Durham Population Lab


The Durham Population Laboratory (DPL) is a new collaborative initiative with Duke and local Durham partners to build an integrated administrative data infrastructure of Durham, North Carolina's county-wide administrative data and electronic health records (EHR).

The DPL will enable DPRC researchers to examine early developmental, social, economic and policy processes which contribute to divergent trajectories in health and well-being in Durham County’s racially and ethnically diverse population. The DPL intends to develop new approaches and new data to accelerate timely research that addresses the population health challenges facing children and families, that may be scaled-up and replicated elsewhere in North Carolina and beyond.

DPL goals are to leverage the expertise of DPRC Scholars who are familiar with the challenges and nuances of working with administrative data to:

  • Identify core data collection efforts and modalities that best leverage these data
  • Develop the best protocols and tools for curating, linking, and analyzing administrative data
  • Provide both virtual and real space for mutually beneficial interagency and inter-institutional convergence and collaboration


Illustrative Activities:

  • M. Giovanna Merli and Sarah Curran (University of Washington) co-hosted a "Workshop on Data Science and Demography", with leaders from U.S. population research centers—including UC Berkeley, Johns Hopkins, and UCLA, as well as the Max Planck Institute of Demographic Research—for a discussion of the possibilities presented through collaborations between data scientists and population scientists.
  • James Moody’s work explores newly available electronic administrative "big data” in hospitals, including prescription information and other electronic health records (EHR), focusing on the challenges this data presents to population scientists, most notably the integration of health records data with personal interviews; the application of social science models to administrative records; the exploration of best practices for the collection of sensitive data; and concerns surrounding population representation and generalizability.
  • Benjamin Goldstein reviews Electronic Health Records available through the Duke University Health System “Working with EHR Data from Duke University Health System: What is it and How Do I do it?"
  • Allison Stolte and Giovanna Merli, in collaboration with Ben Goldstein and other Duke School of Medicine colleagues, have published research in Social Science & Medicine demonstrating the efficacy of using electronic health records (EHRs) to assess children's population health.
  • Giovanna Merli, Jerome Reiter, and Ted Mouw (UNC), along with students in the Duke Department of Statistical Science, have working paper that employs Bayesian record linkage to match survey participants in the Chinese Immigrants in Raleigh-Durham (ChIRDU) Study, to individuals/household records from InfoUSA. The project builds on an original data collection effort to study the spatial distribution and individual characteristics of Chinese immigrants in an effort to understand their social incorporation as measured by residential patterns of spatial assimilation, especially of a rare immigrant population in the US South. As such, the authors use the InfoUSA data to obtain more granular geographical variables on the population of interest.