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Widening Health Disparities: Diabetes and Prediabetes Prevalence from 2001 to 2022 Among Six Asian American Subgroups and Other Racial and Ethnic Groups in California

Oct 2026 · Journal of Social Issues · 0 citations · 33 references

Abstract

Asian Americans, the fastest‐growing racial group in the United States, are frequently aggregated into a single category in national surveillance, masking substantial heterogeneity in diabetes risk across subgroups. Drawing on fundamental cause theory, a life course perspective, and the social determinants of health framework, this repeated cross‐sectional analysis of the California Health Interview Survey (2001–2022) examined 21‐year trends in diabetes and prediabetes prevalence among 320,098 adults aged 50 and older across nine racial and ethnic groups, including non‐Hispanic White (NHW), non‐Hispanic Black (NHB), Hispanic, Chinese, Filipino, Japanese, Korean, South Asian, and Vietnamese. After adjustment for age, sex, nativity, education, income, English proficiency, and health insurance coverage, diabetes prevalence in 2021–2022 was highest among Filipino (32.8%) and South Asian (28.3%) adults. Korean older adults had the fastest adjusted annual percentage change (APC) in diabetes prevalence (3.98%/year), followed by Filipino (3.74%/year) and Vietnamese (3.15%/year). Vietnamese older adults showed the steepest prediabetes increase (16.25%/year). Adjusted prevalence ratios for diabetes relative to NHW adults were 2.13 (95% confidence interval [CI], 1.92, 2.37) for Filipino and 2.03 (95% CI, 1.76, 2.34) for South Asian older adults, exceeding both the NHB‐to‐NHW and the Hispanic‐to‐NHW disparities. Adjusting for body mass index amplified rather than attenuated prevalence estimates for Asian American subgroups. These findings show that aggregating Asian Americans into a single category obscures clinically important heterogeneity in diabetes burden. We discuss structural, social, and cultural factors that may contribute to these disparities and their implications for ethnicity‐specific screening, culturally tailored prevention, and disaggregated data collection policy.

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