Causes and Consequences of Food Choice Kiyah Duffey Department of Nutrition The University of North...
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Transcript of Causes and Consequences of Food Choice Kiyah Duffey Department of Nutrition The University of North...
Causes and Consequences of Food
Choice
Kiyah Duffey Department of Nutrition
The University of North Carolina at Chapel HillNovember 16, 2006
Outline
Nutritional Epidemiology at UNC Factors affecting food choice
Environmental factors Consequences of food choice: obesity
Background Epidemiology/ Current Trends
Specific Research Fast Food Consumption Beverage contribution to overall diet
Nutrition Epidemiology
Epidemiology is the study of the distribution of disease in a population. Population-level causes of disease
At UNC, focus is on the link between diet & physical activity and obesity; nutrition transition in developing countries US, China, Philippines, Russia, Brazil
Factors Influencing Food Choice
An Ecological Model of Diet, Physical Activity, and Obesity
Developed by Dr. Penny Gordon Larsen, UNC Chapel Hill Dept of Nutritionfor the NHLBI Workshop: Predictors of Obesity, Weight Gain, Diet, and Physical Activity; August 2004, Bethesda MD
Food Choice
Dietary Patterns & Nutrient Intake:Type, Amount,
Frequency of foods eaten
Biological & Demographic
Age, sex, SES, genetics, disease states
Psychological
Preferences, emotions, body image,
motivation, knowledge
Social/Cultural
Family factors, peer influencesocial norms, acculturation
Physical Environment
Access, urban design, transportation, advertising
Policies/Incentives
Cost & availability: Environmental regulation
Organizational
Programs & policies in schools, worksite,
community orgs
Factors Affecting Food Choice Physical Environment: Location
Social/Cultural: Visual Cues, portion size
Policies: Avertising
Location
Location of Fast Food Outlets to Chicago Schools: 35% w/ in 400m 80% w/ in 800m
3 - 4 times more FF w/in 1.5 km from schools than expected if distributed randomly
More highly clustered: outside downtown mid-upper income more commercialized
Austin et al. American Journal of Public Health 2005; 95( 9):1575-1581
Neighborhood Affects Access ARIC, nationwide data Examined neighborhood
influence on diet 8% of Blacks vs. 31%
Whites lived in census tract with ≥1 supermarket
Blacks: 1.31 and 2.18 times more likely to meet guidelines for F&V consumption if there was 1 or 2 supermarkets in their census tract
Moreland et al. Am J Public Health. 2002 Nov;92(11):1761-7
Black neighborhoods have Higher Density of Fast Food Outlets Orleans Parish, LA Modeled probability of
fast food outlets (FFO) given race and income
Black neighborhoods had more FFO/mi2 than White neighborhoods: 2.4 vs. 1.5
Black neighborhood explained 19% of the variance in distribution of FFO
Block et al. Am. J. Preventive Med. 2004:27(3):211-7.
Visual Cues
Are all containers created equal? People consumed more
beverage from short fat glasses than tall thin ones
4, 2L bowls vs. 2 4L bowls Self-serve Serving from 4L bowls
resulted in 53% more taken, 56% more calories consumed
≠
Brian Wansink, Cornell University: Nutrition Psychology. Unpublished data
≠
Would you like popcorn with that?
158 moviegoers
Popcorn buckets weighed before and after movie
2-week old popcorn tasted “stale”, “soggy”, “terrible”
Fresh
240 g 120 g
2-Weeks old
240 g 120 g
<< >
33%
>
41%Wansink & Kim. 2005. J Nutr Educ Behav: 37; 242-45
“Bottomless” Bowls Increase Lunch Calories
54 adults 20 min, free lunch Half given 18 oz, half
given “bottomless” bowls 20% more soup (113
kcals) eaten from bottomless bowls
No differences in estimated caloric intake or reported satiety
Brian Wansink. 2005. Obesity Research: 13(1); 93-100
Portion Size
Portion Sizes Increasing, Cheeseburger
Calorie difference: 257 calories
590 calories
20 Years Ago Today
333 calories
Increased Portion Sizes, Bagels
140 calories 3-inch diameter
Calorie Difference: 210 calories
350 calories 6-inch diameter
20 Years Ago Today
Effect of Portion Size on Energy Intake
Rolls et al. 2004, 2005 Altered portion sizes of
sandwiches, macaroni dishes & pre-packaged chips
Participants free to eat at will
Measured plate waste, and asked respondents about level of satiety
Pasta Entrée Size Affects Total Caloric Intake
0
500
1000
1500
2000
2500
100% 150%
Portion Size of Entree
Ene
rgy
Inta
ke (kJ
)
Baked Pasta Accompaniments Side Dishes Desserts Beverages
600
400
200
En
erg
y Inta
ke
(kcal)
600
400
En
erg
y Inta
ke
(kcal)
Diliberti et al. 2004. Obesity Research: 12(3);562-568
*
*
Sandwich Size Affects Energy Intake
Females (n=37)
Males (n=38)
Rolls et al. J Amer Diet Assoc. 2004; 104:367-372
0
200
400
600
800
1000
1200
6 8 10 12 6 8 10 12
Sandwich Size (inches)
En
erg
y In
take (
kcals
)
+ 159 kcals + 355 kcals
Advertising and Other Influences
Advertising Children (<12y) viewed ~4,900 food & restaurant
commercials/year http://www.msnbc.msn.com/id/15095189/
Snickers budget alone 5 x greater than 5-a-day
$11.6 billion $9.5 million
Other Influences Changing food supply
~3900kcal/person available* Significant increases in added sugars
Pricing Availability at home
Carrots slices vs. Doritos Parental Influences
Overweight children are more likely to have overweight parents
*Data are based on ERS estimates of per capita quantities of food available for consumption, imputed consumption data, and on estimates from USDA's. Source: USDA/Center for Nutrition Policy and Promotion, March 3, 2006. http://www.ers.usda.gov/Data/FoodConsumption/NutrientAvailIndex.htm
Consequences of Food Choice: Overweight & Obesity
Senate bill bans obesity lawsuits By Marguerite Higgins
THE WASHINGTON TIMES
'Freshman 15' really 5 or 7, but the gains don't stopCNN , POSTED: 11:37 a.m. EDT, October 23, 2006,
Defining Overweight & Obesity Excess of body fat for a given height and
weight
In adults, defined using BMI (kg/m2)
Example 6’0”, 160 lbs, BMI=21.7; 200 lbs, BMI=27.1 5,4”, 145 lbs ,BMI=24.9; 170 lbs, BMI=29.2
Defining Adult Overweight, Obesity
BMI (kg/m2)BMI Weight Status
<18.5 Underweight
18.5-24.9 Normal weight
25.0-29.9 Overweight
≥ 30.0
30.0-34.9 Obese
35.0-39.9 Severe obesity
≥ 40.0 Morbid obesity
Adapted: National Heart, Lung, and Blood Institute Guidelines. Obes Res.1998:6(suppl 2):51S-209S.
BMI Correlates with % Body Fat
Evidence of a Problem
About 127 million US adults are overweight
60 million obese, 9 million severely obese (equivalent to BMI ≥40)
48
15
56
22
66
32
0
10
20
30
40
50
60
70
Overweight Obese
197619882004
Ogden et al. JAMA 2006: 1549-1555
No Data <10% 10%–14%
(*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
Obesity Trends* Among U.S. AdultsBRFSS, 1985
http://www.cdc.gov/nccdphp/dnpa/obesity/trend/maps/
Obesity Trends* Among U.S. AdultsBRFSS, 2005
(*BMI ≥30, or ~ 30 lbs overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Health Consequences of Obesity
Hypertension Dyslipidemia (for example, high total cholesterol
or high levels of triglycerides) Type 2 diabetes Coronary heart disease Stroke Gallbladder disease Osteoarthritis Sleep apnea and respiratory problems Some cancers (endometrial, breast, and colon)
Study 1: Fast Food vs. Restaurant Food Consumption
Effects on Energy Intake
Duffey, Gordon-Larsen et al., AJCN. In press
Background
Away-From-Home (AFH) eating provides >30% total daily energy intake among adolescents/adults
Evidence suggests that: AFH food typically higher in total calories, saturated fat,
refined carbohydrates and cholesterol
Higher frequency of AFH consumption may be associated with BMI and weight change
Little is known about nutritional differences between restaurant and fast food as they are usually studied together
Study Questions
Do fast foods and restaurant foods have differential effects on weight gain?
What happens to these effects if the two sources of away from home eating are combined into a single measure?
Study Population & Methods
CARDIA: longitudinal data
Study years 0 (1985-86), 7 (1992-93) and 10 (1985-86); adults aged 18-30
Defined patterns of fast food and restaurant food intake
Modeled the association of eating away from home with weight change and total caloric intake
Higher 7-year Fast Food Intake is Associated with
Greater 7-year BMI Gain in White Females*
* Model 1: adjusted prevalence stratified multinomial logistic regression models controlled for: age, income, education, eating pattern (maintain low, decreased, increased, maintain high fast food) and study center.
** p<0.05, compared to referent category (maintained BMI).
0
10
20
30
40
50
60
70
80
Pe
rce
nt
of
wh
ite w
om
en
n=112 n=363 n=557
Fast Food IntakeFrom Year 0 to 7
11
33
56
10
33
57
9
23**
11
34
55
68**
Lost > 1 BMI unit Maintained BMI Gained > 1 BMI unit
Maintained Low
Decreased
Increased
Maintained High
Higher Fast Food (versus Restaurant) Intake is Associated with Higher BMI Gains over a 7-year Period in White
Females*
8
24
13
37
8
33
59
68**
50**
0
10
20
30
40
50
60
70
80
90
Lost > 1 BMI unit Maintained BMI Gained > 1 BMI unit
Pe
rce
nt
of
wh
ite w
om
en Higher Fast Food
Higher Restaurant
Higher Both
n=112 n=363 n=557
* Model 2: adjusted prevalence stratified multinomial logistic regression models controlled for: age, income, education, eating pattern at time 7 (high v. low intake of restaurant, fast food or combination) and study center.
** p<0.05, compared to the referent category (maintained BMI).
Conclusions
Increase in fast food consumption associated with greater likelihood of increase in BMI
Higher restaurant (versus higher fast food) consumption is associated with greater likelihood of maintenance of BMI
Combining AFH to a single measure masks the independent associations of these two food sources with long-term energy intake and BMI
Study 2: Dietary & Beverage Patterns
Predicting Water vs. Soda Consumption
Duffey & Popkin. Journal Nutr. 2006: 2901-07.
Adults are Consuming More Calorically-Sweetened Beverages Increased consumption of calorically-sweetened beverages over the past two decades
Average adult obtains 21% of calories from beverages: providing an additional 150-300 calories/day
Among consumers, there is greater consumption of calorically-sweetened beverages, such as soda, than for caloric beverages with nutrients, such as low-fat milk
52
22.325
26
12 12 11
21
28
0
10
20
30
40
50
60
Water Coffee Tea Diet Low-Fat Milk Fruit Juice VegetableJuice
Fruit Drinks Soda
Beverages
Among Consumers*, Mean Fluid Ounce Intake Greater for
Calorically-Sweetened Beverages
Mea
n C
on
sum
pti
on
(fl
. o
z.)
UnsweetenedUnsweetened
Caloric w/ Caloric w/ NutrientsNutrients
Calorically-Calorically-SweetenedSweetened
* Adults 18+ years from NHANES 1999-2002 survey
DietDiet
52
22.325
26
12 12 11
21
28
0
10
20
30
40
50
60
Water Coffee Tea Diet Low-Fat Milk Fruit Juice VegetableJuice Fruit Drinks Soda
Beverages
Study Questions
Do certain beverages tend to be consumed together (are there patterns of beverage intake)?
If so, how do food patterns associate with these beverage patterns?
Study Population & Methods
National Health and Nutrition Examination Survey 1999-2002, adults ≥19 years
First of two non-consecutive 24 hour recalls
UNC-CH Food Grouping System & cluster analysis Finds patterns in data and generates groups of like
individuals
Modeled effect of food cluster on beverage cluster
Final Food Clusters*
Normal55%
Cereal & LF Meat10%
Vegetables5%
Fruit & LF Dairy5%
Snacks & HF Foods14%
Fast Food11%
*Adults, 19+
Final Beverage Clusters*
Water & Tea14%
Coffee, Water & Tea19%
Diet14%
Coffee & Soda22%
Nutrients & Soda
14%
Soda17%
*Adults, 19+
Fast Food Membership Linked with Decreased Probability* of
Consuming Water-Containing Beverage Patterns
*Predictions from mlogit results controlling for age, race, gender, income, education & overweight status
10.311.9
9.6
26.1
17.8
24.2
15 15.6
11.3
22.5
15.417.3
0
5
10
15
20
25
30
Water & Tea Coffee, Water
& Tea
Diet Coffee &
Soda
Nutrients &
Soda
Soda
Beverage Cluster
Per
cen
t o
f S
amp
le
Fast Food Member Fast Food Non-Member
Vegetable Group Membership Linked with
Decreased Probability* of Consuming Soda
20.9 20.8
8.5
24
17.9
10.9
14.1
17.8
11.2
22.8
15.7
18.4
0
5
10
15
20
25
30
Water &
Tea
Coffee,
Water &
Tea
Diet Coffee &
Soda
Nutrients &
Soda
Soda
Beverage Clusters
Per
cen
t o
f S
amp
le
Vegetable Member Vegetable Non-Member
*Predictions from mlogit results controlling for age, race, gender, income, education & overweight status
In Conclusion Caloric and non-caloric beverages tend to be
consumed independently
Healthier diet patterns associated with healthier beverage patterns
Substituting non-caloric beverages for caloric ones can reduce total energy intake
What’s Next? How does an individual’s
neighborhood impact their food choices, dietary intake, physical activity patterns and weight gain?
Do changes in the environment lead to changes in these outcomes?
Derived Measures Distance Matrices, Network Calculations,Connectivity, Community Classifications
GIS Database
Respondent LocationsPA Resources
Contextual Data
Land Use
Ancillary Data(Roads, Administrative
Boundaries, etc.)
GPS Data
Obesity & The EnvironmntThe University of North Carolina at Chapel Hill
Respondents
Block Group Boundary
Residential Neighborhoods
Obesity & The EnvironmentThe University of North Carolina at Chapel Hill
Respondents
Individual Buffer
Community Study Area
Sampled Block Group
Create Buffer Zones, 5 Mile from Residential Location
Obesity & The EnvironmentThe University of North Carolina at Chapel Hill
Respondents
Individual Buffer
Community Study Area
Sampled Block Group
Block Group Boundary
Food Sources
Measures Food Sources in 5 Mile Buffers
Intended Analysis
Examine average distance to and count of fast food and restaurant places within buffers around residential locations
Determine if proximity → times/week consumption
Determine if consumption → weight gain
8
For consideration… Continued need for unified, simple message
Conventional vs. Organic Whole Foods & Michael Pollan http://gristmill.grist.org/story/2006/6/29/143121/559?source=weekly
Local vs. long-distance foods
Nutrition Labeling at point of purchase; restaurant menus, menu boards
Selected References Books: Food Politics by Marion Nestle The Hungry Gene by Ellen Ruppel Shell Food Fight by Kelly Brownell
Websites http://www.consumersunion.org/pub/core_health_care/002657.html http://www.cdc.gov/nccdphp/dnpa/obesity/trend/maps/ The Food Trust: http://www.thefoodtrust.org/php/programs/farmers.market.program.php Food Policy Institute: http://www.preventioninstitute.org/npp.html Pro Restaurant/Food producers: http://www.consumerfreedom.com/ American Obesity Association: http://www.obesity.org/ North American Association for the Study of Obesity (NAASO): http://www.naaso.org/ Center for Science in the Public Interest: http://www.cspinet.org/ Whole Foods vs. Pollan blog: http://gristmill.grist.org/story/2006/6/29/143121/559?source=weekly Tufts University, Professor of Food Policy- blog :http://www.usfoodpolicy.blogspot.com/ Rudd Center for Food Policy, Yale: http://www.yaleruddcenter.org/home.aspx
Articles: Young & Nestle. Am J Pub Health 2002: 246-47 Gordon-Larsen et al. Obes Res. 2003;11(1):121-129. Albright et al. Health Educ Q. 1990;17(2):157-167. Weinsier et al. Am J Med. Aug 1998;105(2):145-150. Fiske & Cullen. J Am Diet Assoc. 2004;104(1):90-93. Diez Roux et al. N Engl J Med. 2001;345(2):99-106. Jeffery & Utter. Obes Res. 2003;11:12S-22S. Nielsen S et al. Obes Res. 2002;10(5):370-378. French S et al. Annu Rev Public Health. 2001;22:309-335. Rolls et al. Appetite. 2004;42(1):63-69. Block et al. Am J Prev Med. 2004;27(3):211-217. Gordon-Larsen et al. Pediatrics. Feb 2006;117(2):417-424.
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