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Disparities in Sugary Drink Advertising on New York City Streets
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RESEARCH ARTICLE
Disparities in Sugary Drink Advertising on New York City Streets Erin A. Dowling, MPH,1 Calpurnyia Roberts, PhD,1 Tamar Adjoian, MPH,2 Shannon M. Farley, DrPH,2 Rachel Dannefer, MPH3
Introduction: Studies show that outdoor advertisements for unhealthy, consumable products are associated with increased intake and often target youth, low-income neighborhoods, and neighborhoods of color. Despite evidence that overconsumption of sugary drinks contributes to obesity and other chronic conditions, little is known specifically regarding the patterns of outdoor sugary drink advertising.
Methods: The number of outdoor, street-level advertisements featuring sugary drinks was assessed in a random sample of retail-dense street segments (N=953) in low, medium, and high-poverty neighborhoods in each of New York City’s 5 boroughs in 2015. Negative binomial regression was used to determine associations between sugary drink ad density, poverty level, and other census tract-level demographics (2009−2013 estimates) in each borough and New York City overall. Data were analyzed in 2017−2019.
Results: In New York City and in 3 of 5 boroughs, sugary drink ad density was positively associated with increased percentages of black, non-Latino residents (New York City: incidence rate ratio=1.20, p<0.001; Bronx: incidence rate ratio=1.30, p=0.005; Brooklyn: incidence rate ratio=1.18, p<0.001; Manhattan: incidence rate ratio=1.20, p<0.05). Positive associations were also observed with poverty level in Brooklyn (low versus medium poverty: incidence rate ratio=2.16, p=0.09; low versus high poverty: incidence rate ratio=2.17, p=0.02) and Staten Island (low versus medium poverty: incidence rate ratio=3.27, p=0.03).
Conclusions: This study found a consistent positive association between the density of outdoor sugary drink advertisements and the presence of non-Latino black residents in New York City and, in some boroughs, evidence of a positive association with neighborhood poverty. These findings highlight the inequities where sugary drinks are advertised in New York City. Am J Prev Med 2019;000(000):1−9. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
INTRODUCTION
I
t has been estimated that Americans consume more than 300 kilocalories of added sugars per day, on average.1 This equates to well above the 10% of total daily energy intake recommended by 2015−2020 Dietary Guidelines for Americans.2 Beverages are the largest source of added sugars in the American diet, accounting for 47% of added sugars per day for both children and adults.2 Several prospective studies and meta-analyses have demonstrated that excessive consumption of sugary drinks (which include soda, sweetened iced teas, sports drinks, energy drinks, and fruit drinks with added sugar)
is associated with weight gain, as well as an increased risk of obesity, type 2 diabetes, and cardiovascular disease.3−8 As these beverages add extra calories and sugar not From the 1Bronx Neighborhood Health Action Center, New York City Department of Health and Mental Hygiene, New York, New York; 2 Bureau of Chronic Disease Prevention and Tobacco Control, New York City Department of Health and Mental Hygiene, New York, New York; and 3Harlem Neighborhood Health Action Center, New York City Department of Health and Mental Hygiene, New York, New York Address correspondence to: Erin A. Dowling, MPH, New York City Department of Health and Mental Hygiene, 4209 28th Street, Long Island City, NY 11101. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.09.025
© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
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otherwise compensated for in one’s diet, reducing consumption is often identified as a key strategy in preventing obesity and obesity-related diseases.4 In New York City (NYC), sugary drink consumption has significantly declined during the past decade,9 possibly owing in part to citywide media campaigns, policy proposals, and implementation of food and beverage standards in city-owned buildings.10 From 2007 to 2016, the percentage of adults that reported consuming 1 or more sugary drinks per day decreased from 35.9% (95% CI=34.6%, 37.1%) to 22.7% (95% CI=21.5%, 23.9%)9; however, disparities in consumption patterns persist. The percentage of Latino and black, non-Latino residents that report drinking at least 1 sugary drink per day has consistently been double that of white, non-Latino and Asian or Pacific Islander residents.9 Further differences occur along economic lines. In 2016, drinking 1 or more sugary drinks daily was self-reported by 25.7% of adults in low-income households compared with 16.9% of those in high-income households.9 There is also variation across the 5 boroughs of NYC (Bronx, Brooklyn, Manhattan, Queens, and Staten Island), with Bronx residents regularly reporting the highest rates of sugary drink consumption.9 These race, income, and geographic disparities highlight the need to understand further how social and environmental factors influence sugary drink intake. Although NYC is 1 of the most diverse cities in the world, it is also largely segregated along racial and economic lines. This persistent segregation combined with systems of discriminatory practices disproportionately expose low-income, nonwhite residents to factors in their living environments that contribute to poor and preventable health outcomes.11 The pervasiveness of outdoor advertisements is 1 way the built environment may influence community health by encouraging the purchase and consumption of specific products.12−14 Studies show that low-income neighborhoods, neighborhoods of color, those with lower educational attainment, and those with large youth populations are often overburdened by exposure to a higher quantity of outdoor advertisements overall, as well as those featuring harmful or unhealthy content.15−21 Further evidence suggests such targeted marketing of unhealthy food toward children and nonwhite populations contributes to poor dietary behaviors and disparities in health outcomes.22,23 However, few studies have specifically assessed sugary drink advertisements in the outdoor environment; instead, they have been included under the umbrella of unhealthy food advertisements. The present study builds on previous efforts by estimating the density of streetlevel sugary drink advertisements across the 5 boroughs of NYC and describing variation by neighborhood.
METHODS Study Sample Data were collected through the Community Marketing Study, which was conducted to assess the density of outdoor, street level, stationary advertisements of consumable products (nonalcoholic beverages, food products, tobacco products, and alcoholic beverages) within each of the 5 boroughs that constitute NYC. Linear Integrated Ordered Network, a dataset containing information on all street features in NYC,24 and Primary Land Use Tax Lot Output, a database of land use, property-level tax lot, and business locations maintained by NYC Department of City Planning,25 were used to identify retail-dense street segments. Street segments consisted of both sides of a street from 1 cross street to the next, including streets separated by a median. Segments were considered “retail-dense” if ≥50% of building entrances on the segment were for retail establishments.26 Using this method, 7,963 street segments were deemed eligible for inclusion. Identified street segments were matched to the census tract in which they were located and then grouped into 3 levels of neighborhood poverty. Neighborhood poverty was assigned following the recommended guidelines of the NYC Department of Health and Mental Hygiene27 (low poverty, where <10% of census tract residents live below the federal poverty threshold; medium poverty, where 10% to <20% of census tract residents live below the threshold; and high poverty, where ≥20% of census tract residents live below the threshold based on 2009−2013 estimates).28 Street segments that lay within 2 census tracts with differing poverty levels were assigned to the higher-income poverty level group to render more-conservative estimates, as increased advertising density was anticipated in lower-income areas. Street segments were then stratified by borough and randomly sampled, with a 5% oversample to ensure a minimum of 50 segments per stratum in the event some segments were deemed ineligible during data collection procedures. Surveyors photographed advertisements along both sides of sampled segments in the summer of 2015. Advertisements were defined as street level (first floor level up to and including any awnings); stationary signs (e.g., posters, digital signs, stickers, and decals; examples shown in Appendix Figure 1, available online) on outdoor structures, such as newsstands, bus shelters, and payphones; and were included if they displayed a product with the intended purpose of promoting that product or type of product (Appendix Table 1, available online, describes the complete inclusion and exclusion criteria).29-31 During canvassing, segments were excluded if there were no stores and retail doorways (thus, not meeting the definition of retail-dense), advertisements were obscured (i.e., segments were at ≥50% covered with scaffolding; the stores and retail doorways were offset from the sidewalk and therefore not visible from the street), or were otherwise inaccessible (Figure 1).29
Measures Sugary drink ads were defined as those featuring pictures or isolated logos of beverages that have added caloric sweetener and >25 calories per 8-ounce serving (e.g., soda, sweetened iced teas, fruit drinks, sports drinks, energy drinks, flavored milk, milkshakes, sugary coffee drinks; Appendix Table 2,29 available online). A comprehensive coding protocol was developed using nutrition information obtained from corporate websites to www.ajpmonline.org
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Figure 1. Composition of street segments sampled, NYC Community Marketing Study, 2015.29 NYC, New York City.
determine whether featured beverage products met these criteria. In rare cases, when this information was unavailable, the research team visited stores to record nutrition information from products. As a secondary analysis, when ads contained multiple sugary drink products, the number of distinct images was counted (Appendix Figure 2, available online). Identical products appearing multiple times in the same advertisements were not each counted as distinct images. If different varieties of 1 type of product appeared in an ad, including different sizes or flavors, each of these counted as a distinct product image. Sugary drink ad density was standardized per 1,000 feet of a retail-dense street segment (roughly the equivalent of 3 city blocks) to account for the fact that street segments varied in length. The density of ads per street segment was calculated as follows: Sugary Drink Ad Density ¼
number of sugary drink ads on each street segment x 1000: feet of retaildense street segment
Statistical Analysis Sugary drink ad density was analyzed by borough to reduce potential confounding because of difficult-to-measure characteristics unique to or that manifest differently in each borough, such as differences in the built environment and the specific patterns of ethnic and cultural segregation and gentrification. Results were also stratified by neighborhood poverty level (low, medium, and high poverty). Demographic characteristics for the census tracts where segments were located were chosen for inclusion based on previous research indicating potential associations with targeted marketing.15−18 Data for these characteristics were obtained from the 5-year estimates of the 2009−2013 American Community Survey.28 In addition, population density was included to control for differences in population and landmass, which vary drastically between boroughs (Bronx: 1.4 million, 26.9 thousand acres; Brooklyn: 2.5 million, 45.3 thousand acres; Manhattan: 1.6 million, 14.6 thousand acres; Queens: 2.3 million, 69.5 thousand acres; and Staten Island: 470 thousand, 37.4 thousand acres).32 Most of the demographic variables were not normally distributed; thus, medians and IQRs were used to summarize variables within each stratum. & 2019
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Negative binomial regression was used to estimate the relative density of sugary drinks ads per 1,000 feet of retail-dense street segment to account for overdispersion of the response variable. The incidence rate ratio (IRR) was reported as a measure of relative density for poverty level and demographic variables within each borough. Bivariate and multivariate analyses were conducted to assess the association of sugary drink ad density with each variable of interest within each borough. Covariates included: neighborhood poverty level; percentage aged 18−24 years without a high school (HS) diploma; percentage aged <18 years; percentage Latino; percentage white, non-Latino; percentage black, nonLatino; percentage Asian or Pacific Islander; and population density. Except for neighborhood poverty, these variables were treated as continuous and reported in increments of 10-percentage units. The percentage of white, non-Latino residents was excluded in the adjusted analysis as it was often strongly correlated with other variables and presented issues of multicollinearity. A sensitivity analysis was conducted to assess findings by the number of discrete sugary drink images. All data analysis was conducted using SAS Enterprise Guide, version 7.1. This project was submitted to the NYC Department of Health and Mental Hygiene IRB and was determined to be exempt from IRB review, as it did not qualify as human subjects research.
RESULTS The final sample included 953 retail-dense street segments (Figure 1),29 which were roughly evenly distributed in low- (301), medium- (315), and high-poverty (337) neighborhoods (Table 1). The characteristics of sampled segments varied by neighborhood poverty level, both within each borough and across NYC. Higher concentrations of population density, youth, and residents without an HS diploma or equivalent were observed in high-poverty neighborhoods. The highest proportion of black, nonLatinos and Latinos consistently resided in high-poverty neighborhoods, irrespective of county. By contrast, the most significant percentage of white, non-Latinos was in low-poverty neighborhoods in each borough. Overall, 4,356 advertisements were featuring sugary drinks with 8,197 sugary drink images observed in the sample (Table 2). There were between 2.72 (Staten Island, low-poverty census tracts) and 29.91 (Bronx, mediumpoverty census tracts) ads for sugary drinks observed per 1,000 feet of retail-dense street segment in the sample, meaning someone walking the length of 3 city blocks in a retail-dense area would encounter anywhere from about 3 to 30 ads (7−48 images), depending on the neighborhood they were in (Table 2). On average, there were about 2 images per ad in NYC, irrespective of borough. Findings by images did not differ substantively from those for ads. Table 3 shows the unadjusted and adjusted models for the association between census tract-level characteristics and sugary drink ad density. In unadjusted analyses for NYC, sugary drink ad density was 1.54 (95% CI=1.16,
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Table 1. Sociodemographic Characteristics for Sampled Street Segments in Each Strata and for NYC Overall No. census tracts
Total no. of sampled street segmentsa
Population density (per acre)
Under 18 (%)
Low
18
32
49.6 (31.6−53.4)
20.7 (17.0−24.9)
Medium
27
42
55 (44.1−76.3)
21.1 (18.5−24.4)
High
55
66
114 (90.4−160.4)
30.3 (26.3−34.0)
32.3 (23.2−43.6)
Low
40
50
56.6 (47.4−75.0)
16.3 (12.3−20.1)
6.2 (0.0−14.7)
Medium
69
90
73.5 (58.0−88.7)
19.4 (15.7−23.4)
8.9 (3.0−18.1)
High
91
109
92.2 (73.1−118.9)
24.1 (20.7−28.9)
20.4 (11.3−31.4)
19.2 (11.2−40.4)
Low
67
110
121.1 (60.8−176.8)
10.4 (7.7−13.8)
0.0 (0.0−3.1)
6.5 (4.5−9.3)
2.2 (1.0−3.9)
77.7 (67.7−83.6)
9.0 (6.9−12.7)
Medium
35
61
122.6 (80.6−156.2)
7.4 (5.6−13.3)
2.7 (0.0−7.0)
7.9 (7.9−20.1)
4.2 (1.8−6.4)
63.6 (46.7−71.5)
13.2 (10.4−17.6)
High
40
70
154.9 (100.1−184.2) 14.7 (11.5−19.1)
15.9 (11.4−27.3)
26.5 (9.3−50.4)
7.0 (2.9−16.8)
12.8 (6.7−28.3)
24.9 (2.6−59.8)
Low
52
68
35.8 (26.1−47.7)
20.5 (16.7−22.9)
9.4 (1.7−17.7)
13.8 (7.5−22.4)
1.5 (0.5−10.0)
40.6 (10.1−65.2)
21.9 (6.5−27.8)
Medium
65
79
71.3 (52.1−90.1)
19.7 (16.5−22.5)
12.2 (6.8−20.9)
27.0 (17.2−41.1)
2.9 (0.7−9.6)
15.7 (4.9−47.0)
26.7 (12.2−38.9)
High
29
43
93.8 (45.6−130.5)
23.6 (18.8−25.8)
23.0 (16.3−27.2)
40.4 (30.3−72.6)
2.9 (1.3−11.3)
10.2 (2.5−19.5)
16.5 (9.8−42.8)
Neighborhood poverty level per borough
Hispanic or Latino (%)
Black, non-Latino or African American (%)
White, non-Latino (%)
9.1 (2.6−19.0)
24.9 (15.7−33.4)
12.4 (0.5−19.7)
46.8 (4.3−73.2)
1.1 (0.8−8.4)
17.0 (8.7−24.1)
33.8 (21.1−48.9)
37.7 (5.0−67.7)
5.7 (2.2−34.7)
5.3 (1.1−12.5)
65.8 (59.3−73.0)
23.9 (17.7−33.0)
2.5 (1.1−4.7)
1.4 (0.1−2.7)
9.8 (6.1−15.4)
3.1 (0.8−13.3)
66.3 (55.1−74.4)
6.4 (3.3−10.9)
10.6 (6.0−16.0)
2.7 (0.6−58.5)
47.9 (14.3−67.0)
9.5 (3.7−27.7)
12.0 (1.6−69.6)
14.0 (4.3−54.2)
5.1 (1.8−12.8)
Less than HS diploma or GED (%)
Asian or Pacific Islander (%)
Bronx
Queens
Staten Island Low
21
41
19.2 (12.3−21.7)
22.0 (20.3−24.2)
6.6 (0.7−11.8)
13.1 (10.1−22.3)
1.8 (0.1−4.4)
74.8 (69.2−8.7)
Medium
16
43
17.7 (12.2−18.5)
22.5 (21.2−26.6)
10.7 (4.1−17.6)
14.6 (11.6−29.0)
6.9 (3.3−21.0)
67.1 (33.4−72.7)
10.4 (7.1−11.2)
5.6 (2.7−9.4)
High
14
49
25.3 (21.6−33.4)
27.2 (23.6−31.5)
18.3 (15.3−35.0)
42.6 (32.7−47.9)
26.8 (21.3−32.3)
25.8 (12.2−27.4)
2.6 (1.7−7.6)
Low
198
301
50.1 (26.7−96.6)
16.4 (10.7−21.4)
3.9 (0.0−11.8)
10.1 (6.4−18.0)
2.5 (0.8−5.8)
69.2 (50.1−79.5)
8.1 (4.0−14.6)
Medium
212
315
68.9 (44.1−97.2)
19.2 (13.7−22.6)
9.2 (3.4−17.9)
16.5 (9.6−29.0)
4.2 (1.5−22.0)
43.7 (10.8−65.9)
11.3 (6.5−22.5)
High
229
337
95.6 (52.9−143.7)
24.3 (19.1−30.3)
22.9 (14.3−34.0)
39.6 (17.0−62.7)
17.1 (2.9−32.9)
NYC overall
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Note: Values expressed as median (IQR) unless specified otherwise. a Street segments are defined as both sides of a street or “block” from 1 cross street to the next, including those separated by a median. HS, high school; NYC, New York City.
11.0 (3.4−27.4)
5.1 (1.7−16.0)
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Table 2. Total Sugary Drink Advertisements, Images, and Image Density in Each Strata and NYC Overall Neighborhood poverty level per borough Bronx Low Medium High Total Brooklyn Low Medium High Total Manhattan Low Medium High Total Queens Low Medium High Total Staten Island Low Medium High Total NYC overall Low Medium High Total
Total sugary drink ads
Total sugary drink images
Sugary drink ad density (total per 1,000 feet)
Sugary drink image density (total per 1,000 feet)
131 367 429 927
229 601 955 1,785
12.87 29.91 19.66 20.94
22.49 48.98 43.77 40.32
144 581 829 1,554
271 1,018 1,504 2,793
9.46 22.07 24.94 20.78
17.80 38.67 45.24 37.34
245 126 305 676
442 272 568 1,282
6.20 5.36 12.38 7.71
11.18 11.56 23.05 14.62
397 376 242 1,015
752 721 478 1,951
21.89 18.02 16.87 19.03
41.46 34.56 33.33 36.58
36 68 80 184
95 132 159 386
2.72 5.20 6.11 4.67
7.19 10.10 12.14 9.80
953 1,518 1,885 4,356
1,789 2,744 3,664 8,197
9.90 15.80 17.59 14.54
18.58 28.57 34.20 27.37
NYC, New York City.
2.04) times as high for medium- versus low-poverty neighborhoods and 1.66 (95% CI=1.26, 2.19) times as great for high- versus low-poverty neighborhoods. Associations with neighborhood poverty were also observed in Brooklyn (low versus medium poverty: IRR=2.43, 95% CI=1.37, 4.31; low versus high poverty: IRR=2.83, 95% CI=1.62, 4.92), Manhattan (low versus high poverty: IRR=2.10, 95% CI=1.28, 3.43), and Bronx (low versus high poverty: IRR=2.14, 95% CI=1.03, 4.45). However, after adjusting for all other census tract-level characteristics, associations of sugary drink ad density along street segments with neighborhood poverty level citywide were only observed in Brooklyn (low versus medium poverty: IRR=2.16, 95% CI=1.20, 3.87; low versus high poverty: IRR=2.17, 95% CI=1.13, 4.14) and Staten Island (low versus medium poverty: IRR=3.27, 95% CI=1.15, 9.26). & 2019
In the adjusted model, there were associations observed in NYC overall between street-level sugary drink ad density and the percentage of census tract residents with less than an HS diploma (10-unit IRR=1.15, 95% CI=1.04, 1.27); the percentage of Asian or Pacific Islander residents (10-unit IRR=1.10, 95% CI=1.01, 1.19); and the percentage of black, non-Latino residents (10-unit IRR=1.20, 95% CI=1.14, 1.26). When analyzed by borough, the percentage of black, non-Latino residents was also associated with sugary drink ad density in Bronx (10-unit IRR=1.30, 95% CI=1.07, 1.56), Brooklyn (10-unit IRR=1.18, 95% CI=1.09, 1.27), and Manhattan (10-unit IRR=1.20, 95% CI=1.00, 1.43). In Queens, an inverse association was observed for the percentage of Latino residents after adjusting for all other variables (10-unit IRR=0.80, 95% CI=0.66, 0.96), and in Staten Island, population density showed a small association
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Table 3. Association Between Sugary Drink Ad Density and Neighborhood Characteristics for Each Borough and NYC Overall Adjusted IRRa
Unadjusted IRR Characteristics
Bronx
Brooklyn
Manhattan
Queens
Staten Island
NYC overall
Bronx
Brooklyn
Manhattan
Queens
Staten Island
NYC overall
Neighborhood poverty level Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
2.43 (1.37, 4.31)**
0.95 (0.55, 1.62)
0.77 (0.44, 1.33)
1.28 (0.85, 1.90)
1.54 (1.16, 2.04)**
1.54 (0.63, 3.69)
2.16 (1.20, 3.87)**
0.73 (0.42, 1.29)
1.50 (0.80, 2.79)
3.27 (1.15, 9.26)*
1.32 (0.99, 1.75)
Population density
1.70 (0.83, 3.48) 1.00 (0.99, 1.01)
2.83 (1.62, 4.92)*** 1.00 (0.99, 1.00)
2.10 (1.28, 3.43)** 1.00 (1.00, 1.01)*
0.71 (0.37, 1.36) 0.99 (0.98, 0.99)**
1.00 (1.00, 1.00) 1.01 (0.97, 1.05)
1.66 (1.26, 2.19)*** 1.00 (0.99, 1.00)
0.79 (0.27, 2.33) 0.99 (0.99, 1.00)
2.17 (1.13, 4.14)* 0.99 (0.99, 1.01)
0.64 (0.27, 1.51) 1.00 (0.99, 1.01)
1.56 (0.66, 3.64) 0.99 (0.99, 1.01)
0.68 (0.13, 3.58) 1.06 (1.00, 1.12)*
0.99 (0.70, 1.28) 1.00 (0.99, 1.00)
Aged <18 yearsb Less than high school diplomab
1.61 (0.92, 2.79) 1.15 (0.93, 1.43)
0.99 (0.73, 1.36) 1.14 (0.97, 1.33)
2.00 (1.43, 2.80)*** 1.63 (1.35, 1.97)***
1.32 (0.86. 2.07) 1.16 (0.95, 1.41)
0.90 (0.51, 1.59) 1.21 (0.89, 1.62)
1.44 (1.23, 1.68)*** 1.29 (1.17, 1.41)***
1.86 (0.83, 4.14) 1.09 (0.82, 1.56)
0.81 (0.61, 1.08) 1.07 (0.92, 1.27)
0.95 (0.58, 1.53) 1.30 (0.94, 1.79)
1.26 (0.78, 2.01) 1.12 (0.89, 1.41)
0.55 (0.25, 1.90) 1.28 (0.85, 1.91)
1.08 (0.90, 1.28) 1.15 (1.04, 1.27)**
0.96 (0.86, 1.09) 1.07 (0.87, 1.30)
0.83 − − − (0.80, 0.87)*** 1.19 1.30 1.18 1.20 (1.14, 1.24)*** (1.07, 1.56)** (1.09, 1.27)*** (1.00, 1.43)*
−
−
−
1.10 (0.96, 1.25)
0.87 (0.61, 1.24)
1.20 (1.14, 1.26)***
0.79 (0.66, 0.96)* 0.88 (0.76, 1.04)
1.32 (0.84, 2.08) 0.45 (0.20, 1.00)
1.05 (0.97, 1.13) 1.10 (1.01, 1.19)*
High
Whiteb Blackb Latinob Asian or Pacific Islanderb
0.77 0.83 0.84 0.93 (0.67, 0.86)*** (0.77, 0.89)*** (0.78, 0.90)*** (0.85, 1.03) 1.18 1.13 1.32 1.21 (1.04, 1.33)** (1.06, 1.20)*** (1.13, 1.53)*** (1.09, 1.34)*** 0.98 (0.86, 1.09) 0.92 (0.53, 1.57)
1.02 (0.91, 1.16) 0.92 (0.82, 1.04)
1.27 0.83 (1.14, 1.42)*** (0.74, 0.93)*** 0.92 0.83 (0.83, 1.02) (0.72, 0.95)**
1.08 (0.87, 1.33) 0.77 (0.40, 1.47)
1.04 (0.98, 1.09) 0.91 (0.85, 0.97)
1.17 (0.90, 1.51) 1.78 (0.90, 3.49)
1.11 (0.97, 1.27) 1.14 (0.96, 1.33)
1.15 (0.95, 1.39) 1.06 (0.90, 1.25)
Notes: Values are % (95% CI) unless otherwise noted. Boldface indicates statistical significance (*p<0.05, **p<0.01, ***p<0.001). a Negative binomial regression models included neighborhood poverty level; % aged 18−24 years; % without a high school diploma; % aged <18 years; % Latino; % white, non-Latino; % black, nonLatino; % Asian or Pacific Islander; and population density as covariates. b Estimates for all percentage variables are per 10% unit increases. IRR, Incidence Rate Ratio; NYC, New York City.
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Ref 2.39 (1.10, 5.20)*
Medium
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Low
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with sugary drink image density in the adjusted model (IRR=1.08, 95% CI=1.00, 1.16). These adjusted findings remained mostly unchanged, particularly citywide when switching the outcome measure from the number sugary drink ads to the number of sugary drink images (Appendix Table 3, available online).
DISCUSSION These findings demonstrate inequities in the location of sugary drink ads in retail-dense areas of NYC and the demographic characteristics of those residing in these areas. In multivariate analyses, the percentages of black, non-Latino residents; Asian or Pacific Islander residents; and residents without HS diplomas were positively associated with sugary drink ad density in NYC overall. When analyzed at the borough level, the percentage of black, non-Latino residents was the demographic factor most consistently associated with sugary drink ad density, demonstrating a positive association in 3 of 5 boroughs. In Queens, there was a surprising inverse association observed with the percentage of Latino residents, which is the opposite of what previous research has found.20,33 As there is considerable diversity within Latino communities (e.g., country of origin, years of acculturation, income, and education levels) and where Latinos reside in NYC,34 further research may provide additional context to these findings. It is also meaningful that although the percentage of white, non-Latino residents was excluded from adjusted analysis owing to issues of multicollinearity, its significant inverse association at the city level and in 3 of 5 boroughs suggests a protective effect in neighborhoods with large white, non-Latino populations. This study adds to the body of research on targeted, unhealthy advertisements by focusing specifically on sugary drink ads. Previous studies have documented increased density and percentages of unhealthy outdoor advertisements in low-income communities, neighborhoods of color, and those with large youth populations.17,19−21 Collectively, this evidence suggests outdoor advertisements, much like other types of marketing,22 are placed to encourage increased purchasing among specific and often vulnerable populations. Often, targeted marketing appears to influence purchasing and consumption patterns effectively.22,35 The current findings coupled with research demonstrating that areas with higher percentages of food advertisement had increased odds of sugary drink consumption,12,17,20 obesity,12 and diabetes,20 highlights the important role targeted advertising might play in perpetuating poor health and contributing to persistent health-related disparities in NYC neighborhoods. Given the rising national attention around policy proposals to & 2019
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reduce consumption, understanding how sugary drink advertising is placed across neighborhoods can inform community-based and policy efforts that aim to address sugary drink overconsumption. Alaska,37 Hawaii,38Maine,39 and Vermont40 have taken the lead in regulating exposure to outdoor advertising by banning billboards. In California, the 1994 Lee Law requires that no more than a third of the windows and doors of alcohol retailers (including grocery stores) be covered. Though enacted to address issues of public safety following the Los Angeles riots, this policy also helps to limit the public’s exposure to unhealthy advertisements.41 However, First Amendment rights and the feasibility of enforcement are often a consideration in policy proposals of this kind. Instituting a ban of sugary drink advertisements on city-owned property, such as what has been recently instituted for alcohol advertisements in NYC, would be a more viable approach. However, alternative solutions may be needed to reduce the advertising of sugary drinks on privately owned property further. Other suggestions include placing a tax on the purchase of advertising space, providing an incentive to shop owners (such as a seal of approval) for adherence to specific advertising standards, or requiring warnings about the health effects of sugary drinks on all advertisements. In all of these cases, additional research is needed to assess the impact of such interventions.
Limitations There are several limitations to this study. Advertisements that were not at street level (e.g., billboards), stationary (e.g., rotating signs, street-vendors), did not feature logos or images (e.g., signs that just listed “soda”), or that primarily promoted other products were excluded. As a result, the outdoor sugary drink advertising environment was likely not captured in its entirety. The size of the advertisements was also not included in these analyses, which could provide additional insight into the extent of disparities in outdoor marketing. In addition, the language was not captured in the analysis; ads in languages not spoken by residents and passers-by may yield different responses. Content analysis of the ads (e.g., images and text featuring people of color) was not conducted, which limited the ability to elucidate the type of ads that are targeted toward different populations in this study. These results may also not apply to less urban or densely built areas, or those with less pedestrian traffic, which may explain some of the findings in Staten Island and Queens. Finally, though the original study was powered to conduct stratified analyses for all consumable product advertisements, Staten Island had fewer retail-dense street segments and fewer advertisements, making this investigation of sugary drink ads underpowered for this borough.
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CONCLUSIONS In NYC, street-level sugary drink ads were disproportionately displayed in specific neighborhoods, especially those with higher percentages of black, non-Latino residents. These findings demonstrate 1 of the ways communities of color are unequally exposed to environmental factors that may contribute to poor health. More studies and more collaborative initiatives—with input from community members and public and private stakeholders—are needed to raise awareness of targeted marketing and advocate for policy changes to eliminate this discriminatory practice.
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ACKNOWLEDGMENTS The authors wish to thank their colleagues Susan Resnick, Gretchen Culp, Michael Johns, Kevin Konty, and Katherine Bartley, as well as RTI International collaborators Becky Durocher, Nathan Mann, and Brett Loomis and Ewald & Wasserman Research Consultants, LLC. The findings from this study have previously been shared in an oral presentation given during the 2018 American Public Health Association Annual Meeting. This work was supported by a Cooperative Agreement from the Centers for Disease Control and Prevention (No. 6 NU58DP005956-03). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or HHS. Author contributions: EAD planned research aims and completed the analyses, contributed to interpretation, and co-led the writing. CR collaborated on the study design and research plan, supported the analyses, contributed to interpretation, and co-led the writing. TA collaborated on the study design, contributed to interpretation, supported analyses, and participated in writing. SMF collaborated on the study design, contributed to interpretation, and supported analyses and writing. RD collaborated on the study design, contributed to interpretation, and supported writing. No financial disclosures were reported by the authors of this paper.
SUPPLEMENTAL MATERIAL Supplemental materials associated with this article can be found in the online version at https://doi.org/10.1016/j. amepre.2019.09.025.
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30. Advertisement. In: Merriam-Webster Online. www.merriam-webster. com/dictionary/advertisement. Accessed November 29, 2016. 31. WHO. A framework for implementing the set of recommendations on the marketing of foods and non-alcoholic beverages to children. www.who.int/dietphysicalactivity/MarketingFramework2012.pdf. Published 2012. Accessed November 29, 2016. Tag edP32. U.S. Census Bureau. 2010 Census summary File 1. www.census.gov/ prod/cen2010/doc/sf1.pdf. Published 2012. Accessed February 12, 2018. 33. Cassady DL, Liaw K, Miller LM. Disparities in obesity-related outdoor advertising by neighborhood income and race. J Urban Health. 2015;92(5):835–842. https://doi.org/10.1007/s11524-015-9980-1. 34. Greer S, Naidoo M, Hinterland K, et al. 2017 Health of Latinos in NYC. https://www1.nyc.gov/assets/doh/downloads/pdf/episrv/ 2017-latino-health.pdf. Published September 27, 2017. Accessed August 1, 2019. 35. Sadeghirad B, Duhaney T, Motaghipisheh S, Campbell NR, Johnston BC. Influence of unhealthy food and beverage marketing on children’s dietary intake and preference: a systematic review and meta-analysis of randomized trials. Obes Rev. 2016;17(10):945–959. https://doi.org/ 10.1111/obr.12445. 36. Lee BY. 5 More locations pass soda taxes: what’s next for big soda? Forbes; www.forbes.com/sites/brucelee/2016/11/14/5-more-locations-pass-sodataxes-whats-next-for-big-soda/#5073a978ed19TagedEn. Published 2016. Accessed March 29, 2018. 37. Outdoor Advertising Prohibited, A.S. x 19.25.090 et seq. (1949). http:// www.legis.state.ak.us/basis/statutes.asp#19.25.090. Accessed October 9, 2019 38. Control of Outdoor Advertising, H.R.S x264-72 (1966). 39. Unauthorized signs or marks forbidden, 23 M.R.S. x 1151. http://legislature.maine.gov/statutes/23/title23sec1151.html. Accessed October 9, 2019. 40. Prohibition of other outdoor advertising, 10 V.S.A. x488 et seq. (1967). https://legislature.vermont.gov/statutes/section/10/021/00488. Accessed October 19, 2019. Tag edP41. CA B.P.C. x25612.5. (1999). https://leginfo.legislature.ca.gov/faces/ codes_displaySection.xhtml?lawCode=BPC§ionNum=25612.5. Accessed October 9, 2019.
RESEARCH ARTICLE
Disparities in Sugary Drink Advertising on New York City Streets Erin A. Dowling, MPH,1 Calpurnyia Roberts, PhD,1 Tamar Adjoian, MPH,2 Shannon M. Farley, DrPH,2 Rachel Dannefer, MPH3
Introduction: Studies show that outdoor advertisements for unhealthy, consumable products are associated with increased intake and often target youth, low-income neighborhoods, and neighborhoods of color. Despite evidence that overconsumption of sugary drinks contributes to obesity and other chronic conditions, little is known specifically regarding the patterns of outdoor sugary drink advertising.
Methods: The number of outdoor, street-level advertisements featuring sugary drinks was assessed in a random sample of retail-dense street segments (N=953) in low, medium, and high-poverty neighborhoods in each of New York City’s 5 boroughs in 2015. Negative binomial regression was used to determine associations between sugary drink ad density, poverty level, and other census tract-level demographics (2009−2013 estimates) in each borough and New York City overall. Data were analyzed in 2017−2019.
Results: In New York City and in 3 of 5 boroughs, sugary drink ad density was positively associated with increased percentages of black, non-Latino residents (New York City: incidence rate ratio=1.20, p<0.001; Bronx: incidence rate ratio=1.30, p=0.005; Brooklyn: incidence rate ratio=1.18, p<0.001; Manhattan: incidence rate ratio=1.20, p<0.05). Positive associations were also observed with poverty level in Brooklyn (low versus medium poverty: incidence rate ratio=2.16, p=0.09; low versus high poverty: incidence rate ratio=2.17, p=0.02) and Staten Island (low versus medium poverty: incidence rate ratio=3.27, p=0.03).
Conclusions: This study found a consistent positive association between the density of outdoor sugary drink advertisements and the presence of non-Latino black residents in New York City and, in some boroughs, evidence of a positive association with neighborhood poverty. These findings highlight the inequities where sugary drinks are advertised in New York City. Am J Prev Med 2019;000(000):1−9. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
INTRODUCTION
I
t has been estimated that Americans consume more than 300 kilocalories of added sugars per day, on average.1 This equates to well above the 10% of total daily energy intake recommended by 2015−2020 Dietary Guidelines for Americans.2 Beverages are the largest source of added sugars in the American diet, accounting for 47% of added sugars per day for both children and adults.2 Several prospective studies and meta-analyses have demonstrated that excessive consumption of sugary drinks (which include soda, sweetened iced teas, sports drinks, energy drinks, and fruit drinks with added sugar)
is associated with weight gain, as well as an increased risk of obesity, type 2 diabetes, and cardiovascular disease.3−8 As these beverages add extra calories and sugar not From the 1Bronx Neighborhood Health Action Center, New York City Department of Health and Mental Hygiene, New York, New York; 2 Bureau of Chronic Disease Prevention and Tobacco Control, New York City Department of Health and Mental Hygiene, New York, New York; and 3Harlem Neighborhood Health Action Center, New York City Department of Health and Mental Hygiene, New York, New York Address correspondence to: Erin A. Dowling, MPH, New York City Department of Health and Mental Hygiene, 4209 28th Street, Long Island City, NY 11101. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.09.025
© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
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otherwise compensated for in one’s diet, reducing consumption is often identified as a key strategy in preventing obesity and obesity-related diseases.4 In New York City (NYC), sugary drink consumption has significantly declined during the past decade,9 possibly owing in part to citywide media campaigns, policy proposals, and implementation of food and beverage standards in city-owned buildings.10 From 2007 to 2016, the percentage of adults that reported consuming 1 or more sugary drinks per day decreased from 35.9% (95% CI=34.6%, 37.1%) to 22.7% (95% CI=21.5%, 23.9%)9; however, disparities in consumption patterns persist. The percentage of Latino and black, non-Latino residents that report drinking at least 1 sugary drink per day has consistently been double that of white, non-Latino and Asian or Pacific Islander residents.9 Further differences occur along economic lines. In 2016, drinking 1 or more sugary drinks daily was self-reported by 25.7% of adults in low-income households compared with 16.9% of those in high-income households.9 There is also variation across the 5 boroughs of NYC (Bronx, Brooklyn, Manhattan, Queens, and Staten Island), with Bronx residents regularly reporting the highest rates of sugary drink consumption.9 These race, income, and geographic disparities highlight the need to understand further how social and environmental factors influence sugary drink intake. Although NYC is 1 of the most diverse cities in the world, it is also largely segregated along racial and economic lines. This persistent segregation combined with systems of discriminatory practices disproportionately expose low-income, nonwhite residents to factors in their living environments that contribute to poor and preventable health outcomes.11 The pervasiveness of outdoor advertisements is 1 way the built environment may influence community health by encouraging the purchase and consumption of specific products.12−14 Studies show that low-income neighborhoods, neighborhoods of color, those with lower educational attainment, and those with large youth populations are often overburdened by exposure to a higher quantity of outdoor advertisements overall, as well as those featuring harmful or unhealthy content.15−21 Further evidence suggests such targeted marketing of unhealthy food toward children and nonwhite populations contributes to poor dietary behaviors and disparities in health outcomes.22,23 However, few studies have specifically assessed sugary drink advertisements in the outdoor environment; instead, they have been included under the umbrella of unhealthy food advertisements. The present study builds on previous efforts by estimating the density of streetlevel sugary drink advertisements across the 5 boroughs of NYC and describing variation by neighborhood.
METHODS Study Sample Data were collected through the Community Marketing Study, which was conducted to assess the density of outdoor, street level, stationary advertisements of consumable products (nonalcoholic beverages, food products, tobacco products, and alcoholic beverages) within each of the 5 boroughs that constitute NYC. Linear Integrated Ordered Network, a dataset containing information on all street features in NYC,24 and Primary Land Use Tax Lot Output, a database of land use, property-level tax lot, and business locations maintained by NYC Department of City Planning,25 were used to identify retail-dense street segments. Street segments consisted of both sides of a street from 1 cross street to the next, including streets separated by a median. Segments were considered “retail-dense” if ≥50% of building entrances on the segment were for retail establishments.26 Using this method, 7,963 street segments were deemed eligible for inclusion. Identified street segments were matched to the census tract in which they were located and then grouped into 3 levels of neighborhood poverty. Neighborhood poverty was assigned following the recommended guidelines of the NYC Department of Health and Mental Hygiene27 (low poverty, where <10% of census tract residents live below the federal poverty threshold; medium poverty, where 10% to <20% of census tract residents live below the threshold; and high poverty, where ≥20% of census tract residents live below the threshold based on 2009−2013 estimates).28 Street segments that lay within 2 census tracts with differing poverty levels were assigned to the higher-income poverty level group to render more-conservative estimates, as increased advertising density was anticipated in lower-income areas. Street segments were then stratified by borough and randomly sampled, with a 5% oversample to ensure a minimum of 50 segments per stratum in the event some segments were deemed ineligible during data collection procedures. Surveyors photographed advertisements along both sides of sampled segments in the summer of 2015. Advertisements were defined as street level (first floor level up to and including any awnings); stationary signs (e.g., posters, digital signs, stickers, and decals; examples shown in Appendix Figure 1, available online) on outdoor structures, such as newsstands, bus shelters, and payphones; and were included if they displayed a product with the intended purpose of promoting that product or type of product (Appendix Table 1, available online, describes the complete inclusion and exclusion criteria).29-31 During canvassing, segments were excluded if there were no stores and retail doorways (thus, not meeting the definition of retail-dense), advertisements were obscured (i.e., segments were at ≥50% covered with scaffolding; the stores and retail doorways were offset from the sidewalk and therefore not visible from the street), or were otherwise inaccessible (Figure 1).29
Measures Sugary drink ads were defined as those featuring pictures or isolated logos of beverages that have added caloric sweetener and >25 calories per 8-ounce serving (e.g., soda, sweetened iced teas, fruit drinks, sports drinks, energy drinks, flavored milk, milkshakes, sugary coffee drinks; Appendix Table 2,29 available online). A comprehensive coding protocol was developed using nutrition information obtained from corporate websites to www.ajpmonline.org
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Figure 1. Composition of street segments sampled, NYC Community Marketing Study, 2015.29 NYC, New York City.
determine whether featured beverage products met these criteria. In rare cases, when this information was unavailable, the research team visited stores to record nutrition information from products. As a secondary analysis, when ads contained multiple sugary drink products, the number of distinct images was counted (Appendix Figure 2, available online). Identical products appearing multiple times in the same advertisements were not each counted as distinct images. If different varieties of 1 type of product appeared in an ad, including different sizes or flavors, each of these counted as a distinct product image. Sugary drink ad density was standardized per 1,000 feet of a retail-dense street segment (roughly the equivalent of 3 city blocks) to account for the fact that street segments varied in length. The density of ads per street segment was calculated as follows: Sugary Drink Ad Density ¼
number of sugary drink ads on each street segment x 1000: feet of retaildense street segment
Statistical Analysis Sugary drink ad density was analyzed by borough to reduce potential confounding because of difficult-to-measure characteristics unique to or that manifest differently in each borough, such as differences in the built environment and the specific patterns of ethnic and cultural segregation and gentrification. Results were also stratified by neighborhood poverty level (low, medium, and high poverty). Demographic characteristics for the census tracts where segments were located were chosen for inclusion based on previous research indicating potential associations with targeted marketing.15−18 Data for these characteristics were obtained from the 5-year estimates of the 2009−2013 American Community Survey.28 In addition, population density was included to control for differences in population and landmass, which vary drastically between boroughs (Bronx: 1.4 million, 26.9 thousand acres; Brooklyn: 2.5 million, 45.3 thousand acres; Manhattan: 1.6 million, 14.6 thousand acres; Queens: 2.3 million, 69.5 thousand acres; and Staten Island: 470 thousand, 37.4 thousand acres).32 Most of the demographic variables were not normally distributed; thus, medians and IQRs were used to summarize variables within each stratum. & 2019
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Negative binomial regression was used to estimate the relative density of sugary drinks ads per 1,000 feet of retail-dense street segment to account for overdispersion of the response variable. The incidence rate ratio (IRR) was reported as a measure of relative density for poverty level and demographic variables within each borough. Bivariate and multivariate analyses were conducted to assess the association of sugary drink ad density with each variable of interest within each borough. Covariates included: neighborhood poverty level; percentage aged 18−24 years without a high school (HS) diploma; percentage aged <18 years; percentage Latino; percentage white, non-Latino; percentage black, nonLatino; percentage Asian or Pacific Islander; and population density. Except for neighborhood poverty, these variables were treated as continuous and reported in increments of 10-percentage units. The percentage of white, non-Latino residents was excluded in the adjusted analysis as it was often strongly correlated with other variables and presented issues of multicollinearity. A sensitivity analysis was conducted to assess findings by the number of discrete sugary drink images. All data analysis was conducted using SAS Enterprise Guide, version 7.1. This project was submitted to the NYC Department of Health and Mental Hygiene IRB and was determined to be exempt from IRB review, as it did not qualify as human subjects research.
RESULTS The final sample included 953 retail-dense street segments (Figure 1),29 which were roughly evenly distributed in low- (301), medium- (315), and high-poverty (337) neighborhoods (Table 1). The characteristics of sampled segments varied by neighborhood poverty level, both within each borough and across NYC. Higher concentrations of population density, youth, and residents without an HS diploma or equivalent were observed in high-poverty neighborhoods. The highest proportion of black, nonLatinos and Latinos consistently resided in high-poverty neighborhoods, irrespective of county. By contrast, the most significant percentage of white, non-Latinos was in low-poverty neighborhoods in each borough. Overall, 4,356 advertisements were featuring sugary drinks with 8,197 sugary drink images observed in the sample (Table 2). There were between 2.72 (Staten Island, low-poverty census tracts) and 29.91 (Bronx, mediumpoverty census tracts) ads for sugary drinks observed per 1,000 feet of retail-dense street segment in the sample, meaning someone walking the length of 3 city blocks in a retail-dense area would encounter anywhere from about 3 to 30 ads (7−48 images), depending on the neighborhood they were in (Table 2). On average, there were about 2 images per ad in NYC, irrespective of borough. Findings by images did not differ substantively from those for ads. Table 3 shows the unadjusted and adjusted models for the association between census tract-level characteristics and sugary drink ad density. In unadjusted analyses for NYC, sugary drink ad density was 1.54 (95% CI=1.16,
4
Table 1. Sociodemographic Characteristics for Sampled Street Segments in Each Strata and for NYC Overall No. census tracts
Total no. of sampled street segmentsa
Population density (per acre)
Under 18 (%)
Low
18
32
49.6 (31.6−53.4)
20.7 (17.0−24.9)
Medium
27
42
55 (44.1−76.3)
21.1 (18.5−24.4)
High
55
66
114 (90.4−160.4)
30.3 (26.3−34.0)
32.3 (23.2−43.6)
Low
40
50
56.6 (47.4−75.0)
16.3 (12.3−20.1)
6.2 (0.0−14.7)
Medium
69
90
73.5 (58.0−88.7)
19.4 (15.7−23.4)
8.9 (3.0−18.1)
High
91
109
92.2 (73.1−118.9)
24.1 (20.7−28.9)
20.4 (11.3−31.4)
19.2 (11.2−40.4)
Low
67
110
121.1 (60.8−176.8)
10.4 (7.7−13.8)
0.0 (0.0−3.1)
6.5 (4.5−9.3)
2.2 (1.0−3.9)
77.7 (67.7−83.6)
9.0 (6.9−12.7)
Medium
35
61
122.6 (80.6−156.2)
7.4 (5.6−13.3)
2.7 (0.0−7.0)
7.9 (7.9−20.1)
4.2 (1.8−6.4)
63.6 (46.7−71.5)
13.2 (10.4−17.6)
High
40
70
154.9 (100.1−184.2) 14.7 (11.5−19.1)
15.9 (11.4−27.3)
26.5 (9.3−50.4)
7.0 (2.9−16.8)
12.8 (6.7−28.3)
24.9 (2.6−59.8)
Low
52
68
35.8 (26.1−47.7)
20.5 (16.7−22.9)
9.4 (1.7−17.7)
13.8 (7.5−22.4)
1.5 (0.5−10.0)
40.6 (10.1−65.2)
21.9 (6.5−27.8)
Medium
65
79
71.3 (52.1−90.1)
19.7 (16.5−22.5)
12.2 (6.8−20.9)
27.0 (17.2−41.1)
2.9 (0.7−9.6)
15.7 (4.9−47.0)
26.7 (12.2−38.9)
High
29
43
93.8 (45.6−130.5)
23.6 (18.8−25.8)
23.0 (16.3−27.2)
40.4 (30.3−72.6)
2.9 (1.3−11.3)
10.2 (2.5−19.5)
16.5 (9.8−42.8)
Neighborhood poverty level per borough
Hispanic or Latino (%)
Black, non-Latino or African American (%)
White, non-Latino (%)
9.1 (2.6−19.0)
24.9 (15.7−33.4)
12.4 (0.5−19.7)
46.8 (4.3−73.2)
1.1 (0.8−8.4)
17.0 (8.7−24.1)
33.8 (21.1−48.9)
37.7 (5.0−67.7)
5.7 (2.2−34.7)
5.3 (1.1−12.5)
65.8 (59.3−73.0)
23.9 (17.7−33.0)
2.5 (1.1−4.7)
1.4 (0.1−2.7)
9.8 (6.1−15.4)
3.1 (0.8−13.3)
66.3 (55.1−74.4)
6.4 (3.3−10.9)
10.6 (6.0−16.0)
2.7 (0.6−58.5)
47.9 (14.3−67.0)
9.5 (3.7−27.7)
12.0 (1.6−69.6)
14.0 (4.3−54.2)
5.1 (1.8−12.8)
Less than HS diploma or GED (%)
Asian or Pacific Islander (%)
Bronx
Queens
Staten Island Low
21
41
19.2 (12.3−21.7)
22.0 (20.3−24.2)
6.6 (0.7−11.8)
13.1 (10.1−22.3)
1.8 (0.1−4.4)
74.8 (69.2−8.7)
Medium
16
43
17.7 (12.2−18.5)
22.5 (21.2−26.6)
10.7 (4.1−17.6)
14.6 (11.6−29.0)
6.9 (3.3−21.0)
67.1 (33.4−72.7)
10.4 (7.1−11.2)
5.6 (2.7−9.4)
High
14
49
25.3 (21.6−33.4)
27.2 (23.6−31.5)
18.3 (15.3−35.0)
42.6 (32.7−47.9)
26.8 (21.3−32.3)
25.8 (12.2−27.4)
2.6 (1.7−7.6)
Low
198
301
50.1 (26.7−96.6)
16.4 (10.7−21.4)
3.9 (0.0−11.8)
10.1 (6.4−18.0)
2.5 (0.8−5.8)
69.2 (50.1−79.5)
8.1 (4.0−14.6)
Medium
212
315
68.9 (44.1−97.2)
19.2 (13.7−22.6)
9.2 (3.4−17.9)
16.5 (9.6−29.0)
4.2 (1.5−22.0)
43.7 (10.8−65.9)
11.3 (6.5−22.5)
High
229
337
95.6 (52.9−143.7)
24.3 (19.1−30.3)
22.9 (14.3−34.0)
39.6 (17.0−62.7)
17.1 (2.9−32.9)
NYC overall
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Note: Values expressed as median (IQR) unless specified otherwise. a Street segments are defined as both sides of a street or “block” from 1 cross street to the next, including those separated by a median. HS, high school; NYC, New York City.
11.0 (3.4−27.4)
5.1 (1.7−16.0)
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Table 2. Total Sugary Drink Advertisements, Images, and Image Density in Each Strata and NYC Overall Neighborhood poverty level per borough Bronx Low Medium High Total Brooklyn Low Medium High Total Manhattan Low Medium High Total Queens Low Medium High Total Staten Island Low Medium High Total NYC overall Low Medium High Total
Total sugary drink ads
Total sugary drink images
Sugary drink ad density (total per 1,000 feet)
Sugary drink image density (total per 1,000 feet)
131 367 429 927
229 601 955 1,785
12.87 29.91 19.66 20.94
22.49 48.98 43.77 40.32
144 581 829 1,554
271 1,018 1,504 2,793
9.46 22.07 24.94 20.78
17.80 38.67 45.24 37.34
245 126 305 676
442 272 568 1,282
6.20 5.36 12.38 7.71
11.18 11.56 23.05 14.62
397 376 242 1,015
752 721 478 1,951
21.89 18.02 16.87 19.03
41.46 34.56 33.33 36.58
36 68 80 184
95 132 159 386
2.72 5.20 6.11 4.67
7.19 10.10 12.14 9.80
953 1,518 1,885 4,356
1,789 2,744 3,664 8,197
9.90 15.80 17.59 14.54
18.58 28.57 34.20 27.37
NYC, New York City.
2.04) times as high for medium- versus low-poverty neighborhoods and 1.66 (95% CI=1.26, 2.19) times as great for high- versus low-poverty neighborhoods. Associations with neighborhood poverty were also observed in Brooklyn (low versus medium poverty: IRR=2.43, 95% CI=1.37, 4.31; low versus high poverty: IRR=2.83, 95% CI=1.62, 4.92), Manhattan (low versus high poverty: IRR=2.10, 95% CI=1.28, 3.43), and Bronx (low versus high poverty: IRR=2.14, 95% CI=1.03, 4.45). However, after adjusting for all other census tract-level characteristics, associations of sugary drink ad density along street segments with neighborhood poverty level citywide were only observed in Brooklyn (low versus medium poverty: IRR=2.16, 95% CI=1.20, 3.87; low versus high poverty: IRR=2.17, 95% CI=1.13, 4.14) and Staten Island (low versus medium poverty: IRR=3.27, 95% CI=1.15, 9.26). & 2019
In the adjusted model, there were associations observed in NYC overall between street-level sugary drink ad density and the percentage of census tract residents with less than an HS diploma (10-unit IRR=1.15, 95% CI=1.04, 1.27); the percentage of Asian or Pacific Islander residents (10-unit IRR=1.10, 95% CI=1.01, 1.19); and the percentage of black, non-Latino residents (10-unit IRR=1.20, 95% CI=1.14, 1.26). When analyzed by borough, the percentage of black, non-Latino residents was also associated with sugary drink ad density in Bronx (10-unit IRR=1.30, 95% CI=1.07, 1.56), Brooklyn (10-unit IRR=1.18, 95% CI=1.09, 1.27), and Manhattan (10-unit IRR=1.20, 95% CI=1.00, 1.43). In Queens, an inverse association was observed for the percentage of Latino residents after adjusting for all other variables (10-unit IRR=0.80, 95% CI=0.66, 0.96), and in Staten Island, population density showed a small association
6
Table 3. Association Between Sugary Drink Ad Density and Neighborhood Characteristics for Each Borough and NYC Overall Adjusted IRRa
Unadjusted IRR Characteristics
Bronx
Brooklyn
Manhattan
Queens
Staten Island
NYC overall
Bronx
Brooklyn
Manhattan
Queens
Staten Island
NYC overall
Neighborhood poverty level Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
Ref
2.43 (1.37, 4.31)**
0.95 (0.55, 1.62)
0.77 (0.44, 1.33)
1.28 (0.85, 1.90)
1.54 (1.16, 2.04)**
1.54 (0.63, 3.69)
2.16 (1.20, 3.87)**
0.73 (0.42, 1.29)
1.50 (0.80, 2.79)
3.27 (1.15, 9.26)*
1.32 (0.99, 1.75)
Population density
1.70 (0.83, 3.48) 1.00 (0.99, 1.01)
2.83 (1.62, 4.92)*** 1.00 (0.99, 1.00)
2.10 (1.28, 3.43)** 1.00 (1.00, 1.01)*
0.71 (0.37, 1.36) 0.99 (0.98, 0.99)**
1.00 (1.00, 1.00) 1.01 (0.97, 1.05)
1.66 (1.26, 2.19)*** 1.00 (0.99, 1.00)
0.79 (0.27, 2.33) 0.99 (0.99, 1.00)
2.17 (1.13, 4.14)* 0.99 (0.99, 1.01)
0.64 (0.27, 1.51) 1.00 (0.99, 1.01)
1.56 (0.66, 3.64) 0.99 (0.99, 1.01)
0.68 (0.13, 3.58) 1.06 (1.00, 1.12)*
0.99 (0.70, 1.28) 1.00 (0.99, 1.00)
Aged <18 yearsb Less than high school diplomab
1.61 (0.92, 2.79) 1.15 (0.93, 1.43)
0.99 (0.73, 1.36) 1.14 (0.97, 1.33)
2.00 (1.43, 2.80)*** 1.63 (1.35, 1.97)***
1.32 (0.86. 2.07) 1.16 (0.95, 1.41)
0.90 (0.51, 1.59) 1.21 (0.89, 1.62)
1.44 (1.23, 1.68)*** 1.29 (1.17, 1.41)***
1.86 (0.83, 4.14) 1.09 (0.82, 1.56)
0.81 (0.61, 1.08) 1.07 (0.92, 1.27)
0.95 (0.58, 1.53) 1.30 (0.94, 1.79)
1.26 (0.78, 2.01) 1.12 (0.89, 1.41)
0.55 (0.25, 1.90) 1.28 (0.85, 1.91)
1.08 (0.90, 1.28) 1.15 (1.04, 1.27)**
0.96 (0.86, 1.09) 1.07 (0.87, 1.30)
0.83 − − − (0.80, 0.87)*** 1.19 1.30 1.18 1.20 (1.14, 1.24)*** (1.07, 1.56)** (1.09, 1.27)*** (1.00, 1.43)*
−
−
−
1.10 (0.96, 1.25)
0.87 (0.61, 1.24)
1.20 (1.14, 1.26)***
0.79 (0.66, 0.96)* 0.88 (0.76, 1.04)
1.32 (0.84, 2.08) 0.45 (0.20, 1.00)
1.05 (0.97, 1.13) 1.10 (1.01, 1.19)*
High
Whiteb Blackb Latinob Asian or Pacific Islanderb
0.77 0.83 0.84 0.93 (0.67, 0.86)*** (0.77, 0.89)*** (0.78, 0.90)*** (0.85, 1.03) 1.18 1.13 1.32 1.21 (1.04, 1.33)** (1.06, 1.20)*** (1.13, 1.53)*** (1.09, 1.34)*** 0.98 (0.86, 1.09) 0.92 (0.53, 1.57)
1.02 (0.91, 1.16) 0.92 (0.82, 1.04)
1.27 0.83 (1.14, 1.42)*** (0.74, 0.93)*** 0.92 0.83 (0.83, 1.02) (0.72, 0.95)**
1.08 (0.87, 1.33) 0.77 (0.40, 1.47)
1.04 (0.98, 1.09) 0.91 (0.85, 0.97)
1.17 (0.90, 1.51) 1.78 (0.90, 3.49)
1.11 (0.97, 1.27) 1.14 (0.96, 1.33)
1.15 (0.95, 1.39) 1.06 (0.90, 1.25)
Notes: Values are % (95% CI) unless otherwise noted. Boldface indicates statistical significance (*p<0.05, **p<0.01, ***p<0.001). a Negative binomial regression models included neighborhood poverty level; % aged 18−24 years; % without a high school diploma; % aged <18 years; % Latino; % white, non-Latino; % black, nonLatino; % Asian or Pacific Islander; and population density as covariates. b Estimates for all percentage variables are per 10% unit increases. IRR, Incidence Rate Ratio; NYC, New York City.
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Medium
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with sugary drink image density in the adjusted model (IRR=1.08, 95% CI=1.00, 1.16). These adjusted findings remained mostly unchanged, particularly citywide when switching the outcome measure from the number sugary drink ads to the number of sugary drink images (Appendix Table 3, available online).
DISCUSSION These findings demonstrate inequities in the location of sugary drink ads in retail-dense areas of NYC and the demographic characteristics of those residing in these areas. In multivariate analyses, the percentages of black, non-Latino residents; Asian or Pacific Islander residents; and residents without HS diplomas were positively associated with sugary drink ad density in NYC overall. When analyzed at the borough level, the percentage of black, non-Latino residents was the demographic factor most consistently associated with sugary drink ad density, demonstrating a positive association in 3 of 5 boroughs. In Queens, there was a surprising inverse association observed with the percentage of Latino residents, which is the opposite of what previous research has found.20,33 As there is considerable diversity within Latino communities (e.g., country of origin, years of acculturation, income, and education levels) and where Latinos reside in NYC,34 further research may provide additional context to these findings. It is also meaningful that although the percentage of white, non-Latino residents was excluded from adjusted analysis owing to issues of multicollinearity, its significant inverse association at the city level and in 3 of 5 boroughs suggests a protective effect in neighborhoods with large white, non-Latino populations. This study adds to the body of research on targeted, unhealthy advertisements by focusing specifically on sugary drink ads. Previous studies have documented increased density and percentages of unhealthy outdoor advertisements in low-income communities, neighborhoods of color, and those with large youth populations.17,19−21 Collectively, this evidence suggests outdoor advertisements, much like other types of marketing,22 are placed to encourage increased purchasing among specific and often vulnerable populations. Often, targeted marketing appears to influence purchasing and consumption patterns effectively.22,35 The current findings coupled with research demonstrating that areas with higher percentages of food advertisement had increased odds of sugary drink consumption,12,17,20 obesity,12 and diabetes,20 highlights the important role targeted advertising might play in perpetuating poor health and contributing to persistent health-related disparities in NYC neighborhoods. Given the rising national attention around policy proposals to & 2019
7 36
reduce consumption, understanding how sugary drink advertising is placed across neighborhoods can inform community-based and policy efforts that aim to address sugary drink overconsumption. Alaska,37 Hawaii,38Maine,39 and Vermont40 have taken the lead in regulating exposure to outdoor advertising by banning billboards. In California, the 1994 Lee Law requires that no more than a third of the windows and doors of alcohol retailers (including grocery stores) be covered. Though enacted to address issues of public safety following the Los Angeles riots, this policy also helps to limit the public’s exposure to unhealthy advertisements.41 However, First Amendment rights and the feasibility of enforcement are often a consideration in policy proposals of this kind. Instituting a ban of sugary drink advertisements on city-owned property, such as what has been recently instituted for alcohol advertisements in NYC, would be a more viable approach. However, alternative solutions may be needed to reduce the advertising of sugary drinks on privately owned property further. Other suggestions include placing a tax on the purchase of advertising space, providing an incentive to shop owners (such as a seal of approval) for adherence to specific advertising standards, or requiring warnings about the health effects of sugary drinks on all advertisements. In all of these cases, additional research is needed to assess the impact of such interventions.
Limitations There are several limitations to this study. Advertisements that were not at street level (e.g., billboards), stationary (e.g., rotating signs, street-vendors), did not feature logos or images (e.g., signs that just listed “soda”), or that primarily promoted other products were excluded. As a result, the outdoor sugary drink advertising environment was likely not captured in its entirety. The size of the advertisements was also not included in these analyses, which could provide additional insight into the extent of disparities in outdoor marketing. In addition, the language was not captured in the analysis; ads in languages not spoken by residents and passers-by may yield different responses. Content analysis of the ads (e.g., images and text featuring people of color) was not conducted, which limited the ability to elucidate the type of ads that are targeted toward different populations in this study. These results may also not apply to less urban or densely built areas, or those with less pedestrian traffic, which may explain some of the findings in Staten Island and Queens. Finally, though the original study was powered to conduct stratified analyses for all consumable product advertisements, Staten Island had fewer retail-dense street segments and fewer advertisements, making this investigation of sugary drink ads underpowered for this borough.
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CONCLUSIONS In NYC, street-level sugary drink ads were disproportionately displayed in specific neighborhoods, especially those with higher percentages of black, non-Latino residents. These findings demonstrate 1 of the ways communities of color are unequally exposed to environmental factors that may contribute to poor health. More studies and more collaborative initiatives—with input from community members and public and private stakeholders—are needed to raise awareness of targeted marketing and advocate for policy changes to eliminate this discriminatory practice.
4.
5.
6.
7.
ACKNOWLEDGMENTS The authors wish to thank their colleagues Susan Resnick, Gretchen Culp, Michael Johns, Kevin Konty, and Katherine Bartley, as well as RTI International collaborators Becky Durocher, Nathan Mann, and Brett Loomis and Ewald & Wasserman Research Consultants, LLC. The findings from this study have previously been shared in an oral presentation given during the 2018 American Public Health Association Annual Meeting. This work was supported by a Cooperative Agreement from the Centers for Disease Control and Prevention (No. 6 NU58DP005956-03). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or HHS. Author contributions: EAD planned research aims and completed the analyses, contributed to interpretation, and co-led the writing. CR collaborated on the study design and research plan, supported the analyses, contributed to interpretation, and co-led the writing. TA collaborated on the study design, contributed to interpretation, supported analyses, and participated in writing. SMF collaborated on the study design, contributed to interpretation, and supported analyses and writing. RD collaborated on the study design, contributed to interpretation, and supported writing. No financial disclosures were reported by the authors of this paper.
SUPPLEMENTAL MATERIAL Supplemental materials associated with this article can be found in the online version at https://doi.org/10.1016/j. amepre.2019.09.025.
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