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Disaggregating Asian-American Mortality in Drug-Related Overdoses and Behavioral Disorders: A Cross-Sectional Study.
Journal of Racial and Ethnic Health Disparities 2024 March 27
Asian Americans have been historically underrepresented in the national drug overdose discourse due to their lower substance use and overdose rates compared to other racial/ethnic groups. However, aggregated analyses fail to capture the vast diversity among Asian-American subgroups, obscuring critical disparities. We conducted a cross-sectional study between 2018 and 2021 examining Asian-American individuals within the CDC WONDER database with drug overdoses as the underlying cause of death (n = 3195; ICD-10 codes X40-X44, X60-X64, X85, and Y10-Y14) or psychoactive substance-related mental and behavioral disorders as one of multiple causes of death (n = 15,513; ICD-10 codes F10-F19). Proportional mortality ratios were calculated, comparing disaggregated Asian-American subgroups to the reference group (Asian Americans as a single aggregate group). Z-tests identified significant differences between subgroups. Compared to the reference group (0.99%), drug overdose deaths were less prevalent among Japanese (0.46%; p < 0.001), Chinese (0.47%; p < 0.001), and Filipino (0.82%; p < 0.001) subgroups, contrasting with a higher prevalence among Asian Indian (1.20%; p < 0.001), Vietnamese (1.35%; p < 0.001), Korean (1.36%; p < 0.001), and other Asian (1.79%; p < 0.001) subgroups. Similarly, compared to the reference group (4.80%), deaths from mental and behavioral disorders were less prevalent among Chinese (3.18%; p < 0.001), Filipino (4.52%; p < 0.001), and Asian Indian (4.56%; p < 0.001) subgroups, while more prevalent among Korean (5.60%; p < 0.001), Vietnamese (5.64%; p < 0.001), Japanese (5.81%; p < 0.001), and other Asian (6.14%; p < 0.001) subgroups. Disaggregated data also revealed substantial geographical variations in these deaths obscured by aggregated analyses. Our findings revealed pronounced intra-racial disparities, underscoring the importance of data disaggregation to inform targeted clinical and public health interventions.
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