Disparities in COVID-19 testing and outcomes among Asian American and Pacific Islanders: an observational study in a large health care system | BMC Public Health

We uniquely demonstrate large variations in COVID-19 testing proportions, test positivity, and disease severity in the Asian Americans and NH/PIs population in Northern and Central California. Our disaggregated Asian subgroup data reveal previously obscured outcomes.

The total percentage of Asian Americans and NH/PIs tested at our health care sites was lower than their share of the area and health system population prevalence. This documented lack of testing suggests an undercounting of cases, and therefore an underestimate of the COVID-19 disease burden in these populations. Generally, COVID-19 testing rates by race and ethnicity are not well understood due to incomplete data collection. Reports from California regarding testing early during the pandemic and even more recently as of March 2022, were missing a substantial amount, as much as 30–40%, of race/ethnicity information [24]. Decreased testing could be due to different mechanisms. The lack of testing sites in underserved neighborhoods, difficulty locating and navigating to a testing site due to insurance type, language abilities; increased fear to travel due to fear of contracting COVID -19 and fear of anti-Asian violence have all been posited as causes for lack of testing [25,26,27,28].

The percent of test positivity was overall increased for the total Asian Americans and NH/PIs population compared to NHW but differed between Asian subgroups. Test positivity or COVID-19 infection could be affected by differential exposure to SARS-CoV-2 virus; as well as inability to protect against infection if exposed to the virus. Asian subgroups, such as Filipinos, Vietnamese, and Pacific Islanders are known to have a larger share of their population as essential workers in health care, supermarkets, restaurants, and other occupations with potentially increased exposure to the infected and decreased ability to work from home [29,30,31]. In the early phases of the pandemic, lack of adequate PPE was documented for many essential workers such as food workers, mass transit staff, and public safety employees.

Once infected, Asian populations who have lower household incomes may not have been able to take time off from work, or may live in crowded conditions and might not have the ability to isolate, increasing the chance of transmitting and others contracting COVID-19 disease [32]. Asian Americans and NH/PIs are more likely to live in multigenerational households compared to NHW overall, which could increase risk of COVID-19 exposure of individuals at higher risk of serious disease within the same household [33,34,35,36]. Differences among Asian subgroups in educational attainment and occupation may contribute to differences in risk of COVID-19 exposure [7, 37]. Asian Indian, Korean, and Japanese subgroups have the highest educational attainment rates among Asian Americans and NH/PIs, while, Pacific Islanders have one of the lowest educational attainment rates [38]. During the pandemic, some individuals who worked high-income jobs were more likely to be able to telework, decreasing risk of COVID-19 exposure. Although the Chinese, Japanese and Korean subgroups had a lower test positivity rate than NHW, all other Asian Americans and NH/PIs subgroups had a higher test positivity rate compared to NHW, demonstrating that subsuming all subgroups under the umbrella term of “AAPI” renders these differences in test positivity invisible.

The Asian Americans and NH/PIs population as a whole was more likely to be hospitalized compared to NHW. Furthermore, proportions of hospitalized Asian subgroups varied greatly. The likelihood of hospitalization varied from twice as likely as NHW for Chinese, Vietnamese, Filipino, Japanese, and “other Asian” to no significant difference compared to NHW for Asian Indians and Koreans. While we adjusted for the socio-demographic and clinical factors, other mechanisms to develop serious disease requiring hospitalization include delayed testing and diagnosis of COVID-19 as well as delayed access to health care until disease severity required hospitalization [39, 40]. Immigration status, length of time in the country, and primary language spoken can all affect health care access. Newly arrived immigrants or undocumented immigrants may not be eligible for insurance or fear seeking health care due to anti-Asian xenophobia [41]. Furthermore, low-income immigrants may also demonstrate hesitation toward preventative care-seeking until care is urgently necessary (prompting hospitalization ER visits, etc.) due to cost or insurance barriers, which may also lead to worsened health outcomes during the pandemic [42]. Telehealth, a touted technological solution to health care access, might not be as accessible for limited English proficient individuals and individuals with limited technology literacy. In addition, uninsured or low socioeconomic status individuals may not have access to electronic devices or reliable internet connectivity required for a quality telehealth visit [43].

Our work also demonstrates unique profiles among Asian subgroups in the context of COVID-19. We are aware of only one other analysis by Marcello et al. which analyzed outcomes of three Asian subgroups: Chinese, South Asian, and “other Asians” in the New York City public hospital system during the first three months of the pandemic [6]. Of note, this study identified the subgroup population by utilizing EHR race/ethnicity and ancestry data. They found that South Asians had the highest proportions of COVID-19 disease and hospitalization among Asians in their study population. The differences in proportions of South Asian vs. Asian Indian hospitalization between our studies is most probably secondary to the Marcello study definition of South Asian encompassing many subgroups (Afghani, Bangladeshi, Indian, Nepalese, Pakistani, Sri Lankan). This population thus was heterogeneous population including a larger proportion of essential workers and seniors among the South Asian community in New York City compared to our study’s more homogeneous, younger Asian Indian community in Northern and Central California.

We demonstrate uniquely with the COVID-19 pandemic, differential testing proportions and severe outcomes due to the fact that Asian subgroup populations have different demographic, socio-economic (including predominant immigration status and occupations), and health profiles [7, 29, 44]. Aggregated Asian data perpetuates structural perpetuation of racist stereotypes and notions of the “model minority myth” and “healthy immigrant effect” that adversely affect Asian Americans and NH/PIs health generally and during the pandemic [18]. The generalization of good health status among Asian Americans and NH/PIs oversimplifies and obscures worse health outcomes unique to different Asian subgroups. It bears mention that while the rise in anti-Asian violence has impacted the entire Asian Americans and NH/PIs community, the exacerbation of racism and xenophobia has especially impacted Chinese communities due to the initial cases of COVID-19 identified in Wuhan, China [45]. The increased fear of anti-Asian violence not only could have deterred many Asian Americans, and specifically members of the Chinese community, from seeking testing or health care, but possibly exacerbated illness, medical and mental health conditions, leading to increased mortality [41].

Limitations and strengths

This study is an analysis of a single health system in Northern and Central California. The Asian Americans and NH/PIs population studied may not be generalizable to other populations. However, the health system encompasses the largest concentration of Asian Americans and NH/PIs in the continental US. To the best of our knowledge, this is the only large health system that has been committed to systematically disaggregating Asian Americans and NH/PIs data into the six major Asian subgroups, “other Asian”, Pacific Islander, and mixed race subgroups, providing a unique source of timely clinical data.

The San Francisco Bay Area public health systems were among the first to take committed, ongoing measures to control the pandemic in March 2020. Our COVID-19 outcomes might be much better than other areas of the US and consequently, our analyses of subgroup severe outcomes could be limited by sample size to demonstrate statistical significance, for the outcomes of ICU admission and death.

During the beginning of the pandemic, COVID-19 testing was limited and primarily conducted in individuals with symptoms and with a history of travelling to China [46]. Patients could have been tested not at all, or in venues such as trusted cultural community centers outside of the Sutter Health system [26, 47]. However, while the Sutter Health testing data might be limited, our data is a true reflection of the extent to which the patients with COVID-19 had outcomes of hospitalization, ICU admission and death. Careful analysis and interpretation of these data are the beginnings of insight in understanding the effect of the pandemic on Asian Americans and NH/PIs subgroups.