Adolescent medicine (AM) subspecialists provide primary, subspecialty, and consultative care to adolescents and young adults (AYAs). Given insufficient numbers of AM subspecialists to care for all AYAs, the workforce supports AYAs health care capacity through education, research, advocacy, and the development of policies and programs sensitive to their unique needs. A modeling project funded by the American Board of Pediatrics Foundation was developed to forecast the pediatric subspecialty workforce in the United States from 2020 to 2040 on the basis of current trends in each subspecialty. The model predicts workforce supply at baseline and across alternative scenarios, and reports results in headcount and headcount adjusted for percentage of time spent in clinical care, termed “clinical workforce equivalent.” For the AM subspecialty, several scenarios were considered that modified the number of fellows and/or clinical time. The baseline model predicted low growth nationally (27% and 13% increase in total AM subspecialists and AM subspecialists per 100 000 children, respectively) and declines in AM workforce relative to population growth in census divisions with existing geographic workforce disparities. In the alternative scenarios, fellow number and clinical time changes did not significantly change predictions relative to the baseline model, but a 12.5% decrease in fellows predicted a 40% reduction in the workforce from baseline with a widening of geographic workforce disparities. On the basis of the expansive clinical and nonclinical roles of AM subspecialists and these forecasted workforce challenges, significant educational, practice, and policy changes will be necessary to bolster the supply of well-trained clinicians addressing the dynamic health care needs of AYAs.
Adolescence and young adulthood are times of transition marked by rapid and dynamic physical, social, emotional, and cognitive development. Such developmental changes confer challenges to meeting evolving adolescent and young adult (AYA) health care needs. Physical and emotional changes influence the management of chronic conditions and impact AYA’s navigation of sexual and gender identities. Emerging autonomy and identity development may influence participation in, and adherence to, disease management plans. AYA risk tolerance, critical for cognitive and psychosocial development, may be associated with the increased risk-taking behaviors that contribute to leading causes of death among AYA in the United States: Unintentional injury, suicide, and homicide.1,2 Successful AYA transition to independent adulthood influences and is influenced by health status and health care access, occurring in a world where an increasingly diverse AYA population must also negotiate structural racism, politicized health care access, widening gaps in income and opportunity, and discrimination based upon race, ethnicity, gender, sexual orientation, ability, and age.
Adolescent medicine (AM) subspecialists help patients, families, and clinicians manage these complexities, vulnerabilities, and emerging AYA capacities through clinical care, medical education, research, and advocacy to advance AYA health, and provide essential consultation and expert technical assistance to inform policy and program development that is sensitive to unique AYA needs. However, AM is 1 of the smallest pediatric subspecialties with ongoing workforce challenges.1,3
In a collaborative effort to understand the needs and challenges of the pediatric subspecialty workforce over the next 20 years, this article reviews evolving AYA health care needs, describes the current AM subspecialty workforce, and discusses future workforce needs using a pediatric subspecialty workforce model funded by the American Board of Pediatrics (ABP) Foundation and described in this supplement’s introduction.4
Adolescents and Young Adults Presenting to Adolescent Medicine Subspecialists
The World Health Organization defines adolescence from ages 10 to 19 years5 and the American Academy of Pediatrics Bright Futures guidelines define this life stage as 11 to 21 years of age.6 In 2019, adolescents aged 10 to 19 years comprised 12.8% (42 million total) of the US population, and estimates suggest there will be 44 million adolescents in the United States by 2050. Nearly half of adolescents identify as a member of a minoritized racial or ethnic group and one-third live in poverty, which has significant implications given known racial, ethnic, and socioeconomic health disparities. Although most adolescents live in urban areas, approximately one-tenth live in rural areas where they are more likely to live in poverty, rely on public health insurance, and struggle with health care access.7
Many pediatric subspecialists continue to provide care through young adulthood, a critical time marked by ongoing development and transition. Caring for AYAs through 25 years of age by AM subspecialists8–10 is supported by the Society for Adolescent Health and Medicine11 with the goal of bridging the gap between pediatric and adult care.
AM subspecialists provide primary, consultative, and subspecialty AYA care in inpatient and outpatient clinical settings.8,12 They bring expertise in history-taking on sensitive topics, apply motivational interviewing techniques, work within complex youth–family relationships, and build therapeutic alliances with AYAs. Using a strength-based, patient-centered approach, AM subspecialists build on AYAs’ evolving cognitive capacity, ability to learn and apply new skills, and desire for independence to support effective self-management of acute and chronic conditions and successful transitions to adulthood. AM subspecialists promote healthy AYA relationships, sexual and reproductive behaviors, body image, eating and exercise behaviors, and media use while also supporting AYA identity development. In addition, AM subspecialists take a trauma-informed, evidence-based, equitable, inclusive, and culturally responsive approach to screening for and addressing AYA health risks. These risks have, unfortunately, persisted or increased over the last decade, as evidenced by results from the National Survey of Children’s Health,13 the Centers for Disease Control and Prevention Youth Risk Behavior Survey,14 the Monitoring the Future Survey,15 and the Growing Up Today Study.16 Two critical areas of AYA health are highlighted below.
Sexually transmitted infections (STIs) and unintended pregnancy remain persistent threats to AYA health and well-being. AYAs aged 15 to 24 years, approximately 27% of the sexually active US population, have persistently accounted for at least 50% of STIs diagnosed each year.17 Sexual health disparities exist, with a greater burden of STI diagnoses, including HIV, among sexual and gender-diverse (SGD) AYAs and those from minoritized racial and ethnic groups.17 In the United States, the teen birth rate (births per 1000 females aged 15–19 years) has declined annually since 1991, but is characterized by persistent racial and ethnic disparities, remaining higher than rates in other industrialized nations.18 Adolescence is a time of sexual exploration and identity development, but there are biological, cognitive, behavioral, and structural factors that increase AYA risk for adverse sexual and reproductive health outcomes that contribute to health disparities.19 AM subspecialists are mindful of these factors and provide access to comprehensive and inclusive sexual and reproductive care, including contraceptive care,20 pregnancy management,21,22 and the treatment and prevention of STIs, including HIV.23,24
The behavioral mental health crisis is 1 of the most pressing issues currently threatening the health and wellness of AYAs across the country.25,26 In 2021, the American Academy of Pediatrics, American Academy of Child and Adolescent Psychiatry, and Children’s Hospital Association jointly declared a national emergency in child and adolescent mental health.27 In 2021, the Department of Health and Human Services identified mental health challenges as the leading cause of disability and poor outcomes in young people, with 1 in 5 youth having a mental, emotional, developmental, or behavioral disorder.26 Among these, eating and substance use disorders have significantly impacted AYA, particularly those who self-identify as SGD, are from racial and ethnic minoritized groups, and/or live in poverty.26,28–30 On the basis of lifetime prevalence, incidence, and mortality data, it is estimated that 1.9 million children and adolescents experienced an eating disorder in 2018 to 2019.31 During the coronavirus disease 2019 pandemic, patients seeking and receiving eating disorder care increased significantly.32 Eating disorders are associated with increased mortality, high rates of years lost because of disability, and reduced quality of life.33 AYA substance use and overdose are also significant challenges nationwide. Although AYA alcohol use has declined over the past decade, “high-intensity” alcohol use (10+ or 15+ drinks per occasion) remains a significant concern, and use of electronic vaporizer devices has increased.34,35 Cannabis is the leading cause of substance use disorder among AYAs, with daily use at its highest levels in 20 years.29 In 2020, 1 million adolescents (4.1%) and 4.5 million young adults (13.5%) had a cannabis use disorder.36 The developing AYA brain is particularly vulnerable to the toxic effects of alcohol, cannabis, and other substances, and can lead to a number of poor health outcomes and increased mortality risks.37 Since 2020, overdose and poisoning are the third leading cause of death among youth <20 years of age, largely related to unintentional use of fentanyl.38
AM subspecialists have expertise working in multidisciplinary teams to address the interconnected mental, behavioral, psychosocial, and physical health needs of all AYAs, including those who self-identify as SGD or as being from minoritized racial and ethnic groups, who disproportionately experience poor health outcomes.39–45
The Current Adolescent Medicine Workforce
History
The first AM training program began at Boston Children’s Hospital in 1951 when college health services emerged as a new model of care in the post World War II era to support AYAs pursuing postsecondary education and living away from home. Federal funding in the 1960s and 1970s, including the first Leadership Education in Adolescent Health program funded by the US Department of Health and Human Services Maternal and Child Health Bureau, continued the growth and expansion of these training programs throughout the country. The ABP recognized AM as a board-certified subspecialty in 1991 and offered the first AM certifying examination in 1994.3,46 The American Board of Internal Medicine (ABIM) recognized the subspecialty in 1992, and the American Board of Family Medicine (ABFM) began offering a Certificate of Added Qualification in 2000. The first AM fellowship training programs received accreditation from the Accreditation Council for Graduate Medical Education (ACGME) in 1998.3
Basic Numbers and Demographics
AM-certified subspecialists may initially train in pediatrics, internal medicine, combined internal medicine/pediatrics, or family medicine residency programs; the data presented below vary regarding which trainees are included. On the basis of ABP data through June 2023, 836 pediatricians have ever been board-certified in AM by the ABP, 69.4% (580) of whom were actively enrolled in Maintenance of Certification.47 Certifications granted by the ABFM and the ABIM account for another 240 ever-certified AM subspecialists as of June 2022.48
The ABP data on certified subspecialists include individuals who may not be in the current workforce because of recent retirement, death, or other factors. To correct this, subsequent descriptions of the current workforce limit the sample to 555 currently certified pediatricians ≤70 years of age. Of these, 76.8% identified as female, 23.1% as male, and 0.2% as gender nonbinary. Gender nonbinary individuals may be underreported because this was not a response option until 2021. The median age was 50 years and 23.8% were aged 61 to 70 years. Regarding medical training, 81.8% were American medical school graduates (AMGs) with a Doctor of Medicine (MD) degree, 5.9% were AMGs with a Doctor of Osteopathic Medicine (DO) degree, 9.2% were international medical graduates (IMGs) with an MD degree, and 3.1% were IMGs with an international degree.47 Notably, AM is the most racially and ethnically diverse subspecialty in pediatrics, with race and ethnicity estimates from 2018 to 2022 suggesting that ∼26.8% self-identified as underrepresented in medicine (URiM), including 16.5% identifying as Black or African American and 5.8% as Hispanic, Latino, or Spanish origin.49
Work Characteristics
Data on current ABP-certified AM subspecialists are collected through the ABP’s Maintenance of Certification enrollment surveys. Surveys from 2018 to 2022 had a 61.1% response rate reflecting responses from 324 AM subspecialists aged ≤70 years. Because of skip patterns in the survey, percentages reported below are for individual questions.50 The majority of respondents reported full-time employment (84.2%); 47.0% worked ≥50 hours per week on average over the previous 6 months. Similar proportions of women (13.7%) and men (11.3%) indicated part-time employment status. Most respondents (70.2%) spent ≥50% of their time in clinical care, whereas only 5.4% reported spending ≥50% on research.51 The largest proportion of respondents (32.3%) endorsed a primary work setting within a medical school or its parent university, with most (83.7%) having a faculty appointment. Nearly three-quarters (74.4%) worked in an urban environment. Almost half (49.3%) reported that ≥50% of patients received public insurance.52
Geographic Distribution
When limited to those subspecialists in the United States in 2023, there was an average of 14.4 currently certified (across all 3 certification boards) AM subspecialists per US state (range 0–94), which translates to 1.0 AM subspecialists per 100 000 children ages 0 to 18 years (range 0.0–8.7) (Fig 1). On the basis of the US Census Bureau estimates of the AYA population, there are 1.2 AM subspecialists per 100 000 AYAs aged 10 to 25 years.53 Per 2021–2022 ACGME data, states with AM fellowship training programs have more AM subspecialists per 100 000 children aged 0 to 18 years, whereas several states have no ABP-certified AM subspecialists, leading to variable access to AM specialty care. The average driving distance to an ABP-certified AM subspecialist in 2019 ranged from 7.2 miles in New Jersey to 190.5 miles in Wyoming (excluding Alaska, Hawaii, Puerto Rico, and the District of Columbia).52,54,55 Most ABP-certified AM subspecialists are concentrated in urban settings, with very few in rural areas.
Fellowship Pathways
According to the ABP, the total number of first-year AM fellows, including those selected outside of the Match, increased from 35 in 2012 to 2013 to 37 in 2022 to 2023 (+5.7%).54 The total number of AM fellows in standard, noncombined US fellowship programs increased from 68 in 2008 to 93 in 2022 (+36.8%).55 In academic year 2022 to 2023, the 93 AM fellows in these programs identified as female (79.6%). Most (74.2%) held MD degrees from US medical schools; an additional 12.9% were AMGs with a DO degree, and 6.5% were IMGs with an MD degree. These characteristics largely mirror the current workforce, except for the higher percentage of AMG trainees with a DO degree compared with current ABP-certified AM subspecialists (12.9% vs 5.9%).55 The percentage of AM fellows from URiM backgrounds has fallen from 34.1% (2018) to 27.9% (2022), yet AM still remains 1 of the most diverse ABP subspecialties.55
Fellows commonly take their first posttraining position near their training location.56 Figure 1 shows the variability in AM fellowship locations in academic year 2021 to 2022, displaying a pattern similar to the current AM workforce with geographic clustering in the Northeastern and West Coast states and at large academic centers in urban areas of Southern and Midwestern states.
Financial Considerations
A 2021 study by Catenaccio and colleagues estimated a negative financial return for most clinicians entering pediatric subspecialty fellowship training programs compared with those foregoing fellowship training.57 With the exception of developmental/behavioral pediatrics,58 AM fared the worst of all pediatric subspecialties. Although older reports suggest compensation may be a less critical concern for residents choosing a pediatric subspecialty career,59 the increasingly high accrual of education debt may impact career decision-making. In 2022, approximately 45.0% of AM fellows owed ≥$200 000 compared with 39.5% of all pediatric subspecialty fellows.48 Furthermore, there are often racial and ethnic disparities in educational debt, with greater and more persistent indebtedness among trainees from URiM backgrounds.60 As the subspecialty with the highest percentage of trainees from URiM backgrounds and ABP-certified physicians,49 these financial challenges profoundly impact the current and future AM subspecialty workforce.
Modeling the Future Adolescent Medicine Subspecialty Workforce
Methods
A microsimulation model was developed by the Cecil G. Sheps Center for Health Services Research at the University of North Carolina and the Strategic Modeling and Analysis Ltd. to forecast the pediatric workforce aged 70 years and under for 14 pediatric subspecialties in the United States on the basis of current trends in pediatrics, each subspecialty, health care, and society.4,61 This model utilizes ABP, ABFM, and ABIM certification data to forecast the AM subspecialty workforce.61,62 It compares baseline predictions from 2020 to 2040 to several alternative scenarios that forecast the impact on workforce supply and reports these in headcount (HC, absolute number) and HC adjusted for time spent in clinical care, termed “clinical workforce equivalent” (CWE).61 Given that the financial burdens associated with an AM career pathway could threaten fellowship trainee numbers and that competing clinical and nonclinical demands impact the percentage of clinical time AM subspecialists engage in, the scenarios highlighted in the results for this AM workforce analysis include (1) an increase or decrease in fellow trainees, and (2) an increase or decrease in clinical time. The model considers total AM subspecialist HC, as well as HC per 100 000 children aged 0 to 18 years and CWE per 100 000 children for the baseline and alternate scenarios at the national and census region and division levels. The model also takes into account changes in the child population at the national and subnational level based on the US Census Bureau63 ; differences by subspecialty for census regions are discussed in the summary article in this supplement.64 An interactive, Web-based visualization of the model is publicly available online.62
Given the critical role of AM subspecialists in education, research, policy development, and clinical care, HC is focused on below. Additional modeled scenarios for both HC and CWE per 100 000 children at the census division levels that are not discussed in-depth in this article are reflected in Tables 1–2. Numbers reported below may differ from those in the previous section because of differences in years (2020 vs 2023), sample selection criteria, and inclusion of self-reported clinical time. Estimates for 95% confidence intervals are available in Tables 1–2 and on the data visualization tool.
Census Region . | Census Division . | Year 2020 . | Year 2040 . | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Baseline Model . | Baseline Model . | 12.5% Decrease in Fellows . | 12.5% Increase in Fellows . | 7% Reduction in Clinical Time . | 7% Increase in Clinical Time . | Increased Level of Exit at All Ages . | Increased Level of Exit in Midcareer . | Decrease in Fellows, Reduction in Clinical Time, and Increased Early Exit by 5 Years From the Workforce . | Increase in Fellows and an Increase in Clinical Time . | ||
Midwest | East North Central | 0.83 [0.83–0.83] | 1.07 [0.92–1.21] | 1.02 [0.86–1.19] | 1.11 [0.97–1.26] | 1.07 [0.92–1.21] | 1.06 [0.93–1.20] | 1.05 [0.90–1.21] | 1.05 [0.91–1.20] | 1.00 [0.85–1.16] | 1.11 [0.97–1.26] |
(+28%) | (+23%) | (+34%) | (+28%) | (+27%) | (+26%) | (+27%) | (+20%) | (+34%) | |||
West North Central | 0.45 [0.43–0.47] | 0.39 [0.25–0.53] | 0.38 [0.23–0.52] | 0.39 [0.24–0.53] | 0.39 [0.25–0.53] | 0.39 [0.26–0.53] | 0.38 [0.23–0.54] | 0.38 [0.23–0.52] | 0.37 [0.23–0.51] | 0.39 [0.24–0.53] | |
(−14%) | (−16%) | (−14%) | (−14%) | (−13%) | (−15%) | (−16%) | (−18%) | (−14%) | |||
South | East South Central | 0.36 [0.36–0.36] | 0.34 [0.18–0.50] | 0.33 [0.19–0.48] | 0.33 [0.19–0.48] | 0.34 [0.18–0.50] | 0.35 [0.18–0.52] | 0.32 [0.16–0.48] | 0.33 [0.19–0.47] | 0.32 [0.18–0.46] | 0.33 [0.19–0.48] |
(−7%) | (−8%) | (−8%) | (−7%) | (−3%) | (−12%) | (−10%) | (−12%) | (−8%) | |||
South Atlantic | 0.64 [0.63–0.65] | 0.93 [0.82–1.04] | 0.89 [0.76–1.01] | 0.96 [0.83–1.08] | 0.93 [0.82–1.04] | 0.93 [0.82–1.04] | 0.93 [0.81–1.05] | 0.92 [0.81–1.02] | 0.89 [0.79–1.00] | 0.96 [0.83–1.08] | |
(+45%) | (+38%) | (+50%) | (+45%) | (+45%) | (+45%) | (+44%) | (+40%) | (+50%) | |||
West South Central | 0.39 [0.38–0.40] | 0.41 [0.31–0.51] | 0.40 [0.30–0.49] | 0.43 [0.33–0.53] | 0.41 [0.31–0.51] | 0.41 [0.32–0.50] | 0.42 [0.32–0.51] | 0.41 [0.31–0.52] | 0.40 [0.31–0.49] | 0.43 [0.33–0.53] | |
(+4%) | (0%) | (+10%) | (+4%) | (+4%) | (+6%) | (+5%) | (+2%) | (+10%) | |||
Northeast | Middle Atlantic | 1.66 [1.65–1.67] | 1.31 [1.12–1.51] | 1.26 [1.08–1.45] | 1.37 [1.18–1.56] | 1.31 [1.12–1.51] | 1.32 [1.13–1.50] | 1.32 [1.12–1.53] | 1.32 [1.15–1.49] | 1.28 [1.09–1.47] | 1.37 [1.18–1.56] |
(−21%) | (−24%) | (−17%) | (−21%) | (−21%) | (−20%) | (−21%) | (−23%) | (−17%) | |||
New England | 2.14 [2.12–2.15] | 2.86 [2.42–3.31] | 2.74 [2.34–3.13] | 3.02 [2.59–3.44] | 2.86 [2.42–3.31] | 2.81 [2.40–3.23] | 2.88 [2.43–3.34] | 2.88 [2.43–3.33] | 2.74 [2.27–3.21] | 3.02 [2.59–3.44] | |
(+34%) | (+28%) | (+41%) | (+34%) | (+32%) | (+35%) | (+35%) | (+28%) | (+41%) | |||
West | Mountain | 0.43 [0.43–0.43] | 0.35 [0.25–0.45] | 0.34 [0.24–0.44] | 0.35 [0.23–0.46] | 0.35 [0.25–0.45] | 0.34 [0.24–0.44] | 0.35 [0.25–0.45] | 0.35 [0.24–0.45] | 0.33 [0.24–0.43] | 0.35 [0.23–0.46] |
(−20%) | (−22%) | (−20%) | (−20%) | (−21%) | (−19%) | (−20%) | (−23%) | (−20%) | |||
Pacific | 0.84 [0.83–0.84] | 1.16 [1.04–1.28] | 1.11 [0.98–1.24] | 1.20 [1.07–1.33] | 1.16 [1.04–1.28] | 1.16 [1.03–1.29] | 1.15 [1.02–1.28] | 1.16 [1.03–1.28] | 1.11 [1.00–1.22] | 1.20 [1.07–1.33] | |
(+38%) | (+32%) | (+43%) | (+38%) | (+38%) | (+37%) | (+38%) | (+32%) | (+43%) |
Census Region . | Census Division . | Year 2020 . | Year 2040 . | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Baseline Model . | Baseline Model . | 12.5% Decrease in Fellows . | 12.5% Increase in Fellows . | 7% Reduction in Clinical Time . | 7% Increase in Clinical Time . | Increased Level of Exit at All Ages . | Increased Level of Exit in Midcareer . | Decrease in Fellows, Reduction in Clinical Time, and Increased Early Exit by 5 Years From the Workforce . | Increase in Fellows and an Increase in Clinical Time . | ||
Midwest | East North Central | 0.83 [0.83–0.83] | 1.07 [0.92–1.21] | 1.02 [0.86–1.19] | 1.11 [0.97–1.26] | 1.07 [0.92–1.21] | 1.06 [0.93–1.20] | 1.05 [0.90–1.21] | 1.05 [0.91–1.20] | 1.00 [0.85–1.16] | 1.11 [0.97–1.26] |
(+28%) | (+23%) | (+34%) | (+28%) | (+27%) | (+26%) | (+27%) | (+20%) | (+34%) | |||
West North Central | 0.45 [0.43–0.47] | 0.39 [0.25–0.53] | 0.38 [0.23–0.52] | 0.39 [0.24–0.53] | 0.39 [0.25–0.53] | 0.39 [0.26–0.53] | 0.38 [0.23–0.54] | 0.38 [0.23–0.52] | 0.37 [0.23–0.51] | 0.39 [0.24–0.53] | |
(−14%) | (−16%) | (−14%) | (−14%) | (−13%) | (−15%) | (−16%) | (−18%) | (−14%) | |||
South | East South Central | 0.36 [0.36–0.36] | 0.34 [0.18–0.50] | 0.33 [0.19–0.48] | 0.33 [0.19–0.48] | 0.34 [0.18–0.50] | 0.35 [0.18–0.52] | 0.32 [0.16–0.48] | 0.33 [0.19–0.47] | 0.32 [0.18–0.46] | 0.33 [0.19–0.48] |
(−7%) | (−8%) | (−8%) | (−7%) | (−3%) | (−12%) | (−10%) | (−12%) | (−8%) | |||
South Atlantic | 0.64 [0.63–0.65] | 0.93 [0.82–1.04] | 0.89 [0.76–1.01] | 0.96 [0.83–1.08] | 0.93 [0.82–1.04] | 0.93 [0.82–1.04] | 0.93 [0.81–1.05] | 0.92 [0.81–1.02] | 0.89 [0.79–1.00] | 0.96 [0.83–1.08] | |
(+45%) | (+38%) | (+50%) | (+45%) | (+45%) | (+45%) | (+44%) | (+40%) | (+50%) | |||
West South Central | 0.39 [0.38–0.40] | 0.41 [0.31–0.51] | 0.40 [0.30–0.49] | 0.43 [0.33–0.53] | 0.41 [0.31–0.51] | 0.41 [0.32–0.50] | 0.42 [0.32–0.51] | 0.41 [0.31–0.52] | 0.40 [0.31–0.49] | 0.43 [0.33–0.53] | |
(+4%) | (0%) | (+10%) | (+4%) | (+4%) | (+6%) | (+5%) | (+2%) | (+10%) | |||
Northeast | Middle Atlantic | 1.66 [1.65–1.67] | 1.31 [1.12–1.51] | 1.26 [1.08–1.45] | 1.37 [1.18–1.56] | 1.31 [1.12–1.51] | 1.32 [1.13–1.50] | 1.32 [1.12–1.53] | 1.32 [1.15–1.49] | 1.28 [1.09–1.47] | 1.37 [1.18–1.56] |
(−21%) | (−24%) | (−17%) | (−21%) | (−21%) | (−20%) | (−21%) | (−23%) | (−17%) | |||
New England | 2.14 [2.12–2.15] | 2.86 [2.42–3.31] | 2.74 [2.34–3.13] | 3.02 [2.59–3.44] | 2.86 [2.42–3.31] | 2.81 [2.40–3.23] | 2.88 [2.43–3.34] | 2.88 [2.43–3.33] | 2.74 [2.27–3.21] | 3.02 [2.59–3.44] | |
(+34%) | (+28%) | (+41%) | (+34%) | (+32%) | (+35%) | (+35%) | (+28%) | (+41%) | |||
West | Mountain | 0.43 [0.43–0.43] | 0.35 [0.25–0.45] | 0.34 [0.24–0.44] | 0.35 [0.23–0.46] | 0.35 [0.25–0.45] | 0.34 [0.24–0.44] | 0.35 [0.25–0.45] | 0.35 [0.24–0.45] | 0.33 [0.24–0.43] | 0.35 [0.23–0.46] |
(−20%) | (−22%) | (−20%) | (−20%) | (−21%) | (−19%) | (−20%) | (−23%) | (−20%) | |||
Pacific | 0.84 [0.83–0.84] | 1.16 [1.04–1.28] | 1.11 [0.98–1.24] | 1.20 [1.07–1.33] | 1.16 [1.04–1.28] | 1.16 [1.03–1.29] | 1.15 [1.02–1.28] | 1.16 [1.03–1.28] | 1.11 [1.00–1.22] | 1.20 [1.07–1.33] | |
(+38%) | (+32%) | (+43%) | (+38%) | (+38%) | (+37%) | (+38%) | (+32%) | (+43%) |
Numbers denote HC per 100 000 children [95% confidence interval]. Percentages indicate change from baseline year 2020.
Census Region . | Census Division . | Year 2020 . | Year 2040 . | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Baseline Model . | Baseline Model . | 12.5% Decrease in Fellows . | 12.5% Increase in Fellows . | 7% Clinical Reduction in Clinical Time . | 7% Increase in Clinical Time . | Increased Level of Exit at All Ages . | Increased Level of Exit in Midcareer . | Decrease in Fellows, Reduction in Clinical Time, and Increased Early Exit by 5 Years From the Workforce . | Increase in Fellows and an Increase in Clinical Time . | ||
Midwest | East North Central | 0.45 [0.45–0.45] | 0.57 [0.50–0.65] | 0.55 [0.46–0.64] | 0.60 [0.52–0.68] | 0.53 [0.46–0.61] | 0.61 [0.53–0.69] | 0.57 [0.48–0.65] | 0.57 [0.49–0.65] | 0.50 [0.42–0.58] | 0.64 [0.56–0.72] |
(+28%) | (+23%) | (+34%) | (+19%) | (+37%) | (+27%) | (+27%) | (+12%) | (+43%) | |||
West North Central | 0.24 [0.23–0.25] | 0.21 [0.13–0.28] | 0.20 [0.13–0.28] | 0.21 [0.13–0.29] | 0.19 [0.12–0.26] | 0.23 [0.15–0.30] | 0.21 [0.12–0.29] | 0.20 [0.12–0.28] | 0.18 [0.11–0.26] | 0.22 [0.14–0.31] | |
(−14%) | (−16%) | (−13%) | (−20%) | (−7%) | (−14%) | (−16%) | (−24%) | (−7%) | |||
South | East South Central | 0.19 [0.19–0.19] | 0.18 [0.10–0.27] | 0.18 [0.10–0.26] | 0.18 [0.10–0.26] | 0.17 [0.09–0.25] | 0.20 [0.11–0.30] | 0.17 [0.09–0.26] | 0.18 [0.10–0.25] | 0.16 [0.09–0.23] | 0.19 [0.11–0.28] |
(−2%) | (−4%) | (−4%) | (−9%) | (+8%) | (−7%) | (−6%) | (−14%) | (+3%) | |||
South Atlantic | 0.35 [0.34–0.35] | 0.50 [0.44–0.56] | 0.48 [0.41–0.54] | 0.52 [0.45–0.58] | 0.47 [0.41–0.52] | 0.54 [0.47–0.60] | 0.50 [0.43–0.56] | 0.49 [0.44–0.55] | 0.45 [0.40–0.50] | 0.55 [0.48–0.63] | |
(+45%) | (+38%) | (+50%) | (+35%) | (+55%) | (+45%) | (+43%) | (+30%) | (+60%) | |||
West South Central | 0.21 [0.21–0.22] | 0.22 [0.17–0.27] | 0.21 [0.16–0.26] | 0.23 [0.18–0.29] | 0.20 [0.15–0.25] | 0.24 [0.18–0.29] | 0.22 [0.17–0.27] | 0.22 [0.16–0.28] | 0.20 [0.16–0.24 | 0.25 [0.19–0.31] | |
(+2%) | (−1%) | (+8%) | (−5%) | (+10%) | (+4%) | (+4%) | (−7%) | (+16%) | |||
Northeast | Middle Atlantic | 0.89 [0.88–0.90] | 0.70 [0.60–0.81] | 0.68 [0.58–0.77] | 0.73 [0.63–0.83] | 0.65 [0.56–0.75] | 0.75 [0.65–0.86] | 0.71 [0.60–0.82] | 0.70 [0.61–0.79] | 0.64 [0.54–0.73] | 0.78 [0.68–0.89] |
(−21%) | (−24%) | (−18%) | (−27%) | (−15%) | (−21%) | (−21%) | (−28%) | (−12%) | |||
New England | 1.16 [1.15–1.17] | 1.53 [1.29–1.76] | 1.46 [1.25–1.67] | 1.61 [1.38–1.84] | 1.42 [1.20–1.64] | 1.60 [1.37–1.84] | 1.54 [1.29–1.78] | 1.54 [1.30–1.77] | 1.36 [1.12–1.59] | 1.72 [1.48–1.96] | |
(+32%) | (+26%) | (+39%) | (+22%) | (+38%) | (+32%) | (+32%) | (+17%) | (+48%) | |||
West | Mountain | 0.23 [0.23–0.23] | 0.19 [0.14–0.24] | 0.19 [0.13–0.24] | 0.19 [0.13–0.25] | 0.18 [0.13–0.23] | 0.20 [0.14–0.26] | 0.19 [0.14–0.25] | 0.19 [0.13–0.25] | 0.17 [0.12–0.22] | 0.20 [0.14–0.27] |
(−17%) | (−19%) | (−18%) | (−23%) | (−13%) | (−17%) | (−18%) | (−26%) | (−12%) | |||
Pacific | 0.45 [0.45–0.45] | 0.64 [0.57–0.70] | 0.61 [0.54–0.68] | 0.66 [0.59–0.73] | 0.59 [0.53–0.65] | 0.69 [0.61–0.76] | 0.63 [0.57–0.70] | 0.64 [0.57–0.71] | 0.57 [0.51–0.63] | 0.71 [0.63–0.78] | |
(+41%) | (+35%) | (+46%) | (+31%) | (+52%) | (+41%) | (+41%) | (+26%) | (+57%) |
Census Region . | Census Division . | Year 2020 . | Year 2040 . | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Baseline Model . | Baseline Model . | 12.5% Decrease in Fellows . | 12.5% Increase in Fellows . | 7% Clinical Reduction in Clinical Time . | 7% Increase in Clinical Time . | Increased Level of Exit at All Ages . | Increased Level of Exit in Midcareer . | Decrease in Fellows, Reduction in Clinical Time, and Increased Early Exit by 5 Years From the Workforce . | Increase in Fellows and an Increase in Clinical Time . | ||
Midwest | East North Central | 0.45 [0.45–0.45] | 0.57 [0.50–0.65] | 0.55 [0.46–0.64] | 0.60 [0.52–0.68] | 0.53 [0.46–0.61] | 0.61 [0.53–0.69] | 0.57 [0.48–0.65] | 0.57 [0.49–0.65] | 0.50 [0.42–0.58] | 0.64 [0.56–0.72] |
(+28%) | (+23%) | (+34%) | (+19%) | (+37%) | (+27%) | (+27%) | (+12%) | (+43%) | |||
West North Central | 0.24 [0.23–0.25] | 0.21 [0.13–0.28] | 0.20 [0.13–0.28] | 0.21 [0.13–0.29] | 0.19 [0.12–0.26] | 0.23 [0.15–0.30] | 0.21 [0.12–0.29] | 0.20 [0.12–0.28] | 0.18 [0.11–0.26] | 0.22 [0.14–0.31] | |
(−14%) | (−16%) | (−13%) | (−20%) | (−7%) | (−14%) | (−16%) | (−24%) | (−7%) | |||
South | East South Central | 0.19 [0.19–0.19] | 0.18 [0.10–0.27] | 0.18 [0.10–0.26] | 0.18 [0.10–0.26] | 0.17 [0.09–0.25] | 0.20 [0.11–0.30] | 0.17 [0.09–0.26] | 0.18 [0.10–0.25] | 0.16 [0.09–0.23] | 0.19 [0.11–0.28] |
(−2%) | (−4%) | (−4%) | (−9%) | (+8%) | (−7%) | (−6%) | (−14%) | (+3%) | |||
South Atlantic | 0.35 [0.34–0.35] | 0.50 [0.44–0.56] | 0.48 [0.41–0.54] | 0.52 [0.45–0.58] | 0.47 [0.41–0.52] | 0.54 [0.47–0.60] | 0.50 [0.43–0.56] | 0.49 [0.44–0.55] | 0.45 [0.40–0.50] | 0.55 [0.48–0.63] | |
(+45%) | (+38%) | (+50%) | (+35%) | (+55%) | (+45%) | (+43%) | (+30%) | (+60%) | |||
West South Central | 0.21 [0.21–0.22] | 0.22 [0.17–0.27] | 0.21 [0.16–0.26] | 0.23 [0.18–0.29] | 0.20 [0.15–0.25] | 0.24 [0.18–0.29] | 0.22 [0.17–0.27] | 0.22 [0.16–0.28] | 0.20 [0.16–0.24 | 0.25 [0.19–0.31] | |
(+2%) | (−1%) | (+8%) | (−5%) | (+10%) | (+4%) | (+4%) | (−7%) | (+16%) | |||
Northeast | Middle Atlantic | 0.89 [0.88–0.90] | 0.70 [0.60–0.81] | 0.68 [0.58–0.77] | 0.73 [0.63–0.83] | 0.65 [0.56–0.75] | 0.75 [0.65–0.86] | 0.71 [0.60–0.82] | 0.70 [0.61–0.79] | 0.64 [0.54–0.73] | 0.78 [0.68–0.89] |
(−21%) | (−24%) | (−18%) | (−27%) | (−15%) | (−21%) | (−21%) | (−28%) | (−12%) | |||
New England | 1.16 [1.15–1.17] | 1.53 [1.29–1.76] | 1.46 [1.25–1.67] | 1.61 [1.38–1.84] | 1.42 [1.20–1.64] | 1.60 [1.37–1.84] | 1.54 [1.29–1.78] | 1.54 [1.30–1.77] | 1.36 [1.12–1.59] | 1.72 [1.48–1.96] | |
(+32%) | (+26%) | (+39%) | (+22%) | (+38%) | (+32%) | (+32%) | (+17%) | (+48%) | |||
West | Mountain | 0.23 [0.23–0.23] | 0.19 [0.14–0.24] | 0.19 [0.13–0.24] | 0.19 [0.13–0.25] | 0.18 [0.13–0.23] | 0.20 [0.14–0.26] | 0.19 [0.14–0.25] | 0.19 [0.13–0.25] | 0.17 [0.12–0.22] | 0.20 [0.14–0.27] |
(−17%) | (−19%) | (−18%) | (−23%) | (−13%) | (−17%) | (−18%) | (−26%) | (−12%) | |||
Pacific | 0.45 [0.45–0.45] | 0.64 [0.57–0.70] | 0.61 [0.54–0.68] | 0.66 [0.59–0.73] | 0.59 [0.53–0.65] | 0.69 [0.61–0.76] | 0.63 [0.57–0.70] | 0.64 [0.57–0.71] | 0.57 [0.51–0.63] | 0.71 [0.63–0.78] | |
(+41%) | (+35%) | (+46%) | (+31%) | (+52%) | (+41%) | (+41%) | (+26%) | (+57%) |
Numbers denote CWE per 100 000 children [95% confidence interval]. Percentages indicate change from baseline year 2020.
Results
The baseline model predicts a 27% increase in the total number of AM subspecialists nationally (653–832), a 13% increase in HC (0.8–0.9 per 100 000 children), and a 13% increase in CWE per 100 000 children (0.43–0.49). Of the 14 subspecialties in the model, the only subspecialty with a smaller supply projection is child abuse pediatrics.
This modest increase is not uniform across the United States, with decreasing AM HC per 100 000 children in nearly half of the 9 census divisions, including the East South Central, Mountain, and West North Central divisions (Table 1, Fig 2A). Notably, with the exception of the Middle Atlantic, these divisions already have the lowest numbers of practicing AM subspecialists. The remaining 5 census divisions account for the overall increase in AM subspecialist HC per 100 000 children, including the East North Central, New England, Pacific, South Atlantic, and West South Central divisions.
If the number of fellows entering AM training increased by 12.5% by 2030, there would be an anticipated 17% growth in AM subspecialist HC per 100 000 children by 2040. This increase is not significantly different from the baseline model prediction, given the relatively small numbers in the current AM workforce (Table 1). The same decline in AM subspecialist HC per 100 000 children by US census division at baseline is seen in this scenario, because growth remains concentrated in the East North Central, New England, Pacific, South, and West South Central divisions (Fig 2C).
Conversely, should AM fellow trainee numbers decrease by 12.5% by 2030, there is only an anticipated 8% growth in the AM specialty workforce (Table 1, Fig 2C), a nearly 40% decrease in the workforce compared with baseline. Like the baseline model predictions, the Middle Atlantic, Mountain, West North Central, and East South Central divisions show a decrease in HC per child population, whereas other regions show an increase or no change.
In the fourth and fifth model predictions, on the basis of a basis of a 7% decrease or increase in clinical time, there is very little difference when compared with the baseline models or 1 another (Table 1). The geographic disparities previously discussed persist in both models, with no significant differences between the models. For the best- and worst-case scenarios, the West North Central, Middle Atlantic, and Mountain all have HC projections less than baseline. The model predicts a similar pattern across census regions (Fig 3) and divisions (Table 2) for changes in CWE per 100 000 children.
Looking Toward Solutions to Improve Child Health
The anticipated growth of the AM subspecialty workforce by 2040 is overall low, with some census divisions seeing a decline relative to population growth. These findings are concerning given the demand for AM subspecialists to provide clinical care, educate trainees and other clinicians, advance research, and inform policy and programmatic development. Increasing the number of AM subspecialist trainees predicted only a slight increase in the workforce above baseline. Should there be a 12.5% decline in trainees, existing geographic workforce disparities would widen. Changing percentage of clinical time had no bearing on AM workforce predictions. Therefore, creative solutions will be needed to address current and future AYA health care needs.
Education and Training
General pediatricians outnumber AM subspecialists 70-fold, and, along with other physicians and advanced practice providers, they provide the bulk of adolescent clinical care. However, education and training in AYA care are limited and arguably insufficient for these providers. Only 1 month of adolescent-specific training is required in a 3-year general pediatrics residency training program,65 and family medicine residency has no AM-specific training requirements.66 Physician assistant pediatric expertise only requires a supervisor’s attestation that the trainee understands “how and when to apply the appropriate techniques and methods” related to adolescent care.67 There are no specified AM clinical hour requirements for the certification of pediatric or family nurse practitioners and limited precepted adolescent health clinical experiences for nurse practitioner trainees.68 It is therefore not surprising that primary care providers report a low degree of comfort in providing many aspects of adolescent health care.69–71
Such limited training creates a dangerous gap in AYA care. One solution is to create more opportunities for longitudinal AM training in pediatric residency training programs.72 Additionally, states with limited AM subspecialty presence could augment the AM training of advanced practice providers. States with AM training programs for all disciplines could facilitate the creation of rural clinical rotations or mobile health care experiences, bringing AYA health care where it is most lacking. Required continuing education in AYA health care is also needed to keep practicing clinicians up to date on the ever-changing AYA health care landscape.
Practice
The specialized needs of AYAs who have access to AM subspecialists will be addressed. However, many AYAs do not have access to inclusive care in confidential spaces with clinicians who have sufficient time and education to screen them appropriately and/or provide necessary interventions. A multidisciplinary model of AYA care, utilizing the expertise of mental health clinicians, resource experts, case managers, and dieticians, is ideal, though not typically available in resource-limited areas. Given the widening geographic disparities in the AM subspecialty workforce, clinicians with challenging cases may need to refer patients out of state and/or access specialty consultation through virtual health care platforms, 2 solutions that will likely both require policy changes. The Society for Adolescent Health and Medicine provides resources, education, advocacy, and a professional community of clinicians for support.73
Policy
Policies requiring more AYA health care education and clinical training across health care professions, including pediatrics, are critical. Licensing bodies should require continuing education in adolescent health for all practicing pediatric clinicians. In pediatric residency training, more longitudinal AM experiences supervised by board-certified AM subspecialists should be required, especially for those entering general practice upon graduation. On the basis of the data presented here, the ACGME should consider expanding AM requirements for pediatric trainees.
AM subspecialist compensation must also be addressed. The lifetime earning potential of an AM subspecialist is diminished relative to general pediatricians and most other pediatric subspecialists.57 This is associated with lower mean fellowship fill rates and geographic workforce disparities, resulting in greater distances traveled by patients to see a subspecialist.74 Given the enormous burden of medical education debt, future income matters. Current physician reimbursement models hurt cognitive subspecialties, like AM, with few reimbursable procedures and clinical encounters, which tend to be prolonged, given the psychosocial and behavioral underpinnings of patients’ health care needs. Additionally, AM subspecialist efforts to inform research, policy, and program development are not associated with reimbursable relative value units, but are critical to optimizing health care access and the health and well-being of AYA populations. Given these realities, policies addressing the opportunity cost of fellowship training or providing a financial incentive for AM fellowship trainees, such as loan repayment or forgiveness, should be facilitated. Such incentives would be significant for individuals willing to practice in underserved census divisions and URiM trainees disproportionately burdened by education debt. The recently launched Health Resources and Services Administration Pediatric Health Workforce Pediatric Specialty Loan Repayment Program75 is undoubtedly a move in the right direction, but requires 36 hours per week of clinical care for 3 consecutive years of training. This far exceeds what is possible in an academic training program seeking to train educators, policymakers, and researchers, in addition to subspecialty clinicians.
Lastly, state, local, and hospital/clinic policies and laws impact AYA health care. For those census divisions with a declining AM workforce, more liberal cross-state telemedicine and insurance coverage regulations may be needed to provide essential AYA services. Legislative restrictions to evidence-based AYA health care, such as abortion and gender-affirming care, present serious barriers and reinforce disparities in evidence-based services. Frequently punitive, these policies not only deny youth access to essential health care, but they also put AM specialty providers at risk and reinforce increasing geographic disparities in the AM workforce.76–79
Future Workforce Research
How will AYA health care be addressed with a declining subspecialty workforce in geographic areas with increasing need? High priority must be placed on studying the impact of interventions to increase access to AM subspecialists (eg, improved compensation, loan repayment/forgiveness, additional training opportunities, funded consultation models), as well as the detrimental effects of state laws criminalizing evidence-based health care on critical AYA health outcomes.
Conclusions
AM subspecialists provide necessary AYA clinical care while also educating medical trainees and practicing pediatric clinicians and informing AYA-related program development, research, policies, and legislation. A career in AM is rewarding, given the opportunities to impact youth’s physical, emotional, and social health trajectories. Yet, the AM subspecialty workforce remains small and is predicted to decline in relation to US population increases in multiple census divisions. If the United States cares about the future of AYA health care, significant educational, practice, and policy changes must be enacted to bolster the supply of well-trained clinicians addressing this population’s dynamic health care needs.
Acknowledgments
We thank Emily McCartha, Andy Knapton, and Adriana R. Gaona for their review of the modeling data. We also thank Virginia A. Moyer and Patience Leino for their editorial support. We also thank the leadership of the Society for Adolescent Health and Medicine for comments on the manuscript. Last, we thank the pediatricians who shared their information with the ABP Foundation and made this supplement possible.
Drs Fields, Louis-Jacques, Kas-Osoka, and Pitts drafted the initial manuscript and critically reviewed and revised the manuscript; Drs Holland-Hall, Richardson, Ott, and Leslie critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
FUNDING: Funded by the American Board of Pediatrics Foundation. Drs Ott, Pitts, and Richardson acknowledge funding support from the Health Resources and Services Administration and Maternal and Child Health Bureau Leadership Education in Adolescent Health training grants T71MC45699, T71MC00009, and 5T71MC242101000, respectively. The American Board of Pediatrics Foundation, the Carolina Health Workforce Research Center at the University of North Carolina at Chapel Hill’s Sheps Center for Health Services Research, and the Strategic Modelling Analytics & Planning Ltd partnered in the design and conduct of this study. The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement by, the Health Resources and Services Administration, the Department of Health and Human Services, the US government, the American Board of Pediatrics, or the American Board of Pediatrics Foundation.
CONFLICT OF INTEREST DISCLOSURES: Dr Leslie is an employee of the American Board of Pediatrics. Dr Pitts is a member of the American Board of Pediatrics Education and Training Committee. Dr Holland-Hall is a member of the American Board of Pediatrics Adolescent Medicine subboard. Dr Fields has served on an advisory board for Roche Diagnostics and Gilead Sciences. Dr Ott’s spouse is an employee of Eli Lilly, Inc, and together they are small stockholders. The other authors have indicated they have no conflicts of interest relevant to this article to disclose.
- ABFM
American Board of Family Medicine
- ABIM
American Board of Internal Medicine
- ABP
American Board of Pediatrics
- ACGME
Accreditation Council for Graduate Medical Education
- AM
adolescent medicine
- AMG
American medical graduate
- AYA
adolescent and young adult
- CWE
clinical workforce equivalent
- DO
Doctor of Osteopathy
- HC
headcount
- IMG
international medical graduate
- MD
Doctor of Medicine
- SGD
sexual and gender diverse
- STI
sexually transmitted infection
- URiM
underrepresented in medicine
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