The Journal of School Nursing2021, Vol. 37(4) 298–305© The Author(s) 2019Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/1059840519868766journals.sagepub.com/home/jsn
The number of children who are obese and overweight continues as a public health challenge despite decades of research. The purpose of this article is to describe trends in body mass index (BMI) percentile data collected from 11- to 14-year-old school children in 2008–2009 and 2015–2016 in rural Wisconsin. The BMI percentiles from 1,347 students were compared using time, gender, age, and school (public vs. parochial) as predictors. The trend over time indicated a decrease in students of healthy weight and an increase in those overweight or obese. Also noted was a significantly higher proportion of children who were overweight or obese in parochial compared to public schools. Discussed are the observed trends, community-wide initiatives implemented, as well as how schools can employ a more comprehensive approach to childhood obesity that first ensures community readiness and involves school, home, and community.
body mass index percentiles, child health, obesity, overweight, partnerships, public health nursing, school health, school nurse
In the past 20–30 years, the percentage of people who are overweight or obese has become one of the greatest public health challenges internationally, with children and adolescents being particularly vulnerable (Centers for Disease Control and Prevention [CDC], 2011a; Helseth, Riiser, Holmberg Fagerland, Misvaer, & Glavin, 2017; Schrecker & Miller, 2018). Decades of assessments and research have failed to show a decrease in the prevalence of U.S. children who are overweight or obese (Ogden, Carroll, Kit, & Flegal, 2012). The National Health and Nutrition Examination Survey indicates the U.S. childhood obesity rate in 2015–2016 was 18.5% for 2- to 19-year-olds (Hales, Carroll, Fryar, & Ogden, 2017; The State of Obesity Project, n.d.). Further, the subgroup of 12- to 19-year-olds had the highest rate of obesity at 20.9% (The State of Obesity, n.d.). Children who are obese are more likely to become adults who are obese. This leads to the potential for a wide array of health problems such as diabetes, hypertension, dyslipidemia, cardiovascular disease, and disability (Hendriksson et al., 2019; Styne et al., 2017) as well as psychological issues, including low self-esteem and depression (CDC, 2016; Styne et al., 2017). Additionally, data from the CDC indicate obesity may be more problematic for rural residents, with the non-metropolitan overweight or obesity prevalence at 34.2%, compared to 27.8% for those living in urban areas (Lundeen et al., 2018).
In Wisconsin specifically, 29.5% of children aged 10–17 years were obese or overweight in 2016, ranking it 31st among the 50 states for this issue (The State of Obesity Project, n.d.). Unfortunately, the prevalence of children who are overweight is no longer publicly reported by county in Wisconsin as many schools do not identify or track obesity as they do other chronic conditions such as diabetes or asthma. For example, 34.8% of Wisconsin secondary schools use school records to identify and track obesity, compared to 97.6% of schools tracking students with asthma (Brener et al., 2017).
The high national and state prevalence of overweight or obese children and the serious implications of childhood obesity on adult health are well known. In response to these issues, a community coalition formed in a rural northern Wisconsin county in 2006, with the issue of childhood obesity as its priority and part of the local Community Health Improvement Plan (CHIP). The coalition members (including school and public health nurses [PHNs], health-care providers, and teachers) identified the need for baseline data describing the childhood overweight and obesity issue for the county. As such, data were collected for children enrolled in schools in this county between fall 2008 and spring 2009 (hereafter referred to as 2008–2009). After the collection of these baseline data, the coalition reviewed federal and state best practices on good nutrition, such as Healthy People 2020 (Office of Disease Prevention and Health Promotion, 2014) and What Works for Health (University of Wisconsin Population Health Institute, 2010), and planned evidence-based initiatives for the entire county. A similar data collection process was repeated between fall 2015 and spring 2016 (hereafter referred to as 2015–2016), with the goal of quantifying any change in rates of children aged 11–14 who were overweight or obese in this county. The purpose of this article is to summarize the observed trends in body mass index (BMI) percentiles in these children in a rural Wisconsin county at two points in time (2008–2009 and 2015–2016), during which community-wide initiatives were implemented, and to provide suggestions for school nurses planning similar endeavors related to childhood obesity.
The description of the data collection and participants given below was determined by the coalition members. University researchers were then asked to complete a secondary analysis of previously collected, de-identified data from a convenience sample of students in schools in this rural county in 2008–2009 and 2015–2016. The proposed analysis of these data was reviewed and approved as “Exempt†by the University of Wisconsin-Green Bay Institutional Review Board in both 2009 and 2016.
In 2008–2009, the heights and weights of kindergarten through 12th grade students were measured in 13 of 15 (87%) schools in two cities within the county under study. These schools included three (of five) parochial schools, two public high schools, and eight public middle and/or elementary schools.
However, only 5 of 15 (33%) schools (two public and three parochial)—all of which enrolled 11- to 14-year-olds—agreed to be surveyed again in 2015–2016. Additionally, one parochial school was excluded from analysis due to a small sample size in 2015–2016 (n = 5). Thus, for meaningful comparisons, analyses only included data from four schools assessed during both measurement periods, all with students aged 11–14 years (2008–2009: n = 511, 2015–2016: n = 836).
Age- and gender-specific BMI percentiles were used to compare 11- to 14-year-old students across measurement periods. Prior to data collection, the lead PHN contacted each school in the county on behalf of the CHIP coalition, explained the BMI percentile project, and offered assistance with BMI measurements. Participating schools were given an information sheet for parents describing the height and weight collection process, assuring anonymity of student data (one of the safeguards suggested by the CDC [2011b]) and stating that participation was voluntary. Each school requested and received individual parental consent before measurement of students began. Following the Community Partnership Model (Brosnan, Upchurch, Meininger, Hester, & Eissa, 2005), students’ heights and weights were collected by physical education teachers, a PHN (as a contracted school nurse), and registered nurses who were BSN students from two universities and were mentored by PHNs as part of a community and public health nursing practicum. All students were measured in a private area to assure confidentiality. Single height and weight measurements were taken on each student with the students’ shoes removed. The physical education teachers at two schools had already assessed weight and height before the coalition data collection began, using a calibrated scale with a stadiometer that was owned by each respective school. The health department purchased, calibrated, and used a digital scale for consistency in the remaining weight measurements taken by the PHN and students. Height measurements on these students were collected using a measurement device affixed to a wall, with students required to have their heels tight to the wall.
After data collection, information about participants was de-identified to ensure anonymity and privacy. Only height, weight, gender, age, and school (which was further collapsed into public vs. parochial by the authors) were available for analysis. The age- and gender-adjusted BMI percentiles were then calculated using the CDC’s BMI tool for children and teens, specifically designed for school use (CDC, 2015). The BMI tool required an individual’s date of birth, not age, which was not available to the investigators in the de-identified data. As a result, each participant’s date of birth was arbitrarily set to October 1 and the birth year was calculated as Year Measured (2008 or 2015)—Age (at year of measurement). The CDC defines the following classifications based on age- and gender-adjusted BMI percentiles for children: underweight: <5th percentile; healthy weight: 5th to <85th percentile; overweight: 85th to <95th percentile; and obese: 95th percentile or higher (CDC, 2016).
Tests of independence for two categorical variables. A chi-square (χ2 ) or Fisher’s exact test of independence (using tables of observed counts; raw data omitted) was used to look for evidence of a relationship (dependency) between two categorical variables of interest, BMI percentile category and gender. When all expected counts were at least five, the χ2 test was used, whereas otherwise Fisher’s was employed. If the overall test of independence was significant at the level of α = .05, post hoc analyses were conducted to determine which subgroups (e.g., obese males, healthy-weight females) had higher or lower probabilities (percentages) of occurrence than expected.
Since only results from χ2 tests were ultimately significant, post hoc analyses were based on large-sample approximations. Specifically, the cells of a contingency table with standardized residuals (SRs) larger than two in magnitude were deemed those likely driving significant results (Agresti, 2007).
Logistic regression for more than two variables. In instances where multiple categorical predictors (e.g., gender, year of data collection, and their interaction) were of interest in predicting a binary BMI percentile outcome (i.e., obese vs. other [nonobese]), logistic regression was used, and likelihood ratio tests were applied to assess the significance of interactions and main effects (Agresti, 2007). To trust the results of these likelihood ratio tests in this special case where all variables are categorical, Agresti (2007) states all fitted counts should be “at least about five,†which was satisfied by these data. Analyses were conducted in R Version 3.4.1 for PC (R Core Team, 2017), and the lrtest function from the lmtest package (Zeileis & Hothorn, 2002) was used in some instances.
Table 1 describes the number and age of students, aged 11–14 years, by school (two public, two parochial), gender, and year of data collection. Table 2 provides the percentage of students in each BMI percentile category by gender and year of data collection, along with the change in percentage points across the two measurement periods. Lastly, Figure 1 shows BMI percentile categories by school and year of data collection.
The percentages of children (11–14 years) in each BMI percentile category, given year of data collection (Table 2), showed no evidence of a statistically significant relationship using a χ2 test of independence between BMI percentile category (underweight, healthy, overweight, obese) and year of data collection (p = .150, α = .05). However, the general trend from 2008–2009 (n = 511) to 2015–2016 (n = 836) showed a decrease in students who were healthy weight (from 59.1% to 55.9%), while all other categories (overweight, obese, and even underweight) increased (see Table 2). Specifically, children who were overweight increased from 18.4% to 19.4%, while those deemed obese increased from 21.3% to 21.8%.
In 2008–2009, 20.9% of females and 21.8% of males were obese, while in 2015–2016, the percentages were 20.1% and 23.3%, respectively. There was no evidence of a relationship between BMI percentile category and gender in either data collection year (2008–2009: Fisher’s exact test: p = .985; 2015–2016: χ2 test: p = .508; Table 2). It is widely recognized that the height and weight changes in adolescent years are highly individualized due to developmental differences. Reflecting this, the analysis of overweight and obese percentages by age (11–14 years), year of data collection, and their interaction gave no evidence of a relationship (logistic regression; all p values > α = .05).
Finally, BMI percentiles from two public and two parochial schools were compared as they were the only schools assessed in both measurement periods (aside from one previously noted and excluded due to a small sample size). In 2008–2009, there was no evidence of a relationship between BMI percentile category and school (χ2 test: p = .934). However, in 2015–2016, the proportion (percentage) of students in various BMI percentile categories differed by school type (parochial vs. public; χ2 test: p = .023). SRs were used to determine what percentages in 2015–2016 were lower or higher than expected if BMI percentile category and school were truly independent (Figure 1). In Public School A, there were more students who were underweight or healthy (64.8%, SR: 2.59) and fewer who were obese (15.7%, SR: −3.12) than expected statistically (underweight and healthy categories were collapsed due to a small number of underweight students). In Public School B, there were moderately fewer students who were underweight or healthy weight (56.4%, SR: −1.71) than expected and more who were obese (24.7%, SR: 2.41). Regarding parochial schools, School C had moderately more students who were overweight (33.3%, SR: 1.64) than expected, and Parochial School D had moderately more students who were obese (33.3%, SR: 1.72).
Two rounds of BMI percentile surveillance indicate this Wisconsin county was comparable to or surpassed the national and/or state average rates of children in the same grade levels who were overweight or obese (Hales, Fryar, Carroll, Freedman, & Ogden, 2018). These rates also rose from 2008–2009 to 2015–2016.
Other notable findings arose from this study. First, these data indicated there were more obese male than female children in each measurement period (Table 2), although the differences were not statistically significant. A national study in the United States between 1999–2000 and 2009–2010 noted an increase in obesity prevalence among males aged 2–19 years, but not in females (Ogden et al., 2012). Similar findings of nonstatistically significant higher incidences of overweight males have been noted in the literature (Obesity Health Alliance, 2016; Ogden, Flegal, Carroll, & Johnson, 2002; Stark, Niederhauser, Camacho, & Shirai, 2011). More research is needed on the concept of gender differences in childhood obesity as also suggested by Wisniewski and Chernausek (2009).
Second, there was a lack of evidence for a relationship between BMI percentile category and school in 2008–2009 but notable differences in the proportions (percentages) across schools in 2015–2016 (Figure 1). These data show that, compared to all other schools, Parochial School D had a higher percentage of children who were obese than expected statistically, while Parochial School C had the highest percentage of children who were overweight. These findings oppose those by Li and Hooker (2010), who found that higher BMI percentiles were found in children who attended public, not private or parochial, schools. A more focused design to study differences between public and parochial schools is recommended.
Limitations exist in this coalition-led project. Utilizing local university research assistance before beginning data collection could have enhanced the design and results of this study. Specifically, measuring the same students across both time periods with standardized measurement procedures for all students would have been advantageous. Also, between the two measurement periods, community-wide health initiatives were implemented, but which—if any—students were exposed to these initiatives was not tracked, so their impact on BMI percentiles could not be determined. These initiatives included community and school gardens and the addition of refrigerators for fruits and vegetables in convenience stores in food desert areas. Healthy weight and nutrition content were presented through another county program in some school classrooms with high percentages of low-income students. Some teachers implemented “healthy snack†policies, however, efforts for school-wide policies by the coalition were met with resistance. Careful planning, implementation, and tracking of initiatives relative to a control group of students may have strengthened the design and outcomes of this project. It is potentially notable, however, that despite these initiatives and increased attention to healthy eating throughout the community, the prevalence of children who were overweight or obese in this community still rose over time in general.
Next, it is possible the results from first measurement period (2008–2009) were not disseminated properly so as to impact community awareness and action, were not incorporated into community wellness plans, or did not reach target audiences in the intended fashion. In this instance, the project background, data collection process, and results were summarized by the principal author and presented to the county health department and board of health. The PHNs intended to present the results to the schools, community, and medical providers, but attempts to be placed on the local school board agenda were unsuccessful. This may indicate the school board was not interested in obesity prevention or did not share the same vision as the coalition, as discussed above. Community medical providers were provided a summary report, but it is unclear how many—if any—of these providers reviewed the report, as no subsequent action was noted. However, if school nurses employ the strategies discussed below to engage key stakeholders in community readiness before screening, it is possible community response to the local childhood obesity trend may be realized.
Changes in public health and school staff, along with community health priorities, further stymied efforts. The continued collaboration between the county health department, schools, medical providers, and coalition members to implement and determine the efficacy of obesity prevention and intervention programs was difficult to sustain (American Heart Asssociation, n.d.). For example, the next 5-year CHIP period began during the middle of this project, in 2013. As a result, local public health focus shifted to identifying new community issues to include in this next 5-year cycle (2013–2018), taking time away from the program’s current efforts. Sustaining and engaging the school and community members in the voluntary coalition was also difficult due to competing demands for time and energy for new, urgent health issues (e.g., opioid addiction and mental health). Literature indicates that using the Community Readiness Model (Findholt, 2007), along with the adoption of the CDC’s strategies for physical activity and safeguards for schools, might be beneficial before launching a BMI percentile screening program (CDC, 2011b; Sliwa, Brener, Lundeen, & Lee, 2019). Another recent option to garner community support to prevent chronic disease (including that resulting from childhood obesity) and promote health and well-being of children and adolescents in schools is the Whole School, Whole Community, Whole Child model, which offered (USS 998) $15.4 million in funding in fiscal year 2019 (CDC, 2019).
Lastly, school nurse involvement in obesity prevention programs is supported by the literature (Quelly, 2013). Limited school nursing services were available in some of the schools in this study, which hampered obesity screening, surveillance, and the ability to engage fully in education and prevention of this chronic health issue. School nurse staffing varies across public school districts and between public and parochial schools. For example, 30.8% of schools in Wisconsin are without a full-time equivalent school nurse, with 22% of the 86 public health departments contracting with schools to provide limited services instead of full-time school nursing (B. Carr, personal communication, January 4, 2018). Therefore, assessing and responding to individual students with unhealthy BMI percentiles could not be fully realized due to limited nurse time and other student health issues of perceived higher priority. Determining “adequate†staffing of school nurses is complex, as state laws and practice acts must be followed, and student acuity, student health needs, and social determinants within the community must be considered (National Association of School Nurses, 2015). Thus, increasing the amount of time nurses are available to work with children, as well as mandating school nurses in all districts, would potentially allow for increased use of screening, surveillance, and initiatives to help curb childhood obesity. School nurses should remain active and vigilant in policy advocacy related to school nursing services.
Despite the aforementioned shortcomings, there are additonal lessons school nurses may glean from this study. First, collection of students’ baseline data using professional volunteers was a cost-effective method. Next, while community coaltion members had a vision, the schools, parents, and rest of the community may not have been ready to support it. Namely, the goal was to provide a snapshot of the community’s health, rather than a mispercevied focus on individual student measurements, the latter of which may result in defensiveness and resistance to measurement. Not as many schools participated in the second round of height and weight measurements (2015-2016), possibly due to parental resistance; lack of school nurses, coalition members, and volunteers to screen; and time to complete the measurements. School nurses who provide more education for these groups about the program’s importance and identify and address other barriers before program implementation may have better success (Schroeder & Smaldone, 2017). The use of key messages, or standardized educational phrases, may also have help parents, schools and the community to understand the importance of the screenign and increased participation (Ruggieri, Bass, Alhaji & Gordon, 2018).
Children’s school years provide one of the best environments for supporting and educating them about lifelong healthy skills related to nutrition, physical activity, and lifestyle. The importance of tracking childhood obesity trends—as is done for other chronic diseases in students—is critical for the health of the public and students themselves, and to determine the effectiveness of community, school, and public health initiatives. Screening, surveillance, and assessing trends in such data can also be useful to develop programs, guidelines, and tools to improve school health policy and curricula to prevent chronic diseases (CDC, 2019; Harvard School of Public Health, 2019). Ultimately, a multifaceted, innovative approach to childhood obesity initiatives that confirms community readiness and targets the home, school, and community is the most comprehensive, but also demanding, and thus may only be effective with the assurance of adequate resources.
Janet Reilly contributed to conception, design, acquisition, analysis, or interpretation; drafted the manuscript; critically revised the manuscript; gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Le Zhu contributed to conception, design, acquisition, analysis, or interpretation; drafted the manuscript; critically revised the manuscript; gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Megan J. Olson Hunt contributed to acquisition, analysis, or interpretation; drafted the manuscript; critically revised the manuscript; gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Rebecca Hovarter contributed to acquisition, analysis, or interpretation; drafted the manuscript; critically revised the manuscript; gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy. M. Brigid Flood contributed to conception, design, acquisition, analysis, or interpretation; critically revised the manuscript; gave final approval, and agreed to be accountable for all aspects of work ensuring integrity and accuracy.
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.
Janet Reilly, DNP, APNP-BC, RN https://orcid.org/0000-0002-0788-6479
Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken, NJ: John Wiley.
American Heart Association. (n.d.). Policy position statement on Body Mass Index (BMI) surveillance and assessment in schools. Retrieved from http://www.heart.org/idc/groups/heart-public/@wcm/@adv/documents/downloadable/ucm_301789.pdf
Brener, N. D., Demissie, Z., McManus, T., Shanklin, S. L., Queen, B., & Kann, L. (2017). School health profiles: Characteristics of health programs among secondary schools. Retrieved from https://www.cdc.gov/healthyyouth/data/profiles/pdf/2016/2016_profiles_report.pdf
Brosnan, C., Upchurch, S., Meininger, J., Hester, G. J., & Eissa, M. (2005). Student nurses participate in public health research and practice through a school-based screening program. Public Health Nursing, 22, 260–266. doi:10.1111/j.0737-1209.2005.220310.x
Centers for Disease Control and Prevention. (2011a). School health guidelines to promote healthy eating and physical activity. Morbidity and Mortality Weekly, 6. Retrieved from https://www.cdc.gov/healthyschools/npao/pdf/MMWR-School-Health-Guidelines.pdf
Centers for Disease Control and Prevention. (2011b). Strategies to prevent obesity and other chronic diseases: The CDC guide to strategies to increase physical activity in the community. Atlanta, GA: U.S. Department of Health and Human Services.
Centers for Disease Control and Prevention. (2015). Children’s BMI tool for schools. Retrieved from https://www.cdc.gov/healthyweight/assessing/bmi/childrens_BMI/tool_for_schools.html
Centers for Disease Control and Prevention. (2016). Defining childhood obesity. Retrieved from https://www.cdc.gov/obesity/childhood/defining.html
Centers for Disease Control and Prevention. (2019). Healthy schools. Retrieved from https://www.cdc.gov/chronicdisease/resources/publications/factsheets/healthy-schools.htm
Findholt, N. (2007). Application of the community readiness model for childhood obesity prevention. Public Health Nursing, 24, 565–570. doi:10.1111/j.1525-1446.2007.00669.x
Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among adults and youth: United States, 2015–2016 (Data Brief No. 288). Hyattsville, MD: National Center for Health Statistics.
Hales, C. M., Fryar, C. D., Carroll, M. D., Freedman, D. S., & Ogden, C. L. (2018). Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. Journal of American Medical Association, 319, 1723–1725. doi:10.1001/jama.2018.3060
Harvard School of Public Health. (2019). Obesity prevention source, school obesity prevention recommendations: Complete list. Retrieved from https://www.hsph.harvard.edu/obesity-prevention-source/obesity-prevention/schools/school-obesity-pre vention-recommendations-read-and-print/
Helseth, S., Riiser, K., Holmberg Fagerlund, B., Misvaer, N., & Glavin, K. (2017). Implementing guidelines for preventing, identifying and treating adolescent overweight and obesity—School nurses perceptions of the challenges involved. Journal of Clinical Nursing, 26, 4716–4725. doi: 10.1111/jocn.13823
Hendriksson, P., Henricksson, H., Tynelius, P., Berglind, D., Löf, M., Lee, I., ...Ortega, F. B. (2019). Fitness and body mass index during adolescence and disability later in life: A cohort study. Annals of Internal Medicine, 170, 230–239. doi:10.7326/M18-1861
Li, J., & Hooker, N. H. (2010). Childhood obesity and school: Evidence from the national survey of children’s health. Journal of School Health, 80, 96–103. doi:10.1111/j.1746-1561.2009.00471.x
Lundeen, E. A., Park, S., Pan, L., O’Toole, T., Matthews, K., & Blanck, H. M. (2018). Obesity prevalence among adults living in metropolitan and nonmetropolitan counties—United States 2016. Morbidity and Mortality Weekly, 67, 653–658. Retrieved from http://dx.doi.org/10.15585/mmwr.mm6723a1
National Association of School Nurses. (2015). School nurse workload—Staffing for safe care: A position statement. Retrieved from https://www.nasn.org/advocacy/professional-practice-documents/position-statements/ps-workload
Obesity Health Alliance. (2016). 3 in 5 of England’s most deprived boys will be overweight or obese by 2020. Retrieved from http://obesityhealthalliance.org.uk/2016/10/11/3-5-englands-deprived-boys-will-overweight-obese-2020/
Office of Disease Prevention and Health Promotion. (2014). Healthy People 2020 topics & objectives: Nutrition and weight status. Retrieved from https://www.healthypeople.gov/2020/topics-objectives/topic/nutrition-and-weight-status
Ogden, C., Caroll, M. D., Kit, B. K., & Flegal, K. M. (2012, January). Prevalence of obesity in the United States, 2009-2010 (Data Brief No. 82), Atlanta, GA: Center for Disease Control and Prevention.
Ogden, C., Flegal, K. M., Carroll, M. D., & Johnson, C. L. (2002). Prevalence and trends in overweight among US children and adolescents, 1999-2000. Journal of American Medical Association, 288, 1728–1732. doi:10.1001/jama.288.14.1728
Quelly, S. (2013). Influence of perceptions of school nurse practices to prevent childhood obesity. Journal of School Nursing, 30, 292–302. doi:10.1177/1059840513508434
R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
Ruggieri, D. G., Bass, S. B., Alhajji, M., & Gordon, T. F. (2018). Understanding parents’ perceptions of school-based BMI screening and BMI report cards using perceptual mapping: Implications for school nurses. The Journal of School Nursing. Advance online publication. doi:10.1177/1059840518789243
Schrecker, T., & Miller, E. (2018). A big, fat complicated public health problem. Journal of Public Health, 40, 217–218. doi:10.1093/pubmed/fdy118
Schroeder, K., & Smaldone, A. (2017). What barriers and facilitators do school nurses experience when implementing an obesity intervention? Journal of School Nursing, 33, 456–466. doi:10.1177/1059840517694967
Sliwa, S. A., Brener, N. D., Lundeen, E. A., & Lee, S. M. (2019, February 26). Do schools that screen for body mass index have recommended safeguards in place? The Journal of School Nursing, 35, 299–308. doi:10.1177/1059840518758376
Stark, M. J., Niederhauser, V. P., Camacho, J. M., & Shirai, L. (2011). The prevalence of overweight and obesity in children at a health maintenance organization in Hawai’i. Hawai’i Medial Journal, 70, 27–31.
The State of Obesity. (n.d.). The state of childhood obesity (2014-2018). Retrieved from https://stateofobesity.org/childhood-obesity-trends/
The State of Obesity Project in Wisconsin. (n.d.). The state of obesity in Wisconsin: The state of obesity by the state (2014-2018). Retrieved from https://stateofobesity.org/states/wi/
Styne, D. M., Arslanian, S. A., Connor, E. L., Farooqi, I. S., Murad, M. H., Silverstein, J. H., & Yanovski, J. A. (2017). Pediatric obesity, assessment, treatment, and prevention: An endocrine society clinical practice guideline. Journal of Clinical Endocrinology and Metabolism, 102, 709–757. doi:10.1210/jc.2016-2573
University of Wisconsin Population Health Institute. (2010). What works for health: Policies and programs to improve Wisconsin’s health. Retrieved from http://whatworksforhealth.wisc.edu/factor.php?id=12
Wisniewski, A. B., & Chernausek, S. D. (2009). Gender in childhood obesity: Family environment, hormones, and genes. Gender Medicine, 6, 76–85. doi:10.1016/j.genm.2008.12.001
Zeileis, A., & Hothorn, T. (2002). Diagnostic checking in regression relationships. R News, 2, 7–10.
Janet Reilly, DNP, APNP-BC, RN, is an associate professor at the Department of Nursing and Health Studies in the University of Wisconsin-Green Bay, Green Bay, Wisconsin.
Le Zhu, PhD, RDN, is an associate professor in Human Biology at the University of Wisconsin-Green Bay, Green Bay, Wisconsin.
Megan J. Olson Hunt, PhD, is an associate professor in Mathematics and Statistics at the University of Wisconsin-Green Bay, Green Bay, Wisconsin.
Rebecca Hovarter, DNP, APHN-BC, CHP, RN, is a senior lecturer in Nursing and Health Studies at the University of Wisconsin-Green Bay, Green Bay, Wisconsin.
M. Brigid Flood, BSN, RN, is a retired public health nurse from a county health department, Wisconsin.
1 Nursing and Health Studies, University of Wisconsin-Green Bay, Green Bay, WI, USA
2 Human Biology, University of Wisconsin-Green Bay, Green Bay, WI, USA
3 Mathematics and Statistics, University of Wisconsin-Green Bay, Green Bay, WI, USA
4 Retired from a Wisconsin county health department, WI, USA
Corresponding Author:Janet Reilly, DNP, APNP-BC, RN, Nursing and Health Studies, University of Wisconsin-Green Bay, 2420 Nicolet Drive, Green Bay, WI 54311, USA.Email: reillyj@uwgb.edu
