Neighborhood Immigrant Acculturation and Diet among Hispanic Female Residents of NYC

Abstract

Objective

To identify predominant dietary patterns among Hispanic women and to determine whether adherence to dietary patterns is predicted by neighborhood level factors: linguistic isolation, poverty rate and the retail food environment.

Design

Cross-sectional analyses of predictors of adherence to dietary patterns identified from principle component analyses of data collected using the Study of Women’s Health Across the Nation (SWAN) food frequency questionnaire. Census data were used to measure poverty rates and the percent of Spanish speaking families in the neighborhood in which no one ≥ 14 years old spoke English very well (linguistic isolation) and the retail food environment was measured using business listings data.

Setting

New York City.

Subjects

345 Hispanic women.

Results

Two major dietary patterns were identified: a healthy diet pattern loading high for vegetable, legumes, potato, fish, and other seafood which explained 17% of the variance in the FFQ data and an energy dense diet pattern loading high for red meat, poultry, pizza, french fries, and high energy drink, which explained 9% of the variance in the FFQ data. Adherence to the healthy diet pattern was positively associated with neighborhood linguistic isolation and negatively associated with neighborhood poverty. More fast food restaurants per Km² in the neighborhood was significantly associated with lower adherence to the healthy diet. Adherence to the energy dense diet pattern was inversely, but not significantly, associated with neighborhood linguistic isolation.

Conclusions

These results are consistent with the hypothesis that living in immigrant enclaves is associated with healthy diet patterns among Hispanics.

Introduction

Racial and ethnic minority populations have been acutely affected by the epidemic of overweight and obesity in the United States. Hispanics, along with Black Americans and Native Americans, face a higher risk of obesity than Caucasians ⁽¹⁾. Multiple studies have shown that first-generation immigrants have a lower body size than second- and third-generation immigrants, and that the number of years since immigration is correlated with increased body size ^(2–7). These studies have been interpreted to suggest that over time immigrants adapt to the ‘obesogenic’ environment of the U.S., which promotes dietary and physical activity patterns favoring a positive energy balance and thus gain weight ^(3–6). Thus acculturation, conceptualized as the process by which immigrants adopt the cultural norms, specifically in this case dietary and physical activity patterns, of the host society, has been identified as an important topic for study regarding body size among Hispanics ^{(4, 8, 9)}.

There is a growing literature showing that among Hispanics, increasing generation since immigration, duration of residence in the US, and other measures of increased acculturation are associated with diets that are lower in rice, beans and fruits and higher in sugar and sugar sweetened drinks ⁽¹⁰⁾. Much of the research has focused on individual-level measures of acculturation such as the respondent’s use of English or years of US residence. However a few studies have shown that residence in primarily immigrant and co-ethnic neighborhoods, areas likely to have groceries and restaurants selling familiar home-country foods ⁽¹¹⁾, is linked to healthier diets ^(12–14). While presented as an indicator of neighborhood-level acculturation status, foreign-born composition is, conceptually, a less direct measure of neighborhood-level immigrant acculturation than measures of linguistic isolation ⁽⁷⁾. Linguistic isolation is a term used by the Census to characterize households in which no person aged 14 or over speaks English at least “very well” [http://www.doleta.gov/reports/CensusData/Glossary.cfm]. For example, neighborhoods could have similar proportions of immigrant residents but the neighborhoods could differ in level of linguistic isolation and thus the extent to which they are isolated from obesogenic dietary norms and practices prevalent in the U.S. Neighborhood linguistic isolation has been found to be a stronger predictor of BMI among Hispanics than neighborhood foreign-born composition ⁽⁷⁾. In addition, the association between nativity and BMI was weaker among Hispanics living in neighborhoods with higher levels of linguistic isolation than among those living in neighborhoods with lower levels of linguistic isolation ⁽⁷⁾.

To date, the vast majority of the research on immigrant acculturation and diet has focused on consumption of individual or groups of nutrients, such as fats and sugars, or on consumption of food groups, such as fruits and vegetables ⁽¹⁰⁾. Referred to as “nutritionism’, this focus on nutrients or food items high in specific nutrients as the predominant outcome of interest has been critiqued as being overly reductionist, failing to account for how foods are eaten in combination as part of meals and cuisines ^(15–18). Since nutrients and food items are commonly consumed as part of a dietary pattern or preference, analyses of associations between indicators of acculturation and single nutrients or food items may be confounded by the effects of acculturation on overall dietary preferences. Inter-individual variation in dietary patterns, as opposed to variation in single nutrients, may be a better indicator of the effects of individual- and neighborhood-level acculturation on diet ⁽¹⁹⁾.

Using data from food frequency questionnaires, we sought to identify major dietary patterns among Hispanic women and to evaluate the extent of individual women’s adherence to those patterns. We then examined the association of adherence to the dietary patterns with individual and neighborhood-level acculturation and the neighborhood retail food environment.

Experimental methods

Sample

We analyzed sociodemographic and food frequency questionnaire (FFQ) data for the Hispanic female guardians (typically the mother; alternatively, a grandmother) of Head Start children enrolled in a study of risk factors for asthma. The asthma study has been described extensively elsewhere. Head Start, a federal program that focuses on the healthy development of children between the age of three and five, provides education, health, nutrition, and other services to low-income children and their families. In brief, 1,026 children were recruited from 50 Head Start Programs in northern Manhattan, Bronx and Brooklyn; their guardians responded to a questionnaire on asthma symptoms and risk factors ⁽²⁰⁾. Of these, 547 children were enrolled into a sub-study involving a home visit during which further questionnaire data were collected and environmental samples were taken ⁽²¹⁾. Recruitment and data collection occurred between November 2003 and August 2006. The Columbia Presbyterian Medical Center Institutional Review Board approved the study protocol and all participants provided written informed consent.

Individual-Level Data

The parents of all the recruited children took part in an extensive questionnaire based interview and provided socio-demographic information. Data from this questionnaire that were assessed as potential individual-level predictors of diet included: Hispanic ethnicity (Mexican, Puerto Rican, Dominican, or other/mixed), age, nativity, language use at home, whether the respondent currently attended school, years of schooling completed, and whether the respondent worked outside the home. As part of the home visit protocol that the sub-set of the families participated in, the children’s guardians were asked to complete the Block “Study of Women’s Health Across the Nation” (SWAN) food frequency questionnaire; the Spanish-language version of which includes the 103 core SWAN FFQ items, plus nine additional food questions for items common to Hispanic cuisine ⁽²²⁾.

Neighborhood-Level Data

Home addresses were geocoded to the appropriate tax lot using Geosupport software and data on neighborhood food and social environment characteristics were constructed using aerial weighting methods for 0.5 Km radial buffers around each respondent’s home. US Census 2000 Summary File 3 data were used to measure neighborhood socio-demographic characteristics, including: percent of population below the federal poverty line referred to here as ‘percent poverty’; percent of the population reporting Hispanic ethnicity referred to here as ‘percent Hispanic’; and percent of Spanish speaking households that were linguistically isolated (defined by the Census Bureau as a household in which no one 14 years old and over speaks only English or speaks a non-English language and speaks English “very well”) and referred to here as ‘percent linguistically isolated’. Data on the locations of supermarkets, grocery stores, produce markets, convenience stores, bodegas, meat stores, fish stores, candy and nut stores, bakeries, fast-food restaurants, and pizza parlors in 2005 were obtained from a commercial database purchased from Dun and Bradstreet ^{(23, 24)}. Locations of farmers markets in 2006 were obtained from the New York City Coalition Against Hunger, the Council on the Environment of New York City and the Farmer’s Market Federation of New York.

Statistical Analyses

A modified version of the principal component analysis (PCA) approach described by Hu and colleagues for the Health Professionals Follow-up Study was used to identify dietary patterns with the SWAN FFQ data ⁽¹⁷⁾. Hu and colleagues grouped food items from the Willet FFQ into 40 food groups and summed the servings per day of each food, in each group. The food groups used by Hu and colleagues were used with the SWAN FFQ with a few modifications. Because the SWAN FFQ includes more soy-based items than in the Willet FFQ, an additional food group for soy-based products was created. In addition, because study subjects reported very few servings of alcohol on the FFQ, the separate beer, wine, and hard liquor groups used by Hu and colleagues were combined into one alcoholic beverage group.

The few prior studies that have used PCA with an FFQ designed for Hispanics provide little guidance on how to group the Hispanic food items ^{(25, 26)}. We tried two approaches and compared the dietary patterns they identified. Our first approach, was to distribute the Hispanic food items into the food groups devised by Hu et al., based on culinary usage (e.g. we placed flour tortillas in the refined grains group which includes white breads, muffins and bagels). Our second approach was to place all the Hispanic food items in their own food group. For each approach, we used PCA with orthogonal varimax rotation, retaining components that had eigenvalue >1 and were interpretable. A factor score was calculated as a measure of adherence to each component using the observed food group servings and the component loading scores ⁽¹⁷⁾.

As in our past studies, because of the large number of types of retail outlets considered, retail food outlets were grouped into categories considered to be “BMI healthy” (supermarkets, produce markets, farmers markets and health food stores), “BMI unhealthy” (fast food, pizza, convenience stores, Bodegas, meat markets, candy and nut stores) and “BMI neutral” (restaurants excluding fast food and pizzerias, small grocery stores, fish markets and specialty stores) ^{(23, 24)}. The identification and classification of retail outlets and the rationale for the groupings of outlets has been explained extensively previously ⁽²⁴⁾. For the analyses presented here, we calculate the density (outlets per Km² land area in the 0.5 Km radial buffer) of BMI healthy, BMI unhealthy and BMI neutral retail outlets. The density of BMI-healthy food outlets has been found to be inversely associated with maternal obesity and BMI in adults ^{(23, 24)}. In addition, because of the interest among NYC policymakers in the role of these particular food outlets, separate analyses examined whether the presence or density of fast food restaurants, pizza parlors, supermarkets, and bodegas in the study subject’s neighborhood predicted adherence to the dietary patterns.

Generalized estimating equation (GEE) analyses were used to evaluate the association between adherence to the identified dietary patterns and individual and neighborhood-level socio-demographic characteristics. Our GEE models used the respondent’s community district to designate larger neighborhood areas in the calculation of robust standard errors ⁽²⁷⁾. Community districts represent named neighborhoods in NYC such as Central Harlem or Washington Heights in which local community boards have influence over development, zoning and licensing. Analyses began with consideration of the individual-level socio-demographic characteristics; neighborhood-level variables were subsequently added to the model. The individual level variables, Hispanic ethnicity, US or foreign place of birth, and use of English at home proved to be highly inter-correlated and all three variables could not be entered into models simultaneously. Of the three variables, Hispanic ethnicity and language used at home proved to be the least inter-correlated, and analyses focused on these variables. Neighborhood percent Hispanic and percent linguistically isolated were strongly correlated (r=0.55), and because linguistic isolation had greater face validity as a measure of acculturation status, analyses concentrated on this variable as a measure of neighborhood level acculturation status. Because neighborhood level socio-economic status may have an effect on diet above and beyond being just a proxy of individual level socio-economic status and may also correlate with neighborhood immigrant composition we included neighborhood percent poverty in the model as a potential confounder. The neighborhood retail food environment variables were added to the statistical model to evaluate whether differences in the density of retail outlets explained differences in diet by individual and neighborhood level socio-demographic characteristics.

Results

From the 547 families who took part in the home visit portion of the study, a total of 355 female guardians, who self-reported their ethnicity as Hispanic, provided FFQ data. Of these, 345 provided the complete socio-demographic data required for the analyses presented here. Table 1 provides descriptive statistics for the study population.

Table 1.

Descriptive statistics of the full study population

Categorical individual level variables	N (%) Sample Size = 345
Hispanic Ethnicity
Mexican	141 (41%)
Dominican	97 (28%)
Puerto Rican	41 (12%)
Other Hispanic	66 (19%)
Current Schooling
Not in School	261 (76%)
In School	84 (24%)
Work status
Not-Employed	261 (76%)
Employed	84 (24%)
Language at Home
English	83 (24%)
Spanish	262 (76%)
Continuous individual level variables	Mean, median
Years of school	Mean 10.45, median 12
Age	Mean 32.35, median 31.22

The PCA analyses of the FFQ data with Hispanic food items distributed across food groups identified two major components. The first component loaded high on the vegetable, legumes, potato, and fish and seafood food groups, and explained 17.28% of the variance in the data. This component was named the ‘Healthy Diet’. The second component loaded high on the processed meat, high energy drink, fries, poultry, pizza, and red meat food groups, and explained 9.15% of the variance in the data. This component was named the ‘Energy Dense Diet’. Table 2 shows the top 10 loading items for the Healthly Diet and the Energy Dense Diet. The two approaches used to group the Hispanic food items in the FFQ produced almost identical results, a 99% correlation for adherence to the Healthy Diet and 94% correlation for adherence to the Energy Dense Diet. We therefore focused on the results from the PCA analyses based on distributing the Hispanic food items among the existing food groups.

Table 2.

Principle components identified in the FFQ data

Component 1 Healthy Diet		Component 2 Energy Dense Diet
Food groups	Loading weights	Food groups	Loading weights
Cruciferous vegetables	0.75	Processed meat	0.67
Leafy green vegetables	0.74	High energy drink	0.61
Dark-yellow vegetables	0.68	Fries	0.58
Legumes	0.62	Poultry	0.50
Potatoes	0.55	Pizza	0.44
Fish and seafood	0.52	Red meat	0.39
Fruits	0.51	Eggs	0.38
Other vegetables	0.38	Fish seafood	0.35
Soup	0.32	Potatoes	0.29

Table 3 shows the results of GEE analyses of predictors of adherence to the Healthy Diet pattern. Model 1 assessed associations between individual-level socio-demographic characteristics and adherence to the Healthy Diet pattern. Compared to those reporting a Mexican ethnicity, those reporting a Puerto Rican ethnicity had lower adherence to the Healthy Diet pattern. Adherence was also associated with being in school, increasing age, and total estimated calorie consumption, but was not associated with use of English in the home. Model 2 included variables for individual and neighborhood socio-demographic characteristics. Adherence to the Healthy Diet pattern was significantly negatively associated with increasing neighborhood poverty and significantly positively associated with linguistic isolation. Model 3 included all socio-demographic characteristics as well as characteristics of the neighborhood food environment. As the density of three types of food outlets (BMI healthy, unhealthy and neutral) are strongly correlated (Spearman rank correlations between 0.60 and 0.82), initial analyses considered each of the three food outlet types as predictors of adherence to the Healthy Dietary pattern separately and then a single model was fitted to consider all three outlets types concurrently. The results were consistent regardless of modeling approach: the densities of BMI healthy, unhealthy and neutral retail food outlets were not associated with adherence to the Healthy Diet pattern, and inclusion of these variables in the model did not alter the associations between neighborhood socio-demographic characteristics and adherence. In separate analyses [not shown] considering fast food restaurants, pizzerias, supermarkets, and bodegas, the only association observed for these specific food outlets was an inverse association between the density of fast food restaurants and adherence to the Healthy Diet (beta = −0.03, p=0.02). In these analyses, poverty rate and the proportion of the Spanish-speaking households that were linguistically isolated remained significant predictors of adherence to the Healthy Diet.

Table 3.

Predictors of Adherence to the Healthy Diet Pattern

Predictor	Model 1		Model 2		Model 3
	Beta Coefficient	P-value	Beta Coefficient	P-value	Beta Coefficient	P-value
Hispanic Ethnicity
Mexican	Ref		Ref		Ref
Dominican	0.23	0.13	0.25	0.12	0.24	0.13
Puerto Rican	−0.56	0.02	−0.44	0.08	−0.44	0.08
Other	−0.06	0.68	−0.03	0.79	−0.02	0.87
Currently in school	0.28	0.02	0.23	0.08	0.24	0.06
Currently employed	−0.07	0.59	−0.06	0.62	−0.07	0.60
English used at home	0.27	0.19	0.27	0.18	0.26	0.19
Age in years	0.02	0.04	0.02	0.05	0.02	0.06
Years of education	0.02	0.21	0.02	0.14	0.02	0.14
Total calorie intake (per 100 calories)	0.02	0.00	0.02	0.00	0.02	0.00
Neighborhood poverty rate			−1.93	0.00	−2.26	0.00
Neighborhood percent Spanish language linguistic isolation			2.72	0.00	2.66	0.00
Density of healthy food outlets (outlets/Km2)					0.01	0.61
Density of unhealthy food outlets (outlets/Km2)					0.00	0.27
Density of neutral food outlets (outlets/Km2)					0.00	0.29

Table 4 shows the results of GEE analyses of predictors of adherence to the Energy Dense Diet pattern. Results for model 1 show that compared to respondents who reported Mexican ethnicity, those reporting Puerto Rican ethnicity were more adherent to the Energy Dense Diet pattern. Adherence was also associated with employment outside the home and with total calorie consumption and inversely associated with increasing age. Model 2 showed that adherence to the Energy Dense Diet pattern was significantly positively associated with increasing neighborhood poverty. Model 3 includes additional variables for the characteristics of the neighborhood food environment. Of the food environment variables, adherence to the Energy Dense Diet pattern was positively associated with the density of BMI-neutral retail outlets. A large component of the BMI-neutral retail outlet category is restaurants other than fast food or pizzerias, and the association between adherence to the Energy Dense Diet pattern and the density of BMI-neutral retail outlets was driven almost entirely by the association between adherence to the Energy Dense Diet pattern and the density of restaurants other than fast food or pizzerias (beta = 0.006 per restaurant /Km², p=0.03). Density of other specific retail food outlet types was not associated with adherence to the Energy Dense Diet pattern. A comparison of Model 2 and Model 3 shows that the inverse association between linguistic isolation and adherence to Energy Dense Diet pattern increases and reaches borderline statistical significance when the food environment variables are included in analyses.

Table 4.

Predictors of Adherence to the Energy Dense Diet Pattern.

Predictor	Model 1		Model 2		Model 3
	Beta Coefficient	P-value	Beta Coefficient	P-value	Beta Coefficient	P-value
Hispanic Ethnicity
Mexican	Ref		Ref		Ref
Dominican	0.12	0.19	0.12	0.17	0.15	0.11
Puerto Rican	0.74	0.00	0.71	0.00	0.71	0.00
Other	−0.12	0.19	−0.09	0.26	−0.12	0.12
Currently in school	−0.12	0.19	−0.09	0.26	−0.12	0.12
Currently employed	0.17	0.05	0.17	0.05	0.18	0.06
English used at home	0.11	0.27	0.12	0.24	0.13	0.15
Age in years	−0.02	0.00	−0.02	0.00	−0.02	0.00
Years of education	−0.01	0.29	−0.01	0.35	−0.01	0.43
Total Calorie Intake (per 100 calories)	0.04	0.00	0.04	0.00	0.04	0.00
Neighborhood poverty rate			1.05	0.01	2.06	0.00
Neighborhood percent Spanish language linguistic isolation			−0.38	0.53	−1.83	0.05
Density of healthy food outlets (outlets/Km2)					0.00	0.77
Density of unhealthy food outlets (outlets/Km2)					0.00	0.32
Density of neutral food outlets (outlets/Km2)					0.01	0.03

Discussion

In this population of low-income Hispanic women, two major dietary patterns were identified, one that could be construed as being healthy, high in vegetables, fruits, and legumes (table 2), and a second that could be construed as being less healthy, comprised of energy dense foods (table 2). In our study, adherence to the Healthy Diet was strongly predicted by neighborhood linguistic isolation, even after control for neighborhood poverty. The association with neighborhood linguistic isolation could not be accounted for by differences in the neighborhood retail food environment. Adherence to the Energy Dense Diet pattern was initially modestly inversely, but not significantly, associated with neighborhood linguistic isolation, an association that increased after adjustment for measures of the neighborhood retail food environment.

These results are consistent with the hypothesis that living in immigrant enclaves is associated with healthy dietary patterns among Hispanic immigrants ^{(12, 14)}. Past research among Hispanics has found associations between measures of acculturation and diet, but this work has focused mainly on specific nutrients or consumption of specific food groups such as fruit and vegetables, and individual level measures of acculturation such as the study subject’s use of English. However, nutrients and food items are not consumed in isolation; analyses that focus on isolated nutrients or food groups as the measure of diet may find associations between indicators of acculturation and diet that are not observed when the overall dietary pattern is considered as the outcome of interest ⁽¹⁷⁾. For instance, past studies among immigrants have found associations between speaking English and the consumption of foods items or groups, but we did not observe an association between the study subject’s use of English and overall dietary pattern ⁽¹⁰⁾. In our analysis, neighborhood level acculturation context is a major correlate of dietary patterns, appearing to promote one pattern of consumption while at the same time protecting a second pattern of consumption.

The nine additional food items, included in the SWAN FFQ to reflect Hispanic cuisine, did not, as a group, load highly on either the identified Healthy Diet or Energy Dense dietary patterns. Since these “Hispanic” FFQ items include both foods that could be considered healthy (e.g. cassava or cooked green papers) and foods that could be considered energy dense (e.g. condensed milk or flan), the failure of this grouping to load highly on either pattern is not surprising. Neighborhood linguistic isolation does not thus appear to promote consumption of the “Hispanic” food items added to the 103 item core SWAN FFQ, but rather the consumption of an overall dietary pattern high in vegetables, legumes, fish, seafood, and fruits; items that are part of the core FFQ. If, as the literature suggests, the rise in obesity among immigrants results, at least in part, from the loss of home country diets and acquisition of obesogenic American diets, then interventions that facilitate the maintenance of home country diets may make important contributions to obesity prevention ^{(10, 28, 29)}. Our results suggest however, that such interventions should focus on the maintenance of patterns of home country dietary practices that are healthy (e.g., patterns that maintain higher levels of consumption of fruits, rice, and legumes and lower sugar consumption) rather than on the consumption of specific food items or even types of cuisines considered to be “traditional” ⁽¹⁰⁾. Lee and colleagues suggest that, in the face of increasing market penetration of processed foods and U.S. chain restaurants in Korea, a multi-pronged national campaign to promote retention of the vegetable-centered traditional Korean diet resulted in significant positive health effects ⁽³⁰⁾. A similar approach that acknowledges healthy dietary patterns and their practices that many immigrants bring from their countries of origin, and encouragement the retention of the best elements of those practices, an approach Yeh and colleagues refer to as ‘selective acculturation’ ^{(29, 31, 32)} may be of use among immigrant groups in the U.S.

In these analyses, our construct of “BMI healthy” retail outlets, which has previously been shown to predict lower BMI in adults and lower risk of maternal obesity ^{(23, 24)} did not predict adherence to either of the identified dietary patterns. Whereas adherence to the Healthy Diet pattern might be expected to be associated with lower energy intake and the Energy Dense Diet pattern to be associated with higher energy intake, higher calorie intake was positively associated with higher adherence to both dietary patterns. Since our prior work has found an association between the density of BMI-healthy outlets with lower BMI, we considered that control for calorie intake in models that include food environment variables might represent over-adjustment for a potential intervening variable. However, the results for the food environment variables were not materially different in analyses that did not control for calorie intake. The only association between our previously defined food environment measures and diet was between a higher density of BMI-neutral outlets and higher adherence to the Energy Dense Diet pattern, an association primarily driven by the density of restaurants other than fast food or pizza. It is likely that an abundance of restaurants is linked to higher consumption of restaurant meals and exposure to non-home country cuisines and higher levels of acculturation. In addition, meals eaten outside the home are less healthy than meals prepared at home ^{(33, 34)}. In the analyses examining specific retail outlets, the only observed association was between higher density of fast food restaurants and lower adherence to the Healthy Diet pattern. While past work has not found strong links between the presence of fast food outlets and diet or obesity, it is possible that in this population an abundance of fast food restaurants is linked to more meals being consumed in these restaurants ^{(24, 35–37)}. Such restaurants are not major sources of the vegetables, legumes, and fruits associated with the Healthy Diet pattern. Again, while there were a few associations between the dietary pattern data and aspects of the retail food environment, the food environment measures did not explain the association between neighborhood Spanish language linguistic isolation and adherence to the Healthy Diet pattern.

In conclusion, these analyses suggest that measures of neighborhood-level immigrant acculturation may predict differences in dietary patterns among Hispanic women that cannot be explained by variation in the neighborhood retail food environment. This study contributes to a small but growing literature that considers how neighborhood contexts may moderate the influence of individual ethnicity or acculturation on health behavior. Strengths of the study include the representation of several Hispanic ethnicities in the study sample, the use of objective measures of the food environment, and the use of dietary pattern analysis to characterize study subjects’ food intake. The primary limitation is use of a sample that includes only women. Interventions and nutritional counseling that focus on preserving and promoting the home country diets of immigrants may be effective in preventing the weight gain that is commonly seen among immigrants with longer duration of residence in the United States and successive generations of immigrants.