Shelley H Liu, PhD
Dr. Shelley Liu is an Associate Professor in the Department of Population Health Science and Policy at the Icahn School of Medicine at Mount Sinai. She is a data scientist and biostatistician working at the intersection of precision environmental health, metabolic and cognitive health, with an emphasis on health equity and policy. She has a strong interest in developing rigorous data science models that allow for fair and equitable insights for all people, to protect the health of population subgroups that may be under-represented in medical research.
Her research expertise includes modeling how our cumulative exposure to everyday chemicals, such as PFASs (toxic forever chemicals) and plastics, impact our health, and identifying vulnerable populations with high exposure burden. She uses advanced psychometric models, known as item response theory methods, to quantify a person’s cumulative exposure burden to chemical mixtures. Her team developed a PFAS exposure burden calculator using U.S. nationally representative data from 2017-2018. The calculator allows researchers to calculate PFAS burden scores of their participants onto a common scale even if they did not measure the exact same set of PFAS analytes, to support data pooling and harmonization across studies. Her recent work also quantifies cumulative exposure burden to PFASs in a way that takes into account the exposure heterogeneity in our diverse U.S. population. By accounting for this, she found they could uncover hidden disparities in exposure burden across race/ethnicity groups. They found that Asian Americans had significantly higher PFAS burden scores than non-Hispanic Whites and all other race/ethnicity groups.
Dr. Liu also applies rigorous data science and psychometric methods to cognitive functioning data. Her work focuses on increasing the precision of modeling cognitive trajectories by integrating data from multiple tests, to better enable researchers to understand how environmental and socio-demographic factors may affect cognitive health over time. She is also collaborating on a grant to increase Asian American participation in aging research, by developing a Chinese language version of common cognitive functioning test and using psychometric methods to identify potentially biased items for adaptation. Dr. Liu also collaborates and provides statistical expertise on grants focused on endocrine disrupting chemicals and bone health, stress and air pollution interactions on cognition, and policy levers to reduce inequities in diabetes, and equity in Medicaid coverage. Dr. Liu uses statistical models such as psychometrics and item response theory, latent variable modeling, longitudinal data analysis, and Bayesian inference.
Her research is supported by a National Institutes of Health (NIH) Mentored Quantitative Research Career Development Award (K25) and NIH R03, in addition to collaborative grants. Dr. Liu is also a member of the Center for Biostatistics and the Institute for Exposomic Research at the Icahn School of Medicine at Mount Sinai.
Key papers include:
1. Liu, SH, Kuiper J, Chen Y, Feuerstahler L, Teresi J, Buckley JP. Developing an exposure
burden score for chemical mixtures using item response theory, with applications to PFAS mixtures.
Environmental Health Perspectives 2022; 130(11):117001. PMID: 36321842.
2. Liu SH, Feuerstahler L, Chen Y, Braun JM, Buckley JP. Toward advancing precision environmental health: Developing a customized exposure burden score to PFAS mixtures to enable equitable comparisons across population subgroups, using mixture item response theory. Environmental Science and Technology. 2023;doi:10.1021/acs.est.3c00343.
3. Chen Y, Feuerstahler L, Martinez-Steele E, Buckley JP, Liu SH. Phthalate mixtures and insulin resistance: An item response theory approach to quantify exposure burden to phthalate mixtures. Journal of Exposure Science and Environmental Epidemiology, 2023. doi: 10.1038/s41370-023-00535-z.
4. Liu SH, Juster RP, Dams-O'Connor K, Spicer J. Allostatic load scoring using item response theory. Comprehensive Psychoneuroendocrinology. 2020 Dec 17;5:100025. doi: 10.1016/j.cpnec.2020.100025. PMID: 35754455.
Her research has been featured in the media:
Biostatistics, Environmental Health, Epidemiology, Patient Centered Outcomes Research, Pediatrics, Psychiatry, Public Health