Background Information: Early-onset breast cancer (EOBC), breast cancer diagnosed at a younger age, presents a unique public health challenge because many younger patients are outside the age range for routine breast cancer screening. While factors such as genetics and tumor biology can influence when cancer is detected, access to healthcare may also play an important role. This project aims to investigate whether geographic and socioeconomic barriers to accessing breast cancer diagnostic services are associated with later-stage diagnoses among younger patients in Los Angeles County. Understanding these disparities could help identify communities where improvements in healthcare access may have the greatest impact.

We will combine cancer registry with geographic information on breast cancer diagnostic facilities, public transportation, road networks, and neighborhood characteristics. Using geographic accessibility models, we will estimate how easily patients can reach diagnostic services by car and public transportation and examine whether differences in accessibility are associated with diagnostic delay. We will also investigate how socioeconomic disadvantage and racial and ethnic disparities factor into access to care.

This project is a strong fit for students interested in cancer, health disparities, and quantitative public health research. While no experience is required, interest or experience in statistics, GIS, and R coding will be relevant to the project. The project will also introduce students to the clinical side of cancer and social and structural factors that shape health outcomes through literature review and scientific writing.

Background Information: Language barriers between patients and healthcare providers remain a persistent obstacle to equitable clinical care, particularly in linguistically diverse regions like LA County. Patients with limited English proficiency often experience worse communication, lower comprehension of diagnoses and treatment plans, and poorer health outcomes when language-concordant care or professional interpretation is unavailable. To bridge this gap, clinicians and health systems rely on a range of strategies, including professional interpreters, bilingual clinical staff, and increasingly, AI-based translation tools or Google Translate, each of which carries practical trade-offs that are not yet well understood from the perspective of those using them day to day. Understanding these dynamics from the frontline is essential to closing persistent gaps in language access and reducing communication-related health disparities. 

To examine these dynamics, this study will conduct semi-structured, one-on-one interviews with interpreters and clinicians across LA County, including interpreters employed by PALS for Health, clinicians who work alongside them, bilingual clinicians, and clinicians who use AI or Google Translate as an interpretation tool. The methodology will use qualitative coding and thematic analysis to identify recurring challenges, such as language concordance gaps among bilingual staff and coordination demands across specialty visits for patients managing complex conditions, as well as the changes participants themselves believe would improve the current system.

This project would be a good fit for individuals with a passion for health equity and language justice, along with an interest in learning and applying qualitative research skills such as interviewing, transcript coding, and thematic analysis. Participants should be adaptable and comfortable engaging directly with working healthcare professionals, especially in a qualitative project that may shift its focus based on emerging themes in the interview data. 

Background Information: Generative AI tools have quickly become part of college students’ academic lives, whether they are used to brainstorm ideas, explain difficult concepts, study for exams, or edit work. While these tools may help students manage demanding workloads, emerging research has also linked patterns of AI use and reliance with academic stress. However, much of the existing research relies on students’ self-reported experiences, which leaves questions about how AI use relates to both students’ perceived stress and their physiological responses during the naturally changing workload of an academic quarter. 

In this project, we’re hoping to investigate the relationship between AI use and stress among the UCLA undergraduate population. Participants will complete surveys assessing their academic AI use and perceived stress at multiple points throughout the academic quarter. We also hope to incorporate standardized resting heart rate variability (HRV) measurements as a physiological measure related to stress regulation. By comparing measurements across the span of a quarter, we are hoping to better understand how AI use relates to changes in both perceived and physiological stress as academic demands fluctuate.

This project will be a good fit for you if you’re interested in student wellness in light of AI, and learning how to design surveys and apply statistical models to them.

Background Information: Major depressive disorder with perinatal presentation, more commonly known as postpartum depression, is a condition that affects approximately 9 to 15 percent of people who have given birth, and frequently lasts several months. However, until 2021 only 60 days of Medicaid was allotted to new parents after delivery. In 2021, the Biden Administration passed the American Rescue Plan Act, which gave all states the option to extend Medicaid coverage in the postpartum period from 60 days after birth to a full year. As of 2026, all states except for Arkansas have made these changes to their public healthcare systems. This study aims to investigate whether the postpartum medicaid extension in most states alleviated issues with utilization of postpartum healthcare services, most notably in postpartum depression identification and treatment. In order to do this, we are collecting information from certain states’ all-payer claims databases and using a differences-in-differences model to show if the new coverage window alleviated discrepancies in the identification and treatment of postpartum depression.

Applicants who would be a good fit for the project should have a strong interest or background in postpartum and/or maternal health. Experience in coding with R and using large governmental databases and datasets would also be helpful. Anyone without these skills should show high interest in learning them.

Background Information: Cancer mortality is not distributed evenly across communities. Differences in access to healthcare, socioeconomic conditions, and environmental exposures can all contribute to disparities in cancer outcomes. However, public health efforts often focus on identifying communities where disparities are already visible. This project asks whether we can use existing data to predict which communities may be at higher risk of cancer mortality in the future. Identifying these vulnerable communities earlier could help policymakers better target resources and investigate the factors that contribute to worsening cancer outcomes.

This project will have two main components. First, we will combine cancer mortality data with community-level socioeconomic factors and geographic data related to healthcare access. Using machine learning methods, we will train models on this historical data to predict which communities are likely to experience elevated cancer mortality in the future. Second, we will map these high-risk communities and compare them with the current availability and accessibility of healthcare resources. This will allow us to identify communities where high predicted need may overlap with limited access to resources and explore potential geographic disparities in the capacity to address future cancer burden.

This project is a good fit for students interested in epidemiology, biostatistics, or data science applied to questions of health disparities. While no prior experience with machine learning is required, students should be comfortable using R and/or Python.

Abstract: Prolonged exposure to heat can have harmful effects on the cognitive performance and health of students. Further research is needed to assess heat impacts on university students specifically. Our study examines the relationship between indoor heat exposure and student-reported outcomes among students residing in UCLA dormitories. We administered a voluntarily longitudinal survey to eligible students to assess these outcomes. This data was cleaned, combined with NOAA temperature data, and analyzed using R. Though not statistically significant, we found some evidence to suggest that students without air conditioned dorms experience higher levels of adverse outcomes than students with air conditioning.

Research Associates: Evalyse Estradaflores, Hannah Butalid, Kaelen Spiegel, & Nina Zhang

Mentors: Dr. Daniel Eisenberg

Poster Presentation: Semel Healthy Campus Initiative (2026)

Oral Presentation: UCLA Undergraduate Research Week (2026)

Background Information: Healthcare access remains a crucial challenge in South Los Angeles, especially for immigrant and Spanish-speaking families. Systemic barriers, language difficulties, and cultural disconnect often contribute to unmet healthcare needs. This study intends to amplify the voices of the community with a qualitative design that focuses on semi-structured interviews. By connecting with immigrant and first-generation family members, community leaders, and community health workers, the project aims to explore how these three key groups engage with and navigate the healthcare system. Partnerships with local clinics, organizations, and schools will help build trust and provide a safe, confidential environment for the participants. We will record, transcribe, and translate all interviews, and then analyze them to understand healthcare barriers, coping mechanisms, self-medication, and healthcare opinions, with a focus on community resilience. 

This project is a strong fit for those fluent in Spanish (but not a requirement) and interested in community-based projects and health and immigrant justice. Cultural humility, strong communication, and empathy are essential for this project as we aim to directly engage with a community that has been historically marginalized. This study is perfect for those with lived experiences or a deep connection with immigrant communities.

Background Information: This project examines water-pipe (hookah) use among college-aged students, with particular emphasis on the social, cultural, and health-related factors that influence its popularity. Water-pipe smoking is often perceived as less harmful than cigarettes due to misconceptions about water filtration, flavorings, and cultural traditions. Through a qualitative study, this project aims to examine these potential misconceptions and health risk assumptions among Middle Eastern and North African (MENA) students to improve public health awareness and interventions. Using insights from existing public health literature and peer-reviewed studies, we will explore how hookah use is shaped by peer networks, campus culture, and cultural identity.

This study will interview UCLA students to understand their experiences and perceptions of hookah use, analyze demographic and behavioral data, and compare responses across different MENA cultural and social groups. Based on existing literature on perceived harm associated with hookah use, the results will be used to contextualize our findings within broader health trends. The aim of this research is to advocate for ways hookah-use can be managed among MENA college-aged youth and to inform better tailored cultural messaging, health training, and community outreach.

This project is suitable for individuals interested in the intersection of culture and public health and passionate about substance use prevention among MENA youth. Given the qualitative nature of this study, experience with literature reviews and familiarity conducting literature reviews is preferred. 

Abstract: California is home to the largest immigrant population in the U.S; 28% of residents are foreign-born. Despite this, noncitizens are disproportionately uninsured due to the “chilling effect” (Watson, 2014): withdrawal from government programs due to fear of deportation or change to legal status.3,4 California has continued to expand MediCal, with the Jan. 2024 policy extending coverage to all income-eligible adults regardless of citizen status.

This study examines the impact of the January 2024 expansion of Medi-Cal eligibility to undocumented immigrants on enrollment rates, focusing on whether more conservative districts experienced different enrollment than democratic districts.

Using fixed-effects regression, our results suggest that the change in the rate of enrollment post policy expansion was not significantly different between the conservative and liberal districts, and they follow similar trends post-expansion. Other covariates such as the median household income, rurality, and the percentage of foreign-born residents were stronger contributors in deterring the change in enrollment rate. 

The results suggest that California’s strong, liberal baseline may minimize local-level chilling effects. We recognize our limitation through inability to measure undocumented-specific enrollment; the datasets only provide overall enrollment counts. Additionally, the reliance on a static measure of political ideology may fail to capture the changing political sentiment over our study period.

Abstract: Focusing on global health policy requires capturing not only average health outcomes but also the distribution of health across population subgroups. The Health Equity Metric (HEM), developed by Zimmerman (2019), addresses this by quantifying population health loss attributable to unequal health distributions, aggregating individual shortfalls from a reference level of potential health into a single summary measure of health disutility. While HEM has been validated in the United States, its applicability in low- and lower-middle-income settings remains underexplored. This study applies HEM to 14 Sub-Saharan African countries using Demographic Health Survey data (DHS-8, 2019-2024). Self-reported health serves as the primary outcome, mapped to a continuous health index using the weighting scheme of van Doorslaer and Jones (2003). The privileged reference group is defined through joint stratification across wealth, urbanicity, and education. HEM scores across all 14 countries ranged narrowly from 0.95 to

0.99, with minimal cross-country variation. This compression is attributable to a ceiling effect in self-reported health data; nearly 75% of women reported good or very good health, causing the privileged group median to match the overall population median in 13 of 14 countries. As a result, most individuals had near-zero shortfalls from the benchmark, limiting HEM's empirical sensitivity. These findings suggest that HEM's performance depends critically on the distributional properties of the health measure used.

Research Associates: Anne Wang, Surdish Randhawa, & Caden Mulloy

Mentor: Dr. Frederick J. Zimmerman

Poster Presentation: Community Service Commission Service Impact Summit in Los Angeles, California (2026)

Oral Presentation: UCLA Undergraduate Research Week in Los Angeles, California (2026)

Abstract: Following the COVID-19 pandemic, there was a marked decline in routine childhood vaccination rates, attributed to reduced healthcare access and increased vaccination hesitancy. Consequently, measles, which requires ~95% coverage to maintain herd immunity, has re-emerged in recent years. The Vaccines for Children (VFC) program provides free vaccines to children with limited access to health insurance. However, it is unclear whether access to VFC providers is associated with MMR vaccination trends. We examined the association between VFC provider density and changes in MMR vaccination coverage among school-aged children across California counties from 2021 to 2025. VFC provider density was defined as the number of VFC providers relative to the VFC-eligible child population in each county. We fit an ordinal logistic regression model to evaluate this relationship and adjusted for county-level covariates identified in the existing literature, including insurance rates, poverty rates, and political leanings. VFC provider density was not significantly associated with change in MMR vaccination coverage after adjusting for covariates (p=0.40). However, the percentage of individuals under 19 with insurance was associated with 52% higher odds of increased MMR coverage (p < 0.0001). These findings suggest that greater availability of VFC providers alone may not be sufficient to support post-pandemic recovery in childhood MMR vaccination coverage in California. Broader barriers to healthcare access, including insurance coverage, may play an important role in vaccination recovery and should be considered alongside efforts to expand vaccine provider availability.

Research Associates: Catherine Wang, Noa Figlin, Kashish Priyam, Shayan Gharagozlou, & Khwaish Sethiya

Mentor: Zichen Liu, PhD Candidate

Poster Presentation: Community Service Commission Service Impact Summit in Los Angeles, California (2026)

Oral Presentation: UCLA Undergraduate Research Week in Los Angeles, California (2026)

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