We all know that data and data analysis are essential to the work of population health. The question is, do you have the data you need to adequately predict and prevent disease? A health risk assessment can give you a lot of insights—both direct and indirect—into the social determinants and other health needs of your population.
Payers, providers, and employers often use electronic medical record (EMR) and claims data to track chronic disease trends for their population health management efforts. EMR data can tell you whether someone’s blood pressure is being controlled well with statins or lifestyle as medicine. Claims data can tell you how many people in your population have been diagnosed with hypertension or who has gone to the emergency room for uncontrolled blood sugar, for example. However, these data sources only tell the story of illness. They are looking in the rearview mirror at conditions that might have been prevented with early intervention.
Another significant data source is social determinants of health (SDoH). SDoH considers the factors that contribute to health or disease, such as economic stability, geographical location, whether someone has transportation or social connection, whether they are accessing healthy foods, and if natural and built environments encourage physical activity. Since chronic disease can be prevented through changes in health behaviors, an evidence‑based health risk assessment (HRA) that collects these SDoH factors and more is a valuable tool to show individuals the connection between their health behaviors and risk for chronic disease.
Any strategy that addresses SDoH starts and ends with data. And you can get a lot of that data from a HRA. HRAs are questionnaires that capture self-reported data about an individuals’ health and lifestyle behaviors. Some example HRA questions that collect valuable insights into SDoH might include:
The overall aim of an HRA is to gather an individual's health and lifestyle data to determine their current and future risk for disease or chronic conditions, such as diabetes, heart disease, cancer, or obesity. It also provides data on wether an individual has social support, is satisfied with life, or is current with recommended exams. For example, over half of the Medicaid members who took the Wellsource HRA were not up to date on their vaccinations. Health professionals use this data to inform programs for both individuals and, at a higher level, across populations.
There are two types of SDoH insights that can be derived from health risk assessment data: direct and indirect.
Direct insights are the specific, concrete pieces of information you gather about a person or a population by asking them directly. Some examples of direct SDoH insights might be where they live, what their food sources are, how they get around town, and whether they have social connection.
But what about the harder-to-ask questions, like income or employment? These are sensitive topics that a person may be uncomfortable answering. Some HRA administrators are hesitant to include these types of questions, but education and income are both predictors of health and mortality. All is not lost, however. Even if you aren’t able to collect direct insights, you can use HRA data to inform wellness programs targeting SDoH. This is where indirect insights come into play.
Indirect insights take a bit more of an investigative approach and a basic understanding of the patterns of health and lifestyle behaviors that signal a SDoH to understand what your population may be encountering. For example, sleep issues are common among disadvantaged populations. Factors that contribute to poor sleep are associated with health disparities including poor nutrition, lower income and education levels, and sociocultural and built environments. If you know your population is experiencing lack of sleep, it’s a good indicator that you may want to investigate further to determine if these additional factors—including SDoH—may be at play.
Let’s look at an example of direct and indirect insights. Your data can identify individuals with a health condition who may not have it under control. An individual diagnosed with prediabetes may report drinking a lot sugar-sweetened beverages and eating a lot of saturated fats. This is a direct insight.
You data can also tell you why they are choosing the foods they eat. Do they buy most of their food from a minimart? Individuals who’s primary food source is vending machines, convenience stores (e.g., minimart, corner store, or gas station), or fast food restaurants are more likely to not meet the recommended daily intakes of fruits, vegetables, and whole grains than those who get their food elsewhere.
Indirect inferences gathered on a population are still informed by evidence and are valuable for gaining a full picture of total wellbeing.
You can use the direct and indirect insights you have gathered from an HRA—including change readiness—to segment and stratify your population, enabling you to deploy programs with personalized messaging that will resonate with the individuals for whom they are intended. create a data-driven plan of action that considers both personal and socioeconomic factors.
With any implementation, you will need to measure, plan, and improve on the programs implemented to address SDoH, reduce health disparities, and improve health outcomes. HRA data will be an integral part of your planning and evaluation, allowing you to gain direct and indirect insights into your population’s SDoH and see trends in your population’s health.
Interested in learning more about health risk assessment data and social determinants of health? Download The Second Annual Wellsource Data Review, which analyzes self-reported health and lifestyle HRA data from more than 24,000 individuals to see the influence common SDoH might have on overall population health.