural divides were not fully explored. The classification of institutions into T50NIH and NonT50NIH may oversimplify the influence of institutional prestige, as other factors, such as local healthcare market dynamics, may also play a significant role.

Additionally, although the 500-mile threshold was selected based on prior workforce studies and its relevance to U.S. geography, any categorical cutoff may oversimplify mobility and should be interpreted as a general marker of substantial relocation rather than a strict boundary. Lastly, the 2020 to 2023 data spans a period that included the COVID-19 pandemic, which could have impacted mobility patterns in ways that are not yet fully understood.

Future Directions

Future research should address these limitations by incorporating qualitative data through surveys with Urology graduates to better understand personal and professional factors influencing mobility decisions. Expanding analyses to include more granular geographic data, such as rural versus urban transitions, could reveal additional insights into workforce distribution. Furthermore, examining the impact of institutional policies, such as loan repayment programs or residency stipends, on retention could provide actionable data for improving workforce stability. Investigating other public health factors, such as access to preventive care and regional healthcare infrastructure, would help contextualize the findings within broader healthcare dynamics. Finally, exploring how workforce distribution impacts patient outcomes would strengthen the connection between workforce dynamics and population health.

Conclusion

The findings highlight the importance of strategically aligning urology workforce distribution with regional healthcare needs. Policymakers, training programs, and healthcare leaders must collaborate to address disparities in urologist availability, especially in regions with high smoking prevalence and underserved urban populations. Expanding fellowship opportunities, enhancing rural practice incentives, and fostering partnerships with public health organizations can create a more equitable and sustainable urology workforce. By addressing both the systemic and public health challenges identified in this study, the healthcare system can ensure improved access to high-quality urological care and better outcomes for patients across all regions.

Supplemental Material

Supplemental Material - Geographic Mobility of Urology Graduates from Residency to First Practice in the United States

Supplemenal Material for Geographic Mobility of Urology Graduates from Residency to First Practice in the United States by Mikhil Patel, Chakravarthy Nulu, Raag Patel, Sriharsha Sripadrao, Rohan Vuppala, Nikhil Jaganathan, Bradley Morganstern, Sherita King and Martha Terris in Journal of Medical Education and Curricular Development.

Footnotes

ORCID iD

Mikhil Patel

Author Contributions

Conceptualization, M.P., C.N., R.P. and R.V.; methodology, C.N., R.P. and R.V.; validation, C.N., R.P., R.V., S.S., N.J., M.P., M.T., B.M., S.K.; formal analysis, C.N., R.P. and R.V.; investigation, M.P., C.N., R.P. and R.V.; data curation, C.N., R.P. and R.V.; writing—original draft preparation, C.N., R.P., R.V., S.S., N.J., M.P., M.T., B.M., S.K.; writing—review and editing, C.N., R.P., R.V., S.S., N.J., M.P., M.T., B.M., S.K.; All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

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