The ODP seeks to better understand the NIH’s investment in prevention research by methodically characterizing the NIH prevention research portfolio, providing more information about what the NIH is funding and in greater detail than previously available.

More specific identification and analysis of NIH-funded prevention research enables the ODP to assess the progress and changes in NIH-funded prevention research over time. These efforts help the ODP describe trends in NIH-funded prevention research and identify gaps in the NIH prevention research portfolio that could benefit from targeted investments, potentially addressing important modifiable risk factors and, therefore, reducing the burden of preventable disease.

The ODP provides leadership for the development, coordination, and implementation of prevention research in collaboration with NIH Institutes and Centers and other partners. Fulfilling this vision depends on the ODP’s ability to accurately characterize studies across a number of dimensions such as topic area, study design, population studied, and type of prevention research.

Defining and Coding Prevention Research

The ODP defines prevention research as encompassing both primary and secondary prevention research in humans, as well as prevention-related methods for use in humans—it does not include basic or preclinical studies that could still be years away from preventing disease or disability.

Working closely with the Office of Portfolio Analysis (OPA), the ODP developed new methods to apply a machine learning approach to the NIH’s prevention research portfolio based on the ODP's definition of prevention research.

Once the machine learning tools identify relevant NIH prevention research projects, the ODP’s prevention research taxonomy (PDF)—along with a detailed protocol (PDF)—serves as a set of rules for coding project abstracts. The taxonomy is a framework for classifying research and includes 140 non-mutually exclusive topics grouped into six categories. The protocol provides teams of coders with instructions, definitions, and examples to support the accurate, standardized classification of research projects.

The ODP continually refines and trains its machine learning algorithms to identify prevention research projects. Efforts are currently underway to apply machine learning to support coding more specific details of individual prevention research grants based on the ODP taxonomy, such as the health condition, study population, study design, and type of prevention research.

• Publication: A Machine Learning Approach To Identify NIH-Funded Applied Prevention Research (Villani J, Schully SD, Meyer P, Myles RL, Lee JA, Murray DM, Vargas AJ. American Journal of Preventive Medicine. 2018;55(6):926-931. doi: 10.1016/j.amepre.2018.07.024. Epub 2018 Oct 25. PMID: 30458951; PMCID: PMC6251715.)¹
• ODP Director’s Message: New Papers from the ODP: Characterizing the NIH Prevention Research Portfolio Using a Novel Machine Learning Approach (November 27, 2018)
   

¹Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 1.0 (PDF)
 

Summary and Early Findings

• The ODP coded more than 11,000 research projects across 12 activity codes for grants awarded in fiscal years 2012–2017, leading to the first-ever detailed analysis of the NIH prevention research portfolio.
• These 12 activity codes represent 91.7% of all new projects and 84.1% of all dollars used in NIH research supported by extramural grants and collaborative agreements.
• The ODP collaborated with the OPA to develop novel machine learning algorithms that identify prevention research projects. Because the machine learning method was specifically trained to recognize applied prevention research, it more accurately identified applied prevention grants than other approaches.
• Using these new tools and methods, the ODP found that primary and secondary prevention research represents 16.7% of NIH research projects and 22.6% of NIH research funding.
• A large proportion of prevention research projects included observational studies (63.3%), analysis of existing data (43.4%), or methods research (23.9%). Projects using a randomized clinical trial design represented less than a fifth (18.2%) of the NIH prevention research portfolio.

Primary and Secondary Prevention Research

• Publication: NIH Primary and Secondary Prevention Research in Humans During 2012−2017 (Murray DM, Villani J, Vargas AJ, Lee JA, Myles RL, Wu JY, Mabry PL, Schully SD. American Journal of Preventive Medicine. 2018;55(6):915-925. doi: 10.1016/j.amepre.2018.08.006. Epub 2018 Oct 25. PMID: 30458950; PMCID: PMC6251492.)¹
• Publication: A Machine Learning Approach To Identify NIH-Funded Applied Prevention Research (Villani J, Schully SD, Meyer P, Myles RL, Lee JA, Murray DM, Vargas AJ. American Journal of Preventive Medicine. 2018;55(6):926-931. doi: 10.1016/j.amepre.2018.07.024. Epub 2018 Oct 25. PMID: 30458951; PMCID: PMC6251715.)¹
• ODP Director’s Message: New Papers from the ODP: Characterizing the NIH Prevention Research Portfolio Using a Novel Machine Learning Approach (November 27, 2018)
• Presentation: Primary and Secondary Prevention Research in Humans Funded by NIH During 2012-2017 (PDF) (Society for Prevention Research, May 2018)

The ODP regularly publishes the results of its analyses of the NIH prevention research portfolio in specific areas, which are listed below. New publications will be added as they become available.
 

Diet and Physical Activity

• Publication: Diet and Physical Activity Prevention Research Supported by the U.S. NIH From 2012-2017 (Vargas AJ, Sprow K, Lerman JL, Villani J, Regan KS, Ballard RM. American Journal of Preventive Medicine. 2019;57(6):818-825. doi: 10.1016/j.amepre.2019.07.023. PMID: 31753263; PMCID: PMC6894494.)¹

Leading Causes and Risk Factors of Death and Disability

• Publication: Assessment of Prevention Research Measuring Leading Risk Factors and Causes of Mortality and Disability Supported by the US National Institutes of Health (Vargas AJ, Schully SD, Villani J, Ganoza Caballero L, Murray DM. JAMA Network Open. 2019;2(11):e1914718. doi: 10.1001/jamanetworkopen.2019.14718. PMID: 31702797; PMCID: PMC6902772.)¹
• ODP Director’s Message: ODP Study Suggests the U.S. Could Benefit from More Prevention Research on Leading Risk Factors and Causes of Death and Disability (November 8, 2019)
• NIH Media Advisory: Study finds leading risk factors and causes of death and disability underrepresented in NIH-supported prevention research (November 8, 2019)

Substance Use

• Publication: Substance use prevention research funded by the NIH (Villani J, Ganoza L, Sims BE, Crump AD, Godette DC, Hilton ME, Vargas AJ. Drug and Alcohol Dependence. 2020;206:107724. doi: 10.1016/j.drugalcdep.2019.107724. PMID: 31753731.)¹
   

¹Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 1.0 (PDF)