Abstract
Objective
(i) To estimate the independent and combined effects of race/ethnicity and region on the variety of fruits and vegetables consumed in the USA in 2011; and (ii) to assess whether and to what extent race/ethnicity and region may synergistically influence variety of fruit and vegetable consumption.
Design
Cross-sectional analysis. Multivariate logistic regression predicted the likelihood of meeting fruit and vegetable variety indicators independently and in combination for each race/ethnicity and region. Interaction effects models were used to test for interaction effects between race/ethnicity and region on fruit and vegetable variety.
Setting
The 2011 Behavioral Risk Factor Surveillance System (BRFSS).
Subjects
The sample consisted of 275 864 adult respondents.
Results
Fewer than half of respondents consumed fruit and all vegetable subcategories at least once weekly. The adjusted likelihood of meeting fruit and vegetable variety indicators varied significantly by race/ethnicity and region (P<0·05). Significant interactions between race/ethnicity and region were found for at least once weekly consumption of beans, orange vegetables, all vegetables, and fruit and all vegetables (P<0·05).
Conclusions
Our results reinforce previous findings that the variety of vegetable consumption is lacking and is particularly evident among some population subgroups, such as non-Hispanic blacks in the Midwest USA, who may benefit from targeted dietary interventions.
Keywords: Fruit and vegetable, Race/ethnicity, Region, BRFSS
Consuming a diet rich in fruits and vegetables is associated with a decreased risk of CVD( 1 – 4 ), the leading cause of mortality in the USA( 5 ). When incorporated into an overall healthy lifestyle, regular consumption of fruits and vegetables is associated with a reduced risk of all-cause mortality( 6 ). A growing body of evidence also shows a positive relationship between dietary variety and micronutrient intake( 7 – 9 ). Accordingly, the 2010 Dietary Guidelines for Americans provide recommendations for consuming adequate amounts and variety of fruits and vegetables. Yet, despite an ongoing national programme to improve consumption habits( 10 ), the proportion of Americans meeting these guidelines remains low( 11 – 14 ). Marked differences in consumption patterns have been observed across race/ethnic groups( 15 , 16 ) as well as regions of residence( 12 ) and, importantly, some evidence suggests that race/ethnicity and region of residence may synergistically influence consumption patterns( 17 – 19 ). These findings suggest that determinants of fruit and vegetable consumption may be more nuanced than had previously been understood and that highly targeted dietary interventions may be needed for certain population groups. Examining the relationship of race/ethnicity and region of residence with fruit and vegetable consumption is therefore critical to improving Americans’ food consumption habits.
Differences in fruit and vegetable consumption across racial/ethnic groups have been well demonstrated( 11 , 12 , 14 – 16 , 19 – 25 ). Studies have found that non-Hispanic blacks consume fewer fruits and vegetables than other race/ethnic groups( 14 – 16 , 20 , 22 , 24 – 27 ), although more recent findings suggest otherwise( 11 , 21 ). Several studies have described differences in fruit and vegetable consumption across regions( 16 , 28 , 29 ), with results indicating more favourable consumption patterns in the West and Northeast compared with the South and Midwest. According to the US Census Bureau( 30 ), the West region includes the Pacific Coast and stretches east to Colorado, where it meets the Midwest and the South. The South stretches from Texas and Oklahoma up to New Jersey, where the Northeast begins. Few studies have explored the interaction between region and race/ethnicity on food consumption and nutrient intake, but those that have suggest region may have a modifying effect on race/ethnicity( 17 – 19 ). Our objectives in the present work were to: (i) estimate the independent and combined effects of race/ethnicity and region on the variety of fruits and vegetables consumed; and (ii) assess whether and to what extent race/ethnicity and region may synergistically influence variety of fruit and vegetable consumption.
Materials and methods
We used the 2011 Behavioral Risk Factor Surveillance System (BRFSS) for the current analysis. The BRFSS is a telephone survey administered by US state health departments in partnership with the Centers for Disease Control and Prevention. Data are collected on health risk behaviours and preventive health practices of the US civilian population aged ≥18 years. Over 500 000 individuals are interviewed each year, making it the largest telephone-based survey in the world. Random digit dialing is used to select an independent probability sample of residents in each state and trained interviewers administer identical core questionnaires to one adult per household.
Two key changes were made to the 2011 BRFSS sampling design that make this data set particularly attractive for our analysis. A cell phone sampling frame was added to account for the nearly one-third of US households that use a cell phone instead of a landline telephone( 31 ). As racial/ethnic minorities, renters and low-income populations are more likely to live in households with a cell phone instead of a landline telephone, this sampling frame better represents the population of interest in our study compared with BRFSS sampling frames from previous years( 32 ). Additionally, the weighting methodology was changed from a post-stratification approach to a raking approach (also known as iterative proportional fitting), where the sample data is weighted to fit the distribution of control variables, making the sample more reflective of sociodemographic characteristics within each state( 31 ).
Fruit and vegetable consumption frequencies were measured by six questions in the BRFSS core module: (i) ‘During the past month, how many times per day, week or month did you drink 100 % PURE fruit juices? Do not include fruit-flavoured drinks with added sugar or fruit juice you made at home and added sugar to. Only include 100 % fruit juice.’ (ii) ‘During the past month, not counting juice, how many times per day, week or month did you eat fruit? Count fresh, frozen or canned fruit.’ (iii) ‘During the past month, how many times per day, week or month did you eat cooked or canned beans, such as refried, baked, black, garbanzo beans, beans in soup, soyabeans, edamame, tofu or lentils? Do NOT include long green beans.’ (iv) ‘During the past month, how many times per day, week or month did you eat dark green vegetables; for example, broccoli or dark leafy greens including romaine, chard, collard greens or spinach?’ (v) ‘During the past month, how many times per day, week or month did you eat orange-coloured vegetables such as sweet potatoes, pumpkin, winter squash or carrots?’ (vi) ‘Not counting what you just told me about, during the past month, about how many times per day, week or month did you eat OTHER vegetables? Examples of other vegetables include tomatoes, tomato juice or V-8 juice, corn, eggplant, peas, lettuce, cabbage and white potatoes that are not fried, such as baked or mashed potatoes?’
An overall fruit and vegetable variety indicator was operationalized as consuming fruit as well as each subgroup of vegetables (beans, dark green vegetables, orange vegetables and other vegetables) ≥1 time/week, informed by the Food Patterns recommendations for vegetable subgroup consumption on a weekly basis in the Dietary Guidelines for Americans 2010 from the US Department of Agriculture( 33 ). Individual vegetable subgroups and fruit consumed ≥1 time/week were also analysed to explore differences in variety of consumption. As the Food Patterns subcategory recommendations are servings-based and tailored to energy intake, adherence to variety recommendations could not be modelled due BRFSS data limitations.
Main predictor variables were race/ethnicity (non-Hispanic white, non-Hispanic black, non-Hispanic other races, Hispanic) and region (South, Midwest, Northeast, West). A total of 504 408 people completed the interviews. Covariates were identified and operationalized according to precedent set by others and included gender, age, education, income, smoking status, BMI and physical activity( 16 , 28 ). Physical activity level included occupational and leisure-time physical activity, and was categorized as sufficient (150 min of moderate-intensity or 75 min of vigorous intensity aerobic exercise per week)( 34 ), insufficient (active but not sufficient) and inactive (no occupational or leisure-time physical activity). Respondents omitting information on fruit and vegetable consumption or covariates, in addition to those who reported consuming fruit >16 times/d or vegetables >23 times/d, were excluded( 12 , 16 , 22 ) (n 228 544). Persons who responded to all fruit and vegetable questions but reported zero consumption were assigned a frequency of 0·1 times/month to allow for inclusion in the analysis( 16 ). A total of 275 864 respondents were included in the final sample. Respondents were grouped into four regions (South, Northeast, Midwest, and West) according to the US Census Bureau categorization scheme( 30 ).
Multivariate logistic regression was performed on weighted data to estimate odds ratios after adjusting for covariates. Main effects models predicted the likelihood of consuming fruits and vegetable subcategories at least once weekly independently and in combination for each race/ethnicity and region. Interaction effects models were used to test for the presence and magnitude of interaction effects between race/ethnicity and region on fruit and vegetable variety. The statistical significance of interaction terms was computed in a two-step process. Logistic regression models were used to estimate the odds ratios for interaction terms and a subsequent Wald test was executed to determine whether the odds ratios for the interaction terms within each fruit and vegetable category were significantly different from one another in aggregate. Interaction terms were statistically significant if deemed so by the logistic regression model and the Wald test. Statistical significance was set at P<0·05 and all hypothesis tests were two-tailed. The statistical software package Stata 12 was used to account for the complex sampling design and to calculate weighted effect sizes.
Results
Respondent characteristics
Approximately half of all respondents in each region were female. The age distribution of respondents in each region was similar, with the greatest proportion of respondents aged 45–54 years and the lowest proportion aged ≥75 years (Table 1). Non-Hispanic white respondents represented the majority racial/ethnic group in all regions, but the greatest proportions were found in the Midwest and Northeast. Compared with other regions, a greater proportion of non-Hispanic black respondents were found in the South, and a greater proportion of Hispanic respondents and those reporting other race/ethnicities were located in the West. More than half of respondents in each region reported completing some college, with the highest proportion located in the Northeast. More than half of respondents in each region were non-smokers, with the highest proportion located in the West. At least two-thirds of respondents in each region reported sufficient physical activity levels. The proportion of respondents reporting BMI≥30·0 kg/m2 ranged from nearly 23 % in the West to over 27 % in the South. Within each region, the greatest proportion of respondents reported earning ≥$US 75 000 per annum.
Table 1.
Demographic characteristics of respondents by region, Behavioral Risk Factor Surveillance System (BRFSS) 2011
| Characteristic | South (n 76 160) (%*)† | Midwest (n 67 991) (%*) | Northeast (n 53 386) (%*) | West (n 78 327) (%*) | P value‡ |
|---|---|---|---|---|---|
| Sex (% female) | 48·5 | 48·8 | 47·9 | 48·3 | 0·054 |
| Age group (years) | <0·001 | ||||
| 18–24 | 12·2 | 12·4 | 11·5 | 12·8 | |
| 25–34 | 19·5 | 18·7 | 17·4 | 19·2 | |
| 35–44 | 19·3 | 18·5 | 18·4 | 19·0 | |
| 45–54 | 20·0 | 20·2 | 21·3 | 19·5 | |
| 55–64 | 15·0 | 15·5 | 16·0 | 15·0 | |
| 65–74 | 8·7 | 8·7 | 8·9 | 8·6 | |
| ≥75 | 5·8 | 6·0 | 6·4 | 5·9 | |
| Race/ethnicity | <0·001 | ||||
| Non-Hispanic white | 66·5 | 82·2 | 75·2 | 60·0 | |
| Non-Hispanic black | 16·3 | 8·7 | 9·0 | 4·0 | |
| Non-Hispanic, other | 4·7 | 4·1 | 6·6 | 13·6 | |
| Hispanic | 12·5 | 5·1 | 9·2 | 22·6 | |
| Education | <0·001 | ||||
| Less than high school | 11·5 | 8·6 | 9·0 | 12·1 | |
| High school or equivalent | 26·9 | 28·2 | 26·9 | 22·8 | |
| Some college | 31·5 | 33·3 | 27·5 | 34·1 | |
| College graduate | 30·1 | 29·9 | 36·6 | 31·0 | |
| Smoking status | <0·001 | ||||
| Non-smoker | 56·2 | 54·0 | 55·2 | 60·8 | |
| Former smoker | 24·0 | 25·4 | 27·5 | 24·7 | |
| Current smoker | 19·8 | 20·6 | 17·3 | 14·5 | |
| Physical activity level | <0·001 | ||||
| Inactive | 2·7 | 1·7 | 2·3 | 1·5 | |
| Insufficient | 30·5 | 30·0 | 27·6 | 27·1 | |
| Sufficient | 66·8 | 68·3 | 70·1 | 71·4 | |
| BMI (kg/m2) | <0·001 | ||||
| <25 | 35·9 | 35·6 | 39·7 | 40·6 | |
| 25–<30 | 36·8 | 37·6 | 37·3 | 36·8 | |
| ≥30 | 27·4 | 26·8 | 23·0 | 22·6 | |
| Annual income ($US) | <0·001 | ||||
| <10 000 | 6·0 | 4·6 | 4·8 | 6·6 | |
| 10 000–<15 000 | 5·7 | 4·4 | 4·2 | 5·9 | |
| 15 000–<20 000 | 8·4 | 6·9 | 6·9 | 6·6 | |
| 20 000–<25 000 | 9·5 | 9·0 | 7·6 | 8·1 | |
| 25 000–<35 000 | 10·7 | 11·7 | 9·3 | 10·6 | |
| 35 000–<50 000 | 14·3 | 14·9 | 13·1 | 13·3 | |
| 50 000–<75 000 | 15·2 | 18·1 | 15·9 | 15·5 | |
| ≥75 000 | 30·2 | 30·4 | 38·2 | 33·3 |
Percentages are weighted.
Sample sizes are unweighted.
Inter-regional differences were examined using the two-tailed design-adjusted Pearson χ² statistic.
Prevalence and likelihood of consuming fruit and vegetables at least once weekly
Approximately 42 % of respondents reported consuming fruit and all vegetables ≥1 time/week, with the lowest proportion reported among non-Hispanic blacks (28·6 %) and respondents in the Midwest (37·3 %); similar consumption patterns were observed for consuming all vegetables ≥1 time/week (Table 2). There were no large disparities between races or regions for consuming fruit, other vegetables or dark green vegetables ≥1 time/week. Nearly two-thirds of respondents reported consuming orange vegetables ≥1 time/week, which was similar across all regions and race/ethnicities, except for non-Hispanic blacks. Nearly two-thirds of respondents reported consuming beans ≥1 time/week, which was highest among Hispanics and in the West, and lowest among non-Hispanic blacks and in the Northeast.
Table 2.
Percentage of respondents consuming fruit and vegetables at least once weekly by race/ethnicity and region, Behavioral Risk Factor Surveillance System (BRFSS) 2011
| Vegetables | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fruit* | Beans | Dark green | Orange | Other | All vegetables | Fruit and all vegetables | ||||||||
| Characteristic | % | se | % | se | % | se | % | se | % | se | % | se | % | se |
| Overall | 95·4 | 0·08 | 63·8 | 0·19 | 85·5 | 0·14 | 63·9 | 0·19 | 94·1 | 0·11 | 42·4 | 0·19 | 41·9 | 0·19 |
| Race/ethnicity | ||||||||||||||
| Non-Hispanic white | 95·5 | 0·08 | 63·1 | 0·20 | 86·0 | 0·15 | 65·4 | 0·20 | 96·2 | 0·09 | 43·1 | 0·21 | 42·6 | 0·21 |
| Non-Hispanic black | 93·5 | 0·34 | 51·8 | 0·71 | 81·8 | 0·57 | 51·3 | 0·57 | 86·4 | 0·50 | 29·3 | 0·63 | 28·6 | 0·63 |
| Non-Hispanic, other | 96·2 | 0·31 | 64·2 | 0·85 | 88·4 | 0·53 | 68·0 | 0·81 | 93·9 | 0·40 | 45·9 | 0·87 | 45·5 | 0·87 |
| Hispanic | 96·2 | 0·27 | 77·1 | 0·62 | 84·1 | 0·57 | 63·6 | 0·71 | 89·5 | 0·47 | 47·1 | 0·72 | 46·5 | 0·72 |
| Region | ||||||||||||||
| South | 94·3 | 0·16 | 66·2 | 0·33 | 84·0 | 0·27 | 61·2 | 0·35 | 93·5 | 0·19 | 41·6 | 0·34 | 40·9 | 0·34 |
| Midwest | 95·4 | 0·17 | 58·3 | 0·40 | 82·6 | 0·31 | 61·6 | 0·40 | 95·1 | 0·20 | 37·8 | 0·38 | 37·3 | 0·38 |
| Northeast | 95·7 | 0·18 | 56·2 | 0·45 | 87·9 | 0·31 | 66·6 | 0·44 | 94·3 | 0·22 | 40·1 | 0·45 | 39·7 | 0·45 |
| West | 96·7 | 0·13 | 70·5 | 0·36 | 88·5 | 0·25 | 67·8 | 0·37 | 94·0 | 0·22 | 49·0 | 0·39 | 48·6 | 0·39 |
All values are statistically significant at the 0·01 level; differences were examined using the two-tailed design-adjusted Pearson χ² statistic.
Design-adjusted df=496 586.
Whole fruit and/or 100 % fruit juice.
Compared with non-Hispanic white respondents, non-Hispanic black respondents were least likely to consume each category of fruit and vegetables ≥1 time/week except for dark green vegetables, and Hispanics were most likely to consume each category of fruit and vegetables ≥1 time/week except for other vegetables (Table 3). Compared with respondents in the South, respondents in the Midwest were less likely to consume each category of fruits and vegetables ≥1 time/week except for fruit and other vegetables, and respondents in the West were more likely to consume each category of fruits and vegetables ≥1 time/week except for other vegetables.
Table 3.
Adjusted odds ratios of respondents consuming fruit and vegetables at least once weekly, Behavioral Risk Factor Surveillance System (BRFSS) 2011
| Vegetables | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fruit* | Beans | Dark green | Orange | Other | All vegetables | Fruit and all vegetables | ||||||||
| Characteristic | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI |
| Race/ethnicity | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 0·98 | 0·87, 1·12 | 0·66 | 0·62, 0·70 | 1·02 | 0·93, 1·11 | 0·68 | 0·64, 0·72 | 0·35 | 0·31, 0·39 | 0·65 | 0·61, 0·70 | 0·64 | 0·60, 0·68 |
| Non-Hispanic, other | 1·10 | 0·92, 1·30 | 0·95 | 0·88, 1·02 | 1·15 | 1·03, 1·28 | 1·10 | 1·02, 1·19 | 0·67 | 0·58, 0·78 | 1·04 | 0·96, 1·11 | 1·04 | 0·96, 1·12 |
| Hispanic | 1·69 | 1·43, 2·00 | 1·96 | 1·81, 2·11 | 1·23 | 1·12, 1·36 | 1·17 | 1·09, 1·25 | 0·59 | 0·53, 0·67 | 1·39 | 1·30, 1·48 | 1·38 | 1·30, 1·48 |
| Region | ||||||||||||||
| South | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Midwest | 1·24 | 1·12, 1·36 | 0·71 | 0·68, 0·75 | 0·87 | 0·82, 0·93 | 0·99 | 0·94, 1·03 | 1·09 | 0·98, 1·21 | 0·83 | 0·79, 0·87 | 0·84 | 0·80, 0·87 |
| Northeast | 1·22 | 1·10, 1·35 | 0·62 | 0·59, 0·65 | 1·26 | 1·17, 1·35 | 1·16 | 1·11, 1·22 | 0·93 | 0·84, 1·04 | 0·86 | 0·82, 0·90 | 0·87 | 0·83, 0·91 |
| West | 1·47 | 1·33, 1·63 | 1·06 | 1·01, 1·11 | 1·36 | 1·27, 1·45 | 1·19 | 1·14, 1·25 | 0·96 | 0·87, 1·07 | 1·19 | 1·14, 1·25 | 1·20 | 1·15, 1·25 |
Design-adjusted df=496 586.
Adjusted for gender, age, education, smoking status, physical activity, BMI and income.
Whole fruit and/or 100 % fruit juice.
Populations with relatively low likelihoods of meeting fruit and vegetable variety indicators can be identified by multiplying race/ethnicity coefficients with region coefficients. This reveals relatively low likelihoods of consuming fruit and all vegetables ≥1 time/week for non-Hispanic blacks in the Midwest (OR: 0·64×0·84=0·54) and non-Hispanic blacks in the Northeast (OR: 0·64×0·87=0·56; Table 3), who were almost 50 % less likely to meet this indicator than non-Hispanic whites in their regions. Non-Hispanic blacks in these regions also had the lowest likelihoods for consuming all vegetables ≥1 time/week and beans ≥1 time/week.
Interaction effect of race/ethnicity and region on consuming fruit and vegetables at least once weekly
Table 4 displays the odds ratios for race/ethnicity×region interaction terms for each race/ethnicity and region combination. Statistically significant interactions were observed for fruit and all vegetables, all vegetables, beans and orange vegetables. All statistically significant interaction effects were observed in the Northeast and Midwest, with most observed in the Northeast. For consuming beans ≥1 time/week, orange vegetables ≥1 time/week, all vegetables ≥1 time/week, and fruit and all vegetables ≥1 time/week, the relationship between non-Hispanic blacks and non-Hispanic whites in the Northeast was significantly different from the relationship between these race/ethnicities in all other regions.
Table 4.
Odds ratios for race/ethnicity×region interaction terms, Behavioral Risk Factor Surveillance System (BRFSS) 2011
| Vegetables | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fruit* | Beans† | Dark green | Orange† | Other | All vegetables† | Fruit and all vegetables† | ||||||||
| Outcome | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI |
| South | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic, other | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Hispanic | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Midwest | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 1·27 | 0·91, 1·76 | 1·04 | 0·88, 1·23 | 1·17 | 0·95, 1·45 | 1·06 | 0·90, 1·25 | 0·97 | 0·74, 1·27 | 1·03 | 0·85, 1·24 | 1·02 | 0·84, 1·24 |
| Non-Hispanic, other | 0·79 | 0·48, 1·30 | 1·28‡ | 1·01, 1·63 | 1·28 | 0·95, 1·73 | 1·35‡ | 1·06, 1·72 | 1·01 | 0·61, 1·67 | 1·35‡ | 1·07, 1·71 | 1·35‡ | 1·07, 1·71 |
| Hispanic | 0·96 | 0·49, 1·90 | 0·99 | 0·75, 1·31 | 0·74 | 0·53, 1·04 | 0·84 | 0·64, 1·10 | 0·99 | 0·62, 1·59 | 0·91 | 0·71, 1·17 | 0·92 | 0·71, 1·18 |
| Northeast | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 1·01 | 0·72, 1·43 | 2·00§ | 1·71, 2·35 | 1·05 | 0·82, 1·35 | 1·19‡ | 1·01, 1·40 | 0·95 | 0·73, 1·23 | 1·56§ | 1·32, 1·85 | 1·59§ | 1·34, 1·89 |
| Non-Hispanic, other | 1·09 | 0·63, 1·90 | 1·49§ | 1·18, 1·87 | 1·20 | 0·84, 1·73 | 1·18 | 0·93, 1·50 | 1·03 | 0·65, 1·62 | 1·36§ | 1·08, 1·71 | 1·35§ | 1·07, 1·70 |
| Hispanic | 0·69 | 0·47, 1·03 | 1·61§ | 1·30, 2·00 | 0·74 | 0·56, 0·97 | 0·76§ | 0·62, 0·93 | 0·75 | 0·54, 1·03 | 1·09 | 0·9, 1·33 | 1·09 | 0·90, 1·32 |
| West | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 0·93 | 0·58, 1·50 | 1·05 | 0·84, 1·31 | 0·97 | 0·72, 1·32 | 1·07 | 0·86, 1·32 | 1·07 | 0·75, 1·52 | 1·12 | 0·89, 1·39 | 1·11 | 0·89, 1·39 |
| Non-Hispanic, other | 0·93 | 0·63, 1·37 | 0·92 | 0·76, 1·10 | 1·17 | 0·90, 1·52 | 1·07 | 0·90, 1·29 | 0·84 | 0·57, 1·23 | 0·94 | 0·79, 1·11 | 0·94 | 0·79, 1·12 |
| Hispanic | 1·04 | 0·73, 1·49 | 1·13 | 0·95, 1·33 | 0·90 | 0·73, 1·11 | 1·04 | 0·89, 1·21 | 0·80 | 0·60, 1·05 | 1·02 | 0·88, 1·17 | 1·03 | 0·89, 1·18 |
Adjusted for gender, age, education, smoking status, physical activity, BMI, income, region and race/ethnicity.
Whole fruit and/or 100 % fruit juice.
Race×region interaction term in column jointly statistically significant at the 0·05 level (Wald test).
Race×region interaction term statistically significant at the 0·05 level.
Race×region interaction term statistically significant at the 0·01 level.
Examination of intraregional disparities by incorporating interaction effects
The likelihood of meeting variety indicators by region and races within regions, accounting for interaction effects, is displayed in Table 5. Non-Hispanic blacks were ~40 % less likely than non-Hispanic whites to consume fruit and all vegetables ≥1 time/week in the South, Midwest and West (Table 5). There was no difference between non-Hispanic blacks and whites in the Northeast on this indicator, due to a synergistic effect between race/ethnicity and region highlighted in Table 4. A similar pattern was observed for non-Hispanic blacks consuming all vegetables ≥1 time/week.
Table 5.
Adjusted odds ratios for respondents consuming fruit and vegetables at least once weekly by region and races within regions, Behavioral Risk Factor Surveillance System (BRFSS) 2011
| Vegetables | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fruit* | Beans | Dark green | Orange | Other | All vegetables | Fruit and all vegetables | ||||||||
| Outcome | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI | OR | 95 % CI |
| Region | ||||||||||||||
| South | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Midwest | 1·22† | 1·10, 1·35 | 0·69† | 0·66, 0·72 | 0·87† | 0·82, 0·93 | 0·98 | 0·93, 1·02 | 1·11 | 0·99, 1·25 | 0·82† | 0·78, 0·85 | 0·82† | 0·78, 0·86 |
| Northeast | 1·25† | 1·12, 1·40 | 0·54† | 0·51, 0·56 | 1·28† | 1·19, 1·38 | 1·16† | 1·10, 1·23 | 0·99 | 0·87, 1·13 | 0·80† | 0·76, 0·84 | 0·81† | 0·77, 0·85 |
| West | 1·46† | 1·31, 1·62 | 1·05† | 1·00, 1·10 | 1·36† | 1·27, 1·45 | 1·16† | 1·11, 1·22 | 1·04 | 0·91, 1·19 | 1·19† | 1·14, 1·25 | 1·20† | 1·14, 1·25 |
| South | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 0·95 | 0·80, 1·12 | 0·58† | 0·53, 0·63 | 0·98 | 0·87, 1·10 | 0·65† | 0·60, 0·70 | 0·36† | 0·31, 0·41 | 0·59† | 0·54, 0·64 | 0·58† | 0·54, 0·64 |
| Non-Hispanic, other | 1·16 | 0·90, 1·50 | 0·89 | 0·77, 1·02 | 1·01 | 0·84, 1·21 | 1·00 | 0·88, 1·15 | 0·71† | 0·53, 0·97 | 0·98 | 0·86, 1·12 | 0·98 | 0·86, 1·12 |
| Hispanic | 1·76† | 1·34, 2·32 | 1·73† | 1·51, 1·97 | 1·38† | 1·17, 1·63 | 1·21† | 1·08, 1·37 | 0·68† | 0·55, 0·84 | 1·37† | 1·22, 1·53 | 1·36† | 1·21, 1·52 |
| Midwest | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 1·20 | 0·91, 1·60 | 0·60† | 0·52, 0·69 | 1·15 | 0·96, 1·38 | 0·68† | 0·59, 0·79 | 0·35† | 0·27, 0·44 | 0·61† | 0·51, 0·72 | 0·60† | 0·50, 0·71 |
| Non-Hispanic, other | 0·91 | 0·59, 1·40 | 1·14 | 0·94, 1·39 | 1·29† | 1·02, 1·63 | 1·36† | 1·11, 1·66 | 0·72 | 0·48, 1·08 | 1·32† | 1·09, 1·61 | 1·32† | 1·09, 1·60 |
| Hispanic | 1·69 | 0·91, 3·16 | 1·71† | 1·33, 2·19 | 1·03 | 0·76, 1·38 | 1·02 | 0·80, 1·30 | 0·67 | 0·44, 1·03 | 1·24 | 0·99, 1·56 | 1·24 | 0·99, 1·56 |
| Northeast | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 0·96 | 0·71, 1·30 | 1·16† | 1·01, 1·33 | 1·03 | 0·83, 1·29 | 0·77† | 0·67, 0·89 | 0·34† | 0·27, 0·42 | 0·92 | 0·80, 1·07 | 0·93 | 0·80, 1·08 |
| Non-Hispanic, other | 1·26 | 0·77, 2·07 | 1·32† | 1·10, 1·59 | 1·21 | 0·89, 1·65 | 1·18 | 0·97, 1·44 | 0·73 | 0·52, 1·03 | 1·33† | 1·11, 1·60 | 1·32† | 1·10, 1·59 |
| Hispanic | 1·22 | 0·91, 1·64 | 2·78† | 2·34, 3·29 | 1·02 | 0·82, 1·27 | 0·92 | 0·78, 1·09 | 0·51† | 0·39, 0·65 | 1·50† | 1·28, 1·76 | 1·48† | 1·26, 1·74 |
| West | ||||||||||||||
| Non-Hispanic white | Referent | Referent | Referent | Referent | Referent | Referent | Referent | |||||||
| Non-Hispanic black | 0·88 | 0·57, 1·38 | 0·61† | 0·50, 0·75 | 0·95 | 0·72, 1·27 | 0·69† | 0·56, 0·84 | 0·38† | 0·27, 0·53 | 0·66† | 0·54, 0·81 | 0·65† | 0·53, 0·80 |
| Non-Hispanic, other | 1·08 | 0·81, 1·44 | 0·82† | 0·73, 0·92 | 1·18 | 0·98, 1·42 | 1·08 | 0·96, 1·22 | 0·60† | 0·47, 0·76 | 0·92 | 0·82, 1·02 | 0·92 | 0·83, 1·03 |
| Hispanic | 1·84† | 1·43, 2·36 | 1·95† | 1·74, 2·17 | 1·25† | 1·09, 1·43 | 1·26† | 1·15, 1·39 | 0·54† | 0·45, 0·65 | 1·39† | 1·27, 1·52 | 1·39† | 1·27, 1·53 |
Adjusted for gender, age, education, smoking status, physical activity, BMI, income, race/ethnicity, and race/ethnicity×region
Whole fruit and/or 100 % fruit juice
Statistically significant at the 0·05 level.
Non-Hispanic blacks in the South, Midwest and West were ~40 % less likely to consume beans than non-Hispanic whites in these regions (Table 5). However, in the Northeast, non-Hispanic blacks were 16 % more likely to consume beans ≥1 time/week than non-Hispanic whites, due to the positive interaction effect displayed in Table 4. In all regions, Hispanics were more likely to consume beans ≥1 time/week than non-Hispanic whites (Table 5), but the positive interaction effect in the Northeast (Table 4) enhanced this gap.
There was no difference in the likelihood of consuming orange vegetables ≥1 time/week between Hispanics and non-Hispanic whites in the Northeast (Table 5); this was the only example found of a negative interaction (i.e. OR <1·00) between race/ethnicity and region (Table 4). In the South and West, Hispanics were 21 and 26 % more likely, respectively, to consume orange vegetables compared with non-Hispanic whites in these regions (Table 5).
Discussion
The present study provides findings of the prevalence and likelihood of meeting fruit and vegetable variety indicators and elucidates the possible interaction effects between race/ethnicity and region of residence. Using the BRFSS 2011 data, which included a new survey methodology to enhance the representativeness of minority populations, and revised FFQ categories, these data may better reflect potential disparities in fruit and vegetable consumption than prior BRFSS iterations.
The results generated by the study show that a greater proportion of respondents consumed fruit ≥1 time/week and other vegetables ≥1 time/week compared with each of the other fruit and vegetable categories (Table 2). This may be concerning for two reasons. First, nearly 30 % of respondents reported consuming their fruit as juice ≥50 % of the time (data not shown) and nearly 10 % of respondents reported consuming their fruit as juice ≥70 % of the time. Diets that include more fruit juice than whole fruit have been associated with an increased risk of type 2 diabetes( 35 ), which suggests that the form in which fruit is consumed may be just as important as the amount of fruit that is consumed. Second, our results reinforce previous findings that the variety of vegetable consumption is lacking( 9 , 36 ). Among all race/ethnic groups, consumption of vegetables is skewed towards other vegetables (which includes starchy vegetables, such as non-fried potatoes), a finding that has also been observed by others( 13 , 37 ). Further research should assess which regions and race/ethnicities may require targeted interventions to substitute whole fruit for fruit juice and to increase the variety of vegetable consumption.
Testing the interaction between race/ethnicity and region allows for the identification of synergistic influences between race/ethnicity and region on fruit and vegetable consumption. Hispanics in the Northeast were impacted by an interaction, but in a mixed way. Being Hispanic in the Northeast was associated with a significant synergistic effect on consuming beans ≥1 time/week and a negative effect on consuming orange vegetables ≥1 time/week.
We identify non-Hispanic blacks in the Northeast as the population group that was most affected by the synergistic effect between race/ethnicity and region. Non-Hispanic blacks had the lowest prevalence of consuming fruit and all vegetables at least once weekly. Examining the independent and combined effects of race/ethnicity and region of residence, without accounting for effect modification, highlighted non-Hispanic blacks in the Northeast and Midwest as subpopulations with particularly low likelihoods of consumption. However, being non-Hispanic black in the Northeast was associated with a positive effect on consuming fruit and all vegetables ≥1 time/week (Table 4). Compared with other regions, the Northeast appears to provide conditions that positively influence fruit and vegetable consumption among non-Hispanic blacks. Accounting for synergies between race/ethnicity and region in meeting the variety indicators also adds increased precision in targeting: non-Hispanic blacks in the Midwest may be a critical subpopulation to engage with to increase variety of fruit and vegetable consumption.
Regional and cultural dietary patterns may contribute to the effects of race/ethnicity and region that were observed in the present study. For example, Mahaffey et al.( 38 ) found that women living in coastal regions consumed more fish than their non-coastal counterparts and others( 39 ) have described the unique diet pattern of blacks in the South. Additionally, dietary acculturation in foreign-born populations may be a factor contributing to observed synergistic effects. Dietary acculturation tends to reduce fruit and vegetable consumption( 40 , 41 ), although there are differences between racial/ethnic groups and within ethnic subgroups( 41 – 43 ). As acculturation increases among Hispanic immigrant populations, consumption of legumes and fruit tends to decrease and non-leguminous vegetable consumption increases( 41 ). More acculturated Hispanics consume diets similar to non-Hispanic whites( 41 ). Far fewer studies document dietary acculturation trends among non-Hispanic black foreign-born populations. Those that do often compare this subpopulation with non-Hispanic blacks born in the USA, who tend to consume less fruit( 43 ) and different kinds of vegetables (e.g. more green leafy vegetables and white potatoes, fewer beans and yellow vegetables)( 43 ) than foreign-born, non-Hispanic blacks.
US regions are markedly different from one another in the proportion and diversity of their residents who were born outside the USA( 44 ). New York and New Jersey (located in the Northeast) are the states with the second and third largest percentage of their populations as foreign born, respectively( 45 ). The Northeast is the most diverse region in terms of its foreign-born residents: no country of origin made up >10 % of the foreign-born population in 2010, unlike other regions( 44 ). Immigrants from the Dominican Republic and Jamaica were two out of top five immigrant groups in the Northeast, whereas there were no black ethnicities among the top five immigrant groups in other regions( 45 ).
The dynamics between dietary acculturation in foreign-born populations and the degree of urbanization within the Northeast could explain the synergistic effects observed in the present study. Approximately 80 % of the population in the Northeast is located in urban areas, compared with 66 and 63 % in the South and Midwest, respectively( 46 ). Furthermore, four of the top ten most populous urban areas in the USA are located in the Northeast( 46 ). Residing in ethnic enclaves in densely populated urban centres may mediate dietary acculturation via social and/or food environmental structures. For example, Dubowitz et al.( 40 ) observed that the proportion of a neighbourhood that is foreign born was positively associated with individuals’ fruit and vegetable consumption, regardless of whether they were foreign or native to the USA.
Local and community-level food environments in the Northeast may contribute favourably towards individuals’ fruit and vegetable consumption( 47 ). Fruits, vegetables and nuts represent the majority of direct food sales (e.g. farmers’ markets and community-supported agriculture operations) and direct sales are densely concentrated in the Northeast region( 48 ). Of the ten states with the highest share of direct food sales out of total food sales, eight are located in the Northeast( 49 ). A greater proportion of primary grocery shoppers make food purchases at farmers’ markets in the Northeast compared with other regions, and they are ~40–60 % more likely to purchase fruits and vegetables through direct sales during some parts of the year compared with other regions( 50 ).
The limitations to the present study are several. It is a cross-sectional analysis, which precludes analysis of time trends in disparities in meeting fruit and vegetable consumption outcomes. A cross-sectional approach was necessary due to the novel design of the 2011 BRFSS module and further research is needed to complement this baseline once subsequent BRFSS data are published. The BRFSS is a telephone survey that relies on self-reported data for information, which may over- or under-report particular characteristics or behaviours. The accuracy of dietary surveys relies on respondents’ memory and respondents may adjust their reported consumption patterns to simplify the interview process as well as to impress the interviewer( 51 , 52 ). Respondents may also differ in how they categorize fruits and vegetables and this may be different between race/ethnic groups( 51 , 53 ). The BRFSS also does not capture fruits and vegetables consumed as part of mixed dishes. Additionally, the 2010 Dietary Guidelines( 33 ) provide fruit and vegetable recommendations on the basis of daily servings, whereas the BRFSS captures daily frequencies.
Although the National Health and Nutrition Examination Survey (NHANES) provides dietary data on the basis of mass (which can be converted to servings), NHANES data are not collected at the state level and therefore are not as useful as the BRFSS for estimating regional food consumption patterns. Finally, all published studies validating the FFQ module for BRFSS were completed for versions prior to 2011, although a similar version of the 2011 FFQ was pilot tested in several states and reliability tested in one state (K Grimm, Centers for Disease Control and Prevention, personal communication, 3 April 2013). Further research is needed to assess the validity and reliability of these and other BRFSS modules as a result of the 2011 changes( 54 ).
Conclusion
The present findings suggest that targeted dietary interventions may be needed for some population groups, such as non-Hispanic blacks in the Midwest. Interventions should focus on increasing consumption of all types of vegetables, particularly orange vegetables and beans. Further research is needed to determine the drivers behind the synergistic effects of race/ethnicity and region in the Northeast and Midwest.
Acknowledgements
Acknowledgements: The authors gratefully acknowledge Robert Houser, PhD and Parke Wilde, PhD for input on statistical analysis. Financial support: This research received no specific grant from any finding agency in the public, commercial or not-for-profit sectors. Conflict of interest: None. Authorship: N.T. conceived the study. N.T. and Z.C. designed the study, conducted statistical analysis and wrote the manuscript. Both authors read and approved the final manuscript. Ethics of human subject participation: Ethical approval was not required.
References
- 1. Danaei G, Ding EL, Mozaffarian D et al. (2009) The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med 6, 1–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. He FJ, Nowson CA, Lucas M et al. (2007) Increased consumption of fruit and vegetables is related to a reduced risk of coronary heart disease: meta-analysis of cohort studies. J Hum Hypertens 21, 717–728. [DOI] [PubMed] [Google Scholar]
- 3. Dauchet L, Amouyel P, Hercberg S et al. (2006) Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr 136, 2588–2593. [DOI] [PubMed] [Google Scholar]
- 4. Hung H-C, Joshipura KJ, Jiang R et al. (2004) Fruit and vegetable intake and risk of major chronic disease. J Natl Cancer Inst 96, 1577–1584. [DOI] [PubMed] [Google Scholar]
- 5. US Department of Health and Human Services, Centers for Disease Control and Prevention (2010) Ten Leading Causes of Death and Injury. www.cdc.gov/injury/wisqars/leadingcauses.html (accessed February 2013).
- 6. Loef M & Walach H (2012) The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis. Prev Med 55, 163–170. [DOI] [PubMed] [Google Scholar]
- 7. Griep LMO, Verschuren WMM, Kromhout D et al. (2012) Variety in fruit and vegetable consumption and 10-year incidence of CHD and stroke. Public Health Nutr 15, 2280–2286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Murphy SP, Foote JA, Wilkens LR et al. (2006) Simple measures of dietary variety are associated with improved dietary quality. J Am Diet Assoc 106, 425–429. [DOI] [PubMed] [Google Scholar]
- 9. Foote JA, Murphy SP, Wilkens LR et al. (2004) Dietary variety increases the probability of nutrient adequacy among adults. J Nutr 134, 1779–1785. [DOI] [PubMed] [Google Scholar]
- 10. Produce for Better Health Foundation (2015) Fruits and Veggies: More Matters. http://www.fruitsandveggiesmorematters.org/ (accessed February 2015).
- 11. Demydas T (2011) Consumer segmentation based on the level and structure of fruit and vegetable intake: an empirical evidence for US adults from the National Health and Nutrition Examination Survey (NHANES) 2005–2006. Public Health Nutr 14, 1088–1095. [DOI] [PubMed] [Google Scholar]
- 12. Grimm KA, Blanck HM, Scanlon KS et al. (2010) State-specific trends in fruit and vegetable consumption among adults – United States, 2000–2009. MMWR CDC Surveill Summ 59, 1125–1130. [PubMed] [Google Scholar]
- 13. Kimmons J, Gillespie C, Seymour J et al. (2009) Fruit and vegetable intake among adolescents and adults in the United States: percentage meeting individualized recommendations. Medscape J Med 11, 26. [PMC free article] [PubMed] [Google Scholar]
- 14. Casagrande SS, Wang Y, Anderson C et al. (2007) Have Americans increased their fruit and vegetable intake? The trends between 1988 and 2002. Am J Prev Med 32, 257–263. [DOI] [PubMed] [Google Scholar]
- 15. Kant AK, Graubard BI & Kumanyika SK (2007) Trends in black–white differentials in dietary intakes of US adults, 1971–2002. Am J Prev Med 32, 264–272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Serdula MK, Gillespie C, Kettel-Khan L et al. (2004) Trends in fruit and vegetable consumption among adults in the United States: Behavioral Risk Factor Surveillance System, 1994–2000. Am J Public Health 94, 1014–1018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Newby PK, Noel SE, Grant R et al. (2012) Race and region have independent and synergistic effects on dietary intakes in black and white women. Nutr J 11, 25–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Newby PK, Noel SE, Grant R et al. (2011) Race and region are associated with nutrient intakes among black and white men in the United States. J Nutr 141, 296–303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Thompson B, Demark-Wahnefried W, Taylor G et al. (1999) Baseline fruit and vegetable intake among adults in seven 5 A Day study centers located in diverse geographic areas. J Am Diet Assoc 99, 1241–1248. [DOI] [PubMed] [Google Scholar]
- 20. Kim SAP, Moore LVP, Galuska DP et al. (2014) Vital signs: Fruit and vegetable intake among children – United States, 2003–2010. MMWR CDC Surveill Summ 63, 671–676. [PMC free article] [PubMed] [Google Scholar]
- 21. Lutfiyya MN, Chang LF & Lipsky MS (2012) A cross-sectional study of US rural adults’ consumption of fruits and vegetables: do they consume at least five servings daily? BMC Public Health 12, 280. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Blanck HM, Gillespie C, Kimmons J et al. (2008) Trends in fruit and vegetable consumption among US men and women, 1994–2005. Prev Chronic Dis 5, 1–10. [PMC free article] [PubMed] [Google Scholar]
- 23. Forshee RA & Storey ML (2006) Demographics, not beverage consumption, is associated with diet quality. Int J Food Sci Nutr 57, 494–511. [DOI] [PubMed] [Google Scholar]
- 24. Li R, Serdula M, Bland S et al. (2000) Trends in fruit and vegetable consumption among adults in 16 US states: Behavioral Risk Factor Surveillance System, 1990–1996. Am J Public Health 90, 777–781. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Krebs-Smith SM, Cook A, Subar AF et al. (1995) US adults’ fruit and vegetable intakes, 1989 to 1991: a revised baseline for the Healthy People 2000 objective. Am J Public Health 85, 1623–1629. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Ervin RB (2011) Healthy Eating Index–2005 Total and Component Scores for Adults Aged 20 and Over: National Health and Nutrition Examination Survey, 2003–2004. National Health Statistics Reports no. 44. http://www.cdc.gov/nchs/products/nhsr.htm (accessed November 2014). [PubMed]
- 27. Diaz VA, Mainous Iii AG, Koopman RJ et al. (2005) Race and diet in the overweight: association with cardiovascular risk in a nationally representative sample. Nutrition 21, 718–725. [DOI] [PubMed] [Google Scholar]
- 28. Troost JP, Rafferty AP, Luo Z et al. (2012) Temporal and regional trends in the prevalence of healthy lifestyle characteristics: United States, 1994–2007. Am J Public Health 102, 1392–1398. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Hajjar I & Kotchen T (2003) Regional variations of blood pressure in the United States are associated with regional variations in dietary intakes: the NHANES-III data. J Nutr 133, 211–214. [DOI] [PubMed] [Google Scholar]
- 30. US Census Bureau (2010) Geographic Terms and Concepts – Census Divisions and Census Regions. http://www.census.gov/geo/reference/gtc/gtc_census_divreg.html (accessed November 2014).
- 31. US Department of Health and Human Services, Centers for Disease Control and Prevention (2013) BRFSS 2011 Survey Data and Documentation: Comparability of Data. http://www.cdc.gov/brfss/annual_data/annual_2011.htm (accessed April 2014).
- 32. Hu SS, Balluz L, Battaglia MP et al. (2010) The impact of cell phones on public health surveillance. Bull World Health Organ 88, 799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. US Department of Health and Human Services & US Department of Agriculture (2010) Dietary Guidelines for Americans 2010. Washington, DC: US Government Printing Office; available at http://www.health.gov/dietaryguidelines/ [Google Scholar]
- 34. US Department of Health and Human Services (2008) 2008 Physical Activity Guidelines for Americans. Washington, DC: US Government Printing Office; available at www.health.gov/paguidelines/guidelines/ [Google Scholar]
- 35. Muraki I, Imamura F, Manson JE et al. (2013) Fruit consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ 347, f5001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Marcoe K, Juan WY, Yamini S et al. (2006) Development of food group composites and nutrient profiles for the MyPyramid food guidance system. J Nutr Educ Behav 38, 6 Suppl., S93–S107. [DOI] [PubMed] [Google Scholar]
- 37. Bachman JL, Reedy J, Subar AF et al. (2008) Sources of food group intakes among the US population, 2001–2002. J Am Diet Assoc 108, 804–814. [DOI] [PubMed] [Google Scholar]
- 38. Mahaffey KR, Clickner RP & Jeffries RA (2009) Adult women’s blood mercury concentrations vary regionally in the United States: association with patterns of fish consumption (NHANES 1999–2004). Environ Health Perspect 117, 47–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Bovell-Benjamin A, Dawkins N, Pace R et al. (2010) Dietary consumption practices and cancer risk in African Americans in the rural South. J Health Care Poor Underserved 21, 57–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Dubowitz T, Subramanian SV, Acevedo-Garcia D et al. (2008) Individual and neighborhood differences in diet among low-income foreign and US-born women. Womens Health Issues 18, 181–190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Ayala GX, Baquero B & Klinger S (2008) A systematic review of the relationship between acculturation and diet among Latinos in the United States: implications for future research. J Am Diet Assoc 108, 1330–1344. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Okafor M-TC, Carter-Pokras OD & Zhan M (2014) Greater dietary acculturation (dietary change) is associated with poorer current self-rated health among African immigrant adults. J Nutr Educ Behav 46, 226–235. [DOI] [PubMed] [Google Scholar]
- 43. Lancaster KJ, Watts SO & Dixon LB (2006) Dietary intake and risk of coronary heart disease differ among ethnic subgroups of black Americans. J Nutr 136, 446–451. [DOI] [PubMed] [Google Scholar]
- 44. Grieco EM, Trevelyan E, Larsen L et al. (2012) The Size, Place of Birth, and Geographic Distribution of the Foreign-born Population in the United States: 1960 to 2010. Washington, DC: US Government Printing Office; available at http://www.census.gov/people/publications/popworkingpapers.html [Google Scholar]
- 45. Grieco EM, Acosta Y, de la Cruz P et al. (2012) The Foreign-Born Population in the United States: 2010. Washington, DC: US Government Printing Office; available at http://www.census.gov/topics/population/foreign-born.html# [Google Scholar]
- 46. US Department of Commerce, Census Bureau (2010) Census 2010, Summary File 1, Table P2, generated by Nicole Tichenor. http://www.factfinder2.census.gov (accessed October 2014).
- 47. US Department of Health and Human Services, Centers for Disease Control and Prevention (2013) State Indicator Report on Fruits and Vegetables, 2013. Table 2. http://www.cdc.gov/nutrition/professionals/data/ (accessed November 2014).
- 48. Martinez S, Hand M, Da Pra M et al. (2010) Local Food Systems: Concepts, Impacts, and Issues. Washington, DC: US Government Printing Office; available at http://www.ers.usda.gov/media/122868/err97_1_.pdf [Google Scholar]
- 49. Diamond A & Soto R (2009) Facts on Direct-to-Consumer Food Marketing: Incorporating Data from the 2007 Census of Agriculture. 10.9752/MS035.05-2009 (accessed November 2014). [DOI]
- 50. Blanck HM, Thompson OM, Nebeling L et al. (2011) Improving fruit and vegetable consumption: use of farm-to-consumer venues among US adults. Prev Chronic Dis 8, A49. [PMC free article] [PubMed] [Google Scholar]
- 51. Roark RA & Niederhauser VP (2013) Fruit and vegetable intake: issues with definition and measurement. Public Health Nutr 16, 2–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Woodside JV, Young IS & McKinley MC (2013) Fruits and vegetables: measuring intake and encouraging increased consumption. Proc Nutr Soc 72, 236–245. [DOI] [PubMed] [Google Scholar]
- 53. Thompson FE, Willis GB, Thompson OM et al. (2011) The meaning of ‘fruits’ and ‘vegetables’. Public Health Nutr 14, 1222–1228. [DOI] [PubMed] [Google Scholar]
- 54. Pierannunzi C, Hu SS & Balluz L (2013) A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004–2011. BMC Med Res Methodol 13, 49. [DOI] [PMC free article] [PubMed] [Google Scholar]