Unmet Healthcare Needs in Asian Americans: Challenging the Myth of Model Minority

Background: A substantial proportion of the Asian American population comprises foreign-born immigrants with language barriers, but national surveys are often unable to address their cultural and linguistic challenges. Many population-based surveys use English as their primary survey language, which systematically limits the participation of persons with limited English proficiency. Findings based on English-proficient samples of Asian Americans are likely to be biased upward. For example, according to the Medical Expenditure Panel Survey (MEPS), Asian Americans have the lowest rate (2.8%) of unmet healthcare needs of all racial/ethnic groups.

The overall purpose of this study was to revisit unmet healthcare needs of Asian Americans, using a sample collected through culturally and linguistically sensitive approaches. Aim 1 is to identify the risk group typology of healthcare access based on general risk factors (health insurance, usual place for care, and income) and immigrant-specific risk factors (nativity, length of stay in the U.S., English proficiency, and acculturation). Aim 2 is to examine the characteristics of the identified groups and their contribution to predicting unmet healthcare needs.    

Methods: Data came from the 2,609 participants in the 2015 Asian American Quality of Life (AAQoL) survey. As part of the culturally and linguistically sensitive approaches, the AAQoL survey questionnaires were available in 8 languages (English, Chinese, Korean, Vietnamese, Hindi, Gujarati, and Tagalog). Self-administrated surveys using a paper and pencil format were conducted from August to December, 2015 in Central Texas. Eligible individuals were self-identified Asians 18 years or older. Unmet healthcare needs were assessed by asking respondents whether there was a time in the past 12 months when they needed medical care but could not get it.

Latent profile analysis was conducted using the general risk factors (health insurance, usual place for care, and income) and immigrant-specific risk factors (nativity, length of stay in the U.S., English proficiency, and acculturation). Logistic regression was estimated to examine the contribution of group membership to the unmet healthcare needs.   

Results: Among a total of 2,609 participants, almost half (48.5%) used non-English versions of the survey questionnaire, which indicates that our culturally and linguistically sensitive approaches enabled many individuals who are conventionally unrepresented to be included. Compared with the 2.8% observed in the MEPS, a substantially higher proportion (11.5%) of the present sample had unmet healthcare needs.

Latent profile analysis identified a three-cluster model (BIC=23,243.99, Entropy= .99, LMR-LRT= p < .001, BLRT= p < .001) as the most optimal, and they were labeled the “low-risk group,” “moderate-risk group,” and “high-risk group.” Compared with the low-risk group, the odds of having an unmet need was 1.52 times more likely in the moderate-risk group (95% confidence interval [CI] = 1.01−2.30, p < .05) and 2.24 times more likely in the high-risk group (95% CI = 1.45−3.46, p < .001).    

 

Conclusions: Challenging the myth of model minority, the present sample of Asian Americans demonstrates its vulnerability in access to healthcare. Findings also show the heterogeneity in healthcare access risk profiles.