The Association Between the Social Vulnerability Index and Access to California High School Athletic Trainers Open Access

• Most California Interscholastic Federation high schools do not have a certified athletic trainer on staff.

• Increased numbers of elderly and pediatric household members, individuals with disabilities, single-parent households, and poor English language proficiency are associated with decreased odds of access to school-based athletic training services.

• Lower socioeconomic status and increased vulnerability in housing type and transportation at the census tract level are also associated with reduced odds of access to school-based athletic training services.

Athletic trainers (ATs) serve a crucial role in caring for athletes, as they are frequently the first to assess and treat injured athletes. However, availability of ATs in California high school–based settings is inconsistent, as no mandates exist to require the presence of ATs in schools. Despite having the second largest number of high school athletes in the United States at around 763 000, California is one of the few remaining states to lack licensure for ATs.¹  Prior researchers have revealed that over 54 000 student-athletes in California received care from unqualified health personnel serving in the role of an AT.¹  Efforts to protect youth athletes through athletic training licensure have been attempted in the past through bills proposed to the Califorinia state legislature but have been unsuccessful.²  The lack of licensure for ATs in California directly affects the availability and quality of care for high school athletes. In areas with fewer resources or less access to qualified health professionals, student-athletes may receive inadequate care from unlicensed individuals whom lack the necessary skills and training. Marginalized communities and underresourced schools are more likely to experience gaps in access to qualified care, potentially contributing to health disparities and worse health outcomes for student-athletes. Although reasons for a lack of ATs in the school setting are varied, in the literature, it has been suggested that the presence of a school-based AT may help to mitigate the effects of the social determinants of health (SDoHs) on athlete health and well-being.^3–7  Social determinants of health are factors that can influence one’s health, such as policies and laws, geographic location, the built environment (eg, sidewalks, open spaces), race, socioeconomic status (SES), as well as access to education, nutritious foods, physical activity opportunities, and health care.⁸ 

A growing body of evidence has revealed inequitable SDoHs exist for athletes, which can lead to health disparities among different populations. Authors of one study found that Black athletes are less knowledgeable about concussion symptoms than their White counterparts, which may delay access to appropriate treatment and subsequent return to sports.⁹  Authors of prior work have also identified inequities in access to surgical care for those who have been injured. Pediatric athletes who were Black, Hispanic, or publicly insured are more likely to experience a greater delay to surgery after an anterior cruciate ligament rupture than their counterparts.¹⁰ 

Evaluating AT access in the context of SDoHs is important because it affects how sports-related injuries are evaluated and managed. For example, among racial minorities, the presence of ATs has been associated with increased athlete knowledge about concussion as well as increased survival after sudden cardiac arrest.^7,9,11,12  Although SDoHs are known to affect overall access to youth sports, the role of SDoHs on access to school-based athletic training services has not been fully elucidated.

Authors of prior studies examining the relationship between SDoHs and school-based athletic training access did not consider the intersectionality of SDoHs.^4,5 Intersectionality is a theoretical framework that examines how different parts of a person’s identity or status can work together to create advantages, challenges, or both, experienced on the individual level as well as reflect the systems of power and marginalization that exist at the larger macro societal level.¹³  Studying intersectionality is crucial when examining SDoHs because it acknowledges the complex realities of everyday life and helps to explain how multiple factors interact to shape health outcomes. Some individuals may hold multiple identities—such as low SES, being a racial minority, and being female—that compound and can ultimately lead to health inequities and disparities. While no metric is perfect at capturing the nuances of intersectionality, the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry’s (CDC/ATSDR’s) Social Vulnerability Index (SVI) provides an opportunity to assess multiple SDoHs all at once. The SVI was originally created to help public health officials and local planners better prepare for and respond to emergency events like hurricanes and disease outbreaks and has also been applied to social ecological research. It is a measure of social vulnerability that considers 16 US Census variables across four domains: (1) racial and ethnic minority status, (2) household characteristics, (3) SES, and (4) housing type and transportation (Supplemental Figure 1).¹⁴  Through the lens of the social ecological model of health, a theoretical framework which explains how factors in various layers of the environment interact together and can influence one’s health (Supplemental Figure 2), we set our primary aim to evaluate the association between county and census tract–level SVI scores and California Interscholastic Federation (CIF) high school access to school-based certified ATs and unverified trainers (UTs).¹⁵  Our secondary aim was to describe the variability in SVI scores between and within counties in California. We hypothesize that a higher overall SVI score and domain-specific SVI scores are associated with less access to school-based certified ATs and UTs.

Using the social ecological model as the theoretical framework, we conducted a retrospective cross-sectional study using publicly available data. We used the state-based CDC/ATSDR’s SVI database to collect county and census tract–level SVI scores within California.¹⁶  Given that a single county can have large variations in SDoHs and thus may not be representative of individual schools, SVI scores were collected at both the county and census tract levels. Census tracts are small geographic areas used by the US Census Bureau to analyze local patterns in demographics, housing, health, and other social factors. Multiple census tracts exist within a single county. The census tract level is the most granular level of data available from the SVI database. The database is based on the US Census Bureau’s American Community Survey data from 2020, as these were the most up-to-date data available. The CIF Participation Census is an annual survey that collects information on its member schools.¹⁷  The 2022–2023 CIF Participation Census (Supplemental Figure 3) contained the most up-to-date data available and was used to collect demographic information (eg, name, address, school type) of individual high schools. Of 1609 CIF member high schools in the 2022–2023 academic year, 1598 (99%) schools responded to the annual Participation Census. We did not conduct an a priori power analysis since all schools who responded to the 2022–2023 Participation Census were included in this study. The University of California, San Francisco, Institutional Review Board approved our study.

We extracted participating school names, the city of address for each school, school type (public, public/charter, or private), total student enrollment, total student-athlete enrollment, access to school-based UTs, and access to school-based certified ATs from the CIF census database.¹⁷  We then manually cross-referenced the school names and city of addresses with the California Department of Education’s online California School Directory, the school’s Website, or both, to determine the school’s address. Subsequently, we used each school’s address (or geographic coordinates found on Google Maps when the address was inadequate) to match each school to its corresponding county and census tract using an online geocoder developed by the US Census Bureau.^18,19  Then we matched each school to its corresponding county and census tract–level SVI scores using the state-based CDC/ATSDR’s SVI database.¹⁴  The primary outcomes of interest were (1) access to school-based UTs and (2) access to school-based certified ATs. Secondary outcomes of interest included intercounty and intracounty variability in SVI scores.

We summarized demographic data using descriptive statistics with medians (interquartile ranges [IQRs]) summarizing continuous variables and frequencies (%) summarizing categorical variables. We built separate multivariable logistic regressions to assess the relationships between census-level SVI scores (overall and all SVI domains) and access to school-based UTs and certified ATs. Then we used generalized linear mixed effects models, with a binary outcome and logit link, to assess the relationships between county-level SVI scores (overall and SVI domains) and access to school-based UTs and certified ATs. We included a random effect for counties in these models to account for correlation among schools within the same county. We adjusted all models for school enrollment size, total sport participation, and school type. If a school did not know whether an AT was on staff, we removed that school from analyses involving ATs as an outcome. We used coefficients of variation to assess intercounty and intracounty variability in SVI scores. SAS version 9.4 (SAS Institutes) was used for all analyses and statistical significance was set a priori at α = .05.

Based upon the CIF census data, of the 1598 respondent schools, 65% were public, 24% private, and 11% charter (Table 1). The median student enrollment per school was 928 students (IQR = 327, 1817). The median number of athletes per school was 460 (IQR = 174, 750); however, this number does not account for multisport athletes; thus, the number of unique athletes may be overcounted. Forty-nine percent of high schools reported having an AT on staff, of which 41% were certified ATs. Most high school–based ATs worked part time (61%).

When adjusted for school type, school enrollment size, and total sport participation, an increased SVI score (indicating increased vulnerability) in household characteristics was associated with lower odds of having a high school UT on staff at both county (OR = 0.89 [95% CI = 0.80, 0.99]; P = .04) and census tract levels (OR = 0.93 [95% CI = 0.89, 0.97]; P = .002; Table 2). Higher scores in SES, racial and ethnic minority status, and housing type and transportation were not significantly associated with access to high school UTs at both the county and census tract levels. Increased overall SVI scores were associated with lower odds of having a high school UT at the census tract level (OR = 0.95 [95% CI = 0.90, 0.99]; P = .02) but not the county level (P = .75).

Using the same adjustments for analyses, increased SES SVI scores were associated with lower odds of having a high school certified AT at the census tract level (OR = 0.94 [95% CI = 0.89, 0.98]; P = .006) but not the county level (P = .16; Table 2). A higher SVI score in household characteristics was associated with lower odds of having a high school certified AT on staff at both county (OR = 0.86 [95% CI = 0.77, 0.96]; P = .009) and census tract levels (OR = 0.91 [95% CI = 0.87, 0.96]; P < .001). Higher SVI scores in racial and ethnic minority status were not significantly associated with access to high school certified ATs at both the county and census tract levels. Increased vulnerability in housing type and transportation at the census tract level was associated with lower odds of having a high school certified AT (OR = 0.95 [95% CI = 0.91, 0.99]; P = .02) but not at the county level (P = .73). Increased overall SVI scores were associated with lower odds of having a high school certified AT at the census tract level (OR = 0.92 [95% CI = 0.88, 0.97]; P < .001) but not at the county level (P = .22).

Intercounty (between-counties) variability was highest in household characteristics SVI scores and lowest in racial and ethnic minority status SVI scores (Table 3). Marin County has the greatest intracounty (within-county) variability in overall SVI score (Table 4; Figure).

Access to high-quality youth sports includes the adequate staffing of ATs, who are first-line triagers for injuries, recognize life-threatening medical conditions, and manage medical emergencies.²⁰  Social determinants of health are known to affect overall access to youth sports; however, it is not fully understood how multiple SDoHs, outside of SES, may affect the access to school-based ATs.⁴  Additionally, increased quality of health equity research methodologies, which consider the assessment of multiple contributors and their effect on health disparities in sports and exercise medicine, is needed.²¹  Using the social ecological model of health as a theoretical framework and the SVI as a measure of multiple SDoHs, we examined 16 distinct SDoHs and found differential access to athletic training services based on the geographical location of a school and certain SDoHs affecting that region.

In our study, increased vulnerability in household characteristics (eg, elderly and pediatric household members, individuals with disabilities, single-parent households, and poor English language proficiency) at both the county and census tract levels stood out as the primary SVI domain associated with decreased access to school-based UTs and certified ATs. To our knowledge, this is a novel finding that has not been demonstrated in prior research. One possible explanation is that immigrants, in addition to racial and ethnic minority families, frequently live in multigenerational households to pool resources both in finances as well as supervised care for dependents such as the elderly, children, and individuals with disabilities.²²  Due to the stretching of limited resources, parents in these settings may not advocate for ATs at their school districts, given that their efforts are spent elsewhere providing for the entire family. Moreover, these parents may simply not be aware of the existence or value of ATs due to language barriers. However, as household characteristics have not been previously studied by researchers examining SDoHs and their relationship to school-based AT access, drawing definitive conclusions proves challenging. Our results also reveal that an increased overall vulnerability at the census tract level is associated with decreased odds of access to both school-based UTs and certified ATs. Given that the other SVI domains had variable associations with access to athletic training services, household characteristics may be the most influential driving force behind overall social vulnerability. This underscores the importance of further research into household characteristics as a crucial factor influencing access to school-based ATs.

At the more granular census tract level, our analysis revealed that lower odds of certified AT access were also associated with increased vulnerability in the SES as well as housing type and transportation domains. Our findings are congruent with prior studies in which authors have demonstrated that decreased SES is associated with decreased AT access.^1,23  For example, authors of one cross-sectional study found that, although access to ATs positively influenced student-athletes’ health care, schools with lower SES had reduced access to AT services.⁴  The researchers of that study used county median household income and percentage of students eligible for free or reduced-price lunch as a proxy for SES. The disparities in funding for ATs in California high schools, which are often not reimbursed by third-party payers like Medicaid or commercial insurers, can be linked to the broader context of California's school funding evolution.^24,25  While legislative changes like Serrano v Priest and Proposition 13 have addressed some financial disparities among school districts, ongoing inequalities driven by local income variations and economic fluctuations persist.²⁵  Consequently, school districts frequently prioritize resources for teacher and staff salaries over funding for athletics departments, possibly relegating financial support for ATs to a lower priority.^26,27  In addition to variations in school funding, if parents or the community in which they live have low levels of educational attainment and SES, then children are less likely to have access to youth sports and more likely to drop out of organized sports.^23,28–30  This may be related to the increasing cost of youth sports, which can become burdensome for those who have financial struggles.^30,31  Reduced sport participation may then also lead to reduced employment of ATs in school settings given the decreased need for athletic training services. Authors of mixed-method studies have also shown that lack of awareness regarding the importance and effect of ATs may also contribute to AT understaffing.^5,6  These factors ultimately lead school administrators to rely on coaches or other medical providers, such as school nurses, to fulfill the duties of ATs.^5,6  Disparities in funding for ATs in California’s high schools, coupled with socioeconomic and educational disparities within communities, highlight the ongoing challenges in ensuring equitable access to sports health care professionals.

Housing type and transportation also have not yet been studied in relation to school-based AT access. However, our results could demonstrate the intersectionality of SDoHs affecting access to school-based medical care for student-athletes in resource-limited environments. A plausible explanation is that those who rely on public transit and live in multiunit or mobile homes in crowded spaces are more likely to have lower SES, which negatively affects local school funding and may result in even less distributed funds to athletic departments. Another possibility is that the lack of private vehicles as a mode of transportation may serve as a barrier for youth to attend practices and games, resulting in overall reduction in school sport participation and therefore insufficient justification for school administrators to allocate funds to employ school-based athletic training services. Of note, limited transportation and housing insecurity have previously been suggested as barriers to health as perceived by secondary school ATs.³²  It is unclear whether increased vulnerability in housing type and transportation is an independent risk factor for decreased access to ATs in the school setting.

Race and ethnicity is one of the most-researched SDoHs in sports medicine, and minority ethnicities are often associated with poor health outcomes.²¹  It is often used as a proxy for other SDoHs and is thus at risk for being labeled inappropriately as a mediator for social vulnerability.²¹  We did not find an association between racial and ethnic minority status and access to school-based UTs or certified ATs in our study. This is not to say that a relationship between the two does not exist. A recently published study showed that California secondary schools without access to ATs had a larger proportion of Hispanic or Latino as well as African American students.³³  Contrary to previously documented inequitable access to health care affecting racial and ethnic minorities, Barter et al found that White-identifying populations experienced limited access to AT services in public secondary schools.⁴  Of note, Barter et al acknowledge they did not include state-level race or ethnicity demographics as part of their inclusion criteria, which could have biased their results.⁴  In the context of our study, it may be that California is a more racially and ethnically diverse state than some of the states included in the Barter et al study, and therefore, race and ethnicity as measured by the SVI played a lesser role with regard to AT access.⁴  In addition, other SDoHs may have influenced the Barter et al study findings on race, such as SES and geographic locations of secondary schools.⁴  As previously mentioned, our results are like those of Barter et al in that lower access to AT services is associated with lower SES.⁴  Geographic location of secondary schools has previously been linked to differential access to AT services, with those in rural settings having decreased access to ATs.³⁴  It may be that the effects of race and ethnic minority status on access to AT services are amplified when combined with a lower SES and attending secondary school in a rural setting. This possibility emphasizes the importance of evaluating multiple SDoHs and considering intersectionality when investigating access to ATs.

The SDoH-mediated differential access to athletic training services ultimately contributes to health disparities among youth athletes. The contributing SDoH themes identified in this study—SES, housing type and transportation, and most importantly, household characteristics—summarize the complex interplay of several SDoHs which may affect access to school-based ATs. We found that increased vulnerability in these factors is associated with reduced access to athletic training services. However, it is in the communities that have the greatest social vulnerability where the effect of athletic training services may be felt most profoundly.

Insufficient or nonexistent athletic training services can result in reduced prevention as well as delayed or inadequate treatment of injuries affecting high school student-athletes. In contrast, the presence of ATs may be a golden opportunity for increased access to overall health care. Athletic trainers frequently function as the first and often the only point of contact that a student-athlete has with the health care system.^35,36  Moreover, ATs in the secondary school setting often serve roles beyond what is typically expected. Athletic trainers are uniquely positioned to serve as health educators, coordinators, advocates, and navigators for student-athletes and their families.^35–38  In these roles, ATs have the potential to reduce the negative effects of SDoHs. Focusing on the SDoHs identified in this study could aid in developing targeted interventions to increase access to athletic training services in California high schools and to help vulnerable communities take a step toward achieving health equity.³⁶ 

We cannot determine causality or temporality between independent and dependent variables due to the nature of a cross-sectional study design. Other SDoHs may not have been captured in this study, such as access to primary care and food insecurity, which may affect access to an AT. Additionally, we did not capture the nuances and unique experiences of each individual student-athlete. As such, we forwent individual athlete demographics such as sexual orientation and presence of disability that may affect youth participation in sport.

Given that the CDC/ATSDR’s SVI database is based on the US Census Bureau’s American Community Survey results, the most granular level of data provided is at the census tract level. The SVI scores at the county and census tract levels may not be representative of individual schools. However, viewing data at these levels may help to identify regions where higher SVI scores may exist in general and thus help policymakers and researchers understand these communities and target solutions.

The SVI database was based on the American Community Survey from 2020, whereas the CIF participation survey was from the 2022–2023 academic year. We wanted to use the most up-to-date information available to reflect the current landscape of access to school-based athletic training services but acknowledge that limitations exist in doing so, given the difference in time points. Additionally, the CIF did not collect any survey data during the 2020–2021 academic year due to the COVID-19 pandemic.

Limitations also exist in the instrument used to collect our primary outcomes. The CIF Participation Census is a self-reporting instrument which depends on California high school officials to accurately respond to each question. The subjective nature of these surveys may put our study at risk of recall bias, with either overreporting or underreporting of the outcomes of interest. However, the requirement for all CIF member schools to submit responses likely stemmed from a motive to collect accurate data statewide and subsequently resulted in a large sample of schools. Also, given that all participants are CIF member schools, this can subject our research to selection bias. The instrument itself also has measurement issues, as evidenced by its double-barreled question as well as an overlap in two of its available answer choices. Authors of subsequent iterations of this work should revise the instrument questions to improve the validity of responses.

Future studies investigating the role of household characteristics as a SDoH in sports and exercise are warranted. Researchers can consider other SDoHs not included in this study as well as their intersectionality to capture their effect on youth sports. Moreover, mixed methods can be used to elicit rich narratives from individual athletes. Finally, SDoHs should also be investigated at a more granular level, such as within census tracts. These research directions can help develop specific interventions to bridge health disparities.

Addendum: Since the online publication of this manuscript, the California Business & Professional Code 2529.8.1 came into effect. This new law is a title act and a form of regulation of ATs; however, it is short of a practice act and does not enforce licensure for existing ATs nor mandate California high schools to employ ATs in their athletic departments. While the enactment of this law is a step toward a license that regulates athletic training practice in the state, more work needs to be done to ensure safety and to achieve health equity for California high school student athletes. As this was a retrospective study utilizing data from the American Community Survey of 2020 and the CIF Participation Census of 2022–2023, the California Business & Professional Code 2529.8.1 does not affect the results or interpretation of this study.

The authors acknowledge the CIF for providing their survey data for use in this research, Katerina Bernabe for her assistance in data collection, as well as the AMSSM Collaborative Research Network (CRN) for their methodological and statistical support.

Dr Hatamiya reports nonfinancial support from CIF and personal fees from Global Ultrasound Institute (GUSI) outside the submitted work.

Supplemental Figure 1. US Census variables used in calculating the overall Social Vulnerability Index (SVI) score. Adapted with permission from the Centers for Disease Control and Prevention (CDC).

Supplemental Figure 2. Social ecological model.

Supplemental Figure 3. California Interscholastic Federation (CIF) Participation Census 2022–2023 questions. Reprinted with permission.