Abstract

Much research has debated whether immigrants' health advantages over natives decline with their duration at destination. Most such research has relied on (pooled) cross-sectional data and used years since immigration as a proxy for the duration of residence, leading to the challenge of distilling the duration effect from the confounding cohort-of-arrival and age-of-arrival effects. Because longitudinal studies tend to use self-rated health as the outcome, the changes they observed may reflect shifts in immigrants' awareness of health problems. We illuminate the debate by examining how immigrants' mortality risk—a relatively unambiguous measure tied to poor health—changes over time compared to natives' mortality risk. Our analysis uses the National Health Interview Survey (1992–2009) with linked mortality data through 2011 (n = 875,306). We find a survival advantage for U.S. immigrants over the native-born that persisted or amplified during the 20-year period. Moreover, this advantage persisted for all immigrants, regardless of their race/ethnicity and gender or when they began their U.S. residence. This study provides unequivocal evidence that immigrant status' health protection as reflected in mortality is stable and long-lasting.

Introduction

Despite immigrants' lower socioeconomic status and less access to health care (Derose et al. 2009; Park and Myers 2010) relative to the native-born, they tend to have better health in many aspects, including mortality, heart and circulatory disease, obesity, and smoking status (e.g., Cunningham et al. 2008; Lariscy et al. 2015; Singh and Hiatt 2006). This phenomenon, known as the “immigrant health advantage” (e.g., Markides and Eschbach 2005; Riosmena et al. 2017), is argued to decrease with immigrants' length of U.S. stay (Akresh 2007; Lara et al. 2005; Lopez-Gonzalez et al. 2005). Researchers have attributed the declining health advantage to immigrants' unhealthy assimilation to the diet, smoking habits, and other health behaviors of the native-born (Abraido-Lanza et al. 2005; Finch et al. 2001; Kimbro 2009) and exposure to racial discrimination and other negative environmental experiences (Carrasquillo et al. 2000; Hunter 2000; Leclere et al. 1994).

Most studies addressing immigrants' diminishing health advantage have used (pooled) cross-sectional data and compared immigrants with varying lengths of time in the destination country to infer the existence of unhealthy assimilation (Antecol and Bedard 2006; Cho et al. 2004). In such data, immigrants' duration of residence is exactly the survey year minus the arrival year or the age at the survey minus the age of arrival. Therefore, research trying to address all these factors often suffers from identification problems. In most cases, research inevitably confounds immigrants' length of stay with the cohort or age of arrival. Because immigrants arriving at various periods may differ in selectivity owing to shifts in the origin's sociodemographic conditions and policy changes in the destination, the cohort of arrival could explain why immigrants of varying durations of stay exhibit differing extents of health advantage in cross-sectional data. Similarly, immigrants arriving in childhood and adulthood likely migrate for different reasons, with those migrating for work being more selective healthwise than those migrating for family reasons (Gubernskaya 2015). Therefore, the health disparities observed at a single time point among those with varying durations of stay may reflect age-based health selectivity rather than the duration effect.

Given the difficulties cross-sectional analyses face, a handful of studies have started using longitudinal data that follow immigrants over time (Choi 2012; Wakabayashi 2010). Results from longitudinal analyses, however, are mixed regarding the over-time convergence of immigrants' and natives' health. Gubernskaya (2015), for example, found faster self-rated health declines for immigrants than for the native-born population, whereas Lu and colleagues (2017) showed that the foreign-born are able to maintain their health advantage. Moreover, prior studies relied exclusively on an older population (Choi 2012; Gubernskaya 2015), in which immigrants may be especially few and selected, or used self-rated health as an outcome, which might reflect immigrants' changing perceptions of their health instead of their actual health (Jasso et al. 2004; McDonald and Kennedy 2004).

Building on the limited longitudinal research on the importance of duration of stay to immigrants' health, this study utilizes the National Health Interview Survey (NHIS) with linked mortality data to follow the survival status of a U.S. national adult sample for up to 19 years. Unlike previous research, we focus on over-time shifts in immigrants' mortality advantage, a measure fairly unambiguous relative to self-reported health conditions or self-rated health (Angel 2006). Although mortality is conceptually different from health, the health protection from immigrant status might be reasonably expected to lower mortality risk. In fact, research has shown a lower mortality rate for immigrants than for natives (Arias et al. 2010; Borrell and Lancet 2012; Singh and Hiatt 2006) and considered this gap as corroborating evidence for the immigrant health advantage (e.g., Angel et al. 2010; Lariscy et al. 2015). In this sense, examining how immigrants' mortality advantage changes with their length of stay can shed light on general knowledge concerning the durability of health protection for immigrants.

To uncover the effect of duration of residence on immigrants' mortality, we analyze the patterns and disparities in mortality risk over real time while accounting for age-related mortality hazards. Because studies based on (pooled) cross-sectional data often divided immigrants by the number of years since immigration (YSI) and compared their health conditions (Antecol and Bedard 2006; Cho et al. 2004), we further break down the foreign-born by YSI group to show how these groups' mortality hazards evolve over time. We thereby assess the extent to which differences among YSI groups indeed reflect over-time shifts in immigrants' mortality risk for immigrants relative to natives. Finally, because immigrants of various races and ethnicities may assimilate at different paces and may be exposed to differing levels of discrimination (Villarreal and Tamborini 2018), their temporal mortality patterns are potentially diverse. Thus, we also examine long-term changes in mortality risk by immigrants' ethnoracial identity.

Background

Immigrant Health and Mortality Advantages and Unhealthy Assimilation

Much research shows that immigrants have better health conditions (e.g., Cunningham et al. 2008; Markides and Coreil 1986) and lower mortality rates (Arias et al. 2010; Borrell and Lancet 2012; Mehta et al. 2016) than their native-born counterparts. These differences can be attributed to three factors. First, healthier individuals are more likely to self-select into migration (Akresh and Frank 2008; Bosdriesz et al. 2013; Guillot et al. 2018). Second, immigrants' unique behavioral patterns and social capital enhance their health (Blue and Fenelon 2011; Eschbach et al. 2004). Specifically, immigrants' relatively favorable health behaviors (e.g., low smoking rates) and tight social networks have protective effects on their health, leading to their lower mortality (e.g., Fenelon 2013; Gallo et al. 2009; Kimbro 2009). Third, the “salmon bias”—the bias caused by the likely returns of unhealthy immigrants to their origins—may explain immigrants' better health and lower mortality (e.g., Arenas et al. 2015; Palloni and Ewbank 2004). Although some evidence supports the salmon bias, many researchers argue that it insufficiently explains immigrants' advantages in health or mortality (e.g., Elo et al. 2004; Hummer et al. 2007; Riosmena et al. 2013).

Despite the abundant evidence on immigrants' health and morality advantages, some researchers contend that such advantages are short-lived and appear mostly early in an immigrant's U.S. stay (e.g., Riosmena et al. 2017). Immigrants with longer U.S. residence or greater acculturation to U.S. society have worse health and more illness risk factors than those with shorter residence or less acculturation (e.g., Hunt et al. 2004; Lara et al. 2005; Lopez-Gonzalez et al. 2005). This phenomenon, often referred to as unhealthy assimilation (Antecol and Bedard 2006), suggests that health protection for immigrants is only short term but that assimilation to natives' unhealthy diet and behaviors (e.g., smoking) occurs in the medium to long run, ultimately eroding immigrants' health advantage (Cho et al. 2004; Finch et al. 2001). The other explanation for the declining health protection from immigrant status is the exposure to racial discrimination and other negative social, economic, and environmental experiences that differentially affect immigrants (Carrasquillo et al. 2000; Finch and Vega 2003). Immigrants' exposure to such negative forces increases with their length of stay such that their health can be expected to deteriorate and converge with natives' health.

To the extent that poor health increases mortality risk, the unhealthy assimilation depicted in prior research should also erode immigrants' mortality advantage over time. Nevertheless, few researchers have investigated whether immigrants' mortality advantage dissipates as their length of stay extends. Instead, most studies have relied on intergenerational comparisons, finding lower mortality for first-generation immigrants than for their children and grandchildren (Elo et al. 2004; Hummer et al. 1999; Palloni and Arias 2004) and a shrinking immigrant mortality advantage over the native-born across generations (e.g., Eschbach et al. 2007). This line of research, however, does not directly address how immigrants' mortality advantage may change within their own generation. As an exception, Angel and colleagues (2010) addressed within-generation shifts in mortality hazards using age at migration as a proxy for the length of stay. They found that among Mexican-origin immigrants aged 65 or older, those who arrived after age 50 had lower mortality than those who arrived in childhood or midlife. But as we explain later, the findings based on age at migration do not necessarily imply unhealthy assimilation over the duration of residence because they can be influenced by age-based health selection.

Counterarguments and Methodological Challenges

Researchers have questioned the argument of unhealthy assimilation on conceptual and methodological levels. Conceptually, although immigrants face an initial disadvantage in health care access (Laroche 2000; Leclere et al. 1994; McDonald and Kennedy 2004), especially if they are undocumented (Hacker et al. 2015), they could experience changes over time in legal status, expansion of local ties, and improved knowledge of the destination language and resources. In turn, immigrants likely have increased receipt of preventative health checks, diagnoses, and medical treatments as their stay lengthens, which should widen their health or mortality advantage over the native-born. Immigrants are also likely to experience economic assimilation, which offsets their early disadvantages in income, employment, and living environments (Borjas 1995; Duleep and Regets 2002; Hu 2000; Schoeni 1997; Villarreal and Tamborini 2018; Zheng and Yu 2021). Considering the potential growth of immigrants' resources and access over time, some researchers have suggested that the finding of immigrants' shrinking health advantage may result from how prior studies measured health. Immigrants' improved health care access with longer residence may increase the diagnoses of preexisting conditions (e.g., Jasso et al. 2004; McDonald and Kennedy 2004), which could lead to worse self-rated health, an outcome variable widely used in studies about unhealthy assimilation (Cho et al. 2004; Hamilton et al. 2015). If, despite their self-perceptions, immigrants became increasingly healthier than natives with longer stays in the host country, then we should find that their mortality advantage—which is unaffected by their subjective views—persists and even expands with time.

Methodologically, most studies supporting the argument of unhealthy assimilation, which renders the competing hypothesis that immigrants' survival advantage will diminish with their time of stay, have relied on cross-sectional or pooled cross-sectional data (Antecol and Bedard 2006; Cho et al. 2004). Such studies have generally used retrospective information on YSI to measure duration of residence and test how immigrants' health varies across the values of this indicator. In any given survey year, however, the effect of duration of residence on health could capture the impact of compositional differences among immigrants who arrive in a particular year (i.e., cohort-of-arrival effect) and the impact of the age at which immigrants arrive (i.e., age-of-arrival effect) because duration is a function of both. The specific relationships can be expressed as follows:

Duration of residence = Year of survey – cohort of arrival;
(1)
Duration of residence = Age at survey – age of arrival.
(2)

Because both age and survey year are potentially relevant to individuals' health and should be controlled for (when data from multiple survey years are used), any attempt to simultaneously address duration of residence and cohort or age at arrival with cross-sectional or pooled cross-sectional data naturally suffers from identification problems.

Separating the effect of duration of stay from that of cohort of arrival is crucial because there are at least three reasons to expect differences in characteristics, including health endowment and mortality risk, among immigrant cohorts arriving in different periods. First, the health distribution in sending countries may change over time (Lu et al. 2017). For example, health endowment has generally improved across cohorts in less industrialized countries because of improvements in living standards, nutrition, and health care. As a result, the immigrant cohorts arriving in more recent decades may be heathier. Second, the migration selection process could shift. Because enhanced living standards in the sending countries increase the opportunity cost of immigration, especially for the relatively well-off, more recent immigrant cohorts may disproportionally consist of individuals from lower socioeconomic backgrounds. Third, changes in legal and social environments at destination, such as the implementation of anti-immigrant laws, may amplify (or reduce) immigration costs (Hamilton et al. 2015). Within the United States, the shifts in immigrants' destination states over time further add time-related variation in immigration costs (Massey 2008): states differ in their policies and treatments of immigrants. Changing costs are likely to alter the composition of incoming immigrants (e.g., by legal status or education) and accelerate or decelerate return migration rates, both of which can cause disparities in health and mortality risk across immigrant arrival cohorts.

Owing to the concern of confounding cohort-of-arrival effect, studies using data from multiple survey years have tried to control for this factor and survey year simultaneously and estimate the net effect of duration of residence at destination (e.g., Antecol and Bedard 2006). After cohort of arrival was controlled for, some researchers found no negative relationship between immigrants' length of stay and health for all immigrants (Lu et al. 2017) or among Black immigrants (Hamilton and Hummer 2011), whereas others reported downward health assimilation (Antecol and Bedard 2006; Cho et al. 2004; Hamilton et al. 2015). This line of research has also produced some perplexing cohort-of-arrival patterns. For example, despite the nutrition transition and obesity epidemic at origin and rising obesity and overweight prevalence rates among immigrants over time in the United States, recent Hispanic immigrants have lower body mass index than those who immigrated in 1980 or before (Antecol and Bedard 2006: tables 5 and 6). At the same time, though, recent immigrant cohorts are more likely to report poorer health, worse physical conditions, and more activity limitations than earlier ones (Antecol and Bedard 2006: tables 3 and 4). These conflicting findings cannot be explained by selection; it is unlikely that increasing selectivity over time filters out Hispanic immigrants with higher body mass index but does not remove those with poorer health. Even more puzzling, Hamilton and colleagues (2015: tables 3 and 4) used the same analytic strategy and found that recent Hispanic immigrants are less likely to report poor health than early immigrants. Although these discrepancies can potentially come from different data (NHIS data in Antecol and Bedard 2006; March Current Population Survey data in Hamilton et al. 2015), they might also result from the instability in the model estimates due to the collinearity among cohort of arrival, YSI, and survey year.

Relatively few cross-sectional studies have recognized that the duration-of-residence effect can also be confounded with the age-of-arrival effect. Younger immigrants are likely a less selective group than older immigrants. Whereas child immigrants tend to arrive via their parents, young adult immigrants move primarily for personal aspirations and job opportunities, the pursuit of which requires them to be relatively healthy. Even elderly immigrants, who tend to move for family unification, must be healthy enough to migrate. Consistent with this age-based health selection, previous studies found that at the same baseline age (e.g., age 50), immigrants who migrated during childhood or adolescence have worse health than those who migrated in young adulthood or later life (Choi 2012; Gubernskaya 2015). Because these studies also found the former to experience a slower health decline since the baseline age than the latter, it is unlikely that the longer duration of stay and greater extent of unhealthy assimilation or exposure to discrimination explain child immigrants' worse health at the baseline age. Access only to information on health patterns at the baseline age, as in cross-sectional analyses, would have led to erroneously taking the worse health for those who migrated at an earlier age as evidence for immigrants' declining health advantage. Alternatively, collinearity and identification problems would arise from an attempt to account simultaneously for age, age of arrival, and duration of stay in the models.

An Alternative Approach

The foregoing discussion explains the identification challenges in using (pooled) cross-sectional data to capture the variation in health by immigrants' duration of residence. Even if the identification problems can be solved, cross-sectional analyses do not directly observe how within-individual health conditions or mortality hazards change over time. A conceptually clearer and methodologically cleaner approach is to utilize longitudinal data and track both immigrants' and natives' health or mortality over real time. This approach can also bypass the two aforementioned identification problems inherent in (pooled) cross-sectional estimates.

Few studies have exploited longitudinal data to examine U.S. immigrant health trajectories. Gubernskaya (2015) used the 1992–2008 Health and Retirement Study to model self-rated health trajectories beginning at age 50. She found that foreign-born individuals report better health than native-born individuals at age 50 but that some foreign-born groups, such as Hispanics and those who migrated at older ages, experience steeper health declines since age 50 than the native-born. Lu and colleagues (2017) utilized the 1996, 2001, 2004, and 2008 panels of Survey of Income and Program Participation, with a follow-up period of two to four years for each panel. Contrary to Gubernskaya, they found that immigrants maintain their self-rated health advantage over natives during the short follow-up period. Lu and colleagues also showed that Latin American and Asian immigrants are particularly likely to sustain their health advantage.

Although these two studies represent substantial improvements over analyses with (pooled) cross-sectional data, their mixed results call for further investigation. Moreover, both studies relied on a subjective measure of health. As discussed earlier, immigrants' duration of stay may affect their perceived health status through improved health care access and screening. Gubernskaya's (2015) finding of greater declines in self-rated health among certain immigrant groups than among natives could reflect this change in perception. Although the Lu et al. (2017) study found that immigrants' self-rated health is largely stable, their follow-up period of two to four years might be too short for immigrants to experience improved health care access and heightened awareness of health problems. Self-rated health is also problematic because different racial/ethnic and nativity groups, who have different cultures and reference groups, may assess their health using diverse criteria (Finch et al. 2002; Kimbro et al. 2012).

Compared with self-rated health, mortality risk is far less ambiguous and is not subject to the same criticism. Taking advantage of surveys with mortality follow-up data, a handful of studies have investigated how elderly immigrants' subsequent mortality depends on their age at migration (Angel et al. 2010; Choi 2012). Those who migrated at older ages have lower subsequent mortality than those who migrated in childhood or midlife. Although this finding can be a joint product of an age-of-arrival effect and a duration effect, the fact that health behaviors barely explain mortality differences among those who migrated at varying ages suggests that the differences are more likely due to age-based migration selection than to unhealthy assimilation (Angel et al. 2010: table 2). In any case, no prior study of mortality risk directly tested whether immigrants' survival advantage over the native-born changes over real time.

To add evidence on the durability of health protection for immigrants, we utilize an unusually large data set containing a general adult population and a sizable number of immigrants. This data set tracks respondents' mortality status for a long period, allowing us to examine changes in immigrants' survival advantage and avoid the reporting bias inherent in subjective health measures. We avoid comparing subsequent mortality among immigrants with different ages of migration (Angel et al. 2010), which may capture both age-of-arrival and duration effects, or comparing natives and immigrants at each age (Gubernskaya 2015), which may confound the duration effect with life course patterns. Instead, we investigate mortality disparities over elapsed time (time since the survey interview) while accounting for age-related survival patterns. If unhealthy assimilation occurs, we should observe convergence in mortality risk between immigrants and natives over time regardless of immigrants' years in the United States before the survey.

Methods

Data and Participants

Our analysis uses the IPUMS NHIS from 1992–2009 with linked mortality records through 2011 (Blewett et al. 2018). The NHIS is an annual, cross-sectional, multistage probability sample survey of the noninstitutionalized civilian U.S. population conducted by the National Center for Health Statistics (NCHS). The NHIS began to measure nativity in 1989, but it lacked detailed information about Asian heritages until 1992. We thus restrict the sample to data from 1992 onward. Unlike other national surveys on immigration, which have small samples or focus on a specific immigrant group, the NHIS contains a large sample and immigrants of diverse origins. We can therefore compare immigrants with different ethnoracial identities. The survey data are linked to death records in the National Death Index (NDI) through probabilistic record-matching methods, which use 13 criteria to ascertain the vital status of each respondent.1 At the time of data analysis, death records at quarter-year intervals from the NHIS 1992–2009 surveys were available through the end of 2011.

We pool the NHIS respondents from 1992 to 2009 and restrict the sample to individuals aged 26–85 at the time of the survey.2 Setting the lower bound of age at 26 ensures that most respondents will have finished their education. Within this sample, 92% have eligible mortality records (n = 922,193).3 Our analysis compares four foreign-born populations—non-Hispanic Whites, non-Hispanic Blacks, non-Hispanic Asians, and Hispanics—with their native-born coethnics. Non-Hispanic Asians include Chinese, Filipinos, and Asian Indians. We exclude other Asian ethnic groups because they were very small or disproportionately foreign- or native-born in the NHIS data. Hispanics include those originating from Mexico and other Latin American countries. We omit individuals of other racial groups (3.7% of the sample with eligible mortality records; n = 34,145) and those with missing data on covariates (1.0%; n = 9,419). The analytic sample consists of 748,106 native-born individuals, among whom 590,833 are White, 105,525 are Black, 2,863 are Asian, and 48,885 are Hispanic; 130,523 are foreign-born individuals, among whom 28,467 are White, 9,803 are Black, 15,196 are Asian, and 77,057 are Hispanic. Although only 1% of the eligible sample has missing values on the covariates, we conducted a separate analysis that incorporated those with invalid values (by adding an “unknown” category to the covariates). The results were virtually unchanged.

To compare immigrants' mortality risk to native-born people's over elapsed time, we reshape the data set to a person–year format, which starts from the year of the interview and ends in the year of the respondent's death or 2011, whichever is earlier. The NHIS supplies information on the time of birth, interview, and death by quarter-year, allowing us to compute the time exposed to mortality risk (i.e., elapsed time) since each respondent was interviewed. We measure the duration of exposure by year instead of quarter-year to avoid generating an unnecessarily large data set that is computationally difficult to handle. For the respondents who died during the observed period, the duration of exposure is a time-varying measure of the number of years from their interview to each subsequent calendar year until their death. For surviving respondents, we calculate their time-varying durations through 2011, the last time point with mortality status in our data. After the transformation to the person–year format, our analytic sample contains 9,870,755 observations.

Measures

The outcome of interest is mortality status. By December 31, 2011, 125,531 of the NHIS respondents had died; 115,345 of them were natives (15.4% of natives), and 10,186 were immigrants (7.8% of immigrants). Because the NHIS data are linked to mortality records, we can determine the exact elapsed time since the survey year in which a death occurred. We code mortality status as 1 if a respondent died in that year and as 0 otherwise.

We measure the main predictor, nativity status, in three ways. The first and simplest measure is a binary indicator distinguishing native-born from foreign-born individuals on the basis of self-reports. The second measure similarly includes a category for natives but divides foreign-born respondents into four groups according to their YSI, an indicator often used in prior research to infer the process of unhealthy assimilation. The NHIS asked the foreign-born to select whether they had been in the United States for 0–4, 5–9, 10–15, or more than 15 years. Because the survey did not distinguish among those who had immigrated more than 15 years ago, we cannot create a time-varying measure to indicate precisely an immigrant's length of U.S. stay. Therefore, our analysis instead focuses on how respondents' mortality hazards change with the time elapsed since the survey. At the same time, we compare the time-based shifts among different YSI groups to gauge the extent to which the group differences reflect the process of unhealthy assimilation. Our sample contains 14,263 individuals with 0–4 YSI, 18,870 with 5–9 YSI, 19,236 with 10–14 YSI, 75,725 with 15+ YSI, and 2,429 with undetermined YSI (1.9% of immigrants). We retain immigrants with undetermined YSI in the analysis to maximize the sample size but exclude them from the models specifically addressing differences between YSI groups.

Because immigrants of different ethnoracial identities may vary in their selectivity and legal-status composition, which have implications for their health and mortality, we also investigate whether changes in immigrants' survival advantage are contingent on their race or ethnicity. Thus, we construct a third variable differentiating immigrants by race/ethnicity, with five categories: natives, non-Hispanic White immigrants, non-Hispanic Black immigrants, non-Hispanic Asian immigrants, and Hispanic immigrants. In some models, we also divide the native-born into the same four ethnoracial groups to compare immigrants with their coethnics.

To account for differences in characteristics between native-born and foreign-born respondents, we introduce gender, age at the survey, education, poverty status, and marital status in the models.4 The NHIS recorded gender as binary (women vs. men), so we measure it accordingly. Age at the survey is centered on the grand mean. Education is categorized as less than high school, high school diploma, some college, and college degree or more. We use three categories to indicate poverty status: above the U.S. Census Bureau's poverty threshold, below the poverty threshold, and unknown poverty status.5 Marital status is categorized as married, widowed, divorced, separated, and never-married.

Analytic Strategy

We employ discrete-time survival analysis (logistic regression). We treat elapsed time, ranging from 0 to 19 years, as 20 duration interval–specific dummy variables or as a continuous variable. The overall findings are similar either way, although a continuous variable for elapsed time produces a smoother trend. For parsimony, we present the findings from models using a continuous elapsed-time variable in the main text; those based on the 20 elapsed-time dummy variables are shown in the online appendix. A logistic regression coefficient indicates the logarithm of the odds of a given group (e.g., immigrants) experiencing the outcome over the odds of the reference group (e.g., natives), thus representing the relative risk of the dependent variable associated with the covariate. This regression coefficient can be referred to as the log-odds coefficient or log odds ratio. The key variables of interest in the survival analysis are the interactions between the three measures of nativity status and elapsed time. With natives as the reference group, positive coefficients for the interactions indicate that the immigrant survival advantage diminishes over time on a relative scale, whereas negative coefficients indicate an extended health advantage.6 To account for possible confounding life course mortality patterns and potential native–immigrant differences in such patterns, we adjust for age at the survey and the interaction between nativity status and age at the survey. We add other individual characteristics (e.g., gender, race/ethnicity, education, poverty status, and marital status) to consider nativity differences in these compositions. We also include dummy variables for survey year to control for temporal trends in mortality.

In addition to presenting results in log-odds coefficients, we also calculate predicted hazard probabilities over elapsed time from the survival analysis. In the calculation, all the categorical variables are set to be the reference groups, and continuous variables are at the grand means. In the case of predicted hazard probabilities, a widening gap (difference) in the probabilities between the immigrants and natives over elapsed time indicates an extended immigrant survival advantage based on an absolute scale, whereas a narrowing gap indicates a diminished immigrant health advantage.7 The patterns based on log odds ratios indicate the chances of dying for immigrants relative to natives, and the patterns based on differences in predicted hazards demonstrate the absolute gap in mortality risk by nativity; these two sets of patterns may not always be consistent. The epidemiological literature has debated and discussed the choice of using the relative or absolute scale to interpret an interaction between covariates (here, nativity and elapsed time) since the 1970s (Brown 1986; Rothman et al. 1986; Walter and Holford 1978). Some social scientists have called for using the absolute scale to test temporal changes in health inequalities (Mehta et al. 2019). Given that the appropriate scale to examine immigrants' health advantage is not clear-cut from the literature, we follow prior studies in presenting findings on both scales (Harper and Lynch 2005; Vandenbroucke et al. 2007; VanderWeele and Knol 2014).

Results

Table 1 shows the nativity, race, and ethnicity compositions of the sample and other basic descriptive statistics. Among immigrants, 2.9% of the 0–4 and the 5–9 YSI groups had died by the end of 2011, compared with 3.9% and 10.9% in the 10–14 and 15+ YSI groups, respectively. Among the native-born, 15.4% died between the survey year and 2011. These numbers appear to suggest that immigrants have a survival advantage and that this advantage diminishes with the duration of residence. However, YSI group differences could also reflect age effects (given that the mean age at survey increases from 37.9 to 50.5 across the four YSI groups) and the cohort of arrival. A better way to identify the duration of residence effect is by comparing mortality risk among immigrants relative to the native-born over elapsed time.

Table 1

Descriptive statistics

Years Since Immigration
Native-bornForeign-born0–45–910–1415+
Number of Observations 748,106 130,523 14,263 18,870 19,236 75,725 
Number of Deaths 115,345 10,186 416 543 754 8,277 
% of Deaths 15.4 7.8 2.9 2.9 3.9 10.9 
Men (%) 47.0 47.6 47.3 47.2 48.5 47.4 
Race/Ethnicity (%)       
 Non-Hispanic White 79.0 21.8 19.3 14.6 12.6 26.6 
 Non-Hispanic Black 14.1 7.5 7.1 8.3 8.2 7.2 
 Non-Hispanic Asian 0.4 11.6 16.9 14.2 13.5 9.6 
 Hispanic 6.5 59.0 56.7 62.9 65.7 56.6 
Age at Survey 49.6 45.6 37.9 38.1 39.5 50.5 
Education (%)       
 Less than high school 15.0 37.9 36.3 40.2 42.6 36.4 
 High school diploma 35.2 24.3 21.2 23.3 23.9 25.1 
 Any college 25.7 16.5 12.3 14.2 14.3 18.6 
 College degree+ 24.1 21.2 30.1 22.3 19.3 19.9 
Poverty Status (%)       
 Above threshold 76.2 65.5 55.1 61.8 64.3 69.5 
 Below threshold 7.4 15.6 23.8 20.5 18.4 12.2 
 Unknown 16.4 19.0 21.1 17.7 17.3 18.3 
Marital Status (%)       
 Married 65.9 71.5 72.6 73.2 74.3 70.1 
 Widowed 8.2 5.7 2.8 2.5 2.8 7.8 
 Divorced 11.3 7.0 3.7 4.5 5.2 8.7 
 Separated 2.5 3.8 3.3 4.1 4.6 3.7 
 Never married 12.3 12.0 17.7 15.7 13.0 9.7 
Years Since Immigration
Native-bornForeign-born0–45–910–1415+
Number of Observations 748,106 130,523 14,263 18,870 19,236 75,725 
Number of Deaths 115,345 10,186 416 543 754 8,277 
% of Deaths 15.4 7.8 2.9 2.9 3.9 10.9 
Men (%) 47.0 47.6 47.3 47.2 48.5 47.4 
Race/Ethnicity (%)       
 Non-Hispanic White 79.0 21.8 19.3 14.6 12.6 26.6 
 Non-Hispanic Black 14.1 7.5 7.1 8.3 8.2 7.2 
 Non-Hispanic Asian 0.4 11.6 16.9 14.2 13.5 9.6 
 Hispanic 6.5 59.0 56.7 62.9 65.7 56.6 
Age at Survey 49.6 45.6 37.9 38.1 39.5 50.5 
Education (%)       
 Less than high school 15.0 37.9 36.3 40.2 42.6 36.4 
 High school diploma 35.2 24.3 21.2 23.3 23.9 25.1 
 Any college 25.7 16.5 12.3 14.2 14.3 18.6 
 College degree+ 24.1 21.2 30.1 22.3 19.3 19.9 
Poverty Status (%)       
 Above threshold 76.2 65.5 55.1 61.8 64.3 69.5 
 Below threshold 7.4 15.6 23.8 20.5 18.4 12.2 
 Unknown 16.4 19.0 21.1 17.7 17.3 18.3 
Marital Status (%)       
 Married 65.9 71.5 72.6 73.2 74.3 70.1 
 Widowed 8.2 5.7 2.8 2.5 2.8 7.8 
 Divorced 11.3 7.0 3.7 4.5 5.2 8.7 
 Separated 2.5 3.8 3.3 4.1 4.6 3.7 
 Never married 12.3 12.0 17.7 15.7 13.0 9.7 

Immigrant Survival Advantage Over Elapsed Time

Table 2 presents the log-odds coefficients from the discrete-time survival analysis using alternative indicators of nativity status. In Model 1, being foreign-born is associated with a 19% [(1 – exp(−0.215)) × 100] reduction in the odds of death at Time 0 (i.e., year of the survey). This survival advantage amplified with each elapsed year, although odds of death increased with time for both natives and immigrants. Model 2 includes the interaction between being foreign-born and age at the survey. The nonsignificant coefficient (0.001) suggests that foreign-born individuals' survival advantage persisted over the life course. More importantly, adding this interaction barely alters the coefficient estimate of the interaction between being foreign-born and time. Thus, health disparities by nativity at each age cannot explain immigrants' increasing survival advantage over time.

Table 2

Coefficient estimates from discrete-time survival analysis on immigrant survival advantage

Model 1Model 2Model 3
Nativity       
 Foreign-born −0.215*** (0.020) −0.215*** (0.027)   
Time 0.098*** (0.001) 0.098*** (0.001) 0.098*** (0.001) 
 Foreign-born × time −0.011*** (0.002) −0.011*** (0.002)   
Years Since Immigration (YSI) (ref. = native-born)       
 0–4     −0.440*** (0.097) 
 5–9     −0.291*** (0.084) 
 10–14     −0.115 (0.072) 
 15+     −0.231*** (0.032) 
 0–4 × time     −0.017 (0.010) 
 5–9 × time     −0.028** (0.009) 
 10–14 × time     −0.019* (0.007) 
 15+ × time     −0.005* (0.002) 
Age at Survey 0.091*** (0.001) 0.091*** (0.001) 0.091*** (0.001) 
 Foreign-born × age at survey   0.001 (0.001)   
 0–4 YSI × age at survey     −0.014*** (0.003) 
 5–9 YSI × age at survey     −0.009*** (0.003) 
 10–14 YSI × age at survey     −0.011*** (0.002) 
 15+ YSI × age at survey     0.001 (0.001) 
Constant −6.121*** (0.014) −6.121*** (0.014) −6.122*** (0.014) 
N 9,870,755  9,870,755  9,844,455  
Likelihood Ratio Test 244,842.99  244,842.99  244,626.53  
Pseudo-R2 .182  .182  .182  
Model 1Model 2Model 3
Nativity       
 Foreign-born −0.215*** (0.020) −0.215*** (0.027)   
Time 0.098*** (0.001) 0.098*** (0.001) 0.098*** (0.001) 
 Foreign-born × time −0.011*** (0.002) −0.011*** (0.002)   
Years Since Immigration (YSI) (ref. = native-born)       
 0–4     −0.440*** (0.097) 
 5–9     −0.291*** (0.084) 
 10–14     −0.115 (0.072) 
 15+     −0.231*** (0.032) 
 0–4 × time     −0.017 (0.010) 
 5–9 × time     −0.028** (0.009) 
 10–14 × time     −0.019* (0.007) 
 15+ × time     −0.005* (0.002) 
Age at Survey 0.091*** (0.001) 0.091*** (0.001) 0.091*** (0.001) 
 Foreign-born × age at survey   0.001 (0.001)   
 0–4 YSI × age at survey     −0.014*** (0.003) 
 5–9 YSI × age at survey     −0.009*** (0.003) 
 10–14 YSI × age at survey     −0.011*** (0.002) 
 15+ YSI × age at survey     0.001 (0.001) 
Constant −6.121*** (0.014) −6.121*** (0.014) −6.122*** (0.014) 
N 9,870,755  9,870,755  9,844,455  
Likelihood Ratio Test 244,842.99  244,842.99  244,626.53  
Pseudo-R2 .182  .182  .182  

Notes: All models include race/ethnicity, gender, education, poverty status, marital status, and dummy variables for survey year. Standard errors are shown in parentheses.

*

p < .05; **p < .01; ***p < .001

Because odds are not straightforward to interpret, we convert odds to hazard probabilities. Table 3 and panel a of Figure 1 display these probabilities over elapsed time by nativity status. The probability of death was 0.04 percentage points lower for foreign-born respondents than for native-born respondents at the beginning of the observation period (year of survey) and 0.47 percentage points lower 19 years later. Table 3 also shows the immigrant–native ratios of hazard probabilities for these two time points. Similar to the log odds ratios in Table 2, these ratios indicate immigrant–native mortality disparities on a relative scale, although hazard probability ratios are more intuitive. The hazard probability for foreign-born individuals was approximately 19% lower in the year of the survey and 34% lower 19 years later. Thus, regardless of whether we rely on differences (an absolute measure) or ratios (a relative measure) of hazard probabilities, foreign-born individuals' survival advantage persisted and expanded throughout the 20 years of observation.

Fig. 1

Predicted hazard probabilities of mortality over elapsed time since the survey, by nativity and years since immigration (YSI)

Fig. 1

Predicted hazard probabilities of mortality over elapsed time since the survey, by nativity and years since immigration (YSI)

Close modal
Table 3

Predicted hazard probabilities of mortality

Table 2, Model 2/
Figure 1, Panel a
Table 2, Model 3/
Figure 1, Panel b
Table 4, Model 1/
Figure 2 
Elapsed TimeNative-bornForeign-born0–4 YSI5–9 YSI10–14 YSI15+ YSIWhiteBlackAsianHispanic
0.0022 0.0018 0.0014 0.0016 0.0020 0.0017 0.0011 0.0015 0.0017 0.0018 
0.0024 0.0019 0.0015 0.0018 0.0021 0.0019 0.0013 0.0016 0.0018 0.0019 
0.0027 0.0021 0.0017 0.0019 0.0023 0.0021 0.0014 0.0018 0.0020 0.0021 
0.0029 0.0023 0.0018 0.0020 0.0025 0.0023 0.0016 0.0019 0.0021 0.0023 
0.0032 0.0025 0.0020 0.0022 0.0027 0.0025 0.0018 0.0021 0.0023 0.0024 
0.0036 0.0027 0.0021 0.0023 0.0029 0.0028 0.0020 0.0023 0.0025 0.0026 
0.0039 0.0030 0.0023 0.0025 0.0031 0.0030 0.0022 0.0025 0.0027 0.0029 
0.0044 0.0033 0.0025 0.0027 0.0034 0.0033 0.0024 0.0027 0.0029 0.0031 
0.0048 0.0036 0.0027 0.0029 0.0037 0.0036 0.0027 0.0030 0.0031 0.0033 
0.0053 0.0039 0.0029 0.0031 0.0040 0.0040 0.0030 0.0032 0.0034 0.0036 
10 0.0058 0.0042 0.0032 0.0033 0.0043 0.0044 0.0033 0.0035 0.0036 0.0039 
11 0.0064 0.0046 0.0035 0.0035 0.0047 0.0048 0.0037 0.0038 0.0039 0.0042 
12 0.0071 0.0051 0.0038 0.0038 0.0051 0.0053 0.0041 0.0042 0.0042 0.0046 
13 0.0078 0.0055 0.0041 0.0041 0.0055 0.0058 0.0046 0.0046 0.0046 0.0049 
14 0.0086 0.0060 0.0044 0.0044 0.0059 0.0064 0.0051 0.0050 0.0049 0.0053 
15 0.0095 0.0066 0.0048 0.0047 0.0064 0.0070 0.0056 0.0054 0.0053 0.0057 
16 0.0105 0.0072 0.0052 0.0050 0.0070 0.0076 0.0063 0.0059 0.0057 0.0062 
17 0.0116 0.0078 0.0056 0.0054 0.0075 0.0084 0.0070 0.0064 0.0062 0.0067 
18 0.0127 0.0085 0.0061 0.0058 0.0081 0.0092 0.0078 0.0070 0.0067 0.0072 
19 0.0140 0.0093 0.0066 0.0062 0.0088 0.0101 0.0086 0.0076 0.0072 0.0078 
Immigrant–Native Gap at Time 0 
 Difference  −0.0004 −0.0008 −0.0006 −0.0002 −0.0005 −0.0011 −0.0007 −0.0005 −0.0004 
 Ratio (%)  81 64 75 89 79 52 68 77 82 
Immigrant–Native Gap at Time 19 
 Difference  −0.0047 −0.0074 −0.0079 −0.0052 −0.0039 −0.0055 −0.0065 −0.0069 −0.0063 
 Ratio (%)  66 47 44 63 72 61 54 51 56 
Table 2, Model 2/
Figure 1, Panel a
Table 2, Model 3/
Figure 1, Panel b
Table 4, Model 1/
Figure 2 
Elapsed TimeNative-bornForeign-born0–4 YSI5–9 YSI10–14 YSI15+ YSIWhiteBlackAsianHispanic
0.0022 0.0018 0.0014 0.0016 0.0020 0.0017 0.0011 0.0015 0.0017 0.0018 
0.0024 0.0019 0.0015 0.0018 0.0021 0.0019 0.0013 0.0016 0.0018 0.0019 
0.0027 0.0021 0.0017 0.0019 0.0023 0.0021 0.0014 0.0018 0.0020 0.0021 
0.0029 0.0023 0.0018 0.0020 0.0025 0.0023 0.0016 0.0019 0.0021 0.0023 
0.0032 0.0025 0.0020 0.0022 0.0027 0.0025 0.0018 0.0021 0.0023 0.0024 
0.0036 0.0027 0.0021 0.0023 0.0029 0.0028 0.0020 0.0023 0.0025 0.0026 
0.0039 0.0030 0.0023 0.0025 0.0031 0.0030 0.0022 0.0025 0.0027 0.0029 
0.0044 0.0033 0.0025 0.0027 0.0034 0.0033 0.0024 0.0027 0.0029 0.0031 
0.0048 0.0036 0.0027 0.0029 0.0037 0.0036 0.0027 0.0030 0.0031 0.0033 
0.0053 0.0039 0.0029 0.0031 0.0040 0.0040 0.0030 0.0032 0.0034 0.0036 
10 0.0058 0.0042 0.0032 0.0033 0.0043 0.0044 0.0033 0.0035 0.0036 0.0039 
11 0.0064 0.0046 0.0035 0.0035 0.0047 0.0048 0.0037 0.0038 0.0039 0.0042 
12 0.0071 0.0051 0.0038 0.0038 0.0051 0.0053 0.0041 0.0042 0.0042 0.0046 
13 0.0078 0.0055 0.0041 0.0041 0.0055 0.0058 0.0046 0.0046 0.0046 0.0049 
14 0.0086 0.0060 0.0044 0.0044 0.0059 0.0064 0.0051 0.0050 0.0049 0.0053 
15 0.0095 0.0066 0.0048 0.0047 0.0064 0.0070 0.0056 0.0054 0.0053 0.0057 
16 0.0105 0.0072 0.0052 0.0050 0.0070 0.0076 0.0063 0.0059 0.0057 0.0062 
17 0.0116 0.0078 0.0056 0.0054 0.0075 0.0084 0.0070 0.0064 0.0062 0.0067 
18 0.0127 0.0085 0.0061 0.0058 0.0081 0.0092 0.0078 0.0070 0.0067 0.0072 
19 0.0140 0.0093 0.0066 0.0062 0.0088 0.0101 0.0086 0.0076 0.0072 0.0078 
Immigrant–Native Gap at Time 0 
 Difference  −0.0004 −0.0008 −0.0006 −0.0002 −0.0005 −0.0011 −0.0007 −0.0005 −0.0004 
 Ratio (%)  81 64 75 89 79 52 68 77 82 
Immigrant–Native Gap at Time 19 
 Difference  −0.0047 −0.0074 −0.0079 −0.0052 −0.0039 −0.0055 −0.0065 −0.0069 −0.0063 
 Ratio (%)  66 47 44 63 72 61 54 51 56 

Model 3 of Table 2 compares YSI groups with the native-born. All the YSI groups had lower odds of death than natives, and their survival advantages persisted or grew over elapsed time. Panel b of Figure 1 illustrates the predicted hazard probabilities over elapsed time. This panel similarly shows that the survival advantages of all four YSI groups, especially the 0–4 and 5–9 YSI groups, over natives increased over time. For example, the 0–4 YSI group's mortality hazard probability was 0.08 percentage points lower than that of the native-born in the year of the survey and 0.74 percentage points lower after 19 years (Table 3). The 15+ YSI group's hazard probability also changed from 0.05 percentage points lower to 0.39 percentage points lower than natives' during the 20-year period. If we instead look at the ratio of hazard probabilities, the pattern is similar: the 15+ YSI group's hazard probability has changed from 79% to 72% of natives' during the 20 years of observation.

Although the mortality hazard probabilities for the 10–14 and 15+ YSI groups are higher than those for the 0–4 and 5–9 YSI groups, the differences do not necessarily indicate unhealthy assimilation. If the mortality gap between, say, the 10–14 YSI group and the 0–4 YSI group results from the former's unhealthy assimilation during their extra 10 years in the country, the survival advantage of the 0–4 YSI group compared with the native-born should have shrunk after their U.S. stays of 10 or more years. By the same token, the survival advantages of the 10–14 and 15+ YSI groups should have continued to shrink starting from the year of the survey. Rather than unhealthy assimilation, the differing hazard probabilities among the YSI groups most likely reflect influences of factors other than duration of residence, such as the age and cohort of arrival. Because the differences in mortality hazards are not linearly correlated with YSI, we suspect that the cohort-of-arrival effect may contribute more to the YSI-related pattern. Those in the YSI groups with U.S. arrival in earlier periods might have had worse health than those arriving later.

Racial/Ethnic and Gender Heterogeneities

Table 4 presents the racial/ethnic and gender heterogeneities in the immigrant survival advantage over natives. According to Model 1, White immigrants had lower log odds of death than natives and other immigrants in the year of the survey. However, their survival advantage compared with natives narrowed over elapsed time, although this difference is not statistically significant. In contrast, Black, Asian, and Hispanic immigrants' survival advantages over natives remained or grew over time. Figure 2 displays the predicted hazard probabilities from Model 1. The figure shows that the gap in hazard probabilities between natives and immigrants—an absolute measure of immigrant survival advantage—widened for all immigrant groups. The difference between White immigrants and natives increased from 0.11 percentage points to 0.55 percentage points during the 20 years, and the differences between other immigrant groups and natives grew more (Table 3). However, the ratio of hazard probabilities—a relative measure—suggests that the advantage for White immigrants shrank slightly, with the hazard probability increasing from 52% to 61% of that of the native-born (Table 3). This is not the case for other immigrant groups, whose survival advantages were amplified even with ratios of hazard probabilities. Note that over-time comparisons using a relative scale based on log odds ratios or hazard probability ratios could be less meaningful than those on an absolute scale. When the hazard probability at baseline is substantially lower than in subsequent years, the change over time can make a small difference on the absolute scale while being artificially large on the relative scale (Mehta et al. 2019).

Fig. 2

Predicted hazard probabilities of mortality over elapsed time, by nativity and race/ethnicity

Fig. 2

Predicted hazard probabilities of mortality over elapsed time, by nativity and race/ethnicity

Close modal
Table 4

Coefficient estimates from discrete-time survival analysis on immigrant survival advantage compared with the native-born population, by race/ethnicity and gender

Model 1: AllModel 2: MenModel 3: Women
Immigrant Racial/Ethnic Group (ref. = native-born) 
 White −0.653*** (0.052) −0.448*** (0.067) −0.901*** (0.081) 
 Black −0.389*** (0.095) −0.288* (0.129) −0.484*** (0.141) 
 Asian −0.266*** (0.080) −0.209* (0.104) −0.359** (0.125) 
 Hispanic −0.204*** (0.032) −0.231*** (0.043) −0.173*** (0.049) 
Time 0.098*** (0.001) 0.091*** (0.001) 0.106*** (0.001) 
 White × time 0.008 (0.004) −0.003 (0.005) 0.019** (0.005) 
 Black × time −0.012 (0.009) −0.033* (0.013) 0.007 (0.012) 
 Asian × time −0.022** (0.007) −0.024* (0.010) −0.018 (0.011) 
 Hispanic × time −0.021*** (0.003) −0.017*** (0.005) −0.024*** (0.005) 
Age at Survey 0.091*** (0.000) 0.091*** (0.000) 0.091*** (0.000) 
 White × age at survey 0.014*** (0.001) 0.009*** (0.002) 0.020*** (0.002) 
 Black × age at survey −0.003 (0.003) 0.000 (0.004) −0.007 (0.004) 
 Asian × age at survey −0.011*** (0.003) −0.012*** (0.003) −0.009* (0.004) 
 Hispanic × age at survey −0.006*** (0.001) −0.007*** (0.001) −0.006*** (0.001) 
Constant −6.117*** (0.014) −5.552*** (0.019) −6.188*** (0.020) 
N 9,870,755  4,615,448  5,255,307  
Likelihood Ratio Test 244,729.41  115,150.24  129,219.60  
Pseudo-R2 .182  .172  .191  
Model 1: AllModel 2: MenModel 3: Women
Immigrant Racial/Ethnic Group (ref. = native-born) 
 White −0.653*** (0.052) −0.448*** (0.067) −0.901*** (0.081) 
 Black −0.389*** (0.095) −0.288* (0.129) −0.484*** (0.141) 
 Asian −0.266*** (0.080) −0.209* (0.104) −0.359** (0.125) 
 Hispanic −0.204*** (0.032) −0.231*** (0.043) −0.173*** (0.049) 
Time 0.098*** (0.001) 0.091*** (0.001) 0.106*** (0.001) 
 White × time 0.008 (0.004) −0.003 (0.005) 0.019** (0.005) 
 Black × time −0.012 (0.009) −0.033* (0.013) 0.007 (0.012) 
 Asian × time −0.022** (0.007) −0.024* (0.010) −0.018 (0.011) 
 Hispanic × time −0.021*** (0.003) −0.017*** (0.005) −0.024*** (0.005) 
Age at Survey 0.091*** (0.000) 0.091*** (0.000) 0.091*** (0.000) 
 White × age at survey 0.014*** (0.001) 0.009*** (0.002) 0.020*** (0.002) 
 Black × age at survey −0.003 (0.003) 0.000 (0.004) −0.007 (0.004) 
 Asian × age at survey −0.011*** (0.003) −0.012*** (0.003) −0.009* (0.004) 
 Hispanic × age at survey −0.006*** (0.001) −0.007*** (0.001) −0.006*** (0.001) 
Constant −6.117*** (0.014) −5.552*** (0.019) −6.188*** (0.020) 
N 9,870,755  4,615,448  5,255,307  
Likelihood Ratio Test 244,729.41  115,150.24  129,219.60  
Pseudo-R2 .182  .172  .191  

Notes: All models include education, poverty status, marital status, and dummy variables for survey year. Standard errors are shown in parentheses.

*

p < .05; **p < .01; ***p < .001

Models 2 and 3 of Table 4 present the results by gender and race/ethnicity. From the log odds estimates, immigrants' survival advantage expanded over time for all subgroups except White and Black women, for whom it significantly or nonsignificantly narrowed. Again, though, it is more important to examine the difference in hazard probabilities, an absolute measure. Figure 3 presents the corresponding graphs for hazard probabilities. The figure shows that in the absolute sense, the immigrant survival advantage over natives amplified for all subgroups, especially for Black and Asian men and Asian and Hispanic women. Compared with other immigrants, White male immigrants have the smallest increase in survival advantage.

Fig. 3

Predicted hazard probabilities of mortality over elapsed time among immigrant racial/ethnic groups and the native-born population, by gender

Fig. 3

Predicted hazard probabilities of mortality over elapsed time among immigrant racial/ethnic groups and the native-born population, by gender

Close modal

Table 5 compares the mortality advantages of immigrants relative to their native-born coethnics by gender. The overall findings are consistent with those in Table 4, although the significance levels vary, perhaps because of the small sample size of some native-born coethnics (e.g., Asians). The coefficients for the three-way interactions of nativity, time, and race/ethnicity in Model 1 suggest that the magnitude by which the immigrant survival advantage grew with time could be greater among Black and Asian men. Among women, White and Black immigrants' survival advantages over their native-born counterparts hardly changed over elapsed time; Asian and Hispanic immigrants' advantages strengthened, although the coefficient is nonsignificant for Asian women. The predicted hazard probabilities calculated from models in Table 5 (not presented here) reveal similar patterns.

Table 5

Coefficient estimates from discrete-time survival analysis on immigrant survival advantage by race/ethnicity and gender compared with native-born coethnics

Model 1: MenModel 2: Women
Nativity     
 Foreign-born −0.134** (0.050) −0.289*** (0.053) 
Race/Ethnicity (ref. = White)     
 Black 0.192*** (0.022) 0.229*** (0.021) 
 Asian −0.523** (0.203) −0.328 (0.205) 
 Hispanic −0.088* (0.038) −0.177*** (0.042) 
 Foreign-born × Black −0.273* (0.115) −0.564*** (0.125) 
 Foreign-born × Asian 0.287 (0.224) 0.065 (0.231) 
 Foreign-born × Hispanic −0.063 (0.067) 0.167** (0.069) 
Time 0.093*** (0.001) 0.109*** (0.001) 
 Foreign-born × time −0.013** (0.005) 0.001 (0.005) 
 Black × time −0.008*** (0.003) −0.017*** (0.002) 
 Asian × time 0.005 (0.023) 0.010 (0.023) 
 Hispanic × time −0.007 (0.004) −0.003 (0.005) 
 Foreign-born × time × Black −0.015 (0.014) 0.024 (0.013) 
 Foreign-born × time × Asian −0.013 (0.026) −0.027 (0.026) 
 Foreign-born × time × Hispanic 0.005 (0.008) −0.021** (0.008) 
Age at Survey 0.091*** (0.000) 0.091*** (0.000) 
 Foreign-born × age at survey −0.002* (0.001) 0.002 (0.001) 
Constant −5.575*** (0.019) −6.213*** (0.020) 
N 4,615,448  5,255,307  
Likelihood Ratio Test 115,293.09  129,328.99  
Pseudo-R2 .173  .191  
Model 1: MenModel 2: Women
Nativity     
 Foreign-born −0.134** (0.050) −0.289*** (0.053) 
Race/Ethnicity (ref. = White)     
 Black 0.192*** (0.022) 0.229*** (0.021) 
 Asian −0.523** (0.203) −0.328 (0.205) 
 Hispanic −0.088* (0.038) −0.177*** (0.042) 
 Foreign-born × Black −0.273* (0.115) −0.564*** (0.125) 
 Foreign-born × Asian 0.287 (0.224) 0.065 (0.231) 
 Foreign-born × Hispanic −0.063 (0.067) 0.167** (0.069) 
Time 0.093*** (0.001) 0.109*** (0.001) 
 Foreign-born × time −0.013** (0.005) 0.001 (0.005) 
 Black × time −0.008*** (0.003) −0.017*** (0.002) 
 Asian × time 0.005 (0.023) 0.010 (0.023) 
 Hispanic × time −0.007 (0.004) −0.003 (0.005) 
 Foreign-born × time × Black −0.015 (0.014) 0.024 (0.013) 
 Foreign-born × time × Asian −0.013 (0.026) −0.027 (0.026) 
 Foreign-born × time × Hispanic 0.005 (0.008) −0.021** (0.008) 
Age at Survey 0.091*** (0.000) 0.091*** (0.000) 
 Foreign-born × age at survey −0.002* (0.001) 0.002 (0.001) 
Constant −5.575*** (0.019) −6.213*** (0.020) 
N 4,615,448  5,255,307  
Likelihood Ratio Test 115,293.09  129,328.99  
Pseudo-R2 .173  .191  

Notes: All models include education, poverty status, marital status, and dummy variables for survey year. Standard errors are shown in parentheses.

*

p < .05; **p < .01; ***p < .001

Overall, our results indicate that on the absolute scale, the immigrant survival advantage over natives increased over the observation period for all gender and racial groups, especially Black men, Asian men and women, and Hispanic women. Even on the relative scale, the advantage persisted or magnified for most immigrant subgroups, especially compared with their native-born coethnics. In general, the over-time increase in the survival advantage was more pronounced for non-White immigrants than for White immigrants, especially among men. Perhaps non-White immigrants, who tend to be from less developed countries, needed to overcome more obstacles to immigrate than White immigrants. The former may therefore be more selected on health or health behaviors, leading to their greater survival advantages. This differential health selection might be more likely for men than women: men more commonly immigrate for work, which requires relatively robust health.

Robustness Checks

To be certain about the persistence of immigrant survival advantage, we first check the robustness of our findings by using different model specifications. Model 1 in Table A1 (online appendix) measures elapsed time with a set of dummy variables. The overall results are very similar to Model 1 in Table 2. Model 2 uses attained age (time-varying years of age in each year since the survey) as the time metric and includes the interaction between attained age and foreign-born status to account for nativity disparities in life course mortality patterns. Changes in immigrants' survival advantage over time are still indicated by the interactions between elapsed time and foreign-born status in this model. Because attained age is highly correlated with elapsed time, the main effect of elapsed time is omitted from Model 2. Findings from the model point to the same conclusion: immigrants' odds of death became increasingly lower than those of the native-born—from 19% lower [= 1 – exp(−0.902 + 0.692)] to 40% lower [= 1 – exp(−0.902 + 0.391)]—over the elapsed time.

Second, we check for the possibility of salmon bias, which may cause an overestimation of immigrants' health advantage because return migrants tend to be less healthy than those who remain in the United States. The NHIS does not include information on whether a migrant returned to their country of origin. The data set nevertheless identifies 47,161 native-born and 15,169 immigrant respondents without eligible death records (i.e., without eligible NDI linkage).8 We include these immigrants in a sensitivity analysis with the bold assumption that they are all return migrants. Although this assumption may not be accurate, it sets up an upper bound for the estimate of the impact of salmon bias. We create hypothetical scenarios in which varying proportions of these migrants died by 2011, the end of our mortality follow-up data. According to Table 1, 15.4% of natives and 7.8% of immigrants among those with eligible death records died by 2011. In the hypothetical scenarios, the immigrants without eligible mortality records are set to be equally likely (15%), twice as likely (30%), and greater than three times as likely (50%) than natives to have died by 2011. In other words, we assume these immigrants to be 2–7 times as likely to die than the immigrants with eligible mortality records. As shown in Table A2 (online appendix), immigrants' health advantage would have persisted over the elapsed time in these extreme scenarios except when 50% of them are assumed to have died by 2011. Under such a scenario, the odds of death for immigrants compared with natives would have increased with elapsed time, implying a decline in the immigrants' survival advantage. Nevertheless, the likelihood that one half of the immigrants with missing mortality records had returned to their home countries and died is very low. On the basis of this additional analysis, we think that the salmon bias is unlikely to fully account for the persistent immigrant survival advantage over the length of stay.

Discussion and Conclusions

Much research has debated whether immigrants' health advantage over natives declines with their duration at destination. Most such research faces the challenge of distinguishing the influences of multiple time-related factors—including age, survey year, the period of arrival, and age at arrival—from the effect of duration of stay on immigrants' health because of their use of (pooled) cross-sectional data. Longitudinal studies are similarly limited partly because of their reliance on self-reported health as the outcome variable. Because poor health generally adds mortality risk and because mortality is an unambiguous measure that has long been used to assess immigrants' health advantage (e.g., Angel et al. 2010; Choi 2012), we shed light on the debate on the durability of this advantage by examining changes in mortality risk of immigrants compared with natives over real time. The analysis shows that U.S. immigrants enjoy a survival advantage over the native-born. This advantage, when assessed on the absolute scale, is enduring and ever-growing for all immigrants, regardless of their race/ethnicity, gender, or the number of years since arrival. Even on the relative scale, the survival advantage is persistent over time for nearly all immigrant subgroups. Thus, to the extent mortality is tied to health, this study provides unequivocal evidence that the health protection of immigrant status is stable and long-lasting, with no sign of waning after two decades.

Our results suggest that immigrants' initially greater health endowment and better health behaviors, along with increased economic assimilation and improved access to health care with time, ultimately offset any unhealthy assimilation and amplify their survival advantage over natives in the long run. Even after we consider the higher likelihood that unhealthy immigrants will return to their origin countries (the salmon bias), immigrants' survival advantage generally remains over time. Despite our robust findings, we cannot examine the specific mechanisms behind immigrants' enduring and often increasing survival advantage because we have no time-varying information other than mortality. However, immigrants of various races/ethnicities—including White immigrants, who tend to be more assimilated upon arrival and rely less on immigrant community resources—universally experience persistent mortality protection. This fact leads us to suspect that conditions common to all immigrants, such as health-based selection, may be primarily responsible for their lasting survival advantage. Regardless of the mechanisms, this research suggests that regarding mortality risk, the argument for negative acculturation and its negative effects on immigrant health might be exaggerated. Nevertheless, future studies should collect detailed longitudinal data on immigrants' experiences and behaviors over the duration of their stay to better understand how immigrants maintain their survival advantage over time.

Findings from this study also help explain the literature's inconsistent conclusions regarding long-term changes in immigrants' health advantages. As we have argued, the health discrepancies between different YSI groups in cross-sectional observations might largely reflect the disparities between immigrant cohorts arriving at different time points. Different arrival cohorts are likely to have differing health endowment and selection because of the time-varying conditions at both their origins and destinations. The cohorts might also vary in their compositions of the sending countries and U.S. geographic destinations. All these factors might cause immigrants' long-term health trajectories and mortality risks to differ by their cohort of arrival. This study shows that the relationship between health and years of stay inferred by various YSI groups' mortality hazards does not need to be consistent with the pattern of change in mortality risk that respondents experienced over time. Our finding that the magnitude of immigrants' survival advantage is not linearly associated with YSI also suggests that health and mortality differences among YSI groups likely reflect heterogeneity across arrival cohorts: this heterogeneity might be shaped by factors that are not linearly correlated with time (e.g., law changes). Overall, our research highlights the need for more caution in interpreting findings regarding disparities among immigrant groups with different lengths of stay in the host country.

Although this study focuses on U.S. immigrants, its results may have implications for migrant health elsewhere. Longitudinal studies have found that immigrants' self-assessed health declines with their duration in Australia (e.g., Chiswick et al. 2008) and declines more than the native-born population in Canada (Newbold 2005; Setia et al. 2012). These countries differ from the United States in population health (with the U.S. population being less healthy), immigrants' origins, health care systems, and labor market opportunities, making it difficult to draw direct comparisons between these studies and ours. Nonetheless, our findings suggest the importance of replicating the pattern of unhealthy assimilation found elsewhere using more unambiguous outcomes (e.g., mortality). Our research design tracking shifts in immigrants' survival advantage over real time, instead of with age or by age at arrival, can also be useful for studies in other countries.

Despite this study's contribution to the knowledge of immigrants' long-term health trajectories, it has a few limitations. First, because the NHIS data combine all those who migrated more than 15 years ago and because a large proportion of immigrants belong to this group, we cannot more precisely distinguish immigrants on their year or age of arrival. Consequently, we cannot say conclusively why the YSI groups demonstrate different degrees of relative survival advantage over the 20 years of observation; we know only that the duration of stay alone is unlikely to explain the group differences.

Second, although using death as the outcome variable helps avoid self-reporting bias, which can easily be affected by immigrants' acculturation and health care access, mortality does not capture all aspects of health. Among the survivors, immigrants may suffer more from chronic illnesses, disabilities, or other serious physical limitations than their native-born counterparts. Given immigrants' greater language barriers and typically worse access to health care, however, the chances of surviving severe illnesses should be worse for immigrants than for natives. If so, we would be more likely to find that the native-born who lived through the 20-year period had more major health problems than their foreign-born counterparts.

Third, because our analysis relies on linked mortality data from administrative records, the results could be biased if immigrants' and natives' deaths were documented with different levels of accuracy. Indeed, we find that relative to the native-born, a larger proportion of immigrants had no eligible mortality records. Such a discrepancy may be due to return migration for some immigrants, the difficulty of linking undocumented immigrants' records, or less accurate filing of death certificates among immigrants. In any case, our additional analysis shows that even if the foreign-born without linked death records had similar or considerably higher mortality hazards than native-born individuals, immigrants' long-term survival advantage over natives generally remained.

Fourth, our data lack information that would allow us to further investigate heterogeneity among immigrants. Specifically, we do not know each immigrant's legal status, making it impossible to determine whether the immigrant survival advantage varies by legal status. However, we show that immigrants from all ethnoracial groups, who likely vary in the proportion with undocumented status, enjoyed a lasting survival advantage over the native-born and that the differences between immigrant groups are relatively small (see Figure 2). Thus, although a change from undocumented to documented legal status after some years of U.S. residence might be one explanation for the widening survival gap between immigrants and natives, we do not think that accounting for legal status would alter our overall results.9

Beyond contributing to the debate on the durability of immigrants' health advantage over time, our study adds to general knowledge of mortality disparities across sociodemographic groups. Mortality research has long documented the Black–White mortality crossover: a pattern in which the survival rate of the Black population (which generally displays high mortality) converges with that of the White population (which generally has lower mortality) with increases in age. This pattern has been attributed to mortality selection (e.g., Johnson 2000; Manton and Stallard 1981). Because members of higher mortality groups die at faster rates, the survivors in such groups are increasingly selected with age, enabling them to close the mortality gap with the low-mortality group at the aggregate level.10 In contrast to this pattern, we find that the survival chances of the native-born (a higher mortality group) increasingly fell behind that of immigrants (a lower mortality group) over time. This widening gap may be due to the greater heterogeneity of the foreign-born population compared with the native-born population; if the health distribution is more bimodal for immigrants than for the native-born, then the death of unhealthy individuals could boost the immigrants' average survival rate more, even though the native-born have more deaths. Alternatively, immigrants may experience proportionally much greater increases in socioeconomic resources and access to health care than natives over time, with the greater health protection from these increases ultimately offsetting the mortality selection effect in shaping the nativity gap in mortality over time. Although examining the exact mechanisms behind the widening mortality gap between immigrants and the native-born over time is beyond the scope of this article, our study demonstrates a different way in which mortality disparities between population subgroups evolve and calls for research on conditions that may counteract the influence of differential mortality selection.

Acknowledgments

This publication was supported by grants P2CHD058484 and P2CHD041022, funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or the Department of Health and Human Services.

Notes

1

The NCHS links the NHIS survey records to the NDI records using the following identifying information in both records: social security number; first name, middle initial, and last name; father’s surname; month, day, and year of birth; state of birth; state of residence; sex; race/ethnicity; and marital status. The NHIS participants are ineligible for linkage if the submission records do not meet the minimum data requirements. Therefore, some NHIS participants who died and had death certificates filed may not have their death records linked because of missing information in the NHIS records. Throughout the article, we refer to those without linked death records for this or any other reason as those “without eligible mortality records.” The issue of missing record matches may be more serious for the foreign-born than for the native-born because of return migration and undocumented migrants’ missing social security numbers. Nevertheless, the robustness check presented later helps address this issue.

2

The NHIS top-coded age at survey at age 85 from 1997 forward. To be consistent, we top-code age at 85 for the 1992–1996 waves.

3

In total, 83,863 respondents are without eligible mortality records (i.e., without eligible NDI linkage).

4

Although immigrants’ legal status can affect their access to health care (Hacker et al. 2015), the NHIS did not ask about legal status. The survey asked about citizenship status from 1998 forward, but we cannot infer the legal status for noncitizens, who could be permanent residents, legal temporary migrants, or undocumented migrants. The differentiation of immigrants by race/ethnicity in our analysis is likely to capture some of the impact of legal status, given that immigrants from different regions vary considerably in their documentation status. We consider the implications of our inability to control for legal status in the Discussion and Conclusions section.

5

We do not include income in the models because of a considerable proportion of missing values (31%). The combination of education and poverty status, however, should approximate respondents’ socioeconomic status fairly well.

6

By design, logistic regression forces covariates to operate multiplicatively because additivity on a logarithmic scale implies multiplicativity on the untransformed scale (Mehta et al. 2019). The coefficient of an interaction term between two covariates on a logarithmic scale implies whether the odds ratio for one covariate differs across levels of the other covariate on the untransformed scale.

7

The difference (not the ratio) in the hazard probability measures the absolute risk of the dependent variable associated with the covariate.

8

The sum of the two numbers is smaller than the number listed in footnote 3 (83,863) because it excludes respondents with missing data on any covariates.

9

The legal status could also matter if most undocumented immigrants were not matched in the mortality records. As our robustness check shows, though, even if all those without linked mortality records were mostly undocumented and had an unusually high mortality rate because of their lack of access to health care, it would hardly affect our argument about immigrants’ persistent survival advantage.

10

The group-level pattern results from mortality selection. Thus, once we account for this selection, we can still find cumulative disadvantage with age for individuals from higher mortality groups—that is, a widening gap in mortality risk between them and otherwise similar people from lower mortality groups (Zheng 2020).

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