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Article |

School-Based Body Mass Index Screening and Parent Notification:  A Statewide Natural Experiment FREE

Kristine A. Madsen, MD, MPH
[+] Author Affiliations

Author Affiliation: Department of Pediatrics, University of California, San Francisco.


Arch Pediatr Adolesc Med. 2011;165(11):987-992. doi:10.1001/archpediatrics.2011.127.
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Published online

Objective To assess the impact of body mass index (BMI) screening with parental notification on weight status for California public school students.

Design A natural experiment wherein nearly all California school districts conducted annual BMI screening in the fifth, seventh, and ninth grades, but parental notification of BMI screening results was optional.

Setting Data from mandatory fitness testing in California public schools for 2001 through 2008.

Participants A total of 6 967 120 fifth-, seventh-, and ninth-grade youth (73% of enrolled students).

Intervention School-based BMI screening with optional parent notification.

Main Outcome Measure Body mass index z score was the main outcome in adjusted mixed-effects linear regression models, assessing whether notifying parents of their child's BMI in a given year predicted BMI z score 2 years hence.

Results Rates of parental notification of BMI screening results increased from 35% in 2001 to 52% in 2008. Body mass index notification in fifth and/or seventh grade had no impact on subsequent BMI z scores (95% confidence interval, −0.03 to 0.01) compared with no notification. No differences in the impact of notification were seen by race/ethnicity. Results did not vary with sensitivity analyses.

Conclusions These findings suggest that while BMI screening itself could have benefits, parental notification in its current form may not reduce pediatric obesity. Until effective methods of notification are identified, schools should consider directing resources to policies and programs proven to improve student health.

The broad scope of the pediatric obesity epidemic13 calls for public health approaches.4 School-based strategies have the potential to reach the vast majority of youth and may address increasing racial and ethnic disparities in obesity.5 The Institute of Medicine recommends school-based body-mass index (BMI) screening with parental notification of results as a public health means of reducing pediatric obesity.6 Theoretically, notifying parents of BMI screening results can inform parents that their child is at risk, thereby allowing parents to take effective action to improve their child's weight status. However, the Centers for Disease Control and Prevention,7 the American Heart Association,8 and the US Preventive Services Task Force9 do not include school-based BMI screening in their recommendations, citing a lack of evidence to support its effect.

Despite the lack of evidence, as of 2006, 41% of school districts nationwide mandated height and weight assessments, and 72% of those districts required reporting results to parents.10 The state of California was an early adopter of BMI screening. For the past decade, almost all California public schools have collected BMI data annually on fifth-, seventh-, and ninth-grade students. However, notifying parents of BMI screening results remains optional. Mandated screening with optional notification creates a natural experiment through which to examine the effect of the now widespread use of parental notification. California's natural experiment was used to determine if notifying parents of school-based BMI screening results reduces obesity at the population level and if notification has the potential to reduce health disparities.

STUDY POPULATION

Participants were fifth-, seventh-, and ninth-grade youth who underwent mandatory fitness testing in California public schools from 2001 through 2008. The Committee on Human Research at the University of California, San Francisco, certified this study as exempt.

FITNESS ASSESSMENT DATA

Pursuant to California Education code,11 California public schools assessed the fitness status of fifth, seventh, and ninth graders each spring in 1999 and from 2001 through 2008 using the FITNESSGRAM.12 The FITNESSGRAM, a battery of 6 fitness assessments, including body composition, is widely used nationally; of 29 states that either require or recommend BMI or fitness assessment in schools, 15 (52%) recommend use of the FITNESSGRAM.13 The California Department of Education (CDE) allows schools to use BMI, skin-fold measures, or bioelectrical impedance to estimate body composition; over 95% of California schools use BMI (e-mail communication from the CDE, April 2010). School districts are required to send student-level data to the CDE and may contract with outside vendors to obtain student-level reports of FITNESSGRAM performance to provide to parents.14

Deidentified student-level FITNESSGRAM data were available from the CDE for the years 2001 through 2008. Individual identifiers were not available, and data from an individual student could not be linked across years. Data records include student grade, sex, age in years, height to the nearest inch, weight to the nearest pound, and race/ethnicity (African American, American Indian/Alaskan Native, Asian, Filipino, Hispanic/Latino, Pacific Islander, or white–not of Hispanic origin). To protect student confidentiality, the CDE included Filipino and Pacific Islander students in the Asian category. The CDE redacted data for students who, based on sex, grade, and race/ethnicity, were among a group of 10 or fewer students in their school district. The BMI z scores were calculated using the CDC's SAS program.15

DISTRICT-LEVEL DATA

Data were obtained directly from the CDE Web site16 for total enrollment; percentage of students eligible for free or reduced-price meals; urban level (based on an 8-point scale from the US Census Bureau, ranging from a large city to a rural area); and an index of retention (the percentage of students enrolled in the district continuously from October 2007 until standardized testing in spring 2008).

INTERVIEWS WITH DISTRICTS

Structured telephone interviews were used to determine if, when, and how districts notified parents of student fitness assessment results. Researchers called the main telephone number for all 438 kindergarten through eighth-grade (K-8) and 376 kindergarten through 12th grade (K-12) school districts and asked to speak to the person with the greatest knowledge of physical fitness test procedures. The identified individual was asked if the current district policy was to notify parents of FITNESSGRAM results and, if so, in what year notification began, and by what method parents were notified. Interviewees who stated that their district did not currently notify parents of results were asked if that policy had ever changed. All interviewees were asked if they thought any schools within the district deviated from the district-level policy regarding parent notification. Interviewees in 17 districts reported not knowing the answer to 1 or more questions and either referred researchers to another individual or sought information from another district employee in order to provide complete information.

STATISTICAL ANALYSIS

Linear regression, adjusted for year and clustering by district code, identified predictors of missing data (Table 1) and the Wilcoxon rank sum test identified predictors of notification status in 2001 and 2008 (Table 2). To determine if parent notification when a child is in fifth grade predicts BMI z score in seventh grade, and if notification in seventh grade predicts BMI z score in ninth grade, a mixed-effects linear regression model with a random effect for district (to account for repeated measures within districts over time and clustering of students within districts) and a random intercept was used. The primary outcome was student-level BMI z score, and the primary predictor was district-level prior notification, a time-varying binary indicator of a district's notification status 2 years before. Prior notification was coded as 0 for all fifth graders; seventh- and ninth-grade students were assigned prior notification values based on their current district's previous notification policy. The full model included the following student-level factors: grade (fifth grade as reference), sex, race/ethnicity (non-Hispanic white as reference), and cohort year (to account for temporal trends in BMI z score with students in fifth grade in 2001 as reference group). The model was adjusted for district-level factors: proportion of students eligible for free or reduced-price meals, and urban level (categorical, with large city as reference). To determine if parent notification might reduce health disparities, an interaction term was included in the full model for prior notification × race/ethnicity. Statistical analyses were performed with SAS software (version 9.2; SAS Institute Inc, Cary, North Carolina).

Table Graphic Jump LocationTable 1. Enrollment and Body Mass Index (BMI) Record Data
Table Graphic Jump LocationTable 2. Differences in District-Level Factors by Notification Status in 2001 and 2008a
SENSITIVITY ANALYSES

To isolate the impact of timing of notification, a categorical term compared no prior notification with notification in fifth grade only, notification in seventh grade only, and notification in both fifth and seventh grades. In addition, the full model was rerun restricting data to (1) 380 districts with a retention index of at least 95% (which included 65% of valid data), (2) 700 districts with a retention index higher than 87% (which included 97% of valid data), (3) districts with enrollment of 75 000 or less (which excluded 4 large districts), (4) the years 2004 through 2008 (to account for potential lack of institutional memory for earliest years of FITNESSGRAM administration). Finally, the full model was rerun, excluding 106 districts reporting that individual schools might deviate from the districts' policy or reporting that the district did not have a policy requiring notification and left the decision to individual schools.

Valid BMI data were available for 6 967 120 students, representing 72.7% of all fifth, seventh, and ninth graders enrolled during the years 2001 through 2008 (Table 1). The number of districts with no FITNESSGRAM data records available declined from 70 in 2001 to 29 in 2008 (representing 4.5% and 0.2% of enrolled students, respectively). There were small district-level correlations between the amount of missing data and the proportion of students eligible for free and reduced-price meals and the mean student BMI z score (Table 1).

Researchers conducted telephone interviews with 429 of 438 K-8 districts (98%) and 355 of 376 K-12 school districts (94%). Districts that did not respond to interview requests were not different in enrollment, proportion of students eligible for free or reduced-price meals, or mean BMI z score in 2008 from districts interviewed. The number of districts notifying parents of BMI screening results increased from 261 (35%) in 2001 to 386 (51%) in 2008 (the proportion notifying in 2008 was 53% when 26 districts that did not know notification status prior to 2008 were included). Districts that notified were larger, had fewer missing FITNESSGRAM data, lower mean student BMI z score (in 2001 but not in 2008), and slightly more African American and Asian students, and were more urban than districts that did not notify parents (Table 2). Associations between missing data and free meals or BMI z score (Table 1) did not vary by notification status (P >> .19 for all interaction terms). To notify parents of FITNESSGRAM results, most districts sent a letter either via US mail (70% of those notifying) or with students to bring home to their parents (19%). Of all districts that ever notified, only 12 stopped doing so.

In the mixed-effects model, prior parental notification had no impact on subsequent BMI z scores (effect size, −0.01 BMI z score units; 95% confidence interval [CI], −0.03 to 0.01) after adjusting for grade, sex, race/ethnicity, urban level, and free or reduced-price meal eligibility (Table 3). In a similarly adjusted model with type of notification as a categorical predictor, neither mailing a letter nor sending a letter home with the child significantly affected BMI z score, nor were the effects different from each other in a pair-wise comparison. No differences in the impact of notification were seen by race/ethnicity ( = .64 value for interaction term). To determine if the proportion of obese students in a district modified the effect of BMI reporting, an interaction term between prior notification and district-level quartiles for the proportion of obese students (mean proportion of students with a BMI at or above the 95th percentile across all data years) was included. While there was significant interaction between prior notification and quartile of proportion of obese students (P < .001), the effect was not significant within any quartile, nor was there a step-wise effect with increasing proportion of obese students (Table 4). No other sensitivity analyses yielded results that differed from the primary model.

Table Graphic Jump LocationTable 3. Regression Coefficients From Mixed-Effects Model Predicting BMI z Score
Table Graphic Jump LocationTable 4. Impact of Prior Notification on Body Mass Index z Score, by Quartile of Proportion of Obese Youth (Mean Over Years 2001-2008)

This study, which took advantage of California's statewide natural experiment, did not find that notifying parents of school-based BMI screening results for fifth- and seventh-grade students had an effect on pediatric obesity. While notifying parents of their child's weight status might be part of a multifaceted approach to reducing obesity, these results suggest that current notification methods used in the fifth and seventh grades are not sufficiently effective to warrant the practice on a large scale. California, which is home to almost 1 in 8 youth living in the United States, has the largest Latino population in the country.17 This study, therefore, provided a unique opportunity to evaluate the impact of school-based BMI screening and notification in one of the ethnic groups most susceptible to obesity and type 2 diabetes mellitus.18

Theoretically, BMI screening and reporting can notify parents that their child is overweight or obese and prompt them to act on this knowledge. There is consistent evidence demonstrating that many parents of overweight and obese children are not cognizant of their child's weight status.19 However, perceptions of weight status and the risks associated with obesity are complex and may not be changed by experts' reports of risk.20 Many adults who are obese or otherwise at risk for cardiovascular disease do not perceive themselves to be at increased risk, despite experts' opinions to the contrary.21,22 The long-term risks of childhood obesity are particularly difficult to convey because parents frequently believe their child will “grow into their weight.”23

Two studies have demonstrated that BMI reporting can improve the accuracy of parents' perceptions of their child's weight status.24,25 West et al25 demonstrated this effect among both African American and white parents. Chomitz et al24 further found that parents of children in kindergarten through eighth grade reported being motivated to attempt lifestyle changes as a result of BMI reporting, although it should be noted that the parents in the study by Chomitz et al24 were of relatively high socioeconomic status. Also noteworthy is the fact that in the study by Chomitz et al,2,4 1 to 6 weeks after a BMI report was sent, only 63% of parents recalled having received the report, suggesting that BMI reporting may be a weak intervention. In a large-scale effort among a diverse population in West Virginia, Harris and Neal26 found that BMI reporting did not change parents' perceptions of their child's weight. Further work to enhance the impact of BMI reporting should explore parents' perceptions of the causes of obesity, its associated risks, and what can and should be done at the individual, family, and community levels. This “mental models” approach has been successfully used to improve risk communications in other arenas.27 A better understanding of parents' mental models might suggest communication methods to provide critical missing information and dispel misconceptions about pediatric obesity that affect parents' willingness or ability to make changes. It will be particularly important to explore mental models among distinct racial/ethnic and socioeconomic subgroups, given these factors' impact on weight perception.28

Even if BMI reporting can alter perceptions in diverse groups, school-based BMI reporting fails one of the most salient aspects of a useful screening test: having an effective therapy if the disease (or condition) is detected.29 Lifestyle interventions to treat pediatric obesity are largely ineffective,30 and recommending individual behavior change is unlikely to meet with success, if the experience of multidisciplinary pediatric obesity clinics is any guide.31,32 Thus, expecting a single BMI report to parents to have a meaningful effect on a child's weight status, in the absence of environmental changes, may be wishful thinking.

Arkansas did see a halt in the progression of obesity after implementing BMI screening and notification as part of Act 1220 of 2003.33 However, Act 1220 simultaneously called for changes in cafeteria food offerings, increased physical activity requirements, and healthier vending machine options, making attribution to any 1 intervention difficult. A recent study34 using a similar multifaceted approach to alter the school environment demonstrated a significant impact on obesity, without implementing BMI screening and notification. There is evidence that focused interventions can have a positive impact on pediatric obesity. For example, policies banning the sale of sugar-sweetened beverages and snacks that are high in fat or sugar during the school day seem to be related to declines in obesity seen after 2005 in California.35 Increased quality and quantity of physical education has been associated with decreased obesity and improved fitness.3638 Until a cost-effective method of BMI notification can be found, notification resources would be better invested in changing youths' environment, particularly in low-income communities.

The present study could not assess the impact of BMI screening itself, and it is possible that screening alone may heighten community awareness, which, in turn, could lead to changes in school or community policies over a period of years. These changes might have an impact on obesity that the present model, which looked to see if notification predicted weight status 2 years hence for a cohort of children, could not detect. It will be important to study the impact of screening itself on obesity.

STUDY LIMITATIONS

Several limitations should be considered in interpreting these findings. Misclassification of the predictor variable, which would make it more difficult to detect an effect of BMI reporting, could occur if institutional memory was poor or if individual schools deviated from the district policy regarding notification. In addition, if students changed districts between fifth and ninth grades and notification status differed between the old and new districts, their data would have been misclassified. Sensitivity analyses to address misclassification (limiting data to the most recent years, excluding districts indicating that individual schools might deviate from district policy and excluding districts with high mobility) yielded similar findings. Parents who did not receive results as intended would have been misclassified, and sensitivity analyses would not have addressed this.

Data could not be linked across years for individual students, and while it would be unusual, associations at the student level could be different from those at the district level owing to confounding at either the student or district level. Data were differentially missing for districts with heavier students and districts with a higher proportion of students eligible for free or reduced-price meals; however, notification status did not modify this association, so it is unlikely that this bias in missing data would have affected the findings beyond potentially limiting generalizability.

The quality of school-based BMI screening data is unknown. There is no surveillance of FITNESSGRAM test administration, and the integrity of data collection methods likely varies (which will decrease precision of estimates) and may vary by school (which might bias results). Nonetheless, random error is not likely an issue given the very small 95% CI for the effect. Bias in measurements is possible, but unless there is also bias in the change over time, it should not affect results for the effect of notification.

The widespread use of BMI screening and reporting is heartening because it reflects schools' willingness to dedicate resources to address the obesity epidemic. However, current methods of reporting school-based BMI screening results to parents do not seem to have an impact on pediatric obesity. While BMI screening itself may have value, further work to evaluate different approaches to providing parents with BMI screening information should be pursued before BMI reporting is implemented on a large scale. In addition, research could explore how this type of information might be used more broadly with other stakeholders and in policy. In the meantime, schools will likely see greater benefits if resources are used to increase opportunities for physical activity and improve nutrition.

Correspondence: Kristine A. Madsen, MD, MPH, Department of Pediatrics, University of California, San Francisco, 3333 California St, Box 0503, San Francisco, CA 94118 (madsenk@peds.ucsf.edu).

Accepted for Publication: April 13, 2011.

Published Online: July 4, 2011. doi:10.1001/archpediatrics.2011.127

Financial Disclosure: None reported.

Funding/Support: This study was funded by Robert Wood Johnson Foundation Active Living Research grant No. 65715, Eunice Kennedy Shriver National Institute of Child Health and Human Development grant No. 1K23HD054470-01A1, and American Heart Association grant No. 0865005F.

Disclaimer: No funders were involved in any aspect of the analyses contained herein or in the preparation of this manuscript.

Olshansky SJ, Passaro DJ, Hershow RC,  et al.  A potential decline in life expectancy in the United States in the 21st century.  N Engl J Med. 2005;352(11):1138-1145
PubMed   |  Link to Article
Bibbins-Domingo K, Coxson P, Pletcher MJ, Lightwood J, Goldman L. Adolescent overweight and future adult coronary heart disease.  N Engl J Med. 2007;357(23):2371-2379
PubMed   |  Link to Article
Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS. The relation of childhood BMI to adult adiposity: the Bogalusa Heart Study.  Pediatrics. 2005;115(1):22-27
PubMed
Ludwig DS. Childhood obesity: the shape of things to come.  N Engl J Med. 2007;357(23):2325-2327
PubMed   |  Link to Article
Madsen KA, Weedn AE, Crawford PB. Disparities in peaks, plateaus, and declines in prevalence of high BMI among adolescents.  Pediatrics. 2010;126(3):434-442
PubMed   |  Link to Article
Preventing Childhood Obesity.  Health in the Balance. Washington, DC: Institute of Medicine, Committee on Prevention of Obesity in Children and Youth; National Academies Press; 2005
Ikeda JP, Crawford PB, Woodward-Lopez G. BMI screening in schools: helpful or harmful.  Health Educ Res. 2006;21(6):761-769
PubMed   |  Link to Article
 Policy position statement on body mass index (BMI) surveillance and assessment in schools: American Heart Association. http://www.heart.org/idc/groups/heart-public/@wcm/@adv/documents/downloadable/ucm_301789.pdf. Accessed October 22, 2010
US Preventive Services Task Force.  Screening and interventions for overweight in children and adolescents: recommendation statement.  Pediatrics. 2005;116(1):205-209
PubMed   |  Link to Article
Nihiser AJ, Lee SM, Wechsler H,  et al.  Body mass index measurement in schools.  J Sch Health. 2007;77(10):651-671
PubMed   |  Link to Article
 The Pupil Nutrition, Health, and Achievement Act of 2001, SB 19 (Escutia), Chapter 1 and Chapter 913 (Ca 2001) 
Welk GJ, Morrow JRJ, Falls HBE. New Aerobic Capacity and Body Composition Healthy Fitness Zone Standards. Cooper Institute Web site. www.cooperinstitute.org/products/grams/documents/FITNESSGRAM_ReferenceGuide.pdf. Accessed October 22, 2010
Linchey J, Madsen KA. State requirements and recommendations for school-based screenings for body mass index or body composition, 2010. Prev Chronic Dis. In press
 2011 California Physical Fitness Test. California Department of Education Website. http://www.pftdata.org/files/2010-11_PFT_Coordinator_Manual.pdf. Accessed May 20, 2011
 An SAS program for the CDC Growth Charts. Division of Nutrition, Physical Activity and Obesity, National Center for Chronic Disease Prevention and Health Promotion Web site. http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm. Accessed May 20, 2011
 DataQuest. http://data1.cde.ca.gov/dataquest/. Accessed June 2010
Guzman B. The Hispanic population: Census 2000 Brief. US Census Bureau Web site. http://www.census.gov/prod/2001pubs/c2kbr01-3.pdf. Accessed October 22, 2010
Centers for Disease Control and Prevention.  National Diabetes Fact Sheet, 2011: Fast Facts on Diabetes. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2011
Ritchie LD, Welk G, Styne D, Gerstein DE, Crawford PB. Family environment and pediatric overweight: what is a parent to do?  J Am Diet Assoc. 2005;105(5):(suppl 1)  S70-S79
PubMed   |  Link to Article
Slovic P. Trust, emotion, sex, politics, and science: surveying the risk-assessment battlefield.  Risk Anal. 1999;19(4):689-701
PubMed
van der Weijden T, van Steenkiste B, Stoffers HE, Timmermans DR, Grol R. Primary prevention of cardiovascular diseases in general practice: mismatch between cardiovascular risk and patients' risk perceptions.  Med Decis Making. 2007;27(6):754-761
PubMed   |  Link to Article
Gregory CO, Blanck HM, Gillespie C, Maynard LM, Serdula MK. Perceived health risk of excess body weight among overweight and obese men and women: differences by sex.  Prev Med. 2008;47(1):46-52
PubMed   |  Link to Article
Jain A, Sherman SN, Chamberlin LA, Carter Y, Powers SW, Whitaker RC. Why don't low-income mothers worry about their preschoolers being overweight?  Pediatrics. 2001;107(5):1138-1146
PubMed   |  Link to Article
Chomitz VR, Collins J, Kim J, Kramer E, McGowan R. Promoting healthy weight among elementary school children via a health report card approach.  Arch Pediatr Adolesc Med. 2003;157(8):765-772
PubMed   |  Link to Article
West DS, Raczynski JM, Phillips MM, Bursac Z, Heath Gauss C, Montgomery BE. Parental recognition of overweight in school-age children.  Obesity (Silver Spring). 2008;16(3):630-636
PubMed   |  Link to Article
Harris CV, Neal WA. Assessing BMI in West Virginia schools: parent perspectives and the influence of context.  Pediatrics. 2009;124:(suppl 1)  S63-S72
PubMed   |  Link to Article
Atman CJ, Bostrom A, Fischhoff B, Morgan MG. Designing risk communications: completing and correcting mental models of hazardous processes, part I.  Risk Anal. 1994;14(5):779-788
PubMed   |  Link to Article
Chang VW, Christakis NA. Self-perception of weight appropriateness in the United States.  Am J Prev Med. 2003;24(4):332-339
PubMed   |  Link to Article
Newman TB, Kohn MA. Screening Tests: Evidence-Based Diagnosis. New York, NY: Cambridge University Press; 2009
Oude Luttikhuis H, Baur L, Jansen H,  et al.  Interventions for treating obesity in children.  Cochrane Database Syst Rev. 2009;(1):CD001872
PubMed
Madsen KA, Garber AK, Mietus-Snyder ML,  et al.  A clinic-based lifestyle intervention for pediatric obesity: efficacy and behavioral and biochemical predictors of response.  J Pediatr Endocrinol Metab. 2009;22(9):805-814
PubMed   |  Link to Article
Pinhas-Hamiel O, Lerner-Geva L, Copperman N, Jacobson MS. Insulin resistance and parental obesity as predictors to response to therapeutic life style change in obese children and adolescents 10-18 years old.  J Adolesc Health. 2008;43(5):437-443
PubMed   |  Link to Article
Thompson JW, Card-Higginson P. Arkansas' experience: statewide surveillance and parental information on the child obesity epidemic.  Pediatrics. 2009;124:(suppl 1)  S73-S82
PubMed   |  Link to Article
Foster GD, Linder B, Baranowski T,  et al; HEALTHY Study Group.  A school-based intervention for diabetes risk reduction.  N Engl J Med. 2010;363(5):443-453
PubMed   |  Link to Article
Sanchez-Vaznaugh EV, Sanchez BN, Baek J, Crawford PB. “Competitive” food and beverage policies: are they influencing childhood overweight trends?  Health Aff (Millwood). 2010;29(3):436-446
PubMed   |  Link to Article
Datar A, Sturm R. Physical education in elementary school and body mass index: evidence from the early childhood longitudinal study.  Am J Public Health. 2004;94(9):1501-1506
PubMed   |  Link to Article
Madsen KA, Gosliner W, Woodward-Lopez G, Crawford PB. Physical activity opportunities associated with fitness and weight status among adolescents in low-income communities.  Arch Pediatr Adolesc Med. 2009;163(11):1014-1021
PubMed   |  Link to Article
Sollerhed AC, Ejlertsson G. Physical benefits of expanded physical education in primary school: findings from a 3-year intervention study in Sweden.  Scand J Med Sci Sports. 2008;18(1):102-107
PubMed   |  Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1. Enrollment and Body Mass Index (BMI) Record Data
Table Graphic Jump LocationTable 2. Differences in District-Level Factors by Notification Status in 2001 and 2008a
Table Graphic Jump LocationTable 3. Regression Coefficients From Mixed-Effects Model Predicting BMI z Score
Table Graphic Jump LocationTable 4. Impact of Prior Notification on Body Mass Index z Score, by Quartile of Proportion of Obese Youth (Mean Over Years 2001-2008)

References

Olshansky SJ, Passaro DJ, Hershow RC,  et al.  A potential decline in life expectancy in the United States in the 21st century.  N Engl J Med. 2005;352(11):1138-1145
PubMed   |  Link to Article
Bibbins-Domingo K, Coxson P, Pletcher MJ, Lightwood J, Goldman L. Adolescent overweight and future adult coronary heart disease.  N Engl J Med. 2007;357(23):2371-2379
PubMed   |  Link to Article
Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS. The relation of childhood BMI to adult adiposity: the Bogalusa Heart Study.  Pediatrics. 2005;115(1):22-27
PubMed
Ludwig DS. Childhood obesity: the shape of things to come.  N Engl J Med. 2007;357(23):2325-2327
PubMed   |  Link to Article
Madsen KA, Weedn AE, Crawford PB. Disparities in peaks, plateaus, and declines in prevalence of high BMI among adolescents.  Pediatrics. 2010;126(3):434-442
PubMed   |  Link to Article
Preventing Childhood Obesity.  Health in the Balance. Washington, DC: Institute of Medicine, Committee on Prevention of Obesity in Children and Youth; National Academies Press; 2005
Ikeda JP, Crawford PB, Woodward-Lopez G. BMI screening in schools: helpful or harmful.  Health Educ Res. 2006;21(6):761-769
PubMed   |  Link to Article
 Policy position statement on body mass index (BMI) surveillance and assessment in schools: American Heart Association. http://www.heart.org/idc/groups/heart-public/@wcm/@adv/documents/downloadable/ucm_301789.pdf. Accessed October 22, 2010
US Preventive Services Task Force.  Screening and interventions for overweight in children and adolescents: recommendation statement.  Pediatrics. 2005;116(1):205-209
PubMed   |  Link to Article
Nihiser AJ, Lee SM, Wechsler H,  et al.  Body mass index measurement in schools.  J Sch Health. 2007;77(10):651-671
PubMed   |  Link to Article
 The Pupil Nutrition, Health, and Achievement Act of 2001, SB 19 (Escutia), Chapter 1 and Chapter 913 (Ca 2001) 
Welk GJ, Morrow JRJ, Falls HBE. New Aerobic Capacity and Body Composition Healthy Fitness Zone Standards. Cooper Institute Web site. www.cooperinstitute.org/products/grams/documents/FITNESSGRAM_ReferenceGuide.pdf. Accessed October 22, 2010
Linchey J, Madsen KA. State requirements and recommendations for school-based screenings for body mass index or body composition, 2010. Prev Chronic Dis. In press
 2011 California Physical Fitness Test. California Department of Education Website. http://www.pftdata.org/files/2010-11_PFT_Coordinator_Manual.pdf. Accessed May 20, 2011
 An SAS program for the CDC Growth Charts. Division of Nutrition, Physical Activity and Obesity, National Center for Chronic Disease Prevention and Health Promotion Web site. http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm. Accessed May 20, 2011
 DataQuest. http://data1.cde.ca.gov/dataquest/. Accessed June 2010
Guzman B. The Hispanic population: Census 2000 Brief. US Census Bureau Web site. http://www.census.gov/prod/2001pubs/c2kbr01-3.pdf. Accessed October 22, 2010
Centers for Disease Control and Prevention.  National Diabetes Fact Sheet, 2011: Fast Facts on Diabetes. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2011
Ritchie LD, Welk G, Styne D, Gerstein DE, Crawford PB. Family environment and pediatric overweight: what is a parent to do?  J Am Diet Assoc. 2005;105(5):(suppl 1)  S70-S79
PubMed   |  Link to Article
Slovic P. Trust, emotion, sex, politics, and science: surveying the risk-assessment battlefield.  Risk Anal. 1999;19(4):689-701
PubMed
van der Weijden T, van Steenkiste B, Stoffers HE, Timmermans DR, Grol R. Primary prevention of cardiovascular diseases in general practice: mismatch between cardiovascular risk and patients' risk perceptions.  Med Decis Making. 2007;27(6):754-761
PubMed   |  Link to Article
Gregory CO, Blanck HM, Gillespie C, Maynard LM, Serdula MK. Perceived health risk of excess body weight among overweight and obese men and women: differences by sex.  Prev Med. 2008;47(1):46-52
PubMed   |  Link to Article
Jain A, Sherman SN, Chamberlin LA, Carter Y, Powers SW, Whitaker RC. Why don't low-income mothers worry about their preschoolers being overweight?  Pediatrics. 2001;107(5):1138-1146
PubMed   |  Link to Article
Chomitz VR, Collins J, Kim J, Kramer E, McGowan R. Promoting healthy weight among elementary school children via a health report card approach.  Arch Pediatr Adolesc Med. 2003;157(8):765-772
PubMed   |  Link to Article
West DS, Raczynski JM, Phillips MM, Bursac Z, Heath Gauss C, Montgomery BE. Parental recognition of overweight in school-age children.  Obesity (Silver Spring). 2008;16(3):630-636
PubMed   |  Link to Article
Harris CV, Neal WA. Assessing BMI in West Virginia schools: parent perspectives and the influence of context.  Pediatrics. 2009;124:(suppl 1)  S63-S72
PubMed   |  Link to Article
Atman CJ, Bostrom A, Fischhoff B, Morgan MG. Designing risk communications: completing and correcting mental models of hazardous processes, part I.  Risk Anal. 1994;14(5):779-788
PubMed   |  Link to Article
Chang VW, Christakis NA. Self-perception of weight appropriateness in the United States.  Am J Prev Med. 2003;24(4):332-339
PubMed   |  Link to Article
Newman TB, Kohn MA. Screening Tests: Evidence-Based Diagnosis. New York, NY: Cambridge University Press; 2009
Oude Luttikhuis H, Baur L, Jansen H,  et al.  Interventions for treating obesity in children.  Cochrane Database Syst Rev. 2009;(1):CD001872
PubMed
Madsen KA, Garber AK, Mietus-Snyder ML,  et al.  A clinic-based lifestyle intervention for pediatric obesity: efficacy and behavioral and biochemical predictors of response.  J Pediatr Endocrinol Metab. 2009;22(9):805-814
PubMed   |  Link to Article
Pinhas-Hamiel O, Lerner-Geva L, Copperman N, Jacobson MS. Insulin resistance and parental obesity as predictors to response to therapeutic life style change in obese children and adolescents 10-18 years old.  J Adolesc Health. 2008;43(5):437-443
PubMed   |  Link to Article
Thompson JW, Card-Higginson P. Arkansas' experience: statewide surveillance and parental information on the child obesity epidemic.  Pediatrics. 2009;124:(suppl 1)  S73-S82
PubMed   |  Link to Article
Foster GD, Linder B, Baranowski T,  et al; HEALTHY Study Group.  A school-based intervention for diabetes risk reduction.  N Engl J Med. 2010;363(5):443-453
PubMed   |  Link to Article
Sanchez-Vaznaugh EV, Sanchez BN, Baek J, Crawford PB. “Competitive” food and beverage policies: are they influencing childhood overweight trends?  Health Aff (Millwood). 2010;29(3):436-446
PubMed   |  Link to Article
Datar A, Sturm R. Physical education in elementary school and body mass index: evidence from the early childhood longitudinal study.  Am J Public Health. 2004;94(9):1501-1506
PubMed   |  Link to Article
Madsen KA, Gosliner W, Woodward-Lopez G, Crawford PB. Physical activity opportunities associated with fitness and weight status among adolescents in low-income communities.  Arch Pediatr Adolesc Med. 2009;163(11):1014-1021
PubMed   |  Link to Article
Sollerhed AC, Ejlertsson G. Physical benefits of expanded physical education in primary school: findings from a 3-year intervention study in Sweden.  Scand J Med Sci Sports. 2008;18(1):102-107
PubMed   |  Link to Article

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