BackgroundOne-third (34.2%) of children aged six to eleven years are overweight or obese (1) and childhood obesity continues to be a major focus of public health efforts in the United States (2). Childhood obesity is associated with risks for developing conditions such as hyperlipidemia, hypertension, and type 2 diabetes (3–5), as well as social and emotional health challenges, including being bullied (6;7), poor self-esteem and depression (8). Good health and social outcomes are important goals for school health policy and program efforts (9), and a socio-ecological approach should be employed for achieving positive health and social outcomes in schools (10). Schools can play a vital role in addressing childhood obesity through the coordination of strategic planning, implementation, and evaluation of school-based healthy eating and physical activity policies and practices (11).
The Centers for Disease Control and Prevention (CDC) have synthesized research and best practices related to promoting healthy eating and physical activity in schools, providing nine guidelines with multiple strategies for implementation (11). Although it is unknown how many strategies are needed to achieve health outcomes, it is widely accepted that there should be multiple strategies implemented at multiple socio-ecological levels to increase physical activity and healthy eating, and reduce obesity (12–14). A recent review of evaluated obesity prevention studies and their impact on BMI found strong evidence to support the efficacy of school-based prevention programs, particularly for elementary school-aged children (15). Recommendations from the review suggest future studies should be designed to evaluate both impact (reduction in obesity) and process (implementation) (15).
It has been suggested that future research have more practical utility for decision makers and be broadened to enhance usability in the “real world” (12;16). A challenge to researchers implementing multi-strategy obesity prevention interventions is how best to compare and determine the overall impact of diverse intervention strategies using a common metric. The Center for Community Health and Evaluation [CCHE]) (17) has proposed an approach to estimate the impact of multi-strategy interventions on an average person’s behavior. In other words, the relative change in behavior of across both those who have been exposed to an intervention strategy, and those who have not been exposed.. CCHE refers to this estimated impact as dose. Dose is a product of the intervention’s reach and strength (a quantitative measure of impact based on frequency, intensity and outcomes from the literature).
The primary aim of this case study was to describe prevalence of overweight and obesity among elementary school students in a rural Mid-western community between 2006 and 2012. The secondary aim was to use a novel approach called population dose to retrospectively evaluate the impact dose of each strategy implemented and its estimated potential population level impact on changes in overweight and obesity.
MethodsStudy PopulationKearney Public School (KPS) District is located in Kearney, Nebraska, a Mid-western community of approximately 30,000 people. Between 2006–2012, approximately 2400 elementary students were enrolled each year in grades kindergarten through fifth in nine schools. Five of the nine schools had over 40% of students receiving free or reduced federal meals, the threshold for Title I designation (18), and the district was primarily Caucasian (85%).
Intervention StrategiesA chronological view and description of the strategies included in this case study can be found in Table 1. KPS implemented the following strategies district-wide: body mass index (BMI) screening and referral program; local school wellness policy; the Carol M. White Physical Education Program grant (PEP grant # Q215F080323); district wellness team; healthier school meal program; and a new physical education curriculum. In addition to implementing the district-wide strategies, each of the nine individual schools implemented, to varying degrees, a comprehensive school physical activity program (CSPAP) and healthier school food environment strategies. Supportive and promotional education strategies including the implementation of the wellness policy, formation of wellness teams, school to family education programs, educational presentations to school staff, and data evaluation by administrators and teachers were implemented to build capacity in support of physical activity and healthy eating related strategies. All strategies were not implemented simultaneously, but were phased-in over the six years. Kearney Public Schools provided existing aggregate data for this study and the use of these data was approved by the University of Nebraska Kearney Institutional Review Board.
BMI Screening and Referral ProgramKPS has been measuring each student’s (k-5th grade) weight and height annually since 2006 as part of yearly health screenings completed by the school nurses and trained university volunteers. Individual student data were not followed over time; this was a series of seven annual, cross-sectional screenings from 2006–2012. Weight was measured using a Befour platform digital scale (PS6600, Befour Inc., Saukville, WI) to the nearest 0.1 pounds. Height was assessed using a standard portable stadiometer, measured to the nearest 0.25 inch. Both instruments were calibrated routinely. Both weight and height were measured without shoes and in normal street clothes without jackets and sweatshirts. These data were then entered into a BMI web application developed at the University of Nebraska Kearney. Each student’s BMI (kg·m2) was calculated and percentile determined using the gender specific BMI-for-age percentiles from the CDC 2000 Growth Charts. The accepted definition for normal weight was defined as a BMI percentile between the 5th and 84.9th percentile, overweight was defined as 85th–94.9th percentile, and obesity defined as equal or greater to the 95th percentile (19). Each year, parents received a BMI report card describing their child’s BMI. Students identified as obese were referred to a community-based child obesity treatment program (20).
Evaluation ProceduresBecause there was variability in both district-wide and individual school strategy implementation and subsequent exposure among the schools, we calculated the dose using an approach developed by CCHE (17) with evidence for predictive validity (21). The dose was retrospectively calculated for four independent strategy groupings (set of coordinated activities (21): (1) CSPAP which included both quality physical education and physical activity opportunities outside of physical education (e.g., recess, classroom physical activity breaks, after school programs), (2) school food environment which included all food in school outside of the meal program such as classroom food rewards, classroom parties and fundraisers, (3) BMI screening, reporting and community obesity treatment program, and (4) school meal program.
The CCHE defines dose as an estimate of community-level change in the expected desirable outcome as a result of a community change strategy or strategies (21). We used implementation data regarding frequency, duration, magnitude of changes, and evidence from the literature to estimate behavior change and their estimated impact on BMI change. Behavioral outcomes of interest were increasing physical activity, decreasing unhealthy/high calorie foods, and increasing healthy food consumption. The dose of each strategy is the product of reach and strength of the strategy.
Reach calculationReach was equal to the percentage of students enrolled in KPS grades K-5 who were exposed to a strategy (number of students exposed (participated) / number enrolled in each school). Reach was calculated for each individual school (n=9). For example, if 50 students in a school with 150 enrollment participated in the lunchtime walking program, then reach of that strategy would be 33%.
Strength calculationStrength is equal to the degree to which students exposed to a strategy might change their healthy eating and/or physical activity behaviors to make healthier choices as a result of being exposed. Frequency of exposure, intensity of exposure, degree to which the healthy choice is the only choice, and supporting promotional and educational strategies are all factors that can be used to determine strength (17).
Strength scores were based on empirical evidence collected and analyzed by CCHE (17). CCHE calculated strength scores in a blinded manner, only reviewing implementation data for each strategy without knowing the BMI trends over time, to help ensure an unbiased analysis. Strength was calculated for each individual school (n=9). For example, if a new physical education curriculum was implemented in a school and it increased moderate/vigorous physical activity minutes from 10 minutes to 12 minutes every day, then the strength of that strategy would be 1.1%. In the absence of baseline data, we use CDC estimates of physical activity that states that elementary aged children get an average of 85 minutes of MVPA per day (22). If we increase activity by 2 minutes to baseline on 5 of 7 days a week, during eight months a year that school is in session we get 1.1% change in physical activity overall.
AnalysisThe absolute and relative change in percent of children whose BMI percentile was between the 85th and 94th percentile (overweight) and equal to or greater than the 95th percentile (obese) were calculated between 2006 and 2012. Each year, a census was collected from all students in grades k-5.
The number of district-level strategies were described each year between 2006 and 2012 in an additive format and graphed. A dose score was calculated at the end for individual school strategies based on level of implementation (reach x strength) as described above.
ResultsNinety-seven percent of the total student body was screened for height and weight each year with minimal fluctuation in percent of students receiving free and reduced lunch (7.1%) and a 9.75% mobility rate within the elementary schools (Table 3). Therefore, the change in the prevalence of overweight and obesity reflects the actual difference in the population. Figure 1 shows the percent of overweight and obese students attending KPS elementary schools annually from 2006–2012. The absolute change in prevalence of obesity decreased 2.5%, from 16.4% to 13.9%, indicating a 15.2% relative change in prevalence of obesity in 6 years. The prevalence of overweight decreased from 15.5% in 2006 to 14.3% in 2012 indicating a relative percent change of 7.6%. The prevalence of overweight and obesity combined from 2006–2012 decreased by 3.7% (31.9% to 28.2%, an 11.6% relative decrease). However, there was a wide range within schools of BMI trends over time with a range of overweight and obesity change from a 10% increase in school A to a 12% decrease in school F. Figure 2 illustrates the number of annual district-wide strategies implemented from 2006–2012 and the corresponding annual district-wide prevalence of overweight and obesity (≥85th percentile for BMI).
Table 2 provides a detailed schematic of how dose scores were calculated for each strategy based on estimated strength and reach within each school. The highest dose scores calculated were for CSPAP (5.6–9.7%), due to the relatively high reach AND strength of the strategies. The BMI screening, reporting, and obesity treatment program had the lowest dose (0.6% – 1.6%) due to the low reach of the treatment program even though the strength was very high for those who participated in the intensive obesity reduction classes.
Figure 3 represents the dose for each strategy implemented at the individual school-level from 2006–2012. As shown in Table 2, dose scores were calculated over the six years with frequency and duration impacting strength scores. Schools that showed absolute decreases in overweight and obesity prevalence of greater than 10% are noted in Figure 3.
DiscussionThis retrospective case study revealed a 2.5% absolute decrease in obesity from 16.4% in 2006 to 13.9% in 2012, a 15.2% relative change. The prevalence of overweight and obesity combined decreased from 31.9% in 2006 to 28.2% in 2012, an 11.6% relative change. Although these changes only reflect one school district, they are in contrast to the NHANES national data that documented a 2.6% absolute increase in obesity prevalence between 2006 and 2012 among 6 to 11 year old children (15.1% [11.3–20.1] in 2005–2006 to 17.7% [14.5–21.4] in 2011–2012, p>0.05) (1). It is also worth noting that there was a wide range within schools, with some schools showing as much as a 12% reduction in overweight/obese and other schools showing as much as a 10% increase percent overweight/obese over this same time period.
The overall reduction of overweight and obesity prevalence from 2006 to 2012 may have been the result of several strategies being implemented across KPS. Establishing causality is difficult using a retrospective study design (23), and was not an aim of this study. Some strategies were district-wide and potentially reached all students, whereas other strategies were implemented at the school-level to varying degrees. The five school-level strategies included CSPAP, school food environment, BMI reporting and obesity treatment program, school meal program, and supportive/promotional education programs. These strategies are identified in the literature to have potential impact on obesity, physical activity, or nutrition, and represent a socio-ecological approach to obesity prevention (11;13;14;20;24–28). Multifaceted school-based programs for 6 to 11 year olds that include both nutrition and physical activity components have been found to both improve health and be cost saving (12). The current retrospective case study describes efforts to reduce obesity and would be considered a more natural intervention compared to past studies that were more controlled intervention studies. Each strategy was evaluated at the individual school-level, which allowed us to differentiate between the schools.
The dose scores derived in this study are based on all enrolled students at each elementary school in KPS, even those who were not exposed to all strategies (17). According to CCHE (17)(17)(17), cumulative dose scores for each school suggest that for all elementary students enrolled, there was an estimated 8.9% to 17.4% change in healthy eating and or physical activity behaviors (17). These estimates are not meant to be taken literally, but rather indicated to us that significant, measurable changes in behaviors that impact BMI were occurring in these schools. It is generally accepted that the main cause of obesity is due to imbalance between energy intake and energy expenditure. We would therefore expect that collectively the strategies implemented throughout KPS which had most impact on nutrition and physical activity behaviors to show greater reduction in overweight/obesity, and this inverse relationship is in fact what we found. The greatest reductions in overweight/obesity prevalence occurred in Schools F, H, and C (Figure 3). Dose scores for these schools were also higher, ranking 4th, 1st, and 2nd out of 9 and ranging from 12.8 to 17.4%. Comparatively, Schools A, D, G, and I with the least change in BMI or who showed increases, were ranked lowest in terms of dose scores (8.9% to 11.1%). One of the greater discrepancies in dose between School G and School F, H, or C include the participation rate in the obesity treatment program. School G had the highest school enrollment amongst all schools and given their overweight/obesity rate translates into approximately 120 overweight/obese students of which only 9.0%, or approximately 11 students participated. Comparatively, School F had an average attendance of 290 students, a similar baseline overweight/obesity rate (30.6% to School G (29.0%), and 34% of students participate in the obesity treatment program. This would equate to approximately 30 students who participated in the obesity treatment program, nearly three times that of School G.
Interestingly, School C only reported 4.0% of obese students participating in the obesity treatment program, but it also had the highest overall dose score and the highest baseline overweight/obesity rate (42.8%). These findings suggest that it may be important to implement strategies at the primary (e.g., CSPAP), secondary (e.g., BMI screening program), and tertiary (e.g., obesity treatment program) levels of prevention. This hypothesis can be tested in future studies. Parents have reported supporting the BMI screening program in KPS (Heelan, et al., unpublished) and the family-based pediatric obesity treatment program has demonstrated efficacy (20). While we cannot pinpoint exact commonalities between schools with the greatest reduction in obesity, it does appear that having a high dose cluster of strategies, regardless of their makeup, is a common factor. The use of a retrospective study has certainly provided data to generate hypotheses for future research (23).
The adoption and implementation of district and school-level strategies were not uniform across schools. The district-level wellness policy was important for identifying specific physical activity and nutrition strategies that schools should implement. It was difficult to get individual school administrators to agree to make significant changes within their schools until they were presented with the district-wide and individual school overweight and obesity prevalence data in December 2009. The data demonstrated to school principals the importance of physical activity and healthy eating.
The discrepancy between schools in degree of implementation may be the result of differences in funding, teacher-student ratio, general infrastructure, and capacity for implementing the process of health promotion in schools using a socio-ecological approach (10). Any combination of these factors could lead to natural variation in the timing of adoption and degree of implementation of strategies (29). For example, all elementary schools changed their policies on classroom parties, snacks in the classroom and food rewards. However, level of implementation varied considerably as one school prohibited all food outside of school meals, while other schools required, to varying degrees, only healthy food brought into the school for snacks and fundraisers.
The findings of this retrospective case study are not generalizable to other school districts. Conversely, an advantage of retrospective case studies include the opportunity to study rare occurrences (23), in this case a school district whose prevalence of obesity decreased during the same period when obesity remained level nationally (1). Additionally, this type of study can act as a good pilot study to help identify feasibility issues and generate hypotheses for future studies (23). As a result of this case study, valuable insights into interpreting the differences in implementation of several strategies across a school district were gleaned. Calculating dose could allow stakeholders to better comprehend the differences in implementation between schools and how each strategy could impact obesity prevalence, even in situations where yearly BMI measurements are not feasible. Community stakeholders can work together to determine the feasibility issues surrounding sustained measurement and reporting of strategy implementation.
The study has several limitations. First, the study did not evaluate changes in environments outside of the school setting such as the home or the community that may have also influenced a child’s weight status over time. Changes in these environments may have also contributed to observed changes in obesity status. Second, while strategy exposure and participation data were collected throughout the years of the study, dose was assessed at the end of the study period and reflects an estimation of implementation at the end of the six years of the evaluation.
Finally, a quantitative number was assigned to categorize the often times qualitative implementation data for a given strategy. However, the calculated dose scores do allow for relative comparisons of strategy implementation between schools. The dose score has been helpful to visualize that a “district-wide” policy or strategy does not necessarily suggest that all strategies will be uniformly implemented. Future research should test approaches for implementing district policies at the school-level and their relationship to health-related outcomes. Future research should also continue to focus on developing data collection methods that are user-friendly to practitioners whom are conducting non-controlled studies in the area of obesity prevention, as well as, evaluating the validity of the method employed for this study.