RESEARCH AND PRACTICE
Access to Supermarkets and Fruit and Vegetable Consumption Anju Aggarwal, PhD, Andrea J. Cook, PhD, Junfeng Jiao, PhD, Rebecca A. Seguin, PhD, Anne Vernez Moudon, DrSc, Philip M. Hurvitz, PhD, and Adam Drewnowski, PhD
Socioeconomic disparities in diet quality are well established in the United States. Low income and low levels of education have been consistently linked with poor diets and poor health.1,2 Multiple individual-level factors are known to influence diet quality, including economic barriers, inadequate nutrition knowledge and awareness, food preferences and attitudes, and cultural factors.2---6 In the past decade, availability of healthy foods at the neighborhood level has been proposed as an environmental determinant of diet.7---12 In the absence of data on actual food shopping destinations, it has been assumed that mere availability of supermarkets in one’s neighborhood reflects accessibility. Thus, physical proximity to neighborhood supermarkets, based on either aggregated data (e.g., the presence11,13,14 or density of supermarkets within a given neighborhood census tract15---17) or individual-level measures (e.g., the distance between one’s home and the nearest supermarket13,16,18,19), has been linked with higher consumption of fruits and vegetables and with higher overall diet quality. As a result, ensuring physical access to supermarkets in low-income neighborhoods has recently become the focus of public health policies designed to improve diets and health.20,21 However, a few recent studies have produced inconsistent results.21---23 Some of these investigations have shown that physical distance to a supermarket is not associated with fruit and vegetable intake or overall diet quality and body weight,22---24 even when varying distances from respondents’ homes are assessed.25,26 Interestingly, a few studies that probed for actual food shopping locations revealed that most people did not even shop at their nearest supermarkets.22,23,27,28 Such studies are beginning to imply that physical proximity may not be the most salient variable in determining access to supermarkets, particularly among those who shop by car.26,29,30 Rather, it has been proposed that a host of other proximal and distal determinants of
Objectives. We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity. Methods. The Seattle Obesity Study geocoded respondents’ home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. Results. Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. Conclusions. Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors. (Am J Public Health. 2014;104:917–923. doi:10.2105/AJPH.2013.301763)
dietary intake, such as personal choices, psychosocial factors, and unobserved measures of socioeconomic status (SES), may determine supermarket choice, food shopping decisions, and, in turn, diets and health.23,28 We examined whether physical proximity to supermarkets or underlying personal factors would more strongly predict access to supermarkets and consumption of fruits and vegetables. We were able to investigate this issue because, unlike most other studies, we had available data on actual food shopping destinations. Novel measures were used to conceptualize each of these variables. First, we measured physical proximity using street network distance between respondents’ homes and the supermarket they reported as their primary food source. Second, we used choice of primary supermarket as a proxy for unmeasured underlying personal factors such as economic and psychosocial barriers and food preferences. We hypothesized that supermarket choice would better predict respondents’ fruit and vegetable consumption than mere physical
May 2014, Vol 104, No. 5 | American Journal of Public Health
distance. The question was whether supermarket choice would outweigh physical proximity in predicting fruit and vegetable consumption even among low-income or other vulnerable groups.
METHODS The 2008---2009 Seattle Obesity Study enrolled a stratified, population-based sample of 2001 adult residents of King County, Washington. Details on the study’s methodology and procedures have been published elsewhere.23,31 A commercial database was used to match telephone numbers with residential addresses, and prenotification letters were mailed. Adult respondents were randomly selected from each household. Eligible respondents who provided verbal consent completed a 25-minute telephone survey modeled on the Behavioral Risk Factor Surveillance System (BRFSS) survey. The survey collected detailed individual-level data on fruit and vegetable consumption, food
Aggarwal et al. | Peer Reviewed | Research and Practice | 917
RESEARCH AND PRACTICE
shopping and eating habits, and sociodemographic and health variables. Each respondent was asked to report the name of the primary store at which most of the food in his or her household was purchased, as well as the store’s location. Our analyses were restricted to the 1670 participants who reported supermarkets as their primary food source. After removal of missing data, the final analytical sample consisted of 1386 respondents. During the telephone survey, respondents were asked to report the frequency at which they consumed whole fruits, 100% fruit juices, salads, potatoes, carrots, and other vegetables (the standard diet quality items used in the BRFSS survey during 2008 and 200932). The reported frequencies of consumption for each of these foods were combined to compute respondents’ daily consumption of fruits and vegetables.
Geocoding and Network Distance Measures The home address of each respondent was obtained during the telephone survey. ArcGIS version 9.3.1 (ESRI, Redlands, CA) was used to geocode respondents’ home addresses to the centroid of their home parcel or lot (according to 2008 King County assessment parcel data). The 30% of address records that did not result in a match in the automatic geocoding were manually matched via a digital map environment augmented by online resources such as Google Maps. Each home point was double checked for plausibility and accuracy. Details on the matching methodology used have been published elsewhere.23 We obtained data on all food store locations in King County from 2008 food permit records provided by Public Health---Seattle & King County (PHSKC). We used 2008 King County assessment parcel data and ArcGIS to geocode all food locations to the centroid of the parcel. The approximately 12% of address records that did not result in a match were again manually matched via a digital map environment augmented by online resources. Of the 10 254 overall food permit addresses, 10 215 (99.6%) were geocoded. We used ArcGIS to compute network distances from each respondent’s home to all supermarkets in the data set. Network distances
(in miles) represented the fastest probable route respondents would take from their homes to their primary supermarkets and the nearest supermarkets along the existing road network. On the basis of the data distribution obtained, the distance to primary supermarket variable was grouped into the following categories: less than 1 mile, 1 to 1.99 miles, 2 to 2.99 miles, and 3 miles or more (more than 25% of the study population traveled at least 3 miles to their primary store). In addition, respondents were stratified according to whether or not their primary supermarkets were the nearest supermarkets. Those who reported using their nearest supermarket or using a supermarket within a 0.1-mile buffer were categorized as nearest supermarket users. The 0.1-mile buffer allowed us to differentiate between those traveling to supermarkets within a few blocks of their nearest supermarket and those who actually traveled longer distances to shop at their primary supermarket of choice.
Characterization of Supermarkets A market basket database of 100 commonly consumed foods was developed to characterize each of the primary supermarkets according to food availability and price33 (details on market basket procedures and classification of supermarkets have been published elsewhere23,34). We made in-person visits to collect data on food availability and prices for 8 supermarket chains—Safeway, Fred Meyer, Quality Food Centers, Puget Consumer Co-op, Albertsons, Trader Joe’s, Whole Foods, and Metropolitan Market—at which 90% of the sample reported shopping. In the case of each store, the lowest price available for each item in the market basket was used; most often this was the store brand price. For the remaining 5 stores, at which 10% of the sample reported shopping, data were collected from the company Web sites or through contact with store managers. Across the stores, the rate of availability of market basket foods was found to be close to 100%; however, there was significant variation by price. We used cluster analyses to classify supermarkets into 3 price strata: low, medium, and high. The average price of the market basket at low-cost supermarkets was $224; food at medium-cost supermarkets was 30%
918 | Research and Practice | Peer Reviewed | Aggarwal et al.
to 40% more expensive ($305 on average) than that at low-cost supermarkets, and food at the highest-cost supermarkets was 60% more expensive ($393 on average).
Socioeconomic and Demographic Measures Data on sociodemographic variables (e.g., age, gender, race/ethnicity, household size, car ownership, residential density) were collected during the telephone survey. Annual household income and highest level of education completed were used as indicators of socioeconomic status. For analytical purposes, income was grouped into 2 categories (< $50 000 and ‡ $50 000). In addition, we recoded the 6-category education variable into 3 categories: high school or less, some college, and college or more. Race/ethnicity was grouped into 2 categories (non-Hispanic White and other). Car ownership data were obtained from the telephone survey and dichotomized. Finally, we obtained information on residential density (number of dwelling units within a 10-minute walking radius, treated as a continuous variable in our analyses) from the King County assessment parcel database.
Statistical Analyses Fruit and vegetable consumption was assessed by sociodemographic variables, street network distance to primary supermarket, and type of supermarket (low, medium, or high cost). We computed means along with medians and interquartile ranges. A series of regression analyses were conducted to assess the relationship of fruit and vegetable consumption with network distance to primary supermarket and supermarket type. Model 1 consisted of a set of linear regressions adjusting for age, gender, race/ ethnicity, household size, residential density, and car ownership. Frequency of fruit and vegetable consumption was the dependent variable, and network distance to primary supermarket, supermarket type, annual household income, and education were independent variables. Model 2 evaluated whether supermarket type continued to be associated with consumption of fruits and vegetables after further adjustment for household income and education in a multivariate model. The goal of model 3 was to assess whether the observed
American Journal of Public Health | May 2014, Vol 104, No. 5
RESEARCH AND PRACTICE
relationships persisted after the addition of the distance variable to the model. We conducted descriptive analyses to characterize the sociodemographic profile and supermarket choice of respondents who did and did not bypass their nearest supermarket for their primary food source. We calculated omnibus P values assessing the overall unadjusted relationships between the demographic variables. In all of the models, linear regressions with robust standard errors were used to correct for the skewed dependent variable.35 In addition, we used log-linear regression models to conduct sensitivity analyses; in these models, the log of fruit and vegetable consumption was the outcome, which allowed an assessment of the robustness of the primary results. In these log-linear regression models, we also evaluated whether spatial correlation existed after adjustment for the baseline covariates. According to the Akaike information criterion,36 models without any spatial correlation exhibited a better goodness of fit. The findings from all models remained the same after the sensitivity analyses; hence, only the linear regression results are presented for ease of interpretation. All confidence intervals (CIs) and P values were 2-sided and based on the Wald statistic; the level of statistical significance was set at P < .05. We used R software in conducting the data analyses.37
TABLE 1—Fruit and Vegetable Consumption, by Sociodemographic Variables, Distance to Primary Supermarket, and Supermarket Type: Seattle Obesity Study, 2008–2009 Frequency of Fruit and Vegetable Consumption per Day Variables
No. of Respondents
Mean (SD)
Median (Interquartile Range)
Gender Male
521
3.89 (2.06)
3.49 (2.44–4.99)
Female
865
4.36 (2.15)
4.00 (2.93–5.43)
Age group, y 18–44
363
4.03 (2.12)
3.78 (2.46–5.29)
45–64
717
4.22 (2.22)
3.82 (2.71–5.35)
‡ 65
306
4.29 (1.90)
4.00 (3.00–5.29)
1135
4.18 (2.10)
3.82 (2.74–5.29)
251
4.19 (2.23)
3.92 (2.57–5.29)
556 830
4.03 (2.11) 4.29 (2.13)
3.64 (2.57–5.24) 3.97 (2.85–5.35)
Race/ethnicity Non-Hispanic White Non-White Annual household income, $ < 50 000 ‡ 50 000 Education < high school
245
3.79 (2.11)
3.29 (2.35–5.14)
Some college
357
3.87 (2.18)
3.43 (2.49–4.78)
> college
784
4.45 (2.07)
4.19 (3.07–5.57)
1287
4.19 (2.10)
3.86 (2.72–5.29)
99
4.07 (2.45)
3.57 (2.29–5.43)
Car ownership Yes No Distance to primary supermarket, miles .05 for each). However, a significant race/ethnicity trend was observed. Only 26.6% of non-White respondents shopped at their nearest supermarket, a percentage significantly lower than that among White respondents (35.3%) (P = .009). Analyses of shopping patterns according to type of supermarket revealed a significant relationship (P < .001). Only small percentages of respondents who shopped at low- and high-cost supermarkets (25% and 30%, respectively) used their nearest supermarket for primary food shopping; the majority of respondents traveled much farther. Figure 1 depicts an example of the shortest network route from home to the primary supermarket for 2 same-area residents. One respondent bypasses medium- and high-cost
supermarkets that are in physical proximity to the respondent’s home for the low-cost supermarket of choice. The other respondent bypasses physically proximal medium- and low-cost supermarkets to travel to a high-cost supermarket.
DISCUSSION To our knowledge, this is one of the few US-based studies to collect data on primary food shopping destinations and to characterize respondents’ choice of a primary store according to its pricing. There were several unique findings. First, contrary to common assumptions, the majority of the respondents did not use their nearest supermarket for primary food shopping. The nearest supermarket was the primary source for only one third of the respondents; the remaining two thirds shopped for food elsewhere. These findings imply that mere physical proximity
TABLE 2—Results of Multivariate Regression Analyses Assessing Fruit and Vegetable Consumption According to Demographic Characteristics, Distance to Primary Supermarket, and Supermarket Type: Seattle Obesity Study, 2008–2009 Model 1 Variables Annual household income, $
a
b (95% CI)
Model 2 P
0.00
a
b (95% CI)
Model 3 P
0.00
a
b (95% CI)
P
0.00
< 50 000 (Ref) ‡ 50 000
0.27 (0.02, 0.51)
.033
0.06 (–0.19, 0.31)
.637
0.06 (–0.19, 0.31)
.627
Education < high school (Ref)
0.00
Some college
0.14 (–0.21, 0.48)
.43
0.12 (–0.22, 0.46)
.49
0.12 (–0.22, 0.46)
.479
0.72 (0.41, 1.03)
Access to Supermarkets and Fruit and Vegetable Consumption Anju Aggarwal, PhD, Andrea J. Cook, PhD, Junfeng Jiao, PhD, Rebecca A. Seguin, PhD, Anne Vernez Moudon, DrSc, Philip M. Hurvitz, PhD, and Adam Drewnowski, PhD
Socioeconomic disparities in diet quality are well established in the United States. Low income and low levels of education have been consistently linked with poor diets and poor health.1,2 Multiple individual-level factors are known to influence diet quality, including economic barriers, inadequate nutrition knowledge and awareness, food preferences and attitudes, and cultural factors.2---6 In the past decade, availability of healthy foods at the neighborhood level has been proposed as an environmental determinant of diet.7---12 In the absence of data on actual food shopping destinations, it has been assumed that mere availability of supermarkets in one’s neighborhood reflects accessibility. Thus, physical proximity to neighborhood supermarkets, based on either aggregated data (e.g., the presence11,13,14 or density of supermarkets within a given neighborhood census tract15---17) or individual-level measures (e.g., the distance between one’s home and the nearest supermarket13,16,18,19), has been linked with higher consumption of fruits and vegetables and with higher overall diet quality. As a result, ensuring physical access to supermarkets in low-income neighborhoods has recently become the focus of public health policies designed to improve diets and health.20,21 However, a few recent studies have produced inconsistent results.21---23 Some of these investigations have shown that physical distance to a supermarket is not associated with fruit and vegetable intake or overall diet quality and body weight,22---24 even when varying distances from respondents’ homes are assessed.25,26 Interestingly, a few studies that probed for actual food shopping locations revealed that most people did not even shop at their nearest supermarkets.22,23,27,28 Such studies are beginning to imply that physical proximity may not be the most salient variable in determining access to supermarkets, particularly among those who shop by car.26,29,30 Rather, it has been proposed that a host of other proximal and distal determinants of
Objectives. We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity. Methods. The Seattle Obesity Study geocoded respondents’ home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. Results. Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. Conclusions. Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors. (Am J Public Health. 2014;104:917–923. doi:10.2105/AJPH.2013.301763)
dietary intake, such as personal choices, psychosocial factors, and unobserved measures of socioeconomic status (SES), may determine supermarket choice, food shopping decisions, and, in turn, diets and health.23,28 We examined whether physical proximity to supermarkets or underlying personal factors would more strongly predict access to supermarkets and consumption of fruits and vegetables. We were able to investigate this issue because, unlike most other studies, we had available data on actual food shopping destinations. Novel measures were used to conceptualize each of these variables. First, we measured physical proximity using street network distance between respondents’ homes and the supermarket they reported as their primary food source. Second, we used choice of primary supermarket as a proxy for unmeasured underlying personal factors such as economic and psychosocial barriers and food preferences. We hypothesized that supermarket choice would better predict respondents’ fruit and vegetable consumption than mere physical
May 2014, Vol 104, No. 5 | American Journal of Public Health
distance. The question was whether supermarket choice would outweigh physical proximity in predicting fruit and vegetable consumption even among low-income or other vulnerable groups.
METHODS The 2008---2009 Seattle Obesity Study enrolled a stratified, population-based sample of 2001 adult residents of King County, Washington. Details on the study’s methodology and procedures have been published elsewhere.23,31 A commercial database was used to match telephone numbers with residential addresses, and prenotification letters were mailed. Adult respondents were randomly selected from each household. Eligible respondents who provided verbal consent completed a 25-minute telephone survey modeled on the Behavioral Risk Factor Surveillance System (BRFSS) survey. The survey collected detailed individual-level data on fruit and vegetable consumption, food
Aggarwal et al. | Peer Reviewed | Research and Practice | 917
RESEARCH AND PRACTICE
shopping and eating habits, and sociodemographic and health variables. Each respondent was asked to report the name of the primary store at which most of the food in his or her household was purchased, as well as the store’s location. Our analyses were restricted to the 1670 participants who reported supermarkets as their primary food source. After removal of missing data, the final analytical sample consisted of 1386 respondents. During the telephone survey, respondents were asked to report the frequency at which they consumed whole fruits, 100% fruit juices, salads, potatoes, carrots, and other vegetables (the standard diet quality items used in the BRFSS survey during 2008 and 200932). The reported frequencies of consumption for each of these foods were combined to compute respondents’ daily consumption of fruits and vegetables.
Geocoding and Network Distance Measures The home address of each respondent was obtained during the telephone survey. ArcGIS version 9.3.1 (ESRI, Redlands, CA) was used to geocode respondents’ home addresses to the centroid of their home parcel or lot (according to 2008 King County assessment parcel data). The 30% of address records that did not result in a match in the automatic geocoding were manually matched via a digital map environment augmented by online resources such as Google Maps. Each home point was double checked for plausibility and accuracy. Details on the matching methodology used have been published elsewhere.23 We obtained data on all food store locations in King County from 2008 food permit records provided by Public Health---Seattle & King County (PHSKC). We used 2008 King County assessment parcel data and ArcGIS to geocode all food locations to the centroid of the parcel. The approximately 12% of address records that did not result in a match were again manually matched via a digital map environment augmented by online resources. Of the 10 254 overall food permit addresses, 10 215 (99.6%) were geocoded. We used ArcGIS to compute network distances from each respondent’s home to all supermarkets in the data set. Network distances
(in miles) represented the fastest probable route respondents would take from their homes to their primary supermarkets and the nearest supermarkets along the existing road network. On the basis of the data distribution obtained, the distance to primary supermarket variable was grouped into the following categories: less than 1 mile, 1 to 1.99 miles, 2 to 2.99 miles, and 3 miles or more (more than 25% of the study population traveled at least 3 miles to their primary store). In addition, respondents were stratified according to whether or not their primary supermarkets were the nearest supermarkets. Those who reported using their nearest supermarket or using a supermarket within a 0.1-mile buffer were categorized as nearest supermarket users. The 0.1-mile buffer allowed us to differentiate between those traveling to supermarkets within a few blocks of their nearest supermarket and those who actually traveled longer distances to shop at their primary supermarket of choice.
Characterization of Supermarkets A market basket database of 100 commonly consumed foods was developed to characterize each of the primary supermarkets according to food availability and price33 (details on market basket procedures and classification of supermarkets have been published elsewhere23,34). We made in-person visits to collect data on food availability and prices for 8 supermarket chains—Safeway, Fred Meyer, Quality Food Centers, Puget Consumer Co-op, Albertsons, Trader Joe’s, Whole Foods, and Metropolitan Market—at which 90% of the sample reported shopping. In the case of each store, the lowest price available for each item in the market basket was used; most often this was the store brand price. For the remaining 5 stores, at which 10% of the sample reported shopping, data were collected from the company Web sites or through contact with store managers. Across the stores, the rate of availability of market basket foods was found to be close to 100%; however, there was significant variation by price. We used cluster analyses to classify supermarkets into 3 price strata: low, medium, and high. The average price of the market basket at low-cost supermarkets was $224; food at medium-cost supermarkets was 30%
918 | Research and Practice | Peer Reviewed | Aggarwal et al.
to 40% more expensive ($305 on average) than that at low-cost supermarkets, and food at the highest-cost supermarkets was 60% more expensive ($393 on average).
Socioeconomic and Demographic Measures Data on sociodemographic variables (e.g., age, gender, race/ethnicity, household size, car ownership, residential density) were collected during the telephone survey. Annual household income and highest level of education completed were used as indicators of socioeconomic status. For analytical purposes, income was grouped into 2 categories (< $50 000 and ‡ $50 000). In addition, we recoded the 6-category education variable into 3 categories: high school or less, some college, and college or more. Race/ethnicity was grouped into 2 categories (non-Hispanic White and other). Car ownership data were obtained from the telephone survey and dichotomized. Finally, we obtained information on residential density (number of dwelling units within a 10-minute walking radius, treated as a continuous variable in our analyses) from the King County assessment parcel database.
Statistical Analyses Fruit and vegetable consumption was assessed by sociodemographic variables, street network distance to primary supermarket, and type of supermarket (low, medium, or high cost). We computed means along with medians and interquartile ranges. A series of regression analyses were conducted to assess the relationship of fruit and vegetable consumption with network distance to primary supermarket and supermarket type. Model 1 consisted of a set of linear regressions adjusting for age, gender, race/ ethnicity, household size, residential density, and car ownership. Frequency of fruit and vegetable consumption was the dependent variable, and network distance to primary supermarket, supermarket type, annual household income, and education were independent variables. Model 2 evaluated whether supermarket type continued to be associated with consumption of fruits and vegetables after further adjustment for household income and education in a multivariate model. The goal of model 3 was to assess whether the observed
American Journal of Public Health | May 2014, Vol 104, No. 5
RESEARCH AND PRACTICE
relationships persisted after the addition of the distance variable to the model. We conducted descriptive analyses to characterize the sociodemographic profile and supermarket choice of respondents who did and did not bypass their nearest supermarket for their primary food source. We calculated omnibus P values assessing the overall unadjusted relationships between the demographic variables. In all of the models, linear regressions with robust standard errors were used to correct for the skewed dependent variable.35 In addition, we used log-linear regression models to conduct sensitivity analyses; in these models, the log of fruit and vegetable consumption was the outcome, which allowed an assessment of the robustness of the primary results. In these log-linear regression models, we also evaluated whether spatial correlation existed after adjustment for the baseline covariates. According to the Akaike information criterion,36 models without any spatial correlation exhibited a better goodness of fit. The findings from all models remained the same after the sensitivity analyses; hence, only the linear regression results are presented for ease of interpretation. All confidence intervals (CIs) and P values were 2-sided and based on the Wald statistic; the level of statistical significance was set at P < .05. We used R software in conducting the data analyses.37
TABLE 1—Fruit and Vegetable Consumption, by Sociodemographic Variables, Distance to Primary Supermarket, and Supermarket Type: Seattle Obesity Study, 2008–2009 Frequency of Fruit and Vegetable Consumption per Day Variables
No. of Respondents
Mean (SD)
Median (Interquartile Range)
Gender Male
521
3.89 (2.06)
3.49 (2.44–4.99)
Female
865
4.36 (2.15)
4.00 (2.93–5.43)
Age group, y 18–44
363
4.03 (2.12)
3.78 (2.46–5.29)
45–64
717
4.22 (2.22)
3.82 (2.71–5.35)
‡ 65
306
4.29 (1.90)
4.00 (3.00–5.29)
1135
4.18 (2.10)
3.82 (2.74–5.29)
251
4.19 (2.23)
3.92 (2.57–5.29)
556 830
4.03 (2.11) 4.29 (2.13)
3.64 (2.57–5.24) 3.97 (2.85–5.35)
Race/ethnicity Non-Hispanic White Non-White Annual household income, $ < 50 000 ‡ 50 000 Education < high school
245
3.79 (2.11)
3.29 (2.35–5.14)
Some college
357
3.87 (2.18)
3.43 (2.49–4.78)
> college
784
4.45 (2.07)
4.19 (3.07–5.57)
1287
4.19 (2.10)
3.86 (2.72–5.29)
99
4.07 (2.45)
3.57 (2.29–5.43)
Car ownership Yes No Distance to primary supermarket, miles .05 for each). However, a significant race/ethnicity trend was observed. Only 26.6% of non-White respondents shopped at their nearest supermarket, a percentage significantly lower than that among White respondents (35.3%) (P = .009). Analyses of shopping patterns according to type of supermarket revealed a significant relationship (P < .001). Only small percentages of respondents who shopped at low- and high-cost supermarkets (25% and 30%, respectively) used their nearest supermarket for primary food shopping; the majority of respondents traveled much farther. Figure 1 depicts an example of the shortest network route from home to the primary supermarket for 2 same-area residents. One respondent bypasses medium- and high-cost
supermarkets that are in physical proximity to the respondent’s home for the low-cost supermarket of choice. The other respondent bypasses physically proximal medium- and low-cost supermarkets to travel to a high-cost supermarket.
DISCUSSION To our knowledge, this is one of the few US-based studies to collect data on primary food shopping destinations and to characterize respondents’ choice of a primary store according to its pricing. There were several unique findings. First, contrary to common assumptions, the majority of the respondents did not use their nearest supermarket for primary food shopping. The nearest supermarket was the primary source for only one third of the respondents; the remaining two thirds shopped for food elsewhere. These findings imply that mere physical proximity
TABLE 2—Results of Multivariate Regression Analyses Assessing Fruit and Vegetable Consumption According to Demographic Characteristics, Distance to Primary Supermarket, and Supermarket Type: Seattle Obesity Study, 2008–2009 Model 1 Variables Annual household income, $
a
b (95% CI)
Model 2 P
0.00
a
b (95% CI)
Model 3 P
0.00
a
b (95% CI)
P
0.00
< 50 000 (Ref) ‡ 50 000
0.27 (0.02, 0.51)
.033
0.06 (–0.19, 0.31)
.637
0.06 (–0.19, 0.31)
.627
Education < high school (Ref)
0.00
Some college
0.14 (–0.21, 0.48)
.43
0.12 (–0.22, 0.46)
.49
0.12 (–0.22, 0.46)
.479
0.72 (0.41, 1.03)
