Table 1.
Summary of PRIME Projects.
| Project (Institutions(s)) 1 | Summary | Exposures 2 | Study Populations 3 |
|---|---|---|---|
| Development and testing of response surface methods for investigating the epidemiology of exposure to mixtures (BU/Harvard) |
Combines aspects of response surface modeling with index methods into the Bayesian Multiple Index Method (BMIM) and incorporates toxicological information. Special cases are a single index model and a full response surface of all exposures as in BKMR. | Dioxin-like compounds, PCBs, phthalates, parabens, bisphenols triclosan, UV filters, BFRs, PBDEs | RCC, EARTH |
| Principal Component Pursuit to assess exposure to environmental mixtures in epidemiologic studies (Columbia) |
Adapts the method Principal Component Pursuit (PCP), used in computer vision applications, to the epidemiologic setting of mixtures of environmental pollutants. | PCBs, metals, air pollution | CHDS, CCCEH, SHS, SPARCS |
| Structured nonparametric methods for mixtures of exposures (Duke) |
Incorporates chemical structure data and mechanistic constraints into nonparametric Bayesian regression methods to improve stability, performance, and interpretation in estimating dose response. Supplemental funding develops Bayesian modeling frameworks for including exposures in epidemiological models of infectious disease spread, as well as flexible spatiotemporal modeling with applications to study exposure effects on COVID-19 hospitalizations. | Phenols, OPs, perchlorate, PFCs, phthalates, BFRs, PAHs, pyrethroids, air pollutants | MSSM, NHANES, CHAMACOS, CLEAR, CDC COVID Data Tracker, NYTimes COVID Data, State Population by Characteristics |
| Methods for data integration and risk assessment for environmental mixtures (MSSM/Harvard) |
Integrates temporally resolved exposure into models, evaluates how early (“priming” or “protective”) exposures can impact susceptibility to later exposures, and estimates regulatory guideline values for mixtures. | Tooth metal biomarkers; EDCs, dietary data | Colorado birth data; SELMA |
| Bringing Modern Data Science Tools to Bear on Environmental Mixtures (Notre Dame/Rice) |
Develops data architecture to capture complex spatial location data for families, environmental exposures, and social stressors that vary over time. Leverages modern data science by applying rapidly evolving techniques for architecting data combined with hierarchical Bayesian models with variable selection, spatial models, and machine learning algorithms to large-scale environmental mixture and social exposure datasets of direct importance to child outcomes. | Air pollution, lead, social stressors | Aggregate North Carolina birth records, blood lead surveillance data, and educational system data to social and environmental exposures |
| Innovative Methodologic Advances for Mixtures Research in Epidemiology (UI Chicago) |
Adapts genomics approaches to evaluate the total main effects and interactions of chemical exposures. Applies novel multivariate models for analyzing the complex relationship between health outcomes, biological intermediates, and environmental pollutants. | POPs, PCBs, OCPs, BFRs, PFCs, dioxins, heavy metals | NHANES, GLFCS, HCHS/SOL |
1 Listed in alphabetical order, by institution. Project details available at NIH RePORTER: https://reporter.nih.gov/, accessed on 21 December 2021. Institutions: Columbia University Mailman School of Public Health, University of Illinois Chicago, Icahn School of Medicine at Mount Sinai, Harvard T.H. Chan School of Public Health, University of Notre Dame, Rice University, Boston University School of Public Health, Duke University. 2 BFRs: Brominated Flame Retardants; EDCs: Endocrine Disrupting Chemicals, OCPs: Organochlorine Pesticides; OPs: Organophosphorus Pesticides; PAHs: Polycyclic Aromatic Hydrocarbons; PBDEs: Polybrominated Diphenyl Ethers; PCBs: Polychlorinated Biphenyls; PFCs: Perfluorinated Chemicals; POPs: Persistent Organic Pollutants; UV: Ultraviolet. 3 CCCEH: Columbia Center for Children’s Environmental Health; CDC COVID Data Tracker: https://covid.cdc.gov/covid-data-tracker/#variant-proportions and https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-Jurisdi/unsk-b7fc, accessed on 21 December 2021; CHAMACOS: Center for the Health Assessment of Mothers and Children of Salinas; CHDS: Child Health and Development Studies; CLEAR: Climate Change, Environmental Contaminants and Reproductive Health; EARTH: Environment And Reproductive Health cohort; GLFCS: Great Lakes Fish Consumption Study; HCHS-SOL: Hispanic Community Health Study/Study of Latinos; MSSM: Mount Sinai Children’s Environmental Health Study; NHANES: National Health and Nutrition Examination Survey; NYTimes COVID Data: https://github.com/nytimes/covid-19-data, accessed on 21 December, 2021 RCC: Russian Children’s Cohort; SELMA: Swedish Environmental Longitudinal Mother and child, Asthma and allergy study; SHS: Strong Heart Study; SPARCS: NY Statewide Planning and Research Cooperative System; State Population by Characteristics: published by the U.S. Census Bureau breaks down 2019 U.S. state populations by Age. From Single Year of Age and Sex Population Estimates: 1 April 2010 to 1 July 2019—CIVILIAN (SC-EST2019-AGESEX-CIV) https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-detail.html, accessed on 21 December 2021, WAS: Wisconsin Angler Study.