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Correspondence: Address correspondence to Dr. Sadhna Diwan, School of Social Service Administration, University of Chicago, 969 E. 60th Street, Chicago, IL 60637. E-mail: diwan{at}uchicago.edu
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Abstract |
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 TOP Abstract Older Immigrants and...The Life Stress ModelMethodsResultsDiscussionReferences |
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Key Words: Depression • Social support • Ethnicity • Asian and Pacific Islanders • Mental Health
This study examines factors related to psychological well-being in a sample of Asian Indian immigrants, who are the third largest group within the API category (Barnes & Bennett, 2002). In general, Asian Indian immigrants are highly educated, with the average education level being a bachelor's degree, and a median income higher than that of non-Hispanic Whites (Bennett & Martin, 1997). Asian Indian immigrants in the United States are unique in that there is significant diversity in educational level, socioeconomic status (SES), and within-group difference in language, diet, and religious affiliation as compared with other minority immigrant groups. Despite being structurally assimilated (having entered the occupational structure at middle and upper-middle levels), these immigrants have maintained their cultural distinctiveness (Gawlick, 1997). The average life expectancy for Asian Indians is 62 years (63 years for men and 64 years for women; Population Reference Bureau, 2003), which is the lowest among the major groups comprising APIs (i.e., Chinese, Japanese, Filipinos, and Koreans). These statistics are for the countries of origin, because separate data for Asian immigrants in the United States are not available. In terms of health, there is an increasing body of research on Asian Indian immigrants in other countries, notably the U.K., Canada, and Singapore, which highlights the risk of increased mortality and morbidity especially as a result of cardiovascular disease among this immigrant group as compared with natives and other immigrants (Balarajan, 1991). Hughes, Raval, and Raftery (1989) observed myocardial infarction (MI) to occur at an earlier age among Asian Indians (50 years of age) as compared with Whites (55 years of age), with a marked increase (2.1 times higher) in the incidence of MIs in 30- to 39-year-olds, suggesting that cardiovascular disease is accelerated in the immigrant Asian Indian population in the U.K. Limited data from seven states with the highest API population in the United States indicate that heart disease is the leading cause of death among both Asian Indian men and women, although the risk factors that predispose this population to the disease are unclear (Hoyert & Kung, 1997). As this minority group continues to grow older and more numerous, there is a need to better understand the multiplicity of factors, physiological, psychosocial, and behavioral, that may contribute to the prevalence of chronic disease.
An examination of factors related to psychological well-being in this ethnic group thus provides an opportunity for us to develop a better understanding of the interplay of acculturation, SES, gender, and personal coping resources on the psychological well-being of immigrant Asian Indians when they are faced with stressful life events.
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Older Immigrants and Psychological Well-Being |
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Among older adults, much attention has been devoted to examining depression, which is a common problem in this age group. Depressive symptoms have been associated with poor health, poor self-rated health, and slower recovery from illness, making it important to screen for and treat depression among older adults (Roberts, Kaplan, Shema, & Strawbridge, 1997). Women and individuals who have lower incomes, fewer social supports, and lesser feelings of personal control or mastery are much more likely to report depressive symptoms (Balaswamy & Richardson, 2001; Fernandez, Mutran, Reitzes, & Sudha, 1998; Lin, Ye, & Ensel, 1999; Pearlin & Schooler, 1978). Religious involvement has been associated with a beneficial impact on depressive symptoms, loneliness, and anxiety (see Levin & Chatters, 1998).
In examining depression, we find it important to note that positive affect and negative affect are two distinct dimensions of depression, and it is helpful to examine these aspects separately when trying to improve overall psychological well-being. However, as noted by Folkman and Moskowitz (2000), the coping literature has almost exclusively focused on negative outcomes in the stress process, which results in a relative dearth of knowledge of "how people generate and sustain positive affect under [stressful] conditions" (p. 652). Thus, our interest is to examine how different risks and resources are associated with each type of affect among individuals faced with stressful life events. From a clinical perspective, knowledge of the different impact of risks and resources may be useful in planning interventions that influence both positive and negative affect in order to improve overall psychological well-being. Positive affect in the context of chronic stress may help prevent worse outcomes such as clinical depression or adverse physiological consequences of stress (Folkman & Moskowitz, 2000).
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The Life Stress Model |
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Stress is known to have an impact on well-being by increasing depressive symptoms. It is not clear whether it just increases negative affect or it reduces positive affect as well. Further, it is not known whether the resources that influence negative affect have the same impact on positive affect. That is, are the resources needed to cope with life stresses different for improving both aspects of well-being?
The conceptual model used in this study extends the life stress model by examining the psychological well-being of immigrants and including religiosity and acculturation as coping resources that may influence both positive and negative affect. This study specifically examines the influence of demographics, personal coping resources, and social support on both positive and negative affect, when stressful life events are controlled for. Stressful life events are considered to be risk factors, whereas certain demographic characteristics, personal coping resources, and social-support variables generally enhance psychological well-being and are therefore considered as protective factors. Figure 1 depicts the life stress model used to guide the analysis.
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Methods |
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The sampling strategy used for this study was purposive and nonrandom, as we attempted to include a wider range of Asian Indian immigrants than previously studied in the literature. We interviewed eligible spouses whenever possible in order to get an adequate representation of men and women. The sampling frame consisted of members of 10 Asian Indian community organizations; 8 of these represented different linguistic communities, 1 organization generally represented Indians from varied communities, and 1 was a professional network consisting of members from varied communities. Many older, first-generation Asian Indian immigrants belong to one or more of these ethnic community organizations, given the significance of these organizations in maintaining social, cultural, and religious traditions. As Kim and McHenry (1998) note, Asian Americans have a greater likelihood than other minorities of participating in nationality groups.
As we can see in the sample characteristics (Table 1), there was heterogeneity in education, income, and religious affiliation. The sample is described in detail elsewhere (see Diwan & Jonnalagadda, 2001) and is therefore described briefly here. We sent letters announcing the study and inviting participation to a total of 542 individuals, of whom 6% were unreachable because we lacked correct phone numbers for them and 26% were unreachable despite the fact that we made numerous attempts to contact them at varying times. As suggested by Dillman (1978), we calculate the response rate only for those who were successfully contacted. Of the 365 individuals successfully contacted by telephone, 237 (65%) completed the survey, 75 (20%) refused, 40 (11%) did not meet the age criterion, and 13 (4%) were unable to communicate in English. We mistakenly included these last two categories in the sampling frame. After a preliminary analysis of the data, we found 11 respondents in our final sample to be younger than 50 years of age, and we excluded them from further analysis in this study; this gave us a total sample of 226 individuals.
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Individual Characteristics
These included demographics and health status. Demographics included age, gender, education (ordinal scale measuring years of schooling), and household income, which we measured in intervals of $15,000 ranging from below $10,000 to $100,000 or more. We measured health status by using an index of six self-reported health conditions, that is, heart disease, stroke, high blood pressure, diabetes, cancer, and arthritis.
Personal Coping Resources
We defined these as mastery, religiosity, and acculturation. We assessed mastery with five items from the Pearlin and Schooler (1978) mastery scale, which assesses beliefs about control over life and capacity to deal with life's difficulties. The items were as follows: feel helpless in dealing with problems; have little control over things; have no way to solve problems; little you can do to change important things; and feel pushed around. The response categories were as follows: most of the time, some of the time, and hardly ever. The reliability (alpha) coefficient for mastery is 
= 0.79, with higher scores indicating greater mastery. We measured religiosity by using five items that assessed regular performance of the following religious or spiritual activities: meditation, prayer, reading holy books, participation in spiritual discourses, and attendance of religious functions or events. Higher scores indicated performance of more religious activities and therefore greater religiosity. The reliability (alpha) coefficient for religiosity is = 0.65. The validity of the religiosity measure can be evidenced by its significant bivariate correlations with two variables: (a) one item asking respondents to rate how important religion was to them (r =.49, p =.000), and (b) frequency of going to worship (r =.50, p =.000). We assessed acculturation by using two variables: length of residence in the United States, measured in number of years, and cultural or ethnic identity or orientation, which was a single item asking respondents to rate themselves as being 1, very Indian; 2, mostly Indian; 3, Indian and American; 4, mostly American; and 5, very American (see Sodowsky & Carey, 1988). Higher scores indicate a more American identity or orientation. The validity of the identity measure as a measure of acculturation can be observed through its significant bivariate correlations with other proxy measures of acculturation. These variables include age at immigration (r = –.19, p =.004), in which the younger the age at immigration, the more bicultural or American the identity; preference for type of food (mostly Indian to mostly American) consumed at home (r =.35, p =.000) and when dining out (r =.32, p =.000), indicating that a more Indian identity is associated with greater preference for Indian food; desire to return to India upon retirement (r = –.25, p =.000), indicating that a more Indian identity is associated with a desire to return to India; and finally, the correlation between length of stay in the United States and identity, which was also significant (r =.23, p =.000), indicating that a longer length of stay is associated with a more bicultural or American identity.
Social Resources
We assessed these through one variable examining satisfaction with one's friendships. Individuals were asked to rate their satisfaction with friendships, with the response categories being 1, very satisfied; 2, somewhat satisfied; 3, somewhat dissatisfied; and 4, very dissatisfied. Thus, higher scores indicated greater dissatisfaction. A large body of evidence suggests that subjective evaluations of support are more strongly related to psychological well-being than objective markers of the social-support process such as the frequency of contact with others (see Krause, 1995).
Positive and Negative Affect
We assessed these by using the eight-item Center for Epidemiological Studies–Depression scale (CES-D) short form that is used in the Health and Retirement Survey and the Assets and Health Dynamics Study of older adults (see Turvey, Wallace, & Herzog, 1998). The short form with the abbreviated responses has been found to be a valid measure of depression among older people (see Turvey et al.). A confirmatory factor analysis of the short form CES-D (see Diwan, Jonnalagadda, & Gupta, in press) yielded two factors—positive affect (two items, i.e., happy and enjoyed life) and negative affect with physical symptoms (five items, i.e., feeling sad, depressed, or lonely; could not get going; or sleep was restless). We dropped one item (effort), because it was poorly correlated with either factor. Participants were asked to respond (yes or no) to whether they had experienced each item much of the time during the past week. Stacey and Gatz (1991) found that the CES-D had convergent validity with the Affect Balance Scale, which measures both affects separately.
Analysis
We used descriptive data (mean, standard deviation, and frequencies) to provide a profile of the sample. We calculated Pearson correlation coefficients to examine bivariate relationships. Because the dependent variables (positive and negative affect) are based on a count of the number of symptoms reported by an individual, we used negative binomial regression (see McCullagh & Nelder, 1989). Count outcomes are characterized by the fact that most individuals have a score of zero and the proportion of individuals with a specific positive value decreases as the value of the count increases (Frone, 1997). Models in which counts are overdispersed (i.e., where the variance is greater than the mean) are commonly estimated by using a negative binomial regression model (Frone, 1997; Zorn, 1998). Thus, as a way to maintain consistency in the regression analyses, high scores on negative affect indicate greater negative affect, whereas high scores on positive affect indicate a lack of positive affect; that is, most individuals have positive affect (0), with the proportion of individuals lacking positive affect decreasing as the value of the count variable increases. Long (1997) noted that the coefficients of a negative binomial regression are interpreted in the same manner as those from a Poisson regression (p. 237). The simplest way to interpret results is by using the factor changes in the expected count (p. 228). Because the regression model is based on a logarithm of the outcome, the expression 100[exp (coefficient) – 1] provides the incidence rate ratio (IRR), which represents the percent change in the dependent variable (a count measure) for one category of a categorical independent variable relative to the reference category, holding other variables constant. For a continuous independent variable the incidence rate ratio shows the expected factor change in the dependent variable for each point increase in the value of the independent variable.
Using STATA (StataCorp, 2002), we conducted negative binomial regression analyses of the predictors of positive and negative affect by using a robust variance estimator for clustered data because of the presence of several married couples (n = 40) in the sample, where some correlation is likely between the responses of husbands and wives. The robust variance estimator developed by White (1980) yields consistent standard errors for clustered data and is widely used in the literature. We undertook two separate hierarchical regression analyses to examine the impact of stressful life events, personal coping resources, and social resources on positive and negative affect, respectively. Because we were utilizing the life stress model, we expected that similar factors as well as unique variables would predict each aspect of well-being.
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Results |
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Table 2 presents the Pearson correlation coefficients for all the variables used in the study. The relationship between positive and negative is modest (0.36), indicating that these two dimensions are somewhat independent albeit related to each other.
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We performed a separate bivariate analysis of variance to better understand how positive affect was related to cultural or ethnic identity by dividing the groups into three: those claiming a more Indian identity (Group 1), those claiming a more American identity (Group 2), and those claiming both cultures (Group 3). The mean scores for positive affect show the best outcomes for Group 2 (1.95), followed by Group 3 (1.94) and Group 1 (1.82). Thus, those claiming a more American identity are very similar to those who claim both cultures.
For negative affect, stressful life events, gender, religiosity, and mastery (Steps 1, 2, and 3) meet the criterion for inclusion in the equation for prediction of negative affect (Table 4). The full model with inclusion of these variables was significant, with a Wald chi-square of 80.71 (df =10, n = 188, p =.001). As shown in Table 4, all the models were significant and the log likelihood decreased in each step, indicating a better fit from the previous model. The stressful life events variable, which was a significant predictor of negative affect in Step 1 (without controlling for other variables), became only marginally significant (p =.053) in predicting negative affect after all other variables were controlled for. In Step 2, women had a 116% higher rate of reporting negative affect (IRR = 2.16) as compared with men. Of all the personal resources (Step 3), mastery and religiosity functioned as resources in reducing negative affect. As the level of mastery increased, the rate of reporting negative affect decreased by 15% (IRR = 0.85), and as the level of religiosity increased, the rate of reporting negative affect decreased by 16% (IRR = 0.84).
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Discussion |
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The findings from the study reveal that different types of resources influence positive and negative affect. The common risk factors for poorer outcomes on both positive and negative affect are stressful life events and being female, which is consistent with a large volume of literature that finds women to be at greater risk for depression (Nolen-Hoeksema, Larson, & Grayson, 1999). The resources that influence positive affect are (a) satisfaction with one's social network (friendships), which is similar to the findings reported by Balaswamy and Richardson (2001), who found satisfaction with one's social network or support to be predictive only of positive affect; and (b) a cultural identity or orientation that is either bicultural or more American, which is similar to the findings reported by Mehta (1998), who found acculturation to be predictive of less depression. However, our study finds the relationship between acculturation and well-being to hold only for positive affect. Both of these variables (satisfaction with friendships and cultural or ethnic identity) appear to relate to social integration in the sense of belongingness and relationship to the larger community, which have been documented in the literature to have a salutary effect on well-being (Seeman, 1996). Thus, interventions to increase positive affect and well-being among middle-aged and older members of this immigrant community could focus on increasing opportunities for social integration—both through increasing opportunities for social contacts and through cognitive interventions that help individuals develop more adaptive resources to manage the stress that comes with migration and living in a new environment. Folkman and Moskowitz (2000) reported that positive reappraisal (i.e., reframing a situation to see it in a positive light) is a coping strategy that helps maintain positive affect in the face of chronic stress. Adapting this strategy for use with first-generation immigrants would involve helping more ethnically oriented individuals experiencing stress to reevaluate the benefits of living in another culture and helping them find support (e.g., through support groups) to promote adaptation, growth, and greater sense of well-being by increasing positive affect.
By contrast, the resources that reduce negative affect are feelings of mastery and increased religious or spiritual activity. These findings are similar to the findings of Pearlin and Schooler (1978) and Levin and Chatters (1998), who examined the impact of these variables on depression. Religious activities may reduce negative affect by offering meaning and purpose for coping with stressful life events. Similarly, to the degree that stressful events do not erode an individual's sense of mastery or control, mastery offers a significant buffer against negative affect. Interventions to decrease negative affect then should attend to the spiritual needs of older immigrants and offer ways to increase a sense of mastery in one's life. As Folkman and Moskowitz (2000) noted, problem-focused coping strategies (i.e., efforts directed at solving or managing the problem that is causing distress) engender a sense of mastery and can help explain reports of positive affect in the midst of distress.
Interestingly, length of stay in the United States, which is another measure of acculturation, was not a significant predictor for either affect. At the suggestion of one of the reviewers, we conducted additional analyses that included each variable (length of stay and ethnic identity) in the models separately. These analyses indicate that length of stay acts as a suppressor variable, which is defined by Tabatchnik and Fidell (2001) as one that "suppresses variance that is irrelevant to the prediction of the DV. Thus, a suppressor variable is defined not by its own regression weight, but by its enhancement of the effects of other variables in the set of IVs" (p. 148). Thus, length of stay is not itself significantly related to positive affect, but it is related to other predictors of positive affect, namely ethnic identity and satisfaction with friendships. When included in the model with ethnic identity, length of stay improves the fit of the model by decreasing the log likelihood, increasing the chi-square, and improving the performance of the satisfaction-with-friendships variable. Neither length of stay nor ethnic identity is significantly related to negative affect. On a conceptual level, it is quite possible for individuals to maintain strong ethnic identities and notions of what is culturally appropriate despite having lived in another country for a number of years. The correlation between length of stay in the United States and cultural or ethnic identity or orientation is modest, and length of stay may be a measure of acculturation more in terms of adopting specific behaviors rather than changing overall attitudes. For example, greater length of stay has been associated with differences in diet-related behaviors for this immigrant group (Jonnalagadda & Diwan, 2002), but it does not appear to have an impact on their psychological well-being. Thus, in this sample, length of stay does not appear to be an adequate measure of acculturation for social-psychological processes as compared with behavioral processes. Although acculturation is typically assessed through scales consisting of several items measuring multiple dimensions of acculturation, the advantage of a single-item, self-reported, global, cultural or ethnic identity measure is that it is brief, it imposes a smaller burden on the respondent, and it may be a useful heuristic for mental health practitioners working with this minority group. However, more research is necessary on validating this measure.
One of the limitations of this study is the nonrandom nature of the sample. It is possible that the findings of this study may be attributable to sample-selection issues. Thus, generalizability of the findings is limited to the groups represented in the sample (members of various organizations) but not to other individuals, for example, those who could not speak English or those who elect not to belong to such ethnic organizations. Nonjoiners of ethnic organizations may differ from joiners in the areas of social integration; that is, they may not perceive a need for remaining connected with the ethnic community. They may also differ in their description of their cultural identity; that is, less ethnically identified individuals may not see the need to belong to ethnic organizations, but instead may be members of other mainstream groups. Those who do not speak English are likely to be more ethnically identified but may or may not be well integrated into the community. Finally, we do not have any data on the individuals who refused to participate in the study and therefore cannot say for sure whether any systematic differences existed between the respondents and nonrespondents.
Despite the nonrandom nature of the sample, its strength is that it does represent various segments of the immigrant Asian Indian community with respect to diversity in income, and religious and linguistic affiliation. Despite its limitations, this study provides useful and interesting data on the impact of personal and social resources on psychological well-being in this immigrant community. It highlights the usefulness of examining the correlates of positive and negative affect separately in order to better understand which variables contribute to both types of affect when one is faced with stress. Thus, the study offers greater insight into the role played by religiosity, mastery, social support, and acculturation in influencing well-being among this group of immigrants.
Future research will have to encompass greater numbers of women, older individuals who have immigrated later in life, and those who are unable to communicate in English in order for us to better understand variations in the well-being among members of this immigrant group. Additional exploration of the nature of social support received from both the Indian and the larger community may provide a better description of the process of social integration for members of this community. Qualitative explorations of the cultural or ethnic identity variable may also provide greater insight into the particular aspects of culture and adaptation or ways of coping that influence the experience of positive affect.
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Footnotes |
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1 School of Social Service Administration, University of Chicago, Illinois.
2 Department of Nutrition, Georgia State University, Atlanta.
3 College of Social Work, Ohio State University, Columbus.
Decision Editor: Linda S. Noelker, PhD
Received for publication May 20, 2003. Accepted for publication September 23, 2003.
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