Calorie labeling and consumer estimation of calories purchased

  • Glen B Taksler1Email author and

    Affiliated with

    • Brian Elbel2, 3

      Affiliated with

      International Journal of Behavioral Nutrition and Physical Activity201411:91

      DOI: 10.1186/s12966-014-0091-2

      Received: 30 September 2013

      Accepted: 2 July 2014

      Published: 12 July 2014

      Abstract

      Background

      Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation.

      Findings

      Researchers surveyed customers and collected purchase receipts at fast food restaurants in the United States cities of Philadelphia (which implemented calorie labeling policies) and Baltimore (a matched comparison city) in December 2009 (pre-implementation) and June 2010 (post-implementation). A difference-in-difference design was used to examine the difference between estimated and actual calories purchased, and the odds of underestimating calories.

      Participants in both cities, both pre- and post-calorie labeling, tended to underestimate calories purchased, by an average 216–409 calories. Adjusted difference-in-differences in estimated-actual calories were significant for individuals who ordered small meals and those with some college education (accuracy in Philadelphia improved by 78 and 231 calories, respectively, relative to Baltimore, p = 0.03-0.04). However, categorical accuracy was similar; the adjusted odds ratio [AOR] for underestimation by >100 calories was 0.90 (p = 0.48) in difference-in-difference models. Accuracy was most improved for subjects with a BA or higher education (AOR = 0.25, p < 0.001) and for individuals ordering small meals (AOR = 0.54, p = 0.001). Accuracy worsened for females (AOR = 1.38, p < 0.001) and for individuals ordering large meals (AOR = 1.27, p = 0.028).

      Conclusions

      We concluded that the odds of underestimating calories varied by subgroup, suggesting that at some level, consumers may incorporate labeling information.

      Keywords

      Diet Health policy Energy intake Caloric restriction Obesity

      Calorie labeling legislation has been introduced in several United States cities and states to reduce obesity rates. Nationally, the Patient Protection and Affordable Care Act is expected to require restaurants with ≥20 locations to post calories for all regular food and drink items [[1]].

      Yet, studies suggest that calorie labeling has little impact on the number of calories purchased. Studies from Philadelphia [[2]] and low-income areas in New York City [[3]] found that labeling was associated with consumers noticing calorie labels but no significant change in calories purchased. Most other controlled studies have found similar results [[4]-[7]], although one study found that consumers at Starbucks purchased 12 fewer calories following calorie labeling [[8]]. Experimental studies have found mixed results [[9],[10]].

      Despite little evidence of a change in number of calories purchased, recent work has considered whether labeling is associated with greater accuracy in estimates of the number of calories purchased [[11]]. That is, while consumers purchase a similar number of calories, do they better judge the caloric content of foods following labeling policies? Such a finding could indicate that, at some level, consumers absorb calorie labeling information. Given the time associated with behavior change, such a mechanism could indicate an important first step in the potential longer-term impact of labeling. One prior study suggests that consumers were 9 percentage points more accurate in correctly predicting calories purchased (within 100 calories, from 15% before labeling to 24% after labeling) [[11]], but was limited to New York City. Other prior work has attributed caloric underestimation to a lack of visual cues [[12],[13]]. In one study, subjects who ate from self-refilling soup bowls (lacking the visual control of a bowl for portion size) were found to consume 73% more soup than controls; however, both groups estimated similar caloric consumption [[12]]. Caloric underestimation may also be related to nutritional status (overestimation of energy content for unhealthy foods) [[14]], less overall health consciousness [[15]], and lower education [[16]]. More generally, food labels appear most often used when easier-to-understand [[17],[18]], though some literature suggests an association to health literacy [[19]-[22]], female gender [[21]-[23]], and higher education [[21],[22]].

      Using a larger and more diverse sample than previous research, researchers examine the influence of calorie labeling on estimation of calories purchased in Philadelphia.

      Findings

      Methods

      Data were collected as part of a larger study to examine the influence of calorie labeling implemented in Philadelphia in 2010 [[2]]. A difference-in-difference design was used to examine the difference between estimated and actual calories purchased in Philadelphia in December 2009 (pre-calorie labeling) versus June 2010 (post-calorie labeling), as compared to Baltimore (a matched comparison city without calorie labeling rules) during the same month. The Appendix describes difference-in-difference methodology in more detail. Baltimore was selected as the city most comparable to Philadelphia by calculating Euclidean distances between Philadelphia and each of the largest 100 US cities using standardized city-level measures derived from Census 2000 data, including population size, poverty, unemployment, education, race/ethnicity, and income measures [[2]]. Full methods are available elsewhere [[2]].

      Research staff stood outside locations of McDonald’s and Burger King during lunch (approximately 11:30 am-2:30 pm) or dinner (approximately 5:00 pm-8:00 pm) on weekdays, and approached entering customers appearing to be ≥18 years old and asked them to bring back their receipt in exchange for $2 [[2]]. Participants who agreed were asked questions including which items were ordered for him/herself (versus other individuals); the exact nature of items (added cheese, mayonnaise, etc.); how often they visited “big chain” fast food restaurants; and how many calories they estimated to be in their purchase. The receipt provided was used to calculate actual calories purchased, based on nutrition information provided by each restaurant (as of May 2010) [[2]].

      First, summary statistics were calculated for the full sample (N = 1835) and subgroups based on number of calories purchased (≤median [850 calories] vs. >median), gender, race/ethnicity, education, and food vs. beverage. Summary statistics were calculated for each city, both pre- and post-calorie labeling. T-tests of unadjusted statistical significance were run for 4 groups: Philadelphia vs. Baltimore pre-calorie labeling, Philadelphia vs. Baltimore post-calorie labeling, Philadelphia pre- versus post-calorie labeling, and Baltimore pre- versus post-calorie labeling.

      Researchers then examined the difference between estimated and actual calories using multiple regression models. The dependent variable was estimated minus actual calories for each respondent. A positive number meant an overestimate and a negative number meant an underestimate of actual calories. The key independent variable of interest was an interaction term between Philadelphia (versus Baltimore) and post-calorie labeling (versus pre-calorie labeling). That is, researchers sought to measure the marginal contribution of calorie labeling policies to the accuracy of estimates in Philadelphia. Independent covariates included age, gender, race/ethnicity, education, number of items purchased, purchase of a combination meal, to-go vs. eat-in consumption, number of fast food restaurant visits per week, city, and time period (pre- vs. post-calorie labeling).

      Finally, consistent with prior research suggesting that consumers tend to underestimate calories [[2],[3],[11],[24]], logistic regression models were used to consider whether subjects underestimated by >100, >250, and >500 calories. (Researchers verified that consumers in the sample, on average, underestimated calories; results shown below.) This analysis was used to consider broad patterns in accuracy pre- vs. post-calorie labeling, as opposed to the magnitude difference between estimated and actual calories. Odds ratios were adjusted for the same covariates described above.

      Standard errors were clustered by restaurant. Tests were performed with a two-sided alpha = 0.05. This study was approved by the Institutional Review Board of New York University School of Medicine.

      Results

      Table 1 presents summary statistics. Respondents were primarily male, black or African American, and held a high school or lower education. No significant differences were observed in the actual number of calories purchased, though some differences existed across cities (a larger proportion of females in Philadelphia, and larger proportion of blacks and fast food visits/week in Baltimore) and time periods (a larger proportion of females and blacks in Philadelphia, and less missing data in Baltimore, in the post-calorie labeling period).
      Table 1

      Summary statistics

       

      All

      Philadelphia

      Baltimore

      Significance tests

      Pre-

      Post-

      Pre-

      Post-

      Pre-

      Post-

      Pre vs. Post

      Mean

      SD

      Mean

      SD

      Mean

      SD

      Mean

      SD

      Mean

      SD

      Philadelphia

      Baltimore

      N

      1835

       

      470

       

      534

       

      394

       

      437

           

      Mean

                    

      Age

      39.1

      13.9

      39.7

      14.1

      37.4

      14.4

      40.8

      13.5

      38.9

      13.4

        

      *

      *

      Number of calories purchased, actual

      951

      685

      987

      757

      927

      704

      974

      696

      923

      559

          

      Percent

                    

      Gender

                    

        Male

      55.2

      49.8

      58.3

      49.4

      52.1

      50.0

      51.5

      50.0

      58.8

      49.3

      *

      *

      *

       

        Female

      37.4

      48.4

      37.5

      48.5

      45.5

      49.8

      28.9

      45.4

      35.2

      47.8

      **

      **

      **

       

        Missing

      7.4

      26.2

      4.3

      20.2

      2.4

      15.4

      19.5

      39.7

      6.0

      23.7

      ***

      **

       

      ***

      Race/Ethnicity

                    

        Black

      70.1

      45.8

      60.4

      49.0

      70.8

      45.5

      73.9

      44.0

      76.4

      42.5

      ***

      *

      ***

       

        Caucasian

      20.8

      40.6

      23.0

      42.1

      17.8

      38.3

      22.1

      41.5

      21.1

      40.8

        

      *

       

        Other/Missing

      4.1

      19.8

      6.0

      23.7

      5.2

      22.3

      3.3

      17.9

      1.4

      11.7

       

      **

        

      Education

                    

        High school or less

      60.9

      48.8

      54.7

      49.8

      63.3

      48.2

      62.2

      48.6

      63.4

      48.2

      *

       

      **

       

        Some college or AA

      25.2

      43.4

      29.8

      45.8

      23.0

      42.1

      22.6

      41.9

      25.2

      43.5

      *

       

      *

       

        BA or above

      10.6

      30.8

      11.7

      32.2

      8.1

      27.2

      12.4

      33.0

      10.8

      31.0

          

        Missing

      3.4

      18.1

      3.8

      19.2

      5.6

      23.1

      2.8

      16.5

      0.7

      8.3

       

      ***

       

      *

      Type of order

                    

        To go

      67.6

      46.8

      60.6

      48.9

      70.4

      45.7

      68.0

      46.7

      71.4

      45.2

      *

       

      **

       

        Eat in

      26.3

      44.1

      25.1

      43.4

      24.2

      42.8

      28.4

      45.2

      28.4

      45.1

          

        Missing

      6.1

      23.9

      14.3

      35.0

      5.4

      22.7

      3.6

      18.5

      0.2

      4.8

      ***

      ***

      ***

      ***

      Number of times usually eat in big chain fast food restaurant per week

               

        ≤1

      56.4

      49.6

      62.3

      48.5

      64.0

      48.0

      49.0

      50.1

      47.1

      50.0

      ***

      ***

        

        2

      15.8

      36.4

      12.3

      32.9

      12.7

      33.4

      17.5

      38.1

      21.5

      41.1

      *

      ***

        

        ≥3

      34.4

      47.5

      26.4

      44.1

      30.7

      46.2

      39.6

      49.0

      42.8

      49.5

      ***

      ***

        

        Missing

      3.1

      17.4

      7.7

      26.6

      2.4

      15.4

      1.5

      12.3

      0.5

      6.8

      ***

      *

      ***

       

      Number of items purchased

                    

        1

      23.2

      42.2

      18.5

      38.9

      25.7

      43.7

      25.9

      43.9

      22.7

      41.9

      **

       

      **

       

        2

      20.0

      40.0

      21.5

      41.1

      18.9

      39.2

      19.0

      39.3

      20.4

      40.3

          

        3

      31.6

      46.5

      31.7

      46.6

      32.2

      46.8

      31.0

      46.3

      31.4

      46.4

          

        4

      11.3

      31.7

      10.2

      30.3

      9.2

      28.9

      12.2

      32.8

      14.4

      35.2

       

      *

        

        ≥5

      14.0

      34.7

      18.1

      38.5

      14.0

      34.8

      11.9

      32.5

      11.2

      31.6

      *

         

      Purchased combination meal

      24.5

      43.0

      21.5

      41.1

      25.7

      43.7

      25.4

      43.6

      25.6

      43.7

          

        Restaurant

                    

        McDonald’s

      64.2

      48.0

      66.2

      47.4

      70.2

      45.8

      61.7

      48.7

      57.0

      49.6

       

      ***

        

        Burger King

      35.8

      48.0

      33.8

      47.4

      29.8

      45.8

      38.3

      48.7

      43.0

      49.6

       

      ***

        

      ***P < 0.001, **P < 0.01, *P < 0.05.

      Table 2 shows regression results for the difference between estimated and actual calories. In the full sample and every subgroup, participants in both cities and time periods tended to underestimate calories purchased, by an average of 216–409 calories. The difference-in-difference coefficient was typically positive, meaning that respondents in Philadelphia were more accurate relative to Baltimore post-calorie labeling, but was only significant for 2 subgroups: respondents who purchased ≤ median number of calories (coefficient = 78, p = 0.04) and respondents with some college education (coefficient = 231, p = 0.03).
      Table 2

      Actual versus estimated calories, Philadelphia versus Baltimore

       

      Actual

      Estimated

      Estimated minus actual

      Difference-in-Difference

      Pre

      Post

      Pre

      Post

      Pre

      Post

      Significance tests

      Unadj

      Adj (95% CI)

      P

      Pre

      Post

      Full sample

        Philadelphia

      987

      927

      578

      581

      −409

      −346

      **

       

      177

      122 (−809, 1052)

      0.35

        Baltimore

      974

      923

      758

      593

      −216

      −330

           

      Purchased >850 calories (median)

        Philadelphia

      1480

      1450

      780

      758

      −700

      −692

      *

       

      223

      191(−2301,2682)

      0.51

        Baltimore

      1430

      1390

      1032

      777

      −398

      −613

           

      Purchased ≤850 calories (median)

        Philadelphia

      446

      459

      357

      422

      −89

      −37

        

      105

      78 (20, 136)

      0.04

        Baltimore

      463

      486

      450

      420

      −13

      −65

           

      Male 1

        Philadelphia

      982

      943

      575

      609

      −407

      −334

        

      130

      124 (−998, 1245)

      0.39

        Baltimore

      1006

      968

      692

      597

      −314

      −370

           

      Female 1

        Philadelphia

      987

      925

      602

      562

      −385

      −363

        

      −41

      −87 (−386, 213)

      0.17

        Baltimore

      993

      834

      689

      591

      −305

      −243

           

      Black 1

        Philadelphia

      933

      858

      543

      585

      −389

      −273

        

      173

      100 (−760, 959)

      0.38

        Baltimore

      1007

      895

      745

      577

      −262

      −318

           

      White 1

        Philadelphia

      1088

      990

      684

      751

      −405

      −239

        

      384

      250 (−524, 1025)

      0.15

        Baltimore

      886

      950

      815

      661

      −71

      −290

           

      High school or less 1

        Philadelphia

      968

      885

      545

      475

      −423

      −409

        

      169

      54 (−590, 698)

      0.48

        Baltimore

      954

      934

      698

      523

      −256

      −411

           

      Some college or AA 1

        Philadelphia

      1028

      977

      582

      811

      −447

      −166

        

      170

      231 (77, 385)

      0.03

        Baltimore

      1065

      914

      758

      718

      −307

      −196

      *

          

      BA or above 1

        Philadelphia

      1065

      1141

      650

      696

      −414

      −445

      *

       

      149

      231(−2138,2600)

      0.43

        Baltimore

      968

      900

      919

      671

      −49

      −229

           

      Food only

        Philadelphia

      801

      691

      521

      528

      −279

      −163

        

      180

      205 (−514, 924)

      0.17

        Baltimore

      774

      719

      618

      500

      −156

      −219

           

      Beverage only

        Philadelphia

      203

      308

      204

      231

      1

      −77

        

      −13

      −60 (−1450,1329)

      0.68

        Baltimore

      306

      368

      341

      338

      35

      −31

           

      Purchased 1 item

        Philadelphia

      320

      316

      221

      286

      −99

      −30

      **

       

      167

      181 (−864, 1226)

      0.27

        Baltimore

      319

      339

      364

      286

      45

      −53

           

      Purchased >1 item

        Philadelphia

      1139

      1138

      660

      683

      −480

      −455

      *

       

      128

      112 (−932, 1156)

      0.40

        Baltimore

      1202

      1093

      895

      683

      −307

      −411

           

      Purchased combination meal

        Philadelphia

      1441

      1512

      768

      738

      −674

      −774

        

      9

      −15(−2050,2019)

      0.94

        Baltimore

      1482

      1383

      932

      723

      −550

      −659

           

      Did not purchase combination meal

        Philadelphia

      863

      725

      527

      527

      −337

      −198

      **

       

      252

      167 (−539, 872)

      0.20

        Baltimore

      801

      764

      698

      548

      −102

      −216

      *

          

      1May not sum to the full sample because of missing gender, race, and/or education for some subjects.

      Unadj: Unadjusted. Adj: Adjusted.

      **P < 0.01, *P < 0.05.

      Table 3 shows the logistic regression results for subjects’ likelihood to underestimate calories, versus overestimating or correctly estimating calories. In the full sample, the odds of underestimation by >100 calories was similar post- vs. pre-calorie labeling legislation, with an adjusted odds ratio[AOR] of 0.90 (95% = 0.67-1.21, p = 0.48). However, gross underestimates were less likely; the AOR for underestimation by >500 calories was 0.75 (95% CI = 0.73-0.77, p < 0.001). Accuracy in Philadelphia post-calorie labeling was most improved for subjects with a BA or higher education (AOR = 0.25, 95% CI = 0.12-0.50, p < 0.001) and for subjects ordering less than the median number of calories (AOR = 0.54, 95% CI = 0.37-0.78, p = 0.001). Accuracy deteriorated among females (AOR = 1.38, p < 0.001), respondents who purchased more than the median number of calories (AOR = 1.27, p = 0.028), and respondents who purchased a combination meal (AOR = 1.23, p = 0.012).
      Table 3

      Error in estimate of number of calories purchased, Philadelphia vs. Baltimore

       

      Philadelphia

      Baltimore

      Difference-in-Difference

      Pre-

      Post-

      Pre-

      Post-

      Unadj.

      Odds ratio

      P

      (95% CI)

      Percent

      Error in estimate of number of calories (kcal) purchased

      Full sample, correct within 100 kcal

        Overestimated by >100 kcal

      11.9

      14.4

      25.6

      15.3

         

        Correctly estimated within 100 kcal

      18.9

      15.5

      10.2

      14.2

         

        Underestimated by >100 kcal

      69.2

      70.0

      64.2

      70.5

      −5.4

      0.90 (0.67-1.21)

      0.48

      Full sample, correct within 250 kcal

        Overestimated by >250 kcal

      9.6

      10.3

      21.6

      11.7

         

        Correctly estimated within 250 kcal

      32.6

      34.8

      29.7

      33.9

         

        Underestimated by >250 kcal

      57.9

      54.9

      48.7

      54.5

      −8.7

      0.82 (0.65-1.04)

      0.095

      Full sample, correct within 500 kcal

        Overestimated by >500 kcal

      6.8

      5.6

      14.5

      7.3

         

        Correctly estimated within 500 kcal

      53.6

      58.8

      53.3

      55.6

         

        Underestimated >500 kcal

      39.6

      35.6

      32.2

      37.1

      −8.8

      0.75 (0.73-0.77)

      <0.001***

      Purchased >850 kcal (median)

        Overestimated by >100 kcal

      9.8

      11.1

      25.0

      8.5

         

        Correctly estimated within 100 kcal

      7.3

      4.8

      3.4

      6.2

         

        Underestimated >100 kcal

      82.9

      84.1

      71.6

      85.3

      −12.5

      1.27 (1.03-1.56)

      0.028*

      Purchased ≤850 kcal (median)

        Overestimated by >100 kcal

      14.3

      17.4

      26.3

      21.7

         

        Correctly estimated within 100 kcal

      31.7

      25.2

      17.7

      21.7

         

        Underestimated >100 kcal

      54.0

      57.5

      55.9

      56.6

      2.7

      0.54 (0.37-0.78)

      0.001**

      Male

        Overestimated by >100 kcal

      12.0

      16.2

      20.7

      14.0

         

        Correctly estimated within 100 kcal

      17.5

      16.6

      9.9

      14.4

         

        Underestimated >100 kcal

      70.4

      67.3

      69.5

      71.6

      −5.3

      0.81 (0.60-1.08)

      0.15

      Female

        Overestimated by >100 kcal

      11.4

      13.2

      21.9

      17.5

         

        Correctly estimated within 100 kcal

      20.5

      13.6

      8.8

      14.3

         

        Underestimated >100 kcal

      68.2

      73.3

      69.3

      68.2

      6.2

      1.38 (1.25-1.53)

      <0.001***

      Black

        Overestimated by >100 kcal

      12.0

      15.3

      25.4

      15.0

         

        Correctly estimated within 100 kcal

      22.2

      16.1

      8.6

      13.5

         

        Underestimated >100 kcal

      65.9

      68.5

      66.0

      71.6

      −2.9

      0.96 (0.60-1.52)

      0.86

      White

        Overestimated by >100 kcal

      13.9

      14.7

      26.4

      17.4

         

        Correctly estimated within 100 kcal

      14.8

      10.5

      13.8

      16.3

         

        Underestimated >100 kcal

      71.3

      74.7

      59.8

      66.3

      −3.1

      1.29 (0.85-1.96)

      0.22

      High school or less

        Overestimated by >100 kcal

      8.6

      12.1

      26.5

      11.9

         

        Correctly estimated within 100 kcal

      21.4

      14.8

      9.0

      13.0

         

        Underestimated >100 kcal

      70.0

      73.1

      64.5

      75.1

      −7.6

      0.82 (0.60-1.13)

      0.22

      Some college or AA

        Overestimated by >100 kcal

      13.6

      18.7

      21.4

      20.0

         

        Correctly estimated within 100 kcal

      15.7

      17.1

      7.9

      16.4

         

        Underestimated >100 kcal

      70.7

      64.2

      70.8

      63.6

      0.7

      1.16 (0.93-1.44)

      0.18

      BA or above

        Overestimated by >100 kcal

      14.6

      20.9

      26.5

      23.4

         

        Correctly estimated within 100 kcal

      12.7

      18.6

      16.3

      14.9

         

        Underestimated >100 kcal

      72.7

      60.5

      57.1

      61.7

      −16.8

      0.25 (0.12-0.50)

      <0.001***

      Food only

        Overestimated by >100 kcal

      10.4

      16.7

      25.2

      15.3

         

        Correctly estimated within 100 kcal

      19.8

      16.1

      10.3

      19.6

         

        Underestimated >100 kcal

      69.8

      67.3

      64.5

      65.0

      −3.0

      0.88 (0.44-1.81)

      0.77

      Beverage only

        Overestimated by >100 kcal

      14.6

      20.9

      26.5

      23.4

         

        Correctly estimated within 100 kcal

      12.7

      18.6

      16.3

      14.9

         

        Underestimated >100 kcal

      72.7

      60.5

      57.1

      61.7

      −16.8

      1.71 (0.12-12.76)

      0.63

      Purchased 1 item

        Overestimated by >100 kcal

      12.6

      17.5

      27.5

      18.2

         

        Correctly estimated within 100 kcal

      40.2

      32.9

      24.5

      24.2

         

        Underestimated >100 kcal

      47.1

      49.6

      48.0

      57.6

      −7.0

      0.73 (0.34-1.60)

      0.44

      Purchased >1 item

        Overestimated by >100 kcal

      11.8

      13.4

      25.0

      14.5

         

        Correctly estimated within 100 kcal

      14.1

      9.6

      5.1

      11.2

         

        Underestimated >100 kcal

      74.2

      77.1

      69.9

      74.3

      −1.5

      0.97 (0.81-1.15)

      0.72

      Purchased combination meal

        Overestimated by >100 kcal

      15.8

      8.8

      21.0

      8.9

         

        Correctly estimated within 100 kcal

      6.9

      5.1

      2.0

      5.4

         

        Underestimated >100 kcal

      77.2

      86.1

      77.0

      85.7

      0.2

      1.23 (1.05-1.44)

      0.012*

      Did not purchase combination meal

        Overestimated by >100 kcal

      10.8

      16.4

      27.2

      17.5

         

        Correctly estimated within 100 kcal

      22.2

      19.1

      12.9

      17.2

         

        Underestimated >100 kcal

      66.9

      64.5

      59.9

      65.2

      −7.8

      0.84 (0.61-1.16)

      0.29

      Unadj.,Unadjusted. kcal: Calories.

      ***P < 0.001, **P < 0.01, *P < 0.05.

      Discussion

      Numerous studies suggest that respondents purchase a similar number of calories pre- and post-calorie labeling [[3]-[5]]. This result has often been interpreted as suggesting that consumers do not use calorie-labeling information.

      Researchers found that consumers in Philadelphia, which implemented calorie-labeling policies, were less likely to grossly underestimate calories (by >500 calories) post-labeling, relative to Baltimore, which did not implement such policies. These results suggest that at some level, consumers may incorporate labeling information, a novel result. Categorical accuracy for underestimation by >100 calories varied widely by subgroup, with improved accuracy among more educated consumers and those ordering small meals, and lower accuracy among women, consumers ordering large meals, and consumers ordering combination meals. No significant differences by race were found. Further research exploring why consumers choose to purchase a high number of calories despite increased awareness of the number of calories purchased is needed.

      Perhaps most notably, respondents with a BA education or higher had a 75% reduction in odds for underestimating by >100 calories in Philadelphia post- versus pre-labeling (Table 3). This finding suggests that public health campaigns to promote understanding of calorie labeling may best be centered around less educated populations, who are less likely to report using posted information [[2]]. While females had 38% increased odds for underestimating by >100 calories post-calorie labeling (Table 3), this finding may be tempered by an 8.1 percentage point increase in the proportion of females in Philadelphia post-calorie labeling (p = 0.010, Table 1), compared with an insignificant change in the proportion of females in Baltimore (p = 0.053, Table 1). We therefore would be cautious not to overinterpret differences in use of calorie labeling by gender, although some prior work in psychology has found greater calorie underestimation by women [[25]]. Additionally, while consumers could have purchased differently as a result of the survey or incentive ($2), the data collection procedures were consistent across all periods and locations, suggesting that this should not influence the impact estimates [[2]].

      We also found that the odds of underestimating calories post-calorie labeling declined in respondents who purchased ≤ median number of calories (AOR = 0.54, p < 0.001) but increased in respondents who purchased > median calories (AOR = 1.27, p = 0.028) (Table 3). Since respondents who purchased combination meals bought twice as many calories as other respondents (medians = 1340 and 670 calories, respectively), it is possible that calorie labels for combination meals were more confusing. These calorie labels typically gave wider ranges (“500-2000 calories”) that required individuals wanting further information to look-up calories for each item in the combination meal. Future research should consider whether providing more detailed information on combination meal calorie labels might improve overall accuracy.

      Appendix

      The change in calories purchased in Philadelphia post-calorie labeling legislation was assumed to derive from two potential factors, calorie labeling legislation or secular trends. To measure secular trends, researchers surveyed calories purchased in Baltimore, a control city, during the same time periods as for Philadelphia. Researchers assumed that the change in calories purchased in Baltimore would represent the secular trend, and any remaining change in calories purchased would be due to calorie labeling legislation. The difference in calories purchased in Philadelphia, relative to the change in calories purchased in Baltimore, is sometimes called the “difference-in-difference.” The regression model was as follows:
      y = a + β 0 × Philadelphia + β 1 × Post + β 2 × Philadelphia * Post + δ × X + ε http://static-content.springer.com/image/art%3A10.1186%2Fs12966-014-0091-2/MediaObjects/12966_2014_Article_91_Equ1_HTML.gif
      where α = constant; Philadelphia = 1 if Philadelphia, 0 if Baltimore; Post = 1 if post-calorie labeling legislation, 0 if pre-calorie labeling legislation; X = an array of all other independent variables (with a corresponding array of coefficient estimates δ); and ε = error term.

      β 2 , the interaction between Philadelphia and post-calorie labeling legislation, represented the difference-in-difference estimate.

      Declarations

      Acknowledgements

      This project was supported by grant number R01HL095935 from the NIH/NHLBI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

      This study was completed while Dr. Taksler was with the Departments of Population Health and Medicine, New York University School of Medicine, New York, NY.

      Funding

      This project was supported by grant number R01HL095935 from the NIH/NHLBI.

      Authors’ Affiliations

      (1)
      Medicine Institute, Cleveland Clinic
      (2)
      Departments of Population Health and Medicine, New York University School of Medicine
      (3)
      New York University Wagner School of Public Service

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      Copyright

      © Taksler and Elbel; licensee BioMed Central Ltd. 2014

      This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.

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