Abstract
The healthy immigrant effect—where immigrants are on average healthier than the native born— have been well studied. However, little is known about the relationship between immigration and the health of the native born. This study fills this important research gap by examining the association between neighborhood immigrant density and several population health measures among native-born Americans. We used data from the Los Angeles County Health Survey to analyze four individual-level health behaviors and outcomes, including regular fast food consumption, fruit and vegetable consumption, body mass index, and hypertension. We conducted multilevel logistic regressions to assess the association between neighborhood immigrant density and the four health behaviors and outcomes. The results showed that neighborhood immigrant density was negatively associated with regular fast food consumption (OR=0.33; 95% CI, 0.18–0.59), BMI (β= −2.16, 95% CI, −3.13 - −1.19), and hypertension (OR=0.58; 95% CI, 0.38–0.89), and positively associated with fruit/vegetable consumption (OR=1.64; 95% CI, 1.01–2.66) among native-born Americans. In conclusion, native-born Americans who live in a neighborhood with a high density of immigrants had healthier behaviors and better health outcomes compared to those who live in a neighborhood with a low density of immigrants.
Keywords: Neighborhood environment, immigrant health, diet, chronic disease, urban health
Introduction
The “healthy immigrant effect,” whereby immigrants have better health than natives with comparable socioeconomic status (SES), has been documented for various health behaviors and outcomes such as diet, mental illness, and cardiovascular diseases.1–3 For example, a study showed that foreign-born Mexicans consumed nearly twice as much energy from healthy foods such as legumes, soybeans, fruits, and vegetables as US-born Mexicans, while US-born Mexicans consumed 56 kilocalorie more per day from unhealthy fast food compared to foreign-born Mexicans.1 In addition, US-born immigrants had a faster acculturation of unhealthy behaviors, such as smoking and physical inactivity, compared to foreign-born immigrants.4 The “healthy immigrant effect” also holds at the community level as, for example, low-income immigrants living in a community with a high percentage of immigrants have a higher average life expectancy compared to those who live in a community with more natives.5 Some of the favorable health outcomes among immigrants can be attributed to healthier behaviors among immigrants such as the low prevalence of smoking and the preference for a healthy diet.6
In this study, we hypothesize that the “healthy immigrant effect” may have a “spillover” benefit on the native-born Americans living in communities with a high immigrant density. In other words, these healthy immigrants may influence their native-born neighbors in the same community. Although immigration may reduce social solidarity and social capital in the short term,7 it has a positive impact on neighborhood population health in the long run through possible pathways such as changing the peer effect in smoking and healthy eating, making healthy ethnic foods more accessible, and lowering the crime rate.8,9 However, previous studies have not assessed the association between neighborhood immigrant density and health behaviors and outcomes among native-born Americans. This study aims to fill this research gap.
Using a large population health survey conducted in Los Angeles County, we examined whether living in communities with a high density of immigrants was associated with healthy behaviors such as less fast food consumption and more fruit and vegetable intake, as well as lower risks for developing obesity and hypertension. We used a multilevel approach to assess the extent to which neighborhood immigrant density may impact these health behaviors outcomes. The findings may provide important evidence that can be used to inform public health practices as well as immigration policies.
Methods
Study population
The study sample was from the adult respondents in the Los Angeles County Health Survey (LACHS). LACHS is a population-based random digit-dial telephone survey of Los Angeles County households.10 This dataset includes ZIP code information, which allows researchers to study population health questions at both individual and neighborhood level. We combined the LACHS data with zip-code-tabulated-area-level characteristic data to form a new dataset. We have recently used this combined dataset to study the association between immigrant acculturation and health behaviors.11,12 For this study, the non-uniform spatial distribution of the foreign-born population in Los Angeles County provides us with the variation we need for analyzing the association between immigrant density and the health outcomes.
Measures on dietary behaviors and health outcomes
The dependent variables we examined in the study included dietary behaviors and health outcomes. Dietary behaviors were measured by fast food consumption and fruit and vegetable intake. Fast food consumption was measured by responses to the following question, “how often do you eat any food, including meals and snacks, from a fast-food restaurant, like McDonald’s, Taco Bell, Kentucky Fried Chicken or another similar type of place?” The respondents were asked to select their consumption levels from five choices, “1=‘4+times/week’, 2=‘1–3times/week’, 3=‘<1time/week but >=1time/month’, 4=‘<1time/month’, 5=‘never’.” In our analysis, we recoded the frequency of fast food consumption as a binary outcome: 1 = regular fast food consumption, defined as eating any food from fast food restaurants at least once per week (choices 1 or 2); 0 = not regular fast food consumption defined as eating fast food less than once per week (choices 3, 4, or 5).
Fruit/vegetable intake was derived from the LACHS question about the daily intake of fruits and vegetables. Those who reported five or more servings of fruits and vegetables were coded 1 and those who reported four or fewer were coded as 0 since five or more servings of fruits and vegetables was the level of intake recommended by the United States Centers for Disease Control and Prevention (CDC).13
Health outcomes were measured by body mass index (BMI) and self-reported hypertension diagnosis. BMI was calculated from self-reported height and weight. It was used in the model as a continuous variable. Hypertension was coded 1 if the respondent answered “yes” to the survey question about “whether a health care professional had told him/her that he/she had hypertension,” and 0 if the answer was “no”.
Measures on immigrant density and food environment
Geographic clusters used in our multilevel analyses here were defined by zip-code-tabulated areas (ZCTA) in the analysis. We obtained ZCTA characteristics data—including median household income and percent of residents who were foreign-born—from the US Census (www.census.gov). We also obtained the number of fast food restaurants within each neighborhood from the California Department of Public Health, originally derived from the Dun & Bradstreet restaurants database (https://www.dnb.com).14
Immigrant density in this study was defined as the area-level density of foreign-born immigrants (i.e., first-generation immigrants). Food environment was measured by the number of fast food restaurants (e.g., McDonald’s, Taco Bell, Kentucky Fried Chicken, Burger King, and other chain restaurants) per 100,000 residents within a neighborhood. Area-level economic characteristics were controlled in the model using median household income as a proxy. Median household income was log transformed to adjust for its skewed distribution. We also merged about 30% of adjacent ZCTAs because the number of respondents in those areas was too small to provide statistical power in our analysis, ending up with 271 areas as the geographic clusters in our multilevel analyses.11 For the merged areas, we calculated the area-level household income, immigrant density and fast food density using weighted averages of the zip-code level estimates.
The covariates in the model included individual-level demographic and socioeconomic variables, including age, gender, race/ethnicity, educational attainment, and household income. Household income was categorized into four categories using federal poverty level (FPL).
Statistical analysis
Since BMI is a continuous variable, we applied multilevel linear regression to examine the link between area-level immigrant density and BMI, with the geographic areas described above as the cluster variable and the covariates described above as the control variables. For hypertension, fast food consumption, and fruit and vegetables intake, we used multilevel logistic regressions to examine their link with area-level immigrant density, with the geographic areas described above as the cluster variable and the covariates described above as the control variables. The statistical analysis was performed using STATA.
Results
Table 1 presents the descriptive statistics of the study sample. About one third of the respondents reported regular fast food consumption, 17% of respondents ate five or more servings of fruits and vegetables per day, 31% of respondents had hypertension, and the mean BMI of the respondents is 26.85. The average age of the study sample was about 51 years old. More than 40% of the respondents had a college degree, and more than half had a household income of above 300% federal poverty level. The majority of the respondents (about 60%) were non-Latino whites.
Table 1.
Descriptive statistics of the four samples of US-born residents in Los Angeles County
| Individual-level variables | Analysis sample for regular fast food consumption | Analysis sample for >5 fruits &vegetable/day | Analysis sample for body mass index | Analysis sample for Hypertension |
|---|---|---|---|---|
| N | 4244 | 9166 | 8968 | 9451 |
| >1 time of fast food consumption/week | 34.83% | |||
| >5 servings of Fruits/vegetable/day | 17.13% | |||
| Body mass index | 26.85 (5.58) | |||
| Hypertension | 30.94% | |||
| Age | 52.84 (18.21) | 50.88 (17.84) | 50.87 (17.94) | 50.77 (17.84) |
| Female | 52.97% | 51.25 | 49.33% | 51.34% |
| Educational attainment | ||||
| Below high school | 7.73% | 7.47% | 7.52% | 7.67% |
| Finished high school | 18.71% | 19.84% | 19.86% | 19.80% |
| Some college | 30.84% | 31.58% | 31.32% | 31.44% |
| College graduation | 42.72% | 41.11% | 41.30% | 41.09% |
| Household income | ||||
| < federal poverty line (FPL) | 11.55% | 7.62% | 10.72% | 11.00% |
| 100%−200% FPL | 16.38% | 19.92% | 16.01% | 16.12% |
| 200%−300% FPL | 15.17% | 17.70% | 16.87% | 17.00% |
| Above 300% FPL | 56.90% | 54.77% | 56.40% | 55.88% |
| Race/ethnicity | ||||
| Non-Latino Whites | 60.77% | 59.31% | 62.29% | 61.97% |
| Latino | 21.89% | 20.08% | 20.55% | 20.76% |
| African American | 12.32% | 12.89% | 12.82% | 12.90% |
| Asian American | 5.02% | 4.04% | 4.34% | 4.36% |
| Others | 2.45% | 2.44% | 3.19% | 3.21% |
| Area-level variables (mean/SD) | ||||
| Median household income | $24,624 ($15,966) | |||
| Percent foreign-born | 33.06% (14.55%) | |||
| # of Fast food restaurants per 10,000 residents | 23.2 (10.78)2 | |||
Note:
Standard errors in parentheses
Median=23, Interquartile range=13.
Table 1 also shows the neighborhood characteristics. Specifically, there were on average 23.2 (SD: 10.8; Median: 23; Interquartile range: 13) fast food restaurants per 10,000 residents at the neighborhood level. About one third of respondents were born in foreign countries and they had lived in the U.S. for a median of 20 years (interquartile range=19). The average median household income across different neighborhoods was $24,624.
Table 2 presents the ORs estimated from multilevel regression analysis. Neighborhood immigrant density had a negative association with regular fast food consumption among native-born Americans (OR=0.33; 95% CI, 0.18–0.59), controlling for all other variables in the model. Similarly, living in a neighborhood with a high percentage of immigrants was associated with a lower likelihood of having hypertension (OR=0.58; 95% CI, 0.38–0.89). Moreover, people living in a neighborhood with a high percentage of immigrants were more likely to eat a recommended serving of fruits and vegetables (OR=1.64; 95% CI, 1.01–2.66). Lastly, our results show that neighborhood immigrant density was negatively associated with BMI (β= −2.16 kg/m2, 95% CI, −3.13 – −1.19).
Table 2.
Multilevel Models of Fast Food Consumption, Fruits/Vegetable Consumption, Body Mass Index and Hypertension
| Regular fast food consumption (Odds Ratio) | Fruits/vegetable >5 servings per day (Odds Ratio) | Body mass index (Regression slope) | Hypertension (Odds Ratio) | |
|---|---|---|---|---|
| 95% Confidence Interval in parentheses | ||||
| N | 4244 | 9166 | 8968 | 9451 |
| Key independent variable | ||||
| Percent foreign-born | 0.330 [0.183,0.596] | 1.638 [1.007,2.663] | −2.160 [−3.128,−1.192] | 0.582 [0.383,0.886] |
| Individual-level variables | ||||
| Age | 1.009 [0.988,1.031] | 0.992 [0.973,1.010] | 0.334 [0.299,0.369] | 1.153 [1.130,1.176] |
| Age squared | 1.000 [0.999,1.000] | 1.000 [1.000,1.000] | −.003 [−.003,−.003] | 0.999 [0.999,0.999] |
| Gender: reference=male | 0.569 [0.498,0.651] | 2.031 [1.809,2.280] | −1.398 [−1.619,−1.177] | 0.839 [0.760,0.925] |
| Educational attainment: reference =below high school | ||||
| Finished high school | 1.056 [0.795,1.404] | 1.015 [0.739,1.395] | −0.603 [−1.081,−0.124] | 1.097 [0.883,1.362] |
| Some college | 0.947 [0.718,1.250] | 1.757 [1.302,2.370] | −0.585 [−1.053,−0.118] | 0.976 [0.790,1.205] |
| College graduation | 0.743 [0.555,0.995] | 2.347 [1.729,3.186] | −1.528 [−2.018,−1.039] | 0.829 [0.665,1.033] |
| Household income: reference =above 300% federal poverty line (FPL) | ||||
| Below FPL | 0.919 [0.724,1.166] | 0.808 [0.640,1.020] | 0.735 [0.323,1.146] | 1.582 [1.319,1.898] |
| 100%−200%FPL | 1.011 [0.828,1.235] | 0.863 [0.720,1.034] | 0.449 [0.109,0.788] | 1.349 [1.162,1.566] |
| 200%−300%FPL | 1.239 [1.017,1.508] | 0.854 [0.725,1.006] | 0.125 [−0.191,0.441] | 1.133 [0.987,1.301] |
| Race/ethnicity: reference =non-Latino Whites | ||||
| Latino | 1.144 [0.943,1.387] | 0.719 [0.598,0.864] | 1.212 [0.880,1.544] | 0.998 [0.854,1.166] |
| African American | 1.061 [0.851,1.325] | 0.660 [0.538,0.809] | 1.269 [0.898,1.640] | 1.889 [1.615,2.209] |
| Asian American | 1.201 [0.886,1.629] | 0.809 [0.612,1.070] | −1.112 [−1.666,−0.558] | 1.087 [0.833,1.419] |
| Other | 0.698 [0.446,1.092] | 0.844 [0.607,1.172] | 0.053 [−0.578,0.685] | 0.978 [0.748,1.278] |
| Neighborhood-level variables | ||||
| fast food restaurant density (No./1000 residents) | 1.004 [0.998,1.011] | 0.999 [0.993,1.004] | 0.008 [−0.003,0.018] | 1.000 [0.995,1.005] |
| Median income | 0.511 [0.423,0.617] | 1.220 [1.051,1.415] | −1.518 [−1.824,−1.213] | 0.743 [0.651,0.848] |
Discussion
This study examined the association between immigrant density (measured as percent foreign born at the neighborhood level) and health behaviors and outcomes among native-born Americans. For health behaviors, native-born Americans may eat less fast food and more fresh fruits and vegetables if they live in a neighborhood with a higher immigrant density. As to health outcomes, there were significant associations between a higher neighborhood immigrant density and a lower BMI and a lower risk of hypertension for native-born Americans. Overall, we observed that high immigrant density in a community was a protective factor against unhealthy behaviors and poor health outcomes for native-born Americans. A previous study showed that non-Latino whites living in an area with a higher percentage of Asians had a lower BMI and 28% lower odds for obesity.15 Findings from our study were consistent with the previous study and provided more generalized evidence on the impact of neighborhood immigrant density on population health.
There are several explanations to the associations between neighborhood immigrant density and health behaviors and outcomes. The increased fruit and vegetable consumption in a neighborhood with a higher density of immigrants, for example, may be caused by the increased access to ethnic food outlets, the enhanced social norm on fruit/vegetable consumption, and food price reduction attributable to immigrant labor supply.16 It has been found that ethnicity has a stronger effect on the choice of food retailors than a narrowly defined “economic rationality” for immigrants.17 The immigrant-driven growth of ethnic food outlets may, in turn, benefit the native-born Americans living in the area and improve their dietary behaviors. Also, given the documented evidence that Mexico-born Americans consumed more fruits and vegetables than native-born Americans,18 there is a stronger social norm towards the consumption of fruits and vegetables in a neighborhood with a high density of Mexico-born Americans or other immigrants.
Our study has several limitations. First, we used self-reported measures for our outcome variables, which could be subject to measurement errors such as underreporting weight and over-reporting height as well as misclassifying undiagnosed hypertension as not having hypertension.19 That being said, the validity of self-reported hypertension was found to be high across different population groups.20 For neighborhood-level variables, we used the density of fast food restaurants and median household income as control variables, but we were not able to include more detailed neighborhood variables such as the density of ethnic food outcomes due to limited data. In addition, we used cross-sectional data in our analysis, so there might be potential mechanisms for the reverse effect. For example, it is possible that some immigrants chose to live in neighborhoods that are healthier such as those ethnic enclaves with native-born Americans from the same country of origin. Finally, we do not have access to the most recent data to examine the associations and the findings from Los Angeles County may not be generalizable to other parts of the country. Knowing the obesity increase in parts of Asia and Latin America in recent decades,21 we need to be aware of the possible changing health norms among new cohorts of immigrants in Los Angeles County and United States in general, which could change what it means to live among immigrants today. In follow-up studies, we plan to obtain datasets that represent larger geographic areas with more neighborhood-level health environment measures as well as objective measures of individual-level health outcomes (e.g., hypertension-related health care expenditure from datasets such as Medical Expenditure Panel Survey).
Few studies have explored the association between immigrant density and the health of native-born Americans. Our results showed that native-born Americans living in neighborhoods with a higher immigrant density were more likely to eat a recommended level of fruits and vegetables, go to fast food restaurants less frequently, have a lower body mass index, and a lower risk for hypertension. Our findings add to the discussion about the role of immigration in the receiving country’s population health and is consistent with the existing evidence about the health benefit of desegregation.22 Further studies are needed to examine the causal effects of neighborhood immigrant density on population health.
Acknowledgments
Source of Funding: This study was supported, in part, by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number R01HL141427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Conflict of Interest Disclosure: None.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
References
- 1.Duffey KJ, Gordon-Larsen P, Ayala GX, Popkin BM. Birthplace Is Associated with More Adverse Dietary Profiles for US-Born Than for Foreign-Born Latino Adults. J Nutr 2008;138(12):2428–2435. doi: 10.3945/jn.108.097105 [DOI] [PubMed] [Google Scholar]
- 2.McDonald JT, Kennedy S. Insights into the ‘healthy immigrant effect’: health status and health service use of immigrants to Canada. Soc Sci Med 2004;59(8):1613–1627. doi: 10.1016/j.socscimed.2004.02.004 [DOI] [PubMed] [Google Scholar]
- 3.Corlin L, Woodin M, Thanikachalam M, Lowe L, Brugge D. Evidence for the healthy immigrant effect in older Chinese immigrants: a cross-sectional study. BMC Public Health 2014;14:603. doi: 10.1186/1471-2458-14-603 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gordon-Larsen P, Harris KM, Ward DS, Popkin BM. Acculturation and overweight-related behaviors among Hispanic immigrants to the US: the National Longitudinal Study of Adolescent Health. Soc Sci Med 2003;57(11):2023–2034. [DOI] [PubMed] [Google Scholar]
- 5.Chetty R, Stepner M, Abraham S, et al. The Association Between Income and Life Expectancy in the United States, 2001–2014. JAMA 2016;315(16):1750–1766. doi: 10.1001/jama.2016.4226 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.O’Loughlin J, Maximova K, Fraser K, Gray-Donald K. Does the “healthy immigrant effect” extend to smoking in immigrant children? J Adolesc Health 2010;46(3):299–301. doi: 10.1016/j.jadohealth.2009.08.005 [DOI] [PubMed] [Google Scholar]
- 7.Putnam RD. E pluribus unum: Diversity and community in the twenty-first century the 2006 Johan Skytte Prize Lecture. Scand Polit Stud 2007;30(2):137–174. [Google Scholar]
- 8.Osypuk TL, Roux AVD, Hadley C, Kandula NR. Are immigrant enclaves healthy places to live? The Multi-ethnic Study of Atherosclerosis. Soc Sci Med 2009;69(1):110–120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lally P, Bartle N, Wardle J. Social norms and diet in adolescents. Appetite 2011;57(3):623–627. doi: 10.1016/j.appet.2011.07.015 [DOI] [PubMed] [Google Scholar]
- 10.Simon Paul A., Wold Cheryl M., Cousineau Michael R., Fielding Jonathan E.. Meeting the Data Needs of a Local Health Department: The Los Angeles County Health Survey. Am J Public Health 2001;91(12):1950–1952. doi: 10.2105/ajph.91.12.1950 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Shi Lu, Zhang Donglan, Jeroen van Meijgaard Kara E. MacLeod, Fielding Jonathan E.. The Interaction Between an Individual’s Acculturation and Community Factors on Physical Inactivity and Obesity: A Multilevel Analysis. Am J Public Health 2015;105(7):1460–1467. doi: 10.2105/ajph.2014.302541 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhang D, van Meijgaard J, Shi L, Cole B, Fielding J. Does neighbourhood composition modify the association between acculturation and unhealthy dietary behaviours? J Epidemiol Community Health 2015;69(8):724–731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Guenther PM, Dodd KW, Reedy J, Krebs-Smith SM. Most Americans Eat Much Less than Recommended Amounts of Fruits and Vegetables. J Am Diet Assoc 2006;106(9):1371–1379. doi: 10.1016/j.jada.2006.06.002 [DOI] [PubMed] [Google Scholar]
- 14.Singleton CR, Sen B, Affuso O. Disparities in the availability of farmers markets in the United States. Environ Justice 2015;8(4):135–143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kirby JB, Liang L, Chen H-J, Wang Y. Race, place, and obesity: the complex relationships among community racial/ethnic composition, individual race/ethnicity, and obesity in the United States. Am J Public Health 2012;102(8):1572–1578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Balkan B, Tumen S. Immigration and prices: quasi-experimental evidence from Syrian refugees in Turkey. J Popul Econ 2016;29(3):657–686. doi: 10.1007/s00148-016-0583-2 [DOI] [Google Scholar]
- 17.Wang L, Lo L. Immigrant Grocery-Shopping Behavior: Ethnic Identity versus Accessibility. Environ Plan Econ Space 2007;39(3):684–699. doi: 10.1068/a3833 [DOI] [Google Scholar]
- 18.Batis C, Hernandez-Barrera L, Barquera S, Rivera JA, Popkin BM. Food Acculturation Drives Dietary Differences among Mexicans, Mexican Americans, and Non-Hispanic Whites. J Nutr 2011;141(10):1898–1906. doi: 10.3945/jn.111.141473 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Gorber SC, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev 2007;8(4):307–326. doi:doi: 10.1111/j.1467-789X.2007.00347.x [DOI] [PubMed] [Google Scholar]
- 20.Giles WH, Croft JB, Keenan NL, Lane MJ, Wheeler FC. The Validity of Self-reported Hypertension and Correlates of Hypertension Awareness Among Blacks and Whites Within the Stroke Belt. Am J Prev Med 1995;11(3):163–169. doi: 10.1016/S0749-3797(18)30468-9 [DOI] [PubMed] [Google Scholar]
- 21.Abarca-Gómez L, Abdeen ZA, Hamid ZA, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet 2017;390(10113):2627–2642. doi: 10.1016/S0140-6736(17)32129-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Shen M The effects of school desegregation on infant health. Econ Hum Biol 2018;30:104–118. doi: 10.1016/j.ehb.2018.06.002 [DOI] [PubMed] [Google Scholar]