ODP plays an important role in characterizing the NIH prevention research portfolio. ODP has been analyzing the NIH research portfolio since 2013, and in 2015, ODP began developing more precise machine learning tools and approaches to better describe and understand NIH-funded prevention research. We published our initial findings in 2018 and continue to release new data on a regular basis.
ODP’s process and the major results of our work are highlighted below.
NIH Investment in Prevention Research
According to ODP's most recent analysis (published in March 2021):
- Primary and secondary prevention research represents 20.7% of NIH research projects and 27.4% of NIH research funding.
- A large proportion of prevention research projects included observational studies (63.9%), analysis of existing data (46.5%), or methods research (24.0%).
- Projects using a randomized clinical trial design represented just over a tenth (12.3%) of the NIH prevention research portfolio.
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ODP has looked at more than 14,000 NIH-funded research projects across 12 activity codes for grants awarded in fiscal years 2012–2019, continuing our detailed analysis of the NIH prevention research portfolio. These 12 activity codes represent 90.8% of all new projects and 80.1% of all dollars used in NIH research supported by extramural grants and collaborative agreements.
ODP collaborated with the Office of Portfolio Analysis (OPA) to develop and validate 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 identifies applied prevention projects than other, earlier approaches.
ODP regularly publishes the results of its analyses of the NIH prevention research portfolio in specific areas, which are listed below. New publications are added as they become available.
Primary and Secondary Prevention Research
- Publication: New NIH Primary and Secondary Prevention Research During 2012-20192
Murray DM, Ganoza LF, Vargas AJ, Ellis EM, Oyedele NK, Schully SD, Liggins CA. American Journal of Preventive Medicine. 2021 Mar 18:S0749-3797(21)00087-8. doi: 10.1016/j.amepre.2021.01.006. Epub ahead of print. PMID: 33745818.
- Publication: NIH Primary and Secondary Prevention Research in Humans During 2012−20171
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.
- Publication: A Machine Learning Approach To Identify NIH-Funded Applied Prevention Research1
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.
- 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)
1Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 1.0 (PDF)
2Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 3.1 (PDF)
Research on Leading Causes and Risk Factors of Death and Disability
- Publication: Assessing National Institutes of Health Prevention Research on Leading Causes of Death Focused on Racial or Ethnic Minority Populations, Fiscal Years 2016-20202
Ganoza LF, Alvidrez J, Liggins CA. Ethnicity and Disease. 2023 Jan 1; 33 (1): 44–50. doi: https://doi.org/10.18865/1718.
- Publication: Assessment of Prevention Research Measuring Leading Risk Factors and Causes of Mortality and Disability Supported by the US National Institutes of Health1
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.
- 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)
1Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 1.0 (PDF)
2Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 3.1 (PDF)
Study Designs Used in NIH Prevention Research
- Publication: NIH Primary and Secondary Prevention Research in Humans: a Portfolio Analysis of Study Designs Used in 2012-20192
Oyedele NK, Ganoza LF, Schully SD, Liggins CA, Murray DM. Prevention Science. 2022 Jan 22. doi: 10.1007/s11121-022-01337-9. Epub ahead of print. PMID: 35064895.
2Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 3.1 (PDF)
Diet and Physical Activity
- Publication: Diet and Physical Activity Prevention Research Supported by the U.S. NIH From 2012-20171
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.
1Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 1.0 (PDF)
Health Care Delivery
- Publication: Investment in Prevention Health Care Delivery Research by the National Institutes of Health2
Ganoza LF, Villani J, Klabunde CN. Medical Care. 2023 Feb 1;61(2):75-80. doi: 10.1097/MLR.0000000000001809. Epub 2022 Dec 6. PMID: 36630558.
2Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 3.1 (PDF)
Substance Use
- Publication: Substance use prevention research funded by the NIH1
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.
1Protocol: Coding Abstracts Using the ODP Prevention Taxonomy - version 1.0 (PDF)
Working closely with the 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)