NIH – Getting To Zero: Understanding HIV Viral Suppression and Transmission in the United States (R01 Clinical Trial Not Allowed)

November 21, 2017 by School of Medicine Webmaster

The purpose of this FOA is to address gaps in our understanding of HIV viral suppression and transmission using m/eHealth, Big Data Science, or similar novel technologies and computational approaches. The findings will hopefully lead to effective strategies to achieve and sustain HIV suppression and halt HIV transmission in the U.S.


Viral suppression is key to preventing HIV transmission. While initial progress to control the U.S. epidemic has been substantial, continued progress on the U.S. HIV elimination goals will require novel approaches to achieve sustained suppression.  Currently, U.S. rates of first linkage to care, as reported to the CDC by 32 states and D.C. (2015) vary from 48% to 86.7% among individuals aged 13 and older. In addition, retention in care after initial linkage is poor, between 36.7% and 49%. Unfortunately, linkage is worse (30.4%) in those age 13-24 years. Poor linkage to care in this age group is mirrored by this age group having the highest rates of HIV infection. Finally, important geographic differences exist with urban and rural populations facing different challenges in HIV care and adherence.

Big Data Science technologies, computing, informatics and analytics can be used to address gaps in our understanding of HIV suppression and transmission. Big Data Science refers to research that capitalizes on the rapid advances in technology that have increased data storage capacity and the liquidity of data in fields such as health, genomics, community and the environment, social media and individual, group, and commercial activities. Data sources are characterized by their high volume and variety, and the field is developing approaches to capture, manage, organize, harmonize and analyze data to extract useful information. mHealth refers to the use of mobile phones or other wireless technologies in medical care and personal assistive applications.  Examples of mHealth include the use of mobile phones to spread prevention messages, conduct disease surveillance and management, and epidemic tracking. eHealth is the use of electronic medical records in care and research.

Focused epidemiology is needed to elucidate the correlates and predictors of successful viral suppression and develop data driven public health approaches to achieve “getting to zero”. Data is currently insufficient in some areas of the country to fully describe the epidemiologic patterns. Furthermore, data is not as timely as is necessary to describe the rapidly changing landscape of transmission. Current surveillance systems operate on a slower basis resulting in delays of years at the national level.  The advent of treat-all strategies, increases in the use of pre-exposure prophylaxis, changes in health care access, fluctuations in substance abuse impact transmission risk on a relatively rapid time course. Methods that can measure these fluctuations much closer to real-time can appropriately guide public health responses and improve the ultimate outcome.

Research Objectives and Areas of Interest

This initiative is intended to support the application of Big Data Science methods and analyses to the complex factors impacting HIV viral suppression.  Research should be focused on the U.S. epidemic with the intent to develop more focused and ever more effective approaches to achieving viral suppression. Data from clinical care, laboratory measures, clinical trials, observational research, human behavior surveys, viral phylogenetics, bioinformatics, sexual networks, transmission clusters, geospatial mapping, and the larger human and environmental digital footprint can be used synergistically to improve the understanding of viral suppression. Applicants to this initiative should propose approaches to increase the precision and timeliness of measures along the treatment cascade and to determine critical components of rapid and sustained viral suppression at the individual and population level.  Proposed research may include:

  • Improved accuracy and speed to describe the epidemiology of HIV care indicators: testing, linkage, engagement, suppression of viral load at a jurisdictional or national population level.
  • Evaluations of the epidemiology of HIV care to monitor progress overall and in sub-populations, such as by gender, transmission risk, race, age, or geographic region.
  • Research to uncover novel correlates and predictors of initial and sustained HIV viral suppression capitalizing on a rich understanding of the contextual factors impacting suppression.
  • Measurements of long-term viral suppression and the correlates and predictors of success.
  • Implementation strategies addressing coverage, service delivery systems, barriers to service delivery and stigma that can be obtained from existing data or estimated in modelling applications will be accepted.

Applicants may propose innovative crowd source approaches to generate new shared data resources based on electronic individual level data which can be triangulated with other data sources to validate inferences from big data methods.

All data generated by this project are expected to be shared consistent with the FAIR (findable, accessible, interoperable, reusable) principles and  in accordance with NIH’s policies.

Use of Common Data Elements in NIH-funded Research

Many NIH Institutes and Centers (ICs) encourage the use of common data elements (CDEs) in basic, clinical, and applied research, patient registries, and other human subject research to facilitate broader and more effective use of data and advance research across studies.  CDEs are data elements that have been identified and defined for use in multiple data sets across different studies.  Use of CDEs can facilitate data sharing and standardization to improve data quality and enable data integration from multiple studies and sources, including electronic health records.  NIH ICs have identified CDEs for many clinical domains (e.g., neurological disease), types of studies (e.g. genome-wide association studies (GWAS)), types of outcomes (e.g., patient-reported outcomes), and patient registries (e.g., the Global Rare Diseases Patient Registry and Data Repository).  NIH has established a “Common Data Element (CDE) Resource Portal” to assist investigators in identifying NIH-supported CDEs when developing protocols, case report forms, and other instruments for data collection.  The Portal provides guidance about and access to NIH-supported CDE initiatives and other tools and resources for the appropriate use of CDEs and data standards in NIH-funded research.  Investigators are encouraged to consult the Portal and describe in their applications any use they will make of NIH-supported CDEs in their projects.

Deadlines:  March 14, 2018; March 14, 2019; March 13, 2020


Filed Under: Funding Opportunities