Jarrod E Dalton,  PhD

Jarrod E Dalton, PhD

Assistant Staff

Lerner Research Institute, 9500 Euclid Avenue, Cleveland, Ohio 44195
Location: JJN3-426
Email: daltonj@ccf.org
Phone: (216) 444-9924


My research involves understanding and integrating into clinical practice social, behavioral and environmental factors that affect health. My current research project, which is funded through the National Institute on Aging, involves understanding how the primary drivers of cardiovascular risk might vary across the age spectrum and across the socioeconomic spectrum. Our team is also investigating ways to dynamically adapt cardiovascular risk predictions in response to changes in health status and treatments over time. This work involves the integration of regional electronic medical record data with location-based data from public sources (such as the U.S. Census Bureau, the Environmental Protection Agency, the Centers for Disease Control and Prevention, etc.). We have supported this work by developing open-source software tools for a variety of biomedical research-oriented tasks, primarily through the R statistical programming language.

In other words ...

My research expertise is in modeling complex systems by combining data from a variety of sources (for instance, electronic medical records, and neighborhood characteristics from agencies such as the U.S. Census and the U.S. Centers for Disease Control and Prevention). In particular, I am currently leading an effort to understand the extent to which various neighborhood and environmental characteristics can be helpful in identifying who is likely to have a heart attack or stroke.

  1. Dalton JE, Perzynski AT, Zidar DA, Rothberg MB, Coulton CJ, Milinovich AT, et al. Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study. Annals of Internal Medicine 167: 456–64. 2017 [PubMed]
  2. Dalton JE, Zidar DA, Udeh BL, Patel MR, Schold JD, and Dawson NV. Practice Variation among Hospitals in Revascularization Therapy and Its Association with Procedure-Related Mortality. Medical Care 54(6): 623-31. 2016 [PubMed]
  3. Dalton JE, Dawson NV, Sessler DI, Schold JD, Love TE, and Kattan MW.  Empirical Treatment Effectiveness Models for Binary Outcomes. Medical Decision Making 36(1):101-14. 2016 [PubMed]

  4. Dalton JE and Nutter B. HydeNet: Hybrid Bayesian Networks Using R and JAGS. R package version 0.9.0. URL: http://CRAN.R-project.org/package=HydeNet. 2015
  5. Dalton JE. Flexible Recalibration of Binary Clinical Prediction Models. Statistics in Medicine. 32(2): 282-289. 2013 [PubMed]
  6. Dalton JE, Glance LG, Mascha EJ, Ehrlinger J, Chamoun N, Sessler DI. Impact of present-on-admission indicators on risk-adjusted hospital mortality measurement. Anesthesiology 118(6): 1298-1306. 2013 [PubMed] [software link for POARisk model]
  7. Dalton JE, Kurz A, Turan A, Mascha EJ, Sessler DI, Saager L. Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients. Anesthesiology 114(6):1336-1344. 2011 [PubMed] [software link for RQI model]
  8. Dalton JE, Kattan MW. Recent advances in evaluating the prognostic value of a marker. Scand J Clin Lab Invest Suppl.242:59-62. 2010 [PubMed]


10/03/2017 |  

Cardiovascular Risk Models Exclude Socioeconomic Factors; New Grant to Develop Enhanced Model

A team of Lerner researchers led by Jarrod Dalton, PhD, assistant staff in the Department of Quantitative Health Sciences, have shown that current cardiovascular risk prediction models systematically underestimate risk in individuals from low income and disadvantaged neighborhoods. With a newly awarded four-year, $2.2 million grant from the National Institute on Aging, part of the National Institutes of Health, the team will develop a more sophisticated model—one that incorporates both clinical and socioeconomic factors—to better predict risk for major cardiovascular disease-related health events such as stroke and heart attack. In addition to helping clinicians provide more targeted prevention and treatment options for patients, the team hopes that better understanding the underlying neighborhood-level risks associated with these events might ultimately inform community development initiatives to create healthier environments.