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Background: This article examines the utilization patterns of community birth (CB) and midwife-attended birth (MAB) among Asian/Pacific Islander (API) populations in the United States. It highlights the presence of significant racial-ethnic disparities and discusses cultural variations that influence these birth choices. Objectives: To describe variation in the probability of CB and MAB in low-risk pregnancies across API communities and to explore contributors to these variations, including traditional birth practices, cultural beliefs, and acculturation. Methods: The study employs logistic regression analysis of 2010-2020 birth certificate data to examine the probability of CB and MAB across pan-ethnic racial-ethnic groups and API subgroups. The data include information on place of birth, birth attendant, maternal demographics, and race-ethnicity, providing a comprehensive view of maternity care utilization among diverse populations. Results: The findings reveal that CB and MAB rates are significantly lower among API groups compared to other pan-ethnic groups. Among API subgroups, there is substantial heterogeneity in the uptake of CB and MAB, with lower rates among Asian Indians and Chinese birthing people and higher rates in Hawaiian, Japanese, and Guamanian populations. Conclusion: The study underscores the importance of addressing racial-ethnic disparities in maternity care and promoting culturally sensitive approaches. Factors such as traditional birth customs, cultural beliefs, and acculturation significantly influence the choice of maternity care among API communities. Tailored interventions that consider the cultural differences and values of API subgroups are essential to enhancing the adoption of low-intervention care models and reducing healthcare disparities in maternal and infant outcomes.
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Not All Asians are the Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social Media
Recent policy initiatives have acknowledged the importance of disaggregating data pertaining to diverse Asian ethnic communities to gain a more comprehensive understanding of their current status and to improve their overall well-being. However, research on anti-Asian racism has thus far fallen short of properly incorporating data disaggregation practices. Our study addresses this gap by collecting 12-month-long data from X (formerly known as Twitter) that contain diverse sub-ethnic group representations within Asian communities. In this dataset, we break down anti-Asian toxic messages based on both temporal and ethnic factors and conduct a series of comparative analyses of toxic messages, targeting different ethnic groups. Using temporal persistence analysis, š-gram-based correspondence analysis, and topic modeling, this study provides compelling evidence that anti-Asian messages comprise various distinctive narratives. Certain messages targeting sub-ethnic Asian groups entail different topics that distinguish them from those targeting Asians in a generic manner or those aimed at major ethnic groups, such as Chinese and Indian. By introducing several techniques that facilitate comparisons of online anti-Asian hate towards diverse ethnic communities, this study highlights the importance of taking a nuanced and disaggregated approach for understanding racial hatred to formulate effective mitigation strategies.
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- Award ID(s):
- 2210137
- PAR ID:
- 10568026
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400701719
- Page Range / eLocation ID:
- 2615 to 2626
- Subject(s) / Keyword(s):
- Anti-Asian sentiment, Racism against Asian, Panethnicity, Disaggregated Asian American data, Topic modeling, Social media mining
- Format(s):
- Medium: X
- Location:
- Singapore Singapore
- Sponsoring Org:
- National Science Foundation
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Background: This article examines the utilization patterns of community birth (CB) and midwife-attended birth (MAB) among Asian/Pacific Islander (API) populations in the United States. It highlights the presence of significant racial-ethnic disparities and discusses cultural variations that influence these birth choices. Objectives: To describe variation in the probability of CB and MAB in low-risk pregnancies across API communities and to explore contributors to these variations, including traditional birth practices, cultural beliefs, and acculturation. Methods: The study employs logistic regression analysis of 2010-2020 birth certificate data to examine the probability of CB and MAB across pan-ethnic racial-ethnic groups and API subgroups. The data include information on place of birth, birth attendant, maternal demographics, and race-ethnicity, providing a comprehensive view of maternity care utilization among diverse populations. Results: The findings reveal that CB and MAB rates are significantly lower among API groups compared to other pan-ethnic groups. Among API subgroups, there is substantial heterogeneity in the uptake of CB and MAB, with lower rates among Asian Indians and Chinese birthing people and higher rates in Hawaiian, Japanese, and Guamanian populations. Conclusion: The study underscores the importance of addressing racial-ethnic disparities in maternity care and promoting culturally sensitive approaches. Factors such as traditional birth customs, cultural beliefs, and acculturation significantly influence the choice of maternity care among API communities. Tailored interventions that consider the cultural differences and values of API subgroups are essential to enhancing the adoption of low-intervention care models and reducing healthcare disparities in maternal and infant outcomes.more » « less
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Anti-Asian prejudice increased during the COVID-19 pandemic, evidenced by a rise in physical attacks on individuals of Asian descent. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media platforms such as Twitter. In the present study, we investigated temporal and geographic patterns in the prevalence of social media content relevant to anti-Asian prejudice within the U.S. and worldwide. Specifically, we used the Twitter Data Collection API to query over 13 million tweets posted during the first 15 months of the pandemic (i.e., from January 30, 2020 to April 30, 2021), for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. Results of a range of exploratory and descriptive analyses offer novel insights. For instance, in the U.S., results from a burst analysis indicated that the prevalence of negative (anti-Asian) and positive (counter-hate) messages fluctuated over time in patterns that largely mirrored salient events relevant to COVID-19 (e.g., political tweets, highly-visible hate crimes targeting Asians). Other representative findings include geographic differences in the frequency of negative and positive keywords that shed light on the regions within the U.S. and the countries worldwide in which negative and positive messages were most frequent. Additional analyses revealed informative patterns in the prevalence of original tweets versus retweets, the co-occurrence of negative and positive content within a tweet, and fluctuations in content in relation to the number of new COVID-19 cases and reported COVID-related deaths. Together,more » « less
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Recent studies have documented increases in anti-Asian hate throughout the COVID-19 pandemic. Yet relatively little is known about how anti-Asian content on social media, as well as positive messages to combat the hate, have varied over time. In this study, we investigated temporal changes in the frequency of anti-Asian and counter-hate messages on Twitter during the first 16 months of the COVID-19 pandemic. Using the Twitter Data Collection Application Programming Interface, we queried all tweets from January 30, 2020 to April 30, 2021 that contained specific anti-Asian (e.g., #chinavirus, #kungflu) and counter-hate (e.g., #hateisavirus) keywords. From this initial data set, we extracted a random subset of 1,000 Twitter users who had used one or more anti-Asian or counter-hate keywords. For each of these users, we calculated the total number of anti-Asian and counter-hate keywords posted each month. Latent growth curve analysis revealed that the frequency of anti-Asian keywords fluctuated over time in a curvilinear pattern, increasing steadily in the early months and then decreasing in the later months of our data collection. In contrast, the frequency of counter-hate keywords remained low for several months and then increased in a linear manner. Significant between-user variability in both anti-Asian and counter-hate content was observed, highlighting individual differences in the generation of hate and counter-hate messages within our sample. Together, these findings begin to shed light on longitudinal patterns of hate and counter-hate on social media during the COVID-19 pandemic.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3589334.3645630
