The Arizona Genetic Counseling Landscape: Exploring the Impact of a Limited Genetic Counseling Workforce

The Arizona Genetic Counseling Landscape: Exploring the Impact of a Limited Genetic Counseling Workforce | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Arizona Genetic Counseling Landscape: Exploring the Impact of a Limited Genetic Counseling Workforce Lauren Maynard, Laura Engle, Elizabeth Chavez, Valerie M. Schaibley This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6607449/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Certified genetic counselors (GCs) are healthcare professionals uniquely trained to understand the complex interactions between genetics and health. As utilization of genetic testing has increased, the workforce of GCs in the United States (US) has struggled to match increasing demand. In this study, we characterized the genetic counseling workforce in Arizona as an example of a limited workforce in a geographically large, spread-out state. Analysis of the per-capita distribution of GCs across the US using data from the 2023 National Society of Genetic Counselors (NSGC) Professional Status Survey (PSS) shows that Arizona is ranked 40 th out of 50 for the number of GCs per 100,000 people, despite the state’s large size and population. We then surveyed GCs residing in Arizona (N=28) to understand demographics, work environment and practices, and estimated clinic measures and compared responses to the 2023 NSGC PSS. Although certain genetic counseling subspecialties are underrepresented in Arizona, including prenatal, neurology and cardiology, there were no significant differences in the demographics, area of practice, employers, and salary between Arizona GCs and those nationwide. Results indicate low wait times and under-utilization of prenatal genetic counseling services in Arizona and long wait times and full clinic loads for pediatrics and adult general genetics clinics. Together, our results demonstrate a need for genetic counseling workforce expansion in Arizona to meet demand and address wait times. Data from this study may assist with expansion of the genetic counseling workforce as similar issues may exist in other geographically large, underserved states. Genetic Counseling Genetic services Medical Genetics and Health Workforce Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The complexity of genetic disease has long required specialized individuals to interpret, diagnose, and treat those affected by hereditary conditions. Certified genetic counselors (GCs) and medical geneticists comprise a subset of health professionals that are uniquely trained to understand the complex interactions between genetics and health. However, as identification of genetic conditions and the utilization of genetic testing has increased, the workforce of genetics professionals in the United States has struggled to keep pace with added demand. A study of the genetic counseling workforce projected a deficiency of GCs until 2023 or 2024 with a ratio of one GC per 100,000 and up until 2030 with a ratio of one GC per 75,000 (Hoskovec et al., 2018 ). A recent study reported a ratio of one GC per 71,842 nationwide, but did not differentiate between GCs in patient-facing compared to non-patient facing roles (Triebold et al., 2021 ). Similar issues exist with the scarcity of medical geneticists, where the average age of clinical geneticists is greater than 50, and up to 24.6% of the geneticists surveyed in 2019 expected to retire in the next five years (Jenkins et al., 2021 ); (Maiese et al., 2019 ). Aside from the nationwide shortage of genetics professionals, there is a scarcity of genetic services outside of metropolitan centers. Genetic services are concentrated in urban areas, and in states with higher populations (Government Accountability Office, 2020 ). In a survey of medical geneticists, 40% of geneticists practiced in only five U.S. states and there was a deficit in the Southwest (Jenkins et al., 2021 ). A geographical analysis of GCs throughout the United States showed that 98.7% of GCs live or work in major metropolitan areas (Triebold et al., 2021 ). Finally, a study of genetic services in California found that the average distance traveled to access genetic services was 76.6 miles and individuals from 9.2% of the state traveled more than 100 miles to see a genetic specialist (Penon-Portmann et al., 2020 ). The geographic disparity of genetics services seems to be further exasperated by financial disparities experienced by those living in rural areas. In the southern U.S., counties which have a practicing GC were found to have a significantly higher income than those without a GC, at least 20% higher in six of the 17 states studied (Villegas & Haga, 2019 ). In these southern states, rural counties and those with lower median incomes had notably lower access to GCs (Villegas & Haga, 2019 ). Arizona is the 14th largest state in the U.S. with a population of 7,151,502 according to the 2020 U.S. Census and has grown 11.9% in 10 years (United States Census Bureau, 2020 ). Although it is the 14th largest state in the U.S. by population, it is the 6th largest state in the U.S. by area, with most of the population concentrated in the greater Phoenix and Tucson metropolitan areas (United States Census Bureau, 2020 ). In 2020, Maricopa County, which contains the greater Phoenix area, had just over 60% of the state’s population and Pima county, which encompasses Tucson, has just under 15% of the state’s population (United States Census Bureau, 2020 ). The remaining population in the state of Arizona resides in cities with populations of 100,000 people or fewer (United States Census Bureau, 2020 ) and many of these cities are 100 miles or more from Phoenix or Tucson. Thus, while only 10.7% of the state’s population live in a “rural” area as defined by the 2020 U.S. Census, Arizona is a large, geographically spread-out state and travel to major metropolitan areas can be a burden for many residents. In addition to the need for an appropriate number of genetic specialists in both urban and rural areas, many other barriers have been documented that prohibit patients from receiving genetic services. These barriers include wait time, length of genetics visits, issues with insurance reimbursement, and misconceptions regarding genetic services (Maiese et al., 2019 ; Raspa et al., 2021 ). Non-genetics providers do not appropriately identify the need for a genetics referral and in some cases provide genetic services themselves (Maiese et al., 2019 ). Barriers to referrals for appropriate genetic services include a lack of awareness of the patient risk factors, inadequate gathering of family history, inadequate knowledge of genetics and genetic conditions, and insufficient knowledge of availability of genetic services (Delikurt et al., 2015 ). Non-geneticist healthcare providers often receive limited training in genetics, which limits their ability to adequately refer patients to genetics. In a small Arizona specific study, 80% of non-genetics physicians reported little to no genetics training, and these providers were not likely to order genetic tests or implement use of genetics into their practice (Schaibley et al., 2022 ). The combination of limited genetics training for non-genetics physicians and the inability to appropriately refer patients for a genetics consultation creates both a lack of referrals to genetics clinics for patients who require these services and a lack of awareness that these services are needed. Although barriers to genetics care and genetic counseling workforce shortages have been documented at a national level, there have been no workforce studies of genetic counseling on a local level to determine if national workforce issues reflect state-to-state environments. After comparing the per-capita genetic counseling workforce by state, we surveyed GCs in the state of Arizona to characterize the genetic counseling workforce in the state and compared our findings to national genetic counseling workforce data. The goal of this analysis is to understand if a highly underserved, geographically large and spread-out state, such as Arizona, has different workforce issues and characteristics as those documented at a national level. METHODS This study was approved by the institutional review board at the University of Arizona (STUDY00001714). Instrumentation We developed a survey to assess the current genetic counseling workforce in Arizona. The survey consisted of 27 or 52 primarily multiple-choice questions, with some Likert scale questions and write-in response options. Skip logic was used to vary the survey depending on whether the participant indicated they practice direct-patient care, mixed direct and non-direct patient care, or non-direct patient care. Survey questions were developed with feedback from the Expansion of Genetic Service Committee of the Arizona Genetic Alliance (AGA), a local chapter of National Society of Genetic Counselors (NSGC). Questions focused on demographic information, estimates of clinical measures (such as referrals, patient wait times, and clinical capacity), clinical practices (such as billing practices, training, support staff, and management structure), and workplace benefits (such as paid time off and salary). All survey responses were anonymous. Demographic question response options were based on the 2022 NSGC Professional Status Survey. The survey was conducted through Qualtrics software (Qualtrics, 2020 ) and took approximately 10–20 minutes to complete. The full version of the survey is available in Appendix A. Participants The survey was distributed to GCs throughout the state and was open between 9/30/22–11/04/22. GCs who were board certified or board eligible through the American Board of Genetic Counseling (ABGC) residing in Arizona at the time of the survey were considered eligible. Based on the ABGC directory, there were a total of 52 GCs in Arizona at the time of the survey. Prior to completing the survey, eligible participants consented to participating in the study on the first page of online survey. The survey was distributed through a QR code provided at the annual AGA Educational Conference (09/30/22) and through a post on the AGA listserv. Data analysis Surveys which were 75% or more complete were included in data analysis. Statistical analysis was performed and figures were generated in RStudio (Version 2023.12.1 + 402). The AZ workforce data were compared to the 2023 NSGC PSS (National Society of Genetic Counselors, 2023 ). U.S. population data was obtained from the US Census (United States Census Bureau, 2020 ). Demographic options with a small number of responses were not always displayed to maintain participant confidentiality. Responses for some demographic questions, such as age, gender identity, and years in practice, were condensed to aide statistical analysis. Fishers exact test was used to identify differences between the AZ workforce and the 2023 PSS data. In order to examine wait times, referral rates and billing data for GCs working with patients in AZ, we analyzed a subset of responses from AZ GCs indicating they were at least partly involved with direct patient care and were not employed by either a commercial laboratory or a private telegenetics company (N = 17). Because this subset differs slightly from those who identify as GCs practicing in direct patient care roles in our cohort, which includes GCs employed by commercial laboratories and private telegenetics companies, this subset is referred to as clinic-based GCs in this publication. RESULTS We first sought to understand how the genetic counseling workforce compares between states in the U.S.. Using the 2023 NSGC PSS data (National Society of Genetic Counselors, 2023 ) and state population data from the 2023 U.S. Census (United States Census Bureau, 2020 ), we calculated the number of GCs in each state per 100,000 people (Fig. 1 ). Despite having a population of over 7 million people, the PSS reports that Arizona has only 29 GCs (National Society of Genetic Counselors, 2023 ). This leads to 0.39 GCs in Arizona per 100,000 people. In comparison, Tennessee and Washington have similar population sizes to Arizona, with 7.1 and 7.8 million, respectively. The 2023 PSS data reports 46 GCs in Tennessee and 88 GCs in Washington, leading to 0.65 and 1.12 GCs per 100,000 people in Tennessee and Washington, respectively (Fig. 1 ). Overall, Arizona ranks 40th across all states for the number of GCs per 100,000 people. To better understand the distribution of the local workforce in the context of the state geography, we analyzed the location of clinic-based genetic counselors. Arizona is a large state, with two major metropolitan areas: Phoenix and Tucson. When ranked by population, all of the top 10 incorporated cities in Arizona are in the greater Phoenix and Tucson metropolitan areas. Only five out of top 25 cities ranked by population fall outside of this region: Yuma, Flagstaff, Casa Grande, Lake Havasu City, and Prescott Valley. According the AGA directory of GCs practicing in Arizona at the time of the survey (Arizona Genetics Alliance, 2022 ), all clinic-based GCs in the state are located in the greater Phoenix area (89%) and the Tucson area (11%) (Fig. 2 ). Patients living outside these areas who require GC services need to travel significant distances for an in-person consultation with a GC. From the five cities listed above, the distance a patient would have to travel to see a genetic counselor would be at least 184 miles if they lived in Yuma, at least 147 miles if they lived in Flagstaff, at least 49 miles if they lived in Casa Grande, at least 194 miles if they lived in Lake Havasu City, and at least 92 miles if they lived in Prescott Valley (Fig. 2 ). Next, we conducted a workforce assessment survey between September and November 2022. The survey was designed to capture data about the current GC workforce in Arizona to identify barriers and opportunities for workforce expansion. At the time of the survey, there were 52 GCs in Arizona according to the American Board of Genetic Counseling Membership Directory (American Board of Genetic Counseling). A total of 37 participants responded to the survey, nine responses were less than 75% complete and excluded from analysis, leaving 28 responses and a survey response rate of 53.8%. Demographic data of survey respondents compared to the 2023 NSGC PSS (National Society of Genetic Counselors, 2023 ) is outlined in Table 1 . There were no significant differences in demographic information between the Arizona GCs and the 2023 PSS data. Participants described themselves primarily as white (86%) or Hispanic or Latinx (11%) females (93%), similar to the 2023 PSS demographic data, but with a slightly higher proportion of Hispanic or Latinx respondents (11% compared to 3%, Table 1 ). Of note, 64% of respondents resided in the state prior to attending their genetic counseling program (Table 1 ). In line with the PSS data, the majority of Arizona GCs are board-certified (96%), work full-time (97%) and are under the age of 40, with 39% aged 20–29 and 43% aged 30–39 (Table 1 ). Most respondents have been practicing for fewer than 10 years, similar to the PSS data (Table 1 ). Most GCs in Arizona (61%) provide predominantly direct patient care, with the remainder practicing in a setting where they provide non-direct patient care (28%), or mixed direct and non-direct patient care (11%) (Table 1 ). Table 1 Demographics of Arizona GC Workforce Demographic AZ Workforce 2023 PSS N % N % Number of GCs in AZ 28 - 29 - Prior AZ Resident 18 64% - - CGC 27 96% 2,837 97% Full-Time 26 93% - 91% Race and Ethnicity White 25 89% 2,588 89% Asian 1 4% 270 9% Hispanic or Latinx 3 11% 100 3% Middle Eastern or North African - - 57 2% Black, African American, or of African descent - - 62 2% American Indian, Alaskan Native, or Indigenous Peoples of Canada - - 6 < 1% Native Hawaiian or other Pacific Islander - - 5 =50 3 11% 333 12% Years in Practice =25 years 2 7% 235 8% Patient Care Direct Patient Care 17 61% - 53% Non-Direct Patient Care 8 28% - 27% Mixed 3 11% - 20% Salary Mean Mean All $ 104,529 $ 104,664 Direct Patient Care $ 92,218 $ 90,800 Non-Direct Patient Care $ 127,938 $ 129,079 Mixed $ 107,767 $ 107,174 Participants could select more than one option for race and ethnicity. Percentages are out of all responses and do not add to 100%. Employers varied, with most respondents working for an academic medical center (29%), a public hospital or medical center (21%), or a commercial laboratory (18%) (Table 2 ). The largest proportion of genetic counselors in Arizona practice in adult cancer genetics (39%), followed by pediatrics (25%) (Table 2 ). Only 14% of genetic counselors in Arizona reported providing any prenatal counseling as part of their practice, compared to 26% of 2023 PSS respondents (Table 2 ). No genetic counselors in Arizona report practicing in settings which include preimplantation genetic testing, ART/IVF or infertility, which makes up 6% of practice specialties reported in the 2023 PSS (Table 2 ). Neurogenetics, cardiology, reproductive/preconception screening, ophthalmology, hematology, public health and psychiatrics are also poorly represented in Arizona compared to national data (Table 2 ). Table 2 Practice Information of Arizona GCs Demographic AZ Workforce 2023 PSS N % N % Practice Specialty Adult cancer genetics 11 39% 1,176 41% Prenatal 4 14% 729 26% Pediatrics 7 25% 644 23% Laboratory Science 3 11% 360 13% Neurogenetics 1 4% 342 12% Genomic medicine 2 7% 245 9% Cardiology 2 7% 366 13% Preimplantation genetic testing, ART/IVF, or infertility - - 177 6% Preconception/reproductive screening 3 11% 540 19% Metabolic disease 3 11% 198 7% General adult genetics 4 14% 489 17% Pediatric cancer genetics 4 14% 316 11% Ophthalmology - - 113 4% Consumer /personal genomics 2 7% 65 2% Hematology - - 122 4% Newborn screening 1 4% 164 6% Public health - - 54 2% Psychiatric - - 34 1% Other 4 14% 202 7% Employer Hospital/medical facility-academic medical center 8 29% 1,052 37% Hospital/medical facility- public 6 21% 343 12% Hospital/medical facility-private 2 7% 353 12% Laboratory -commercial 5 18% 494 17% Laboratory-noncommercial 1 4% 51 2% Private company-telegenetics/consulting 1 4% 108 4% University, college, or training program 1 4% 100 4% Insurance company/benefit management company 1 4% 38 1% Not for profit organization 2 7% 89 3% Physician's private practice 1 4% 52 2% Government organization or agency - - 43 2% Private company- biotech or research - - 24 1% Private company- digital health or software - - 27 1% Private company-pharmaceutical - - 21 1% Self-employed or private practice - - 10 0% Participants could select more than one option for practice specialty. Percentages are out of all responses and do not add to 100%. To better understand the deficiency of clinical genetic services, we asked respondents practicing in direct patient care roles to suggest clinical areas that would benefit from additional genetic counseling services in their institution. Twelve genetic counselors responded, with the most common responses including neurology (n = 10), cardiology (n = 6) and NICU/pediatrics (n = 5). Endocrinology, OBGYN and oncology were each suggested by two respondents and nephrology and adult primary care were each suggested once. We also sought to understand how the limited GC workforce impacts wait times for patients. Clinic-based GCs in AZ (N = 17) reported a wide range of wait times for non-urgent appointments, with 24% reporting wait times less than one week and 35% reporting wait times greater than six months (Fig. 3). In contrast, wait times for non-urgent GC appointments reported in the 2023 PSS are distributed somewhat evenly across the possible options, with only 8% of PSS respondents indicating wait times greater than six months (Fig. 3). We found significant variability between patient wait times among clinic-based GCs in AZ when we stratified the wait times by clinical specialty. Clinical specialties were categorized into four major groups: adult general genetics, adult cancer genetics, pediatrics and prenatal. Participants who indicated more than one clinical specialty were labeled into a fifth “mixed" category. Clinic-based GCs in adult general genetics, adult cancer genetics and mixed specialties reported varied wait times for patients in AZ. In contrast, GCs in prenatal genetics reported wait-times less than one week and all clinic-based GCs in pediatrics reported wait-times of greater than six months (Fig. 4 A). In addition to wait time, we also asked clinic-based GCs if they receive more, less, or an appropriate number of referrals for patients in the community they serve who would meet criteria for a genetic counseling referral. Most respondents (47%) indicated they receive fewer referrals than are appropriate, 41% receive an appropriate number of referrals and 12% receive more referrals than appropriate (Fig. 4 B). Interestingly, all clinic-based prenatal GCs (N = 2) reported receiving fewer referrals than appropriate (Fig. 4 B). Furthermore, when asked about patient capacity compared to actual number of patients seen, both prenatal GCs indicated that they have the capacity to see 50–99 patients per month, but actually see fewer than 25. In contrast, most genetic counselors practicing in general adult genetics, adult cancer, pediatrics, and mixed specialties reported seeing a monthly patient load equal to their monthly capacity. Finally, we analyzed the differences in billing practices. We found a significant difference (p = 0.002) between the billing practices of the 2023 PSS respondents and the clinic-based GCs in AZ. Most clinic-based GCs in AZ bill using a facility fee (N = 12, 71%), typically billed under the physician’s name (N = 7, 41%; Table 3 ). This contrasts with the 2023 NSGC PSS data, in which 33% of GCs who bill for in-person services bill a professional fee in the GCs name (Table 3 ). We found that 59% of clinic-based GCs in AZ (N = 10) use the 96040 CPT code for billing compared to 72% of GCs in the 2023 PSS, although the distribution of billing code usage between the AZ GCs and PSS data was not significantly different. Table 3 Billing Practices for Clinic-Based GCs in Arizona Billing Method AZ Workforce 2023 PSS N % N % Professional fee, billed in the genetic counselor's name 3 18% 305 33% Professional fee, billed in the physician's name 2 12% 251 27% Facility fee, billed in the genetic counselor's name 5 29% 187 20% Facility fee, billed in the physician's name 7 41% 181 20% N/A, services are not billed 1 6% 538 33% Unsure 1 6% 147 16% Participants could select more than one option for billing practices. Percentages are out of all responses and do not add to 100%. Wait times for all clinic-based GCs practicing in Arizona (blue) compared to the 2023 PSS data for non-urgent GC only appointments (black) or an appointment with both a GC and physician (grey). DISCUSSION When comparing the number of GCs per 100,000 in the United States, only 15 out of 50 states are above the one GC per 100,000 population mark, demonstrating that there is an uneven distribution of the GC workforce and much of the country is still considered underserved despite the previously reported ratio of one GC per 71,842 nationwide (Triebold et al., 2021 ). There are fewer GCs in AZ per 100,000 compared to other states with similar populations. With AZ raking 40th out of 50, the state is underserved in genetic counseling workforce and this is even further exacerbated due to the large size of the state and the population distribution. All of the GCs in AZ are localized to the most populated counties in Arizona: Maricopa and Pima counties. While this largely reflects national trends in the clinic-based genetics workforce clustered around major metropolitan areas (Jenkins et al., 2021 ; Triebold et al., 2021 ), the geographic size of Arizona leads to significant disparities in access to clinical genetics services for populations who live outside these counties. This group of nearly 1.7 million individuals, which makes up roughly 24% of the state's population (United States Census Bureau, 2020 ), need to travel significant distances (~ 50 to 200 miles) for in-person genetic services, depending on where they live and if they have access to remote genetic counseling services. A full characterization of remote genetic counseling services available to these individuals provides an important area for future research. Despite the small workforce of GCs compared to other state, the demographic makeup of the GC workforce in AZ is similar to the national workforce. Years in practice, GC specialty, direct vs. non-direct or mixed patient care, employer type and salary did not differ for genetic counselors in AZ compared to the national workforce. However, our sample size was small (N = 28), and we may not have had sufficient power to identify differences in demographic distributions. Although we found a higher proportion of Hispanic or Latinx genetic counselors in Arizona compared to genetic counselors nationally, this is not fully reflective of the local population, in which 31% of the population identifies as Hispanic or Latinx ethnicity based on the U.S. Census data (United States Census Bureau, 2020 ). We found longer wait times for GC services in AZ compared to national data, but we uncovered surprising findings when we stratified our data by subspecialty. While all pediatric GCs in Arizona report wait times of greater than six months, all prenatal genetic counselors in Arizona report wait times of less than 1 week for a non-urgent referral. Interestingly, all prenatal GCs also report underutilization of their clinical availability and that they are receiving fewer than appropriate referrals from the providers in their area. In contrast, GCs in the pediatrics, adult cancer, general adult, and mixed specialties report that they are seeing a monthly patient load equal to their monthly capacity and more often report an appropriate number of referrals. Based on the small number of practicing prenatal GCs and the short clinic wait time to see a prenatal GC, it is likely that patients in AZ are being under-referred for prenatal GC services. In contrast, the long wait times in pediatrics and general adult clinics suggests that the workforce of genetic counselors is too small to meet the current patient demand in these subspecialties. One area of future study should assess provider’s knowledge and views of genetic testing and GC referral practices in AZ, especially for prenatal services. Billing practices for GC services were significantly different in AZ compared to national data. While the majority of GCs nationally bill a professional fee, we found that the majority of GCs in AZ bill a facility fee, typically in a physician's name. Arizona is one of the few states in the U.S. that does not have licensure for GCs, which may impact billing practices at institutions within the state. In contrast, we found no difference in the use of CPT codes for billing between our data and national trends. There have been few studies on billing and reimbursement for genetic counseling and many of the recent studies have been focused on the use of telegenetics services since the start of the COVID-19 pandemic. After the creation of the 96040 CPT code, NSGC completed a study of billing practices and found that while 69% of respondents billed for genetic counseling services, only 24% used the 96040 CPT code (Harrison et al., 2010 ). This study also identified differences in billing based on specialty, with a higher proportion of GCs practicing in prenatal and cancer genetic counseling using the 96040 CPT code than those in pediatrics (Harrison et al., 2010 ). Subsequently, NSGC gathered data on billing practices via the annual PSS. In the 2023 PSS, 63% of GCs reported they bill for services and 72% of these GCs report utilizing the 96040 CPT code (National Society of Genetic Counselors, 2023 ), however, this was not broken down by area of practice. Furthermore, studies of reimbursement for genetic counseling have been focused on the use of the 96040 CPT code, and most have focused on states which have licensure (Leonhard et al., 2017 ; Spinosi et al., 2021 ). A study from 2011 from the Cleveland Clinic, found that 62.6% of encounters billed to third party private payors, which excludes Medicare and Medicaid, received at least partial reimbursement when the 96040 CPT code was billed under a supervising physician’s NPI (Gustafson et al., 2011 ). Because reimbursement for genetic counseling services is likely to drive creation of clinic-based GC positions, this is an important future area of study, in particular comparing reimbursement models between states with and without licensure. An interesting finding of our study is that 64% of GCs practicing in AZ are a prior resident of the state. Although there is no national data to compare this finding to, it provides a useful piece of data to help organizations working to expand the GC workforce in the state of Arizona, as well as employers of GCs. These data suggest that recruitment efforts into the profession focused on local high schools and colleges may be effective at growing the local workforce. Additionally, employers of GCs may consider advertising job postings to local GCs students, as well as offering GC rotation sites or employ GC assistants, given that these are the likely future candidates for job recruitment. Our study has several limitations. Because there are a limited number of GCs in Arizona, our sample size was small. This restricted our ability to perform additional analyses and comparisons to national data, especially within certain subspecialties of genetic counseling. Although we believe that findings from this study may be applicable to other large states with a limited GC workforce, our study did not directly measure workforce differences state by state. Additionally, clinical data, such as wait times, billing, and clinic volume, are based on the GCs recall and not data gathered directly from clinical metrics. Lastly, while the characterization of the GC workforce may suggest some barriers to services, it does not provide a direct measure of patient experience. CONCLUSIONS This study found that Arizona has fewer GCs for the population than other states of similar size and is ranked 40th with 0.39 GCs per 100,000 people. The GC workforce in Arizona, a large and geographical spread out state, roughly mirrors the national GC workforce, but the limited workforce leads to a low number of GCs practicing within each sub-specialty. While the consequence appears to be high wait times in some subspecialties, such as pediatrics, there are low wait times for others, such as prenatal, indicating a potential pattern of under-referral and under-utilization of GCs in these sub-specialties of genetics. Future directions include focusing studies on provider’s opinions and use of genetic counseling and testing, particularly in prenatal genetics, as well as provider education in addition to further characterization of state-by-state GC workforce issues particularly for other geographically large, underserved states. Declarations Competing Interests: Lauren Maynard, Laura Engle, Elizabeth Chavez, Valerie M. Schaibley declare they have no conflicts of interest. Consent to participate: Informed consent was obtained from all patients included in the study. Author Contribution All authors contributed to study design and conception. All authors approved the final version before publication. Lauren Maynard and Laura Engle distributed the survey. Lauren Maynard, Laura Engel, and Valerie Schaibley analyzed the data and wrote the manuscript. Acknowledgements: We would like to thank the Arizona Genetics Alliance for their assistance with survey distribution. Comparisons to data from the National Society of Genetic Counselors (NSGC) Professional Status Survey are made with explicit permission of the NSGC. Survey of genetic counselors in AZ was completed as part of the Master of Science capstone project in Genetic Counseling for Laura Engle. Data Availability Survey participants of this study did not provide written consent for raw data generated by this study to be shared. Due to the small number of survey participants and sensitive data (i.e. salary), data from this study is unavailable to the public. References American Board of Genetic Counseling. ABGC Find a Certified Genetic Counselor Directory . Retrieved October 2022 from https://abgc.learningbuilder.com/Search/Public/MemberRole/Verification Arizona Genetics Alliance. (2022). Arizona Genetic Counselor Contact List . Retrieved May 24, 2024 from https://www.azgeneticsalliance.com/member-resources Delikurt, T., Williamson, G. R., Anastasiadou, V., & Skirton, H. (2015). A systematic review of factors that act as barriers to patient referral to genetic services. Eur J Hum Genet , 23 (6), 739-745. https://doi.org/10.1038/ejhg.2014.180 Government Accountability Office. (2020). Genetic services: Information on genetic counselor and medical geneticist workforces . Washington, D.C.: U.S. Government Printing Office: GAO Publication No. 20-593 Retrieved from https://www.gao.gov/products/gao-20-593 Gustafson, S. L., Pfeiffer, G., & Eng, C. (2011). A large health system's approach to utilization of the genetic counselor CPT(R) 96040 code. Genet Med , 13 (12), 1011-1014. https://doi.org/10.1097/GIM.0b013e3182296344 Harrison, T. A., Doyle, D. L., McGowan, C., Cohen, L., Repass, E., Pfau, R. B., & Brown, T. (2010). Billing for medical genetics and genetic counseling services: a national survey. J Genet Couns , 19 (1), 38-43. https://doi.org/10.1007/s10897-009-9249-5 Hoskovec, J. M., Bennett, R. L., Carey, M. E., DaVanzo, J. E., Dougherty, M., Hahn, S. E., LeRoy, B. S., O'Neal, S., Richardson, J. G., & Wicklund, C. A. (2018). Projecting the Supply and Demand for Certified Genetic Counselors: a Workforce Study. J Genet Couns , 27 (1), 16-20. https://doi.org/10.1007/s10897-017-0158-8 Jenkins, B. D., Fischer, C. G., Polito, C. A., Maiese, D. R., Keehn, A. S., Lyon, M., Edick, M. J., Taylor, M. R. G., Andersson, H. C., Bodurtha, J. N., Blitzer, M. G., Muenke, M., & Watson, M. S. (2021). The 2019 US medical genetics workforce: a focus on clinical genetics. Genet Med , 23 (8), 1458-1464. https://doi.org/10.1038/s41436-021-01162-5 Leonhard, J. R., Munson, P. J., Flanagan, J. D., De Berg, K. L., Thompson, P. A., Dean, L. W., & Stein, Q. P. (2017). Analysis of Reimbursement of Genetic Counseling Services at a Single Institution in a State Requiring Licensure. J Genet Couns , 26 (4), 852-858. https://doi.org/10.1007/s10897-016-0062-7 Maiese, D. R., Keehn, A., Lyon, M., Flannery, D., Watson, M., & Working Groups of the National Coordinating Center for Seven Regional Genetics Service, C. (2019). Current conditions in medical genetics practice. Genet Med , 21 (8), 1874-1877. https://doi.org/10.1038/s41436-018-0417-6 National Society of Genetic Counselors. (2023). 2023 Professional Status Survey . https://www.nsgc.org/Policy-Research-and-Publications/Professional-Status-Survey Penon-Portmann, M., Chang, J., Cheng, M., & Shieh, J. T. (2020). Genetics workforce: distribution of genetics services and challenges to health care in California. Genet Med , 22 (1), 227-231. https://doi.org/10.1038/s41436-019-0628-5 Qualtrics. (2020). https://www.qualtrics.com/ Raspa, M., Moultrie, R., Toth, D., & Haque, S. N. (2021). Barriers and Facilitators to Genetic Service Delivery Models: Scoping Review. Interact J Med Res , 10 (1), e23523. https://doi.org/10.2196/23523 Schaibley, V. M., Ramos, I. N., Woosley, R. L., Curry, S., Hays, S., & Ramos, K. S. (2022). Limited Genomics Training Among Physicians Remains a Barrier to Genomics-Based Implementation of Precision Medicine. Front Med (Lausanne) , 9 , 757212. https://doi.org/10.3389/fmed.2022.757212 Spinosi, F., Khan, S., Seymour, C., & Ashkinadze, E. (2021). Trends in coverage and reimbursement for reproductive genetic counseling in New Jersey by multiple payers from 2010 to 2018. J Genet Couns , 30 (6), 1748-1756. https://doi.org/10.1002/jgc4.1443 Triebold, M., Skov, K., Erickson, L., Olimb, S., Puumala, S., Wallace, I., & Stein, Q. (2021). Geographical analysis of the distribution of certified genetic counselors in the United States. J Genet Couns , 30 (2), 448-456. https://doi.org/10.1002/jgc4.1331 United States Census Bureau. (2020). 2020 Decennial Census . Retrieved July 3, 2024 from https://data.census.gov/profile/Arizona?g=040XX00US04 Villegas, C., & Haga, S. B. (2019). Access to Genetic Counselors in the Southern United States. J Pers Med , 9 (3). https://doi.org/10.3390/jpm9030033 Additional Declarations No competing interests reported. Supplementary Files AppendixASurvey.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6607449","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459423061,"identity":"6fcd42b1-0737-40e6-b178-0f816023e285","order_by":0,"name":"Lauren Maynard","email":"data:image/png;base64,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","orcid":"","institution":"University of Arizona Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Lauren","middleName":"","lastName":"Maynard","suffix":""},{"id":459423062,"identity":"5f18d08a-67de-4138-b250-5548859b5d9f","order_by":1,"name":"Laura Engle","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Engle","suffix":""},{"id":459423064,"identity":"fd2827b3-0ba9-4abe-9897-81f6c749d29c","order_by":2,"name":"Elizabeth Chavez","email":"","orcid":"","institution":"Swedish Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Chavez","suffix":""},{"id":459423065,"identity":"c26ac9d3-a823-4fd6-a851-b3467f08104b","order_by":3,"name":"Valerie M. Schaibley","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Valerie","middleName":"M.","lastName":"Schaibley","suffix":""}],"badges":[],"createdAt":"2025-05-07 03:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6607449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6607449/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83280850,"identity":"f36a2da8-5996-40be-a473-7403a68d77e5","added_by":"auto","created_at":"2025-05-22 10:18:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGC Workforce Distribution Across U.S. States\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe per-capita GCs (calculated as the number of GCs per 100,000 people) are plotted from highest to lowest proportion. Arizona is ranked 40\u003csup\u003eth\u003c/sup\u003e across all U.S. states and is highlighted in blue.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6607449/v1/4514ed8f0d7c9253dc381ae7.png"},{"id":83280435,"identity":"1714c52e-22ac-4bfb-8155-52031b8a2219","added_by":"auto","created_at":"2025-05-22 10:10:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGeographic Distribution of GCs in Arizona\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe location of clinic-based GCs practicing in Arizona are shown in orange, with the size of the dot reflecting the proportion of GCs in that location. Shading of counties reflects the population of each county. The seven most populus cities in Arizona are indicated on the map.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6607449/v1/4d364cb87d868119ef766c37.png"},{"id":83280436,"identity":"724c131b-a772-4ee8-8578-aa65043b7388","added_by":"auto","created_at":"2025-05-22 10:10:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNon-Urgent Wait Times for Appointments\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWait times for all clinic-based GCs practicing in Arizona (blue) compared to the 2023 PSS data for non-urgent GC only appointments (black) or an appointment with both a GC and physician (grey).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6607449/v1/e6583dafe53868a57109c255.png"},{"id":83280442,"identity":"34f112d6-a5b6-46fd-8ba0-4cb4bab37fd6","added_by":"auto","created_at":"2025-05-22 10:10:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eWait Times and Referral Patterns Reported by GCs by Practice Specialty\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClinic-based GCs who reported practicing in one of the most prevalent specialties (adult cancer genetics, general adult genetics, pediatrics, or prenatal) were included (N=17). GCs who reported involvement in more than one of these practice specialties were categorized as mixed practice. A) Patient wait times by major specialty. B) GC perception of the number of referrals they received by major specialty.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6607449/v1/f7fb682dc83e9471bdaaba86.png"},{"id":93753193,"identity":"6e65ba9d-8ea6-4c8e-a48b-5c56cec6fd7a","added_by":"auto","created_at":"2025-10-17 08:09:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6607449/v1/98ec39b6-ba52-45bc-9b2a-0947bdb273e6.pdf"},{"id":83279391,"identity":"7f84e2d2-6fce-466a-a3b0-78eeaeae1a8e","added_by":"auto","created_at":"2025-05-22 10:02:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":43396,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixASurvey.docx","url":"https://assets-eu.researchsquare.com/files/rs-6607449/v1/9d990c4f132454878cceb6fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Arizona Genetic Counseling Landscape: Exploring the Impact of a Limited Genetic Counseling Workforce","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe complexity of genetic disease has long required specialized individuals to interpret, diagnose, and treat those affected by hereditary conditions. Certified genetic counselors (GCs) and medical geneticists comprise a subset of health professionals that are uniquely trained to understand the complex interactions between genetics and health. However, as identification of genetic conditions and the utilization of genetic testing has increased, the workforce of genetics professionals in the United States has struggled to keep pace with added demand. A study of the genetic counseling workforce projected a deficiency of GCs until 2023 or 2024 with a ratio of one GC per 100,000 and up until 2030 with a ratio of one GC per 75,000 (Hoskovec et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A recent study reported a ratio of one GC per 71,842 nationwide, but did not differentiate between GCs in patient-facing compared to non-patient facing roles (Triebold et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar issues exist with the scarcity of medical geneticists, where the average age of clinical geneticists is greater than 50, and up to 24.6% of the geneticists surveyed in 2019 expected to retire in the next five years (Jenkins et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Maiese et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAside from the nationwide shortage of genetics professionals, there is a scarcity of genetic services outside of metropolitan centers. Genetic services are concentrated in urban areas, and in states with higher populations (Government Accountability Office, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a survey of medical geneticists, 40% of geneticists practiced in only five U.S. states and there was a deficit in the Southwest (Jenkins et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A geographical analysis of GCs throughout the United States showed that 98.7% of GCs live or work in major metropolitan areas (Triebold et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, a study of genetic services in California found that the average distance traveled to access genetic services was 76.6 miles and individuals from 9.2% of the state traveled more than 100 miles to see a genetic specialist (Penon-Portmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The geographic disparity of genetics services seems to be further exasperated by financial disparities experienced by those living in rural areas. In the southern U.S., counties which have a practicing GC were found to have a significantly higher income than those without a GC, at least 20% higher in six of the 17 states studied (Villegas \u0026amp; Haga, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In these southern states, rural counties and those with lower median incomes had notably lower access to GCs (Villegas \u0026amp; Haga, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArizona is the 14th largest state in the U.S. with a population of 7,151,502 according to the 2020 U.S. Census and has grown 11.9% in 10 years (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although it is the 14th largest state in the U.S. by population, it is the 6th largest state in the U.S. by area, with most of the population concentrated in the greater Phoenix and Tucson metropolitan areas (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In 2020, Maricopa County, which contains the greater Phoenix area, had just over 60% of the state\u0026rsquo;s population and Pima county, which encompasses Tucson, has just under 15% of the state\u0026rsquo;s population (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The remaining population in the state of Arizona resides in cities with populations of 100,000 people or fewer (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and many of these cities are 100 miles or more from Phoenix or Tucson. Thus, while only 10.7% of the state\u0026rsquo;s population live in a \u0026ldquo;rural\u0026rdquo; area as defined by the 2020 U.S. Census, Arizona is a large, geographically spread-out state and travel to major metropolitan areas can be a burden for many residents.\u003c/p\u003e \u003cp\u003eIn addition to the need for an appropriate number of genetic specialists in both urban and rural areas, many other barriers have been documented that prohibit patients from receiving genetic services. These barriers include wait time, length of genetics visits, issues with insurance reimbursement, and misconceptions regarding genetic services (Maiese et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raspa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Non-genetics providers do not appropriately identify the need for a genetics referral and in some cases provide genetic services themselves (Maiese et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Barriers to referrals for appropriate genetic services include a lack of awareness of the patient risk factors, inadequate gathering of family history, inadequate knowledge of genetics and genetic conditions, and insufficient knowledge of availability of genetic services (Delikurt et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Non-geneticist healthcare providers often receive limited training in genetics, which limits their ability to adequately refer patients to genetics. In a small Arizona specific study, 80% of non-genetics physicians reported little to no genetics training, and these providers were not likely to order genetic tests or implement use of genetics into their practice (Schaibley et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The combination of limited genetics training for non-genetics physicians and the inability to appropriately refer patients for a genetics consultation creates both a lack of referrals to genetics clinics for patients who require these services and a lack of awareness that these services are needed.\u003c/p\u003e \u003cp\u003eAlthough barriers to genetics care and genetic counseling workforce shortages have been documented at a national level, there have been no workforce studies of genetic counseling on a local level to determine if national workforce issues reflect state-to-state environments. After comparing the per-capita genetic counseling workforce by state, we surveyed GCs in the state of Arizona to characterize the genetic counseling workforce in the state and compared our findings to national genetic counseling workforce data. The goal of this analysis is to understand if a highly underserved, geographically large and spread-out state, such as Arizona, has different workforce issues and characteristics as those documented at a national level.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e This study was approved by the institutional review board at the University of Arizona (STUDY00001714).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInstrumentation\u003c/h2\u003e \u003cp\u003eWe developed a survey to assess the current genetic counseling workforce in Arizona. The survey consisted of 27 or 52 primarily multiple-choice questions, with some Likert scale questions and write-in response options. Skip logic was used to vary the survey depending on whether the participant indicated they practice direct-patient care, mixed direct and non-direct patient care, or non-direct patient care. Survey questions were developed with feedback from the Expansion of Genetic Service Committee of the Arizona Genetic Alliance (AGA), a local chapter of National Society of Genetic Counselors (NSGC). Questions focused on demographic information, estimates of clinical measures (such as referrals, patient wait times, and clinical capacity), clinical practices (such as billing practices, training, support staff, and management structure), and workplace benefits (such as paid time off and salary). All survey responses were anonymous. Demographic question response options were based on the 2022 NSGC Professional Status Survey. The survey was conducted through Qualtrics software (Qualtrics, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and took approximately 10\u0026ndash;20 minutes to complete. The full version of the survey is available in Appendix A.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe survey was distributed to GCs throughout the state and was open between 9/30/22\u0026ndash;11/04/22. GCs who were board certified or board eligible through the American Board of Genetic Counseling (ABGC) residing in Arizona at the time of the survey were considered eligible. Based on the ABGC directory, there were a total of 52 GCs in Arizona at the time of the survey. Prior to completing the survey, eligible participants consented to participating in the study on the first page of online survey. The survey was distributed through a QR code provided at the annual AGA Educational Conference (09/30/22) and through a post on the AGA listserv.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eSurveys which were 75% or more complete were included in data analysis. Statistical analysis was performed and figures were generated in RStudio (Version 2023.12.1\u0026thinsp;+\u0026thinsp;402). The AZ workforce data were compared to the 2023 NSGC PSS (National Society of Genetic Counselors, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). U.S. population data was obtained from the US Census (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Demographic options with a small number of responses were not always displayed to maintain participant confidentiality. Responses for some demographic questions, such as age, gender identity, and years in practice, were condensed to aide statistical analysis. Fishers exact test was used to identify differences between the AZ workforce and the 2023 PSS data. In order to examine wait times, referral rates and billing data for GCs working with patients in AZ, we analyzed a subset of responses from AZ GCs indicating they were at least partly involved with direct patient care and were not employed by either a commercial laboratory or a private telegenetics company (N\u0026thinsp;=\u0026thinsp;17). Because this subset differs slightly from those who identify as GCs practicing in direct patient care roles in our cohort, which includes GCs employed by commercial laboratories and private telegenetics companies, this subset is referred to as clinic-based GCs in this publication.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe first sought to understand how the genetic counseling workforce compares between states in the U.S.. Using the 2023 NSGC PSS data (National Society of Genetic Counselors, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and state population data from the 2023 U.S. Census (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we calculated the number of GCs in each state per 100,000 people (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Despite having a population of over 7\u0026nbsp;million people, the PSS reports that Arizona has only 29 GCs (National Society of Genetic Counselors, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This leads to 0.39 GCs in Arizona per 100,000 people. In comparison, Tennessee and Washington have similar population sizes to Arizona, with 7.1 and 7.8\u0026nbsp;million, respectively. The 2023 PSS data reports 46 GCs in Tennessee and 88 GCs in Washington, leading to 0.65 and 1.12 GCs per 100,000 people in Tennessee and Washington, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, Arizona ranks 40th across all states for the number of GCs per 100,000 people.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e To better understand the distribution of the local workforce in the context of the state geography, we analyzed the location of clinic-based genetic counselors. Arizona is a large state, with two major metropolitan areas: Phoenix and Tucson. When ranked by population, all of the top 10 incorporated cities in Arizona are in the greater Phoenix and Tucson metropolitan areas. Only five out of top 25 cities ranked by population fall outside of this region: Yuma, Flagstaff, Casa Grande, Lake Havasu City, and Prescott Valley. According the AGA directory of GCs practicing in Arizona at the time of the survey (Arizona Genetics Alliance, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), all clinic-based GCs in the state are located in the greater Phoenix area (89%) and the Tucson area (11%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients living outside these areas who require GC services need to travel significant distances for an in-person consultation with a GC. From the five cities listed above, the distance a patient would have to travel to see a genetic counselor would be at least 184 miles if they lived in Yuma, at least 147 miles if they lived in Flagstaff, at least 49 miles if they lived in Casa Grande, at least 194 miles if they lived in Lake Havasu City, and at least 92 miles if they lived in Prescott Valley (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we conducted a workforce assessment survey between September and November 2022. The survey was designed to capture data about the current GC workforce in Arizona to identify barriers and opportunities for workforce expansion. At the time of the survey, there were 52 GCs in Arizona according to the American Board of Genetic Counseling Membership Directory (American Board of Genetic Counseling). A total of 37 participants responded to the survey, nine responses were less than 75% complete and excluded from analysis, leaving 28 responses and a survey response rate of 53.8%.\u003c/p\u003e \u003cp\u003eDemographic data of survey respondents compared to the 2023 NSGC PSS (National Society of Genetic Counselors, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences in demographic information between the Arizona GCs and the 2023 PSS data. Participants described themselves primarily as white (86%) or Hispanic or Latinx (11%) females (93%), similar to the 2023 PSS demographic data, but with a slightly higher proportion of Hispanic or Latinx respondents (11% compared to 3%, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of note, 64% of respondents resided in the state prior to attending their genetic counseling program (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In line with the PSS data, the majority of Arizona GCs are board-certified (96%), work full-time (97%) and are under the age of 40, with 39% aged 20\u0026ndash;29 and 43% aged 30\u0026ndash;39 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most respondents have been practicing for fewer than 10 years, similar to the PSS data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most GCs in Arizona (61%) provide predominantly direct patient care, with the remainder practicing in a setting where they provide non-direct patient care (28%), or mixed direct and non-direct patient care (11%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics of Arizona GC Workforce\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAZ Workforce\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2023 PSS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of GCs in AZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrior AZ Resident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFull-Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRace and Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic or Latinx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle Eastern or North African\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack, African American, or of African descent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican Indian, Alaskan Native, or Indigenous Peoples of Canada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNative Hawaiian or other Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender Identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYears in Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;14 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePatient Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Patient Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Direct Patient Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e104,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e104,664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Patient Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e92,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e90,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Direct Patient Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e127,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e129,079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e107,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e107,174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eParticipants could select more than one option for race and ethnicity. Percentages are out of all responses and do not add to 100%.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEmployers varied, with most respondents working for an academic medical center (29%), a public hospital or medical center (21%), or a commercial laboratory (18%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The largest proportion of genetic counselors in Arizona practice in adult cancer genetics (39%), followed by pediatrics (25%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only 14% of genetic counselors in Arizona reported providing any prenatal counseling as part of their practice, compared to 26% of 2023 PSS respondents (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No genetic counselors in Arizona report practicing in settings which include preimplantation genetic testing, ART/IVF or infertility, which makes up 6% of practice specialties reported in the 2023 PSS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Neurogenetics, cardiology, reproductive/preconception screening, ophthalmology, hematology, public health and psychiatrics are also poorly represented in Arizona compared to national data (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePractice Information of Arizona GCs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAZ Workforce\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2023 PSS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePractice Specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult cancer genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrenatal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePediatrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurogenetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenomic medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreimplantation genetic testing, ART/IVF, or infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreconception/reproductive screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral adult genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePediatric cancer genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOphthalmology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsumer /personal genomics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHematology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNewborn screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychiatric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital/medical facility-academic medical center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital/medical facility- public\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital/medical facility-private\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory -commercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory-noncommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate company-telegenetics/consulting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity, college, or training program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsurance company/benefit management company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot for profit organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysician's private practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment organization or agency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate company- biotech or research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate company- digital health or software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate company-pharmaceutical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed or private practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eParticipants could select more than one option for practice specialty. Percentages are out of all responses and do not add to 100%.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo better understand the deficiency of clinical genetic services, we asked respondents practicing in direct patient care roles to suggest clinical areas that would benefit from additional genetic counseling services in their institution. Twelve genetic counselors responded, with the most common responses including neurology (n\u0026thinsp;=\u0026thinsp;10), cardiology (n\u0026thinsp;=\u0026thinsp;6) and NICU/pediatrics (n\u0026thinsp;=\u0026thinsp;5). Endocrinology, OBGYN and oncology were each suggested by two respondents and nephrology and adult primary care were each suggested once.\u003c/p\u003e \u003cp\u003eWe also sought to understand how the limited GC workforce impacts wait times for patients. Clinic-based GCs in AZ (N\u0026thinsp;=\u0026thinsp;17) reported a wide range of wait times for non-urgent appointments, with 24% reporting wait times less than one week and 35% reporting wait times greater than six months (Fig.\u0026nbsp;3). In contrast, wait times for non-urgent GC appointments reported in the 2023 PSS are distributed somewhat evenly across the possible options, with only 8% of PSS respondents indicating wait times greater than six months (Fig.\u0026nbsp;3). We found significant variability between patient wait times among clinic-based GCs in AZ when we stratified the wait times by clinical specialty. Clinical specialties were categorized into four major groups: adult general genetics, adult cancer genetics, pediatrics and prenatal. Participants who indicated more than one clinical specialty were labeled into a fifth \u0026ldquo;mixed\" category. Clinic-based GCs in adult general genetics, adult cancer genetics and mixed specialties reported varied wait times for patients in AZ. In contrast, GCs in prenatal genetics reported wait-times less than one week and all clinic-based GCs in pediatrics reported wait-times of greater than six months (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to wait time, we also asked clinic-based GCs if they receive more, less, or an appropriate number of referrals for patients in the community they serve who would meet criteria for a genetic counseling referral. Most respondents (47%) indicated they receive fewer referrals than are appropriate, 41% receive an appropriate number of referrals and 12% receive more referrals than appropriate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Interestingly, all clinic-based prenatal GCs (N\u0026thinsp;=\u0026thinsp;2) reported receiving fewer referrals than appropriate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, when asked about patient capacity compared to actual number of patients seen, both prenatal GCs indicated that they have the capacity to see 50\u0026ndash;99 patients per month, but actually see fewer than 25. In contrast, most genetic counselors practicing in general adult genetics, adult cancer, pediatrics, and mixed specialties reported seeing a monthly patient load equal to their monthly capacity.\u003c/p\u003e \u003cp\u003eFinally, we analyzed the differences in billing practices. We found a significant difference (p\u0026thinsp;=\u0026thinsp;0.002) between the billing practices of the 2023 PSS respondents and the clinic-based GCs in AZ. Most clinic-based GCs in AZ bill using a facility fee (N\u0026thinsp;=\u0026thinsp;12, 71%), typically billed under the physician\u0026rsquo;s name (N\u0026thinsp;=\u0026thinsp;7, 41%; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This contrasts with the 2023 NSGC PSS data, in which 33% of GCs who bill for in-person services bill a professional fee in the GCs name (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We found that 59% of clinic-based GCs in AZ (N\u0026thinsp;=\u0026thinsp;10) use the 96040 CPT code for billing compared to 72% of GCs in the 2023 PSS, although the distribution of billing code usage between the AZ GCs and PSS data was not significantly different.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBilling Practices for Clinic-Based GCs in Arizona\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBilling Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAZ Workforce\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2023 PSS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional fee, billed in the genetic counselor's name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional fee, billed in the physician's name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility fee, billed in the genetic counselor's name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility fee, billed in the physician's name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/A, services are not billed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eParticipants could select more than one option for billing practices. Percentages are out of all responses and do not add to 100%.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eWait times for all clinic-based GCs practicing in Arizona (blue) compared to the 2023 PSS data for non-urgent GC only appointments (black) or an appointment with both a GC and physician (grey).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWhen comparing the number of GCs per 100,000 in the United States, only 15 out of 50 states are above the one GC per 100,000 population mark, demonstrating that there is an uneven distribution of the GC workforce and much of the country is still considered underserved despite the previously reported ratio of one GC per 71,842 nationwide (Triebold et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are fewer GCs in AZ per 100,000 compared to other states with similar populations. With AZ raking 40th out of 50, the state is underserved in genetic counseling workforce and this is even further exacerbated due to the large size of the state and the population distribution. All of the GCs in AZ are localized to the most populated counties in Arizona: Maricopa and Pima counties. While this largely reflects national trends in the clinic-based genetics workforce clustered around major metropolitan areas (Jenkins et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Triebold et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the geographic size of Arizona leads to significant disparities in access to clinical genetics services for populations who live outside these counties. This group of nearly 1.7\u0026nbsp;million individuals, which makes up roughly 24% of the state's population (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), need to travel significant distances (~\u0026thinsp;50 to 200 miles) for in-person genetic services, depending on where they live and if they have access to remote genetic counseling services. A full characterization of remote genetic counseling services available to these individuals provides an important area for future research.\u003c/p\u003e \u003cp\u003eDespite the small workforce of GCs compared to other state, the demographic makeup of the GC workforce in AZ is similar to the national workforce. Years in practice, GC specialty, direct vs. non-direct or mixed patient care, employer type and salary did not differ for genetic counselors in AZ compared to the national workforce. However, our sample size was small (N\u0026thinsp;=\u0026thinsp;28), and we may not have had sufficient power to identify differences in demographic distributions. Although we found a higher proportion of Hispanic or Latinx genetic counselors in Arizona compared to genetic counselors nationally, this is not fully reflective of the local population, in which 31% of the population identifies as Hispanic or Latinx ethnicity based on the U.S. Census data (United States Census Bureau, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found longer wait times for GC services in AZ compared to national data, but we uncovered surprising findings when we stratified our data by subspecialty. While all pediatric GCs in Arizona report wait times of greater than six months, all prenatal genetic counselors in Arizona report wait times of less than 1 week for a non-urgent referral. Interestingly, all prenatal GCs also report underutilization of their clinical availability and that they are receiving fewer than appropriate referrals from the providers in their area. In contrast, GCs in the pediatrics, adult cancer, general adult, and mixed specialties report that they are seeing a monthly patient load equal to their monthly capacity and more often report an appropriate number of referrals. Based on the small number of practicing prenatal GCs and the short clinic wait time to see a prenatal GC, it is likely that patients in AZ are being under-referred for prenatal GC services. In contrast, the long wait times in pediatrics and general adult clinics suggests that the workforce of genetic counselors is too small to meet the current patient demand in these subspecialties. One area of future study should assess provider\u0026rsquo;s knowledge and views of genetic testing and GC referral practices in AZ, especially for prenatal services.\u003c/p\u003e \u003cp\u003eBilling practices for GC services were significantly different in AZ compared to national data. While the majority of GCs nationally bill a professional fee, we found that the majority of GCs in AZ bill a facility fee, typically in a physician's name. Arizona is one of the few states in the U.S. that does not have licensure for GCs, which may impact billing practices at institutions within the state. In contrast, we found no difference in the use of CPT codes for billing between our data and national trends. There have been few studies on billing and reimbursement for genetic counseling and many of the recent studies have been focused on the use of telegenetics services since the start of the COVID-19 pandemic. After the creation of the 96040 CPT code, NSGC completed a study of billing practices and found that while 69% of respondents billed for genetic counseling services, only 24% used the 96040 CPT code (Harrison et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This study also identified differences in billing based on specialty, with a higher proportion of GCs practicing in prenatal and cancer genetic counseling using the 96040 CPT code than those in pediatrics (Harrison et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Subsequently, NSGC gathered data on billing practices via the annual PSS. In the 2023 PSS, 63% of GCs reported they bill for services and 72% of these GCs report utilizing the 96040 CPT code (National Society of Genetic Counselors, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), however, this was not broken down by area of practice. Furthermore, studies of reimbursement for genetic counseling have been focused on the use of the 96040 CPT code, and most have focused on states which have licensure (Leonhard et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Spinosi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study from 2011 from the Cleveland Clinic, found that 62.6% of encounters billed to third party private payors, which excludes Medicare and Medicaid, received at least partial reimbursement when the 96040 CPT code was billed under a supervising physician\u0026rsquo;s NPI (Gustafson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Because reimbursement for genetic counseling services is likely to drive creation of clinic-based GC positions, this is an important future area of study, in particular comparing reimbursement models between states with and without licensure.\u003c/p\u003e \u003cp\u003eAn interesting finding of our study is that 64% of GCs practicing in AZ are a prior resident of the state. Although there is no national data to compare this finding to, it provides a useful piece of data to help organizations working to expand the GC workforce in the state of Arizona, as well as employers of GCs. These data suggest that recruitment efforts into the profession focused on local high schools and colleges may be effective at growing the local workforce. Additionally, employers of GCs may consider advertising job postings to local GCs students, as well as offering GC rotation sites or employ GC assistants, given that these are the likely future candidates for job recruitment.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Because there are a limited number of GCs in Arizona, our sample size was small. This restricted our ability to perform additional analyses and comparisons to national data, especially within certain subspecialties of genetic counseling. Although we believe that findings from this study may be applicable to other large states with a limited GC workforce, our study did not directly measure workforce differences state by state. Additionally, clinical data, such as wait times, billing, and clinic volume, are based on the GCs recall and not data gathered directly from clinical metrics. Lastly, while the characterization of the GC workforce may suggest some barriers to services, it does not provide a direct measure of patient experience.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study found that Arizona has fewer GCs for the population than other states of similar size and is ranked 40th with 0.39 GCs per 100,000 people. The GC workforce in Arizona, a large and geographical spread out state, roughly mirrors the national GC workforce, but the limited workforce leads to a low number of GCs practicing within each sub-specialty. While the consequence appears to be high wait times in some subspecialties, such as pediatrics, there are low wait times for others, such as prenatal, indicating a potential pattern of under-referral and under-utilization of GCs in these sub-specialties of genetics. Future directions include focusing studies on provider\u0026rsquo;s opinions and use of genetic counseling and testing, particularly in prenatal genetics, as well as provider education in addition to further characterization of state-by-state GC workforce issues particularly for other geographically large, underserved states.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\n\u003cp\u003eLauren Maynard, Laura Engle, Elizabeth Chavez, Valerie M. Schaibley declare they have no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eConsent to participate:\u003c/h2\u003e\n\u003cp\u003e Informed consent was obtained from all patients included in the study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to study design and conception. All authors approved the final version before publication. Lauren Maynard and Laura Engle distributed the survey. Lauren Maynard, Laura Engel, and Valerie Schaibley analyzed the data and wrote the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the Arizona Genetics Alliance for their assistance with survey distribution. Comparisons to data from the National Society of Genetic Counselors (NSGC) Professional Status Survey are made with explicit permission of the NSGC. Survey of genetic counselors in AZ was completed as part of the Master of Science capstone project in Genetic Counseling for Laura Engle.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eSurvey participants of this study did not provide written consent for raw data generated by this study to be shared. Due to the small number of survey participants and sensitive data (i.e. salary), data from this study is unavailable to the public.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Board of Genetic Counseling. \u003cem\u003eABGC Find a Certified Genetic Counselor Directory\u003c/em\u003e. Retrieved October 2022 from https://abgc.learningbuilder.com/Search/Public/MemberRole/Verification\u003c/li\u003e\n\u003cli\u003eArizona Genetics Alliance. (2022). \u003cem\u003eArizona Genetic Counselor Contact List\u003c/em\u003e. Retrieved May 24, 2024 from https://www.azgeneticsalliance.com/member-resources\u003c/li\u003e\n\u003cli\u003eDelikurt, T., Williamson, G. R., Anastasiadou, V., \u0026amp; Skirton, H. (2015). A systematic review of factors that act as barriers to patient referral to genetic services. \u003cem\u003eEur J Hum Genet\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(6), 739-745. https://doi.org/10.1038/ejhg.2014.180 \u003c/li\u003e\n\u003cli\u003eGovernment Accountability Office. (2020). \u003cem\u003eGenetic services: Information on genetic counselor and medical geneticist workforces\u003c/em\u003e. Washington, D.C.: U.S. Government Printing Office: GAO Publication No. 20-593 Retrieved from https://www.gao.gov/products/gao-20-593\u003c/li\u003e\n\u003cli\u003eGustafson, S. L., Pfeiffer, G., \u0026amp; Eng, C. (2011). A large health system\u0026apos;s approach to utilization of the genetic counselor CPT(R) 96040 code. \u003cem\u003eGenet Med\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(12), 1011-1014. https://doi.org/10.1097/GIM.0b013e3182296344 \u003c/li\u003e\n\u003cli\u003eHarrison, T. A., Doyle, D. L., McGowan, C., Cohen, L., Repass, E., Pfau, R. B., \u0026amp; Brown, T. (2010). Billing for medical genetics and genetic counseling services: a national survey. \u003cem\u003eJ Genet Couns\u003c/em\u003e,\u003cem\u003e 19\u003c/em\u003e(1), 38-43. https://doi.org/10.1007/s10897-009-9249-5 \u003c/li\u003e\n\u003cli\u003eHoskovec, J. M., Bennett, R. L., Carey, M. E., DaVanzo, J. E., Dougherty, M., Hahn, S. E., LeRoy, B. S., O\u0026apos;Neal, S., Richardson, J. G., \u0026amp; Wicklund, C. A. (2018). Projecting the Supply and Demand for Certified Genetic Counselors: a Workforce Study. \u003cem\u003eJ Genet Couns\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e(1), 16-20. https://doi.org/10.1007/s10897-017-0158-8 \u003c/li\u003e\n\u003cli\u003eJenkins, B. D., Fischer, C. G., Polito, C. A., Maiese, D. R., Keehn, A. S., Lyon, M., Edick, M. J., Taylor, M. R. G., Andersson, H. C., Bodurtha, J. N., Blitzer, M. G., Muenke, M., \u0026amp; Watson, M. S. (2021). The 2019 US medical genetics workforce: a focus on clinical genetics. \u003cem\u003eGenet Med\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(8), 1458-1464. https://doi.org/10.1038/s41436-021-01162-5 \u003c/li\u003e\n\u003cli\u003eLeonhard, J. R., Munson, P. J., Flanagan, J. D., De Berg, K. L., Thompson, P. A., Dean, L. W., \u0026amp; Stein, Q. P. (2017). Analysis of Reimbursement of Genetic Counseling Services at a Single Institution in a State Requiring Licensure. \u003cem\u003eJ Genet Couns\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(4), 852-858. https://doi.org/10.1007/s10897-016-0062-7 \u003c/li\u003e\n\u003cli\u003eMaiese, D. R., Keehn, A., Lyon, M., Flannery, D., Watson, M., \u0026amp; Working Groups of the National Coordinating Center for Seven Regional Genetics Service, C. (2019). Current conditions in medical genetics practice. \u003cem\u003eGenet Med\u003c/em\u003e,\u003cem\u003e 21\u003c/em\u003e(8), 1874-1877. https://doi.org/10.1038/s41436-018-0417-6 \u003c/li\u003e\n\u003cli\u003eNational Society of Genetic Counselors. (2023). \u003cem\u003e2023 Professional Status Survey\u003c/em\u003e. https://www.nsgc.org/Policy-Research-and-Publications/Professional-Status-Survey\u003c/li\u003e\n\u003cli\u003ePenon-Portmann, M., Chang, J., Cheng, M., \u0026amp; Shieh, J. T. (2020). Genetics workforce: distribution of genetics services and challenges to health care in California. \u003cem\u003eGenet Med\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(1), 227-231. https://doi.org/10.1038/s41436-019-0628-5 \u003c/li\u003e\n\u003cli\u003eQualtrics. (2020). https://www.qualtrics.com/\u003c/li\u003e\n\u003cli\u003eRaspa, M., Moultrie, R., Toth, D., \u0026amp; Haque, S. N. (2021). Barriers and Facilitators to Genetic Service Delivery Models: Scoping Review. \u003cem\u003eInteract J Med Res\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(1), e23523. https://doi.org/10.2196/23523 \u003c/li\u003e\n\u003cli\u003eSchaibley, V. M., Ramos, I. N., Woosley, R. L., Curry, S., Hays, S., \u0026amp; Ramos, K. S. (2022). Limited Genomics Training Among Physicians Remains a Barrier to Genomics-Based Implementation of Precision Medicine. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e, 757212. https://doi.org/10.3389/fmed.2022.757212 \u003c/li\u003e\n\u003cli\u003eSpinosi, F., Khan, S., Seymour, C., \u0026amp; Ashkinadze, E. (2021). Trends in coverage and reimbursement for reproductive genetic counseling in New Jersey by multiple payers from 2010 to 2018. \u003cem\u003eJ Genet Couns\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(6), 1748-1756. https://doi.org/10.1002/jgc4.1443 \u003c/li\u003e\n\u003cli\u003eTriebold, M., Skov, K., Erickson, L., Olimb, S., Puumala, S., Wallace, I., \u0026amp; Stein, Q. (2021). Geographical analysis of the distribution of certified genetic counselors in the United States. \u003cem\u003eJ Genet Couns\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(2), 448-456. https://doi.org/10.1002/jgc4.1331 \u003c/li\u003e\n\u003cli\u003eUnited States Census Bureau. (2020). \u003cem\u003e2020 Decennial Census\u003c/em\u003e. Retrieved July 3, 2024 from https://data.census.gov/profile/Arizona?g=040XX00US04\u003c/li\u003e\n\u003cli\u003eVillegas, C., \u0026amp; Haga, S. B. (2019). Access to Genetic Counselors in the Southern United States. \u003cem\u003eJ Pers Med\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(3). https://doi.org/10.3390/jpm9030033 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genetic Counseling, Genetic services, Medical Genetics and Health Workforce ","lastPublishedDoi":"10.21203/rs.3.rs-6607449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6607449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCertified genetic counselors (GCs) are healthcare professionals uniquely trained to understand the complex interactions between genetics and health. As utilization of genetic testing has increased, the workforce of GCs in the United States (US) has struggled to match increasing demand. In this study, we characterized the genetic counseling workforce in Arizona as an example of a limited workforce in a geographically large, spread-out state. Analysis of the per-capita distribution of GCs across the US using data from the 2023 National Society of Genetic Counselors (NSGC) Professional Status Survey (PSS) shows that Arizona is ranked 40\u003csup\u003eth\u003c/sup\u003e out of 50 for the number of GCs per 100,000 people, despite the state’s large size and population. We then surveyed GCs residing in Arizona (N=28) to understand demographics, work environment and practices, and estimated clinic measures and compared responses to the 2023 NSGC PSS. Although certain genetic counseling subspecialties are underrepresented in Arizona, including prenatal, neurology and cardiology, there were no significant differences in the demographics, area of practice, employers, and salary between Arizona GCs and those nationwide. Results indicate low wait times and under-utilization of prenatal genetic counseling services in Arizona and long wait times and full clinic loads for pediatrics and adult general genetics clinics. Together, our results demonstrate a need for genetic counseling workforce expansion in Arizona to meet demand and address wait times. Data from this study may assist with expansion of the genetic counseling workforce as similar issues may exist in other geographically large, underserved states.\u003c/p\u003e","manuscriptTitle":"The Arizona Genetic Counseling Landscape: Exploring the Impact of a Limited Genetic Counseling Workforce","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:02:10","doi":"10.21203/rs.3.rs-6607449/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65cd81a5-e129-438f-a4cc-a275176075dd","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T08:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 10:02:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6607449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6607449","identity":"rs-6607449","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}