Schedule:
Wednesday, June 1, 2016
Pacific N/O (Hyatt Regency San Francisco)
* noted as presenting author
Ranell L. Myles, PhD, MPH, CHES, Public Health Analyst, NIH Office of Disease Prevention, Rockville, MD
Jennifer Villani, PhD, MPH, Health Science Policy Analyst, NIH Office of Disease Prevention, Rockville, MD
Jocelyn Lee, PhD, MPH, Health Scientist Administrator, NIH Office of Disease Prevention, Rockville, MD
Sheri Schully, PhD, Health Scientist Administrator, NIH Office of Disease Prevention, Rockville, MD
Patricia Mabry, PhD, Health Scientist Administrator, NIH Office of Disease Prevention, Rockville, MD
Jessica Wu, PhD, Health Science Policy Analyst, NIH Office of Disease Prevention, Rockville, MD
David M. Murray, PhD, Associate Director for Prevention, NIH Office of Disease Prevention, Rockville, MD
Pamela Carter-Nolan, PhD, MPH, Senior Research Manager, IQ Solutions, Inc., Rockville, MD
Richard Panzer, MS, Director of User Experience and Interface Design, IQ Solutions, Inc., Rockville, MD
Introduction: The NIH Office of Disease Prevention (ODP) is developing a method to enable standardized, detailed, automated, rapid, and objective characterization of NIH prevention grants. The approach includes development of a tool and team coding process to expedite this characterization of the prevention portfolio. The coding process utilizes a prevention research taxonomy for coding grant abstracts partnered with the Prevention Abstract Classification Tool (PACT), a custom software designed to capture individual and group consensus coding. The taxonomy contains eight categories: study rationale, exposure and outcome variables, entities studied, study setting, population focus, study design, and type of prevention research. A detailed protocol provides instructions for classifying grants with definitions of terms and topics, and illustrative examples.
Methods: Coding was performed based on the taxonomy and protocol with a 10% quality control by ODP staff members. To ensure high quality data, PACT is integrated with SAS to calculate inter-rater reliability among coders and between coders and ODP staff. Analyses were performed on all grants coded from FY11-FY14 to describe trends in study design and type of prevention research.
Results: Over 3,000 Type 1 R01 grants were classified according to the prevention research taxonomy. Kappa scores for inter-rater reliability exceeded 0.7 across the eight domains of the taxonomy. Trends are reported here by study design and the type of prevention research.
Conclusions: Ultimately, this work will facilitate the identification of patterns, trends, and detailed analysis of the NIH prevention portfolio. These analyses will inform investigator-initiated research applications as well as research areas that may benefit from targeted investments by the NIH Institutes and Centers.
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