About

Thu Nguyen, ScD, MSPH, is an associate professor of epidemiology and biostatistics at the University of Maryland School of Public Health. She is a social epidemiologist whose research focuses on the impact of modifiable social factors on minority health and health disparities. A primary line of her research investigates the influence of racism and discrimination in creating and perpetuating health equities. Dr. Nguyen leads the Big Data for Health Equity (BD4HE) Research Collaborative, which is comprised of faculty, trainees, and students from across the U.S. committed to advancing theories, methods, and findings related to the use of Big Data for health equity research. Her work employs a variety of data sources, including Big Data, and approaches such as quantitative and qualitative research methods to better understand social determinants of health. She is the principal investigator of multiple research projects examining the relationship between discrimination, racial bias, and health disparities, utilizing innovative data sources like social media to track and analyze area-level racial sentiment and its effects on health outcomes. Dr. Nguyen holds a BA in Human Biology from Stanford University, an MSPH in Epidemiology from UNC Gillings School of Global Public Health, and a ScD in Social Epidemiology from Harvard T.H. Chan School of Public Health.

Research topics

• Sociology
• Political Science
• Medicine
• Psychiatry
• Psychology
• Gerontology
• Demography
• Social psychology

Selected publications

• The association between state-level negative racial sentiment and maternal hypertension in the US from 2016 to 2021: An observational study using Twitter data

  PLoS ONE · 2026-04-29

  articleOpen accessSenior author

  BACKGROUND: Racial disparities in maternal hypertension (i.e., prepregnancy and gestational) due to experiences of racism may contribute to ongoing racial disparities in US birth outcomes, including low birth weight and preterm birth. Despite evidence linking racism and adverse birth outcomes, no studies have examined the plausible association between area-level negative racial sentiment and disparities in maternal hypertension. To address this gap, we used 2016-2021 US birth certificate data to examine the associations between state-level Twitter-derived negative sentiments toward racial/ethnic minorities and maternal hypertension. We further examined if these associations increased during periods of heightened racial discrimination. METHODS: We used 2016-2021 US natality data with geographic identifiers for pregnancy data for singleton births (n = 22,618,566) and a random sample of 1% of publicly available tweets from 2016-2021 (n = 56,400,097) using Twitter's Academic Application Programming Interface. We calculated annual state-level negative racial sentiment by averaging sentiment scores of all posts referencing a racial category. These scores, divided into quartiles, included five sentiment measures: one toward all racially minoritized groups and four race-specific (Black, Asian, Latinx, and White). We merged data for each year and used log-binomial regression to estimate prevalence rate ratios (PRRs) for prepregnancy and gestational hypertension, adjusting for individual maternal characteristics and state-level demographics. We additionally stratified analyses into time periods before and during the COVID-19 pandemic and Black Lives Matter movement (2016-2019 and 2020-2021, respectively). RESULTS: In our sample, 2.2% of individuals had prepregnancy hypertension and 7.7% had gestational hypertension. From 2016-2021, the prevalence of both types of maternal hypertension increased for individuals across all racial and ethnic groups. Individuals in states with the highest quartile of negative racial minority sentiment had a 36% higher (95% CI: 1%-83%) prevalence of prepregnancy hypertension and a 20% higher (95% CI: 0%-45%) prevalence of gestational hypertension compared to those in the lowest quartile. In 2020 and 2021, the prevalence of prepregnancy hypertension among individuals in racially minoritized groups was 51% greater (95% CI:12%-103%) in the 4th quartile compared to the 1st quartile and showed a higher magnitude of association compared to 2016-2019. In 2020-2021, among Black individuals, those in the highest quartile of anti-Black sentiment had a 31% higher (95% CI: 2%-69%) prevalence of prepregnancy hypertension, and the 19% increase in prepregnancy hypertension among Asian individuals in the highest quartile of anti-Asian sentiment was borderline significant (95% CI: 0%-44%). Patterns for gestational hypertension were not consistent across time and when examining race-specific sentiment. CONCLUSION: Higher levels of state-level negative racial sentiment are associated with increased prevalence of prepregnancy and gestational hypertension among racially minoritized groups, and the association with prepregnancy hypertension appears to strengthen during periods of heightened racial tension and discrimination. These findings highlight the role of area-level racism as a contributor to maternal health disparities.

  PublisherDOI

• Sexual Orientation and Gender Identity Data Collection in Solid Organ Transplant Programs in the United States

  American Journal of Transplantation · 2025-08-01

  articleOpen access
  PublisherOA PDFDOI

• Mapping the Completeness and Positional Accuracy of <scp>OpenStreetMap</scp> Road Data at the County Level in the Contiguous United States

  Transactions in GIS · 2025-06-01 · 3 citations

  articleOpen access

  The OpenStreetMap (OSM) project allows volunteers in the community to contribute and manage spatial data collaboratively and provides free spatial data with global coverage to the public. OSM data have been widely used in many applications. However, the quality of OSM data can be inconsistent due to the crowdsourcing nature of the OSM project. This study compares the OSM road data with the national road data from the U.S. Census Topologically Integrated Geographic Encoding and Referencing system (TIGER) project in the contiguous United States. Specifically, we used three indicators to examine the completeness and positional accuracy of the OSM road data at the county level. Then we performed spatial analysis to study the patterns of the discrepancies. Our results show that OSM road data are inconsistent in completeness and positional accuracy across different counties. Finally, we compared the three indicators among metropolitan, nonmetropolitan, and rural counties with Analysis of Variance (ANOVA) and Boxplot. The results show that the OSM road data in metropolitan counties have better completeness and positional accuracy than those in nonmetropolitan and rural counties. This study can improve our understanding of the quality of OSM road data in the United States, which in turn can help the OSM community improve the quality of road data and allow data users to better use OSM road data in different applications.

  PublisherOA PDFDOI

• Unseen but Present: Asymptomatic COVID-19 Cases and Air Travel to Hong Kong

  medRxiv · 2025-03-27 · 1 citations

  preprintOpen access

  Summary The global spread of infectious diseases was influenced by human movement dynamics, particularly for highly transmissible diseases like COVID-19. Asymptomatic COVID-19 cases lacked symptoms before diagnosis, posing a challenge for containment. Their contribution to air travel remains understudied. This retrospective cross-sectional study investigated the role of asymptomatic COVID-19 cases in air travel and their impact on the global spread of the virus. Through our analysis of 11,775 COVID-19 cases in Hong Kong (January 2020–April 2021), log-binomial regression models assessed the association between asymptomatic status and air travel behavior 14 days before diagnosis. The Wilcoxon rank-sum test compared median flight durations between asymptomatic and symptomatic cases. Results revealed two-thirds of cases with air travel history were asymptomatic, with asymptomatic airport or flight crew ten times more likely to travel than symptomatic counterparts (adjusted PRR=10, 95% CI: 4.00–25.00). For non-crew individuals, the adjusted PRR was 1.14 (95% CI: 1.12–1.16). Median flight duration for asymptomatic cases was 4.6 person-hours shorter than symptomatic ones (p&lt;0.01). These findings highlight the significant contribution of asymptomatic cases to air travel and suggest under-detection during initial travel restrictions. Our study emphasizes proactive public health measures early in pandemics involving airborne infections, irrespective of symptom presentation.

  PublisherOA PDFDOI

• Harnessing Facebook to Investigate Real-World Mentions of Adverse Events of Glucagon-Like Peptide-1 Receptor Agonist (GLP-1 RA) Medications: Observational Study of Facebook Posts From 2022 to 2024

  JMIR Infodemiology · 2025-07-24 · 4 citations

  articleOpen accessSenior author

  Background: In recent years, there has been a dramatic increase in the popularity and use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for weight loss. As such, it is essential to understand users' real-world discussions of short-term, long-term, and co-occurrent adverse events associated with currently used GLP-1 RA medications. Objective: This study aims to quantitatively analyze temporal and co-occurrent GLP-1 RA adverse event trends through discussions of GLP-1 RA weight loss medications on Facebook from 2022 to 2024. Methods: We collected 64,202 Facebook posts (59,293 posts after removing duplicate posts) from January 1, 2022, to May 31, 2024, through CrowdTangle, a public insights tool from Meta. Using English language social media posts from the United States, we examined discussions of adverse event mentions for posts referencing 7 GLP-1 RA weight loss product categories (ie, semaglutide, Ozempic, Wegovy, tirzepatide, Mounjaro, Zepbound, and GLP-1 RA as a class). All analyses were conducted using Python (version 3; Python Software Foundation) in a Google Colab environment. Results: Temporal time series analysis revealed that the GLP-1 RAs' adverse event mentions on social media aligned with several key events: the Food and Drug Administration's approval of Wegovy for pediatric weight management in December 2022, increased media coverage in August 2023, celebrity endorsement in December 2023, and Medicare Part D coverage expansion for weight loss medications in March 2024. Gastrointestinal (GI)-related adverse events (general term) were most prevalent for posts mentioning the GLP-1 RA class (210/4885, 4.30%) and Mounjaro (241/4031, 5.98%). In contrast, the most prevalent adverse event mentions noted for tirzepatide were headache (78/4202, 1.86%) and joint pain (71/4202, 1.69%). Hypertension (13/1769, 0.73%) was frequently mentioned in Zepbound posts, while pancreatitis was commonly associated with Mounjaro posts (44/4031, 1.08%), and 2.85% (139/4885) of posts broadly referring to the GLP-1 RA class. Furthermore, an integrated node network analysis revealed 3 distinct GLP-1 RA adverse events-mentioned clusters: cluster 1 (purple) contained allergies, anxiety, depression, chronic obstructive pulmonary disease, fatigue, fever, hypertension, indigestion, insomnia, gastroesophageal reflux disease, hives, swelling, restlessness, and seizures. Cluster 2 (pink) contained constipation, dehydration, headache, diarrhea, dizziness, hypoglycemia, sweating, and jaundice. Cluster 3 (brown) contained GI symptoms, such as nausea, pancreatitis, rash, and vomiting. The GI symptoms, such as nausea, vomiting, pancreatitis, diarrhea, and indigestion, were strongly associated together (≥100 co-occurrence mentions), while the mentioned neurological symptoms, such as anxiety, depression, and insomnia, were highly correlated with each other (50-100 co-occurrence mentions). Conclusions: This social media study highlights the adverse event mention patterns for posts referencing GLP-1 RA medications. While further research is needed to rigorously examine and validate these findings, this study demonstrates the importance of monitoring social media discussions to predict novel, underreported, or rare drug adverse events, thereby improving patient care, clinical research, and health policy interventions.

  PublisherDOI

• Intersectional inequities in suicide ideation by race, sexual orientation, and gender among US high school students in the pre- and post-2020 waves of the YRBSS: an application of random effects intersectional MAIHDA

  American Journal of Epidemiology · 2025-05-30 · 4 citations

  articleOpen access

  The US faces a youth mental health crisis. Few studies have examined how the disruptions of 2020 impacted existing mental health inequities. Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is a methodological innovation that provides social epidemiology with a theory-informed and rigorous approach to quantify changing intersectional health inequities. Using 2017-2021 data from the Youth Risk Behavior Surveillance System, we illustrate the use of logistic I-MAIHDA with random effects to estimate intersectional inequities in suicidal ideation among US high-school students by race, sexual orientation, and gender. Before 2020, we found substantial inequities in suicidal ideation prevalence, ranging from 9.8% to 12.7% among heterosexual boys to over 50% among bisexual multirace/Other and White girls. We also found notable changes between the pre-2020 and 2021 waves. Strata at the lowest (heterosexual boys) and highest risk (bisexual girls) showed little change, while middle-ranked strata-Black Other/Questioning and lesbian girls, White Other/Questioning boys and girls, and multirace/Other gay boys-reported large increases in suicidal ideation. Our findings suggest worsening teen mental health in the 2017-2021 period, particularly among racial and sexual minorities. This study highlights the value of I-MAIHDA and population surveys like Youth Risk Behavior Surveillance System for understanding changes in intersectional health inequities. This article is part of a Special Collection on Methods in Social Epidemiology.

  PublisherOA PDFDOI

• Association between smoking cessation and risk for type 2 diabetes, stratified by post-cessation weight change: A systematic review and meta-analysis

  Preventive Medicine · 2025-10-11 · 1 citations

  reviewOpen access

  While smoking cessation reduces health risks, its impact on type 2 diabetes mellitus (T2DM) remains complex when considering post-cessation weight gain. This systematic review and meta-analysis examined the association between smoking cessation and diabetes risk stratified by weight change and cessation duration. We searched seven databases through April 14, 2025. Observational studies examining smoking cessation, weight changes, and T2DM were included. Random-effects models pooled hazard ratios (HRs) comparing recent and long-term quitters to continuous/never smokers, stratified by weight gain. Among eleven cohort studies, quitters with weight gain showed increased diabetes risk versus continuous smokers (HR = 1.71, 95 % CI: 1.12, 2.62), with recent quitters having greater risk (HR = 2.20, 95 % CI: 1.27, 3.82) but long-term quitters showing reduced risk (HR = 0.91, 95 % CI: 0.87, 0.95). Quitters without weight gain demonstrated no increased risk (recent: HR = 0.99, 95 % CI: 0.81, 1.02) and lower risk (long-term: HR = 0.84, 95 % CI: 0.81, 0.87). Compared to never-smokers, recent quitters had a higher T2DM risk regardless of weight status (with gain: HR = 1.61, 95 % CI: 1.03, 2.50; without gain: HR = 1.25, 95 % CI: 1.05, 1.48), while long-term quitters showed no significant difference. Smoking cessation temporarily increases T2DM risk, particularly with weight gain, but becomes protective long-term, emphasizing weight management. • Weight gain after smoking cessation increases type 2 diabetes risk by 71 %. • Recent quitters with weight gain face highest but temporary diabetes risk. • Long-term cessation protects against diabetes regardless of weight status. • Weight-stable quitters avoid increased risk and gain long-term protection. • Weight management should be integrated into smoking cessation programs

  PublisherDOI

• A decade of discourse: Exploring sentiments and trends around immigration on social media from 2014 to 2024

  Social Science & Medicine · 2025-10-25 · 3 citations

  articleOpen access1st authorCorresponding

  INTRODUCTION: Social media discussions contribute to the evolving public perception of refugees and immigrants. However, prior research often relied on a single platform and short-term analyses, offering a fragmented view of a highly dynamic phenomenon. OBJECTIVE: Examine trends in public narratives surrounding refugees and immigrants, including the evolution of sentiment and user engagement on Twitter, Facebook, and Bluesky. METHODS: We analyzed 6.3 million U.S.-based English-language posts from Twitter (2014-2023), Facebook (2014-2024), and Bluesky (2023-2024), using platform APIs. Posts containing one or more of 129 immigration-related keywords were grouped into 76 categories. Sentiment was classified using a supervised Support Vector Machine model, and engagement was aggregated at the keyword level. Twitter geodata enabled state-level sentiment mapping. RESULTS: Peaks in volume and negativity aligned with major events, including the 2014 Syrian refugee crisis, the 2017 travel ban, and the 2018 family separation policy. From 2014 to 2019, negative sentiment increased on both Twitter and Facebook, then became more neutral in subsequent years. Bluesky began with predominantly neutral discourse in 2023 but grew more negative after its public launch. Refugee-related discourse was consistently less negative than immigrant-related discourse across all platforms, while enforcement-related and exclusionary rhetoric keywords emerged as the most negatively evaluated. Twitter geodata revealed widespread negativity across states, although refugee discourse remained more moderate or neutral than immigrant discourse overall. CONCLUSION: Migration discourse is shaped by political events, emotional framing, and platform-specific dynamics, underscoring the need for cross-platform analyses to understand evolving digital narratives.

  PublisherDOI

• Navigating identity in digital spaces: An exploratory study of social engagement and interactions

  Social Sciences & Humanities Open · 2025-01-01

  articleOpen accessSenior author

  Introduction: Through a focus group approach in an exploratory qualitative study, we aim to understand how engagement on digital platforms varies by social identities including race and ethnicity, gender, sexual orientation, and religion, and the respondents' response to both positive and negative interactions. Methods: This study involved data collection from six focus groups, two included members of the general population, two focused on the experiences of People of Color (POC), one that included women gamers, and one for all gamers. Participants were asked various questions to aid in the research team's understanding of participants' perceptions of their online experiences with topics including platform engagement, positive and negative online experiences, social connections, identity-based perceptions online (e.g., race and ethnicity, gender, sexual orientation, and religion), and online protection. Results: The results show that the majority of Black and LGBQ participants report experiencing identity-related discrimination online. Women were more likely to report feeling unsafe while on gaming platforms and more frequently used security measures to conceal their voice, identity, and background to reduce discriminatory experiences. Many study participants reported using social media and gaming platforms to build connections, feel a sense of community, and engage in diverse relationships. Conclusion: This study's findings provide insight into the potential of social media and gaming platforms in promoting self-expression, social connectedness, and a strong sense of community. However, our results also demonstrate the ways in which many digital spaces foster discrimination, harassment, fetishization, and exclusion - all of which can exacerbate negative health outcomes among marginalized individuals.

  PublisherDOI

• Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis

  JMIR Formative Research · 2025-12-10

  articleOpen access

  BACKGROUND: Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs. OBJECTIVE: This study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019. METHODS: In total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income. RESULTS: Washington, DC, experienced a shift toward higher-density housing, with non-single-family homes rising from 66% to 72% of the housing stock. Single-lane roads increased from 37% to 42%, suggesting a shift toward more sustainable and compact urban forms. Gentrification trends were reflected in a rise in college-educated residents (16%-41%), a US $17,490 increase in the median household income, and a US $159,600 increase in property values. Longitudinal analyses revealed that increased construction activity was associated with lower rates of obesity, diabetes, high cholesterol, and cancer, while growth in non-single-family housing was correlated with reductions in the prevalence of obesity and diabetes. However, neighborhoods with higher proportions of African American residents experienced reduced construction activity. CONCLUSIONS: Washington, DC, has experienced significant urban transformation, marked by substantial changes in neighborhood built environments and demographic shifts. Urban development is associated with reduced prevalence of chronic conditions. These findings highlight the complex interplay between urban development, demographic changes, and health, underscoring the need for future research to explore the broader impacts of neighborhood built environment changes on community composition and health outcomes. GSV imagery, along with advances in computer vision, can aid in the acceleration of neighborhood studies.

  PublisherDOI

Recent grants

• SF BUILD: Enabling full representation in science

  NIH · $18.2M · 2014–2025

• Place-level discrimination and birth outcomes

  NIH · $720k · 2018–2023

• Risk and strength: determining the impact of area-level sentiment and protective factors on birth outcomes

  NIH · $3.6M · 2021–2026

• Place-level discrimination and birth outcomes

  NIH · $208k · 2018–2020

Frequent coauthors

• Shaniece Criss

  Brown University

  40 shared

• Melanie Kim

  Brown University

  36 shared

• M. Maria Glymour

  Boston University

  36 shared

• Junaid S. Merchant

  University of Maryland, College Park

  34 shared

• Nhung Thai

  University of California, Berkeley

  29 shared

• Katrina Makres

  University of Maryland, College Park

  29 shared

• Quynh C. Nguyen

  26 shared

• Amani M. Allen

  University of Chicago

  16 shared

Education

• ScD, Social and Behavioral Sciences

  Harvard T.H. Chan School of Public Health

  2014

• MSPH, Epidemiology

  University of North Carolina Gillings School of Global Public Health

  2008

Awards & honors

• American Heart Association Postdoctoral Fellowship (2016 - 2…
• Population Health and Health Equity Scholar, University of C…
• NIH Loan Repayment Program, National Institute on Minority H…