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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2026 Jan 12;81(2):gbaf248. doi: 10.1093/geronb/gbaf248

Heterogeneity in Asian Americans’ mortality trends, 2000–2022

Hui Zheng 1,✉, Yoonyoung Choi 2, Leafia Ye 3, Ming Wen 4
Editor: Marc A Garcia5
PMCID: PMC12794022  PMID: 41524107

Abstract

Objectives

Although Asian Americans are the fastest-growing racial/ethnic group in the United States, their recent mortality trends have not been sufficiently studied. This study provides a comprehensive analysis of years of life lost (YLL) from age 25 to 84 among six largest Asian ethnic groups, comparing them to non-Hispanic Whites.

Methods

We analyzed data from the CDC Multiple Cause of Death database and the American Community Survey (2000–2022) using a cause of death decomposition method.

Results

Among college-educated individuals, all Asian ethnic groups experienced either a smaller decrease or an increase in YLL compared to Whites in 2000–2022. These disparities were not primarily driven by the COVID-19 pandemic, though Filipinos and Indians were disproportionately affected compared to Whites. Instead, the divergence in YLL trends predates 2020. Indians showed the most unfavorable trend, with YLL worsening even before the pandemic, followed by Koreans. At least 75% of the smaller YLL reductions among Asians were due to slower improvements in mortality from circulatory diseases, cancer, and diabetes. These findings indicate a less favorable mortality trend for Asian Americans compared to White Americans, particularly the college-educated, in the early 21st century. They also suggest that, over time, Asians may be deriving diminishing health returns from higher education compared to Whites.

Discussion

We discuss differential trends between Whites and Asians, as well as variation within Asian ethnic and educational groups, in the context of socioeconomic conditions, labor market dynamics, racialization in the United States, and stages of nutrition transition in countries of origin.

Keywords: Years of life lost, Trend, Asian American

Since the 1965 Immigration and Nationality Act, the Asian population in the United States has undergone rapid growth. Between 2000 and 2023, the Asian American population more than doubled–growing at a faster rate than any other racial/ethnic group (Krogstad & Im, 2025). Asian Americans consistently have lower mortality rates and higher life expectancy than other groups (Hummer et al., 2004; Singh & Siahpush, 2002). Their life expectancy exceeds that of non-Hispanic Whites by 6 to 8 years (Acciai et al., 2015). This mortality advantage has been attributed to Asian Americans’ favorable socioeconomic status and a substantial proportion of the population being foreign-born (Elo & Preston, 1997).

Estimates of Asian Americans’ mortality without distinction between ethnicities, however, obscure substantial heterogeneity within the population. Asian Americans are heterogeneous across many dimensions: socioeconomic status (education, occupation, income, and poverty), nativity composition (the share of each group that is foreign-born), health behaviors (e.g., smoking and obesity rate), and exposure to discrimination (Lee & Ramakrishnan, 2020; Sakamoto et al., 2009; Staimez et al., 2013). Due to these differences, various Asian ethnic groups exhibit distinct mortality outcomes (Baluran & Patterson, 2021; Hastings et al., 2015; Lauderdale & Kestenbaum, 2002). Moreover, research on temporal trends in Asian Americans’ mortality advantage is rather scarce (with some notable exceptions, e.g., Baluran, 2023). If various Asian ethnic groups display divergent mortality trends over time, lumping them together would potentially produce misleading estimates of the mortality trend among Asian Americans.

In this study, we present the first comprehensive analysis of mortality trends among six largest Asian ethnic groups in the United States—Chinese, Asian Indian, Filipino, Vietnamese, Korean, and Japanese –from 2000 to 2022. We compare their changing mortality advantage relative to non-Hispanic Whites, breaking down patterns by gender and education. Previous studies suggest that Asian Americans, on average, achieve higher educational attainment than Whites (Kao & Thompson, 2003). However, they are less likely to translate these educational advantages into occupational gains (Kim & Sakamoto, 2010; Tran et al., 2019). How this may influence the temporal trend of their relative mortality advantage remains unknown. Our findings reveal a surprising and unfavorable trend among most Asian groups compared to Whites, especially among those with a college degree.

Our study period includes the COVID-19 pandemic, which may have influenced Asian Americans’ mortality trends. During the pandemic, the health of Asian Americans garnered heightened attention (Park et al., 2023; Yan et al., 2021), with some researchers suggesting that Asian Americans were particularly affected due to increased discrimination (Wang et al., 2020; Wen et al., 2023; Zhang et al., 2022). Furthermore, certain ethnic groups, such as Filipinos, have a high concentration in the healthcare industry, exposing them to severe working conditions and a lack of critical protections (Nazareno et al., 2021). As a result, they may have experienced a disproportionate impact during the pandemic. In our analysis, we examine whether the pandemic served as a “turning point” in Asian Americans’ mortality trends or whether it was simply a continuation of long-term patterns.

Background

Heterogeneity within Asian ethnic groups

Several studies have investigated the variability in mortality schedules and life expectancy among Asian Americans (Hastings et al., 2015; Lauderdale & Kestenbaum, 2002). The period between 2012 and 2016 saw Chinese individuals having the highest life expectancy, followed by Asian Indians, Koreans, Japanese, Filipinos, and Vietnamese (Baluran & Patterson, 2021). However, this ranking does not precisely align with the socioeconomic hierarchy of these groups. Concerning educational attainment, Asian Indians had the highest proportion of the population with a bachelor’s degree or higher, followed by Koreans, Chinese, Japanese, Filipinos, and Vietnamese (Baluran & Patterson, 2021). Yet, when considering median household income, Asian Indians had the highest median income, followed by Filipinos, Chinese, Koreans, and Vietnamese (Tran et al., 2019). In terms of the percentage living in poverty, Chinese exhibited the highest poverty rate, followed by Vietnamese, Koreans, Asian Indians, and Filipinos (Tran et al., 2019). The misalignment between life expectancy and socioeconomic rankings implies that non-material forces may underlie health disparities among Asian ethnic groups (Lauderdale & Kestenbaum, 2002).

Existing literature explores alternative factors–such as health behaviors and differential racialization–that influence health disparities among Asian Americans. For instance, Asian Indians have the highest rates of obesity and diabetes (Staimez et al., 2013), as well as a proportionately high mortality burden from ischemic heart disease (Jose et al., 2014), whereas Vietnamese have the lowest rates of overweight and obesity (Lauderdale & Rathouz, 2000). Nonetheless, Asian Indians have the second highest life expectancy among Asian Americans, while Vietnamese individuals have the lowest. This suggests that body weight alone is unlikely to fully account for ethnic variation in health outcomes.

Literature also suggests that Asian Indians may encounter more racial discrimination than other Asian groups due to their darker skin tone (Lee & Ramakrishnan, 2020). However, it is important to note that while Vietnamese, Korean, and Japanese individuals generally have lighter skin tones, they exhibit lower life expectancies than Asian Indians. This undermines the idea that differential racialization based on skin tone explains the observed life expectancy disparities among Asians. Racialization, however, can extend beyond skin tone. Research indicates that facial features, histories of colonization, and differing relationships with the United States can influence perceptions of “Asian-ness.” South and Southeast Asian Americans (e.g., Indians and Filipinos) may be viewed as less prototypically Asian and may experience more discrimination and unfair treatment than East Asians (Baluran, 2025; Goh & McCue, 2021; Yamashita, 2022). Nonetheless, if racial prototypes rooted in racialization were the primary drivers of health variation among Asian ethnicities, we would not expect Asian Indians—who are often perceived as less prototypically Asian—to have the second highest life expectancy.

It is plausible that the interplay between education, income, racialization, and health behaviors contributes to the observed variation among Asians, making any single explanation insufficient. For instance, although Asian Indians have the highest socioeconomic attainment, this advantage may be offset by less healthy behaviors and a higher degree of racialized experiences. In contrast, despite having mid-range education and income levels, Chinese individuals might benefit from healthier lifestyles, contributing to their position at the top of life expectancy rankings. Additionally, differences in immigrant selectivity and nativity composition may further shape these ethnic variations. Between 2000 and 2022, 75% of Korean Americans were foreign-born, while only 44% of Japanese Americans were foreign-born [authors’ calculations based on American Community Survey (ACS) data]. Different Asian immigrants may have been selected differentially based on health endowment, health behaviors, and socioeconomic background.

Due to the complex variations in socioeconomic status, health behaviors, racialized experiences, and nativity composition, a pan-Asian analysis risks obscuring heterogeneity within Asian Americans. Estimating overall trends for Asian Americans as a group is less informative if different Asian ethnicities follow divergent trajectories that offset each other. However, predicting mortality trends across Asian ethnic groups remains challenging due to the interplay of these factors. In the next section, we will explore how each factor may contribute to different mortality trend projections across Asian ethnicities while acknowledging the complexity and limitations of these predictions.

Trends in socioeconomic status, racialization, and health behaviors

Throughout the first two decades of the 21st century, socioeconomic rankings among Asian ethnic groups have remained relatively stable (Sakamoto et al., 2009; Tran et al., 2019). If socioeconomic status is the primary driver of ethnic variation in life expectancy among Asian Americans, we would expect ethnic groups with higher educational and economic attainment, such as Asian Indians, to show more favorable life expectancy trends over time. Conversely, groups with lower socioeconomic status, such as Vietnamese and Korean individuals, would exhibit less favorable trends. However, if racialization plays a dominant role in shaping health disparities, we would expect Indians and Filipinos to exhibit more negative trends compared to Japanese, Chinese, Korean, and Vietnamese individuals, as the former are more likely to have darker skin tones or deviate from prototypical East Asian features (Baluran, 2023). In short, socioeconomic and racialization mechanisms yield contrasting predictions for mortality trends, particularly for Indians, Koreans, and Vietnamese.

Given that over 70% of the Asian American population is foreign-born, the trend in life expectancy among Asians may be closely tied to the evolving health composition of Asian immigrants over time. The changing dynamics in health can be attributed to factors such as extensive energy intake in developing countries, driven by socioeconomic development and the globalization of food production and marketing. Simultaneously, sedentary lifestyles and reduced energy expenditure due to urbanization contribute to what is known as a “nutrition transition” (Egger & Swinburn, 1997; Popkin, 2001). Countries undergoing a nutrition transition face an elevated risk of individuals becoming overweight, obese, and developing various chronic diseases, including circulatory diseases, cancer, and diabetes. Individuals with higher socioeconomic status may be particularly susceptible to this nutrition transition due to increased purchasing power for obesogenic goods, such as expensive energy-dense foods and cars (He et al., 2014; Jones-Smith et al., 2011). The shifting health distribution in Asian countries has the potential to alter the health profiles of Asian immigrants, especially those with higher education levels. If the nutrition transition in countries of origin, rather than immigration experiences in the United States, plays a more significant role in shaping Asian Americans’ mortality trends, we would expect their mortality advantage over White Americans to decline in recent decades, particularly among highly educated immigrants. Furthermore, this decline would likely be driven by a decreasing advantage in deaths related to circulatory diseases, cancer, and diabetes.

The varying stages of nutrition transition in countries of origin can also shape life expectancy trends across different Asian ethnic groups. We may expect Asian Americans from countries in the mid-stage of nutrition transition (e.g., India, Vietnam, and the Philippines) to experience less favorable mortality trends compared to those from countries in a later stage of transition (e.g., Japan, China, and South Korea). According to Barry Popkin’s nutrition transition model (Popkin, 2001), India, Vietnam, and the Philippines are in an emerging transition stage, where urbanization and economic growth have led to increased consumption of processed foods, sugar, and fats, while undernutrition and micronutrient deficiencies remain prevalent in rural areas. In contrast, Japan, China, and South Korea have entered the modernization or post-transition stage, characterized by a more balanced diet and a continued prevalence of traditional dietary patterns. If Asian Americans’ health and mortality are primarily shaped by their countries of origin, we would expect Indian, Vietnamese, and Filipino individuals to experience less favorable mortality trends than Japanese, Chinese, and Korean individuals.

Education, discrimination, and health

Another perspective to consider when examining life expectancy trends is education. Although Asians, on average, attain higher education levels than Whites, this advantage does not fully translate into occupational gains (Kim & Sakamoto, 2010; Tran et al., 2019). These findings suggest labor market discrimination against Asians, especially highly educated individuals as they navigate more directly within “White” spaces compared to their less educated counterparts (Lai & Babcock, 2013). Prolonged exposure to discrimination can act as chronic stressors (Gee et al., 2007; Gee & Ford, 2011), leading to an early onset of chronic stress-related diseases, disability, and mortality. These arguments suggest that highly educated Asian Americans may experience less favorable mortality trends than their White counterparts due to sustained stress exposure, which can elevate risks of death from chronic conditions such as circulatory diseases, cancer, and diabetes. The implication is that Asians may receive a diminished health return from education over time compared to Whites.

Furthermore, if certain Asian ethnic groups—such as Indian and Filipino Americans—are subjected to more racial discrimination due to darker skin tones or greater perceived distance from prototypical East Asian features (Baluran, 2025; Yamashita, 2022), they may experience even less favorable mortality trends compared to other highly educated Asian groups. Despite stemming from different mechanisms, this argument ultimately leads to a similar prediction as the immigrant selectivity hypothesis discussed earlier.

Methods

Data

All data were obtained from two sources: Weighted midyear population estimates from the IPUMS ACS for the years 2000–2022 (Ruggles et al., 2022) and the number of deaths from the CDC Multiple Cause of Death Data (MCD) spanning 2000–2022 (Centers for Disease Control and Prevention, 2023). We aggregated the data from 2000 to 2022 into five years period groups: 2000–2004, 2005–2009, 2010–2014, 2015–2019, and 2020–2022. The ACS data, provided by the U.S. Census Bureau, included imputed missing information on race and education (United States Census Bureau, 2022). Regarding the MCD data, approximately 4% of death certificates had missing information on education, which was imputed using age, sex, race, ethnicity, and cause of death. This imputation process follows methodologies employed in previous studies (Case & Deaton, 2021; Geronimus et al., 2019). We restricted both the ACS and MCD data to ages 25–84, as most individuals complete their college education by age 25. Additionally, age misreporting and the difficulty of identifying a primary cause of death when multiple conditions coexist become increasingly problematic among individuals aged 85 and older (Preston et al., 1999; Tinetti et al., 2012). As we conducted the analyses by Asian subgroups, the small sample size and limited number of deaths beyond age 84 would also make the estimates less stable and reliable.

Measures

The primary variables of interest comprised race/ethnicity, sex, and educational attainment. Race/ethnicity encompassed non-Hispanic Whites, Chinese, Asian Indians, Filipinos, Vietnamese, Koreans, and Japanese. Sex was categorized as men and women. Age was categorized into 5-year groups: 25–29, 30–34, … , 80–84. Educational attainment was recoded as a dichotomous variable: with a bachelor’s degree (BA) and without a BA. During the study period, mortality data shifted from the 1989 education coding system (recording years of schooling) to the 2003 system (using degree categories), with states transitional at different times. To ensure consistency across coding systems, individuals with 16 or more years of schooling in the 1989 system were classified as having a BA degree, aligning with the 2003 coding scheme. This dichotomous classification captures major patterns of educational inequality in mortality, and mitigates measurement inconsistency in MCD data (Rostron et al., 2010) and potential mismatch bias between the ACS and MCD data sources (Case & Deaton, 2021).

The main outcome variable is years of life lost (YLL) between ages 25 and 84. Years of life lost reflects the difference between the actual life expectancy within a specified age range and the maximum potential years within that range (e.g., 60 years from ages 25–84). Cause-specific YLL indicates the number of years lost due to each specific cause of death. In the MCD database, causes of death were recorded using the 10th Revision of the International Classification of Diseases (ICD). These were recoded into 17 categories: alcohol use, drug poisoning, suicide, homicide, unintentional injuries, HIV, flu/pneumonia & acute respiratory infections, circulatory diseases, cancer, chronic lower respiratory diseases, diabetes, septicemia, nephritis, Alzheimer’s, other internal diseases, fetal/infancy origin, and all remaining (“other”) causes of death. Detailed ICD codes for each cause of death are documented in Supplementary Table 1 (see online supplementary material). In the last period (2020–2022), we included COVID-19 as a cause of death.

Analysis

To address mortality data quality issues among Asian subgroups, we implemented a comprehensive two-step analytical approach. We first corrected death count underestimation using sex- and age-specific Asian American misclassification ratios published by Arias & Xu (2022). Subsequently, we applied the Brass relational model (BRM) (Brass, 1971; Park et al., 2023) to enhance mortality estimates at older ages, which is explained in detail in Supplementary Method 1 (see online supplementary material).

Using refined BRM estimates, we constructed multiple-decrement period life tables in 5-year age groups (25–29, 30–34, 35–39, … , 80–84) by race/ethnicity, sex, and education, for five-time segments (2000–2004, 2005–2009, 2010–2014, 2015–2019, and 2020–2022). To increase the stability of the analysis, we combined the period before the pandemic in 5-year increments. Our focus is on examining changes in YLL from the periods 2000–2004 to 2020–2022 and the relative proportion of changes that occurred before the COVID-19 pandemic (from 2000–2004 to 2015–2019) and during the pandemic (from 2015–2019 to 2020–2022). We examined changes in YLL over time by race/ethnicity, sex, and education, with particular attention to the varying magnitudes of changes among Whites and different Asian ethnicities.

To obtain an overview of the contribution of each cause of death to the shift in YLL, we utilized the Andersen et al. (2013) method to decompose YLL by cause of death. This approach allows for a straightforward breakdown of the absolute number of YLL at a single point in time, and the change in YLL between two time points, by cause of death. This method is based on a multiple-decrement life table and does not assume independent competing risks. Detailed explanations of this method can be found in Supplementary Method 2 (see online supplementary material).

Results

The analysis encompasses 6,751,187 deaths from 2000 to 2004, 6,554,954 deaths from 2005 to 2009, 6,620,952 deaths from 2010 to 2014, 7,165,109 deaths from 2015 to 2019, and 5,196,777 deaths from 2020 to 2022. Death records for Whites, Chinese, Japanese, Koreans, Filipinos, Vietnamese, and Asian Indians total 31,595,113, 174,098, 87,405, 71,158, 175,568, 76,751, and 108,886, respectively. Supplementary Table 2 (see online supplementary material) shows the percentage of individuals in the study population who are foreign-born or hold a BA. Compared to Whites, Asian Americans have a significantly higher proportion of both foreign-born individuals and BA holders. However, these characteristics vary considerably across Asian subgroups and time periods. Supplementary Table 3 (see online supplementary material) shows YLL by gender, education, race/ethnicity, and period. Among those with a BA, most experienced a decline in YLL from 2000 to 2019 but increased a bit during the pandemic. Among those without a BA, most experienced a decrease in YLL from 2000 to 2014 but an increase afterwards. The COVID-19 pandemic further aggravated this negative trend. All Asian ethnic groups had lower YLL than White individuals, therefore the Asian mortality advantage is clearly present in most situations.

Next, we explore how the Asian mortality advantage changes over time relative to Whites. Figure 1 illustrates the change in YLL from 2000–2004 to 2020–2022 by race/ethnicity among individuals with a BA. Among men, YLL decreased by approximately 1.11 years among Whites, while it increased by 0.17 years among Asians on average. Nonetheless, the Pan-Asian estimate masks the substantial heterogeneity and different directions of change among various Asian ethnicities. Japanese fared slightly worse than Whites by experiencing a decrease of YLL of 0.84 years, followed by Chinese (−0.71 years) and Vietnamese (−0.41 years). Koreans, Filipinos and Asian Indians, however, observed an increase in YLL of 0.17, 0.69, and 1.52 years, respectively. The pattern is very similar among women with a BA. White women experienced a substantially larger decrease in YLL compared to Asians (−0.89 years vs. −0.004 years), but there exists substantial heterogeneity among various Asian ethnic groups. Japanese experienced a better improvement than Whites, with a reduction in YLL of 1.21 years, followed by Chinese (−0.37 years), and Vietnamese (−0.24 years). Koreans, Filipinos and Asian Indians, in contrast, experienced an increase in YLL of 0.51, 0.25, and 1.10 years, respectively, indicating a deterioration in life expectancy. Overall, the reduction in YLL is small among college-educated Asians compared with Whites—a pattern observed across various ethnicities, although some Asian groups fared much worse than others.

Figure 1.

Bar graphs comparing Whites and Asians in years of life lost among men with a BA (left panel), and women with a BA (right panel).

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Changes in years of life lost by race/ethnicity among individuals with a BA, by period.

Figure 2 shows the change in YLL from 2000–2004 to 2020–2022 by race/ethnicity among those without a BA. Among men, Whites and Asians experienced an increase in YLL of 2.30 and 1.60 years, respectively. However, various Asian ethnic groups display distinct patterns. Japanese (1.56 years), Filipinos (1.29 years), and Chinese (−0.35 years) have a less negative trend compared to Whites. But Vietnamese, Koreans, and Asian Indians experienced the most significant increase in YLL by 2.37–3.74 years. Among women, Whites experienced an increase in YLL by 1.97 years, while Asians had a slight increase by 0.22 years, which was mainly driven by Vietnamese, Koreans, and Asian Indians. Therefore, in both men and women, the Asian average hides the large increases in YLL for these three ethnic groups.

Figure 2.

Bar graphs comparing Whites and Asians in years of life lost among men without a BA (left panel), and women without a BA (right panel).

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Changes in years of life lost by race/ethnicity among individuals without a BA, by period.

Figures 1 and 2 show that for most Asian groups with a BA and Vietnamese, Korean and Indian men without a BA, they either have a less favorable trend or a more negative trend in YLL change compared to their White counterparts. What may have contributed to this pattern? The first possible explanation is that the pandemic had a much larger impact on Asians than on Whites due to an increase in exposure to discrimination and mental health problems among Asian Americans. If that is the case, then most of the Asians’ unfavorable change in YLL should be attributed to changes since 2020. In order to test this explanation, we break down the overall change in YLL into the change before and during the pandemic.

Figures 1 and 2 also show these decomposition results. The white bar shows the change from 2000 to 2019, while the grey bar shows the change since 2020. The pandemic effect was indeed worse for college-educated Asian Americans overall as depicted in the grey bars, but their lack of improvement in YLL started actually much before that. From 2000 to 2019, they already had a smaller reduction in YLL than Whites as shown in the white bars. Indian men faced a unique disadvantage as they already experienced an increase in YLL before the pandemic, which was further exacerbated by the pandemic. Filipino men experienced evident improvement in YLL between 2000 and 2019, but this progress was entirely negated during the pandemic.

The patterns for college-educated women are very similar. Asian American Women’s lack of improvement in YLL already existed before the pandemic. Between 2000 and 2019, White women with a BA had the most improvement in YLL (−1.12 years), more than Chinese (−0.56 years), Vietnamese (−0.43 years), and Filipinos (−0.35 years). Koreans and Asian Indians, in contrast, had a deterioration in YLL (0.51 and 0.82 years, respectively). The pandemic further aggravated the situation, leading to a net increase in YLL for Filipinos, Koreans, and Indians in the first two decades of the 21st century. Therefore, although the pandemic effects on YLL are evident, it is important to note that the lack of improvement in YLL among college-educated Asian Americans had already taken place much before the pandemic.

Among those without a BA, Filipino men had a similar level of improvement to Chinese before the pandemic. However, they were the most negatively impacted by the pandemic among all the Asian ethnicities, resulting in a net increase in YLL from 2000 to 2022. Vietnamese, Koreans, and Asian Indians had already experienced setbacks in YLL before the pandemic compared to Whites. The mortality increases during the pandemic exacerbated these trends, resulting in substantial setbacks between 2000 and 2022. Among women without a BA, Asians did not fare worse than their White counterparts either before or during the pandemic.

These analyses suggest that the pandemic alone does not fully explain the declining Asian mortality advantage. In fact, many Asian groups (except Asian women without a BA) already fared worse than Whites prior to the pandemic. We then turned to cause-specific YLL to see what causes of death may be particularly influential in reducing Asians’ mortality advantage before the pandemic (Supplementary Tables 4–7, see online supplementary material, show the YLL by causes of death). Our decomposition analysis, presented in Figure 3, indicates that Asians have a smaller decrease in YLL primarily attributed to three major causes of death: circulatory diseases, cancer, and diabetes. Figure 4 illustrates counterfactual changes in YLL among Asian groups from 2000 to 2019, had they experienced the same level of improvement in these three causes of deaths as Whites. Among individuals with a BA, the smaller improvements in these three causes of deaths accounted for at least 75% of the smaller decrease in YLL observed among Asian ethnicities compared to Whites. Among those without a BA, these three causes of deaths fully explained the bigger increase in YLL among Japanese, Vietnamese, Korean, and Indian men compared to their White counterparts.

Figure 3.

Bar graphs comparing Whites and Asians in years of life lost by cause of death among men with a BA (top row), women with a BA (second row), men without a BA (third row), and women without a BA (bottom row).

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Changes in years of life lost by cause of death from 2000–2004 to 2015–2019.

Figure 4.

Bar graphs comparing observed and counterfactual change in years of life lost among Asian American men with a BA (top left panel), Asian American women with a BA (top right panel), Asian American men without a BA (bottom left panel), and Asian American women without a BA (bottom right panel).

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Observed and counterfactual change in years of life lost among Asian ethnicities from 2000–2004 to 2015–2019. If They Had the Same Improvement in Circulatory Diseases, Cancer, and Diabetes-Related Deaths.

Figures 1 and 2 also reveal that higher-educated Asians may have particularly experienced a less favorable mortality trend than Whites. This raises the question: Do Asians experience diminishing health returns from education when compared to Whites? In Figure 5, we explore the disparity in YLL between individuals without and with a BA, separately by race/ethnicity over time. A positive value signifies positive returns from higher education, indicating a reduction in YLL due to obtaining a BA. In the years 2000–2004, BA reduces YLL by 4.9 years for White men. Compared to them, Asian men experience at least 2.4 fewer years of reduction in YLL from a BA. This gap has widened to a minimum of 3.4 years in 2020–2022. Similarly, Asian women, compared to White women, show at least 1.1 fewer years of reduction in YLL from a BA in 2000 to 2004, with this gap increasing to at least 3.8 years in 2020–2022. These findings suggest that over time Asians, particularly Asian women, seem to derive fewer and fewer health benefits from higher education when compared to Whites.

Figure 5.

Line graphs showing gap in years of life lost between individuals without and with a BA by race/ethnicity among men (left panel), and women (right panel).

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Gap in years of life lost between individuals without and with a BA by Race/Ethnicity, 2000–2022.

Discussion and conclusion

This study conducted a comprehensive analysis of the mortality trends among different Asian ethnicities in the United States by gender and education from 2000 to 2022. Among individuals with a BA, all Asian ethnicities experienced a smaller decrease or even increase in YLL compared to Whites. All of them already had a smaller improvement in YLL even much before the pandemic. In addition, Filipinos and Indians were disproportionately affected by the COVID-19 pandemic compared to Whites. Among the Asian groups, Indians had the least favorable trend. They already had worsening YLL before the pandemic. Koreans had the second smallest decrease in YLL prior to the pandemic. Filipinos had a similar decrease in YLL compared to Chinese prior to 2020; however, they were significantly impacted by the COVID-19 pandemic, resulting in an increase in YLL from 2000 to 2022. Regarding individuals without a BA, Asians fared better, with either a decrease or a smaller increase in YLL than Whites. The exceptions here are Vietnamese, Korean, and Indian men. They experienced a bigger increase in YLL either before or during the pandemic compared to White counterparts. Due to differential mortality trends among those with and without a BA, Asians have increasingly experienced fewer health benefits from higher education over time compared to Whites.

The big picture here is that despite Asians generally having a mortality advantage over Whites, their mortality trends in the first two decades of the 21st century have been less favorable, particularly among the higher educated group. This unfavorable trend emerged before the pandemic, primarily due to smaller improvement in circulatory diseases, cancer, and diabetes-related deaths. This resonates with recent research documenting a long-running trend of declining health advantage for Asian Americans in the last two decades (Ye & Zheng, 2025). Another key takeaway of this study is that there is significant heterogeneity in mortality trends within the Asian population, which is concealed by pan-Asian analysis. On average, Chinese, Japanese, and Filipinos exhibit a more favorable trend than Koreans, Vietnamese, and Asian Indians, some of whom even experienced a mortality increase before the pandemic. Despite being the most impacted by the pandemic, which may be attributed to their high concentration in the healthcare industry, exposure to severe working conditions, and a lack of critical protections during the pandemic (Nazareno et al., 2021), Filipinos had a comparable improvement in YLL as Chinese before the pandemic.

What do these findings suggest about the mechanisms driving less favorable mortality trends among Asian Americans compared to Whites, particularly among the highly educated? Additionally, how do these mechanisms contribute to the mortality heterogeneity across Asian ethnic groups? We outlined several possible explanations to predict mortality trends among Asian Americans in the background section, which we now revisit.

First, if socioeconomic factors (e.g., education, income) play a key role in shaping mortality disparities, we would expect Asians—particularly those with higher education—to exhibit more favorable trends than Whites. However, highly educated Asians, especially those with a college degree, have been gradually losing their mortality advantage compared to Whites. Additionally, if socioeconomic status were the primary driver, Asian Indians—who have the highest educational attainment and income levels among Asian groups in the 21st century—should exhibit the most favorable trend. Instead, they show the most negative trend in YLL. Therefore, our findings suggest that socioeconomic status alone is unlikely to be the primary mechanism shaping Asian Americans’ mortality trends.

Second, if differential racialization based on skin tone were a key factor, we would expect Indians and Filipinos to experience more negative mortality trends than Japanese, Chinese, Koreans, and Vietnamese, as the former generally have darker skin tones. Similarly, racialization based on facial features would predict that Indians and Filipinos would exhibit the least favorable trends. However, before the pandemic, Filipinos showed mortality improvements in YLL comparable to those of Chinese, despite having darker skin tones than Vietnamese and Koreans—who, after Asian Indians, experienced the second and third most unfavorable trends. Moreover, although Asian Indians exhibited the most unfavorable trend, they still maintained one of the lowest YLL in most time periods. These findings suggest that racialization based on skin tone or facial features is unlikely to be the predominant mechanism driving divergent mortality trends among Asian Americans. This does not imply that racialization does not negatively affect Asian Americans’ health or life expectancy. Rather, the evidence suggests it is probably not the primary mechanism shaping mortality trends within this population.

Third, the immigrant selectivity perspective suggests that the stage of nutrition transition in the country of origin plays a more significant role in shaping immigrants’ health trends than their experiences in the United States. Based on this argument, we would expect Asian Americans’ mortality advantage over White Americans to decline in recent decades, particularly among highly educated immigrants. This decline would likely be driven by a diminishing advantage in deaths related to circulatory diseases, cancer, and diabetes. Additionally, we would expect Indians, Vietnamese, and Filipinos to experience less favorable mortality trends than Japanese, Chinese, and Koreans, as the former are still in the mid-stage of the nutrition transition. Our findings generally align with this prediction, with two exceptions: Koreans and Filipinos. Although South Korea is more economically advanced than Vietnam, Korean immigrants may be less positively selected, as their income and poverty levels in the United States are closer to those of Vietnamese (Sakamoto et al., 2009; Tran et al., 2019). This may help explain why Koreans exhibit a similarly unfavorable mortality trend as Vietnamese. In contrast, despite the Philippines being in the mid-stage of the nutrition transition, Filipino immigrants appear to be more positively selected, as they have the second highest median income and the lowest poverty rate among the six Asian groups examined (Tran et al., 2019). This may have contributed to their YLL trends being comparable to those of Chinese before the pandemic. These two exceptions suggest that socioeconomic status does play a role in shaping mortality trends, but primarily as a consequence of differential immigrant selectivity.

Fourth, the particularly unfavorable mortality trend among highly educated Asians compared to Whites aligns with the argument that they may face discrimination in the labor market (Lai & Babcock, 2013), which limits their ability to translate educational advantages into occupational and income gains (Kim & Sakamoto, 2010; Tran et al., 2019). Prolonged exposure to discrimination can act as a chronic stressor (Gee et al., 2007; Gee & Ford, 2011), increasing the risk of early-onset chronic stress-related diseases, disability, and mortality. This mechanism may help explain why circulatory diseases, cancer, and diabetes-related deaths are emerging as key contributors to the less favorable mortality trends among Asians compared to Whites. It might also account for the diminishing health benefits of higher education for Asians over time relative to Whites.

We also find that Asian Indians with a BA experienced a particularly pronounced increase in mortality compared to other highly educated Asian groups. At first glance, this pattern appears to support the differential racialization thesis. However, it is important to disentangle this explanation from the immigrant selectivity argument, as both lead to similar empirical predictions. Furthermore, the case of Asian Indians must be interpreted within two broader contexts. First, African Americans—arguably the most racialized group in the United States—experienced substantial gains in life expectancy prior to the COVID-19 pandemic (Case & Deaton, 2021; Zheng et al., 2025). If racialization were the primary driver of the increasing YLL among Asian Indians during this period, we would need compelling evidence that they faced more intense or harmful forms of discrimination than African Americans, which remains unclear. Second, YLL increased by 0.81 years for Indian men and 0.82 years for Indian women with a BA between 2000 and 2019—an increase that actually exceeds the rise during the COVID-19 pandemic (0.71 years for men and 0.23 years for women). It seems unlikely that racialization alone could produce a greater impact on mortality than a global pandemic over a relatively short pre-pandemic period.

Based on the preceding discussion, the third and fourth mechanisms appear to align most closely with our findings. Importantly, these are not competing explanations but rather complementary forces shaping the mortality trends of Asian Americans. Differential racialization across Asian ethnicities may also contribute, though it is unlikely to be the predominant factor driving the observed divergence in mortality patterns. Our interpretations remain tentative, as these mechanisms likely interact in complex ways. Disentangling their relative contributions will require future research utilizing individual-level longitudinal data that can track health outcomes, health behaviors, and experiences of labor market discrimination from the time of immigration onward. In addition, future studies should consider health transitions in countries of origin to better understand the forces behind the less favorable mortality trends observed among Asian Americans. Immigrant selectivity remains a crucial determinant of health, educational, and labor market outcomes (Feliciano, 2020). Ignoring immigrant selection can lead to biased estimates and misinterpretation of the results.

This study has several limitations. First, the education coding system in the mortality data changed during our study period. Additionally, there may be discrepancies between educational attainment as reported in the ACS and as recorded on death certificates (Hendi, 2017). Nevertheless, many studies have used these data sources to construct education-specific mortality rates (Case & Deaton, 2021; Geronimus et al., 2019; Zheng & Choi, 2024), and similar trends have been observed using the National Health Interview Survey (Sasson, 2017), lending support to the validity of our approach. Second, while we applied misclassification ratios to correct for the underestimation of death counts among the Asian American population, these ratios do not account for variation across Asian subgroups, as subgroup-specific data are currently unavailable. If racial misclassification varies by subgroup and over time (Arias et al., 2016), our estimates of mortality trends could be biased. Therefore, findings—particularly the observed increase in YLL among Asian Indians prior to the pandemic—should be interpreted with appropriate caution.

Notwithstanding these limitations, this study reveals a surprising and concerning trend: Asian Americans experienced a less favorable mortality trajectory than Whites in the first two decades of the 21st century, particularly among higher educated individuals. This finding is striking given that Asians in America, on average, possess higher socioeconomic status and engage in healthier behaviors than other racial groups. Yet, our analysis suggests that Asian Americans derive fewer health benefits from higher education compared to Whites over time. In addition, this study suggests that combining various Asian ethnicities into a single group would result in misleading conclusions about the mortality trends of Asian Americans. Pan-ethnic analyses would be heavily influenced by the two largest ethnic groups, Chinese and Indian individuals, who exhibit contrasting trends in mortality reduction over time. It is important to consider the unique origins, experiences and characteristics of different Asian ethnicities to gain a more accurate understanding of their mortality patterns.

Supplementary Material

gbaf248_Supplementary_Data
gbaf248_supplementary_data.zip (12.5MB, zip)

Contributor Information

Hui Zheng, Department of Sociology, University of Toronto, Toronto, Ontario, Canada.

Yoonyoung Choi, Department of Sociology, University of Arizona, Tucson, Arizona, United States.

Leafia Ye, Department of Sociology, University of Toronto, Toronto, Ontario, Canada.

Ming Wen, Department of Sociology, University of Hong Kong, Hong Kong SAR, China.

Marc A Garcia, (Social Sciences Section).

Supplementary material

Supplementary data are available at The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences online.

Funding

None declared.

Conflict of interest

None declared.

Data Availability

All relevant data and computing codes are deposited on the OSF repository https://osf.io/rj738/overview.

Author contributions

All authors have contributed to this paper. H.Z. designed the study and wrote the first draft of the paper. Y.C. conducted the analyses and wrote the first draft of methods section. L.Y. contributed to the visualization. Both L.Y. and M.W. provided suggestions throughout the whole process and reviewed and revised the paper.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gbaf248_Supplementary_Data
gbaf248_supplementary_data.zip (12.5MB, zip)

Data Availability Statement

All relevant data and computing codes are deposited on the OSF repository https://osf.io/rj738/overview.

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