The Robert H. Ebert Career Development Award, named for the founder of Harvard Community Health Plan and former Dean of Harvard Medical School, supports the work of Institute faculty who demonstrate exceptional promise in the area of ambulatory care, primary care, or preventive medicine.

These awards provide resources to develop leading-edge programs in teaching and research and to pursue scientific and professional activities consistent with the department’s mission. Congratulations to this year’s four awardees:
 

Laura Garabedian has a health policy and public health background and her work has focused on evaluating the impact of health insurance policies and programs on health insurance coverage, and health care utilization, quality and costs. State all-payers claims databases (APCDs) are a unique source of data for the evaluation of health insurance policies and include data on health care enrollment and claims for most residents in a state over time across coverage types, allowing for the longitudinal study of insurance transitions and health care utilization and outcomes. Her Ebert Award will enable her to acquire Massachusetts APCD data and build the Institute’s capacity to use APCD for externally-funded projects.

Sharon Lutz is a biostatistician with a primary focus on statistical genetics and genomics. She is interested in determining and understanding the path from gene to disease. Sharon has developed and applied statistical methods for unraveling and testing the genetic overlap of chronic obstructive pulmonary disease and cardiovascular disease given the role of cigarette smoking and sex. She will use her two-year Ebert Award to develop novel statistical methods to account for heterogeneity due to sex in genetic association studies. These methods can be applied to a wide range of phenotypes in multiple studies both within and outside of the Institute, including ADAPT, PREDICT, and Gen3G.

Judy Maro is a systems engineer and data scientist whose main research interest has been implementation of pharmacovigilance techniques, particularly continuous near-real time sequential statistical analysis methods and data-mining/signal identification methods in distributed longitudinal databases. Her Ebert Award, focused on developing and testing methods for longitudinal signal detection, will offer her the ability to demonstrate the value of signal identification in outcomes and disease surveillance settings, e.g., outcome-indexing and COVID. Such demonstration projects will open up new networks and relationships, and provide new funding opportunities.

Jessica Young is a biostatistician whose research focuses on the development and application of statistical methods that remain valid for estimating the causal effects of time-varying treatment strategies on health outcomes in the face of complex time-varying confounding and selection bias. Her Ebert Award will support her work towards improved statistical methods for policy-relevant causal inference in real-world data under transparent assumptions.

Continuing Award:

In addition to these new awardees, Hao Yu received a second year of funding of his initial Ebert Award in 2020. His work includes collaborative research on the effects of large-scale state or national health policies on population and patient-centered measures such as: health outcomes, health system and clinician quality, access to care, health care disparities, and health care costs. He is using the two-year award to further integrate his research into the Institute and expand his research portfolio through new initiatives.