Background Barrett’s esophagus (BE) is often asymptomatic and only a small portion of BE patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with BE. Familial aggregation of BE and esophageal adenocarcinoma (EAC), and the increased risk of EAC for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well-developed. Methods We developed a Barrett’s Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained BE pedigrees and 92 multiplex BE pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex BE pedigrees. Results Our results indicate family information helps to predict BE risk, and predicting in families improves both prediction calibration and discrimination accuracy. Conclusions Our model can predict BE risk for anyone with family members known to have, or not have, had BE. It can predict risk for unrelated individuals without knowing any relatives’ information.

Background: Cleveland, Ohio, is home to three major hospital systems serving approximately 80% of the Northeast Ohio population. The Cleveland Clinic, University Hospitals Health System, and MetroHealth are direct competitors for primary and specialty care, and patient overlap between these systems is high. Fragmentation of health data that exist in silos at these health systems produces an overestimation of disease burden due to double and sometimes triple counting of patients. As a result, longitudinal population-based studies across the Cleveland patient population are impeded unless accurate and actionable clinically derived health data sets can be created.Methods: The Cleveland Institute for Computational Biology has developed the De-Duplicate and De-Identify Research Engine (DeDeRE) that, without any exchange of personal health identifiers (PHI) between health systems, will effectively de-duplicate the patients between one or more health entities. Results:The immediate utility of this software for cancer epidemiology is the increased accuracy in measuring cancer burden and the potential to perform longitudinal studies with de-duplicated, de-identified data sets.Conclusions: The DeDeRE software developed and tested here accomplishes its goals without exposing PHIs using a stateof-the-art, trusted privacy preservation network enabled by a hashbased matching algorithm.Impact: This paper will guide the reader through the functions currently developed in DeDeRE and how a healthcare organization (HCO) employing the release version of this technology can begin sharing data with one or more additional HCOs in a collaborative and noncompetitive manner to create a regional population health resource for cancer researchers.See all articles in this CEBP Focus section, "Modernizing Population Science."

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PurposeA disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned. MethodsWe built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio's cancer registry with community data from the U.S. Census and other sources. Space-and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels. Results OH-CASE contains 723,410 patient records for Ohioans diagnosed with cancer from 1/1/2006 -12/31/2017 across 88 counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface. Conclusion OH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.