Preventive Medicine 74 (2015) 81–85
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Changes in quantity, spending, and nutritional characteristics of adult, adolescent and child urban corner store purchases after an environmental intervention Hannah G. Lawman a,⁎, Stephanie Vander Veur a, Giridhar Mallya b, Tara A. McCoy a, Alexis Wojtanowski a, Lisa Colby b, Timothy A. Sanders a, Michelle R. Lent a, Brianna A. Sandoval c, Sandy Sherman c, Judith Wylie-Rosett d, Gary D. Foster a a
Center for Obesity Research and Education, Temple University School of Medicine, USA Philadelphia Department of Public Health, USA c The Food Trust, USA d Department of Epidemiology and Population Health, Albert Einstein College of Medicine, USA b
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Available online 9 December 2014 Keywords: Dietary intake Bodega Obesity
a b s t r a c t Objectives. The purpose of this study was to assess one-year changes in corner store purchases (nutritional characteristics, amount spent) of children, adolescents, and adults in a low-income urban environment before and after implementing an environmental intervention to increase the availability of healthier products. Methods. Corner store owners were provided tools (trainings, signage, refrigeration) to increase the promotion and availability of several healthy foods. Based on the degree of support provided, stores were classified as “basic” or “high-intensity” intervention stores. Data on purchases and their nutrient content were gathered (n = 8671 at baseline, n = 5949 at follow-up) through customer purchase assessment interviews and direct observation outside of 192 corner stores in Philadelphia from March 2011 to August 2012. Results. At baseline, shoppers spent $2.81 ± 3.52 for 643 ± 1065 kcal. Energy, select nutrients, and the total amount spent did not significantly change in the overall sample from baseline to follow-up. Similarly, there was no effect on energy and nutrient content when comparing changes over time between basic and high-intensity stores. Conclusions. There were no significant changes in the energy or nutrient content of corner store purchases one year after implementation of environmental changes to increase the availability of healthier products. © 2014 Published by Elsevier Inc.
Introduction It is well known that corner stores (sometimes called “bodegas”) are associated with high availability of inexpensive, unhealthy foods (candy, fried snacks, sugary drinks) (Lucan et al., 2010; Borradaile et al., 2009; Laska et al., 2010; Cavanaugh et al., 2013; Larson et al., 2009). Previous work has estimated that on average youth spent $1.07 USD at corner stores for approximately 356 cal (Borradaile et al., 2009). Furthermore, 53% of youth reported visiting corners stores daily, and 42% reported shopping at corners stores twice per day. Research has estimated adults spend $2.96 USD on average for 694 cal at corner stores (Lent et al., in press). Corner stores are also disproportionally located in low-income areas where supermarket ⁎ Corresponding author at: Center for Obesity Research and Education, Temple University, 3223 N. Broad Street suite 175, Philadelphia, PA 19140, USA. Fax: + 1 215 707 6475. E-mail address: [email protected] (H.G. Lawman).
http://dx.doi.org/10.1016/j.ypmed.2014.12.003 0091-7435/© 2014 Published by Elsevier Inc.
access is limited and contribute significantly to energy intake among low-income urban individuals (Larson et al., 2009; Borradaile et al., 2009). Thus, corner store food purchases may be especially problematic for low-income, minority populations who show higher rates of obesity and related chronic health problems compared to their higher-income and non-minority peers (Stimpson et al., 2007; Flegal et al., 2010; Diez Roux et al., 2001; Ogden et al., 2014). Previous research has shown retail environment interventions in corner stores may have promise for increasing access to healthy foods (Bodor et al., 2010; Gittelsohn et al., 2008; Pitts et al., 2013; O'Malley et al., 2013). However, few corner store interventions have been systematically evaluated. Previous evaluations have limitations including reliance on store-owner reported sales (Dannefer et al., 2012; Song et al., 2009), a small sample of stores or consumers (Gittelsohn et al., 2010), not examining youth (Song et al., 2009; Dannefer et al., 2012), and/or not undergoing peer-review (Gittelsohn et al., 2012). Some corner store interventions have shown modest increased sales of healthy foods using self or store owner reports (Gittelsohn et al., 2010, 2012;
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Dannefer et al., 2012; Song et al., 2009). However, no studies have examined energy and nutrient characteristics of youth and adults' actual purchases. If corner store interventions are positioned as a strategy to address obesity, objective data on the energy level of purchases are critical to assess their effectiveness. While corner store interventions have the potential to influence a number of health outcomes related to improved diet quality, reducing energy intake is ultimately essential for any intervention targeting obesity. Thus, the purpose of the current study was to: 1) evaluate the effects of a corner store intervention on the energy and nutrient content of customer purchases and 2) assess differences in the intervention effectiveness by age, sex, and level (dose) of the intervention. Method Study design In March 2010, The Food Trust partnered with the Philadelphia Department of Public Health to implement the Healthy Corner Store Initiative (HCSI) on a city-wide scale in Philadelphia, PA (The Food Trust, 2012). The Food Trust's HCSI is a nationally recognized model to improve access to healthy foods through corner store owner training, incentives, and modest infrastructural changes. The current study is an evaluation of the natural experiment in which Get Healthy Philly implemented the HCSI. Corner stores were defined as businesses b 2000 square feet that primarily sold food, had b 4 aisles, and had only 1 cash register. Stores located in high poverty zip codes (≥ 20% of the population has incomes below 100% of the
Federal Poverty Level) were prioritized for recruitment (Cavanaugh et al., 2013). Proximity to schools and other child-serving institutions was also considered. Store owners were individually invited and assessed for their willingness and ability to participate in the HCSI. Of the 630 corner stores who had agreed to participate in the HCSI intervention (described below), 246 stores were randomly selected to also participate in an observational evaluation. Store enrollment and customer purchase assessment (CPA; described in detail below) flow is shown in Fig. 1. Data on participation rates for the CPAs were not collected. Anecdotally, customers rarely declined to participate (estimated to be less than 10%). Data collection occurred over 6 months (mean 114 ± 61 days) at baseline (February–July 2011) in 192 stores and at follow-up (April–September 2012) in 124 stores. Approximately 64.4% of stores where CPAs were obtained at baseline participated in the follow-up assessment. Because of the timing of funding for the evaluation, some baseline CPAs were conducted after the intervention had begun, and some follow-up CPAs were done before stores had finished making all full intervention changes. Store visits were conducted by the intervention team throughout the entire evaluation period to monitor store changes and their completion relative to assessments. These visits occurred in stores that included 65% (6014 of 9238) of CPAs at baseline and 65% (3957 of 6051) of CPAs at post. Among a subset of CPAs, these visits confirmed that baseline assessments occurred after stores had made changes (n = 556 CPAs from 25 stores) or follow-up assessments occurred before high-intensity stores completed the shelving and refrigeration changes (n = 92 CPAs from 11 stores). Therefore, these CPAs were removed from analyses to ensure a true baseline or followup assessment. Baseline CPAs from these stores that occurred before any intervention changes were made or follow-up assessments that occurred after the full high intensity intervention was implemented were retained.
1989 corner stores
630 enrolled
246 randomly selected for evaluation 54 stores removed (34 opted out, 15 removed for safety concerns, 2 ineligible store type, 3 closed)
192 stores (9,238 CPAs) at baseline - 121 basic (5,535 CPAs) - 71 high-intensity stores (3,703 CPAs)
72 stores lost to follow up (53 attempted but no data collected, 6 stores closed, 6 opted out,7 removed for safety concerns)
173 stores (8,671 CPAs) analyzed - 114 basic (5,454 CPAs) - 59 high-intensity stores (3,217 CPAs)
567 CPAs removed (11=outliers, 556 assessed after store changes made)
120 stores (6,051 CPAs) at follow-up - 80 basic (3,806 CPAs) - 40 high-intensity stores (2,245 CPAs)
113 stores (5,949 CPAs analyzed - 73 basic (3,710 CPAs) - 40 high-intensity stores (2,239 CPAs)
102 CPAs removed (11=outliers, 91 assessed before changes completed) Fig. 1. Healthy Corner Store Initiative store enrollment and customer purchase assessment flow. Note: Data were collected in Philadelphia from March 2011 to August 2012.
H.G. Lawman et al. / Preventive Medicine 74 (2015) 81–85
The Institutional Review Boards at Temple University and the Philadelphia Department of Public Health approved the study. Participation was anonymous, and written informed consent was waived. Healthy Corner Store Initiative intervention For the purposes of the evaluation, two dose levels of the HCSI were retrospectively classified. The HCSI “basic” intervention consisted of several components: 1) including 4 new healthy products, preferably 2 new healthy products from two different healthy food categories (fresh fruits and vegetables, canned/dried fruits and vegetables, low-fat dairy, lean meats, and whole grains), 2) participating in a Healthy Food Identification marketing campaign (window and door clings, in-store banners, shelf labels, and recipe cards), and 3) attending at least one business training (focused on healthy product procurement, promotion, and pricing and, as needed, SNAP certification). All store owners received a $100 incentive and could choose healthy items from the approved categories based on their customer preferences. The goal was to implement inventory and marketing changes within 3 months of enrollment. A subset of stores was provided additional business trainings and mini-grants for shelving and refrigeration to help them store, display, and expand their inventory of healthy foods. This constituted the “high-intensity” conversion intervention. To be eligible for the high-intensity intervention, stores had to perform well with the basic intervention (verified inventory changes through field visits by the intervention team) and demonstrate an ability to plan and execute larger inventory changes. Completion of the high-intensity intervention generally required an additional 3–6 months and verification by the intervention staff that the changes had occurred. During retrospective level classifications, stores were only classified as high-intensity if the research staff verified that the high-intensity level changes to shelving and refrigeration were completed.
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Data analysis The intra-class correlation coefficient (ICC) from an unconditional model showed that 2% and 10% of the variance at baseline and follow-up, respectively, were attributable to clustering by store. Therefore, multilevel modeling (purchases clustered within stores) was used with a random intercept for store to determine if there were any changes in each of the nutrient characteristics from baseline to follow-up. Models included time point (baseline vs. follow-up) as the predictor. In separate models, interaction terms were added, including all lower order terms, to determine if nutrient characteristics differed over time by 1) level of intervention (basic versus high-intensity), 2) sex, and 3) age group (child, adolescent, adult). Age group was not available for 36 CPAs, so they were not included in age-related analyses. There were 22 (11 at baseline, 11 at follow-up) purchases that exceeded 10,000 cal (approximately 10 standard deviations above the mean) and were excluded as outliers. These included purchases of oils or lard products that affected the estimates of fat grams. Thus, final sample sizes were 8671 at baseline and 5949 at post. The p-value was set at .01 for all analyses due to the large sample size and multiple comparisons. To investigate potential confounders (e.g., water ice and ice cream sales increasing during summer months), sensitivity analyses examined purchases: 1) from only June and July both at baseline and at follow-up to control for seasonal effects (n = 11,122 CPAs), 2) only from stores that participated in both time points (n = 13,453 CPAs), 3) only from purchases reported as made for self (versus a purchase made for another person) in stores at both time points (n = 12,305 CPAs), and 4) only from stores that had field staff who verified intervention changes were made (n = 8984 CPAs). Results did not change; therefore, all true baseline and post-CPAs were analyzed to maximize power. Data were analyzed using R version 3.0.3 (R Core Development Team, 2013).
Outcomes
Results Corner store purchases Data were collected outside of the stores using customer purchase assessments (CPAs, sometimes called intercepts) (Lent et al., in press). These CPAs were obtained by the research staff interviewing customers and recording direct observations of purchases at corner stores immediately after the purchase. Store owners were introduced to the trained interviewers, who wore identifiable clothing (shirts and/or jackets). The interviewers asked if patrons would allow their purchases to be recorded. The staff asked patrons what they purchased and requested to look in their bags to record the items' names, product categories, and weights/sizes. Interviewers then recorded each food and beverage item purchased (e.g., soda, candy, cereal, bread, etc.). The evaluation staff also asked patrons how much money they just spent, their age category: 1) adult (≥19 years); 2) adolescent (13–18 years); or 3) child (5–12 years), and if the purchase was made for themselves or for another person. CPAs were not conducted if there were no food or beverage items (i.e., cigarettes, newspapers, cleaning supplies). Each CPA (approximately 1.5 min in duration) took place between 8:00 AM and 5:00 PM for 15-minute intervals. The staff stayed longer than 15 min if store traffic was high. The staff attempted to collect CPAs at 6 different morning and afternoon times before the store was no longer visited. Baseline data on corner store purchases have been previously reported (Lent et al., in press). Nutrition information Nutrition information was obtained for all purchased food and beverage items. For items that were packaged, the staff obtained information from the nutrition label. The staff contacted the product manufacturer or distributor directly (via Web site or telephone) for nutrition information when items were no long available for purchase or nutrition labels were absent. Data were obtained from online databases such as CalorieKing.com when product information was not available directly from the manufacturer. After exhausting these methods, remaining nutrition data were obtained from comparable items (similar in size, weight, and ingredients). These items were typically from local vendors and had a very limited distribution. For items that were prepared, the staff purchased an identical item in the corner store (e.g., deli sandwiches). Identical individual components (e.g., bread, deli meat, condiments) were purchased and prepared with the help of store staff to be sure that the typical amounts and types of items were included. Nutrition information from labels of individual components and estimated portions were then used to calculate nutrition information for prepared items. Nutrient characteristics included energy (kcal), fat (g), protein (g), carbohydrate (g), sugar (g), dietary fiber (g), and sodium (mg).
Store and sample characteristics Age distribution at baseline and follow-up was similar across adults (74.2% and 72.4%), adolescents (15.8% and 16.8%), and children (10.0% and 10.7%), respectively. Slightly more males (58.7% and 59.0%) than females (41.3% and 41.0%) participated at baseline and follow-up. At follow-up, 64.6% of stores were classified as basic and 35.4% as highintensity. Store level attrition was 41.1% with comparable rates between basic (39.7%) and high intensity (42.2%; see Fig. 1) stores.
Overall intervention effects on nutritional characteristics There were no significant changes in the energy or nutrient content of purchases from baseline to follow-up (Table 1). There were also no significant differences between basic and high intensity stores over time in the energy and nutrient content of purchases. When comparing changes in basic and high-intensity stores over time (Table 2), the amount spent per purchase increased significantly more over time in high-intensity stores than in basic stores (p b 0.001).
Table 1 Characteristics of baseline (n = 8671) and one-year (n = 5949) purchases. Sample purchase characteristics
Baseline
Follow-up
Delta
SE
p-Value
Total amount spent, $ Total no. of items Energy, kcal Fat, g Protein, g Carbohydrates, g Sugars, g Dietary fiber, g Sodium, mg
2.81 2.19 643.44 21.10 15.43 101.34 65.06 2.48 910.62
2.86 2.13 635.86 20.58 14.73 101.73 64.69 2.56 942.11
0.05 −0.06 −7.57 −0.51 −0.70 0.39 −0.37 0.08 31.49
0.07 0.04 17.38 0.85 0.63 2.77 2.01 0.14 89.55
0.48 0.12 0.66 0.55 0.26 0.89 0.85 0.56 0.72
Note: Adjusted for clustering within store. Data were collected in Philadelphia from March 2011 to August 2012.
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Table 2 Overall sample purchase characteristics of basic versus high-intensity corner stores. Purchase characteristics
Basic stores (n = 9164)
Amount spent, $ Total no. of items Energy, kcal Fat, g Protein, g Carbohydrates, g Sugars, g Dietary fiber, g Sodium, mg
High-intensity stores (n = 5456)
Basic versus high
Baseline mean
Follow-up mean
Δ
Baseline mean
Follow-up mean
Δ
Δ
p
2.86 2.14 633.29 20.56 15.47 100.02 64.05 2.41 935.07
2.69 2.08 603.44 18.81 14.59 97.78 62.10 2.36 915.04
−0.17 −0.06 −29.85 −1.75 −0.88 −2.24 −1.95 −0.05 −20.03
2.71 2.26 660.38 21.98 15.35 103.56 66.76 2.60 869.52
3.17 2.21 695.88 23.86 14.98 109.03 69.44 2.93 993.03
0.46 −0.05 35.50 1.87 −0.37 5.47 2.68 0.33 123.51
0.63 0.01 65.35 3.62 0.52 7.71 4.63 0.38 143.54
b0.01 0.90 0.07 0.04 0.70 0.19 0.27 0.18 0.44
Note: Means are adjusted for clustering by store. Data were collected in Philadelphia from March 2011 to August 2012. ⁎ p b .01, no other within-condition changes were significant. ⁎⁎ p b .001, a significant change indicates a significant difference between basic and high-intensity stores over time (treatment level by time interaction).
Age and sex effects by intervention intensity When comparing changes between basic and high-intensity stores over time by sex and age group, there were no differences in the energy content of purchases (Table 3).
Discussion The current study assessed changes in energy and other nutrients of purchases before and after one year of a corner store intervention in low-income, urban neighborhoods. In addition, it compared changes over time between stores that received a low-intensity or highintensity version of the intervention. Overall, there were no significant changes from baseline to followup in energy content or nutrient characteristics per purchase after one year of the intervention. Similarly, there were no significant differences in the changes in nutrient characteristics in basic versus high-intensity intervention stores after one year. The lack of change in energy of purchases may suggest that a primary emphasis on increasing healthy foods (fruits, vegetables, skim milk, whole grains, lean meats) may not be sufficient to alter the energy or nutrient content of purchases. In addition, interventions designed to promote healthy foods may have varied effectiveness depending on consumers' perceptions of the tastiness of “healthy” foods (Chandon and Wansink, 2012). Significantly more money was spent in high-intensity stores than in low. The reason for this small (approximately 46 cents) increase in high-intensity stores is unknown but might include that consumers purchased a greater number of items, they purchased the promoted healthier items in addition to what they usually bought, and/or the cost of products, including healthier products, increased. Decreased sales has been cited as a common concern from store owners (O'Malley et al., 2013; Dannefer et al., 2012), though results from the current study and others show that sales can actually increase
with the addition of healthier products (Song et al., 2009; Dannefer et al., 2012). An environmental assessment of the same corner store intervention showed improvements in the availability of some fruits and vegetables, low-fat milk, and some low-fat baked goods. However, there were no changes in lower-calorie snacks (e.g., baked chips, 100-calorie snack packs) or no-calorie beverages (e.g., diet soda, water) (Cavanaugh et al., 2014), which would be expected to produce greater reductions in energy. It is possible that less healthy, higher-calorie foods (chips, sugary drinks, cookies, candies) need to be heavily targeted by corners store interventions as well. Alternatively, the one-year follow-up period in the current study may have been too short to observe changes. The increase in store-level variability from baseline to follow-up (2% to 10%) suggests that store-level characteristics made a larger impact on consumer purchases at follow-up. However, it is not known if this change was attributable to the intervention or other environmental changes during the year. Since the completion of this evaluation, the Philadelphia Healthy Corner Store Initiative has been modified and supplemented through a series of programmatic and policy interventions that may impact its ultimate effectiveness. First, The Food Trust and the Philadelphia Department of Public Health have begun implementation of a certification program for corner stores, which consists of greater inventory and promotional changes, competitive pricing for new healthy items, more tailored business trainings, and, in some cases, façade-improvement grants. In approximately 50 stores, refrigeration cases will be outfitted with sugary drinks counter-marketing decals, emphasizing physical activity calorie equivalents. Such messages have been effective in reducing sugary drinks purchases among teens in urban corner stores (Bleich et al., 2012). Lastly, in December 2012, Philadelphia City Council passed an amendment to the zoning code limiting retail advertising (in a content-neutral fashion) to 20% of window and door space. With the large majority of retail ads featuring tobacco, junk foods, and sugary drinks, such a policy could decrease promotion of unhealthy products
Table 3 Estimated mean energy (kcal) by age and sex in basic versus high-intensity intervention stores. Basic
Sex Males Females Age Children Adolescents Adults
High-intensity
Baseline
Follow-up
Δa
Baseline
Follow-up
609.0 669.5
559.5 666.8
−49.5 −2.7
631.9 704.0
669.6 737.3
37.7 33.4
87.2 36.1
0.03 0.53
483.85 614.89 657.30
501.74 625.01 611.90
17.88 10.12 −45.40
484.18 664.85 681.13
456.45 602.08 720.32
−27.73 −62.77 39.19
−45.61 −72.89 84.59
0.57 0.32 0.06
Note: Means adjusted for clustering by store. Data were collected in Philadelphia from March 2011 to August 2012. a No within-group treatment or time comparisons were significant.
Δa
Basic vs. high
p
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(Hillier et al., 2009). The efficacy of these initiatives in improving the overall energy and nutrient content of purchases is unknown and requires further evaluation. Other corner store intervention studies have reported modest changes in the purchase of targeted healthy products, relying primarily on store owner or customer self-report (Gittelsohn et al., 2010; Dannefer et al., 2012; Song et al., 2009). Among 10 healthy corner store initiatives included in a recent systematic review, 9 showed increased purchasing frequency of at least one promoted healthier food (Gittelsohn et al., 2012). One of the largest prior studies of a corner store intervention, which included 55 bodegas in New York City, found an increase in the self-reported purchase of healthy items from 5% to 16% of customers (Dannefer et al., 2012). A small (n = 24 corner stores) randomized controlled trial found no effect of a first-generation corner store intervention on energy intake, nutrition, or body weight in 4th–6th grade students (Lent et al., 2014). Data from the current study are the first available on objectively-assessed, individual-level energy and nutrient changes in youth and adults after a corner store intervention. They suggest that the intervention had no effect on the energy and nutrient content of individual-level purchases. It may be that increased access to healthier items alone is insufficient and greater marketing, counter-marketing, educational, and pricing-related efforts are needed to promote changes that affect the energy and nutrient content of corner store purchases. The current study had limitations including the lack of a randomized control group, inability to compare purchases for the same customers from baseline to follow-up and the relatively short follow-up at 1 year. In addition, the current study included only one purchase assessment and may not be representative of overall shopping patterns. Due to the lag between when the intervention started and when the evaluation funding occurred as well as the challenges associated with working in low-income, high-crime urban areas, the approximately 40% storelevel attrition was a limitation. It is possible this may have reduced power to find a significant positive or negative effect, although the store and CPA sample sizes were large. The attrition may have also biased the findings if lower-performing stores were less likely to participate at follow-up. However, the sensitivity analyses showed no differences in findings across subsets of purchases. Furthermore, previous studies have either not sampled greater than 24 stores or not assessed interventions lasting longer than 5 months (Gittelsohn et al., 2012). Strengths of the current study include the assessment of energy and nutrient of individual purchases, the availability of both basic and high-intensity interventions to assess dose, the use of observed versus self-reported data, the largest sample size of consumers and corner stores, and the longest evaluation period to date. In conclusion, an intervention to increase the availability and promotion of healthy foods in corner stores did not significantly change the energy content of items purchased by a large sample of adults, teens, and children over one year. These data suggest that more intensive interventions and/or those that focus on less healthy foods may be needed to affect the energy content of corner store purchases. Further research, particularly randomized controlled trials, is needed to determine how corner store interventions can promote environmental changes that improve the energy and nutrient profile of the products consumers purchase. Conflict of interest statement While the study was being conducted, Dr. Foster served on Scientific Advisory Boards for Con Agra Foods, United Health Group and Tate & Lyle. Currently, Foster, Vander Veur, and Wojtanowski are employed by Weight Watchers International. No other authors reported financial disclosures.
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Acknowledgments This study was supported by a grant (Cooperative Agreement #3U58DP002626-01S1) from the Centers for Disease Control and Prevention, and from grants (1-P60-DK020541 and F32 DK100248) from the NIDDK of the National Institutes of Health. References Bleich, S., Herring, B., Flagg, D., Gary-Webb, T., 2012. Reduction in purchases of sugarsweetened beverages among low-income black adolescents after exposure to caloric information. Am. J. Public Health 102, 329–335. Bodor, J.N., Ulmer, V.M., Dunaway, L.F., Farley, T.A., Rose, D., 2010. The rationale behind small food store interventions in low-income urban neighborhoods: insights from New Orleans. J. Nutr. 140, 1185–1188. Borradaile, K.E., Sherman, S., Vander Veur, S., et al., 2009. Snacking in children: the role of urban corner stores. Pediatrics 124, 1293–1298. Cavanaugh, E., Mallya, G., Brensinger, C., Tierney, A., Glanz, K., 2013. Nutrition environments in corner stores in Philadelphia. Prev. Med. 56, 149–151. Cavanaugh, E., Green, S., Mallya, G., Tierney, A., Brensinger, C., Glanz, K., 2014. Changes in food and beverage environments after an urban corner store intervention. Prev. Med. 64, 7–12. Chandon, P., Wansink, B., 2012. Does food marketing need to make us fat? A review and solutions. Nutr. Rev. 70, 571–593. Dannefer, R., Williams, D.A., Baronberg, S., Silver, L., 2012. Healthy bodegas: increasing and promoting healthy foods at corner stores in New York City. Am. J. Public Health 102, e27–e31. Diez Roux, A., Merkin, S., Arnett, D., et al., 2001. Neighborhood of residence and incidence of coronary heart disease. N. Engl. J. Med. 345, 99–106. Flegal, K.M., Carroll, M.D., Ogden, C.L., Curtin, L.R., 2010. Prevalence and trends in obesity among US adults, 1999–2008. JAMA 303, 235–241. Gittelsohn, J., Franceschini, M., Rasooly, I., 2008. Understanding the food environment in a low-income urban setting: implications for food store interventions. J. Hunger Environ. Nutr. 2, 33–50. Gittelsohn, J., Song, H., Suratkar, S., et al., 2010. An urban food store intervention positively affects food-related psychosocial variables and food behaviors. Health Educ. Behav. 37, 390–402. Gittelsohn, J., Rowan, M., Gadhoke, P., 2012. Interventions in small food stores to change the food environment, improve diet, and reduce risk of chronic disease. Prev. Chronic. Dis. 9. Hillier, A., Cole, B., Smith, T., et al., 2009. Clustering of unhealthy outdoor advertisements around child-serving institutions: a comparison of three cities. Health Place 15, 935–945. Larson, N., Story, M., Nelson, M.C., 2009. Neighborhood environments: disparities in access to healthy foods in the U.S. Am. J. Prev. Med. 36, 74–81. Laska, M.N., Borradaile, K.E., Tester, J., Foster, G.D., Gittelsohn, J., 2010. Healthy food availability in small urban food stores: a comparison of four US cities. Public Health Nutr. 13, 1031–1035. Lent, M.R., Vander Veur, S., Mallya, G., et al., 2014. Corner store purchases of adults, adolescents and children: items, nutritional characteristics and amount spent. Public Health Nutr. http://dx.doi.org/10.1017/S1368980014001670 (in press). Lent, M.R., Vander Veur, S.S., Mccoy, T.A., et al., 2014. A randomized, controlled study of a healthy corner store initiative on the purchases of urban, low-income youth. Obesity 22, 2494–2500. Lucan, S., Karpyn, A., Sherman, S., 2010. Storing empty calories and chronic disease risk: snack-food products, nutritive content, and manufacturers in Philadelphia corner stores. J. Urban Health 87, 394–409. O'Malley, K., Gustat, J., Rice, J., Johnson, C.C., 2013. Feasibility of increasing access to healthy foods in neighborhood corner stores. J. Community Health 38, 741–749. Ogden, C.L., Carroll, M.D., Kit, B.K., Flegal, K.M., 2014. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 311, 806–814. Pitts, S.B.J., Bringolf, K.R., Lawton, K.K., et al., 2013. Formative evaluation for a healthy corner store initiative in Pitt County, North Carolina: assessing the rural food environment, part 1. Prev. Chronic. Dis. 10, E121. R Core Development Team, 2013. A Language and Environment for Statistical Computing. Song, H.-J., Gittelsohn, J., Kim, M., Suratkar, S., Sharma, S., Anliker, J., 2009. A corner store intervention in a low-income urban community is associated with increased availability and sales of some healthy foods. Public Health Nutr. 12, 2060–2067. Stimpson, J., Nash, A., Ju, H., Eschbach, K., 2007. Neighborhood deprivation is associated with lower levels of serum carotenoids among adults participating in the Third National Health and Nutrition Examination Survey. J. Am. Diet. Assoc. 107, 1895–1902. The Food Trust, 2012. Philadelphia's Healthy Corner Store Initiative: 2010–2012 [Online]. Available:http://thefoodtrust.org/uploads/media_items/hcsi-y2report-final.original. pdf (Accessed January 22, 2013).
Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Changes in quantity, spending, and nutritional characteristics of adult, adolescent and child urban corner store purchases after an environmental intervention Hannah G. Lawman a,⁎, Stephanie Vander Veur a, Giridhar Mallya b, Tara A. McCoy a, Alexis Wojtanowski a, Lisa Colby b, Timothy A. Sanders a, Michelle R. Lent a, Brianna A. Sandoval c, Sandy Sherman c, Judith Wylie-Rosett d, Gary D. Foster a a
Center for Obesity Research and Education, Temple University School of Medicine, USA Philadelphia Department of Public Health, USA c The Food Trust, USA d Department of Epidemiology and Population Health, Albert Einstein College of Medicine, USA b
a r t i c l e
i n f o
Available online 9 December 2014 Keywords: Dietary intake Bodega Obesity
a b s t r a c t Objectives. The purpose of this study was to assess one-year changes in corner store purchases (nutritional characteristics, amount spent) of children, adolescents, and adults in a low-income urban environment before and after implementing an environmental intervention to increase the availability of healthier products. Methods. Corner store owners were provided tools (trainings, signage, refrigeration) to increase the promotion and availability of several healthy foods. Based on the degree of support provided, stores were classified as “basic” or “high-intensity” intervention stores. Data on purchases and their nutrient content were gathered (n = 8671 at baseline, n = 5949 at follow-up) through customer purchase assessment interviews and direct observation outside of 192 corner stores in Philadelphia from March 2011 to August 2012. Results. At baseline, shoppers spent $2.81 ± 3.52 for 643 ± 1065 kcal. Energy, select nutrients, and the total amount spent did not significantly change in the overall sample from baseline to follow-up. Similarly, there was no effect on energy and nutrient content when comparing changes over time between basic and high-intensity stores. Conclusions. There were no significant changes in the energy or nutrient content of corner store purchases one year after implementation of environmental changes to increase the availability of healthier products. © 2014 Published by Elsevier Inc.
Introduction It is well known that corner stores (sometimes called “bodegas”) are associated with high availability of inexpensive, unhealthy foods (candy, fried snacks, sugary drinks) (Lucan et al., 2010; Borradaile et al., 2009; Laska et al., 2010; Cavanaugh et al., 2013; Larson et al., 2009). Previous work has estimated that on average youth spent $1.07 USD at corner stores for approximately 356 cal (Borradaile et al., 2009). Furthermore, 53% of youth reported visiting corners stores daily, and 42% reported shopping at corners stores twice per day. Research has estimated adults spend $2.96 USD on average for 694 cal at corner stores (Lent et al., in press). Corner stores are also disproportionally located in low-income areas where supermarket ⁎ Corresponding author at: Center for Obesity Research and Education, Temple University, 3223 N. Broad Street suite 175, Philadelphia, PA 19140, USA. Fax: + 1 215 707 6475. E-mail address: [email protected] (H.G. Lawman).
http://dx.doi.org/10.1016/j.ypmed.2014.12.003 0091-7435/© 2014 Published by Elsevier Inc.
access is limited and contribute significantly to energy intake among low-income urban individuals (Larson et al., 2009; Borradaile et al., 2009). Thus, corner store food purchases may be especially problematic for low-income, minority populations who show higher rates of obesity and related chronic health problems compared to their higher-income and non-minority peers (Stimpson et al., 2007; Flegal et al., 2010; Diez Roux et al., 2001; Ogden et al., 2014). Previous research has shown retail environment interventions in corner stores may have promise for increasing access to healthy foods (Bodor et al., 2010; Gittelsohn et al., 2008; Pitts et al., 2013; O'Malley et al., 2013). However, few corner store interventions have been systematically evaluated. Previous evaluations have limitations including reliance on store-owner reported sales (Dannefer et al., 2012; Song et al., 2009), a small sample of stores or consumers (Gittelsohn et al., 2010), not examining youth (Song et al., 2009; Dannefer et al., 2012), and/or not undergoing peer-review (Gittelsohn et al., 2012). Some corner store interventions have shown modest increased sales of healthy foods using self or store owner reports (Gittelsohn et al., 2010, 2012;
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Dannefer et al., 2012; Song et al., 2009). However, no studies have examined energy and nutrient characteristics of youth and adults' actual purchases. If corner store interventions are positioned as a strategy to address obesity, objective data on the energy level of purchases are critical to assess their effectiveness. While corner store interventions have the potential to influence a number of health outcomes related to improved diet quality, reducing energy intake is ultimately essential for any intervention targeting obesity. Thus, the purpose of the current study was to: 1) evaluate the effects of a corner store intervention on the energy and nutrient content of customer purchases and 2) assess differences in the intervention effectiveness by age, sex, and level (dose) of the intervention. Method Study design In March 2010, The Food Trust partnered with the Philadelphia Department of Public Health to implement the Healthy Corner Store Initiative (HCSI) on a city-wide scale in Philadelphia, PA (The Food Trust, 2012). The Food Trust's HCSI is a nationally recognized model to improve access to healthy foods through corner store owner training, incentives, and modest infrastructural changes. The current study is an evaluation of the natural experiment in which Get Healthy Philly implemented the HCSI. Corner stores were defined as businesses b 2000 square feet that primarily sold food, had b 4 aisles, and had only 1 cash register. Stores located in high poverty zip codes (≥ 20% of the population has incomes below 100% of the
Federal Poverty Level) were prioritized for recruitment (Cavanaugh et al., 2013). Proximity to schools and other child-serving institutions was also considered. Store owners were individually invited and assessed for their willingness and ability to participate in the HCSI. Of the 630 corner stores who had agreed to participate in the HCSI intervention (described below), 246 stores were randomly selected to also participate in an observational evaluation. Store enrollment and customer purchase assessment (CPA; described in detail below) flow is shown in Fig. 1. Data on participation rates for the CPAs were not collected. Anecdotally, customers rarely declined to participate (estimated to be less than 10%). Data collection occurred over 6 months (mean 114 ± 61 days) at baseline (February–July 2011) in 192 stores and at follow-up (April–September 2012) in 124 stores. Approximately 64.4% of stores where CPAs were obtained at baseline participated in the follow-up assessment. Because of the timing of funding for the evaluation, some baseline CPAs were conducted after the intervention had begun, and some follow-up CPAs were done before stores had finished making all full intervention changes. Store visits were conducted by the intervention team throughout the entire evaluation period to monitor store changes and their completion relative to assessments. These visits occurred in stores that included 65% (6014 of 9238) of CPAs at baseline and 65% (3957 of 6051) of CPAs at post. Among a subset of CPAs, these visits confirmed that baseline assessments occurred after stores had made changes (n = 556 CPAs from 25 stores) or follow-up assessments occurred before high-intensity stores completed the shelving and refrigeration changes (n = 92 CPAs from 11 stores). Therefore, these CPAs were removed from analyses to ensure a true baseline or followup assessment. Baseline CPAs from these stores that occurred before any intervention changes were made or follow-up assessments that occurred after the full high intensity intervention was implemented were retained.
1989 corner stores
630 enrolled
246 randomly selected for evaluation 54 stores removed (34 opted out, 15 removed for safety concerns, 2 ineligible store type, 3 closed)
192 stores (9,238 CPAs) at baseline - 121 basic (5,535 CPAs) - 71 high-intensity stores (3,703 CPAs)
72 stores lost to follow up (53 attempted but no data collected, 6 stores closed, 6 opted out,7 removed for safety concerns)
173 stores (8,671 CPAs) analyzed - 114 basic (5,454 CPAs) - 59 high-intensity stores (3,217 CPAs)
567 CPAs removed (11=outliers, 556 assessed after store changes made)
120 stores (6,051 CPAs) at follow-up - 80 basic (3,806 CPAs) - 40 high-intensity stores (2,245 CPAs)
113 stores (5,949 CPAs analyzed - 73 basic (3,710 CPAs) - 40 high-intensity stores (2,239 CPAs)
102 CPAs removed (11=outliers, 91 assessed before changes completed) Fig. 1. Healthy Corner Store Initiative store enrollment and customer purchase assessment flow. Note: Data were collected in Philadelphia from March 2011 to August 2012.
H.G. Lawman et al. / Preventive Medicine 74 (2015) 81–85
The Institutional Review Boards at Temple University and the Philadelphia Department of Public Health approved the study. Participation was anonymous, and written informed consent was waived. Healthy Corner Store Initiative intervention For the purposes of the evaluation, two dose levels of the HCSI were retrospectively classified. The HCSI “basic” intervention consisted of several components: 1) including 4 new healthy products, preferably 2 new healthy products from two different healthy food categories (fresh fruits and vegetables, canned/dried fruits and vegetables, low-fat dairy, lean meats, and whole grains), 2) participating in a Healthy Food Identification marketing campaign (window and door clings, in-store banners, shelf labels, and recipe cards), and 3) attending at least one business training (focused on healthy product procurement, promotion, and pricing and, as needed, SNAP certification). All store owners received a $100 incentive and could choose healthy items from the approved categories based on their customer preferences. The goal was to implement inventory and marketing changes within 3 months of enrollment. A subset of stores was provided additional business trainings and mini-grants for shelving and refrigeration to help them store, display, and expand their inventory of healthy foods. This constituted the “high-intensity” conversion intervention. To be eligible for the high-intensity intervention, stores had to perform well with the basic intervention (verified inventory changes through field visits by the intervention team) and demonstrate an ability to plan and execute larger inventory changes. Completion of the high-intensity intervention generally required an additional 3–6 months and verification by the intervention staff that the changes had occurred. During retrospective level classifications, stores were only classified as high-intensity if the research staff verified that the high-intensity level changes to shelving and refrigeration were completed.
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Data analysis The intra-class correlation coefficient (ICC) from an unconditional model showed that 2% and 10% of the variance at baseline and follow-up, respectively, were attributable to clustering by store. Therefore, multilevel modeling (purchases clustered within stores) was used with a random intercept for store to determine if there were any changes in each of the nutrient characteristics from baseline to follow-up. Models included time point (baseline vs. follow-up) as the predictor. In separate models, interaction terms were added, including all lower order terms, to determine if nutrient characteristics differed over time by 1) level of intervention (basic versus high-intensity), 2) sex, and 3) age group (child, adolescent, adult). Age group was not available for 36 CPAs, so they were not included in age-related analyses. There were 22 (11 at baseline, 11 at follow-up) purchases that exceeded 10,000 cal (approximately 10 standard deviations above the mean) and were excluded as outliers. These included purchases of oils or lard products that affected the estimates of fat grams. Thus, final sample sizes were 8671 at baseline and 5949 at post. The p-value was set at .01 for all analyses due to the large sample size and multiple comparisons. To investigate potential confounders (e.g., water ice and ice cream sales increasing during summer months), sensitivity analyses examined purchases: 1) from only June and July both at baseline and at follow-up to control for seasonal effects (n = 11,122 CPAs), 2) only from stores that participated in both time points (n = 13,453 CPAs), 3) only from purchases reported as made for self (versus a purchase made for another person) in stores at both time points (n = 12,305 CPAs), and 4) only from stores that had field staff who verified intervention changes were made (n = 8984 CPAs). Results did not change; therefore, all true baseline and post-CPAs were analyzed to maximize power. Data were analyzed using R version 3.0.3 (R Core Development Team, 2013).
Outcomes
Results Corner store purchases Data were collected outside of the stores using customer purchase assessments (CPAs, sometimes called intercepts) (Lent et al., in press). These CPAs were obtained by the research staff interviewing customers and recording direct observations of purchases at corner stores immediately after the purchase. Store owners were introduced to the trained interviewers, who wore identifiable clothing (shirts and/or jackets). The interviewers asked if patrons would allow their purchases to be recorded. The staff asked patrons what they purchased and requested to look in their bags to record the items' names, product categories, and weights/sizes. Interviewers then recorded each food and beverage item purchased (e.g., soda, candy, cereal, bread, etc.). The evaluation staff also asked patrons how much money they just spent, their age category: 1) adult (≥19 years); 2) adolescent (13–18 years); or 3) child (5–12 years), and if the purchase was made for themselves or for another person. CPAs were not conducted if there were no food or beverage items (i.e., cigarettes, newspapers, cleaning supplies). Each CPA (approximately 1.5 min in duration) took place between 8:00 AM and 5:00 PM for 15-minute intervals. The staff stayed longer than 15 min if store traffic was high. The staff attempted to collect CPAs at 6 different morning and afternoon times before the store was no longer visited. Baseline data on corner store purchases have been previously reported (Lent et al., in press). Nutrition information Nutrition information was obtained for all purchased food and beverage items. For items that were packaged, the staff obtained information from the nutrition label. The staff contacted the product manufacturer or distributor directly (via Web site or telephone) for nutrition information when items were no long available for purchase or nutrition labels were absent. Data were obtained from online databases such as CalorieKing.com when product information was not available directly from the manufacturer. After exhausting these methods, remaining nutrition data were obtained from comparable items (similar in size, weight, and ingredients). These items were typically from local vendors and had a very limited distribution. For items that were prepared, the staff purchased an identical item in the corner store (e.g., deli sandwiches). Identical individual components (e.g., bread, deli meat, condiments) were purchased and prepared with the help of store staff to be sure that the typical amounts and types of items were included. Nutrition information from labels of individual components and estimated portions were then used to calculate nutrition information for prepared items. Nutrient characteristics included energy (kcal), fat (g), protein (g), carbohydrate (g), sugar (g), dietary fiber (g), and sodium (mg).
Store and sample characteristics Age distribution at baseline and follow-up was similar across adults (74.2% and 72.4%), adolescents (15.8% and 16.8%), and children (10.0% and 10.7%), respectively. Slightly more males (58.7% and 59.0%) than females (41.3% and 41.0%) participated at baseline and follow-up. At follow-up, 64.6% of stores were classified as basic and 35.4% as highintensity. Store level attrition was 41.1% with comparable rates between basic (39.7%) and high intensity (42.2%; see Fig. 1) stores.
Overall intervention effects on nutritional characteristics There were no significant changes in the energy or nutrient content of purchases from baseline to follow-up (Table 1). There were also no significant differences between basic and high intensity stores over time in the energy and nutrient content of purchases. When comparing changes in basic and high-intensity stores over time (Table 2), the amount spent per purchase increased significantly more over time in high-intensity stores than in basic stores (p b 0.001).
Table 1 Characteristics of baseline (n = 8671) and one-year (n = 5949) purchases. Sample purchase characteristics
Baseline
Follow-up
Delta
SE
p-Value
Total amount spent, $ Total no. of items Energy, kcal Fat, g Protein, g Carbohydrates, g Sugars, g Dietary fiber, g Sodium, mg
2.81 2.19 643.44 21.10 15.43 101.34 65.06 2.48 910.62
2.86 2.13 635.86 20.58 14.73 101.73 64.69 2.56 942.11
0.05 −0.06 −7.57 −0.51 −0.70 0.39 −0.37 0.08 31.49
0.07 0.04 17.38 0.85 0.63 2.77 2.01 0.14 89.55
0.48 0.12 0.66 0.55 0.26 0.89 0.85 0.56 0.72
Note: Adjusted for clustering within store. Data were collected in Philadelphia from March 2011 to August 2012.
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Table 2 Overall sample purchase characteristics of basic versus high-intensity corner stores. Purchase characteristics
Basic stores (n = 9164)
Amount spent, $ Total no. of items Energy, kcal Fat, g Protein, g Carbohydrates, g Sugars, g Dietary fiber, g Sodium, mg
High-intensity stores (n = 5456)
Basic versus high
Baseline mean
Follow-up mean
Δ
Baseline mean
Follow-up mean
Δ
Δ
p
2.86 2.14 633.29 20.56 15.47 100.02 64.05 2.41 935.07
2.69 2.08 603.44 18.81 14.59 97.78 62.10 2.36 915.04
−0.17 −0.06 −29.85 −1.75 −0.88 −2.24 −1.95 −0.05 −20.03
2.71 2.26 660.38 21.98 15.35 103.56 66.76 2.60 869.52
3.17 2.21 695.88 23.86 14.98 109.03 69.44 2.93 993.03
0.46 −0.05 35.50 1.87 −0.37 5.47 2.68 0.33 123.51
0.63 0.01 65.35 3.62 0.52 7.71 4.63 0.38 143.54
b0.01 0.90 0.07 0.04 0.70 0.19 0.27 0.18 0.44
Note: Means are adjusted for clustering by store. Data were collected in Philadelphia from March 2011 to August 2012. ⁎ p b .01, no other within-condition changes were significant. ⁎⁎ p b .001, a significant change indicates a significant difference between basic and high-intensity stores over time (treatment level by time interaction).
Age and sex effects by intervention intensity When comparing changes between basic and high-intensity stores over time by sex and age group, there were no differences in the energy content of purchases (Table 3).
Discussion The current study assessed changes in energy and other nutrients of purchases before and after one year of a corner store intervention in low-income, urban neighborhoods. In addition, it compared changes over time between stores that received a low-intensity or highintensity version of the intervention. Overall, there were no significant changes from baseline to followup in energy content or nutrient characteristics per purchase after one year of the intervention. Similarly, there were no significant differences in the changes in nutrient characteristics in basic versus high-intensity intervention stores after one year. The lack of change in energy of purchases may suggest that a primary emphasis on increasing healthy foods (fruits, vegetables, skim milk, whole grains, lean meats) may not be sufficient to alter the energy or nutrient content of purchases. In addition, interventions designed to promote healthy foods may have varied effectiveness depending on consumers' perceptions of the tastiness of “healthy” foods (Chandon and Wansink, 2012). Significantly more money was spent in high-intensity stores than in low. The reason for this small (approximately 46 cents) increase in high-intensity stores is unknown but might include that consumers purchased a greater number of items, they purchased the promoted healthier items in addition to what they usually bought, and/or the cost of products, including healthier products, increased. Decreased sales has been cited as a common concern from store owners (O'Malley et al., 2013; Dannefer et al., 2012), though results from the current study and others show that sales can actually increase
with the addition of healthier products (Song et al., 2009; Dannefer et al., 2012). An environmental assessment of the same corner store intervention showed improvements in the availability of some fruits and vegetables, low-fat milk, and some low-fat baked goods. However, there were no changes in lower-calorie snacks (e.g., baked chips, 100-calorie snack packs) or no-calorie beverages (e.g., diet soda, water) (Cavanaugh et al., 2014), which would be expected to produce greater reductions in energy. It is possible that less healthy, higher-calorie foods (chips, sugary drinks, cookies, candies) need to be heavily targeted by corners store interventions as well. Alternatively, the one-year follow-up period in the current study may have been too short to observe changes. The increase in store-level variability from baseline to follow-up (2% to 10%) suggests that store-level characteristics made a larger impact on consumer purchases at follow-up. However, it is not known if this change was attributable to the intervention or other environmental changes during the year. Since the completion of this evaluation, the Philadelphia Healthy Corner Store Initiative has been modified and supplemented through a series of programmatic and policy interventions that may impact its ultimate effectiveness. First, The Food Trust and the Philadelphia Department of Public Health have begun implementation of a certification program for corner stores, which consists of greater inventory and promotional changes, competitive pricing for new healthy items, more tailored business trainings, and, in some cases, façade-improvement grants. In approximately 50 stores, refrigeration cases will be outfitted with sugary drinks counter-marketing decals, emphasizing physical activity calorie equivalents. Such messages have been effective in reducing sugary drinks purchases among teens in urban corner stores (Bleich et al., 2012). Lastly, in December 2012, Philadelphia City Council passed an amendment to the zoning code limiting retail advertising (in a content-neutral fashion) to 20% of window and door space. With the large majority of retail ads featuring tobacco, junk foods, and sugary drinks, such a policy could decrease promotion of unhealthy products
Table 3 Estimated mean energy (kcal) by age and sex in basic versus high-intensity intervention stores. Basic
Sex Males Females Age Children Adolescents Adults
High-intensity
Baseline
Follow-up
Δa
Baseline
Follow-up
609.0 669.5
559.5 666.8
−49.5 −2.7
631.9 704.0
669.6 737.3
37.7 33.4
87.2 36.1
0.03 0.53
483.85 614.89 657.30
501.74 625.01 611.90
17.88 10.12 −45.40
484.18 664.85 681.13
456.45 602.08 720.32
−27.73 −62.77 39.19
−45.61 −72.89 84.59
0.57 0.32 0.06
Note: Means adjusted for clustering by store. Data were collected in Philadelphia from March 2011 to August 2012. a No within-group treatment or time comparisons were significant.
Δa
Basic vs. high
p
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(Hillier et al., 2009). The efficacy of these initiatives in improving the overall energy and nutrient content of purchases is unknown and requires further evaluation. Other corner store intervention studies have reported modest changes in the purchase of targeted healthy products, relying primarily on store owner or customer self-report (Gittelsohn et al., 2010; Dannefer et al., 2012; Song et al., 2009). Among 10 healthy corner store initiatives included in a recent systematic review, 9 showed increased purchasing frequency of at least one promoted healthier food (Gittelsohn et al., 2012). One of the largest prior studies of a corner store intervention, which included 55 bodegas in New York City, found an increase in the self-reported purchase of healthy items from 5% to 16% of customers (Dannefer et al., 2012). A small (n = 24 corner stores) randomized controlled trial found no effect of a first-generation corner store intervention on energy intake, nutrition, or body weight in 4th–6th grade students (Lent et al., 2014). Data from the current study are the first available on objectively-assessed, individual-level energy and nutrient changes in youth and adults after a corner store intervention. They suggest that the intervention had no effect on the energy and nutrient content of individual-level purchases. It may be that increased access to healthier items alone is insufficient and greater marketing, counter-marketing, educational, and pricing-related efforts are needed to promote changes that affect the energy and nutrient content of corner store purchases. The current study had limitations including the lack of a randomized control group, inability to compare purchases for the same customers from baseline to follow-up and the relatively short follow-up at 1 year. In addition, the current study included only one purchase assessment and may not be representative of overall shopping patterns. Due to the lag between when the intervention started and when the evaluation funding occurred as well as the challenges associated with working in low-income, high-crime urban areas, the approximately 40% storelevel attrition was a limitation. It is possible this may have reduced power to find a significant positive or negative effect, although the store and CPA sample sizes were large. The attrition may have also biased the findings if lower-performing stores were less likely to participate at follow-up. However, the sensitivity analyses showed no differences in findings across subsets of purchases. Furthermore, previous studies have either not sampled greater than 24 stores or not assessed interventions lasting longer than 5 months (Gittelsohn et al., 2012). Strengths of the current study include the assessment of energy and nutrient of individual purchases, the availability of both basic and high-intensity interventions to assess dose, the use of observed versus self-reported data, the largest sample size of consumers and corner stores, and the longest evaluation period to date. In conclusion, an intervention to increase the availability and promotion of healthy foods in corner stores did not significantly change the energy content of items purchased by a large sample of adults, teens, and children over one year. These data suggest that more intensive interventions and/or those that focus on less healthy foods may be needed to affect the energy content of corner store purchases. Further research, particularly randomized controlled trials, is needed to determine how corner store interventions can promote environmental changes that improve the energy and nutrient profile of the products consumers purchase. Conflict of interest statement While the study was being conducted, Dr. Foster served on Scientific Advisory Boards for Con Agra Foods, United Health Group and Tate & Lyle. Currently, Foster, Vander Veur, and Wojtanowski are employed by Weight Watchers International. No other authors reported financial disclosures.
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Acknowledgments This study was supported by a grant (Cooperative Agreement #3U58DP002626-01S1) from the Centers for Disease Control and Prevention, and from grants (1-P60-DK020541 and F32 DK100248) from the NIDDK of the National Institutes of Health. References Bleich, S., Herring, B., Flagg, D., Gary-Webb, T., 2012. Reduction in purchases of sugarsweetened beverages among low-income black adolescents after exposure to caloric information. Am. J. Public Health 102, 329–335. Bodor, J.N., Ulmer, V.M., Dunaway, L.F., Farley, T.A., Rose, D., 2010. The rationale behind small food store interventions in low-income urban neighborhoods: insights from New Orleans. J. Nutr. 140, 1185–1188. Borradaile, K.E., Sherman, S., Vander Veur, S., et al., 2009. Snacking in children: the role of urban corner stores. Pediatrics 124, 1293–1298. Cavanaugh, E., Mallya, G., Brensinger, C., Tierney, A., Glanz, K., 2013. Nutrition environments in corner stores in Philadelphia. Prev. Med. 56, 149–151. Cavanaugh, E., Green, S., Mallya, G., Tierney, A., Brensinger, C., Glanz, K., 2014. Changes in food and beverage environments after an urban corner store intervention. Prev. Med. 64, 7–12. Chandon, P., Wansink, B., 2012. Does food marketing need to make us fat? A review and solutions. Nutr. Rev. 70, 571–593. Dannefer, R., Williams, D.A., Baronberg, S., Silver, L., 2012. Healthy bodegas: increasing and promoting healthy foods at corner stores in New York City. Am. J. Public Health 102, e27–e31. Diez Roux, A., Merkin, S., Arnett, D., et al., 2001. Neighborhood of residence and incidence of coronary heart disease. N. Engl. J. Med. 345, 99–106. Flegal, K.M., Carroll, M.D., Ogden, C.L., Curtin, L.R., 2010. Prevalence and trends in obesity among US adults, 1999–2008. JAMA 303, 235–241. Gittelsohn, J., Franceschini, M., Rasooly, I., 2008. Understanding the food environment in a low-income urban setting: implications for food store interventions. J. Hunger Environ. Nutr. 2, 33–50. Gittelsohn, J., Song, H., Suratkar, S., et al., 2010. An urban food store intervention positively affects food-related psychosocial variables and food behaviors. Health Educ. Behav. 37, 390–402. Gittelsohn, J., Rowan, M., Gadhoke, P., 2012. Interventions in small food stores to change the food environment, improve diet, and reduce risk of chronic disease. Prev. Chronic. Dis. 9. Hillier, A., Cole, B., Smith, T., et al., 2009. Clustering of unhealthy outdoor advertisements around child-serving institutions: a comparison of three cities. Health Place 15, 935–945. Larson, N., Story, M., Nelson, M.C., 2009. Neighborhood environments: disparities in access to healthy foods in the U.S. Am. J. Prev. Med. 36, 74–81. Laska, M.N., Borradaile, K.E., Tester, J., Foster, G.D., Gittelsohn, J., 2010. Healthy food availability in small urban food stores: a comparison of four US cities. Public Health Nutr. 13, 1031–1035. Lent, M.R., Vander Veur, S., Mallya, G., et al., 2014. Corner store purchases of adults, adolescents and children: items, nutritional characteristics and amount spent. Public Health Nutr. http://dx.doi.org/10.1017/S1368980014001670 (in press). Lent, M.R., Vander Veur, S.S., Mccoy, T.A., et al., 2014. A randomized, controlled study of a healthy corner store initiative on the purchases of urban, low-income youth. Obesity 22, 2494–2500. Lucan, S., Karpyn, A., Sherman, S., 2010. Storing empty calories and chronic disease risk: snack-food products, nutritive content, and manufacturers in Philadelphia corner stores. J. Urban Health 87, 394–409. O'Malley, K., Gustat, J., Rice, J., Johnson, C.C., 2013. Feasibility of increasing access to healthy foods in neighborhood corner stores. J. Community Health 38, 741–749. Ogden, C.L., Carroll, M.D., Kit, B.K., Flegal, K.M., 2014. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 311, 806–814. Pitts, S.B.J., Bringolf, K.R., Lawton, K.K., et al., 2013. Formative evaluation for a healthy corner store initiative in Pitt County, North Carolina: assessing the rural food environment, part 1. Prev. Chronic. Dis. 10, E121. R Core Development Team, 2013. A Language and Environment for Statistical Computing. Song, H.-J., Gittelsohn, J., Kim, M., Suratkar, S., Sharma, S., Anliker, J., 2009. A corner store intervention in a low-income urban community is associated with increased availability and sales of some healthy foods. Public Health Nutr. 12, 2060–2067. Stimpson, J., Nash, A., Ju, H., Eschbach, K., 2007. Neighborhood deprivation is associated with lower levels of serum carotenoids among adults participating in the Third National Health and Nutrition Examination Survey. J. Am. Diet. Assoc. 107, 1895–1902. The Food Trust, 2012. Philadelphia's Healthy Corner Store Initiative: 2010–2012 [Online]. Available:http://thefoodtrust.org/uploads/media_items/hcsi-y2report-final.original. pdf (Accessed January 22, 2013).
