The Journal of School Nursing2023, Vol. 39(5) 357–367© The Author(s) 2021Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405211024277journals.sagepub.com/home/jsn
The extent to which observed disparities in access to primary pediatric care are mirrored in student access to school nursing services is unknown. Using school employment records, we linked 1,346 nurses to school districts serving 1,141,495 students in Washington state. The percentage of students who are Black is negatively associated with the student-to-nurse ratio, while the percentage of students eligible for free-or-reduced-price lunch is positively associated, and relative to urban districts, rural districts have higher student-to-nurse ratios. Disparities in access to school nursing services mirror access gaps for pediatric care along socioeconomic status and geography. The increased number of nurses working in districts with more racial/ethnic minority students may play a protective role and ameliorate access gaps observed in pediatric primary care. States can likely use existing employment and licensing data to understand where school nurses work and therefore guide resource allocation decisions.
Keywords
administration/management, school nurse ratios, quantitative research, health disparities, school health services, public health, nursing outcomes
Evidence shows that the last two decades have witnessed a significant reduction in inequality in access to care for pediatric populations. For instance, the proportion of pediatric patients with an unmet health care need declined by over 25%. Yet substantial racial, socioeconomic, and geographic disparities in access to childhood health care services still persist (Larson et al., 2016). The role school health services play in providing care has long been overlooked despite evidence that such services may be particularly important to disadvantaged students. In recent years, recognition of the intertwining of students’ physical, emotional, and academic health has led school and public health leaders to coordinate their approach, with the goal of improving health and education outcomes (Lewallen et al., 2015).
A limited body of research suggests that disparities in access to care are partially addressed by school nurses, especially among students of color and those from low socioeconomic statuses (SES; Fleming, 2011). School nurses increase access by providing care without the need for appointments, transportation, insurance, or referrals (Fleming, 2011; Holmes et al., 2016). They provide care where students spend one fifth of their waking hours during the school year (Brixey, 2020).
Yet data on the school nurse workforce is limited. Existing evidence suggests that many public schools (18%) do not have any paid nursing support (Willgerodt et al., 2018). Lack of data on the school nurse workforce has made it challenging to ascertain a comprehensive picture of how school nursing services are allocated across districts and schools and whether access to a school nurse is equitably distributed. We provide a first statewide look at the differences in school nurse availability by students’ race/ethnicity, SES, and geographic location. We fill this gap in the literature using data from Washington State and outline a method for how states, absent of centralized school health databases, can build school nurse workforce data from readily accessible and standardized sources. Using these data, the study aims to understand the extent to which disparities in access to pediatric primary care are mirrored or mediated by access to school nursing services.
Researchers and school health advocacy groups have argued that school health services and school nurses are key players in the social safety net (Barnby & Reynolds, 2018; School-Based Health Alliance, n.d.). School nurses provide health care services free of charge without the need for appointments, referrals, transportation, fees, or insurance (Fleming, 2011; Holmes et al., 2016). Moreover, they play an important role in population health management (National Association of School Nurses [NASN], 2020a). However, it is unclear the extent to which school nurses are working in communities that may rely on their care most. In other words, it is unknown the extent to which disparities in access to school nurses mirror access gaps to traditional pediatric care outside of schools.
While research on whom school nurses serve is minimal, there is an extensive body of literature documenting where access to the provision of traditional pediatric services is limited (e.g., access gaps by race/ethnicity, SES, and geography). Children of color are more likely to be uninsured, to have no consistent source of medical care, and to have not seen a primary care provider in the last year compared to their White peers (Larson et al., 2016; Ortega et al., 2018). Children of poorer families are more likely to have no usual source of care, to be uninsured, and to attend schools with more medication errors than their nonpoor peers (Black & Benson, 2019; Larson et al., 2016; E. D. Maughan, McCarthy, et al., 2018). The geographic availability of care presents further challenges for equitable pediatric access. Relative to children living in urban communities, children living in rural communities live further from hospitals, are more likely to live in Health Professional Shortage Areas, and have fewer pediatricians per capita (Peltz et al., 2016; Shipman et al., 2011). Students attending schools in rural communities are more than two times as likely to have no paid school nurses working in them (Willgerodt et al., 2018).
Despite the amount of literature on disparities in access to pediatric primary care, we know very little about the extent to which there are disparities in school health services. School health services and providers are under different legal requirements and often have different funding streams (Johnson, 2017), potentially negating many of the mechanisms that drive disparities in pediatric primary care. For instance, school nurses in Washington state are bound by education statutes that enforce requirements on medication delegation and screenings, among other things, and school health services may receive funding from federal, state, and local education agencies (Johnson, 2017). On the other hand, the different legal and funding streams underlying school health services could cause the widening or emergence of new disparities in access. Consequently, documented disparities in primary care access may not provide much insight into disparities in the education system.
A limited and dated body of research suggests that a point where many traditionally disadvantaged students get care is through school nursing services. For instance, Fleming (2011) found that, conditional on a school having a nurse, Black students made up 40% of visits but accounted for 24% of the student population. Similarly, Latino students made up 16% of visits but 11% of the student population, and poor students accounted for 57% of visits but only 41% of the student population. And Anyon et al. (2013) notes that higher risk factors associated with students of color and the limited availability of quality health services in their communities could contribute to these higher utilization rates. However, they also find that measures of health risk factors do not completely explain the difference in utilization.
Some of the services that students may be utilizing through school nursing services include clinical care, health education, care coordination across different health care delivery systems, care management, and assessment of behavioral and mental health (Holmes et al., 2016; NASN, 2020a). School nurses also track student health data, provide population health management, prepare for health emergencies, monitor vaccination rates, conduct disease surveillance, and provide leadership on school health policy (Holmes et al., 2016; NASN, 2020a).
Given that there are fewer barriers to the receipt of service, Johnson (2017) argues that school nurses increase health equity and increase access to care for traditionally disadvantaged populations. Moreover, school nurses may act as a liaison to a student’s health home by coordinating across systems of care (Holmes et al., 2016), thereby reducing the burden on parents to facilitate communication, exchange documentation, and take time off work. This is especially important because employees in low-income industries, such as the service sector, are 33% less likely to have paid sick leave than workers in higher paying management and professional positions (Bureau of Labor Statistics, 2020) and are arguably less able to take time off to address the health care needs of their children.
However, school nurses can only address disparities in access if they are located where there is need. Obtaining updated national- or state-level data on the distribution of school nurses is challenging, given the infrequency with which data are collected and the lack of dedicated data infrastructure and standardized measures. What is available relies on voluntary reporting (NASN, 2019a), does not link data to students (Willgerodt et al., 2018), or does not adjust schoollevel findings by student enrollment (Spiegelman, 2020).
Data on the school nurse workforce collected in the 2015–2016 school year via the National Teacher and Principal Survey asked a single question: whether a nurse is working full time or part time at the school. According to the survey findings, more nurses work in urban, suburban, and town schools relative to rural schools, in elementary schools, and in schools with fewer free-or-reduced-price lunch (FRPL)–eligible students (Spiegelman, 2020). However, these findings were not adjusted for total student enrollment at the reporting schools.
More recent data collected from a nationally representative survey of school nurses indicate that 18% of public schools in the United States do not have access to paid nursing services and that many schools rely on volunteers to provide health services (Willgerodt et al., 2018). Rural schools, compared to urban schools, are more likely to have no school nurse (23.5% and 10.3%, respectively). School nurses tend to work in more than one school (56%) and in elementary schools. These data provide a national snapshot of the school nurse workforce but, importantly, are not linked to the student populations they serve.
Maughan (2009) constructed state-by-state student-tonurse ratios for all 50 states by contacting state representatives. State representatives reported using a variety of different methods and data sources to construct their student-to-nurse ratios. However, Maughan notes that approximately half of the states had no system to collect or report data on school nurses. Furthermore, the data that were collected often used different discretized measures of the nursing workforce (e.g., does at least one nurse work part time at a school). And, finally, the majority of state contacts indicated that the data on school nurses represented “guesstimates,†rather than high-quality, systematically collected data.
Arguably the best up-to-date information on the national school nurse workforce is from the NASN, which collects data on staffing, chronic conditions, absenteeism, and health clinic visits (known as the National School Health Data Set: Every Student Counts!). The goals of Every Student Counts! are to provide data to inform policy, identify best practices, and better understand pediatric health (NASN, 2019b). The Every Student Counts! initiative uses a uniform data set with standardized reporting procedures and provides a data infrastructure for school nurses to report their data (E. D. Maughan, Johnson, & Bergren, 2018). However, the initiative notes that “[d]ue to the variance in state participation†the “data are not generalizable†(NASN, 2019a).
Given the potentially important role school nurses play in keeping children healthy (Baisch et al., 2011), particularly in times of increasing health complexity for children, it is imperative to understand where school nurses are located and whom they serve.
This study utilized data from the 2019–2020 school year from three sources: the Washington State Office of Superintendent of Public Instruction (OSPI, 2020, 2021), the Washington State Department of Health (DOH, 2021), and the National Center for Education Statistics (NCES, 2020). The OSPI data included annual staffing records for public school employees, providing information regarding their individual paid assignments, their role and activities, demographic data, their full-time equivalents (FTEs), and buildings or districts those assignments were associated with. Personnel activities and role codes were used to identify school nurses. OSPI data also identify whether a school nurse has a school nursing–specific credential. When employees providing health care services were not clearly identified as nurses, we merged these personnel to the DOH Health Care Provider Credential data. If an individual had a licensed practical nurse, registered nurse, and/or Advanced Registered Nurse Practitioner license, they were coded as nurses. To validate the identification strategy of these “potential†nurses, we looked up nurses and their positions in staff directories, health services webpages, and student handbooks for each district. Due to name changes or spelling mistakes, match rates were not 100%. We estimate that our method captured 89% of school nurses’ FTEs.
After identifying nurses, employment records were linked to school-level data, available through the Washington State Data Report Cards. The Report Cards included data on student enrollment, enrollment by race/ethnicity, gender, FRPL eligibility, Special Education participation, and by English-Language-Learner participation. Due to closures at the end of the 2019–2020 school year, we leveraged the 2018–2019 Report Cards to obtain the percentage of students passing state standardized math tests in Grades 3 through 8. To classify districts as urban or rural, the data set was merged to the most recent year (2018–2019) of the Education Demographic and Geographic Estimates dataset maintained by NCES. All data are either publicly available or are covered by a Family Educational Rights and Privacy Act exception. The University of Washington Institutional Review Board designated the study as exempt.
A nurse’s “caseload,†as measured by the student-to-nurse ratio, was used as the dependent variable and was constructed from nurse FTEs and student enrollment. FTEs were used to standardize employment because of the variability in full-time/part-time status among school nurses. School nurses worked at multiple schools and could not be reliably linked to individual schools; therefore, student-tonurse ratios were constructed as the student enrollment divided by nurse FTEs at the district level. One nursing FTE was considered working 180 days at 8 hours a day.
Our independent variables of interest were race/ethnicity, SES, and geography. In keeping with education literature, we utilized FRPL eligibility as a proxy for SES (Michelmore & Dynarski, 2017). We calculated district-level percentages of student demographics. Using data on population density, land use patterns, and distance to other urban/rural areas, NCES identifies districts as one of four types: urban, suburban, town, or rural. These data were used to classify the urbanicity of school districts.
The student-to-nurse ratio is positively skewed with a Pearson’s moment coefficient of skewness of 2.4. We took the natural log of the ratio to get an approximately normal distribution and used this transformed variable in all analyses. To examine the association between student-to-nurse ratios and student demographics, all analyses were weighted by district enrollment and first correlational analyses were conducted. To account for multiple hypotheses tests, a Sidak correction for testing the statistical significance of the correlation coefficients was applied. A one-way analysis of variance (ANOVA) test of the student-to-nurse ratios by urbanicity and a post hoc comparison of means using Scheffé’s test were run.
To get estimates of the relationship between race/ethnicity, SES, and urbanicity and the student-to-nurse ratio conditional on other district characteristics, an ordinary least squares regression framework was utilized. We regressed the log of the student-to-nurse ratio at the district level for the 2019–2020 school year against a range of student demographics and other district characteristics to assess how the spread of the nurse workforce differed across district types. Exponentiated coefficients can be interpreted as the percentage change in the outcome for a one-unit increase in the independent variable. This regression model is depicted in Equation 1.
In Equation 1, ln(yd) is the natural log transformation of district d’s student-to-nurse ratio, yd. Raced is a vector of the percentage of the student population by race/ethnicity and FRPLd is the percentage of students eligible for FRPL at the district level. Θd is a vector of fixed effects for the urbanicity of the district. Zd is a vector of controls including the percentage of the student population that is female, participating in Special Education, that is an English-Language-Learner, and passing the statewide, standardized math test. ϵd is the error term. All regressions are weighted by district total enrollment.
In the first model, we included variables identifying the percentage of students by race/ethnicity, in our second model, the percentage of students eligible for FRPL, and in the third model, fixed effects for urbanicity. In the fourth model, all variables were simultaneously included. All models control for the percentage of students who are female, participating in Special Education, are English-Language-Learners, and passing statewide, standardized math tests. Analyses were conducted using Stata software Version 15.
The data set contains 1,346 nurses working 978 FTEs linked to 296 districts serving 1,141,495 public school students. Summary statistics are reported in Table 1. Panel A provides information on nursing FTEs disaggregated by urbanicity. Panel B provides analogous data on student demographics.
The statewide student-to-nurse ratio was 1,173, with the lowest ratio being in urban areas. On average, nurses are working 0.73 FTEs. Statewide, 59% of the FTEs were from nurses with school nursing–specific credentials. Urban areas had a higher proportion of nurses with school nursing–specific credentials (74%), while 38% of the nursing workforce in rural areas had a school nursing–specific credential. The nursing workforce was predominantly White, female, with an average approximate age of 50. In Panel B, we present the demographic data of students across the state and by urbanicity. Non-White students were more likely to be in urban districts representing 50.9% of the population, compared to rural districts (36%). Overall, 45% of students were eligible for FRPL, with the lowest percentage of FRPL-eligible students in suburban districts (37.8%) and the highest in towns (55.3%).
Figure 1 plots the student-to-nurse ratio for districts in the 2019–2020 school year and shows that there was significant variation in the student-to-nurse ratio across the state. However, from Figure 1, it is difficult to say what student characteristics were most associated with the differences in the student-to-nurse ratios. To investigate this more closely, we turned to our district-level correlation and regression analyses.
In Panel A of Table 2, we present Pearson’s correlation coefficients between a district’s racial/ethnicity and SES composition and the log of the district’s student-to-nurse ratio. The student-to-nurse ratio can be thought of as a rough approximation of a nurse’s caseload, so positive and significant correlations indicate that these types of districts have higher student-to-nurse ratios, that is, more students per nurse. Four districts with an average enrollment of 9.25 students could not be linked to any school nursing services and were dropped from subsequent analyses. In Column 1, we present statewide results, and in Columns 2 through 5, we disaggregate these results by urbanicity. Results from Table 2 are unadjusted correlations. In other words, they do not control for other characteristics of districts such as the percentage of the student population participating in Special Education.
Statewide results indicate that the student-to-nurse ratio was negatively correlated with the percentage of students who were Black (p value < .001), Native Hawaiian or Other Pacific Islander (p value = .001), or Multiracial (p value = .002). For these groups, when data were disaggregated by urbanicity, the magnitudes of the correlation coefficients were largest for urban districts; however, only the percentage of students who were Black was statistically significant (p value = .003). The percentage of FRPL-eligible students was positively associated with higher student-tonurse ratios, though this correlation was not statistically significant. An ANOVA test of the distribution of nurses across urbanicity types (Panel B) indicated that studentto-nurse ratios differed by urbanicity, with a post hoc Scheffe test indicating that town and rural districts had statistically significant different student-to-nurse ratios compared to urban districts (p values of .022 and .001, respectively).
Table 3 provides results from the ordinary least squares regression analyses. Columns 1 through 3 present models with the controls mentioned in the Method section, but with race/ethnicity, FRPL eligibility, and urbanicity entered individually. Districts with a higher percentage of Black students had lower student-to-nurse ratios (p value < .001), while districts with a higher percentage of FRPL-eligible students had higher student-to-nurse ratios (p value = .002). Column 3 includes urbanicity fixed effects with the base category being urban districts. Town and rural districts had higher student-to-nurse ratios relative to urban districts (p values < .001). When all variables were included simultaneously, FRPL eligibility and percentage Black remained consistent predictors of the student-to-nurse ratio (p values < .001). Suburban, town, and rural districts had higher student-to-nurse ratios than urban districts but conditional on the inclusion of race/ethnicity and SES, were no longer statistically significant. The percentage Hispanic and percentage Native Hawaiian or Other Pacific Islander are now negatively and statistically significantly associated with the student-to-nurse ratio (p values of .028 and .017, respectively).
In this study, we show the importance of understanding how the distribution of school nurses can compound or mediate disparities in access to care. Examination of the associations between caseload and race/ethnicity, SES, and urbanicity can inform decision making and resource allocation. Moreover, this study aims to provide a template using readily available data for how state agencies may understand their school nursing workforce in relation to a key social determinant of health.
Our work confirms prior findings that more school nurse FTEs are located in urban districts rather than rural districts (Willgerodt et al., 2018). We extended this work by showing that the gap in access to nurses between urban and rural districts holds after adjusting for student enrollment. Rural school nurses are more likely to work in multiple schools and spend time driving between them (Willgerodt et al., 2018); hence, the gap reported here likely underestimates the gap in nurse FTEs available to care for students. We further showed that after controlling for race and SES, geography is no longer a statistically significant predictor of the student-to-nurse ratio. In other words, for two districts that have similar race and SES compositions, but different urbanicity types, their student-to-nurse ratios were not statistically significantly different.
Some of our findings did not mirror other data on health care access for the pediatric population. While children of color are less likely to have a usual source of care (Flores & Tomany-Korman, 2008), we found that more school nurse FTEs were concentrated in districts with a higher percentage of Black students. It is important to note that districts contain bundles of demographic characteristics, for example, correlations between race and SES (Domina et al., 2018); this result held both in our raw correlation and regression adjusted results. However, the Black student population is not a particularly large portion of the student body in Washington state (4.4%). The percentage of students from other racial/ethnic minority groups was inconsistently statistically significant in predicting lower student-to-nurse ratios. In our preferred specification (Column 4, the model with the full set of controls), the percentage of students who were Black, Hispanic, or Native Hawaiian, or Other Pacific Islander were predictive of lower student-to-nurse ratios. When we reestimated the model from Column 4 and omitted FRPL eligibility, the coefficients on percentage Hispanic and Native Hawaiian or Pacific Islander were no longer statistically significant. However, when urbanicity was omitted, results remained significant, indicating that the omission of FRPL eligibility from regression analyses controlling for race/ethnicity represents a significant source of omitted variable bias. Put another way, the differences across models highlight the need to simultaneously control for race/ethnicity, SES, and geography when estimating differences in access to care. Rerunning the analyses in Columns 1 and 4 of Table 3 but replacing individual race categories with an aggregate percentage, non-White category yielded coefficients of —0.008 and —0.009 with p values of .001 and .000, respectively. Broadly, Washington state districts with more students of color tend to have lower student-to-nurse ratios (i.e., better access to one dimension of nursing resources).
However, this appears to conflict with national research that finds students of color are more likely to be enrolled in schools that provide fewer distinct types of services for chronic health conditions (Tiu et al., 2019). One possible explanation for this discrepancy is that students of color have a higher prevalence of some chronic health conditions and less access to private insurance (Berry et al., 2010; Larson et al., 2016). While speculative, students of color may be enrolled in districts with more nurse FTEs per capita, but that the increased FTE rate is not accounting for the increased need, thereby limiting the types of services provided. This comports with research that finds school nurse assignments are often not determined in full by student needs (e.g., student acuity or the social determinants of health; Jameson et al., 2020). It is also possible that school nurses in communities with more children of color provide different types of services, that is, other than chronic health care management, thereby potentially explaining the discrepancy between more FTEs, but fewer services for chronic health conditions. Alternatively, Tiu et al.’s (2019) study was conducted at the school level, while this study was conducted at the district level. School nurses may be distributed to districts with more students of color but within districts to schools with a higher percentage of White students. Importantly, because of the complexities of the interrelationships between race/ethnicity, health conditions, and access to care, more work is needed to disentangle and understand the associations between these variables.
In this study, the distribution of school nurses tracked access gaps by SES in the general population. Districts with a higher percentage of FRPL-eligible students had less access to nurses, which may be a consequence of how nursing services are funded. Nationally, 76% of school nurses are funded through local dollars (Willgerodt et al., 2018), and we estimate that in Washington, approximately two thirds of nurse FTEs are funded by local monies. The reliance on local dollars to fund school health services could mean that services in lower income areas are stretched thin (Ostrander, 2015; Owens, 2018). Wang et al. (2014) estimate that staffing one nurse at each school, as recommended by the American Academy of Pediatrics and the NASN (Holmes et al., 2016; NASN, 2020b), costs (salary and fringe benefits) US$69,469 per year. Disproportionate access to school nurses by income may be problematic given that children of lower SES families have a higher prevalence of chronic conditions (Spencer et al., 2015).
Addressing disparities in access to school nurses by SES may not be the only challenge with drafting and implementing policies aimed at equitably distributing nurses in Washington state. Researchers have argued that increasing the diversity of the nursing workforce supports culturally competent care, increases patient–provider trust, and racial/ethnic language concordance, among other things (Williams et al., 2014). To that end, we found that the school nurse workforce was much Whiter (90%) and more female (97%) than the student population they served. For comparison, 47.3% of the student population was non-White. The Washington school nurse workforce was also more White than the national school nurse workforce (84.3%), with approximately the same male-to-female distribution (98% of the national workforce is female; Willgerodt et al., 2018). While to our knowledge no research exists on the association between the diversity of the school nursing workforce and student health outcomes, parallels in the education environment exist. Students who racially match their teachers tend to perform better on statewide, standardized tests than their unmatched peers (Villegas & Irvine, 2010).
Given the lack of data on the school nurse workforce, this study also highlights a methodology that could be used by states to examine the distribution of school nurses. Statelevel administrative data containing information about the number of nurses, the hours they work, and the students they serve typically already exist. State Departments of Education maintain employment information on all employees, which are data automatically collected in a standardized manner. These employment records may identify some or all school nurses by their assigned roles, activities, or pay scale. When they do not identify school nurses directly, employment records can be matched, using identifying information such as names and date of birth, against publicly available DOH health care licensing data to identify nurses. These data may be merged using unique school code identifiers with nationally available Common Core education data. NCES maintains annual Common Core Data at the school level for the roughly 100,000 schools across the country and tracks information on a school’s racial and FRPL eligibility composition. Bringing these data together can be used to create a database and answer questions surrounding who has access to school health services.
There are several limitations that require noting. A nurse’s caseload is only one aspect of a school nurse’s workload that may influence a student’s ability to access care. School nurses’ work is broad and complex, and simple student-to-nurse ratios fail to capture the nuances that may impact access (Jameson et al., 2020). The NASN states that in addition to caseload, student acuity, social needs, school nurse qualifications, and the culture of the school or district are necessary for determining appropriate staffing levels (NASN, 2020b). For example, the student-to-nurse ratio does not capture the prevalence of chronic health conditions and corresponding care needs. Future work should include controls for chronic health conditions, so that observed differences in access are conditional on the same level of health conditions. This may be important as other literature finds that Black students have a higher prevalence of some chronic health conditions (Berry et al., 2010). Without controlling directly for chronic health conditions, we do not know whether the increased access to school nurses is “keeping up†with the greater need of particular student populations.
On the other hand, prior literature documents a higher prevalence of developmental disabilities for low socioeconomic children (Boyle et al., 2011). In this context, not controlling for health conditions biases estimates of the access gap toward zero, that is, our estimate of the gap is conservative without this additional control. Finer measures of nursing workload that relate to student outcomes, better data tying students, their medical conditions and care requirements, and their outcomes to nursing-sensitive metrics are needed (Jameson et al., 2020). Such data should include measures of expected standards of practice, detailed data on care coordination, and a school nurse’s community health responsibilities (Jameson et al., 2020).
It is important to be cautious about the interpretation of these district-level findings. Specifically, we cannot infer how nurses are distributed within districts. That being said, Fleming (2011) reported that within schools, ethnic minority, and children of lower SES, families were more likely to access school nursing services compared to their nonpoor and White peers. Our findings plus Fleming’s (2011) study suggest that school nurses help the most vulnerable students by addressing barriers to care.
Individual states should consider assessing their student populations and health services, as results from Washington may not generalize. For example, Washington has a relatively small percentage of Black students (4.4% compared to 15% nationally), relatively more Asian students (8% compared to 6% nationally), and roughly the same percentage of Hispanic students (24%; Census Bureau, 2019). The extent to which states differ from Washington may greatly limit their ability to draw conclusions from these data. Despite these limitations, our study provides the first glimpse into understanding the distribution of school health services and demonstrates a methodology that has broad applicability to other states.
Financial and logistical barriers prevent many children from accessing the care they need (Fung et al., 2014; Syed et al., 2013), and some of these barriers could be overcome by attending a school with a nurse. Despite evidence that school nurses address health disparities by providing care to traditionally disadvantaged populations (Fleming, 2011), little national or state data exist describing the distribution of school nurses. This study represents a first statewide look at the distribution of nurses across districts and students. We provide a road map for how states can efficiently use existing administrative data to explore which students have access to school nurses. We find that children of color attend schools with more nurse FTEs per student and poorer children attend schools with fewer nurse FTEs per student.
There are an estimated 132,000 school nurses across the country providing health services to students (Willgerodt et al., 2018). Building an understanding of this hidden health care system (Lear, 2007) could provide insight into avenues to improve health and academic outcomes. School health leaders cannot tailor the provision of nursing resources to the school communities of greatest need without foreknowledge of where school nurses work. With a better understanding of the distribution of school nursing services, states could consider expanding school nursing services in communities with less access to health care. Doing so may require states to reevaluate the reliance on local monies to fund school health services. Closing access gaps to school health services has the potential to bolster the social safety net for traditionally disadvantaged student populations. This could be of particular importance as there has been a dramatic rise in the prevalence of chronic health conditions in children (Perrin et al., 2014), and many low socioeconomic communities and communities of color are disproportionately affected by COVID-19. Understanding the distribution of school nurses represents a first step for making data-driven decisions about resource allocation, which in turn has the potential to reduce disparities in access to pediatric care. Future work should tie finer measures of nursing workload, such as staffing rates, student acuity, and school and community factors, to nursing-sensitive outcomes to determine which workforce policies and interventions improve students’ health and educational outcomes.
All authors contributed to the design, analysis, and interpretation of the data. Trevor Gratz drafted the manuscript. All authors critically revised the manuscript and gave final approval.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: the National Center for Analysis of Longitudinal Data in Education Research, which is funded by a consortium of foundations. For more information about CALDER funders, see www.caldercenter.org/about-calder.
Trevor Gratz, MSN, RN https://orcid.org/0000-0002-0050-6496
Mayumi Willgerodt, PhD, MPH, RN, FAAN, FNASN https://orcid.org/0000-0002-9874-3739
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Trevor Gratz, MSN, RN, is a research consultant at the University of Washington.
Dan Goldhaber, PhD, is an affiliate professor and director of the Center for Education Data and Research at the University of Washington. He is the director of the Center for Analysis of Longitudinal Data in Education Research at the American Institutes for Research.
Mayumi Willgerodt, PhD, MPH, RN, FAAN, FNASN, is the associate professor and vice-chair for education in the Department of Child, Family, and Population Health Nursing at the University of Washington.
Nate Brown, MA, is a research manager at the University of Washington.
1 University of Washington, Seattle, WA, USA
2 Center for Education Data and Research, University of Washington, Seattle, WA, USA
3 The Center for Analysis of Longitudinal Data in Education Research, American Institutes for Research, Arlington, VA, USA
4 Department of Child, Family, and Population Health Nursing, University of Washington, Seattle, WA, USA
Corresponding Author:Trevor Gratz, MSN, RN, University of Washington, 3876 Bridge Way N., #201, Seattle, WA 98103, USA.Email: gratzt@uw.edu
