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Race and Social Problems

, Volume 10, Issue 3, pp 259–271 | Cite as

Does the Refugee Experience Overshadow the Effect of SES? An Examination of Self-Reported Health Among Older Vietnamese Refugees

  • Berna M. Torr
  • Eileen T. Walsh
Article

Abstract

Is the consistently poorer health of Vietnamese refugees relative to whites due largely to differences in socioeconomic status, demographic characteristics, and health risk behaviors or the residual impact of the trauma of war and resettlement? Using data from a population-based household survey we use multinomial logistic regression to assess the self-rated health and activity limitations of Vietnamese refugees aged 55 and older compared to whites, adjusting for demographics, socioeconomic status, and lifestyle characteristics. Vietnamese refugees report poorer health and are more likely to report activity limitations than whites. While substantial differences in characteristics exist between the two groups, they explain little of the health differentials. Demographic and socioeconomic factors do not explain the health differential between older Vietnamese refugees and whites, although their lifestyle exerts a protective effect. The trauma of war and the stressful context of immigration likely contribute to the poorer health of Vietnamese refugees.

Keywords

Vietnamese-American Refugee Ethnic enclave Health Immigrant Health disparities 

Introduction

In the last decade, the Asian population in the US increased by 43% to 14.7 million persons. Vietnamese are currently the fourth largest, but fastest growing, Asian group (U.S. Census 2012) and are expected to be the largest Asian group in California by 2030 (Khow and Foo 2014).

Vietnamese immigrants consistently report poorer health and/or more functional limitations than other Asian immigrants (Barnes et al. 2008; Frisbie et al. 2001; Ihara 2009; Sorkin et al. 2008; Walsh et al. 2010a) except other Southeast Asian refugee groups (Maty et al. 2011; Yang et al. 2012). Whereas 9% of Asian adults in the US report fair or poor health, 19% of Vietnamese report poor or fair health (Barnes et al. 2008), but little is known about the factors contributing to the overall poorer reported health of Vietnamese in the United States.

Vietnamese also do not share the socioeconomic advantages of other Asians (U.S. Census Bureau 2016). In the three decades since their arrival in the US, the Vietnamese have neither achieved the educational or occupational status of other Asian immigrants nor accumulated levels of income and wealth comparable to other Asians. As refugees, the Vietnamese differ considerably from other immigrants due to their forced migration experience and the context of reception in the US (Mutchler et al. 2007; Vo and Hom 2018; Zhang and Ta 2009).

Our goal in this study is twofold. First, this study adds to the limited knowledge about Vietnamese refugee health, and addresses some of the limitations in previous studies. Only one other study (Sorkin et al. 2008) examines the self-rated health among older Vietnamese and non-Hispanic whites, finding that Vietnamese report poorer health than their white counterparts. We build on this prior work (Sorkin et al. 2008) by adding controls for an array of socioeconomic measures, health risk behaviors and demographic characteristics, and by adding an additional measure of health among the older cohort of Vietnamese refugees: self-reported functional limitations. Second, our dataset is the only community study with a large enough sample of Vietnamese refugees to use a regression approach which allows us to untangle the contributions of the three blocks of variables: demographic, socioeconomic, and health risk behaviors, on the health of Vietnamese immigrants in a large ethnic enclave.

Vietnamese Refugees

Unlike most Asian groups, the Vietnamese have been in the United States fewer than 40 years, making all those Vietnamese age 55 and older foreign-born. Starting in 1975 with the fall of Saigon, four major waves of immigration have resulted in a diverse population of Vietnamese immigrants. The vast majority come from agricultural, rural backgrounds and have no formal education, although a few hail from urban centers with high education and technical occupations (Rumbaut 2006; Sorkin et al. 2008). The commonality among Vietnamese immigrants is their refugee status and their collective experience of homeland war, political oppression, and strife followed by arduous emigration to several English-speaking countries. The incredible hardships endured by most Vietnamese include separation from family members, resettlement in overcrowded refugee camps, and exposure to diseases such as tuberculosis and hepatitis. For the many “boat people” who escaped Southeast Asia on small boats, the trauma included seeing loved ones drown or die in crossing. Another segment of Vietnamese, the former prisoners of war, experienced some of the harshest trauma prior to emigration. These refugees exhibit lasting physical and mental trauma from the Vietnam experience (Zhang and Ta 2009).

Almost 200,000 Vietnamese live in Orange County, or about 6% of the county’s total population (U.S. Census Bureau 2016). Currently, the largest concentration of people of Vietnamese ancestry outside Vietnam resides in five Orange County, California cities known as ‘Little Saigon.’ Here, Vietnamese comprise a relatively large percentage of the population, especially in Westminster (40%), Garden Grove (28%), Fountain Valley (21%). The enclave is growing into two adjacent cities with the largest populations in the county—Vietnamese are 12% of Anaheim and 9% of Santa Ana (U.S. Census Bureau 2016).

This study focuses on two age-based cohorts of older refugees currently residing in ‘Little Saigon.’ One cohort, born 1943–1952, ranged between the ages of 3 and 12 years at the outset of the Vietnam War that commenced in 1955. The older cohort, born before 1942, experienced childhood in a Vietnam free from war, but experienced military conscription, forced relocation, and internment camps as adolescents and young adults. Both cohorts lived through the brutalities and trauma of dislocation, exile on short notice, and exposure to chemical weapons.

As the first generation to settle in the United States, Vietnamese refugees did not have an existing community to help orient them to U.S. culture. They had no ready access to occupational networks, no established businesses or any congregations to ease the transition. Recent immigrants without such networks and liaisons suffer the most disadvantages (Takei and Sakamoto 2011).

The Vietnamese are also among the most linguistically isolated group among Asians in the United States, with 43% unable to speak English (Adams et al. 2010). Inability to speak English is a well-documented barrier to health access: communication is central to seeking help, describing symptoms, and being able to follow the medical practitioner’s advice (Yoo et al. 2009; Zhang et al. 2012). The underrepresentation of Vietnamese among medical practitioners exacerbates the language and cultural barriers for these older cohorts (Vo and Hom 2018).

Health of Vietnamese in US

Although few studies include any controls for socioeconomic status, demographics, and lifestyle characteristics, descriptive studies and findings from scattered epidemiological studies suggest that Vietnamese demonstrate particular health disadvantages on a wide array of measures, including high cervical cancer rates (Taylor et al. 2009), high hepatitis B and tuberculosis rates (Centers for Disease Control and Prevention 2016), high liver cancer rates (Miller et al. 1996), and high colorectal cancer rates (Walsh et al. 2010b). One advantage the vast majority of Vietnamese do have is citizenship and nearly universal medical insurance coverage as a result of their refugee status. There are, however, an increasing number of Vietnamese immigrants living in the county who do not necessarily have insurance because they are either undocumented or non-refugees with Legal Permanent Resident status—green cards. Estimates are this group of non-refugees countywide is approximately 6% of the Vietnamese immigrants in the decade 2002–2012 (Vo and Hom 2018).

Consistently poorer health of the Vietnamese relative to other Asians and whites is likely due to complicated relationships among demographic, socioeconomic, and health behavior risks as well as the impact of the lifelong trauma of war and resettlement. Consistent with previous findings, we expect lower socioeconomic status to result in poorer health and greater likelihood of reporting functional limitations for both older Vietnamese and whites. However, we anticipate that much of the difference in health between Vietnamese refugees and whites is explained by socioeconomic factors and health risk behaviors, though the cumulative effects of trauma and disadvantage across the life span (Ferraro and Shippee 2009; Johnson and Schoeni 2011; Johnson et al. 2012; Reed and Barbosa 2016) likely also play an important role.

Predictors of Health Disparities

Demographics

Gender, marital status, age, household composition, and race/ethnicity all show consistent relationships with health status. In Western societies, although women have greater life expectancy than men, they report more chronic illnesses and more limitations in activities (Adams et al. 2010; Benyamini et al. 2003; Read and Gorman 2010; Idler 2003). The married are more likely than singles to experiences good health (Idler et al. 2004; Simon 2002). Scholarship also documents persistent ethnic and racial differences in life expectancy and the number of years spent disease-free, without limitations on activity (Brown et al. 2016). Increases in health conditions are a natural part of the aging process.

Socioeconomic Status

Minority group membership is highly correlated with poor socioeconomic status and much of the racial and ethnic disparity in health outcomes results from social and economic stratification in society (Farmer and Ferraro 2005; French et al. 2012; Hayward et al. 2000). Common measures of socioeconomic status include income, wealth, language proficiency, and education.

Income is a strong predictor of health. Adequate income improves access to clean water, safe, temperature controlled housing, nutritious food, medicine, and medical care and reduces the chronic stress that is associated with financial insecurity (Kimbro et al. 2012). However, for older populations, current income may not be an appropriate measure of economic resources. Wealth also correlates positively with health; although comprehensive wealth data are seldom available, home ownership in the United States is a valid proxy for wealth (Conley 2010).

Education remains the strongest socioeconomic predictor of good health; however, among foreign-born immigrants, it may not be as relevant a predictor of health (Kimbro et al. 2008). The effect of education on health is separate from income advantage and probably results from better access to information, improved critical thinking, and improved problem solving as well as less exposure to hazardous occupations.

Lifestyle Risk Behaviors

Lifestyle risk behaviors also affect both health and the ability to perform functional activities. Three important risks are smoking, obesity, and lack of physical exercise. Smoking and obesity contribute to cardiovascular disease and cancer (Kandula et al. 2009). The National Cancer Institute also notes that poor diet and lack of physical exercise account for between 25 and 30% of the incidence of cancer among the non-smoking population.

Self-rated Health, Functional Limitations, and Chronic Illness

Self-rated health (SRH) is a durable, valid and holistic measure of health, which correlates with physician assessments both generally (Abdulrahim and El Asmar 2012) and specifically for the Vietnamese refugee population (Kandula et al. 2007). It is also an excellent predictor of both morbidity and mortality (DeSalvo et al. 2005; Idler et al. 2004).

Functional limitations, or limits on ability or inability to perform activities of daily living, among older populations are an end stage of the disablement process resulting from chronic illnesses and injuries. This is an additional measure of health status among older populations, particularly as it is indicative of how much other health issues are affecting quality of life in everyday activities. Studies suggest that some ethnic minorities experience premature onset of both chronic illnesses and deterioration in the ability to perform activities due to the reach of the long arm of physical and psychological disadvantage earlier in life (Geronimus 2001; Johnson and Schoeni 2011).

Current Study

Although substantial evidence suggests that much of the ethnic disparity in health outcomes results from social and economic stratification in society (Brown et al. 2016), important questions remain regarding the Vietnamese. Is their consistently poorer health relative to whites due largely to differences in socioeconomic status, demographic characteristics, and health risk behaviors? Or, is their poorer reported health a residual impact of the lifelong trauma of war and resettlement? Consistent with previous findings, we expect lower socioeconomic status to contribute to poor health. We anticipate that much of the difference in health between Vietnamese refugees and similarly aged whites is explained by differences in socioeconomic status, demographic characteristics, and health risk behaviors. However, we also expect that the cumulative effects of trauma and disadvantage due to war and resettlement across the life span have a residual negative effect on health and functional limitations.

Data and Method

For our analyses we utilize secondary data from the 2007 Orange County Health Needs Assessment (OCHNA), which is a collaborative project of hospitals, community health clinics, county public health, and human service providers. The project, with both technical and community advocate oversight committees, contracted with Macro International to conduct the interviews (Macro International 2007).

The population-based household survey contains detailed data regarding the general health status and health care use, needs, quality, and access among residents of Orange County, California. The questionnaire includes a broad array of demographic and socioeconomic indicators and detailed coverage of physical and mental health, replicating items from both California Health Interview Survey (CHIS) and the Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance Survey (BRFSS). The survey items have been translated into Vietnamese using the same referred, single forward translation employed to validate the California Health Interview Survey items (Ponce et al. 2004). Designed to conform to state legislation, the questionnaire replicates the CHIS and BRFSS surveys and did not allow measurement of some variables that would have enhanced this study.

The surveys were designed and administered in English, Spanish, or Vietnamese. Data were collected through the use of telephone surveys with randomly selected adults in randomly selected, telephone-equipped Orange County households using the protocols developed by the Center for Disease Control Behavioral Risk Factor Surveillance System. The overall cooperation rate for the survey, or response rate for those households actually contacted (AAPOR 2015), for the entire survey is 61.8% (Macro International 2007).

The project oversampled the adult Vietnamese population and seniors (individuals age 55 and older). The Vietnamese oversample was obtained using a combination of random digit dialing and a random sample from a list of likely Vietnamese households (identified by surname). Similarly, the 55+ over sample was obtained using a combination of random digit dialing and a list of likely households containing elderly respondents.

Analytic Sample and Weights

The major benefit of the 2007 OCHNA for our analysis is the oversample of Vietnamese older adults. As shown in Table 1, the sample includes 352 interviews with Vietnamese adults aged 55 and older and 495 interviews with white adults aged 55 and older, for a total of 847 total interviews with adults 55 and older (from 2613 total interviews in which the person reported their ethnicity). As noted above, we limited the sample to those aged 55 and older to focus on two older cohorts of Vietnamese refugees, those born before 1942 (65+) and those born between 1943 and 1952 (aged 55–64 years) who experienced war and resettlement at very different stages of the life course.

Table 1

2007 OCHNA sample size for adults 55 and older by selected ethnicity

 

Number of interviews

Percent of 55+ interviews (%)

Weighted percent of 55+ in community (%)

Vietnamese

352

37

5.5

Non-Hispanic white

495

52

50.8

 Total analytical sample

847

89

56

All other ethnicities

105

11

44

 Total OCHNA 55+ sample

952

100

100

Roughly 21.8% of the interviews in the total sample are with Vietnamese adults, who represent about 5.5% of the adults in Orange County. Our final analytic sample for self-rated health includes 847 interviews with Vietnamese and white adults over age 55 who provided responses to all of the variables in our models. Our final analytic sample for functional limitations includes 488 interviews with Vietnamese and white adults over age 65 (the question was not asked of those under age 65). Again we excluded the small number of cases (N = 4) with missing data on any of the variables in the model. To limit the effect of missing data we excluded variables where there was a large amount of missing data (e.g., income and mental health measures). We also exclude the 11% of interviews with those aged 55 + in all other ethnic groups.

For our descriptive analyses, we use the OCHNA population weights, which allow estimation to Orange County population totals based on State of California Department of Finance estimates. For the regression analyses, we constructed an analytic weight that weights each adult according to the population weights but retains the correct total number of cases (e.g., N = 847). This adjustment allows us to maintain more accurate estimates of the standard errors.

Analytic Approach

We conducted all analyses using SPSS. We first describe the differences in self-rated health and reports of functional limitations, along with demographic, social class, and health risk behaviors by ethnicity. Since the self-rated health variable has three categories (see measures below), we then use multinomial logistic regression techniques to examine the effect of ethnicity on self-rated health net of the demographic and socioeconomic control variables. Since the functional limitations variable is binary, we use binary logistic regression techniques to examine the effect of ethnicity on and functional limitations net of other independent variables.

We investigate four nested regression models for each dependent variable to examine the contributions of different types of variables (ethnicity, demographic, socioeconomic, and lifestyle factors) and their effect on the relationship between ethnicity and health. We start by examining the effect of ethnicity alone, and then include demographic characteristics, socioeconomic status variables, and lifestyle measures. Model 1 examines the effect of only ethnicity/refugee status on self-rated health and functional limitations. Model 2 adds controls for demographic characteristics, including sex, marital status, and household composition. Model 3 adds controls for social class characteristics, which measure stratification. Model 4 adds controls for health risk factors.

Since self-rated health is an ordinal variable, we considered using an ordered logit approach. However, regression diagnostics (not shown) indicate that the proportional odds assumption is not supported, suggesting that it is not appropriate to use one equation to estimate all comparisons. Thus, we use a multinomial logistic regression approach, which allows us to create a separate equation for each comparison. The results described below do suggest a different effect of the independent variables on the comparison between poor and good health vs. the comparison between excellent and good health. We utilize the same four-model approach for functional limitations, but use a binary logistic regression model.

Measures

Dependent Variables

We examine two different measures of health: For those 55 and older, we examine self-rated heath, and for those 65 and older, we also examine functional limitations on activities of daily living.

Self-rated Health

The measure of self-rated health (SRH), is based on the responses to the OCHNA question “would you say, in general, that your health is: excellent, very good, good, fair, or poor?” We collapse the original five-category scale into a three-category dependent variable that compares those reporting (a) “excellent” health, defined as excellent or very good health, and (b) “poor” health, defined as poor or fair health, to those reporting (c) “good” health. We use three categories rather than the original five because, even with the relatively large sample, we have very few respondents in a few cells. Good health is the reference category and as discussed in more detail below, two separate equations are estimated comparing excellent health to good health and poor health to good health. Table 2 presents the full distribution of self-rated health by ethnicity.

Table 2

Bivariate relationship between ethnicity and self-rated health for white and Vietnamese adults 55 and older from 2007 OCHNA

 

Vietnamese

Non-Hispanic white

Excellent

3.8%

24.1%

Very good

5.8%

31.7%

Good

27.2%

25.2%

Poor

40.0%

11.7%

Fair

22.4%

7.2%

Dk/refused

0.7%

0.1%

Analytic N

352

495

Weighted N

34, 127

499, 814

Functional Limitations

For functional limitations our measure is based on the OCHNA question “How much difficulty do you have doing your daily care activities, such as dressing, bathing, feeding, etc.?” with the response categories: “None, some, a lot, or unable to care for self.” Based on this question, we created a dichotomous measure of limitation, comparing those with no limitations on daily care activities to those who reported at least some limitation of these basic activities. We use a dichotomous measure of functional limitations because few respondents reported a lot of limitation or inability to care for one’s self. For purposes of the regression, we estimate the equation for having any limitation compared to having no limitations. Table 3. presents the full distribution of functional limitations by ethnicity.

Table 3

Bivariate relationship between ethnicity and activity limitations for white and Vietnamese adults 65 and older from 2007 OCHNA

 

Vietnamese

Non-Hispanic white

No difficulty

73.1%

89.4%

Some difficulty

16.1%

9.4%

A lot of difficulty

3.8%

0.8%

Unable to care for self

2.6%

0.0%

Don’t know/refused

4.4%

0.4%

Analytic N

198

290

Weighted N

17,971

305, 419

Independent Variables

A dummy variable for Vietnamese ethnicity (whites as the reference group) is used to operationalize our measure of ethnicity and refugee status. This is our key independent variable. Given the age range of the Vietnamese sample and the limited immigration before the Vietnamese war, the entire Vietnamese sample experienced the trauma of war and resettlement in some form. Although language proficiency is an important marker of acculturation, language of interview and ethnicity are essentially collinear in the data, thus we exclude language proficiency from our model.

We include measures of sex, marital status, presence of children, and age as controls for demographic characteristics. Sex is operationalized as a dummy variable for female (with male as the reference group). We include a dummy variable for whether the respondent is currently married or cohabiting (single or never married and previously married as the reference group). We also include dummy variables for presence of children under 18 in the household. We considered adding an additional variable for household size, however, household size was collinear with the presence of the number of children in the household. We opted to use the presence of children since that is the more theoretically relevant variable for our purposes. For the SRH model, we also include a dummy variable for whether the person was aged 65 and older as older age is likely to be associated with poorer health.

Since there is no measure of total wealth in the OCHNA dataset, following stratification scholars’ lead, we use a dummy variable for home ownership as a proxy for wealth (Conley 2010). We also include a set of categorical measures of highest level of education completed (less than high school or high school diploma, some college, and college degree or higher). Because virtually all Vietnamese and whites in our sample were citizens or permanent residents with health insurance, our model does not include these measures.

We initially planned to use income as another measure of socioeconomic status, but extensive missing data, and vast disparities in the distributions, precluded the use of this variable.

We use three dummy variables intended to measure health risk behaviors that may negatively influence health: no physical activity in last 30 days; current or former smoker; and being overweight or obese, operationalized as a body mass index (BMI) of greater than 25. We exclude physical activity from the model because of collinearity with functional limitations. Tests for collinearity between ethnicity and lifestyle variables (not shown) do not indicate any collinearity problems. We also examine interactions between ethnicity and all other independent variables (not shown) and the few notable effects are discussed below.

Results

Descriptive Statistics

Vietnamese refugees report substantially poorer health on our measures than do older whites. As shown in Table 4, older Vietnamese adults are more likely to report poor or fair health, at 63%, compared to whites, at 19%. Vietnamese are also much less likely than whites to report good or excellent health, at 10 versus 56%. While similar differences in SRH between Vietnamese and whites have been reported elsewhere for all adults (Walsh et al. 2010a), the differences for this older population, which includes only foreign born Vietnamese refugees, are even more pronounced. Vietnamese aged 65 and older are also more than twice as likely as whites to report at least some functional limitation, at 24 versus 10%.

Table 4

Weighted descriptive statistics for white and Vietnamese adults 55 and older in analytic sample from 2007 OCHNA

 

Vietnamese

Non-Hispanic white

Self-rated health

Excellent or very good

9.7%

55.9%

Good

27.4%

25.2%

Poor or fair

62.9%

18.9%

At least some activity limitation (% of 65 plus only)

23.6%

10.2%

Demographics

Female

51.8%

55.3%

Age

  

 55–64

47.3%

38.6%

 65 and older

52.7%

61.4%

Marital status

  

 Married or cohabiting

78.9%

78.9%

 Never married

6.2%

3.1%

 Previously married

14.9%

17.9%

Any child in household

12.0%

4.9%

Interview conducted in english

93.3%

17.3%

Social class

Home owner

31.4%

93.5%

Education

  

 Less than HS

34.9%

2.1%

 HS

35.1%

16.5%

 Some college

15.1%

36.0%

 College or higher

14.9%

45.5%

Income

  

 Less than $20 K

43.7%

2.7%

 $20–$60 K

16.9%

36.5%

 $60–$100 K

2.1%

26.2%

 $100 K or more

1.1%

14.0%

 Income missing

36.2%

20.6%

Lifestyle

No physical activity last 30 days

20.4%

22.8%

Current or former smoker

18.8%

46.6%

Overweight (BMI ≥ 25)

26.6%

58.5%

Analytic N 55+

350

493

Weighted N 55+

33,883

499,235

Analytic Ns differ slightly from Tables 2 and 3 due to exclusion of the small number of cases with missing values on the dependent variables

Our Vietnamese refugee sample is slightly younger than their white counterparts (53% aged 65 or older vs. 61% for whites; average age of is 66.6 for Vietnamese and 68.3 for whites). Vietnamese are more likely than whites to have children living in the household (12 vs. 5%). In addition, Vietnamese refugees report lower socioeconomic status across all our measures. Less than one-third of the Vietnamese in our sample were homeowners, compared with over 90% of whites. More than one-third of Vietnamese had not completed a high school education, compared with fewer than 3% of whites. Furthermore, while only one-third of Vietnamese had attended college, and only 15% graduated, over 80% of whites had at least some college education, and almost half had a college degree.

Finally, Tables 2, 3 and 4 indicate that despite overall poorer health and greater likelihood of reporting functional limitations, the Vietnamese report somewhat more healthful lifestyle characteristics, at least on our measures. Vietnamese are less likely to report being current or former smokers (19 vs. 47%) and less likely to be overweight based on BMI (27 vs. 59%) than whites.

Multivariate Analysis

The descriptive statistics suggest that Vietnamese refugees report poorer health, but also highlight that there are also substantial differences in demographic characteristics, socioeconomic status, and lifestyle characteristics between the two groups. We would expect these differences in characteristics to contribute to health disparities. The multivariate regression analysis examines whether these differences in health remain once we control for demographic, socioeconomic status, and lifestyle differences between the two groups.

Predictors of Poor Health Versus Good Health

Table 5 presents the multinomial logistic regression results examining the effect of being a Vietnamese refugee on the likelihood of reporting fair or poor health (vs. good health). Model 1 shows simply the effect of Vietnamese ethnicity. This model reflects the same relationship described above, but provides a slightly different way of thinking about the comparison. Model 1 in Table 5 shows that the Vietnamese are more than three times as likely (B = 1.116, OR = 3.054, p < .001) as whites to report poor or fair health.

Table 5

Multinomial regression on Self-rated health, fair or poor versus good

 

Model 1

Model 2

Model 3

Model 4

Fair or poor vs. good

Fair or poor vs. good

Fair or poor vs. good

Fair or poor vs. good

B

OR

B

OR

B

OR

B

OR

Ethnicity

 Vietnamese (ref = white)

1.116

3.054***

1.097

2.995***

0.831

2.296^

0.986

2.681*

Demographics

 Female (ref = male)

  

− 0.207

0.813

− 0.276

0.759

− 0.139

0.871

 65 and Older (ref = under 65)

  

− 0.834

0.434***

− 0.908

0.403***

− 1.052

0.349***

 Married (ref = not married)

  

− 0.302

0.739

− 0.276

0.759

− 0.2

0.818

 Child in Household (ref = none)

  

− 0.458

0.633

− 0.466

0.628

− 0.486

0.615

Social class

Home owner (ref = renter)

    

− 0.173

0.842

− 0.138

0.871

Education (ref = HS or less)

        

 Some college

    

− 0.173

0.841

− 0.416

0.66

 College or higher

    

− 0.424

0.655

− 0.303

0.739

Lifestyle

No physical activity last 30 days (ref = some)

      

0.72

2.055**

Current or former smoker (ref = never smoked)

      

0.55

1.734*

Overweight (ref = BMI < 25)

      

− 0.243

0.784

Constant

− 0.286

 

0.615

 

1.097

 

0.747

 

*** denotes significant at p < .001, ** denotes significant at p < .01, * denotes significant at p < .05, ^ denotes significant at p < .10

Model 2 adds controls for differences in demographic characteristics. The older age cohort, over age 65, are significantly less likely to report poor health than those aged 55–64 (because they are more likely to choose reference category of good health). However, differences in demographic characteristics do not explain the greater likelihood of Vietnamese to report poor health relative to whites; they remain three times as likely as whites to do so. Model 3 adds controls for socioeconomic status. Unlike previously reported results for all adults, socioeconomic status does not appear to be related to the likelihood of reporting poor health for older adults in this sample, nor does it explain the greater likelihood of reporting of poor health for Vietnamese. Net of socioeconomic status, Vietnamese are still more likely to report poor health, though the effect is somewhat smaller, with Vietnamese just over twice as likely to report poor health.

Finally, Model 4 adds controls for lifestyle characteristics. Unsurprisingly, smokers and those with no physical activity are more likely to report poor health. Furthermore, the inclusion of lifestyle factors in the model actually increases the negative effect of being a Vietnamese refugee over that observed in Model 3. The Vietnamese remain almost three times as likely to report poor health as whites net of demographic characteristics, socioeconomic status, and lifestyle factors. A test of interactions between ethnicity/refugee status with education and lifestyle factors (data not shown) indicates that the only significant interaction is between ethnicity and physical activity. Lack of physical activity significantly increases the likelihood of reporting poor health for whites, but not for Vietnamese. As we saw in Table 4 above, the Vietnamese report less unhealthy lifestyles than similar-aged white adults in Orange County. Comparing across the models in Table 5 suggests that these lifestyle differences helps to mitigate, to some extent, the lower self-rated health of Vietnamese.

Predictors of Excellent Health Versus Good Health

Table 6 presents the multinomial logistic regression results examining the effect of Vietnamese refugee status on the likelihood of reporting excellent or very good health (as opposed to good health). Model 1 in Table 6 shows that Vietnamese are substantially less likely than whites to report excellent or very good health—84% less likely (or 16% as likely, B = − 1.83, OR = 0.160, p < .001).

Table 6

Multinomial regression on self-rated health, excellent or very good versus good

 

Model 1

Model 2

Model 3

Model 4

Excellent or very good vs. good

Excellent or very good vs. good

Excellent or very good vs. good

Excellent or very good vs. good

B

OR

B

OR

B

OR

B

OR

Ethnicity

 Vietnamese (ref = white)

− 1.830

0.160***

− 1.77

0.17***

− 1.879

0.153**

− 1.888

0.153**

Demographics

Female (ref = male)

  

− 0.028

0.973

− 0.074

0.929

0.124

1.132

65 and older (ref = under 65)

  

− 0.623

0.536***

− 0.658

0.518***

− 0.595

0.552**

Married (ref = not married)

  

0.097

1.102

0.046

1.048

0.172

1.187

Child in household (ref = none)

  

− 1.301

0.272***

− 1.303

0.272***

− 1.117

0.327**

Social class

Home owner (ref = renter)

    

− 0.028

0.973

0.146

1.157

Education (ref = HS or less)

        

 Some college

    

− 0.05

0.951

− 0.181

0.834

 College or higher

    

− 0.164

0.849

− 0.14

0.869

Lifestyle

No physical activity last 30 days (ref = some)

      

− 0.408

0.665^

Current or former smoker (ref = never smoked)

      

− 0.017

0.983

Overweight (ref = BMI < 25)

      

− 0.141

0.86

Constant

0.796

 

1.184

 

1.391

 

1.294

 

*** denotes signficant at p < .001, ** denotes significant at p < .01, * denotes significant at p < .05, ^ denotes significant at p < .10

Model 2 suggests that the older cohort (those over 65) and those with children in the household are significantly less likely to report excellent health, regardless of ethnicity. The oldest adults are again more likely to choose the reference category (good health), perhaps because they have lower expectations. Again, differences in demographic characteristics do little to explain ethnic differences in reporting of excellent health—Vietnamese remain 77% less likely to do so. Adding controls for socioeconomic status (Model 3) has little effect; again, socioeconomic status does not significantly affect reporting of excellent health for either white or Vietnamese older adults. And Vietnamese are still much less likely to report excellent health (88% less likely) than whites, net of demographics and socioeconomic status.

Finally, Model 4 adds controls for lifestyle characteristics. Unsurprisingly, those with no physical activity are less likely to report excellent health, though the effect is marginally significant, and of course the direction of causality between health and physical activity may go both directions. Controlling for differences in lifestyle reduces the effect of Vietnamese ethnicity slightly. However, Vietnamese remain almost 90% less likely to report excellent or very good health, relative to whites net of demographic characteristics, socioeconomic status, and lifestyle factors. A test of interactions with education and lifestyle factors (data not shown) indicates no differences in the predictors of excellent health by ethnicity, and the inclusion of interactions did not significantly improve model fit.

Predictors of At Least Some Functional Limitation

Table 7 presents the binomial logistic regression results examining the effect of Vietnamese ethnicity on the likelihood of reporting at least some functional limitation among those 65 and older (this age bracket corresponds to the older cohort of Vietnamese refugees). Model 1 in Table 7 shows that Vietnamese are almost three times as likely (B = 0.995, OR = 2.704, p < .05) as whites to report at least some limitation.

Table 7

Logistic regression on presence of any limitation on activities of daily living (65 plus only)

Ethnicity

Model 1

Model 2

Model 3

Model 4

Any limitation

Any limitation

Any limitation

Any limitation

B

OR

B

OR

B

OR

B

OR

Ethnicity

 Vietnamese (ref = white)

0.995

2.704*

0.962

2.617^

1.045

2.844^

1.707

5.511*

Demographics

Female (ref = male)

  

0.094

1.098

0.28

1.323

0.26

1.296

Married (ref = not married)

  

− 0.362

0.697

− 0.307

0.736

− 0.603

0.547

Child in household (ref = none)

  

0.339

1.403

0.262

1.3

0.349

1.418

Social class

Home owner (ref = renter)

    

− 0.425

0.654

− 0.226

0.798

Education (ref = HS or less)

        

 Some college

    

0.289

1.335

0.573

1.773

 College or higher

    

0.823

2.278**

0.774

2.168^

Lifestyle

Current or former smoker (ref = never smoked)

      

0.724

2.063*

Overweight (ref = BMI < 25)

      

0.954

2.597**

Constant

− 2.171

 

− 1.978

 

− 2.226

 

− 3.229

 

*** denotes signficant at p < .001, ** denotes significant at p < .01, * denotes significant at p < .05, ^ denotes significant at p < .10

Model 2 adds controls for differences in demographic characteristics. Differences in demographic characteristics do not explain the greater likelihood of Vietnamese to report some limitation; they remain almost three times as likely to do so (OR = 2.6). Model 3 adds controls for socioeconomic status. Net of socioeconomic status, Vietnamese are still almost three times more likely to report some limitation (OR = 2.844). Only having a college degree is related to reports of at least some functional limitation, with college graduates more likely to report some limitation.

Model 4 adds controls for lifestyle characteristics (excluding physical activity), which again increases the effect of Vietnamese ethnicity. Unsurprisingly, smokers and the overweight are more likely to report some limitation. The oldest cohort of Vietnamese refugees are almost five times as likely to report at least some limitation on activities relative to elderly whites net of demographic characteristics, socioeconomic status, and lifestyle factors. However, the effect sizes from Model 4 should be interpreted with some caution due to the small size of the non-Vietnamese group reporting activity limitations and the smaller sample size generally to support the analysis. Regardless, these findings again suggest that the generally more healthful lifestyle of Vietnamese elderly on these standard measures, exerts a protective effect on likelihood of experiencing functional limitations among Vietnamese refugees.

We also explored interactions between ethnicity and education and lifestyle characteristics (data not shown). None of the interactions with Vietnamese ethnicity are significant, though the effect of these variables for whites remain significant. That is, being college educated, a smoker, or overweight substantially increases the propensity of whites to report functional limitations. In contrast, the opposite effect is observed for Vietnamese, although none of the effects are significant. It is not clear if this is the result of a real difference by ethnicity or simply a reduction in statistical power given the effective reduction in sample size. Furthermore, including these interaction terms does not significantly improve model fit (difference in − 2 log likelihood = 3.261, df = 4, X2 p value > 0.10).

Summary and Discussion

Consistent with previous research on Vietnamese adults of all ages, older Vietnamese refugees are substantially more likely to report poor health and less likely to report excellent health than similar-aged whites. In fact, the differences in self-rated health are more pronounced among the refugee population than those observed previously for all Vietnamese adults (Walsh et al. 2010a). Given the fact that all Vietnamese seniors are refugees who were born in Vietnam and experienced the trauma of war in their home country and a stressful context of immigration to the United States, their lower self-rated health compared with whites is not surprising.

What is more surprising is the weak effect of demographic and socioeconomic factors in explaining the differences in self-reported health between the two groups of older adults. This differs from previous findings that socioeconomic status explains much of the difference in higher likelihood of Vietnamese adults of all ages reporting poor health (Walsh et al. 2010a). However, it is important to note that we were unable to include income as a measure of socioeconomic status for this sample due to the large amount of missing data, especially among the Vietnamese, and the large disparities in the income distribution between Vietnamese and white older adults.

The presence of children in the household is associated with poorer self-rated health, and this may in fact reflect a component of socioeconomic status. Almost all such children would be grandchildren or other extended family members of the respondent. Such households may reflect poorer socioeconomic status, poorer health of the older person, cultural preferences for family care giving and multigenerational living, or some combination of the three.

Older Vietnamese refugees are also more than twice as likely as older whites to report at least some functional limitation. It is also possible that some of these Vietnamese elders incurred injuries during the war and resettlement that led to current functional limitations, but unfortunately we could not measure this in the data. However, almost none of the demographic or socioeconomic characteristics in our model are significantly related to reporting of functional limitations for either group, except college education, which increases the likelihood of functional limitation for whites. This may be due to higher expectations for healthy living, better access to care, and greater awareness of limitations. It may also reflect a greater ability to live outside institutional settings among more educated older adults, who can afford to purchase services to assist with aging in place.

Vietnamese refugees clearly experience poorer health, both in the form of self-rated health and functional limitations, and this poorer health is not simply due to differences in demographic or socioeconomic characteristics. Controlling for health behavior risk factors increases the differences in self-rated health and functional limitations. This suggests that the relatively healthier lifestyle of older Vietnamese offers some protection from even poorer outcomes.

Better understanding of the health and functional limitations among Vietnamese refugees is crucial to planning services and anticipating needs of this rapidly growing population. The particularly poor health of Vietnamese refugees has implications for delivery of health services, both in the ethnic enclaves and beyond, as secondary migration occurs. Our findings also highlight the heterogeneity of the Asian population in the United States and reinforce the need for disaggregating the pan-ethnic Asian category. Previous research also suggests that Vietnamese adults are relatively disadvantaged and face more adverse health outcomes than do other Asian Groups (Walsh et al. 2010a). The Vietnamese migration experience and accompanying trauma and its role in the poorer health outcomes of these refugees years later warrants additional exploration on the long arm of trauma on health. This calls not only for a careful disaggregation of ethnic immigrants, but studies that examine the differences between immigrants and refugees of the same ethnic group.

The health of aging Vietnamese refugees also sheds some light on how the context of immigration may affect future health. While we focus on older Vietnamese refugees, this is an important factor to consider for other groups as the foreign-born population ages. However, it is important, to exercise some caution in generalizing findings too far into the future. It remains to be seen whether subsequent generations of Vietnamese Americans will continue to experience the poorer health of foreign-born Vietnamese, which is, at least in part, attributed to their particular context of immigration and reception. The fact that the effect of Vietnamese ethnicity on health is more pronounced for the refugee population than for all adults suggests that there may be some attenuation of the effect of refugee status across generations. On the other hand, the protective effect of the Vietnamese lifestyle may also be a fragile phenomenon that pertains only to this particular cohort of Vietnamese refugees.

Limitations

The data from the Orange County Health Needs Assessment is limited to questionnaire items that mirror the California Health Interview Survey (CHIS) and the Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance Survey (BRFSS); some items were not queried and are, therefore, not included, although the information would have enhanced the analysis; e.g., date of arrival in the U.S.; parents’ occupations in Vietnam; number of household members; type of health insurance; access to linguistically and culturally appropriate health services, and relationship with a primary care physician for routine checkups.

Another set of limitations stem from the nature of this particular refugee population. Although the literature indicates self-rated health is a good predictor of mortality and morbidity for many groups, it may not be a culturally sensitive or valid proxy of health status for Vietnamese refugees whose cultural preference is to show humility and, therefore, not report “excellent” health. In addition, because Vietnamese culture places a high value on privacy and face-saving, the respondents may have avoided reporting mental health issues or any stigmatized condition.

Further, there is no information about the attitude of this group of older refugees towards Western medical models; a lack of trust could contribute to reluctance to seek help despite having insurance coverage and result in living with treatable condition. There is very little difference in reports of doctor visits between the two groups—87% of Vietnamese and 85% of white respondents visited a doctor or other Western medical professional in the past year and there were very few (< 1%) reported visits to acupuncturists or other specifically Eastern medical professionals (data not shown). At the same time, the doctors that Vietnamese visit in the enclave of Little Saigon may be likely to speak Vietnamese and/or have more Eastern influences in their administration of Western medicine.

The vast majority of Vietnamese interviews were conducted in Vietnamese reflecting the fact that elderly Vietnamese have the most limited English proficiency of any Asians in Orange County (Vo and Hom 2018). It is possible that some of the differential in reported health and functional limitations results from differences in understanding of the question or perceived meaning of these questions that were not sufficiently addressed in the validation of the Vietnamese questionnaire. Furthermore, given the association of depression and other mental health issues with lower self-rated health (French, Sargent-Cox, & Luszcz) among the elderly, it is possible that the poorer health of Vietnamese reflects the psychological ramifications of their immigration context. Unfortunately, there was also extreme reluctance among this group to answer the mental health questions in this survey. We plan to explore both these possibilities further using focus groups. If there are differences in understanding of health then this would have implications for health delivery as well.

Due to the unique experiences and cultural proclivities of this group of older refugees, the findings cannot be generalized to other refugee groups whose experiences in their countries of origin and their migration have very different factors. Caution is required to generalize beyond a particular refugee group to others. While we focus on a specific refugee group, our findings also provide additional evidence that refugees in general may have poorer health than their counterparts (Reed and Barbosa 2016) even within the same pan-ethnic or race grouping. Thus it is crucial to disaggregate refugees from other immigrants of the same pan-ethnic or race group and to conduct oversamples that capture sufficient number of cases to provide useful information about the refugees.

Conclusion

Vietnamese refugees are more likely than whites to report poor or fair health, less likely to report excellent or very good health, and more likely to report some functional limitations. Differences in demographic and socioeconomic characteristics explain little of this health disparity. This suggests that the trauma of war and the stressful context of immigration to the United States continues to contribute to the poorer health of Vietnamese refugees. These findings have implications for the delivery of health services in Little Saigon and beyond and for thinking about health among refugee populations more generally.

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of SociologyCalifornia State University FullertonFullertonUSA

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