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RESEARCH AND PRACTICE |
Juhee Kim is with the Departments of Nutrition and Society, Human Development and Health, Harvard School of Public Health, Boston, Mass, and the Institute for Community Health, Cambridge, Mass. Aviva Must is with the Department of Public Health and Family Medicine, Tufts University School of Medicine, Boston. Garrett M. Fitzmaurice is with the Department of Biostatistics, Harvard School of Public Health, and the Division of General Medicine, Brigham and Women’s Hospital and Harvard Medical School. Matthew W. Gillman is with the Department of Ambulatory Care and Prevention, Harvard Pilgrim Health Care and Harvard Medical School, and the Department of Nutrition, Harvard School of Public Health. Virginia Chomitz is with the Institute for Community Health. Ellen Kramer is with the Massachusetts Department of Public Health, Boston. Robert McGowan is with the Cambridge Public Schools, Cambridge, Mass. Karen E. Peterson is with the Departments of Nutrition and Society, Human Development and Health, Harvard School of Public Health.
Correspondence: Requests for reprints should be sent to Juhee Kim, ScD, Department of Nutrition, Harvard School of Public Health, Boston, MA 02115 (e-mail: juheekim{at}hsph.harvard.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. To monitor annual changes in weight status, we determined incidence and remission rates of overweight among school-aged children with longitudinal school-based surveillance.
Methods. We estimated 1-year changes in weight status among students enrolled in public schools in Cambridge, Mass. Physical education teachers measured height and weight annually. Adjusted odds ratios (ORs) were estimated via multivariate logistic regression, accounting for repeated observations of individual children across years.
Results. The 1-year incidence of overweight was 4%, and the remission rate was 15%. Among overweight children, 85% remained overweight at a 1-year follow-up, while 18% of children at risk for overweight became overweight. Overweight incidence rates were higher among children aged 7 and 8 years than among those aged 11 to 13 years (boys: OR=1.68; 95% confidence interval [CI]=1.14, 2.47; girls: OR=1.86; 95% CI=1.25, 2.77).
Conclusions. Both incidence and remission rates were higher among younger children. Children who were at risk for overweight were more likely to change their weight status than those who were already overweight. Our results support targeting overweight prevention efforts toward younger children and children at risk for overweight.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Overweight among children is a particular concern because of the associated long-term health consequences, including hyperlipidemia, hypertension, type 2 diabetes, and respiratory illnesses, as well as higher rates of cardiovascular disease mortality and all-cause mortality.3–5 Research has shown that, independent of baseline socioeconomic status and performance on aptitude tests, overweight adolescents are less likely to marry, they complete fewer years of school, and they are more likely to be poor as adults.6
An expert committee focusing on clinical overweight guidelines has recommended routine preventive screening for overweight in children and referral of those with BMIs at or above the 85th percentile for further evaluation.7 However, few reports have described established screening systems, especially among elementary and middle school children. Furthermore, information from national and state surveys rarely supports assessment of the prevalence or characterization of risks in a smaller geographic area. Therefore, demand is growing for local-level information to support community-based prevention efforts.
Some studies have reported state- and local-level prevalence estimates and provided evidence that schools represent feasible organizational settings in which to monitor childhood overweight. For example, in 1995–1996, overweight prevalence rates among 12 559 Native American children aged 5 to 17 years attending schools in 4 states (South Dakota, North Dakota, Iowa, and Nebraska) were 22% and 18% for boys and girls, respectively.8 In 2000–2001, rates of overweight among 6630 4th-, 8th-, and 11th-grade students in Texas were 22%, 19%, and 16%, respectively.9 In metropolitan Boston, school nurses measured the height and weight of all 4th-grade students (n = 832) enrolled in 38 parochial schools in 200110; results of this investigation showed that 20% of students were overweight and 24% were at risk of overweight. In 2003, 24% of 2681 students attending New York City elementary schools were overweight, and 19% were at risk of overweight.11
Previous school-based studies have involved cross-sectional designs, and only certain grades have been selected for screening. Few longitudinal studies have reported on associations between overweight and physical activity,12 cardiovascular risk factors,13–15 or cardiorespiratory fitness.15,16 No studies, to our knowledge, have derived information from ongoing routine screening of overweight children, and thus research in this area has been based on relatively small and selected study populations. A sustainable surveillance system is needed to fill the current gap in longitudinal information on weight status. Availability of longitudinal follow-up data at both the individual and the group level would strengthen public health efforts to reduce overweight among children. If incidence and remission rates are monitored over time, it will be possible to identify children earlier and thus target prevention and intervention before overweight becomes established.
In the present study, conducted in an urban city in Massachusetts, we used serial measurements of height and weight obtained through an ongoing district-wide school surveillance system to estimate 1-year incidence and remission rates of overweight among children aged 5 to 13 years enrolled in the city’s public schools. We also examined annual changes in weight status according to various sociodemographic characteristics.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Physical education teachers enter data with a data entry form designed via a Microsoft Excel 2000 (Microsoft Corporation, Redmond, Wash.) macro function. When a child’s height and weight information is entered, demographic information from the school administrative database is automatically linked to the child’s growth record. To reduce data entry error, a wide range of acceptable values are specified within the data entry program: 24 to 85 in (61 to 216 cm) for height and 25 to 325 lb (11 to 146 kg) for weight. Height and weight measurements are part of the physical education curriculum and are not optional for students. A letter informing families that students were to be measured was sent home with students approximately 2 weeks before measurements were made.
Study Population
Cross-sectional population.
Of the 9206 children enrolled in the Cambridge elementary schools for at least 1 year between 1999 and 2003, 6.4% did not undergo anthropometric measurements. As recommended by the Centers for Disease Control and Prevention (CDC),18 we excluded 0.3% of the students who had biologically implausible values (BIVs): height-for-age and weight-for-age z scores below –6 or above 6 and BMI z scores below –4 or above 5. The cross-sectional surveillance data set used in constructing the longitudinal study cohort included a total of 21 300 measurements completed with 8643 students aged 5 to 14 years.
Longitudinal study population. Four 1-year complete cohorts (i.e., 1999–2000, 2000–2001, 2001–2002, and 2002–2003) were appended as an analytic cohort to maximize the data on 1-year incidence and remission. Students who were not measured in consecutive years were not included in the study cohort even though they participated in surveillance in some of the years between 1999 and 2003. From a total of 16 136 measurements completed with 7411 students aged 5 to 13 years at baseline, we were able to follow up 5630 students who accounted for 11 910 measurements (overall follow-up rate: 73.8%). Students who moved out of the Cambridge school district or were absent on the day of measurement were lost to follow-up. Physical education teachers did not report reasons for student nonparticipation.
Because recommended BIV cut points were unavailable for longitudinal surveillance data, we constructed gender- and age-specific cut points based on our study population. We defined annual longitudinal BIV cut points as follows: (1) mean changes of plus or minus 3 standard deviations in BMI, (2) height decrements greater than 1 inch, and (3) mean increases in height above 3 standard deviations. After removal of 494 measurements with longitudinal BIVs, the study cohort included 11 416 measurements for 5301 students. Follow-up rates were 66.2% in 1999–2000, 70.0% in 2000–2001, 74.6% in 2001–2002, and 71.7% in 2002–2003. The unit of analysis was one pair of measurements taken during 2 consecutive years; some children contributed more than one measurement (the mean number of contributions per child was 2.2).
Measures
Height and weight.
Identical models of a wall-mounted stadiometer (Seca 216 Accu-Hite, Snoqualmie, Wash.) and electronic balance scale (Seca 216 Bellissima-digital, Snoqualmie, Wash.) were distributed to all schools. Equipment was calibrated before each anthropometry session. Physical education teachers measured each child’s standing height without shoes to the nearest quarter inch. Weight was measured to the nearest tenth of a pound while students were wearing light, indoor clothing. Children were called one at a time to a corner of the gymnasium or behind a screen to provide privacy. The teacher reported the measurement results to the child only and recorded the information on a spreadsheet. School nurses often collaborated with the physical education teachers.
Sociodemographic information. Information on birth date, race/ethnicity, and free or reduced-price lunch program participation was extracted from school administrative records. Age in months was calculated as test date minus birth date, and students were grouped into age categories broken down by month. Lunch program participation, coded as a binary variable, served as an indirect measure of socioeconomic status. Children from families with incomes at or below 130% of the federal poverty level are eligible for free meals, and those with incomes between 130% and 185% of the poverty level are eligible for reduced-price meals.19
Growth indexes. An SAS program provided by CDC was used to generate growth indexes (height-for-age z score, weight-for-age z score, BMI z score, and BMI percentile in comparison with CDC 2000 growth charts).18 BMI was calculated as weight (in kilograms) divided by squared height (in meters).
Data quality. Data quality was assessed via CDC criteria according to assumptions of constant variance in height-for-age z score, weight-for-age z score, and BMI z score distributions.20,21 Standard deviation units for these distributions ranged between 0.95 and 1.19 across years, ages, and schools.
Weight status. We categorized weight status as underweight (less than 5th BMI percentile), healthy weight (at or above the 5th and 85th BMI percentiles), at risk for overweight (at or above the 85th and less than the 95th BMI percentiles), and overweight (95th BMI percentile or above).22
Statistical Analysis
All analyses were performed separately by gender. To assess potential bias due to attrition, we used 
2 tests to compare baseline sociodemographic data between the study children and those lost to follow-up, and we used t tests for comparisons involving age and BMI percentile. Weight status prevalence rates at baseline and incidence and remission rates at the 1-year follow-up were estimated across sociodemographic variables. We used 1-year age group categories for prevalence analyses and 2-year age groups for incidence and remission analyses. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) for incidence and remission with multivariate logistic regression, adjusting for repeated measurements over time. Covariates were age group, race/ethnicity, and lunch program participation status. We also examined annual changes in BMI as a predictor of weight status at follow-up. All analyses were performed using the SAS statistical analysis system, version 8.01 (SAS Institute, Cary, NC).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Overall, 18.9% of children were overweight at baseline (Table 1
). Overweight percentages were highest among Hispanic children (boys, 25.5%; girls, 24.8%) and lowest among Asian children (boys, 12.2%; girls, 3.5%). Overweight prevalence rates among children eligible for the free/reduced-price lunch program were about 9 percentage points higher, in absolute terms, than those among noneligible children.
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. Among children overweight at baseline, approximately 85% remained overweight. Ninety percent of girls in the healthy weight category remained in this category, whereas 18% of those at risk of overweight became overweight and 24% attained a healthy weight. Similar findings were observed among boys.
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. Among students who were not overweight at baseline, 387 (4.2%) became overweight at follow-up. Among boys, the highest incidence rate was observed in the 7- to 8.9-year age group (5.4%), while the lowest was observed in the 11- to 13.9-year group (3.3%). The largest incidence rates were observed among Blacks and Hispanics (5.1% and 5.0%, respectively), whereas rates were 4.1% among White boys and 2.3% among Asian boys. The incidence rate was significantly lower among Asian boys than among White boys. Five percent of boys participating in the free/reduced-price lunch program became overweight, whereas 3.8% of nonparticipants became overweight.
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, factors significantly associated with incidence of overweight were age and race/ethnicity among boys and (marginally) age and school lunch status among girls.
Remission rates and adjusted odds ratios for overweight are shown in Table 3. Among students who were overweight at baseline, 330 (15.3%) were not overweight at follow-up. Among boys, the remission rate was highest in the 5- to 6.9-year age group (19.8%); rates ranged from 13.2% to 15.1% in the other age groups. The remission rate among boys participating in the free/reduced-price lunch program was significantly lower (12.0%) than the rate among nonparticipants (18.3%).
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Mean increases in BMI were 3.2 (SD=1.2) among children who became overweight, 1.4 (SD=1.7) among those who remained overweight, and 0.6 (SD=1.2) among those who had never been overweight; those who remitted exhibited a decrease of 1.3 (SD = 1.4). The adjusted odds of becoming overweight increased with each 1-unit increase in annual BMI among both boys (OR=2.9; 95% CI= 2.6, 3.3) and girls (OR=3.2; 95% CI=2.8, 3.6) after age group, race/ethnicity, and lunch program status had been controlled.
Mean baseline BMI percentile rankings were 90.5 (SD = 7.7) among children who became overweight and 96.4 (SD = 1.0) among children who remitted. When we restricted the analysis to children below the 90th percentile at baseline, the incidence rate was only 1.2%. We also assessed whether our results were sensitive to our longitudinal BIV exclusions. Without any such exclusions, incidence and remission rates were about 1% higher than those described here.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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It is noteworthy that younger boys and girls exhibited not only higher incidence but also higher remission rates. The incidence rate was highest among children aged 7 to 8 years (5.5%), and the remission rate was highest among children aged 5 to 6 years (20.3%). These results indicate that children are more likely to become overweight at earlier ages and are more likely to remain overweight as they become older. Also, our findings suggest that targeting prevention efforts toward children before onset of puberty will be important in reducing incidence rates and promoting remission.
Black ethnicity was marginally associated with greater risk of incident overweight and with lower rates of remission among girls. The differences in incidence and remission rates observed between Black and White girls may reflect different tempos of maturation. Black girls had higher BMIs than White girls at the ages of 9 and 10 years, presumably as a result of earlier maturation.23 Girls engaged in less physical activity than boys, and a much greater decrease in physical activity level was observed among girls during the period of adolescence.24 Thus, maturation and physical activity may contribute to gender and racial/ethnic differences in incidence and remission rates during adolescence.
The prevalence of overweight among children aged 5 to 13 years in the Cambridge school health surveillance system was about 4% higher than the national level of 15% documented in 1999–2000 among children aged 6 to 11 years who took part in the National Health and Nutrition Examination Survey,1 but similar to estimates derived from other school-based studies.9–11 Consistent with previous reports, overweight prevalence rates were higher among Black and Hispanic children than among White and Asian children.1,11 Older children and children from low-income families were also more likely to be overweight.8,9
Given the nature of growth, BMI gains are expected as a normal process of child development. For the purposes of screening and prevention, it is important to estimate the extent to which annual increases or decreases in BMI predict risk of deviating from a healthy growth trajectory. We estimated that an approximately 3-fold higher risk of becoming overweight was associated with each 1-unit annual increase in BMI. Mean yearly increases in BMI among children who became overweight were 3.3 (SD = 1.2) for boys and 3.2 (SD = 1.2) for girls, whereas decreases in BMI among those who remitted were 1.4 (SD = 1.5) for boys and 1.1 (SD = 1.3) for girls. Our findings support current recommendations of annual increases of 3 to 4 BMI units as a screening criterion for overweight in children and adolescents.25
Physical education teachers’ administration of the Cambridge school health surveillance system was an essential component of the system’s sustainability and feasibility. No validation studies were conducted to assess the reliability of anthropometric measurements; instead, we used data quality management at the data entry and cleaning steps. The preset values for outliers in the electronic data entry form alerted physical education teachers to enter the correct values. During data cleaning, we examined whether the height-for-age, weight-for-age, and BMI z score standard deviation distributions all fell within expected ranges for surveillance data.21 In our cohort analyses, we also calculated longitudinal BIVs to ensure data quality. Our findings suggest that careful data cleaning is essential to ensuring measurement quality in organizational settings.
The differences in dropout rates could have threatened the validity of our estimates. Because attrition rates were lower among low-income students and higher among Asian students, we may have overestimated overall incidence rates while underestimating remission rates. There were no differences in baseline BMI percentile rankings between children who dropped out of the study and those who remained. Thus, our outcomes were not likely to have been biased by baseline weight status.
Incidence and remission rates were about 1% higher if we excluded only cross-sectional BIVs. Thus, when conducting a longitudinal analysis based on surveillance data, it is important to apply longitudinal BIVs. Because there are no consensus recommendations for longitudinal BIV cut points, we constructed normative criteria based on our study population. Growth rates varied widely among children, especially during the adolescent growth spurt.25 Our longitudinal BIV cut points represented average values for the analytic cohort that may not have reflected any particular individual growth pattern. As a result, our longitudinal BIV exclusions were more likely to be restrictive and our estimates conservative.
Schools represent a reasonable setting in which to build a system to monitor weight status among school-aged children given the increased need for local data. Our results show the utility of ongoing school-based surveillance in regard to providing cross-sectional and longitudinal estimates of childhood overweight status aggregated at both the individual and group levels. Our evaluation of feasibility was limited to the requirements for staff training, standardized equipment and protocols, and secondary analyses of measurement quality. We did not undertake cost–benefit analyses, nor did we assess the reliability or validity of the anthropometric measurements.
The "data–action cycle," comprising data collection, data analysis, recommendations, program planning, and implementation, requires local-level data.26 The direct linkage between surveillance data and implementation of overweight prevention programs and policies is a primary public health practice function. At the organizational level, surveillance data can help schools and communities target programs to specific populations at higher risk of overweight. Also, subsequent surveillance data can be used to evaluate program, environmental, or policy changes. At the family or individual level, personalized information derived from the surveillance system, especially in combination with referral and resource information, may motivate some individuals to initiate weight control activities.
As an example, our surveillance system played an important role in implementing and evaluating a pilot intervention and in subsequently changing a school policy. We undertook a pilot study, involving telephone interviews conducted in 2001 in 4 selected schools, designed to measure the effects of "health report cards" on parent perceptions and on factors associated with children’s weight status.27 Families of overweight children that received child-specific weight information derived from the surveillance data exhibited increased awareness of their child’s weight status (assessed via comparisons of parental reports with measured data). In addition, these families reported increased intentions to initiate weight management strategies with their child. The majority of parents wanted child-specific information in the future. Since 2003, on the basis of these results and minimal opposition from the school community, the Cambridge public schools have issued annual health report cards to all students for whom there are surveillance data.
To assess whether this pilot intervention study might influence changes in weight status, we conducted a follow-up analysis. However, no changes in mean BMI or prevalence of overweight from 2001 to 2002 were detected between the intervention and control schools. Thus, we did not exclude students from the intervention schools in our study.
We observed relatively higher incidence rates among children younger than 9 years than among older children. It is encouraging that we found evidence of remission in our study population. Of the children who were overweight at baseline, 15% were not overweight at follow-up, and the percentage exhibiting remission was even greater (20.3%) among younger children (those aged 5 and 6 years). Further studies are needed to fully elaborate the factors that prevent the incidence and promote the remission of overweight among school-aged children. Monitoring changes in overweight status and associated determinants among children at younger ages is an essential component of efforts to manage and prevent further development of overweight.
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Acknowledgments |
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We gratefully acknowledge the physical education teachers for data collection, the leadership of the Cambridge Public School Department for its vision in implementing Cambridge Public School Health Surveillance System activities, and the staff at the Institute for Community Health, Cambridge, Mass, for guidance and support of data management, analysis, and dissemination.
Human Participant Protection
This study was approved by the institutional review boards of the Cambridge Health Alliance and the Harvard School of Public Health.
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Footnotes |
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Contributors
J. Kim led study design, database management, analyses, and writing. A. Must assisted in developing the study design and in critical revision of the article. G. M. Fitzmaurice participated in study design and statistical analyses. M. W. Gillman assisted in study design and in revisions. V. Chomitz assisted with implementation of the school surveillance system and with data acquisition. E. Kramer assisted with data acquisition, data analysis, and implementation of the school surveillance system. R. McGowan conceived and implemented all aspects of the school surveillance system. K. E. Peterson supervised the research process and participated in the study design. All of the authors helped to conceptualize ideas and interpret findings and reviewed drafts.
Accepted for publication March 14, 2005.
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References |
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