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Wei H, Hswen Y, Merchant JS, Drew LB, Nguyen QC, Yue X, Mane H, Nguyen TT. From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022. J Med Internet Res 2024; 26:e53050. [PMID: 39250221 PMCID: PMC11420573 DOI: 10.2196/53050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 04/25/2024] [Accepted: 06/20/2024] [Indexed: 09/10/2024] Open
Abstract
BACKGROUND Anti-Asian hate crimes escalated during the COVID-19 pandemic; however, limited research has explored the association between social media sentiment and hate crimes toward Asian communities. OBJECTIVE This study aims to investigate the relationship between Twitter (rebranded as X) sentiment data and the occurrence of anti-Asian hate crimes in New York City from 2019 to 2022, a period encompassing both before and during COVID-19 pandemic conditions. METHODS We used a hate crime dataset from the New York City Police Department. This dataset included detailed information on the occurrence of anti-Asian hate crimes at the police precinct level from 2019 to 2022. We used Twitter's application programming interface for Academic Research to collect a random 1% sample of publicly available Twitter data in New York State, including New York City, that included 1 or more of the selected Asian-related keywords and applied support vector machine to classify sentiment. We measured sentiment toward the Asian community using the rates of negative and positive sentiment expressed in tweets at the monthly level (N=48). We used negative binomial models to explore the associations between sentiment levels and the number of anti-Asian hate crimes in the same month. We further adjusted our models for confounders such as the unemployment rate and the emergence of the COVID-19 pandemic. As sensitivity analyses, we used distributed lag models to capture 1- to 2-month lag times. RESULTS A point increase of 1% in negative sentiment rate toward the Asian community in the same month was associated with a 24% increase (incidence rate ratio [IRR] 1.24; 95% CI 1.07-1.44; P=.005) in the number of anti-Asian hate crimes. The association was slightly attenuated after adjusting for unemployment and COVID-19 emergence (ie, after March 2020; P=.008). The positive sentiment toward Asian tweets with a 0-month lag was associated with a 12% decrease (IRR 0.88; 95% CI 0.79-0.97; P=.002) in expected anti-Asian hate crimes in the same month, but the relationship was no longer significant after adjusting for the unemployment rate and the emergence of COVID-19 pandemic (P=.11). CONCLUSIONS A higher negative sentiment level was associated with more hate crimes specifically targeting the Asian community in the same month. The findings highlight the importance of monitoring public sentiment to predict and potentially mitigate hate crimes against Asian individuals.
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Affiliation(s)
- Hanxue Wei
- Department of City and Regional Planning, Cornell University, Ithaca, NY, United States
| | - Yulin Hswen
- Department of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
| | - Junaid S Merchant
- Department of Epidemiology & Biostatistics, University of Maryland, Maryland, MD, United States
| | - Laura B Drew
- Department of Epidemiology & Biostatistics, University of Maryland, Maryland, MD, United States
| | - Quynh C Nguyen
- Department of Epidemiology & Biostatistics, University of Maryland, Maryland, MD, United States
| | - Xiaohe Yue
- Department of Epidemiology & Biostatistics, University of Maryland, Maryland, MD, United States
| | - Heran Mane
- Department of Epidemiology & Biostatistics, University of Maryland, Maryland, MD, United States
| | - Thu T Nguyen
- Department of Epidemiology & Biostatistics, University of Maryland, Maryland, MD, United States
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Wang C, Bai YX, Li XW, Lin LT. Effects of extreme temperatures on public sentiment in 49 Chinese cities. Sci Rep 2024; 14:9954. [PMID: 38688992 PMCID: PMC11061318 DOI: 10.1038/s41598-024-60804-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/26/2024] [Indexed: 05/02/2024] Open
Abstract
The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.
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Affiliation(s)
- Chan Wang
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China
| | - Yi-Xiang Bai
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China.
| | - Xin-Wu Li
- School of Economics, Nankai University, Tianjin, 300071, People's Republic of China
| | - Lu-Tong Lin
- School of Economics, Guangdong University of Finance and Economics, Guangzhou, 510320, People's Republic of China
- School of Economics, Nankai University, Tianjin, 300071, People's Republic of China
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Kusuma IY, Suherman S. The Pulse of Long COVID on Twitter: A Social Network Analysis. ARCHIVES OF IRANIAN MEDICINE 2024; 27:36-43. [PMID: 38431959 PMCID: PMC10915926 DOI: 10.34172/aim.2024.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/12/2023] [Indexed: 03/05/2024]
Abstract
BACKGROUND Long coronavirus disease (COVID) is a complex and multifaceted health condition with a range of severe symptoms that can last for weeks or even months after the acute phase of the illness has passed. Employing social network analysis (SNA) can rapidly provide significant health information to communities related to long COVID. This study aimed to identify the key themes, most influential users, and overall sentiments in the Twitter discourse on long COVID. METHODS Data were collected from a Twitter search with the specific keywords "long COVID" from December 1, 2022, to February 22, 2023, using NodeXL Pro. Visualizations, including network graphs and key influencers, were created using Gephi, and sentiment analysis was conducted with Azure Machine. RESULTS In total, 119,185 tweets from 94325 users were related to long COVID. Top influencers include medical professionals, researchers, journalists, and public figures, with news media platforms as primary information sources; the most common hashtag was #longCOVID, indicating that it is a significant issue of concern among the Twitter community. In the sentiment analysis, most tweets were negative. CONCLUSION The study highlights the importance of critically evaluating information shared by influential users and seeking out multiple sources of information when making health-related decisions. In addition, it emphasizes the value of examining social media conversations to understand public discourse on long COVID and suggests that future researchers could explore the role of social media in shaping public perceptions and behaviors related to health issues. Strategies for enhancing scientific journal engagement and influence in online discussions are discussed as well.
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Affiliation(s)
- Ikhwan Yuda Kusuma
- Institute of Clinical Pharmacy, University of Szeged, H-6725 Szeged, Hungary
- Pharmacy Study Program, Faculty of Health, Universitas Harapan Bangsa, 53182 Purwokerto, Indonesia
| | - Suherman Suherman
- Doctoral School of Educational Sciences, Faculty Humanities and Social Science, University of Szeged, 6722 Szeged, Hungary
- Mathematics Education, Faculty of Teaching and Teacher Education, Universitas Islam Negeri Raden Intan Lampung, Indonesia
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Nguyen TT, Merchant JS, Criss S, Makres K, Gowda KN, Mane H, Yue X, Hswen Y, Glymour MM, Nguyen QC, Allen AM. Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021. J Med Internet Res 2023; 25:e44990. [PMID: 37115602 PMCID: PMC10182466 DOI: 10.2196/44990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Large racial and ethnic disparities in adverse birth outcomes persist. Increasing evidence points to the potential role of racism in creating and perpetuating these disparities. Valid measures of area-level racial attitudes and bias remain elusive, but capture an important and underexplored form of racism that may help explain these disparities. Cultural values and attitudes expressed through social media reflect and shape public norms and subsequent behaviors. Few studies have quantified attitudes toward different racial groups using social media with the aim of examining associations with birth outcomes. OBJECTIVE We used Twitter data to measure state-level racial sentiments and investigate associations with preterm birth (PTB) and low birth weight (LBW) in a multiracial or ethnic sample of mothers in the United States. METHODS A random 1% sample of publicly available tweets from January 1, 2011, to December 31, 2021, was collected using Twitter's Academic Application Programming Interface (N=56,400,097). Analyses were on English-language tweets from the United States that used one or more race-related keywords. We assessed the sentiment of each tweet using support vector machine, a supervised machine learning model. We used 5-fold cross-validation to assess model performance and achieved high accuracy for negative sentiment classification (91%) and a high F1 score (84%). For each year, the state-level racial sentiment was merged with birth data during that year (~3 million births per year). We estimated incidence ratios for LBW and PTB using log binomial regression models, among all mothers, Black mothers, racially minoritized mothers (Asian, Black, or Latina mothers), and White mothers. Models were controlled for individual-level maternal characteristics and state-level demographics. RESULTS Mothers living in states in the highest tertile of negative racial sentiment for tweets referencing racial and ethnic minoritized groups had an 8% higher (95% CI 3%-13%) incidence of LBW and 5% higher (95% CI 0%-11%) incidence of PTB compared to mothers living in the lowest tertile. Negative racial sentiment referencing racially minoritized groups was associated with adverse birth outcomes in the total population, among minoritized mothers, and White mothers. Black mothers living in states in the highest tertile of negative Black sentiment had 6% (95% CI 1%-11%) and 7% (95% CI 2%-13%) higher incidence of LBW and PTB, respectively, compared to mothers living in the lowest tertile. Negative Latinx sentiment was associated with a 6% (95% CI 1%-11%) and 3% (95% CI 0%-6%) higher incidence of LBW and PTB among Latina mothers, respectively. CONCLUSIONS Twitter-derived negative state-level racial sentiment toward racially minoritized groups was associated with a higher risk of adverse birth outcomes among the total population and racially minoritized groups. Policies and supports establishing an inclusive environment accepting of all races and cultures may decrease the overall risk of adverse birth outcomes and reduce racial birth outcome disparities.
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Affiliation(s)
- Thu T Nguyen
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Junaid S Merchant
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Shaniece Criss
- Department of Health Sciences, Furman University, Greenville, SC, United States
| | - Katrina Makres
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Krishik N Gowda
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Heran Mane
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Xiaohe Yue
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Yulin Hswen
- Department of Epidemiology and Biostatistics, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - M Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Quynh C Nguyen
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, United States
| | - Amani M Allen
- Divisions of Community Health Sciences and Epidemiology, University of California, Berkeley, Berkeley, CA, United States
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Duncan DT, Cook SH, Wood EP, Regan SD, Chaix B, Tian Y, Chunara R. Structural racism and homophobia evaluated through social media sentiment combined with activity spaces and associations with mental health among young sexual minority men. Soc Sci Med 2023; 320:115755. [PMID: 36739708 PMCID: PMC10014849 DOI: 10.1016/j.socscimed.2023.115755] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 01/20/2023] [Accepted: 01/29/2023] [Indexed: 02/01/2023]
Abstract
BACKGROUND Research suggests that structural racism and homophobia are associated with mental well-being. However, structural discrimination measures which are relevant to lived experiences and that evade self-report biases are needed. Social media and global-positioning systems (GPS) offer opportunity to measure place-based negative racial sentiment linked to relevant locations via precise geo-coding of activity spaces. This is vital for young sexual minority men (YSMM) of color who may experience both racial and sexual minority discrimination and subsequently poorer mental well-being. METHODS P18 Neighborhood Study (n = 147) data were used. Measures of place-based negative racial and sexual-orientation sentiment were created using geo-located social media as a proxy for racial climate via socially-meaningfully-defined places. Exposure to place-based negative sentiment was computed as an average of discrimination by places frequented using activity space measures per person. Outcomes were number of days of reported poor mental health in last 30 days. Zero-inflated Poisson regression analyses were used to assess influence of and type of relationship between place-based negative racial or sexual-orientation sentiment exposure and mental well-being, including the moderating effect of race/ethnicity. RESULTS We found evidence for a non-linear relationship between place-based negative racial sentiment and mental well-being among our racially and ethnically diverse sample of YSMM (p < .05), and significant differences in the relationship for different race/ethnicity groups (p < .05). The most pronounced differences were detected between Black and White non-Hispanic vs. Hispanic sexual minority men. At two standard deviations above the overall mean of negative racial sentiment exposure based on activity spaces, Black and White YSMM reported significantly more poor mental health days in comparison to Hispanic YSMM. CONCLUSIONS Effects of discrimination can vary by race/ethnicity and discrimination type. Experiencing place-based negative racial sentiment may have implications for mental well-being among YSMM regardless of race/ethnicity, which should be explored in future research including with larger samples sizes.
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Affiliation(s)
- Dustin T Duncan
- Department of Epidemiology, Columbia University Mailman School of Public Health, NewYork, NY, USA
| | - Stephanie H Cook
- Department of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY, USA; Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA
| | - Erica P Wood
- Department of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY, USA
| | - Seann D Regan
- Department of Epidemiology, Columbia University Mailman School of Public Health, NewYork, NY, USA
| | - Basile Chaix
- French National Institute of Health and Medical Research (INSERM), Sorbonne Université, Institut Pierre Louis D'Epidémiologie et de Santé Publique IPLESP, Nemesis Team, F75012, Paris, France
| | - Yijun Tian
- Department of Computer Science and Engineering, New York University Tandon School of Engineering, New York, NY, USA
| | - Rumi Chunara
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, USA; Department of Computer Science and Engineering, New York University Tandon School of Engineering, New York, NY, USA.
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Carr D. Ageism and late-life mortality: How community matters. Soc Sci Med 2023; 320:115501. [PMID: 36424283 PMCID: PMC9678335 DOI: 10.1016/j.socscimed.2022.115501] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 10/30/2022] [Indexed: 11/23/2022]
Abstract
AGEISM: the deeply entrenched biases that people hold about old age-is a persistent social problem that intensified during the COVID-19 pandemic. The harmful physical, emotional, and cognitive health consequences of individual-level age bias are well-documented, with most studies operationalizing ageism as an older adult's personal encounters with age discrimination, self-perceptions of their own aging, and internalized negative beliefs about old age. However, the impacts of community-level age bias on older adults' well-being have received less attention. This commentary reviews recent evidence (Kellogg et al.,) showing that county-level explicit age bias is associated with lower mortality rates among older adults, with effects limited to older adults residing in counties with relatively younger populations. Effects were not detected in counties with relatively older populations, or for implicit age bias. These counterintuitive findings require further exploration, including the use of more fine-grained measures of community-level ageism, attention to the role of gentrification in communities, and the development of new measures of structural ageism, drawing on approaches used to study the impacts of structural racism. Data science approaches, including the use of social media data in tandem with mortality data, may reveal how age bias affects older adults. Communities are especially important to older adults, who spend much of their time in areas immediately proximate to their homes. As more individuals age in place, and as federal funding for home-based and community services (HCBS) increases, researchers should identify which community-level characteristics, including age bias, undermine or enhance late-life well-being.
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Affiliation(s)
- Deborah Carr
- Department of Sociology and Center for Innovation in Social Science, Boston University, 704 Commonwealth Ave, Boston, MA, 02215, USA.
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A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nat Hum Behav 2023; 7:74-101. [PMID: 36344657 PMCID: PMC9883171 DOI: 10.1038/s41562-022-01460-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 09/16/2022] [Indexed: 11/09/2022]
Abstract
One of today's most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.
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Hswen Y. Online Hate: The New Virus. Am J Public Health 2022; 112:545-547. [PMID: 35319954 PMCID: PMC8961821 DOI: 10.2105/ajph.2022.306754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2022] [Indexed: 11/04/2022]
Affiliation(s)
- Yulin Hswen
- Yulin Hswen is with the University of California San Francisco, Department of Epidemiology and Biostatistics, and the Bakar Computational Health Sciences Institute, San Francisco
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Nguyen TT, Criss S, Michaels EK, Cross RI, Michaels JS, Dwivedi P, Huang D, Hsu E, Mukhija K, Nguyen LH, Yardi I, Allen AM, Nguyen QC, Gee GC. Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter. SSM Popul Health 2021; 15:100922. [PMID: 34584933 PMCID: PMC8455860 DOI: 10.1016/j.ssmph.2021.100922] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 11/30/2022] Open
Abstract
This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names "Ahmaud Arbery," "Breonna Taylor," and "George Floyd" were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019-September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4-9 to 33.66% in June 1-7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.
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Affiliation(s)
- Thu T. Nguyen
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, 20742, USA
- Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA
| | - Shaniece Criss
- Department of Health Sciences, Furman University, Greenville, SC, 29613, USA
| | - Eli K. Michaels
- Division of Epidemiology, University of California, Berkeley, CA, 94704, USA
| | - Rebekah I. Cross
- Department of Community Health Sciences, University of California, Los Angeles, CA, 90095, USA
| | - Jackson S. Michaels
- Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA
| | - Pallavi Dwivedi
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, 20742, USA
| | - Dina Huang
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, 20742, USA
| | - Erica Hsu
- Department of Public Health Science, University of Maryland, College Park, MD, 20742, USA
| | - Krishay Mukhija
- Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA
| | - Leah H. Nguyen
- Department of Public Health Science, University of Maryland, College Park, MD, 20742, USA
| | - Isha Yardi
- Department of Public Health Science, University of Maryland, College Park, MD, 20742, USA
| | - Amani M. Allen
- Division of Epidemiology, University of California, Berkeley, CA, 94704, USA
- Division of Community Health Sciences, University of California, Berkeley, CA, 94704, USA
| | - Quynh C. Nguyen
- Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, 20742, USA
| | - Gilbert C. Gee
- Department of Community Health Sciences, University of California, Los Angeles, CA, 90095, USA
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