The obesity epidemic has drawn attention to food marketing practices that may increase the likelihood of caloric overconsumption and weight gain. We explored the associations of discounted prices on supermarket purchases of selected high-calorie foods (HCF) and more healthful, low-calorie foods (LCF) by a demographic group at high risk of obesity.
Our mixed methods design used electronic supermarket purchase data from 82 low-income (primarily African American female) shoppers for households with children and qualitative data from focus groups with demographically similar shoppers.
In analyses of 6,493 food purchase transactions over 65 weeks, the odds of buying foods on sale versus at full price were higher for grain-based snacks, sweet snacks, and sugar-sweetened beverages (odds ratios: 6.6, 5.9, and 2.6, respectively; all
Strategies that shift supermarket sales promotions from price reductions for HCF to price reductions for LCF might help prevent obesity by decreasing purchases of HCF.
Research on food marketing practices has become a major focus of public health research 1) to identify practices that contribute to obesity or other risks for diet-related chronic diseases and 2) to inform the development of policies to change such practices (
We analyzed supermarket food purchase data from a sample of primarily low-income African American female shoppers with children. Obesity prevalence is substantially higher among African Americans (children and adults) than among whites (
We used a mixed methods quantitative–qualitative design (
Purchase data were drawn from baseline data collected in 2 studies of financial incentives for the purchase of fruits and vegetables before any intervention took place: a pilot study conducted in April 2010 through August 2010 (study 1; n = 30) (
We examined all purchases in the data files to determine which food purchases fit in 1 of 7 study categories (4 high-calorie foods [HCF] and 3 healthful low-calorie foods [LCF]). Our coding scheme for identifying HCF and LCF, adapted from a study of home food environments (JE Holsten, unpublished doctoral dissertation, University of Pennsylvania, 2010), was based on evidence about foods and beverages associated with excess weight gain. Foods were categorized based on their energy density (kilocalories [kcal]/100 g) and other aspects of nutritional quality (
Quantity information was extracted using detailed descriptions contained in the participants’ purchase data files. For foods that did not have a specific volume or weight given in the store data, we used public sources of product information from manufactures, the US Department of Agriculture (
Types of sales promotions analyzed included electronic coupons (discounts redeemed at the point of sale using a loyalty card) for free items, mix-and-match store discounts (discounts for purchasing a set of items from the same manufacturer), and multi-purchase offers. Additional store discounts included price reductions on produce (per pound) and items on quick sale. Price reductions through paper coupons (4% of all discounts observed) usually could not be associated with a specific food product and were not analyzed.
The resulting purchase data were 16,638 purchases of which 1,172 were nonfood items, 61 for which only the brand could be identified, and 7,289 that were not in 1 of the 7 study categories. Of the remaining 8,116 items, 1,623 did not meet the HCF or LCF criteria for the relevant category (eg, dried fruits were excluded from the fruit category). The 6,493 food purchases covered 924 shopping occasions over 65 weeks and involved 2,223 price discounts.
Descriptive statistics were used to report participant characteristics for the full sample of 82 households. The samples from the 2 studies were compared by using unpaired
For each food category and for LCF and HCF combined, we tabulated the percentage of purchases that involved discounts by food category and by unique shopper. We also calculated the mean percentage of weeks during the study periods that foods were offered on sale.
Confidence intervals (CIs) were bias corrected and accelerated with estimates based on 1,000 bootstrap samples. For each shopper, purchases of specific products were coded according to whether they were purchased on sale or at full price, linking date of purchase to the weeks that those products were offered on sale.
We used fixed effects logistic regression to estimate the ratio of the odds that a food was purchased when it was on sale compared with the odds that it was purchased at full price. For these analyses, CIs were constructed from robust standard errors. To assess how much shoppers saved from discounts, we estimated the mean percentage of discount savings (full price minus price paid then divided by full price) per shopping day. We used bivariate and multivariate fixed effects generalized linear models with a gaussian distribution and log links to assess the mean change in shopper spending that was related to increases of $1 in discount savings. To control for changes in amount spent by a shopper related to differences in the amounts of foods purchased, we used multivariate models to adjust statistically for quantities purchased on a given shopping day. In analyses of purchases in each study category except SSBs, we used number of ounces (weight) purchased as a covariate in the models to adjust for quantities of food purchased. For purchases of SSBs, we adjusted for number of fluid ounces (volume). These fixed effects analyses controlled for repeated observations of shoppers.
All statistical models controlled for exposure time. Estimates from models with log links were exponentiated. All statistical tests were 2-sided.
We recruited focus group participants by using flyers placed in supermarket bags at the study store. Adult caregivers with at least 1 child under age 18 years living in the household were eligible to participate. Interested participants contacted the research office, and if eligible, were informed about the purpose of the focus group. Informed consent was obtained over the telephone, after which we obtained basic sociodemographic information about the shopper and his or her household.
A moderator guide (based on results of the quantitative analyses) was developed to elicit information about shopping practices and patterns of buying on sale (
Three members of the research team met to review the purpose of the focus groups and the process to be used in analyzing verbatim focus group transcripts. They separately read through and labeled the transcripts by highlighting key words, phrases, sentences, and chunks of related sequences of text and labeling those responses (
Of 82 household shoppers, most were women and identified themselves as African American. Almost one-third of participants reported an annual household income of less than $15,000; more than half were enrolled in the Supplemental Nutrition Assistance Program (SNAP). More than half had education beyond a high school diploma. Study 1 participants had more children in the household and were younger than study 2 participants (
| Characteristic | Overall Sample (N = 82) | Study 1 (n = 24) | Study 2 (n = 58) |
|
|---|---|---|---|---|
|
| 4.0 (1.4) | 4.4 (1.5) | 3.8 (1.4) | .15 |
|
| 1.9 (1.1) | 2.3 (1.2) | 1.7 (1.0) | .05 |
|
| 47.5 (14.2) | 40.5 (14.6) | 50.4 (13.2) | .002 |
|
| 8.9 (4.7) | 8.1 (4.9) | 9.2 (4.6) | .31 |
|
| ||||
| Female | 68 (82.9) | 21 (87.5) | 47 (81.0) | .75 |
| Male | 14 (17.1) | 3 (12.5) | 11 (19.0) | |
|
| ||||
| African American | 79 (96.3) | 23 (95.8) | 56 (96.6) | .51 |
| Non-Hispanic white | 2 (2.4) | 1 (4.2) | 1 (1.7) | |
| Hispanic | 4 (4.9) | 3 (12.5) | 1 (1.7) | |
|
| ||||
| Married or living with partner | 28 (34.1) | 8 (33.3) | 20 (34.5) | .62 |
| Single or never married | 28 (34.1) | 10 (41.7) | 18 (31.0) | |
| Divorced, separated, or widowed | 26 (31.7) | 6 (25.0) | 20 (34.5) | |
|
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| ≤High school diploma | 37 (45.1) | 11 (45.8) | 26 (44.8) | .52 |
| Some college or associate’s degree | 32 (39.0) | 11 (45.8) | 21 (36.2) | |
| ≥College graduate | 13 (15.9) | 2 (8.3) | 11 (19.0) | |
|
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| <15,000 | 25 (30.5) | 11 (45.8) | 14 (24.1) | .19 |
| 15,000–25,000 | 32 (39.0) | 6 (25.0) | 26 (44.8) | |
| 25,001–50,000 | 20 (24.4) | 6 (25.0) | 14 (24.1) | |
| 50,001–60,000 | 3 (3.7) | 1 (4.2) | 2 (3.4) | |
|
| 47 (57.3) | 11 (45.8) | 36 (62.1) | .22 |
|
| 23 (28.0) | 6 (25.0) | 17 (29.3) | .79 |
|
| 17 (20.7) | 5 (20.8) | 12 (20.7) | >.99 |
Abbreviations: SD, standard deviation; SNAP, Supplemental Nutrition Assistance Program; WIC, Supplemental Nutrition Program for Women, Infants, and Children.
Six of the 30 households in study 1 enrolled in study 2. Data on dually enrolled households are included only in the study 2 column.
Percentages do not sum to 100% because 4 respondents identified as both African American and Hispanic. One respondent chose not to answer.
Category includes some college, associate’s degrees, and technical school degrees.
Percentages do not sum to 100% because 2 respondents chose not to answer.
There was considerable overlap in the percentage of weeks that HCF and LCF were on sale (
Mean percentage of weeks foods were on sale, by food category and aggregate food category, in an urban supermarket, Philadelphia, Pennsylvania, April through August 2010 and December 2010 through October 2012.
Food Category Mean Percentage of Weeks on Sale (95% Confidence Interval)
19.2 (15.4–25.1) Fruit 16.5 (12.7–20.7) Vegetables 21.4 (14.9–31.9) Low-fat dairy 19.0 (5.0–37.1)
25.7 (21.6–30.4) Sweet snacks 15.3 (12.6–20.0) Savory snacks 32.0 (21.8–48.3) Sugar-sweetened beverages 35.0 (26.6–41.9) Grain-based snacks 22.5 (15.5–29.6)
All shoppers bought on sale at least 1 food included in the study. More than half made 30% or more of their purchases on sale. The proportion of purchases made on sale ranged from 3% to 75%.
Shoppers were more likely to purchase sweet snacks (odds ratio [OR], 5.9; 95% CI, 3.5–10.0), SSBs (OR, 2.6; 95% CI, 1.9–3.7), and grain-based snacks (OR, 6.6; 95% CI, 3.6–12.0) when those items were on sale compared with when sold at full price. The likelihood of buying LCF on sale versus at full price was not significant for fruits (OR, 1.1; 95% CI, 0.7–1.7) or vegetables (OR, 1.3; 95% CI, 0.9–1.8) (
| Food Category | OR |
|
|---|---|---|
|
|
|
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| Fruit | 1.1 (0.7 | .61 |
| Vegetables | 1.3 (0.9 | .15 |
| Low-fat dairy | 4.7 (0.9 | .07 |
|
|
|
|
| Sweet snacks | 5.9 (3.5 | <.001 |
| Savory snacks | 1.1 (0.6 | .77 |
| Sugar-sweetened beverages | 2.6 (1.9 | <.001 |
| Grain-based snacks | 6.6 (3.6 | <.001 |
Abbreviations: OR, odds ratio; CI, confidence interval.
Fixed effects logistic regression models predict that food was purchased (“1”) compared with not purchased (“0”) in weeks that food was on sale (“1”) compared with weeks food was sold at full price (“0”). Estimates are based on 79,087 observations from 81 households that had purchase data on more than 1 day. Models adjusted for household exposure time in the study.
95% CIs constructed from robust standard errors.
When shoppers bought foods on sale, the mean discount varied from 3.9% off the full price for sweet snacks to 37.9% for SSBs and 43.8% for low-fat dairy products (
| Food Category | Mean Discount Savings | Mean Change in Spending, Unadjusted | Mean Change in Spending, Adjusted | ||
|---|---|---|---|---|---|
| % (95% CI) |
| % (95% CI) |
| ||
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|
|
|
|
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| Fruit | 4.3 (3.6 | 12.7 (2.5–17.6) | .002 | 6.7 (0.9–11.3) | .03 |
| Vegetables | 22.6 (19.3 | 12.7 (8.3–23.4) | <.001 | −1.2 (−6.0 to 3.7) | .55 |
| Low-fat dairy | 43.8 (42.8–44.4) | 5.8 (−8.9 to 15.0) | .60 | −10.5 (−43.8 to 9.1) | .44 |
|
|
|
|
|
|
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| Sweet snacks | 3.9 (3.3 | 9.9 (6.9 | <.001 | 5.2 (2.2–7.2) | <.001 |
| Savory snacks | 24.8 (22.8 | 23.3 (11.7 | <.001 | 1.6 (−7.9 to 3.0) | .61 |
| Sugar-sweetened beverages | 37.9 (34.9 | 11.4 (10.4 | <.001 | 5.3 (4.5–12.3) | .001 |
| Grain-based snacks | 20.9 (18.9 | 9.1 (5.7 | <.001 | −0.01 (−3.5 to 1.6) | .97 |
Abbreviation: CI, confidence interval.
Spending is defined as the purchase price minus any discount savings and thus equal to the amount paid by the shopper, expressed as a percentage of dollars spent on all foods in the category.
Adjusted analyses control statistically for the number of ounces purchased.
Savings is defined as the dollar amount by which the purchase price was reduced by discounts, expressed as a percentage of dollars spent on all foods in the category.
All 95% CIs are bias corrected and accelerated with estimates based on 1,000 bootstrap samples.
The 3 focus groups had a total of 20 participants, all identified as African American; 90% were women. The mean age was 49.6 years (SD, 15.3; range, 18–74). Focus group participants were demographically similar to shoppers in the purchase data except that a smaller percentage were enrolled in Supplemental Nutrition Program for Women, Infants, and Children (5% in the focus group vs 28% of the shoppers with purchase data;
The importance of buying on sale was the main organizing theme voiced by focus group participants (
So I will buy, if it’s cheap, it’s on sale, and even if I don’t need it, I’ll still buy it. [Focus group 2]
I always use the circulars and very rarely do I buy anything that’s not on sale, especially if it’s a nonnecessity item. [Focus group 1]
I think if it’s something on sale that you need, or that you know you’re going to get to, or you might run out of, and it’s on sale, stock up on that. [Focus group 2]
It’s hard, I mean, if we didn’t actually make it a job to comparison shop, we would be going home with one bag of food for the week. But we really, diligently, compare and it takes two of us to do it. [Focus group 1]
And this is how we gain, it’s not that we don’t need, you always have to have ideas of you could do with what you have. [Focus Group 1]
Well it’s important but if you have a freezer, another freezer besides your refrigerator, that’s when you do that [purchase multiple items]. [Focus Group 3]
You know what I mean? ’Cause I know [my son] likes the high brand, but I mix it in with the cheaper brand. Sometimes he eats this and sometimes he eats that. [Focus Group 3]
Limited users:
Like I have a list and come into the store and I’ll say I’m gonna get all the things on my list, but then when I walk around and see the sales, I can get it. . . . So I’m gonna get what I got on the list and get that sale. And try to get one or two, more than one, you know? [Focus group 3]
Nonusers:
I don’t come in here with a slip. Whatever’s on sale, that’s what I’m going to get. [Focus group 1]
Users:
I try to shop with a list because when I shop with a list it’s, it’s more organized. I already know what I have in my cabinet, and so I’m not double dipping, double buying, um, so I do like to shop with a list. [Focus group 3]
Sometimes they have produce marked down, salad ninety-nine cents, I’ll eat salad all day. So that’s how I shop. I look for fruits, when it’s on sale, I just buy it. So if that week, if that food is not on sale, I’m not going to lose out because I already had it. So that’s how I shop. [Focus group 1]
They would do better if they would mark down the vegetables more because it’s . . . good for you to eat more vegetables. [Focus group 1]
If I see spinach on sale, I'm buyin’ it. I don't see it often. [Focus group 1]
Vegetables, they’re so expensive. . . . It’s like you have to forgo something else, to get some fresh vegetables. [Focus group 1]
Because it’s good for you, anything that’s good for you, it costs a fortune, so you have to make a choice. [Focus group 1]
Yesterday I was in a terrible predicament in the store, like do I buy the soap powder and bleach, or do I buy the fruit and things that I know my grandkids need. [Focus group 2]
The quantitative and qualitative data were congruent in indicating that shoppers in our sample sought out and took advantage of discounts. This finding is not surprising given evidence that low-income shoppers are generally price sensitive (
One explanation for the observed increase in the likelihood for purchasing HCF on sale versus full price might be that HCF were on sale more frequently than LCF and thus more likely to be purchased during a sales period. However, this hypothesis was not confirmed by our data on sweet and grain-based snacks. Sweet snacks, for example, were on sale the least often of any food category and yet were 5 times more likely to be purchased on sale versus at full price. It is possible that the lure to purchase sweet snacks when they were on sale was heightened because sales were less frequent.
The applicability of these results to low-income and African American shoppers adds to the importance of our findings. Consumption patterns of foods in our HCF and LCF categories do not meet dietary guidelines for the US population in general, and the dietary patterns of African American and low-income adults and children are not as healthful as the dietary patterns of whites (
Strategic use of price discounts or subsidies has been suggested as a policy approach for increasing purchases of fruits and vegetables and other healthful foods (
Various taxation strategies have been studied as a way of raising prices to discourage consumption of HCF (
Strengths of our study include the objective, quantitative data from shoppers at a single supermarket, collected over a period that included all 4 seasons, with multiple weeks of purchase data for each shopper. Using data from a single supermarket facilitates attribution of observed effects to the discounting patterns rather than to other retailing variables, which were presumably the same for all shoppers during any given week. Having complementary qualitative data for shoppers from the same store added context to the findings. We focused on a high-risk population and on specific foods in well-defined categories associated with obesity risk. Because we did not have information that would allow us to interview focus group participants while they were actually shopping, a limitation is our inability to tease apart how the sale influenced purchases in terms of factors such as size of discount or promotional format. We also cannot assess the effect that buying sale products had on eating behaviors or overall dietary quality for shoppers or other household members.
Our findings bring a new dimension to studies of how food prices can influence obesity development in high-risk groups. Research focused specifically on the types of foods offered through sales and associated promotional strategies may identify new causal pathways for an influence of food prices on excess weight gain or on the difficulty of losing weight among those already overweight or obese. The feasibility of retailing policies and practices that discourage discounts on HCF should be explored.
This research was supported by grants (nos. 70014 and 70889) from the Robert Wood Johnson Foundation to the African American Collaborative Obesity Research Network (AACORN). Dr Kumanyika is the chair of AACORN, and Dr DiSantis is a consultant to AACORN. We thank Hillit Hassadim, MD, and Jerene Good for their assistance with the coding of food purchases. We also thank Dr Sonya Grier for the review of an earlier version of this manuscript.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.
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