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Is proximity to blame?

Is proximity to blame?. Presented by: Sarah Edwards. Overweight Statistics:. In 2009 the WHO estimated that 110 million children are overweight worldwide During 2007-2008, in the US alone, 31.7% of youth were reported obese or overweight

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Is proximity to blame?

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  1. Is proximity to blame? Presented by: Sarah Edwards

  2. Overweight Statistics: • In 2009 the WHO estimated that 110 million children are overweight worldwide • During 2007-2008, in the US alone, 31.7% of youth were reported obese or overweight • The cost for childhood obesity is estimated > 14 Billion dollarsand expected to amount to 1/6 dollars spent on health care by 2030 • If the current trends continue in the US, the projected overweight/obese prevalence in Mexican-American teens will suffer a two-fold increase and African-American teens will suffer a 1.8-fold increase

  3. 2011 Percentage of obese HS students

  4. Obesogenicneighbourhoods: the impact of neighbourhood restaurants and convenience stores on adolescents’ food consumption behaviors Meizi He, Patricia Tucker, Jennifer D Irwin, Jason Gilliland, Kristian Larsen and Paul Hess

  5. Is the availability of unhealthy options to blame? • “While strategies to curb the childhood obesity epidemic must include more effective education of children and their parents on nutrition and portion control, these efforts will likely fall far short without concurrent environmental change efforts that tip the balance in favor of healthier food purchases.” Simon et al.­­­­­­­­­

  6. Objective: • To examine the relationship between the neighborhood food environment and dietary intake among adolescents

  7. Experimental Methods: • Cross-sectional design • Conducted between 2006-2007 in London, Ontario • Study approved by the Office of Research Ethics at the University of Western Ontario • Informed written consent from parents and adolescents

  8. Participants: • Students from grades 7 and 8 (aged 11-14 years) were selected from a heterogeneous sample of schools • Twenty-one of the fifty-one schools invited agreed to participate • 810/1666 students received parental consent = 49% response rate • Due to missing and implausible dietary data the final sample totaled 632 students

  9. Participants:

  10. Survey instruments: • “Block Kids 2004 Food Frequency Questionnaire” (FFQ) • Assess children’s diets over the past 12 months • Short parental questionnaire • Assess demographics of individual household • (ex. Household postal code, family income, father’s and mother’s education and employment status)

  11. Sociodemographic Profile

  12. Neighborhood Food and Environmental Measures Geographic Information System (GIS): • Postal codes reported to maintain anonymity • Defined using a 1 km “straight line buffer” radius • Data on fast-food outlets, convenience stores, and supermarkets Definitions: • Fast Food (FF): restaurants where one orders at a counter and pays in advance for food • Convenience stores (CS): small food retailers with a floor area < 1000 m2 • Supermarkets: larger food retailers with floor areas > 1000 m2 • Junk Food Density: number of FF outlets and CS within a 1 mile buffer • Junk Food Proximity: shortest distance to the nearest FF outlet and CS

  13. Home neighborhood distress scores • Comprised of 4 variables drawn from the 2006 Canadian census • 1. Low educational attainment • 2. Lone parenthood • 3. Unemployment • 4. Incidence of low income • Z-scores were calculated for each neighborhood (-1, 0, 1) • Socio-economic distress index score range: -4 to +4 • Recorded neighborhood distress index score:

  14. Dietary Intake: • FFQ returned to NutritionQuest for processing • Canadian-specific food items were recalculated • HEI-2005: Healthy Eating Index- comprehensive index used to monitor a population’s eating behaviors and for determining associations pertaining to diet quality and behavioral, social, and environmental correlates in nutrition epidemiology research • Current study used 9/12 components- forming a modified HEI with a maximum score of 80 • Higher score = intakes close to recommended range • Lower score = less compliance with recommended range

  15. Healthy Eating Index standards:

  16. HEI 2010:

  17. Results:

  18. HEI scores for ‘home neighborhood’

  19. HEI scores for ‘school neighborhood’

  20. Discussion Conclusion: Implications: Environments surrounding adolescents may yield negative purchasing behaviors Possible policy and environmental interventions, particularly in controlled environments will result in a decline of adolescent obesity rates • From an adolescent’s home- close proximity to CS was associated with a low diet quality score • From an adolescent’s school- close proximity to CS, FF outlets, and high-density of FF outlets were associated with poor nutritional intake • The closer adolescents are to unhealthy options and the greater the density of options, the more likely they will purchase when guardian not around

  21. Proximity of Fast-food restaurants to schools and adolescent obesity BRENNAN DAVIS AND CHRISTOPHER CARPENTER

  22. Background: • 1977-1995: FF consumption among 2-18 year-olds increased 5-fold • By 1995: FF was consumed at 9% of eating occasions and comprised 12% of daily caloric intake • 2004: Roughly 1/3 of all youths eat FF on any given day • According to Duffy et al. (2007)- weekly consumption of FF by young adults is directly associated with a 0.2 BMI increase • Multiple studies have found that FF near schools  increased access to low-quality foods

  23. Objective: • Examine the relationship between fast-food restaurants near schools and obesity among middle and high school students in California (Note: the study also used the controls: gas stations, motels, and grocery stores)

  24. Participants: • Obtained information from the 2002-2005 California Healthy Kids Survey(CHKS) (http://chks.wested.org/) • Used geocoded data on over 500,000 youths • Grades 5 – 12 (ages 10-18 yr)

  25. % %

  26. Methods: • Outcome of interest was BMI • %: 21.7 (mean) with ~ 28% overweight and 12% obese • Used logistic regression and odds ratio Controlled variables include: • Female gender, age category, grade, ethnicity, PA, school type, % of students eligible for free/reduced lunch, etc. • Created indicator variables such as: soda (68%), fruit (74%), vegetable (75%), juice (70%), and fried potato foods (62%) consumed within a 24 hour period

  27. Methods continued: Proximity measurements were obtained using: • Database of latitude/longitude coordinates for schools • Database of restaurants in California in 2003 • List of restaurant brands classified as “top limited service restaurants” Geographical Indicators: • Within one quarter of a mile • Between one quarter and one half of a mile • Between one half and three quarters of a mile • The number of fast food restaurants within a one half mile radius of the school

  28. Results:

  29. Association between school’s proximity to other types establishments and weight status of students

  30. Videos: • North Hollywood High-School Fast-Food • http://www.youtube.com/watch?v=ZcJ8WsVZd4w • Fast Food by our Schools: Convenience or Problem? • http://www.youtube.com/watch?v=g2Chraomsjg

  31. Global perspective: Agreed: • 2012- Minnesota: Forsyth et al. • 2005- Chicago: Austin et al. • 2011- New Zealand: Day et al. Disagreed: • 2013- Delmonhorst, Germany: Buck et al.

  32. Where do RDs come in? • BB- Caroline- Where do we go with this research? Do you think just effectively educating the parents is best since it's unlikely you can drive out convenience stores/fast food chains that get an abundance of business? Will regulating the density of fast food restaurants in an area really decrease children frequenting them? • EVOs • http://www.evoslunchroom.com/wp-content/uploads/2010/06/evos_HKLP_brochure.pdf

  33. Discussion: • Nutrition Education: • BAM! Body and Mind- Dining Decisions Game • http://www.cdc.gov/bam/nutrition/game.html • BAM! Body and Mind- G.A.M.E • http://www.cdc.gov/bam/

  34. Any questions, comments, or ideas?

  35. References: • He M, Tucker P, Irwin J, Gilliland J, Larsen K, Hess P. Obesogenicneighbourhoods: the impact of neighbourhood restaurants and convenience stores on adolescents’ food consumption behaviours. Public Health Nutrition [serial online]. December 2012;15(12):2331-2339. Available from: Academic Search Complete, Ipswich, MA. Accessed September 22, 2013. • Grier S, Davis B. Are All Proximity Effects Created Equal? Fast Food Near Schools and Body Weight Among Diverse Adolescents. Journal Of Public Policy & Marketing [serial online]. Spring2013 2013;32(1):116-128. Available from: Business Source Complete, Ipswich, MA. Accessed September 22, 2013. • Centers for Disease Control and Prevention. Obese Youth Over Time. http://www.cdc.gov/healthyyouth/obesity/obesity-youth.htm. Accessed September 22, 2013. • Centers for Disease Control and Prevention. Healthy Eating Index. http://www.cnpp.usda.gov/Publications/NutritionInsights/Insight52.pdf. Accessed September 22, 2013. • Davis B, Carpenter C. Proximity of Fast-Food Restaurants to Schools and Adolescent Obesity. American Journal Of Public Health [serial online]. March 2009;99(3):505-510. Available from: Business Source Complete, Ipswich, MA. Accessed September 22, 2013. • Wiecha JL, Peterson KE, Ludwig DS, Kim J, Sobol A, Gortmaker SL. When children eat what they watch: impact of television viewing on dietary intake in youth. Arch PediatrAdolesc Med. 2006; 160: 436-442. • Bowman SA, Gortmaker SL, Ebbeling CA, Pereira MA, Ludqig DS. Effects of fast-food consumption on energy intake and diet quality among children in a national household study. Pediatrics. 2004; 113: 112-118. • Forsyth A, Wall M, Larson N, Story M, Neumark-Sztainer D. Do adolescents who live or go to school near fast-food restaurants eat more frequently from fast-food restaurants?. Health & Place [serial online]. November 2012;18(6):1261-1269. Available from: Academic Search Complete, Ipswich, MA. Accessed September 22, 2013. • Austin S, Melly S, Sanchez B, Patel A, Buka S, Gortmaker S. Clustering of Fast-Food Restaurants Around Schools: A Novel Application of Spatial Statistics to the Study of Food Environments. American Journal Of Public Health [serial online]. September 2005;95(9):1575-1581. Available from: Business Source Complete, Ipswich, MA. Accessed September 22, 2013. • Buck C, Börnhorst C, Pigeot I, et al. Clustering of unhealthy food around German schools and its influence on dietary behavior in school children: a pilot study. International Journal Of Behavioral Nutrition & Physical Activity [serial online]. January 2013;10(1):65-74. Available from: Academic Search Complete, Ipswich, MA. Accessed September 22, 2013.\ • North Hollywood High-School Fast Food. http://www.youtube.com/watch?v=ZcJ8WsVZd4w. Accessed September 22, 2013. • Fast Food by our Schools: Convenience of Problem? http://www.youtube.com/watch?v=g2Chraomsjg. Accessed September 22, 2013. • EVOs: Feel Great Fast Food. Healthy Kids Lunch Program. http://www.evoslunchroom.com/wp-content/uploads/2010/06/evos_HKLP_brochure.pdf. Accessed Septemtber 22, 2013. • Centers for Disease Control and Prevention. BAM! Body and Mind Dining Decisions Game. http://www.cdc.gov/bam/nutrition/game.html. Accessed September 22, 2013. • Centers for Disease Control and Prevention. BAM! G.A.M.E. Bring out the Action Hero in You! http://www.cdc.gov/bam/. Accessed September 22, 2013.

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