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One of the first community-based population health studies in the US, the Healthy Nevada Project launched in 2016 with three straightforward goals: conduct sound science, improve health, and save lives. Now among the nations largest such studies, the ground-breaking health and genetics project is three for three.

Developed by theDesert Research Institute Center for Genomic Medicine, the Healthy Nevada Project offers genetic testing at no cost to Nevada residents who want to learn more about their health and genetic profile.

By combining genetic data, environmental data and individual health information, researchers and physicians are gaining new insights into population health, enabling personalised healthcare while improving the health and wellbeing of entire communities in the state.

Painting an accurate portrait of an individual or population to help understand and anticipate health outcomes requires data representing many life factors, including genetics, socioeconomic backgrounds, physical environments, lifestyle behaviours and quality of healthcare.

One of medicines most complicated questions is, how do you predict what someones health outcome is going to be? says Joseph Grzymski, PhD, who serves as principal investigator of the Healthy Nevada Project, chief scientific officer of Renown Health, and research professor of computational biology and genetics at theDesert Research Institute. Its not just genetics, or your blood pressure or where you live, its trying to model all the impacting factors for diseases. The massive challenge of population health studies is to build better predictive models to understand why some people get sick and others dont, why some live to be 90 and above, and determine what that magical equation is.

Working in tandem with experts in environmental data at the Desert Research Institute, the Center for Genomic Medicine fuels the project with de-identified electronic health records. Researchers supplement this with data from the Environmental Protection Agency (EPA), the US Census Bureau, birth and death records, and other data sources to build a population health portrait.

To form connections between participant genetic information and other health factors, data scientists applymachine learningandartificial intelligencecapabilities to DNA results generated byHelix, a partner specialising in population genomics.

Were working to understand how environmental and other factors can help predict who may be at risk, allow for quicker diagnoses and encourage the development of more precise treatments, says Jim Metcalf, chief data scientist of the Healthy Nevada Project. Statistical and machine learning methods, along with the intuitive data visualisations made possible by SAS, have been critical elements.

In addition to using analyticsto identify populations and subpopulations of people who already have a disease in common, project researchers also apply analytics to get in front of diseases before they manifest in individuals.

After a participants voluntary genetic testing, the team checks for risks for many serious genomic conditions, including the top three identified by the Centers for Disease Control and Prevention as medically actionable (CDC Tier 1):

Most individuals affected by these genetic risks arent aware they have them. The project has genetic counsellors who will call our participants if they have a particular mutation and inform them, so they can talk with their physician and make important health decisions, Metcalf says.

Healthy Nevada Project participant Jordan Stiteler says the unexpected phone call saved her life.

Stiteler, a young mother, had family members who had suffered heart attacks and strokes at early ages. When she learned she carried the FH marker, she received guidance and support to help her make healthy lifestyle and medication choices. Soon several other family members joined the study to learn about their genetic risks.

Genetic screening also makes it possible to get in front of a cancer diagnosis. The ideal is to detect these mutations prior to any kind of a tumour becoming untreatable, Metcalf says. We have cases where people have told us, Thank you so much, you saved my life, because they were able to have preventive surgery, or they found a treatable Stage I tumour because of the results of genetic testing. Those are the things we live for in this project.

Since its initial 10,000 adult participants, the Healthy Nevada Project has grown to more than 52,000 individuals and expanded from northern Nevada to Las Vegas and its outlying areas in the southern part of the state.

According to Grzymski, more genome data from more people equates to greater statistical power and accuracy in understanding the links between who you are and your health outcomes. The underpinning of a population genetics study is access to data and then the ability to extract, transform and study the data for any of the myriad health outcomes we want to focus on, he says.

Providing the foundation for those efforts is a SAS platform, which the project runs in an on-premises computing environment.

The strength of the language, the depth, everything that SAS brings has been rock solid, Metcalf says. We have used SAS to comb through, manipulate and extract 200 terabytes of genetics and health records data. Setting the right parameters, we can look through a billion-record table of physician notes with no problem.

A data collection endeavour of this magnitude required cooperation between organisations, care protection of privacy, and a means to gain consent from participants. When executive leadership at Renown realised Desert Research Institute had a cadre of skilled data scientists on staff able to independently ingest and analyse Renowns electronic health records (EHR) data, they made the decision to begin sharing EHR data with the Center for Genomic Medicine at Desert Research Institute. Consequently, Desert Research Institute became a Health Insurance Portability and Accountability Act (HIPAA) business associate of Renowns.

Implementing and supporting processes to ensure patient privacy while facilitating research is a technically challenging and mentally taxing effort.The very real overprint of adhering to HIPAA requirements should not be underestimated in terms of project cost structure and staffing effort. The Healthy Nevada Project team works closely with Renowns compliance department and the Institutional Review Board at the University of Nevada, Reno, to ensure it adheres to the highest standards and practices of maintaining participant privacy. Healthy Nevada Project cohorts typically number in the tens of thousands of participants, if not more. The team is not looking at individuals in the EHR and would have great difficulty doing so as nearly all personally identifiable information is removed from the EHR to create a HIPAA-defined limited dataset as the first step of data ingestion.

Collecting genetic data requires receiving consent from participants via documents approved by the University of Nevada Institutional Review Board. Participants agree to be in the study knowing their genetic information and medical record will be used for medical research. Participating in the study is not mandatory and participants can withdraw at any time for any reason. The consent documents are written at an 8th grade level and are heavily vetted and tested for participant understanding.

The Healthy Nevada Project continues to bring a variety of data sources to the table for insights into population health, including:

The team uses SAS statistical models and analyses to report results to hospital administrators and research to the teams scientific peers for review.

The SAS platform has been the foundation bedrock of the Healthy Nevada Project, Metcalf says. We have immersed ourselves in the machine learning and AI procedures that SAS has and use those on a continual basis.

For example, a hospital wanted to reduce the time patients spend in the post-anesthesia care unit or stepdown room after surgery. To understand why some patients required more time there, the Healthy Nevada Project used a variety of SAS procedures, such as variable selection in the analytic process, to facilitate machine learning, allowing researchers to identify and eliminate possible causes as key factors.

The researchers found that the top factors most directly contributing to time spent in the stepdown room were the anesthesia type used, the patients age and the patients relative health.

The Healthy Nevada Project has elevated Nevadas profile in doing cutting-edge research, using data to deliver evidence-based, publishable results in peer-reviewed scientific journals and databases, says Grzymski. The entire team is proud of the work weve delivered and its impact as we continue to understand what makes people sick or well and enable preventive care.

Using Data and Analytics Across the Research Lifecycle to Improve Population Health read the whitepaper here.

About the author Sarah Newton Sarah is the manager of public sector health policy at SAS, helping governments leverage data and analytics to improve the health and wellbeing of their citizens.Sarah has a masters in public health, as well as extensive experience working on health policy at the federal and state level.Sarah can be contacted at [emailprotected].

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Improving US citizens' health though machine learning and AI - Global Government Forum