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LDI Research Seminar 1_28_11- Brian Elbel, PhD, MPH
 

LDI Research Seminar 1_28_11- Brian Elbel, PhD, MPH

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    LDI Research Seminar 1_28_11- Brian Elbel, PhD, MPH LDI Research Seminar 1_28_11- Brian Elbel, PhD, MPH Presentation Transcript

    • Obesity, Calorie Labeling and Behavioral Economics Brian Elbel, PhD, MPHAssistant Professor of Medicine and Health Policy NYU School of Medicine NYU Wagner School of Public Service University of Pennsylvania January 28th, 2011
    • Outline of Presentation• Overview of my work/approach• Influence of calorie labeling: NYC• Behavioral economics and obesity: Field experiment• Conclusion/Next steps
    • My Work/Approach• Broadly: How individuals make choices that influence health and health care – Consumers/patients choice – Physician choice• Behavioral Economics: Econ with psych• Role and influence of pubic policy• As well as broader influences on choice
    • My Work/Approach• Methods – Econometric analysis of secondary data – Primary data collection, natural experiments – Field Experiments – Randomized trials – “Lab-based” experiments• Choice in three areas: – Clinical – Policy – Obesity
    • Research Areas• Clinical/Choice: – Changing default for physician prescribing of medication for high cholesterol (NIH/NIA/RC4)• Policy/Choice – Low-income use of quality information, behavioral economics to improve use (RWJF) – Influence of number of options and Medicare supplemental choice (AHRQ/NSF) – Role of cognitive ability in Medicare choices
    • Research Areas• Obesity/Choice – NYU Nutrition and Obesity Policy Research and Evaluation Network (CDC) – Evaluation of NYC initiative to bring supermarkets to “food deserts” via tax credits (RWJF; Aetna Foundation) – “Bellevue Bodega”: Behavioral economics in a “store” set up in a public hospital (CTSI) – Influence of calorie labeling (NIH/NHLBI/R01, RWJF) – Behavioral economics and fast food choice
    • Obesity Trends* Among U.S. Adults BRFSS, 1990, 1998, 2008 (*BMI 30, or about 30 lbs. overweight for 5’4” person) 1990 1998 2008No Data <10% 10–14% 15–19% 20–24% 25–29% ≥30%
    • Attempts to Address Obesity• Formerly, largely “education-based”• Recent: attention to the Food Environment• Environment considered by many to be “toxic” – Little fresh food available – Fast food ubiquitous – Marketing of unhealthy foods – children – Pricing of healthy versus unhealthy foods• Altering the environment where consumers make choices: public policy• Consumer not always “rational” in food choice
    • Calorie Labeling• First large-scale public policy to address obesity, focusing on the food environment• Combines information/education and environmental-based approaches Basic premise: Tell consumers before they order the number of calories in each food item
    • Logic Behind Calorie Labeling• Many people eat at fast food restaurants often: Average 2x week• Fast food eating associated with obesity, heart disease – Correlation data – Some more rigorous studies• Individuals currently poorly judge the caloric content of food **• Simply a “right” to the information• Non-demand effect: restaurants changing menus
    • However…• Food choice is incredibly multifaceted• Nutrition is but one factor in consideration• Everyone wants to start a diet or eat “better”…starting tomorrow: time preferences• Self control• Labeling has a hard climb
    • Calorie Labeling in NYC• First locality to institute calorie labeling• Restaurants w/ more than 15 locations nationally must: – Post the caloric content of all “regular” menu items – Post using the same font/format as the price• Covers 10% of all restaurants in NYC• 2,400 establishments• Labeling began in July 2008
    • PicturesSource: CSPI
    • Menu Labeling Goes National• Labeling was included in the federal health reform bill• FDA must propose national regulation by March 2011.• Includes all restaurants with 20 or more locations nationally• Federal law will preempt state and local laws
    • Our Study• How does labeling influence the fast food choices of individuals in low-income areas?• Difference-in-Difference Design – Natural experiment – Examine calories purchased before and after labeling • NYC • Comparison city: Newark, NJ
    • Where we collected data• Low-income areas of NYC matched w/ Newark• NYC: Harlem, South Bronx, Central Brooklyn• Why Newark? – Matched our target communities in NYC – Similar “urban” feel• 2 restaurants in NYC for 1 in Newark• Wendy’s, McDonald, Burger King, Kentucky Fried Chicken
    • Timing of data collection• 2-week period before labeling, July 2008• Tuesday, Wednesday or Thursday• Lunch (12:30 – 3) and dinner hours (4:30 – 7)• Post-data collected 1-month after labeling – Same restaurant, time, day of week – Enough time for individuals to have exposure to labels – Not enough time where: • Seasons change • Kids back in school • Become desensitized to labels • Other things could influence obesity
    • How data were collected• RAs outside of fast food restaurants• Ask subjects to bring back receipt, answer survey in exchange for $2• RAs confirmed what the subject ate on the receipt, performed a short survey• From receipt and survey calculated number of calories purchased by each person• Estimate a 50-60% response rate
    • Influence of Labeling on Adults• Published in Health Affairs, October 2009
    • Sample Full Sample New York City Newark Pre-Labeling Post-Labeling Pre-Labeling Post-Labeling % N % N % N % N % NMale 38% 426 38% 131 36% 161 40% 68 41% 66Race/Ethnicity Black 66% 760 58% 213 64% 286 74% 130 81% 131 Latino/a 20% 230 25% 91 22% 97 15% 26 10% 16 White/Other 14% 166 17% 64 15% 67 11% 20 9% 15 Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.Age 38.2 14.1 38.3 14.7 37.9 14.7 39.7 12.6 37.4 12.6Education High School of Less 47% 205 48% 72 Not Not Some College or AA 20% 86 16% 24 Collected Collected BA or Above 33% 142 36% 54
    • 28% of those who saw labels indicated the labels influenced their choice No change in calories purchased825 before labeling v. 846 after labeling
    • No differences between those who noticed/indicated responded to labels, demographic groups
    • Results from Children / Adolescents• Focus on NYC Data (n=266)• Adolescents 13-17 without caregiver were asked the same questions as adults• Parents were asked questions about what they ordered for their children• Separate results by: – Adolescents 13+, by themselves (30%) – Children with their caregiver (70%)
    • 16% of adolescents who saw labels indicated they influenced their choice
    • Children/Adolescents: Calories Purchased NYC Sample (n=266) Calories Purchased Mean (sd) n Pre PostWith Caretaker 183 610 (327) 595 (319)Without Caregiver 83 730 (341) 755 (327)
    • Knowledge of Recommended Calorie Allowance• Clearly, labeling less effective if consumers don’t know how many calories they “should” eat• Federal law: requires that restaurants post a statement on recommended allowance—FDA now considering what this should look like• We asked consumers how many calories they thought adults should eat to maintain a healthy weight
    • Knowledge of Recommended Calorie Allowance Full New York City Newark Pre-Labeling Post-Labeling Pre-Labeling Post-Labeling % % % % %Dont Know/Didnt Answer 11% 6% 13% 15% 14%Less than 500 27% 33% 21% 28% 26%500 - 999 6% 8% 6% 5% 5%1,000 - 1,499 16% 14% 18% 16% 15%1,500 - 1,999 9% 8% 9% 10% 8%2,000 21% 20% 22% 20% 21%2,001 - 2,499 2% 2% 3% 0% 0%2,500-2,999 4% 4% 3% 3% 6%3,000+ 4% 4% 4% 2% 6%
    • Change in Estimates of Number of Calories Purchased?• Prior work focused on hypothetical scenarios• We asked for estimate of food and drink calories• 23% indicated they didn’t know NYC Newark Pre Post Pre Post Actual 810 842 829 843 Estimate 619 723 615 667 Difference 191 118 214 117 % Different 31% 16% 26% 21%• Diff-in-Diff not statistically significant
    • Change in Estimates of Number of Calories Purchased?• Assume “correct” if estimates are within 100 calories of actual Food and Drink Pre Post % Overestimating 24% 25% Correct Estimate 15% 24% p<.01 % Underestimating 61% 51% Food Only % Overestimating 21% 23% Correct Estimate 16% 21% ns % Underestimating 63% 55% Drink Only % Overestimating 20% 23% Correct Estimate 37% 45% p<.01 % Underestimating 43% 32% Only those who know RDA % Overestimating 34% 32% Correct Estimate 23% 28% p<.05 % Underestimating 43% 40%
    • Who Noticed and Responded to Labels?• Just over half of individuals see labels, consistent over time• Of those who see, only about a quarter use the information• Who are these people? – People who need it the most? – People who are already ‘nutrition-inclined’?
    • Who Noticed and Responded to Labels? Of Those Who Saw: % % Who Saw Labels % Who Were InfluencedSex Female 64% 55% 31% Male 36% 53% 19% *Race/Ethnicity White/Other 16% 61% 45% Black 63% 53% 24% ** Hispanic/Latino 21% 53% 22% ** NYC, Post-Labeling Sample, n=448; *p<.10; **p<.05
    • O f Tho se W ho Saw : % % W ho Saw Labels % W ho Were InfluencedWhich is More Im portant N utritio n 17% 58% 54% T aste 51% 51% 19% ** B oth 32% 59% 23% **Lim it Food to Maintain W eight? N ever 29% 51% 8% Seldom/Sometimes 36% 54% 20% * O ften 13% 61% 39% ** A lw ays 22% 53% 54% **Education HS or less 47% 47% 22% Some College 20% 64% 34% B A or h igher 33% 59% ** 27%Tim es/W eek Eat Fast Food 0-2 30% 57% 32% 3-5 25% 66% 23% * 6-8 17% 49% 29% 9 or mo re 28% 45% ** 21%Know RD A: b/w 1,500 and 2,500 No 58% 53% 25% Yes 42% 61% * 31%Adjusted M odels; ** < .05; * < .10 n=448 n=448 n=242
    • Limitations• Non-random sample• Limited geographic regions/sample• Can’t observe: – Amount of food eaten – Food eaten later in the day – Whether consumers are avoiding fast food altogether • Substituting to healthier foods? • Substituting to less healthy/equally unhealthy foods?• Single time period, one month post-labeling• Not examining changes in menus
    • Consistent With Other Findings• “Starbucks Study”: Bollinger et al.• “Taco Time Study”: Finkelstein et al.
    • Next Steps: This Data• Prices paid, change in prices paid• Geographic/Community characteristics: location of restaurant, location of residence• Change in “type” of orders• Racial/Ethnic Differences
    • Next Steps: Additional Collection• Expansion to examine calorie labeling in two cities, two comparison cities (R01, NHLBI)• Recently collected data in Philadelphia, Baltimore• Receipt collection, larger cross-section of the population• Telephone Survey: capture substitution behaviors, change in sit down restaurants, greater data on food choice, can labeling be made “better”
    • Conclusions• Marked increase in noticing/responding to calorie information in response to labeling• Did not generally translate into decreases in calories purchased for our study population in our study period
    • Conclusions• Those most likely to be influenced by labels: potentially more ‘nutrition’ inclined; more obese?• Estimates of calories purchased improved; still not perfect• Many don’t know recommended calories; more info better?
    • Other methods to influence foodchoice at the “point of purchase”?The Relative Influence of Behavioral Economic Nudges and Calorie Labels in Altering Fast Food ChoiceCoauthors: Janet Schwartz (Duke), Jason Riis (Harvard) and Dan Ariely (Duke)
    • Our Study• Will customers “downsize” fast food meals?• Self-control: Consumers will often not stop eating when full• Can we prime or initiate self-control at the beginning of the meal, using a simple “nudge” or reminder• Field Experiment, actual fast food restaurant – Asian-Style chain restaurant – College campus, near medical center• All aspects of experiment performed by restaurant staff
    • Framework• Study 1: Do customers downsize their order with nudge, with or without financial incentive?• Study 2: Relative influence calorie labeling v. nudge• Study 3: Are customers actually eating less?
    • Ordering a meal at the fast food restaurantStep 1Order meal sizeStep 2 Step 3 Step 4Order side dish Order entrée(s) Pay Fried rice Steamed rice Chicken dish 1 Beef dish 1 Pork dish 1 … $ Chow Mixed Veggie Etc. Etc. mein veggies dish 1 … 48
    • How the nudge workedStep 1Order meal sizeStep 2 Step 3 Step 4Order side dish Order entrée(s) Pay Fried rice Steamed rice Chicken dish 1 Beef dish 1 Pork dish 1 … $ Chow Mixed Veggie Etc. Etc. mein veggies dish 1 … “Would you like to cut more than 200 calories from your meal by taking a half portion of your side dish?” 49
    • Study 1: Summer 2009• N = 283 – Data gathered from receipt • Side dish, entrée(s), downsize – Gender (estimated by RA) • 75% male – Median age (estimated by RA) • 26 – Summer, largely non-student population
    • Study 1Sun Mon Tue Wed Thu Fri Sat BASELINE BASELINE DOWNSIZE DOWNSIZE no discount no discount 1% 35% BASELINE BASELINE DOWNSIZE DOWNSIZE 25¢ discount 25¢ discount 4% 32% •No difference in discount v not •Taking “Nudge” resulted in 200+ calorie saving
    • Calories per Customer (N=283)1200 Baseline Downsize 10101000 912* * p<.05 800 600 530 516 480 396* 400 200 0 Side Dish Entrée Total Cals
    • Summary• Study 1 – People do not spontaneously ask for less food – Customers will downsize meals if prompted (~90 calories averaging across all in condition) • With and without a discount – No compensation with higher calorie entrees• Study 2 – Is downsizing more or less effective than calorie labeling at reducing consumption? How do they interact?
    • Study 2• Fall 2009• College students back in town• N = 994 – Receipts • Side dish, entrée, downsize – Gender (estimated) • 74% male – Median age (estimated) • 20
    • Study 2 DesignSun Mon Tue Wed Thu Fri Sat 55
    • Study 2 DesignSun Mon Tue Wed Thu Fri Sat CALORIE LABELS GO UP (AND STAY UP) 56
    • Study 2 DesignSun Mon Tue Wed Thu Fri Sat baseline baseline baseline NUDGE NUDGE NUDGE NUDGE baseline baseline baseline CALORIE LABELS GO UP (AND STAY UP) baseline baseline baseline NUDGE NUDGE NUDGE NUDGE baseline baseline baseline 57
    • Study 2 ResultsSun Mon Tue Wed Thu % Accepting Fri Sat baseline baseline baseline 0% NUDGE NUDGE NUDGE NUDGE 21% baseline baseline baseline 1% CALORIE LABELS GO UP (AND STAY UP) baseline baseline baseline 0% NUDGE NUDGE NUDGE NUDGE 14% baseline baseline baseline 0% Still accepting nudge, uptake of nudge is LOWER when labels are present 58
    • Calories per Customer (n=994)1200 Baseline Downsize 1020 1033 10161000 945* * p<.05 800 600 536 555 560 484 508 478 456* 437* 400 •Calorie labeling did not appear to alter food choice for this population• 200 dish calories and total calories decreased with nudge in pre-labeling period Side •Still influence of nudge for side dish when labels are present, smaller 0 Side Dish Entrée Total Cals Side Dish Entrée Total Cals Pre-label Post-label
    • Study 3• Calorie labels still up• Same procedure (baseline v. nudge) – All customers were asked to take part in a survey for $10, asked to bring any leftovers – Leftovers were weighed• Those in the baseline condition started with more food….• But, both conditions left the same amount of food on the plate—almost nothing• Not just ordering less, but eating less
    • Strengths / Weaknesses• Strength: – Field experiment – Objective measure of purchasing – Relative influence of two interventions• Weaknesses: – External validity – Selected sample – Mechanism
    • Conclusions• Nudging did have an influence on food choice and consumption; calorie labeling did not• Labeling did not improve the influence of “nudge”, potentially made it worse• Policy: Greater attention to policy approaches aside from simply information – Defaults – Other Nudges?• Non-policy: model for employers, restaurants?
    • Overall Conclusions• Labeling may be justified/helpful for other reasons, but likely won’t alter food choice in a large scale way by itself• Behavioral economic approaches potentially effective; public policy?• Working with employers, others to implement changes• Multiple approaches, done at same time/place, potentially more effective in influencing obesity/food choice: labeling AND nudge?
    • Thanks Collaborators Funders• Rogan Kersh • NIH/NHLBI: R01HL095935• Beth Dixon • RWJF Healthy Eating• Tori Brescoll Research• Tod Mijanovich • Yale Rudd Center• Gbenga Ogedegbe• Beth Weitzman Staff• Dan Ariely • Courtney Abrams• Janet Schwartz • Joyce Gyamfi• Jason Riis
    • Noticed / Responded to Calorie Information New York City post – labeling sample Noticed Calorie Information? N=450 YES No N=242 (54%) N=208 (46%) Info Influenced Choice? YES No N= 67 (28%) N=175 (72%) Reported purchasing fewer calories? YES N=59 (88%)
    • Calorie Labeling in NYC• The History – First passed in 2006 – Restaurant industry sued – Judge ruled against NYC DOHMH on “technical” grounds – Re-wrote the law in 2008, sued again – Ruling went all the way to 2nd Circuit Court – Ruled in favor of NYC DOHMH
    • What do we currently know about labeling and its effectiveness• When labels are not on the menu board: – Only 0.1% to 10% of people “see” calorie information• NYC DOHMH Study, AJPH: – Looked at voluntary labeling at Subway – Those who saw labels ate 50 fewer calories• Experimental Work: Harnack et al., 2009 – Hotel Ballroom, mimicked fast food restaurant – No reduction in calories ordered – Males: increased calories ordered when labels present• Experimental Work: Roberto, Brownell et al. 2010 – Randomized to: no labels, labels, labels w/ “2000” prompt – Labeling decreased intake, with prompt