Regional Variation in Scope of Practice by Family Physicians
============================================================

* Alyssa Lambert
* Sarah E. Fleischer
* Omer Atac
* Andrew Bazemore
* Lars E. Peterson

## Abstract

*Introduction:* Geographic variation in physician scope of practice (SOP) has been documented but the causes remain unknown. We examined whether geographic variation in family physician (FP) SOP is explained by differences in the characteristics of the FPs, their practices, practice environment, or health care market.

*Methods:* We utilized 2 datasets from the American Board of Family Medicine (ABFM) from 2017 to 2022. The National Graduate Survey captures early career FPs while the Continuous Certification Questionnaire is administered to mid to late career FPs. We used a SOP score that ranges from 0 to 30 with a larger score reflecting a broader SOP. Bivariate analyses assessed for differences by Census division in clinician, practice, community, and health care market characteristics. A series of multilevel linear regression analyses tested if geographic differences in SOP were attenuated by the aforementioned characteristics.

*Results:* Our analytic included 9,378 early career FPs and 28,832 mid to late career FPs in the unadjusted regression model. We found significant differences in clinician characteristics by division and cohort. In unadjusted results, SOP score differed by division and career stage within division (range 11.49 to 14.95 for later career FPs and 15.22 to 17.51 for early career FPs). Adjusting for clinician, practice, community, and health care market characteristics did not attenuate divisional variation in SOP.

*Discussion:* Significant geographic variation in FP SOP was not explainable by adjustment for clinician, practice, community, and health care market characteristics. This suggests that health care variation is multifactorial and will require more multifaceted interventions to ameliorate.

*   Delivery of Health Care
*   Family Medicine
*   Family Physicians
*   Health Workforce
*   Primary Health Care
*   Scope of Practice
*   Secondary Data Analysis

## Introduction

Family physicians (FPs) made up 39.8% of the primary care physician workforce in 2019, and have been shown to provide the broadest scope of services among not only primary care, but all physician specialties.1⇓–3 They often provide essential services that would not otherwise be available in areas with fewer resources.4 The value of broad scope family medicine has been well-documented,5 and includes lower health care costs,6 fewer hospitalizations,6 and decreased rate of burnout for FPs.7 Despite these personal and system level benefits, a declining number of FPs are practicing women’s health care,8,9 caring for children,10,11 delivering babies,12 and performing endoscopies.13 In addition, newly graduated FPs feel more prepared and intend to practice more broadly but have a narrower scope of practice (SOP) than their predecessors.14 As FPs often provide care to those in medically underserved areas,4 declining SOP may reduce equitable access to health care and compound existing concerns over primary care shortage and maldistribution.14⇓–16

There is broad variation in the utilization and costs of health care services in the United States.17 For example, Medicare reimbursements yearly per enrollee are around $3,000 more in the West South Central and East South Central divisions than in the Mountain and Pacific divisions.17 Similarly, FP scope has been shown to geographically vary. Rural FPs have a wider SOP than urban physicians,18⇓–20 but regional variation also exists. FPs practicing in the West and Midwest have higher odds of practicing obstetric21 and pediatric care.22 FPs have greater odds of practicing HIV care in the Northeast and West.23 These differences likely start in residency as those trained in a rural residency, or in the West or Midwest, have a broader SOP.19,24

Two recent articles built conceptual models to understand the ways in which FP SOP is influenced by multiple domains. Russell et al described influence across 4 areas – personal, workplace, environment, and population.25 Personal factors affected desired SOP for FPs while workplace, environment, and population influenced actual SOP.25 This article built on the concepts put forth by Reitz et al, that SOP is influenced by both contextual and developmental factors.26 Contextual factors consist of workplace, health care landscape, and personal factors and developmental factors pertained to the FP’s stage in life and career.26 Noted in both these studies was the tendency for FPs to narrow their scope as they progressed in their career.26 Possible drivers of declining SOP from these articles include lack of employer support or job opportunities,27 loss or lack of skills, and desire to narrow scope as one gets closer to retirement.25,26 Past work supports that specific characteristics within these conceptual domains drive SOP. For instance, graduating residents indicate the wish to practice in a broader scope than current FPs are practicing,19,28,29 which supports changing SOP with career stage. In addition, working in a rural health center or academic practice type has the highest SOP30 and working with a Physician Assistant increases SOP.31 This suggests that practice organization and infrastructure have an impact on SOP. Community characteristics may also affect SOP as FPs in disadvantaged communities tend to have a more narrow SOP.4,32

What remains unknown is whether geographic variation in SOP exists because of differences in the distribution of clinicians, practice, community, and health care market characteristics between divisions. Our objective is to test whether geographic variation in FP scope can be explained by differences in these aforementioned characteristics.

## Methods

We utilized 2 datasets from the American Board of Family Medicine (ABFM) from 2017 to 2022. First, the National Graduate Survey (NGS) is administered to early career FPs 3 years after residency graduation throughout a calendar year. The pooled response rate during our study period was 57.42% and respondents are representative of all eligible graduates.33 Second, the Continuing Certification Questionnaire (CCQ) is mandatory component, achieving a 100% response rate, of the examination registration process for mid to late career physicians seeking to continue their ABFM certification and is completed 3 to 4 months before the examination date.34 Both these instruments have common items on SOP, ownership of the practice, practice type, size, and composition of health care team at the practice. We limited our sample to FPs in the United States and those primarily in continuity settings. Demographic information was obtained from ABFM administrative data sets.

Data on community characteristics were obtained at the county level from different sources. First, division was determined by linking state of practice to its respective US Census regional classification. In the past, studies have used the 4 census regions to control for geographic variation,8⇓⇓–11 but we used the 8 divisions to achieve a more granular geographic perspective on possible drivers of large area variation. Second, we used the 2019 Social Deprivation Index (SDI) to measure county level socioeconomic status across a broad array of indicators.35 Third, we used variables from the 2021 Area Health Resources File on the availability of general community hospital beds and Medicare expenditures per capita, and physician supply. Fourth, we used premature death data from the 2021 County Health Rankings as an indicator of health status at the community level, which has been done using mortality in previous studies.36

Our main outcome was the SOP score which is scored from 0 to 30 with a larger score reflecting a broader SOP.37 The SOP score was developed using the Rasch Model, which creates a measurement of a latent trait based observed data. In this case, the latent trait is SOP and is based on how many clinical services, or activities, and sites where FPs provide care. While there are different items from the NGS and CCQ used to calculate SOP, the scores are on the same scale for each group, allowing the scores to be directly compared.

To account for clinician characteristics, we used ABFM data on FP gender, age, race and ethnicity, medical degree, location of undergraduate medical, and residency training. To account for practice characteristics, we used variables on practice type, size, and specialty mix. To account for community characteristics, we used the SDI, rurality of the practice using rural urban commuting area, and the premature death rate. Finally, we used variables on short term community hospital beds and Medicare costs per capita and the percentage of physicians in primary care (family medicine, general internal medicine, pediatrics) to reflect the health care market. County level variables on premature death rate, hospital beds, and Medicare costs were categorized into tertiles with low being >1 standard deviation below the mean, medium ±1 standard deviation from the mean, and high >1 standard deviation above the mean. In addition, we calculated the percentage of FPs who were interested in doing so but, reported not providing deliveries or inpatient care because it was not available in the job they took or due to challenges with privileging to reflect the ease of FPs practicing broadly in the community.

We conducted parallel analyses for the early career FPs in the NGS data and the mid to late career FPs from the CCQ due to the hypothesized differences in forces shaping SOP early in a career versus later in practice25,26 and data on working with a Physician Assistant only being available on the CCQ. We described the characteristics of the NGS and CCQ cohorts and their practices and communities. We then assessed for differences by division for the personal, practice, community, and health care market characteristics for both the NGS and CCQ cohorts using χ2 and ANOVA tests. Finally, we performed a series of multilevel linear regression analyses with SOP score as the outcome, for both NGS and CCQ cohorts, to test the association of division with SOP. All models accounted for clustering at the state and county level due to variation in health policy and regulation. Model 1 only included division to test for differences in scope by division only. Model 2 adjusted for personal characteristics. Model 3 adjusted for only practice characteristics, Model 4 adjusted for community characteristics, and Model 5 accounted for health care market characteristics.

We conducted all analyses using SAS v9.4 (Cary, NC). This study was approved by the American Academy of Family Physicians Institutional Review Board.

## Results

We began with 11,975 FPs in the NGS cohort and 40,184 FPs in the CCQ cohort. After applying previously noted exclusion criteria, we were left with a final sample size of 9,378 early career FPs and 28,832 mid to late career FPs in the unadjusted regression model.

Significant differences were found in the clinician characteristics by division and cohort (Tables 1 and 2). SOP score not only differed by division, but by career stage within division, with early career FPs having an average SOP score of 16.04 compared with an average scope score of 12.77 for the later career FPs. The variation in SOP between the 2 career stage cohorts is shown in Table 3. Figure 1 depicts the deviation of the average SOP score from the reference division (West South Central), for both the NGS and CCQ cohort. Among early career FPs, the percentage of female FPs ranged from 64.9% in New England to 49.1% in East South Central. Less variation is seen in the percentage of females on the CCQ with the full population consisting of 45.0% females. In the East South Central division, 13.4% of NGS and 11.3% of CCQ respondents attended residency in a small or large rural area which is the highest percentage for both cohorts.

![Figure 1.](http://www.jabfm.org/https://www.jabfm.org/content/jabfp/early/2025/03/28/jabfm.2024.240201R1/F1.medium.gif)

[Figure 1.](http://www.jabfm.org/content/early/2025/03/28/jabfm.2024.240201R1/F1)

Figure 1. 
Divisional variation from mean scope of practice score for early career and mid to late career family physicians.

View this table:
[Table 1.](http://www.jabfm.org/content/early/2025/03/28/jabfm.2024.240201R1/T1)

Table 1. 
Divisional Variation of Clinician, Practice, Community, and Healthcare Market Characteristics of Early Career Family Physicians, 2017 to 2022

View this table:
[Table 2.](http://www.jabfm.org/content/early/2025/03/28/jabfm.2024.240201R1/T2)

Table 2. 
Divisional Variation of Clinician, Practice, Community, and Healthcare Market Characteristics of Mid to Late Career Family Physicians, 2017 to 2022

View this table:
[Table 3.](http://www.jabfm.org/content/early/2025/03/28/jabfm.2024.240201R1/T3)

Table 3. 
Scope of Practice Score by Clinician, Practice, Community, and Healthcare Market Characteristics for Early Career (NGS) and Mid to Late Career (CCQ) Family Physicians, 2017 to 2022

Practice types varied between the 2 cohorts and by division. Academic health centers (AHC) made up 16.7% of practice types for early career FPs in the East North Central division, while rural health centers (RHC) made up 13.0% of practice types in the West North Central division. Overall, the percentages of FPs practicing in either AHCs and RHCs was much smaller on the CCQ, 7.6% and 2.3%, respectively. This pattern persisted with the CCQ cohort: 34.6% of FPs in the Pacific division practiced in a Family Medicine only clinics and 34.1% practiced in clinics of mixed primary care specialties.

Community characteristics also varied by division. West North Central division has the largest percentage of FPs practicing in an isolated, small rural or large rural area (40.4% and 32.9% for the NGS and CCQ, respectively) and has the highest SOP score in both cohorts. East South Central has the second highest percentage of FPs practicing in large and small rural areas but has one of the lower SOP scores on the NGS. The highest percentage of FPs worked in high SDI communities in the Pacific division for the NGS and the West South Central division for the CCQ. The highest premature death rate was in the East South Central division for both cohorts at 24.9% and 26.2%. The lowest premature death rate was found in the Pacific with 89.9% and 98.1% of that division having a low premature death rate.

Health care markets also varied widely with hospital beds per capita in the NGS cohort in the Middle Atlantic division having 86.2% of a high number of hospital beds per capita compared with only 49.2% in the West North Central division. The CCQ cohort displays a similar level of variation with the Middle Atlantic having 84.4% high hospital beds per capita and West North Central division having only 54.0%. Medicare costs per capita were highest in the Pacific and West South Central divisions and lowest in Mountain division for both cohorts. Around 30% of early career FPs and 36% of mid to late career FPs responded that they experienced either difficulty with privileging or lack of opportunity at the job they took in terms of practicing inpatient medicine or obstetric care.

In a series of 5 regression models controlling for variables representing each of our domains, the variation in SOP at the division level was not eliminated in either cohort in any of the models (Figure 2). See Appendix Tables 2 and 3 for the full results of each model. Model 1 only investigated the association between division and SOP score controlling for state to establish the baseline. The highest SOP score was in the West North Central division for both cohorts and the lowest SOP score was in the Middle Atlantic division and the South Atlantic division for the NGS and CCQ cohorts, respectively. Adjusting for FP characteristics (Model 2) had a small impact overall but resulted in the most attenuation of variation compared with our other models. Controlling for practice characteristics (Model 3) attenuated the associations in both cohorts, but again did not fully eliminate the variation. Adjusting for either community (Model 4) or health care market (Model 5) characteristics did not affect the associations in a uniform way, increasing the association in some and decreasing it in others.

![Figure 2.](http://www.jabfm.org/https://www.jabfm.org/content/jabfp/early/2025/03/28/jabfm.2024.240201R1/F2.medium.gif)

[Figure 2.](http://www.jabfm.org/content/early/2025/03/28/jabfm.2024.240201R1/F2)

Figure 2. 
Adjusted associations between scope of practice score and division controlling for clinician, practice, community, and health care market characteristics for early career and mid to late career family physicians.

## Discussion

Despite access to national data on over 40,000 FPs and data across multiple conceptually associated domains, our analyses did not fully explain geographic variation in FP SOP (Figure 1). We did, importantly, identify that controlling for differences in clinician, practice, community, and health care market characteristics does reduce the variation in SOP, and that the differences were minimal and varied between cohorts. These findings suggest that this variation is a complex topic that may not be explained solely by quantifiable variables.

Demographics such as age, race, and international medical graduate status have been linked with SOP,26,29,32 and our analyses redemonstrated these findings in the personal characteristics model. This model attenuated variation in SOP at the division level for early career FPs, suggesting that some of the geographic variation in this cohort may be explained by the variation in physician characteristics in different divisions.

Model 3, adjusting for practice factors, attenuated the variation in the CCQ cohort to the greatest degree. This suggests that to some degree the geographic variation in SOP among mid to late career FPs is accounted for by distribution of practice types and factors. This may also suggest as an FP moves further in their career, practice level factors have an increased impact on their SOP. This would be supported by both studies on drivers of scope of practice which put forth that as physicians advance in their career, they often desire to narrow their SOP and may find less support for a broad SOP. This variation may also be due to an increasing number of hospital-owned practices38 that are influencing the scope of FPs in ways that are difficult to operationalize.25,26

Studies have found that rural FPs have a broader SOP than urban FPs.4,20 The most rural division, West North Central, had the highest SOP score in both cohorts, yet the second most rural division, East South Central, having one of the lowest SOP scores in both cohorts. In Table 3, FPs in all rural areas have higher SOP scores than the urban FPs, but do not show a consistent pattern with FPs in Isolated settings having the highest SOP in the NGS cohort and those in Small Rural having the highest SOP in the CCQ cohort. Our study also redemonstrated an earlier finding that a higher SOP is associated with lower Medicare costs.6

SOP is a multidimensional construct, viewed differently across varying specialty and conceptual lenses.1,39 Our main outcome only captures the range of services FPs perform while other domains include involvement in patient conditions and new problem management. Prior work has shown measures of these different constructs are not associated but are all associated with lower costs and utilization.39 Given these findings, we hypothesize that we would also fail to explain geographic variation using other SOP measures.

Our study is subject to limitations. First, our data are cross-sectional, and we cannot make causal inferences. Second, the variables we chose to operationalize the conceptual domains may not represent their influence on SOP. Finally, health care culture may be the ultimate driver of variation and qualitative methods and creating system level variables will likely be needed to fully understand these relationships.

## Conclusion

These findings further articulate regional and divisional variation in SOP for FPs. And while also revealing that data from the most comprehensive national surveys of FPs still do not permit a definitive explanation of the sources of such variation, these findings still provide more context and support for previous works. Our models showed that personal and practice factors do attenuate this variation for early and mid to late career FPs, respectively. More work is needed on this topic and would likely benefit from qualitative study to provide context for the results we have found.

## Appendix.Appendix Table 1. Adjusted Associations Between Scope of Practice Score and Division Controlling for Clinician, Practice, Community, and Healthcare Market Characteristics for Early Career and Mid to Late Career Family Physicians

  
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## Appendix Table 2. Adjusted Associations Between Scope of Practice Score and Division Controlling for Clinician, Practice, Community, and Healthcare Market Characteristics for Early Career Family Physicians

  
</br>![][2]</img>

  
</br>![][3]</img>

## Appendix Table 3. Adjusted Associations Between Scope of Practice Score and Division Controlling for Clinician, Practice, Community, and Healthcare Market Characteristics for Mid to Late Career Family Physicians

  
</br>![][4]</img>

  
</br>![][5]</img>

## Notes

*   This article was externally peer reviewed.

*   This is the Ahead of Print version of the article.

*   *Conflict of interest:* Ms. Fleishcer and Drs. Bazemore and Peterson are employees of the American Board of Family Medicine.

*   *Funding:* Dr. Lambert’s work as an ABFM Visiting Scholar was supported by the ABFM Foundation.

*   To see this article online, please go to: [http://jabfm.org/content/00/00/000.full](http://jabfm.org/content/00/00/000.full).

*   Received for publication May 21, 2024.
*   Revision received July 17, 2024.
*   Accepted for publication July 22, 2024.

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