The Journal of School Nursing2024, Vol. 40(2) 208–222© The Author(s) 2022Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405211068434journals.sagepub.com/home/jsn
School-age children with overweight or obesity continue to be problematic in the United States, and are associated with many health, social, and financial problems. Schools provide an excellent venue in which to promote healthy weight in students, and school nurses are well-positioned to play an essential role in controlling obesity. The number of studies reporting relationships among school health infrastructure and prevalence of elevated Body Mass Index (BMI) is limited. The present study explored associations between three components of school health infrastructure (staff, services, budget) and the proportion overweight or obese 1st, 3rd, and 6th grade students, after controlling for selected factors (race, county education level, county poverty level, rurality). Study results supported an independent association between elevated BMI and school health staff. Additionally, independent associations between elevated BMI and the following covariates were supported: household income, race, and parents’ educational level. There is an ultimate need for well-designed studies addressing these associations.
KeywordsBMI, school nursing, school health, school health infrastructure, students health
In 2018-19, the percentage of overweight or obesity (Body Mass Index [BMI] at 85th or higher percentile) among children ages 10 to 17 in Florida was 30.3% (Data Resource Center for Child and Adolescent Health, n.d.). Elevated BMI among children and adults is associated with many health problems, such as high blood pressure, high cholesterol, and type 2 diabetes (Centers for Disease Control and Prevention [CDC], 2021; Landi et al., 2018; Kumar & Kelly, 2017). It is also associated with psychological/social problems such as low self-esteem, stigma, and discrimination (CDC, 2021; Kumar & Kelly, 2017). Obesity is associated with high personal and societal costs such as reduced quality of life (CDC, 2021), increased risk for disability (Henriksson et al., 2019), and high medical care expenditures (Robert Wood Johnson Foundation, n.d.). Gordon-Larsen et al. (2010) note that childhood obesity increases the risk for adult obesity, with resultant increased costs from obesity as an adult. The estimated costs related to adults’ obesity in 2013 was $342.2 billion (Biener et al., 2017). If obesity prevalence continues to 2030, those costs could be increased by $48 to $66 billion a year (Wang et al., 2011).
There are millions (National Center for Education Statistics [NCES], n.d.a) of children spending an average of 6.64 h per day in school over the 180-day school year (NCES, n.d.b). Schools provide an excellent venue in which to promote healthy weight in students, and school nurses are well-positioned to play an essential role in preventing and controlling obesity (Schroeder et al., 2016). The position of the National Association of Schools of Nursing (NASN) is “… the registered professional school nurse (hereinafter referred to as the school nurse) has the knowledge, expertize, and skills to promote the prevention and reduction of overweight and obesity among children and adolescents in schools (NASN, n.d.a).” One of the overlooked factors that can help improve obesity/overweight among school students is the availability of effective school health services. Three main components of school health infrastructure are staff, services and budget (Scholz et al., 2015). However, limited studies assessing relationships among school health infrastructure factors (staff, services, and budget) and students’ BMI appear in the literature.
School health staff in Florida includes registered nurses (RNs), licensed practitioner nurses (LPNs), and non-nursing staff (aides, techs) (Florida Department of Health [FDH], n.d.a). School nursing is defined by the NASN (n.d.b) as a specialized practice of nursing which protects and promotes student health, facilitates optimal development, and advances academic success. School nurses, grounded in ethical and evidence-based practice, are the leaders who bridge health care and education, provide care coordination, advocate for quality student-centered care, and collaborate to design systems that allow individuals and communities to develop their full potential (“Definition of School Nursing,” para. 1). School nurses are one of the main components of school health infrastructure and are wellpositioned to reduce obesity effectively (Morrison-Sandberg et al., 2011). They are also seen by school staff as an essential factor for students’ health and saving teaching time (Baisch et al., 2011; Hill & Hollis, 2012; Winland & Shannon, 2004). However, there is a shortage of school nursing staff relative to student numbers in more than 60% of U.S. public schools (Searing & Guenette, 2016). In Florida, the ratio of school nurses to students is 1:2,382 (FDH, n.d.a). That ratio is less than 1/3 of the NASN recommendation of, at minimum, one school nurse to 750 healthy students (Dolatowski et al., 2015). Inadequate nursing staffing has a potentially adverse effect on health services provided and health care outcomes including BMI (Aiken et al., 2010; Blegen et al., 2011; Cimiotti et al., 2012; Schroeder & Smaldone, 2017; Tubbs-Cooley et al., 2013). In one example, experts from the New York City Department of Health and Mental Hygiene developed a school nurse-led intervention program targeting children with severe obesity in New York City schools (Schroeder & Smaldone, 2017). Only 5.1% of the eligible students enrolled in the program. One of the reasons cited for low enrollment was the shortage in school nurses’ availability and knowledge. While schools are an excellent place for monitoring and managing students’ weight, sufficient resources, such as school nurse availability, need to be provided to obtain the desired results.
Nurses provide a number of services to monitor, control, and improve school students’ weight (NASN, n.d.a). However, if the number of qualified nursing staff is insufficient to conduct those weight-related services for all school students, we are missing a significant chance to improve elevated BMI among an essential community group: school students.
No published study was found assessing the relationship between school nursing staff characteristics such as the type of staff (RNs or non-RNs) and their ratio to students and the outcome of student BMI. There are some studies that addressed the association between nursing staff and other student outcomes, such as sending students back to class. One study compared rates of students sent back to class to the presence of a school nurse versus no school nurse (Wyman, 2005), while the other study compared school outcomes of having a school nurse versus other unlicensed school health employes (Pennington & Delaney, 2008). Both studies supported the importance of the presence of school nurses in schools.
Another school health infrastructure factor that can affect students’ BMI is school health services. There are many weight-related health services provided at school, such as BMI screening, counseling parents regarding their children‘s weight, conducting health education programs for students, parents, and staff, and conducting specific health programs targeting children who are affected by obesity (Morrison-Sandberg et al., 2011; Quelly, 2014; Schroeder & Smaldone, 2017; Stalter et al., 2011; Steele et al., 2011). While part of the school nurses’ role, these services are often delegated to other staff or not performed due to the school nurse shortages in many schools (Vollinger et al., 2011; NASN, n.d.a). Generally, research on the effect of school health services on students’ weight status is limited (Tucker & Lanningham-Foster, 2015). Only one study was found which assessed the association between the available school nursing services and students’ obesity (O‘Brien, 2012). The services included in that study were health education, counseling, and collaboration with other health staff. The study used a secondary data analysis design with a sample obtained from elementary and secondary public schools in Massachusetts. That study reported that for every one-unit increase in school nurse services, there was 0.16% (p < .04) decrease in the rate of children who were affected by obesity. Other related studies identified in the literature focused on the effectiveness of one-time interventions targeting student weight or BMI (Melin & Lenner, 2009; Pbert et al., 2013; Robbins et al., 2012; Speroni et al., 2007; Tucker & Lanningham-Foster, 2015; Wright et al., 2013). The health services provided in those programs included measuring BMI, health education, weight management counseling, weight related follow-ups, and/or motivational interviews. Some of those studies found improvement in students’ BMI (Melin & Lenner, 2009; Speroni et al., 2007), while others did not (Pbert et al., 2013; Tucker & Lanningham-Foster, 2015; Wright et al., 2013).
Another vital infrastructure factor is the school health budget. The budget is an essential component in improving students’ weight. It is needed for the following factors which directly or indirectly improve students’ weight: providing salaries for adequate number and type of staff, affording weight-related staff training, conducting obesity programs, participating in weight-related research, and providing essential instruments for applying weight-related services (Hendershot et al., 2008; Hughes et al., 2010; Morrison-Sandberg et al., 2011; Stalter et al., 2010). School nursing services are cost-effective (Baisch et al., 2011; Padula et al., 2018; Rodriguez et al., 2013; Wang et al., 2014). Nurses’ presence in schools positively affects students’ time in class and is important for health promotion and disease prevention among students (Baisch et al., 2011; Hill & Hollis, 2012; Morrison-Sandberg et al., 2011; Winland & Shannon, 2004). However, school nursing services are not prioritized by the education system (Lear, 2007). School administrators often decrease the school nursing budget as a way of reducing costs (Lear, 2007). This leads to either a high ratio of students to school nurses or schools with no nurses or schools with non-nursing staff, which adversely affects students’ health and academic status (Bowllan, 2011; Kirchofer et al., 2007; Maughan, 2009a; Nguyen et al., 2008; Pennington & Delaney, 2008; Wyman, 2005). Inadequate school health budget was one of the main barriers interfering with school nurses conducting obesity prevention services (Guttu et al., 2004; Hendershot et al., 2008; Maughan, 2009a, 2009b; Morrison-Sandberg et al., 2011; Quelly, 2014; Steele et al., 2011).
In the literature review by Hussey et al. (2013), many studies assessing the relationship between budget and health care outcomes/quality in various health care settings, such as hospitals, were identified. However, no study was found which examined the association between school health budget and students’ elevated BMI. Several other studies conducted in school settings indicated that insufficient budget was a barrier to providing weight-related nursing services (Guttu et al., 2004; Hendershot et al., 2008; Maughan, 2009a, 2009b; Morrison-Sandberg et al., 2011; Quelly, 2014; Steele et al., 2011).
Many personal factors can affect BMI. Some of those factors include race, household income, parents’ educational level, and the rurality of the living area. There is an association found between children‘s ethnicity (African American and Hispanic), low income, low parents’ educational level, parents’ elevated BMI, and residence in rural areas and increasing the prevalence of overweight and obesity among children (Johnson et al., 2007; Johnson & Johnson, 2015; G. Lazzeri et al., 2011; G. Lazzeri, Giacchi et al., 2014; Moreno et al., 2013; Rogers et al., 2015; Salois, 2012).
In summary, there is a dearth of studies evaluating associations between school nurses and prevalence of students who are overweight or obese. The purpose of this study was to explore associations between three components of school health infrastructure (staff, services, and budget) and elevated BMI among 1st, 3rd, and 6th graders after controlling for selected intrapersonal, interpersonal and community factors. The study aims are as follows:
Aim 1: Test for the association between proportion of students with overweight or obesity and school health staff (RNs and non-RNs), after controlling for selected intrapersonal, interpersonal and community factors.
Hypothesis: There is an inverse association between the proportion of students with overweight or obesity and school health staff (RNs and non-RNs), after controlling for selected intrapersonal, interpersonal, and community factors.
Aim 2: Test for the association between proportion of students with overweight or obesity and the delivered school health services, after controlling for selected intrapersonal, interpersonal, and community factors.
Hypothesis: There is an inverse association between proportion of students with overweight or obesity and the delivered school health services, after controlling for selected intrapersonal, interpersonal and community factors.
Aim 3: Test for the association between proportion of students with overweight or obesity and school health budget, after controlling for selected intrapersonal, interpersonal, and community factors.
Hypothesis: There is an inverse association between proportion of students with overweight or obesity and annual school health expenditure, after controlling for selected intrapersonal, interpersonal, and community factors.
Aim 4: Test for the association between proportion of students with overweight or obesity and the three factors of school health infrastructure simultaneously, after controlling for selected intrapersonal, interpersonal, and community factors.
Hypothesis: There is an inverse association between proportion of students with overweight or obesity and school health staff, school health services, and school health budget after controlling for selected intrapersonal, interpersonal, and community factors.
The conceptual model used in this study (Figure 1) was derived from the social-ecological model. This theory-based framework incorporates individuals’ personal and environmental factors as determinants of their health or healthrelated behaviors, and has been applied to health promotion (McLeroy et al., 1988; Unicef, n.d.). The social-ecological model identifies five groups of factors as potentially influencing obesity-related behaviors in school-age children. The first group includes the intrapersonal factors such as a person‘s socioeconomic status, knowledge, and beliefs. Second are interpersonal factors such as parents’ educational level, household income, and friendship networks. Third are organizational factors such as schools’ organizational characteristics, including the type of health staff and the provided health services. Fourth are community characteristics such as location, access to health care, recreational infrastructure, transportation, and availability of healthy food. Last, are policy factors such as policies regarding school health budget and number of nursing staff at schools (McLeroy et al., 1988). In this study, the intrapersonal factor is represented by students’ race and county poverty level (see Table 1). The interpersonal factor is represented by county education level. The community factor is represented by level of rurality of the counties where students live. The policy factor is represented by annual school health expenditures. Lastly, the organizational factor includes the school organization, involving both school health staff and services. The selected concepts under the organizational and policy categories represent the main factors of school-health infrastructure (Scholz et al., 2015). This study focused primarily on assessing associations between the organizational and policy factors (school health infrastructure) and students’ elevated BMI, while controlling for confounding, non-intervenable factors within the intrapersonal, interpersonal, and community categories.
This secondary analysis of publicly available data examined associations between proportion of students with overweight or obesity and (a) school health staff, (b) school health services, and (c) school health budget among 1st, 3rd, and 6th graders in Florida, after controlling for selected socio-demographic factors. The covariates included race and county poverty level (intrapersonal factors), county education level (interpersonal factors), and counties’ level of rurality (community factors). Data from three governmental websites were used as primary data sources for the study variables. As data were reported at the county level, county was the unit of analysis, with all 67 state of Florida counties utilized. The study was approved by the University of Florida Institutional Review Board.
Inclusion and Exclusion Criteria. All public, primary/middle schools in Florida who participated in the mandated health screening by the Florida Administrative Code Rule 64F-6.003 and submitted the School Health Services Report for the Year 2016–2017 (Florida Department of State, n.d.) were included in this study. Health screening is not mandatory in Florida‘s private primary and middle schools, and therefore private schools were not included in this study.
Sample Size Justification. Detectable effect size was calculated based on the total sample of 67 counties in Florida. Using a.05 significance level and assuming a.20 R2 among the covariates, there was.80 power to detect an effect size of R2 = .086 for a single predictor, while adjusting for 8 covariates.
Information about students and school characteristics was obtained from three sources (Table 1). BMI data were obtained from 564,888 students in 1st, 3rd, and 6th grades contained in the 2016–2017 Summary of School Health Services Reports (SSHSRs), a report covering the 67 Florida county school systems, and produced by the FDH (n.d.a). Second, the “School-aged Child and Adolescent Profile – 2017 (SACAP) (FDH, n.d.b)” report includes data about all school-age populations in Florida and was created by the FDH (n.d.a). Last, the United States Census Bureau (USCB), a governmental agency that reports statistical data about the American population (U.S. Census Bureau, 2010).
The SSHSRs (FDH, n.d.a) were used to obtain data about the following variables: the proportion of students with overweight or obesity (students with BMI at 85th or higher percentile), school health staff, school health services, and school health budget. The SSHSRs are produced annually by the FDH for quality assurance and improvement of school health services. Each county has one report with nine main parts. Information in these reports include the number of schools, staff, and students, school health staff, school health services, school health expenditures, and results of selected health screenings mandated by the Florida Administrative Code Rule 64F-6.003 (FDH, n.d.a, n.d.b). Mandated screenings only cover 1st, 3rd, and 6th grade students, and BMI is one of the required elements in the annual report. Student BMI category was based on the Centers for Disease Control and Prevention (CDC) Childhood BMI percentile scale as follows: underweight (<5th percentile), overweight (85th to <95th percentile), or obese (≥95th percentile).
The SACAP (FDH, n.d.b) was another source of the study data. It provided race and county education level covariates. The SACAP includes reports about various health and healthrelated aspects of the school-aged populations in Florida. The reports are prepared by the FDH, Division of Public Health Statistics & Performance Management (FDH, n.d.b).
The last data source accessed for this study was USCB, which supplied the rurality covariate. The USCB produced the “County Rurality Level: 2010 report” which provides the counties’ percent of rurality based on the percent of the county population living in rural areas (U.S. Census Bureau, 2010).
Since this study is a secondary analysis, there were no data collected. Instead, many previously collected proxy variables at the county level were used, as shown in Table 1 (students’ BMI, school health staff, school health services, school health budget, students’ race, county education levels, county poverty levels, and rurality) The proxy variables were as following: (1) “students BMI” was measured using the data on the ratio of students with overweight or greater BMI to the number of students tested in each county in Florida; (2) “School health staff” construct was measured in two parts: (a) RNs measured by the ratio of each 1,000 students to a registered nurse in each county and (b) Non-RNs measured by the ratio of each 1,000 students to a non-RN including Licensed Practical Nurses and Health Aides/Techs; (3) “School health services” construct was also measured with two parts: (a) Nursing services represented by the ratio of the number of delivered nursing assessment/counseling services to the number of students in each county and (b) Other services measured by the ratio of the total number of delivered health education classes and nutrition and physical activity classes to the number of students; (4) The “School health budget” variable was measured by perstudent school health expenditures for each county; (5) “Students’ race” was measured by the percent of children age 5 to 11 identified as White for every county in Florida; (6) “Parents’ educational level” variable was measured using the percent of individuals 25 years and over with at least a high school diploma in each county of Florida; (7) The “Household Income” variable was measured using data on the percent of elementary and middle school students eligible for free/reduced lunch in each county; (8) The “Rurality” variable was measured using data on each county‘s percent of rurality. Lastly, all the data used is presented at the county level in the data sources used in the study.
Microsoft Excel version 16.24 was used for data entry from the FDH and the other data sources. Data were then imported into SAS version 9.4 (SAS Institute Inc, n.d.), which was used for all statistical analyzes. Since study data were reported on the county level, the level of analysis was at the county level, with each county being one study subject. Data were first examined for distribution of values, including extreme values and patterns of missing data using univariate descriptive statistics appropriate for measurement level (Polit & Lake, 2010). Multiple regression analysis was used to test associations (SAS Institute Inc, n.d.; Allison, 2012) between study predictors and covariates with the outcome variable, proportion of students with overweight or obesity, calculated by dividing the number of students who are affected by overweight or obesity by the number of all BMI-measured students. Given that the outcome variable was a proportion, distribution of residual values did not meet the normality assumption. To remedy this, we examined two transformations (arcsine and logit) for the outcome variable (Pituch & Stevens, 2015). Although both transformations solved the violation of normality assumption, the arcsine transformation was selected because it performed slightly better in terms of potentially influential observations and R2. A.05 level of significance was used for all hypothesis testing, and 95% confidence intervals (Daniel & Cross, 2013) were produced. The models examined, corresponding to study aims, were as follows: 1. The association between elevated BMI (proportion of overweight/obesity) and number and type of school health staff, after controlling for selected intrapersonal, interpersonal, and community factors; 2. The association between elevated BMI and school health services, after controlling for selected intrapersonal, interpersonal, and community factors; 3. The association between elevated BMI and annual school health expenditures, after controlling for selected intrapersonal, interpersonal, and community factors; and 4. The association between elevated BMI and the three factors of school health infrastructure simultaneously, after controlling for selected intrapersonal, interpersonal, and community factors.
Table 2 provides a description of the demographic characteristics of the 67 counties in Florida examined in this study. One data element, county education level, was missing for one county. The typical county population was white (race) (75%), had at least a high school diploma (county education level) (85%), had a majority of elementary school students eligible for free/reduced school lunch (county poverty level) (61%), and was considered rural (rurality) (37%).
Table 3 provides descriptive statistics for the 67 Florida county school systems examined in this study. The typical county school system spent an average of $82 per student annually for health expenditures. The typical ratio of delivered nursing services and other services to students was 1.14 and 0.07, respectively. The typical ratio of RN and non-RN health aides to students was 0.49 and 1.13, respectively. Lastly, while the arcsine transformed proportion of overweight was used in the analyzes to conform to statistical model assumptions, the raw values are also provided in Table 2 for interpretability. In raw terms, the median proportion of overweight was 0.36 (36%), with lower quartile (Q1) = 0.32, which means 25% of the proportion of students with overweight/obesity lie below (32%) while upper quartile (Q3) = 0.40, which means 75% of the proportion of the overweight students lie below (40%).
Tenability of multiple regression model assumptions was evaluated using residuals plots for fit diagnostics, outliers, and influence measures, including assessing plots of Studentized deleted residual by predicted and leverage values. Collinearity was assessed by examination of the VIF inflation factor, the condition index, and the proportion of variance. As the assumption of normally distributed residuals was violated, an arcsine transformation (Pituch & Stevens, 2015) for the proportion of students with elevated BMI was employed, resulting in distribution of residuals consistent with statistical assumptions.
Model 1. Results for model 1, testing association between proportion of students with overweight and obesity and number and type of school health staff, after controlling for selected intrapersonal, interpersonal, and community factors appear in Table 4. The model included arcsinetransformed proportion having elevated BMI as the outcome variable, number of school health staff, and type of school health staff as predictors of interest, and students’ race, county poverty level, county education level, and county‘s level of rurality as covariates. The test for the overall model supported a linear relationship between the outcome and the set of predictor and covariate variables (R2 = .45, F (6, 59) = 8.04, p < .0001). While three covariates, race (b = −0.001, 95% CI (−0.002, −0.0001), p = .03), education (b = −0.003, 95% CI (−0.006, −0.001), p = .005), and county poverty level (b = −0.001, 95% CI (0.0001, 0.003), p = .04) exhibited independent linear relationships with proportion of elevated BMI, an independent linear association with number of RNs (p = .18) and other health staff (p = .07) was not supported.
Model 2. Results for model 2, testing association between students with overweight and obesity and school health services, after controlling for selected intrapersonal, interpersonal, and community factors appear in Table 5. The model included arcsine-transformed proportion having elevated BMI as the outcome variable, nursing services and other (non-nursing) services as predictors of interest, and students’ race, county poverty level, county education level, and county‘s level of rurality as covariates. The test for the overall model supported a linear relationship between proportion of elevated BMI and the set of predictor and covariate variables (R2 = .43, F (6, 59) = 7.46, p < .0001). While two covariates race (b = −0.001, 95% CI (−0.002, −0.00007), p = .037) and county education level (b = −0.004, 95% CI (−0.007, −0.001), p = .002) exhibited independent linear relationships with proportion of elevated BMI, an independent linear association with nursing services (p = .35) and other (non-nursing) services (p = .20) was not supported.
Model 3. Results for model 3, testing association between proportion of students with overweight and obesity and school health budget, after controlling for selected intrapersonal, interpersonal, and community factors appear in Table 6. The model included arcsine-transformed proportion having elevated BMI as the outcome variable, budget as predictors of interest, students’ race, county poverty level, county education level, and county‘s level of rurality as covariates. The test for the overall model supported a linear relationship between the outcome and set of predictor and covariate variables (R2 = .41, F (5, 60) = 8.51, p < .0001). While two covariates race (b = −0.001, 95% CI (−0.002, −0.0001), p = .029) and county education level (b = −0.004, 95% CI (−0.006, −0.001), p = .003) exhibited independent linear relationships with proportion of elevated BMI, an independent linear association with school health budget was not supported (p = .88).
Model 4. Results for model 4, testing association between proportion of students with overweight and obesity and number and type of school health staff, school health services, and school health budget after controlling for selected intrapersonal, interpersonal, and community factors appear in Table 7. The model included arcsine-transformed proportion having elevated BMI as the outcome variable, number of school health staff, and type of school health staff, nursing services, other (non-nursing) services, and school health budget as predictors of interest, and students’ race, county poverty level, county education level, and county‘s level of rurality as covariates. The test for the overall model supported a linear relationship between the outcome and the set of predictor and covariate variables (R2 = .49, F (9, 56) = 5.96, p < .0001). Only one predictor which is non-RNs health aides, (b = −0.04, 95% CI (−0.07, −0.007), p = .019) and one covariate which is education (p = .003) exhibited independent linear relationships with proportion of elevated BMI; independent linear relationships between the outcome and all other predictors and covariates were not supported, with p values ranging from .067 to .426.
This study examined associations between three factors (staff, services, budget) of school health infrastructure with proportion of students with elevated BMI after controlling for selected intrapersonal, interpersonal, and community factors. Those associations were derived from the social-ecological framework. Although many of the study‘s hypothesized relationships with proportion of students with elevated BMI were not supported, study results supported an independent association between school health staffing (non-RNs) and elevated BMI. There were also independent associations between elevated BMI and the following covariates: county poverty level, students’ race, and county education level. The negative regression weight for number of non-nursing school health staff indicates that as non-nursing school health staff decreases, the proportion of students with elevated BMIs increases. Likewise, counties with lower proportions of White students and adults with at least a high school diploma tend to have higher proportions of students with elevated BMI. The positive regression weights for county poverty level and rurality indicate that counties with higher rurality and higher poverty rates tend to have higher proportions of students with elevated BMI.
The independent associations indicated by this study support previous studies’ findings. No published studies were found that assess the association between non-RNs school health staff and students’ elevated BMI. The current study‘s results for the association between county poverty level and students’ elevated BMI was consistent with findings by Johnson et al. (2007), Rogers et al. (2015), and Moreno et al. (2013). The association between students’ elevated BMI and race was consistent with two published findings (Johnson et al., 2007; Moreno et al., 2013), while Rogers et al. (2015) did not report an association. One main difference between the Rogers’s et al., study (2015) and the other studies (Johnson et al., 2007; Moreno et al., 2013) was that the data about students’ BMI variable was individual-level, while the race data was on the school district level, which could lead to weakening the correlation between the variables (Rogers et al., 2015). Lastly, the association between students’ BMI and county education level in this study was similar to findings of previous research (G. Lazzeri et al., 2011; G. Lazzeri, Giacchi et al., 2014). On the other hand, no published studies were found that assess the association between non-RNs school health staff and students’ elevated BMI to be used as compared to current results.
The lack of support for some of this study‘s hypothesized relationships with proportion of students with elevated BMI could be attributed to several reasons related to numbers of heath staff in Florida schools and the design and sample characteristics used by the study. Our findings did not support associations for elevated BMI with school health staff (RNs), services, budget, and rurality. The lack of support for an independent linear association between school health RN staff and students’ elevated BMI was particularly unexpected. Although RNs are more qualified than non-RN health aides to deal with students’ health (NASN, n.d.a; Pennington & Delaney, 2008; Schroeder et al., 2016), and previous studies have supported the importance of RNs and their services for improving students’ outcomes (Melin & Lenner, 2009; O‘Brien, 2012; Robbins et al., 2012; Speroni et al., 2007; Tucker & Lanningham-Foster, 2015; Wright et al., 2013), that association was not supported by this study. Rather, the independent association of elevated BMI with non-RN health staff was supported. One potential explanation for this finding is the shortage of RNs in Florida schools. The NASN recommends a ratio of, at minimum, one school nurse to 750 healthy students (Dolatowski et al., 2015). However, the ratio of school nurses to students in Florida is 1:2,382 (FDH, n.d.a). The discrepancy with expectations may stem from the larger number of non-RN health staff, which has been found to be related to number and quality of the health services provided, and so may also impact the health outcome (Aiken et al., 2010; Blegen et al., 2011; Cimiotti et al., 2012; Tubbs-Cooley et al., 2013). However, the relatively low availability of RNs may not be sufficient to produce a detectable effect. Moreover, the higher RNs to students’ ratios in Florida could indicate the face-to-face student services are provided by non-RN staff, while RN staff tend to perform services that could not be done by non-RNs, such as organize student multi-disciplinary teams, lead faculty health-related team meetings, and advocate for policies. This might substantiate the independent association of elevated BMI and non-RN staff. The mean number of non-RN health aides (1.13), including LPN and Health Aides/Techs, was higher than the mean number of RNs (0.49) (Table 2), leading to a higher ratio of non-RN staff/students. The question remains as to whether a detectable association would result if the RN to student ratio was improved to meet recommended levels.
Another potential explanation for the findings related to the RN variable derive from the study design and sample characteristics. The study‘s data were county-level, resulting in a sample size of only 67. Aggregation at the county level might have led to covering up associations that could otherwise be detectable if the data were at the individual or school level.
School health services was another variable in which the hypothesis of an independent association with students’ elevated BMI was not supported. Previous research found that school nursing services were important to improve student outcomes (Melin & Lenner, 2009; O‘Brien, 2012; Robbins et al., 2012; Speroni et al., 2007; Tucker & Lanningham-Foster, 2015; Wright et al., 2013), and associations between the number of nursing staff and the number of services provided for the students have also been reported (Guttu et al., 2004; Telljohann et al., 2004). In our study, the ratio of nurses to students was small) 1: 2,382(, which may have limited the number of services that could be offered by the RNs. Another potential explanation for the lack of support for the association between school health services and students’ BMIs could be that this study included services limited to nursing assessment/counseling services, and health education, nutrition, and physical activity classes. However, they did not identify all other BMI related services, such as BMI screening, collaborating with other health staff, parent communication, and referral and follow-up services. That may dilute the association with BMI.
Another variable for which there was a lack of support for independent associations with students’ elevated BMI was school health budget. There were no published studies addressing the relationship between school health budget and rates of students with elevated BMI in schools. Hussey et al., review (2013) assessed the association between the health cost and patients’ outcomes/quality in different health settings that did not include schools. The results of studies included in that review varied; some of them (36%) did not support that relationship, which was in line with our study results. However, many studies conducted in school settings indicated the budget as a barrier to applying weight-related nursing services (Guttu et al., 2004; Hendershot et al., 2008; Maughan, 2009a, 2009b; Morrison-Sandberg et al., 2011; Quelly, 2014; Steele et al., 2011). Also, the previous research indicated that school health/nursing services are cost-effective (Baisch et al., 2011; Padula et al., 2018; Rodriguez et al., 2013; Wang et al., 2014). One of the uses of a school health budget is to provide the resources required for providing an adequate number of qualified school health staff (RNs and non-RN health aides) and the needed tools for applying their role, such as stadiometers to monitor students’ BMIs. However, school administrators decrease the school nursing budget as one of the first steps in reducing costs (Lear, 2007). That budget cut can be one of the critical reasons for the fulltime RNs shortage at schools, so no appropriate health services targeting students’ weight. In the current study, the limitations of the used sample/data are possible reasons for not detecting an association between school health budget and students’ elevated BMI. One limitation is using aggregated data instead of individual-level data, which can affect the statistical results. Also, since the data was not collected mainly for the study purpose and was collected and documented by many school system staff, it may be subject to documentation errors.
Finally, the only covariate having no independent statistically significant association with elevated BMI in any of the models examined was rurality. This finding was inconsistent with most previous research (Johnson & Johnson, 2015). Only one article (Salois, 2012) aligned with our findings regarding the rurality variable. The similarity between our study and Salois’s research (2012) was that both used counties as a unit of analysis, where other studies (Johnson & Johnson, 2015) used individuals as a unit of analysis. That can be one reason of having the contradictory results.
Three limitations associated with the present study include using secondary data, county-level nature of data, and using proxy variables. The data were not collected specifically for this study‘s purpose, with some potentially confounding variables being unavailable, such as students’ level of physical activity and their type of nutrition. Both are not the focus of the study but may contribute to students’ BMI and need to be statistically controlled. Although the BMI data were collected by trained staff, and all used CDC BMI measurement guidelines, inter-rater reliability was not available. The school health infrastructure variables were collected from student files and school documents, which may result in documentation variability. Second, the data were on the county level, leading to a relatively small sample size of 67, which limits the complexity of models that can be examined, as well as power for detecting associations. The use of county-wide data also reduces the precision possible, as individual variability is lost when collapsed into a single value. The use of county-level data to test associations for mechanisms which actually operate more closely at the individual or school level would add to the unexplained (error) variance and limits the ability to detect associations. Last, the present study included proxy variables, such as using the “percent of individuals 25 years and over living in Florida with at least high school diploma” as the proxy for the “county education level” variable; as the variables were not collected specifically from students included in the data, they may not have accurately reflected the study sample.
Current study findings have implications for research, policy, and practice. Most previous studies about school nursing and students’ weight included the recommended ratios of staff and services and tested its association with students’ weight improvement. Almost all studies supported those associations. No school district in this study, however, met recommendations for staff, and so the services may not have been sufficient to produce a detectable effect on students’ BMI. However, there is a need for more rigorous studies testing the impact of school health staff, services, and budget on improving students’ BMI. Some examples include using individual-level data, conducting a randomized clinical trial to compare the schools employing the recommended per-student number of school nursing staff and services with schools employing the typical number of staff and services, and testing change in student BMI. Another approach would be to use schools that do not have a nurse and to provide nurses, randomly assigned, to a subset of those schools, and assesses differences in BMI changes. Lastly, there is a need for experimental studies to examine the effect of different types of school health staff and services targeting students with overweight and obesity to identify effective school health staff and service mix to control student BMI. A case could then be made to school system and health care policy makers to fund those services and staffing.
Although schools can have an important role in improving students’ weight-related healthy behaviors, this study identified additional associated factors not within the purview of the schools, such as county education level, county poverty level, and race. Development and use of personalized interventions appropriate for relevant sociodemographic factors may also be more effective achieving Healthy People goals for BMI in school-age children (Office of Disease Prevention and Health Promotion, n.d.). County education level factor had the strongest independent association with BMI. Approximately 85% of adults in Florida have at most a high school diploma. Since education is positively associated with income, and both are positively associated with public health and health equity, policies to improve educational attainment may be beneficial for controlling overweight. These factors, however, are part of the broader sphere encompassing public health and economic policies and need to be addressed at a societal level. Lastly, development and use of personalized interventions appropriate for relevant socio-demographic factors may also be more effective achieving Healthy People goals for BMI in school-age children.
Practice recommendations for this study might be limited to improving nursing documentation. There might be a need for modifying and expanding of nursing documentation process to capture and document more nursing activities such as communication with parents regarding students’ weight, students’ referrals to other specialties, and management of obesity and other chronic diseases. That would help assess their actual effect on students’ outcomes.
There is a dearth of empirical information regarding associations between school health infrastructure factors (staff, services, and budget) and prevalence of student with overweight and obesity. To address that knowledge gap, the present study explored associations between three components of school health infrastructure (staff, services, and budget) and elevated BMI among 1st, 3rd, and 6th graders, after controlling for selected intrapersonal, interpersonal and community factors. Those associations were tested using secondary data analysis design with county-level data obtained from different government entities from all 67 counties in Florida. Study results supported an independent association between school health staff (non-RNs) and elevated BMI. In addition, independent associations were found between elevated BMI and the students’ race, county poverty level, and county education level. However, many of the study‘s hypothesized school infrastructure relationships with students’ elevated BMI were not supported. Those findings could be a result of several factors, including study design limitations, sample characteristics, documentation issues, and the numbers of RNs and non-RN health aides in Florida schools.
Future studies should test the effect of school health infrastructure factors on student‘s weight using more rigorous research designs such as randomized clinical trials and employ larger sample sizes. Using data at the level of schools or individuals can enhance the power of statistical tests and provide a less granular examination of associations. Although the use of cross-sectional data is common in school nursing research, due to a lack of better data, studies employing a longitudinal design to investigate relationships between elevated BMI and school health infrastructure are needed. Identifying and enrolling some schools that meet the recommended number of school nurses within future studies would provide the ability to better evaluate recommendations for school nurse staffing based on relevant outcomes. There is a need for well-designed experimental studies testing the effect of school nursing staff and services on students’ BMI control. One example would be to randomly assign schools into two groups, one with “usual care school nurse staffing” and the other group with “enhanced school nurse staffing,” which would incorporate the recommended ratio of school nurses and full practice by school nurses. Then, compare the effect of both nursing staffing on students’ BMI. Lastly, by gaining a better understanding of associations between students’ BMI and selected factors incorporated within this study, our findings can help increase understanding regarding school health infrastructure and elevated BMI, provide direction for future studies, gain attention for the importance of school health/nursing for improving students’ BMI, and avoid health complications from obesity or overweight in adulthood. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship and/or publication of this article.
Safiya S. Bakarman https://orcid.org/0000-0001-7840-9218
Michael Weaver https://orcid.org/0000-0002-6630-4758
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Dr. Safiya S. Bakarman is currently an Assistant Professor in the College of Nursing, King Saud University (KSU), Saudi Arabia. Dr. Bakarman received her undergraduate degrees, Master of Nursing Science degree from KSU, and Ph.D. in Nursing Science from the University of Florida - USA. Dr. Bakarman’s research interests are in students’ health, school nursing, and health prevention and promotion. Dr. Bakarman’s six years of clinical instruction in school health provided her with a clear idea about the central problems interface school students and staff. Her master thesis and Ph.D. dissertation were among school students, focusing on some health issues and the role of school health infrastructure factors in improving students’ health and academic outcomes.
Dr. Michael Weaver is Professor and Associate Dean for Research & PhD program at the University of Florida School of Nursing. He holds an MSN from the Medical College of Toledo and a PhD in research & measurement from the University of Toledo, and is a Fellow of the American Academy of Nursing. Dr. Weaver’s research interests are in advanced statistical methods, symptom science, and health prevention and promotion. He has over 140 peer-reviewed publications, and is co-Investigator on two ongoing NIH-funded grants: R01 NR016964 Parker (PI) 08/03/2018-05/ 31/2022 Optimal Feeding Tube Dwell Time in VLBW to Reduce Feeding Tube Contamination and R01 NR016986 Lyon & Stechmiller (MPI) 04/01/2018-01/31/2023 Biobehavioral mechanisms underlying symptoms and healing outcomes in older individuals with CVLU. Dr. Weaver’s full profile can be found here: https://nursing.ufl.edu/profile/weaver-michael/.
Dr. Lisa Scarton’s research focuses on reducing health disparities among racial and ethnic minorities through the development of culturally informed interventions delivered across multiple generations and designed to improve health outcomes for persons with type 2 diabetes and type 2 diabetes linked cancers.
1 College of Nursing, King Saud University, Saudi Arabia
2 College of Nursing, University of Florida, USA
Corresponding Author:Safiya S Bakarman, PhD, RN, MSN, College of Nursing, College of Nursing, King Saud University, Riyadh, Saudi Arabia.Email: sbakarman@ufl.edu
