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Objectives. To illustrate the spatiotemporal distribution of geolocated tweets that contain anti-Asian hate language in the contiguous United States during the early phase of the COVID-19 pandemic. Methods. We used a data set of geolocated tweets that match with keywords reflecting COVID-19 and anti-Asian hate and identified geographical clusters using the space-time scan statistic with Bernoulli model. Results. Anti-Asian hate language surged between January and March 2020. We found clusters of hate across the contiguous United States. The strongest cluster consisted of a single county (Ross County, Ohio), where the proportion of hateful tweets was 312.13 times higher than for the rest of the country. Conclusions. Anti-Asian hate on Twitter exhibits a significantly clustered spatiotemporal distribution. Clusters vary in size, duration, strength, and location and are scattered across the entire contiguous United States. Public Health Implications. Our results can inform decision-makers in public health and safety for allocating resources for place-based preparedness and response for pandemic-induced racism as a public health threat. (Am J Public Health. 2022;112(4):646–649. https://doi.org/10.2105/AJPH.2021.306653

Public Health 3.0 calls for the inclusion of new partners and novel data to bring systemic change to the US public health landscape. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has illuminated significant data gaps influenced by ongoing colonial legacies of racism and erasure. American Indian and Alaska Native (AI/AN) populations and communities have been disproportionately affected by incomplete public health data and by the COVID-19 pandemic itself. Our findings indicate that only 26 US states were able to calculate COVID-19‒related death rates for AI/AN populations. Given that 37 states have Indian Health Service locations, we argue that public health researchers and practitioners should have a far larger data set of aggregated public health information on AI/AN populations. Despite enormous obstacles, local Tribal facilities have created effective community responses to COVID-19 testing, tracking, and vaccine administration. Their knowledge can lead the way to a healthier nation. Federal and state governments and health agencies must learn to responsibly support Tribal efforts, collect data from AI/AN persons in partnership with Indian Health Service and Tribal governments, and communicate effectively with Tribal authorities to ensure Indigenous data sovereignty. (Am J Public Health. 2021;111(S3): S208–S214. https://doi.org/10.2105/AJPH.2021.306415 )

Indigenous Peoples are at an increased risk for infectious disease, including COVID-19, due to the historically embedded deleterious social determinants of health. Furthermore, structural limitations in Canadian federal government data contribute to the lack of comparative rates of COVID-19 between Indigenous and non-Indigenous people. To make visible Indigenous Peoples’ experiences in the public health discourse in the midst of COVID-19, this paper aims to answer the following interrelated research questions: (1) What are the associations of key social determinants of health and COVID-19 cases among Canadian health regions? and (2) How do these relationships relate to Indigenous communities? As both proximal and distal social determinants of health conjointly contribute to COVID-19 impacts on Indigenous health, this study used a unique dataset assembled from multiple sources to examine the associations among key social determinants of health characteristics and health with a focus on Indigenous Peoples. We highlight key social vulnerabilities that stem from systemic racism and that place Indigenous populations at increased risk for COVID-19. Many Indigenous health issues are rooted in the historical impacts of colonization, and partially invisible due to systemic federal underfunding in Indigenous communities. The Canadian government must invest in collecting accurate, reliable, and disaggregated data on COVID-19 case counts for Indigenous Peoples, as well as in improving Indigenous community infrastructure and services.

BackgroundThe coronavirus disease pandemic has disproportionately affected poor and racial/ethnic minority individuals and communities, especially Indigenous Peoples. The object of this study is to understand the spatially varying associations between socioeconomic disadvantages and the number of confirmed COVID-19 cases in New Mexico at the ZIP code level.MethodsWe constructed ZIP code-level data (n=372) using the 2014–2018 American Community Survey and COVID-19 data from the New Mexico Department of Health (as of 24 May 2020). The log-linear Poisson and geographically weighted Poisson regression are applied to model the number of confirmed COVID-19 cases (total population as the offset) in a ZIP code.ResultsThe number of confirmed COVID-19 cases in a ZIP code is positively associated with socioeconomic disadvantages—specifically, the high levels of concentrated disadvantage and income inequality. It is also positively associated with the percentage of American Indian and Alaskan Native populations, net of other potential confounders at the ZIP code level. Importantly, these associations are spatially varying in that some ZIP codes suffer more from concentrated disadvantage than others.ConclusionsAdditional attention for COVID-19 mitigation effort should focus on areas with higher levels of concentrated disadvantage, income inequality, and higher percentage of American Indian and Alaska Native populations as these areas have higher incidence of COVID-19. The findings also highlight the importance of plumbing in all households for access to clean and safe water, and the dissemination of educational materials aimed at COVID-19 prevention in non-English language including Indigenous languages.

Native Americans are disproportionately affected by COVID-19. The present study explores whether areas with high percentages of Native American residents are experiencing the equal risks of contracting COVID-19 by examining how the relationships between structural inequalities and confirmed COVID-19 cases spatially vary across Arizona using a geographically weighted regression (GWR). GWR helps with the identification of areas with high confirmed COVID-19 cases in Arizona and with understanding of which predictors of social inequalities are associated with confirmed COVID-19 cases at specific locations. We find that structural inequality indicators and presence of Native Americans are significantly associated with higher confirmed COVID-19 cases; and the relationships between structural inequalities and confirmed COVID-19 cases are significantly stronger in areas with high concentration of Native Americans, particular on Tribal lands. The findings highlight the negative effects that lack of infrastructure (i.e., housing with plumbing, transportation, and accessible health communication) may have on individual and population health, and, in this case, associated with the increase of confirmed COVID-19 cases.

Objectives We investigated the patterns of foreign-born Hispanic health convergence to U.S.-born Hispanics using an allostatic load index, a subjective biological risk health profile, and we explored whether the health convergence patterns differ by sex. Methods The analytic sample consisted of 3,347 Hispanics from the pooled 2005–2010 National Health and Nutrition Examination Survey. We used negative binomial regression models to investigate the association between duration in the United States and the allostatic load index, while controlling for potential covariates. Results Foreign-born Hispanics who had lived in the United States for 0–9 years and 10–19 years had lower levels of allostatic load than U.S.-born Hispanics; however, those who had lived in the United States for 20 or more years had a level of allostatic load similar to their U.S.-born counterparts. The pattern of immigrant health convergence shows a clear sex difference. In the sex-stratified models, we found that foreign-born Hispanic men converged to the level of allostatic load of U.S.-born Hispanic men after having lived in the United States for approximately 10 years. The health convergence pattern qualitatively differed for foreign-born Hispanic women, who remained healthier than U.S.-born Hispanic women regardless of duration in the United States. Conclusions Foreign-born Hispanics are healthier than their U.S.-born counterparts, providing support for the healthy migrant hypothesis. This relatively better health of foreign-born Hispanics disappears with a longer duration in the United States, providing support for the health convergence hypothesis, but is most noticeable for men. Foreign-born Hispanic women converge to U.S.-born Hispanic women's health status at a slower tempo, compared with foreign-born Hispanic men.

With the substantial demographic changes in racial composition in the United States since 1965, research on racial health inequities must build upon the Black-White binary to assess the complex ways “race” affects health and aging. Considering variation in the prevalence and meanings of aging across racialized groups requires concerted efforts to expand and disaggregate samples. Aligned with the goals of the intersectionality framework, we argue that greater inclusion of Asian Americans is critical to advance both theoretical and methodological considerations that enable us to investigate the lived experiences of Asian Americans. Using caregiving as an example, we discuss how systemic, cultural, and interpersonal marginalization from racism and other oppressive systems intertwine with “race” to produce the race effects. Greater inclusion of Asian Americans helps further provide the opportunity to conceptualize culture as dynamic and interacting with structure to produce different racial patterns. Meaningful inclusion of Asian Americans in research requires more systemic effort to collect accurate, reliable, and quality data for Asian Americans that can be disaggregated by other important axes of stratification.

Indigenous Peoples in the United States have been experiencing disproportionate impacts of COVID-19. American Indian and Alaska Native persons are more likely to be infected, experience complications, and die from coronavirus. Evidence suggests that Indigenous persons have 3.5 times the incidence rate of non-Hispanic/Latinx whites. Unfortunately, this is likely a gross underestimate because of a lack of reliable and accurate COVID-19 data for American Indian and Alaska Native populations. Multiple factors contribute to poor data quality including the lack of Indigenous representation in the data and rampant racial misclassification at both the individual and group levels. The current pandemic has shed light on multiple pre-existing issues related to Indigenous data sovereignty in data collection and management. We discuss the importance of centring Indigenous data sovereignty in the systemic efforts to increase COVID-19 data availability and quality. The federal and state governments must support and promote Tribes' rights to access data. Federal and state governments should also focus on bolstering their data availability and quality for aggregated data on AIAN populations and for providing disaggregated Tribal data to Tribes. Given the pivotal moment in the United States with ongoing and parallel pandemics of coronavirus and racism, we urge demographers and population scientists to reflect on the role of structural racism in data, data collection and analysis.

The Coronavirus 2019 (COVID-19) pandemic has disproportionally affected Indigenous Peoples. Unfortunately, there is no accurate understanding of COVID-19's impacts on Indigenous Peoples and communities due to systematic erasure of Indigenous representation in data. Early evidence suggests that COVID-19 has been able to spread through pre-pandemic mechanisms ranging from disproportionate chronic health conditions, inadequate access to healthcare, and poor living conditions stemming from structural inequalities. Using innovative data, we comprehensively investigate the impacts of COVID-19 on Indigenous Peoples in New Mexico at the zip code level. Specifically, we expand the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) to include the measures of structural vulnerabilities from historical racisms against Indigenous Peoples. We found that historically-embedded structural vulnerabilities (e.g., Tribal land status and higher percentages of house units without telephone and complete plumbing) are critical in understanding the disproportionate burden of COVID-19 that American Indian and Alaska Native populations are experiencing. We found that historically-embedded vulnerability variables that emerged epistemologically from Indigenous knowledge had the largest explanatory power compared to other social vulnerability factors from SVI and COVID-19, especially Tribal land status. The findings demonstrate the critical need in public health to center Indigenous knowledge and methodologies in mitigating the deleterious impacts of COVID-19 on Indigenous Peoples and communities, specifically designing place-based mitigating strategies.

Individual-, family-, and contextual-level factors can simultaneously and interactively affect a child's body mass index (BMI). We examine parental nativity as a key determinant of changes in children's BMI over time. Prior research on this topic has been inconclusive. A longitudinal sample of households with children residing in four low-income, high minority New Jersey cities provided data on demographics, socioeconomic status, anthropometric measures, as well as dietary and physical activity behaviors for one randomly selected child. The baseline interview for two separate cohorts took place in 2009/10 and 2014-15, with a follow-up interview 2–5 years later. The outcome variable, change in BMI z-score was divided into three categories (decrease in BMI z-score; no meaningful change; increase in BMI z-score) and analyzed using ordinal logistic regressions. About 28% of the children in the sample had at least one foreign-born parent. For the two major racial/ethnic groups, i.e., Hispanics and non-Hispanic blacks, having a foreign-born parent was associated with a favorable change in BMI—children of foreign-born parents were more likely to experience a decrease BMI z-score between baseline and follow-up. Multivariate analyses reveal that the initial association between parental nativity and children's BMI change (OR = 0.20; p

This paper assesses how ongoing historical racism and nativism as embedded within U.S. culture requires new and important dialogues about the omnipresence of White supremacy and its interconnected mechanisms that divide communities along the lines of race and perceived in-group status. To assess the role of immigration as it is understood through paradigms of White supremacy and systemic racism, the current study examines individual-level predictors of indifference to the BLM movement based on nativity status among Asian Americans—a racialized pan-ethnic group that is comprised of predominantly foreign-born members. Using the 2016 Collaborative Multiracial Post-Election Survey, one of the few nationally representative surveys that include detailed information about the Black Lives Matter movement, our study includes 1371 Asian immigrants (i.e., foreign-born Asian Americans) and 1635 U.S.-born Asian Americans. Results demonstrate that reporting indifference to the BLM movement differ by nativity such that foreign-born Asian Americans were significantly more likely to report indifference to the BLM movement compared to their U.S.-born Asian American counterparts. However, the impact of nativity disappears once we account for sense of belonging and acknowledgement of anti-Black racism. The sense of belonging was significant in predicting indifference to the BLM movement among U.S.-born Asian Americans only. The findings contribute to our understanding of racial sense making for Asian Americans as well as an understanding of how White supremacy translates to anti-Black racism through multiple and interconnected mechanisms for the maintenance of White supremacy.

Soo Mee Kim and Aggie J. Yellow Horse on the harms of Asian erasure in immigration debates.