domingo, 12 de abril de 2015

Preventing Chronic Disease | Estimating Prevalence of Overweight or Obese Children and Adolescents in Small Geographic Areas Using Publicly Available Data - CDC

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Preventing Chronic Disease | Estimating Prevalence of Overweight or Obese Children and Adolescents in Small Geographic Areas Using Publicly Available Data - CDC



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Estimating Prevalence of Overweight or Obese Children and Adolescents in Small Geographic Areas Using Publicly Available Data

Carlo Davila-Payan, PhD; Michael DeGuzman, MPH; Kevin Johnson, MSc; Nicoleta Serban, PhD; Julie Swann, PhD

Suggested citation for this article: Davila-Payan C, DeGuzman M, Johnson K, Serban N, Swann J. Estimating Prevalence of Overweight or Obese Children and Adolescents in Small Geographic Areas Using Publicly Available Data. Prev Chronic Dis 2015;12:140229. DOI:http://dx.doi.org/10.5888/pcd12.140229External Web Site Icon.
PEER REVIEWED

Abstract

Introduction
Interventions for pediatric obesity can be geographically targeted if high-risk populations can be identified. We developed an approach to estimate the percentage of overweight or obese children aged 2 to 17 years in small geographic areas using publicly available data. We piloted our approach for Georgia.
Methods
We created a logistic regression model to estimate the individual probability of high body mass index (BMI), given data on the characteristics of the survey participants. We combined the regression model with a simulation to sample subpopulations and obtain prevalence estimates. The models used information from the 2001–2010 National Health and Nutrition Examination Survey, the 2010 Census, and the 2010 American Community Survey. We validated our results by comparing 1) estimates for adults in Georgia produced by using our approach with estimates from the Centers for Disease Control and Prevention (CDC) and 2) estimates for children in Arkansas produced by using our approach with school examination data. We generated prevalence estimates for census tracts in Georgia and prioritized areas for interventions.
Results
In DeKalb County, the mean prevalence among census tracts varied from 27% to 40%. For adults, the median difference between our estimates and CDC estimates was 1.3 percentage points; for Arkansas children, the median difference between our estimates and examination-based estimates data was 1.7 percentage points.
Conclusion
Prevalence estimates for census tracts can be different from estimates for the county, so small-area estimates are crucial for designing effective interventions. Our approach validates well against external data, and it can be a relevant aid for planning local interventions for children.

Acknowledgments

Participants at Children’s Healthcare of Atlanta gave feedback on preliminary results, including potential interpretations, and reviewed the final manuscript for confidentiality and accuracy. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of Georgia Institute of Technology or Children’s Healthcare of Atlanta. Funding for this study was provided by Children’s Healthcare of Atlanta and the Harold R. and Mary Nash professorship at Georgia Institute of Technology. The authors have no financial relationships or conflicts of interest relevant to this article to disclose. When this research was done, Mr DeGuzman was affiliated with Children’s Healthcare of Atlanta, Atlanta, Georgia.

Author Information

Corresponding Author: Julie Swann, PhD, Department of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr NW, Atlanta, GA 30333. Telephone: 404-385-3054. Email: jswann@isye.gatech.edu.
Author Affiliations: Carlo Davila-Payan, Kevin Johnson, Nicoleta Serban, Georgia Institute of Technology, Atlanta, Georgia; Michael DeGuzman, Columbia University Medical Center, New York, New York.

References

  1. Winnable battles. Atlanta (GA): Centers for Disease Control and Prevention; 2012. http://www.cdc.gov/winnablebattles/. Accessed May 7, 2014.
  2. Daniels SR. The consequences of childhood overweight and obesity. Future Child 2006;16(1):47–67. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  3. Bethell C, Read D, Goodman E, Johnson J, Besl J, Cooper J, et al. Consistently inconsistent: a snapshot of across- and within-state disparities in the prevalence of childhood overweight and obesity. Pediatrics 2009;123(Suppl 5):S277–86. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  4. Akinbami LJ, Ogden CL. Childhood overweight prevalence in the United States: the impact of parent-reported height and weight. Obesity (Silver Spring) 2009;17(8):1574–80. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  5. Shields M, Connor Gorber S, Janssen I, Tremblay MS. Obesity estimates for children based on parent-reported versus direct measures. Health Rep 2011;22(3):47–58. PubMedExternal Web Site Icon
  6. Assessment of childhood and adolescent obesity in Arkansas: year eight (fall 2010 — spring 2011). Little Rock (AR): Arkansas Center for Health Improvement; 2012. http://www.achi.net/BMIContent/StateReports/2011_Statewide_BMI_Report.pdf. Accessed May 7, 2014.
  7. Day SE, Konty KJ, Leventer-Roberts M, Nonas C, Harris TG. Severe obesity among children in New York City public elementary and middle schools, school years 2006–07 through 2010–11. Prev Chronic Dis 2014;11:E118. PubMedExternal Web Site Icon
  8. Congdon P. Estimating population prevalence of psychiatric conditions by small area with applications to analysing outcome and referral variations. Health Place 2006;12(4):465–78. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  9. Judge A, Welton NJ, Sandhu J, Ben-Shlomo Y. Modeling the need for hip and knee replacement surgery. Part 2. Incorporating census data to provide small-area predictions for need with uncertainty bounds. Arthritis Rheum 2009;61(12):1667–73. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  10. Choy M, Switzer P, De Martel C, Parsonnet J. Estimating disease prevalence using census data. Epidemiol Infect 2008;136(9):1253–60. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  11. Li W, Kelsey JL, Zhang Z, Lemon SC, Mezgebu S, Boddie-Willis C, et al. Small-area estimation and prioritizing communities for obesity control in Massachusetts. Am J Public Health 2009;99(3):511–9. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  12. Gregg EW, Kirtland KA, Cadwell BL, Burrows NR, Barker LE, Thompson TJ, et al. ; Centers for Disease Control and Prevention (CDC). Estimated county-level prevalence of diabetes and obesity — United States, 2007. MMWR Morb Mortal Wkly Rep 2009;58(45):1259–63. PubMedExternal Web Site Icon
  13. Malec D, Sedransk J, Moriarity CL, Leclere FB. Small area inference for binary variables in the National Health Interview Survey. J Am Stat Assoc 1997;92(439):815–26. CrossRefExternal Web Site Icon
  14. Malec D, Davis WW, Cao X. Model-based small area estimates of overweight prevalence using sample selection adjustment. Stat Med 1999;18(23):3189–200. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  15. Zhang X, Onufrak S, Holt JB, Croft JB. A multilevel approach to estimating small area childhood obesity prevalence at the census block-group level. Prev Chronic Dis 2013;10:E68[Accessed May 7, 2014]. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  16. Growth charts — percentile data files with LMS values. Atlanta (GA): Centers for Disease Control and Prevention; 2009. http://www.cdc.gov/growthcharts/percentile_data_files.htm. Accessed May 7, 2014.
  17. Obesity and overweight for professionals. Atlanta (GA): Centers for Disease Control and Prevention; 2012. http://www.cdc.gov/obesity/childhood/basics.html. Accessed May 7, 2014.
  18. R: a language and environment for statistical computing. R Core Team, R Foundation for Statistical Computing; 2014. http://www.R-project.org/. Accessed May 7, 2014.
  19. Lumley T. Analysis of complex survey samples. J Stat Softw 2004;9(1):1–19.
  20. Wickham H. ggplot2: elegant graphics for data analysis. New York (NY): Springer; 2009.
  21. Mei Z, Grummer-Strawn LM, Pietrobelli A, Goulding A, Goran MI, Dietz WH. Validity of body mass index compared with other body-composition screening indexes for the assessment of body fatness in children and adolescents. Am J Clin Nutr 2002;75(6):978–85. PubMedExternal Web Site Icon
  22. Grow HM, Cook AJ, Arterburn DE, Saelens BE, Drewnowski A, Lozano P. Child obesity associated with social disadvantage of children’s neighborhoods. Soc Sci Med 2010;71(3):584–91. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon
  23. Koch R. The 80/20 principle: the secret to achieving more with less. New York (NY): Random House LLC; 2011.
  24. National Survey of Children’s Health. Portland (OR): The Data Resource Center for Child and Adolescent Health. http://www.childhealthdata.org/browse/allstates?q=226. Accessed May 7, 2014.
  25. County-level estimates of obesity. Atlanta (GA): Centers for Disease Control and Prevention, National Diabetes Surveillance System; 2009. http://www.cdc.gov/diabetes/pubs/factsheets/countylvlestimates.htm. Accessed May 7, 2014.
  26. 2010 Geographic terms and concepts — census tract. Washington (DC): US Census Bureau. http://www.census.gov/geo/reference/gtc/gtc_ct.html. Accessed May 7, 2014.
  27. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 2014;311(8):806–14. CrossRefExternal Web Site IconPubMedExternal Web Site Icon
  28. Heart disease behavior. Atlanta (GA): Centers for Disease Control and Prevention; 2013. http://www.cdc.gov/heartdisease/behavior.htm. Accessed September 5, 2014.
  29. Michimi A, Wimberly MC. Spatial patterns of obesity and associated risk factors in the conterminous U.S. Am J Prev Med 2010;39(2):e1–12. CrossRefExternal Web Site IconPubMedExternal Web Site Icon
  30. Eto C, Komiya S, Nakao T, Kikkawa K. Validity of the body mass index and fat mass index as an indicator of obesity in children aged 3–5 year. J Physiol Anthropol Appl Human Sci 2004;23(1):25–30. CrossRefExternal Web Site Icon PubMedExternal Web Site Icon

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