Browsing by Author "Swamy, Geeta"

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    Neighborhood Disadvantage is Associated with High Cytomegalovirus Seroprevalence in Pregnancy.
    (J Racial Ethn Health Disparities, 2017-08-24) Lantos, Paul M; Hoffman, Kate; Permar, Sallie R; Jackson, Pearce; Hughes, Brenna L; Kind, Amy; Swamy, Geeta
    BACKGROUND: Cytomegalovirus (CMV) is the most common infectious cause of fetal malformations and childhood hearing loss. CMV is more common among socially disadvantaged groups, and geographically clusters in poor communities. The Area Deprivation Index (ADI) is a neighborhood-level index derived from census data that reflects material disadvantage. METHODS: We performed a geospatial analysis to determine if ADI predicts the local odds of CMV seropositivity. We analyzed a dataset of 3527 women who had been tested for CMV antibodies during pregnancy. We used generalized additive models to analyze the spatial distribution of CMV seropositivity. Adjusted models included individual-level age and race and neighborhood-level ADI. RESULTS: Our dataset included 1955 CMV seropositive women, 1549 who were seronegative, and 23 with recent CMV infection based on low avidity CMV antibodies. High ADI percentiles, representing greater neighborhood poverty, were significantly associated with the nonwhite race (48 vs. 22, p < 0.001) and CMV seropositivity (39 vs. 28, p < 0.001). Our unadjusted spatial models identified clustering of high CMV odds in poor, urban neighborhoods and clustering of low CMV odds in more affluent suburbs (local odds ratio 0.41 to 1.90). Adjustment for both individual race and neighborhood ADI largely eliminated this spatial variability. ADI remained a significant predictor of local CMV seroprevalence even after adjusting for individual race. CONCLUSIONS: Neighborhood-level poverty as measured by the ADI is a race-independent predictor of local CMV seroprevalence among pregnant women.
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    The Project Baseline Health Study: a step towards a broader mission to map human health.
    (NPJ digital medicine, 2020-01) Arges, Kristine; Assimes, Themistocles; Bajaj, Vikram; Balu, Suresh; Bashir, Mustafa R; Beskow, Laura; Blanco, Rosalia; Califf, Robert; Campbell, Paul; Carin, Larry; Christian, Victoria; Cousins, Scott; Das, Millie; Dockery, Marie; Douglas, Pamela S; Dunham, Ashley; Eckstrand, Julie; Fleischmann, Dominik; Ford, Emily; Fraulo, Elizabeth; French, John; Gambhir, Sanjiv S; Ginsburg, Geoffrey S; Green, Robert C; Haddad, Francois; Hernandez, Adrian; Hernandez, John; Huang, Erich S; Jaffe, Glenn; King, Daniel; Koweek, Lynne H; Langlotz, Curtis; Liao, Yaping J; Mahaffey, Kenneth W; Marcom, Kelly; Marks, William J; Maron, David; McCabe, Reid; McCall, Shannon; McCue, Rebecca; Mega, Jessica; Miller, David; Muhlbaier, Lawrence H; Munshi, Rajan; Newby, L Kristin; Pak-Harvey, Ezra; Patrick-Lake, Bray; Pencina, Michael; Peterson, Eric D; Rodriguez, Fatima; Shore, Scarlet; Shah, Svati; Shipes, Steven; Sledge, George; Spielman, Susie; Spitler, Ryan; Schaack, Terry; Swamy, Geeta; Willemink, Martin J; Wong, Charlene A
    The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis.