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Correlation between high risk obesity groups and low socioeconomic status in school children.

Objective: Obesity is a major health problem among children and adolescents which is potentially affected by socioeconomic status (SES). The high risk group (HRG) comprises those youths with a body mass index (BMI) between the 85th and 95th percentile (at risk for overweight) and [greater than or equal to]95th percentile (overweight). We sought a potential link between the HRG and SES.

Methods: Public schools in Chesterfield County, Virginia measured BMI among students in kindergarten and third, seventh, and tenth grades. We assessed SES based on eligibility for the National School Lunch Program and the percentage of the school-age population living in poverty based on per capita income from the 2000 Census.

Results: From 28 to 38% of children and adolescents were in the high risk group. Low SES had robust and highly significant correlations with HRG status with r-values ranging from 0.565 to 0.842, P < 0.0001.

Conclusions: Low SES appears to be an important factor in childhood and adolescent obesity.

Key Words: adolescents, African-Americans, blacks, body mass index, children, elementary school, ethnicity, middle school, high school, race, sex, socioeconomic, whites

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Childhood obesity is a worsening health problem in the United States. (1,2) For children and adolescents, epidemiologists tend to use the terms at risk for overweight and overweight when assessing this epidemic. Primary and secondary schools provide one model to systematically study obesity among children and adolescents. Race is a common parameter employed when studying childhood obesity and data suggest that there are differences in childhood obesity according to race. (2) Emerging data suggest that socioeconomic status (SES) may be another useful parameter to better define this health problem and derive management approaches. (3)

The Centers for Disease Control and Prevention (CDC) have generated tables defining the relationship between body mass index (BMI), sex and age for US children and adolescents. (4) They define youths with a BMI between the 85th and 95th percentile as at risk for overweight. Youths are overweight if their BMI is [greater than or equal to]95th percentile. The high risk group (HRG) may be defined as those children and adolescents with a BMI [greater than or equal to]85th percentile. (This includes youths both at risk for overweight and overweight.)

The Chesterfield County Health Department, School Health Services in Virginia initiated a school-based health screening program, (5) and their findings, coupled with other demographic and socioeconomic parameters, allowed us to better define the role of SES in determining overweight and at risk for overweight among Virginia youths.

Methods

During the academic school years of 2002 to 2003 and 2003 to 2004, students enrolled in Chesterfield County Public Schools underwent height and weight evaluation using a cluster sampling technique. (5) All the Chesterfield County Public Schools (36 elementary schools, 12 middle schools, and 10 high schools) participated in this evaluation. Students from the kindergarten, third, seventh, and tenth grades were screened. Parents of students in selected grade levels received a general notification of screening. Only a few parents asked that their child be excluded from the screening process.

Kindergarten and third grade students were screened by classroom assignments. Seventh and tenth grade students were screened through their health and physical education classes. Screening was accomplished using electronic scales for student weight and stadiometers for height. Anthropometric measurements were made by public health nurses. Height was measured conventionally (unstretched technique (6)).

Public health nurses checked the accuracy of data obtained at screening. Data were then entered into the Nutstat module of Epi Info made available on the Internet by the CDC. (7) Information contained in this database derives from the 2000 CDC growth charts. (4) Based on these numbers, students were placed into one of four categories: [1] underweight (<5th BMI percentile), [2] normal weight ([greater than or equal to]5th and <85th BMI percentile), [3] at risk for overweight ([greater than or equal to]85th and <95th BMI percentile), and [4] overweight ([greater than or equal to]95th BMI percentile).

Data were evaluated by age, sex, race, grade, and race stratified by sex. Screening was started in the fall and completed in the winter for each academic year.

We assessed SES for the Chesterfield County students using two methods. For elementary and middle school students, we used the National School Lunch Program. (8) This program provides free or low-cost lunches to students based on the student's family size and income. Children from families with incomes at or below 130% of the poverty level are eligible for free meals. Those with incomes between 130% and 185% of the poverty level are eligible for reduced-price meals. The percentage of public school students eligible for free or reduced-price lunches in a particular school is strongly related to child poverty among students of that school. We did not determine SES for each student. Rather, we inferred student SES as stated above.

High school students did not participate in the National School Lunch Program. Therefore, to assess low SES for the high school students, we used the percentage of the school-age population living in poverty based on the per capita income data from the 2000 Census. (9)

In data analysis for the kindergarten, third, and seventh grades, we used the percentage of the student population that was actually eligible for the free- and reduced-lunch program. This is a proxy for SES because it shows the percentage who qualify based on family size and income, as opposed to those who actually participated. If we used the number who actually participated in the program, the percentage might not accurately reflect SES because of the possibility that not all eligible students participated.

One middle school and one high school were specialty schools without geographic specificity and with very small enrollments. These two schools were not included in our analysis of SES. Because different students were enrolled in the kindergarten, third, seventh, and tenth grades each academic year, we considered them separate groups in our analysis of BMI and SES (72 elementary schools, 22 middle schools, and 18 high schools).

Statistics

We used SPSS 13.0 for Windows for general statistical analysis. (10) For power analysis, we used SamplePower Release 2.0. (11)

Results

During the academic years 2002 to 2003 and 2003 to 2004, a total of 29,824 Chesterfield County public school students were screened: 7,081 in kindergarten, 7,623 in third grade, 8,143 in seventh grade, and 6,977 in tenth grade. Ages ranged from 4.3 years to 19.7 years with a median age of 12.1 year. By sex, 51.07% were male and 48.93% were female. By race, 68.02% were white (not of Hispanic origin), 24.55% were black (not of Hispanic origin), 4.25% were Hispanic, 2.69% were Asian or Pacific Islander, and 0.49% were American Indian/Alaskan Native.

Between 28.4% and 38.3% of Chesterfield County students were in the high risk group. There was a steady increase in the magnitude of the HRG from the lower grades to the higher grades peaking in middle school: 28.4% in kindergarten, 35.4% in third grade, and 38.3% in seventh grade. This magnitude decreased to 32.5% in the tenth grade. Table 1 provides the prevalence of the combination of at risk for overweight and overweight in children and adolescents at various ages in reports from the literature and from this paper.

The HRG correlated robustly with low SES (kindergarten, r = 0.593, P < 0.0001; third grade, r = 0.565, P < 0.0001; seventh grade, r = 0.842, P < 0.0001; and tenth grade, r = 0.811, P < 0.0001). Table 2 shows the number of Chesterfield County schools in our analysis, the percentage of HRG and the percentage of low SES in each grade along with the Pearson correlation coefficients with significance and power of each sample to detect a significant difference. More detailed power calculations are available from the authors.

Boys were more likely than girls to be overweight. For the three major racial/ethnic groups, Hispanics had the highest prevalence of overweight followed by blacks. Students classified as white were least likely to be overweight.

Discussion

An important lesson learned in the Chesterfield County public school study was the feasibility of such investigations. Students, parents, public health nurses, and public school officials cooperated in data gathering, which led to a summary report of the school-based health screening program for Chesterfield County. (5)

Based on national data largely derived from measurements made in the 1960s and 1970s, one only expects 15% of students to meet or exceed the 85th percentile for BMI (4) (Table 1). Chesterfield County had students in the HRG at more than twice these "reference" ranges.

We did not attempt a detailed assessment of race as a risk factor for HRG status among Chesterfield County students. However, for the three major racial groups, Hispanics had the highest prevalence of overweight and white students had the lowest prevalence of overweight. Black students were in between.

The Chesterfield County findings provided an opportunity to explore low SES as a factor contributing to at risk for overweight and overweight among children and adolescents (Table 2). It is unclear why the correlation coefficients of high-risk groups versus low SES varied between 0.565 and 0.842. For a sociologic study, these correlations are robust. Interestingly, as the size of the HRG increased, so did the strength of the relation with low SES. Even so, such values do not imply causality but should stimulate us to develop prospective studies that will test the implied hypothesis that low SES may contribute to at risk for overweight and overweight among children and adolescents.

Mississippi Elementary and Middle School Comparisons

Of the 50 states in the United States, the prevalence of obesity is greatest in Mississippi. (12) Kolbo et al (13) estimated the prevalence of overweight and at risk for overweight among Mississippi students in Grades 1 through 8. This group reported that 24.0% of these students were overweight and an additional 14.7% were at risk for overweight. Thus, almost 39% of Mississippi students would fall into our high risk group. If we exclude our findings for Chesterfield County students in the tenth grade, our comparable figure would be 34.0%. If we include our students in the tenth grade, our figure would be 33.6%.

Kolbo et al (13) pointed out that 26.9% of their students in the first grade were already in the HRG. For Chesterfield County, 28.4% of those in kindergarten were in the HRG. Kolbo et al (13) emphasized that self reports of height and weight underestimate the prevalence of childhood obesity. Such values should always be measured.

Socioeconomic Status and Obesity in Children and Adolescents

In this section of our paper we shift from such terms as "at risk for overweight" and "overweight" to "obesity." CDC growth charts (4) do not use the term "obesity." Obesity is a physical condition in which the natural energy reserve of an animal stored as fat expands beyond healthy levels. That is, the excess stores of fat leave the animal vulnerable to disease. "Overweight" simply refers to increased body weight in relation to height when compared with some standard of acceptable or desirable weight. One can be overweight without being "fat." Professional athletes may be very lean and muscular with very little body fat and yet their BMI may exceed established "normal" values. Operationally, there are very few professional athletes whose BMI exceeds "normal" guidelines because of disproportionate muscle mass while there are many "fat" people whose BMI exceeds established guidelines because of excess body fat.

Although we did not employ such measurements as skinfold thickness to estimate total body fat and obesity, (14,15) our nurses stated that the vast majority of our HRG children and adolescents appeared fat rather than excessively muscular. Given the large resources already devoted to our study, mobilizing additional resources to refine our estimates of obesity among Chesterfield County youths by using skinfold thickness could not be justified, even though such parameters have predictive value along with BMI for fatal coronary heart disease in long-term follow-up. (16)

Socioeconomic Status

The surging epidemic of obesity among children and adolescents in the US compels us to look for important factors contributing to this problem. Sex, race, and SES may be among those factors. Sex and race are far easier to measure than SES. We chose to look at SES in our study because of available SES data among Chesterfield County Public School youths.

Low SES may influence a variety of factors including health insurance; neighborhood and personal safety; local schools and their resources; local food stores and the extent to which they carry healthful foods; the price of food; private and public transportation; proclivity to watch television and participate in other sedentary activities; subsidized local, state, and federal programs; and access to gyms and health clubs. Reviewing these specific factors is beyond the scope of this paper.

McMurray et al (17) described the influence of physical activity, SES, and race on weight in adolescents. Overweight was found to be related to watching television on nonschool days. However, when BMI was adjusted for race and SES, overweight ceased to relate significantly to television viewing. But, increased hours playing video games predicted overweight. However, low SES and black race overshadowed the direct effects of television or video games on BMI. The authors concluded that programs to reduce obesity should focus on lower SES communities.

Moore et al (18) looked at the effects of race, sex, and SES on changes in youth overweight over a 7-year period in a longitudinal study of cardiovascular risk factors. Subjects at the start of the study had a mean age of 8.8 [+ or -] 2.0 years and were followed for an average of 7.2 [+ or -] 0.5 years. Over the study period, BMI percentile increased significantly. Neither sex nor race predicted this increase, but SES did. For all participants, overweight prevalence increased from 31 to 40% (P < 0.001). However, for low SES children, overweight prevalence increased from 37 to 67% (P < 0.001). The authors concluded that primary prevention of obesity in children is needed, particularly among those youths from low SES backgrounds.

Neumark-Sztainer et al, (19) studying the dietary habits of Minnesota urban youths, found that 12.5% of girls and 16.6% of boys had BMI percentiles [greater than or equal to]95th percentile compared with a target percentile rate of 5%. Rates in our study were 2 to 2.5 times greater than the Minnesota urban youth rates, probably due to the larger size of our HRG. The percentage of Minnesota youths consuming the recommended portions of calcium, fat, fruits, vegetables, and grains was below target in this study. Minnesota youths in a low socioeconomic category were more likely to be obese and less likely to eat a proper diet. The authors concluded that substantive public health efforts are needed to achieve Healthy People 2010 objectives for obesity and nutrition and to reduce racial and SES disparities in children and adolescents.

Gordon-Larsen et al (20) asked whether racial differences in family income and education explained sex-specific disparities in the prevalence of overweight among US adolescents. Differences in family income and parental education explained little of the disparities in the overall prevalence of overweight, but race/SES differences in overweight were greater among female than male adolescents. For example, the prevalence of overweight decreased with increasing SES among white female adolescents but remained elevated and even increased among higher SES black female adolescents. Therefore, black/white disparity in the prevalence of overweight increased at the highest SES. Conversely, overweight disparity was lessened at the highest SES for white, Hispanic, and Asian female adolescents. Among male adolescents, overweight disparity was lowest at average SES. The authors concluded that one should not automatically assume that the weight advantages of increased SES found among white adolescents applies to other racial groups. To reduce the high prevalence of overweight found among US adolescents, we must look at factors other than family income and parental education. Those other factors include environment, context, biology, and culture.

Goodman et al (21) studied the impact of objective and subjective social status on obesity in a biracial group of adolescents. Objective social status (one type of SES) derived from family income and parental education. Subjective social status (SSS) derived from separate scales for society and school. Black adolescent girls had the lowest societal SSS, lowest school SSS, and highest BMI. Overweight was most common for black girls (26.0%) and black boys (26.2%), intermediate for white boys (17.2%), and least common for white girls (11.6%). Overweight was inversely related to parental education, family income, and school SSS. The authors concluded that perceptions of social stratification are associated independently with overweight among adolescents.

In a separate study, Goodman et al (22) assessed the public health impact of SES on adolescent depression and obesity in a nationally representative sample of 15,112 adolescents. Population attributable risks (PAR) for depression and obesity were large for both income and education. For obesity, the adjusted PAR for income was 32% and for education it was 39%. The authors concluded that low SES is associated with a large disease burden.

Haas et al (23) studied the impact of race, SES, and health insurance on overweight among youths. Among children, both blacks and Hispanics were more likely than whites to be overweight. Among adolescents, Hispanics and Asian/Pacific islanders were more likely to be overweight than blacks and whites. Also among adolescents, no private health insurance or having public-sponsored health insurance was related positively to overweight. This relationship was not found among children. The authors concluded that substantial racial differences exist in the risk for overweight among children and adolescents. Poor health insurance is a risk for overweight among adolescents.

Dekkers et al (24) studied sex, race, and SES differences in the development of adiposity from childhood to adulthood among subjects with a family history of cardiovascular disease. Skinfold thickness was greater among females than males with a larger increase with age. Low-SES subjects demonstrated the most rapid increase in waist circumference with age. Blacks separated earlier than whites in growth curves for obese subjects compared with normal weight subjects. The authors concluded that the rate of adiposity development from childhood to early adulthood is not influenced by race but is influenced by sex and SES.

Chesterfield County Initiatives

Chesterfield County, Virginia recently initiated several programs to combat childhood and adolescent obesity. (25) Programs included teacher grant-funded biking units with the students, a five-day a week physical education program for fifth grade students, fitness testing strategies and materials for children with varying degrees of disabilities, elementary school running clubs organized by physical education teachers, and a Chesterfield County community partnership focused on improving children's health by encouraging better nutrition and increased physical activity.

School Role in Preventing Obesity

Story et al (26) assert that US schools have many opportunities to develop and implement obesity-prevention strategies focused on nutritious foods, physical activity, and obesity-related health services. Virtually all public schools participate in the National School Lunch Program. (27) Similarly, almost 80% of the public schools offering the lunch program offer the School Breakfast Program. (26) Typically, 60% of children and adolescents eligible for the lunch program participate and 37% of youths eligible for the breakfast program participate. (26) However, these meal programs, as presently constituted, protect the youth from under- rather than over-nourishment.

Each student should participate in at least 30 minutes of physical activity daily, according to the Institute of Medicine's Preventing Childhood Obesity: Health in the Balance report. (28) The national recommendations for physical activity developed by the Association for Sport and Physical Education vary--150 minutes a week for elementary school children and 225 minutes a week for middle- and secondary school children. (29) Across the US, only 8% of elementary schools and 6% of middle and high schools adhere to these recommendations. (30)

School-based health centers provide on-site primary care which could address obesity-related issues. (31) However, very few comprehensive programs are presently available in public schools. (26)

Conclusions

Children and adolescents in the Chesterfield County public schools have an unacceptably high prevalence of at risk for overweight and overweight. Low SES appears to be an important contributor to Chesterfield County problems. We do not have sufficient information to separate SES from race in predicting health problems in our study sample.

Our literature review supports SES as a powerful determinant of obesity among children and adolescents in this country. Because obesity most commonly starts before adulthood and obesity-related health problems are now epidemic, interventions during primary and secondary school years must become a high national priority.

A coordinated effort by the public and private sectors is essential to access the vast resources of the Federal, State, and local governments. Also, private employers have the ability and wealth to assist in this process. By these means, strategies may evolve to provide incentives for those at risk as well as those who have a stake in solving this problem.

References

1. Strauss RS, Pollack HA. Epidemic increase in childhood overweight, 1986-1998. JAMA 2001;286:2845-2848.

2. Ogden CL, Flegal KM, Carroll MD, et al. Prevalence and trends in overweight among US children and adolescents, 1999-2000. JAMA 2002;288:1728-1732.

3. Isaacs SL, Schroeder SA. Class--the ignored determinant of the nation's health. N Engl J Med 2004;351:1137-1142.

4. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Adv Data 2000;314:1-28.

5. Chesterfield County Health Department, School Health Services. School-Based Health Screening Program. Height, Weight, Body Mass Index-for-Age. Chesterfield County Health Department, PO Box 100, Chesterfield, Virginia 23832-0100. Chesterfield County Health Department, School Health Services, 2003.

6. Tillmann V, Clayton PE. Diurnal variation in height and the reliability of height measurements using stretched and unstretched techniques in the evaluation of short-term growth. Ann Hum Biol 2001;28:195-206.

7. Division of Public Health Surveillance and Informatics. Epi Info 2002--Revision 2 Released Date: January 30, 2003, pp 1-500. Available at: http://www.cdc.gov/epiinfo. Accessed December 6, 2006.

8. USDA. National School Lunch Program, 2004. Available at: http://www.fns.usda.gov/cnd/Lunch/default.htm.

9. US Census Bureau. 2000 Census. Available at: http://www.census.gov/hhes/www/poverty.html.

10. SPSS 13.0 for Windows [computer program]. Version 13.0. Chicago, SPSS Inc., 2004.

11. SPSS Sample Power Release 2.0 [computer program]. Upper Saddle River, Prentice Hall Inc., 2004.

12. Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003;289:76-79.

13. Kolbo JR, Penman AD, Meyer MK, et al. Prevalence of overweight among elementary and middle school students in Mississippi compared with prevalence data from the Youth Risk Behavior Surveillance System. Prev Chronic Dis 2006;3:A84. Epub 2006 Jun 15.

14. Sardinha LB, Going SB, Teixeira PJ, et al. Receiver operating characteristic analysis of body mass index, triceps skinfold thickness, and arm girth for obesity screening in children and adolescents. Am J Clin Nutr 1999;70:1090-1095.

15. Peterson MJ, Czerwinski SA, Siervogel RM. Development and validation of skinfold-thickness prediction equations with a 4-compartment model. Am J Clin Nutr 2003;77:1186-1191.

16. Kim J, Meade T, Haines A. Skinfold thickness, body mass index, and fatal coronary heart disease: 30 year follow up of the Northwick Park heart study. J Epidemiol Community Health 2006;60:275-279.

17. McMurray RG, Harrell JS, Deng S, et al. The influence of physical activity, socioeconomic status, and ethnicity on the weight status of adolescents. Obes Res 2000;8:130-139.

18. Moore DB, Howell PB, Treiber FA. Changes in overweight in youth over a period of 7 years: impact of ethnicity, gender and socioeconomic status. Ethn Dis 2002;12:S1-83-S1-86.

19. Neumark-Sztainer D, Story M, Hannan PJ, et al. Overweight status and eating patterns among adolescents: where do youths stand in comparison with the healthy people 2010 objectives? Am J Public Health 2002;92:844-851.

20. Gordon-Larsen P, Adair LS, Popkin BM. The relationship of ethnicity, socioeconomic factors, and overweight in US adolescents. Obes Res 2003;11:121-129.

21. Goodman E, Adler NE, Daniels SR, et al. Impact of objective and subjective social status on obesity in a biracial cohort of adolescents. Obes Res 2003;11:1018-1026.

22. Goodman E, Slap GB, Huang B. The public health impact of socioeconomic status on adolescent depression and obesity. Am J Public Health 2003;93:1844-1850.

23. Haas JS, Lee LB, Kaplan CP, et al. The association of race, socioeconomic status, and health insurance status with the prevalence of overweight among children and adolescents. Am J Public Health 2003;93:2105-2110.

24. Dekkers JC, Podolsky RH, Treiber FA, et al. Development of general and central obesity from childhood into early adulthood in African American and European American males and females with a family history of cardiovascular disease. Am J Clin Nutr 2004;79:661-668.

25. Joint Committee of the Board of Education and Board of Health, Commonwealth of Virginia. Final Report. Committee Meeting September 7, 2004.

26. Story M, Kaphingst KM, French S. The role of schools in obesity prevention. Future Child 2006;16:109-142.

27. Fox MK, Hamilton W, Lin BH. Effects of Food Assistance and Nutrition Programs on Health and Nutrition, Volume 3: Literature review. Washington, DC: US Department of Agriculture, Economic Research Service; 2004. Food Assistance and Nutrition Research Report Number 19-3.

28. Koplan JP, Liverman CT, Kraak VI. Preventing Childhood Obesity: Health in the Balance. Washington, DC, National Academic Press; 2005.

29. Physical Activity for Children: A Statement of Guidelines for Children 5-12. 2nd ed. Reston, National Association for Sport and Physical Education, 2004.

30. Burgeson CR, Wechsler H, Brener ND, et al. Physical education and activity: results from the School Health Policies and Programs Study 2000. J Sch Health 2001;71:279-293.

31. Brener ND, Burstein GR, DuShaw ML, et al. Health services: results from the School Health Policies and Programs Study 2000. J Sch Health 2001;71:294-304.

Victor R. Vieweg, MD, Christopher H. Johnston, MA, Jack O. Lanier, MHA, DrPH, Antony Fernandez, MD, and Anand K. Pandurangi, MD

From the Departments of Psychiatry, Preventive Medicine, and Internal Medicine, Medical College of Virginia Campus, Virginia Commonwealth University, Richmond, VA; and the Department of Budget and Strategic Planning, City of Richmond, Richmond, VA.

Reprint requests to Victor Vieweg, MD, 17 Runswick Drive, Richmond, VA 23238-5414. Email: vvieweg@visi.net

Accepted August 14, 2006.

RELATED ARTICLE: Key Points

* Obesity, a major health problem among children and adolescents, is potentially affected by socioeconomic status (SES).

* Public schools in Chesterfield County, Virginia measured body mass index (BMI) among students in kindergarten, third, seventh and tenth grades, providing a database to explore the relationship between BMI and SES.

* We found a robust relationship between increased BMI (for sex and age) and low SES, suggesting that low SES may be a risk factor for childhood and adolescent obesity.

RELATED ARTICLE: Low socioeconomic status may influence a variety of health-related factors:

1. Health insurance

2. Neighborhood and personal safety

3. Local schools and their resources

4. Local food stores and the extent to which they carry healthful foods

5. The price of food

6. Private and public transportation

7. Proclivity to watch television and participate in other sedentary activities

8. Subsidized local, state, and federal programs

9. Access to gyms and health clubs
Table 1. Prevalence of the combination of at risk for overweight and
overweight (high risk group) in children and adolescents at various ages

Group 2-5 years 6-11 years 12-19 years

NHANES 1999-2000 all subjects (2) 20.6% 30.3% 30.4%
NHANES 1999-2000 non-Hispanic 20.5% 26.2% 26.5%
 white both sexes (2)
NHANES 1999-2000 non-Hispanic 19.3% 35.9% 43.8%
 black both sexes (2)
NHANES 1999-2000 Mexican 22.7% 39.3% 43.8%
 American both sexes (2)
NHANES 1999-2000 all males (2) 20.9% 32.7% 30.5%
NHANES 1999-2000 non-Hispanic 21.4% 29.4% 27.4%
 white males (2)
NHANES 1999-2000 non-Hispanic 12.6% 34.5% 35.7%
 black males (2)
NHANES 1999-2000 Mexican 26.0% 43.0% 44.2%
 American males (2)
NHANES 1999-2000 all females (2) 20.4% 27.8% 30.2%
NHANES 1999-2000 non-Hispanic 19.7% 22.8% 25.4%
 white females (2)
NHANES 1999-2000 non-Hispanic 26.6% 37.6% 45.5%
 black females (2)
NHANES 1999-2000 Mexican 19.5% 35.1% 43.5%
 American females (2)
Chesterfield County all 28.4% 35.4% 38.3/32.5%
 students, both years (5)

Table 2. Correlation of high risk groups and National Lunch Program
participants (grades K, 3, and 7) or poverty level (grade 10) (low
socioeconomic status) among four Chesterfield County grades

 Kindergarten Third grade

Number 72 72
High risk groups 28.40 [+ or -] 6.79 35.36 [+ or -] 0.65
 (percentage)
Low socioeconomic status 23.54 [+ or -] 20.62 23.54 [+ or -] 20.62
 (percentage)
Pearson correlation 0.593 0.565
 coefficient
Pearson p-value <0.0001 <0.0001
Power 0.9999 0.9999

 Seventh grade Tenth grade

Number 22 18
High risk groups 38.32 [+ or -] 5.65 32.48 [+ or -] 5.36
 (percentage)
Low socioeconomic status 19.03 [+ or -] 13.56 5.46 [+ or -] 2.03
 (percentage)
Pearson correlation 0.842 0.811
 coefficient
Pearson p-value <0.0001 <0.0001
Power 0.9999 0.9999

Because we evaluated students during two academic years--different
students each year, we multiplied the number of schools by 2. Of the 12
middle schools and 20 high schools, we excluded one middle school and
one high school each year because they were specialty schools without
geographic specificity and with very small enrollments.
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Author:Pandurangi, Anand K.
Publication:Southern Medical Journal
Geographic Code:1USA
Date:Jan 1, 2007
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