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Is Digital Health Closing Gaps in Health Equity?

Type 2 diabetes (T2D) disproportionately affects racial and ethnic minority groups. Black Americans are 50% more likely to have T2D, and are at higher risk for complications, than whites.1 They are also the group with the highest rates of obesity. 40% of Latinos with T2D have not been formally diagnosed, and 45% struggle with obesity. This isn’t just a chronic disease epidemic, it’s a health equity crisis.

What are some of the sources of health equity gaps in diabetes care?

Many health equity gaps in care may be attributable to social determinants of health (SDOH). SDOH are the social and environmental conditions that people grow up, work, and live in that influence their health, like level of income, quality of housing, access to nutritious food, and experiences of discrimination.

In type 2 diabetes, lack of access to healthy and affordable foods can mean an increased likelihood of developing the disease. And lack of access to quality healthcare might mean that diabetes progresses to an uncontrolled state, increasing the chances of catastrophic outcomes.

How can new care models address health equity gaps in diabetes care?

So given these challenges, is the new wave of digital health solutions making any headway towards closing health equity gaps in type 2 diabetes?

There are a few key areas where new care delivery models show promising results for reversing disparities in diabetes care:

  1. Equal access to providers: Most diabetes patients only meet with their primary care physician for a few short visits a year, and some don’t have regular access to a provider at all. Remote monitoring care gives patients virtual access to their clinical team.
  2. Continuous oversight and engagement: Tracking and reporting key biomarkers regularly, even daily, helps patients stay engaged and generates important clinical data that can be used to guide needed interventions in near real-time.
  3. Food-as-medicine approaches to reverse, not manage, diabetes: More evidence for using nutrition to prevent and reverse diabetes continues to emerge.  Nutrition therapy can be easily adapted to each individual’s unique needs, values, and preferences. Telehealth lends us the technology to deliver food-as-medicine interventions to people of all different backgrounds, at scale.
  4. Behavioral support and community: Lifestyle changes are hard, and success often depends on support from loved ones, care teams, and others with similar lived experience. To reduce disparities in health outcomes, creating spaces where people feel understood is critical. This might include tailoring treatment to language preferences, and receiving compassionate and culturally competent support from care teams.

Health Equity Case Study: Virta patient's health outcomes across socioeconomic conditions, race and ethnicity

Virta Health is on a mission to reverse type 2 diabetes in 100 million people using innovations in technology and nutrition science. With little data showing outcomes by race, ethnicity, and socioeconomic status in digital diabetes interventions, Virta wanted to better understand how telehealth and food-as-medicine interventions impact different groups. So in 2022, their researchers set out to understand how health outcomes vary across important demographic factors in the real world, including socioeconomic conditions, race, and ethnicity.

Clinical Success Across All Socioeconomic Groups

Virta researchers mapped their patient population to the Area Deprivation Index (a measure of a neighborhood's socioeconomic advantage or disadvantage) and found that Virta enrolls patients living in areas with a ride range of socioeconomic conditions.2

Analysis showed that patients achieve clinically and statistically significant improvements in A1c 6 months after enrollment, regardless of where they live.3 All socioeconomic groups on average achieved an A1c below 7.0% – the American Diabetes Association’s target for blood sugar outcomes associated with less risk of complications. And some groups even reduced average A1c to below 6.5%, the diagnostic threshold for diabetes.

Clinical Success Across Race and Ethnicity Groups

Socioeconomic disparities are not the only differences that impact the prevalence and outcomes of type 2 diabetes. Diabetes also disproportionately impacts racial and ethnic minority groups.

Virta’s researchers disaggregated outcomes and assessed them within self-reported racial and ethnic groups. Results showed that all groups achieved at least a 1% reduction in A1c on average, irrespective of race or ethnicity.3

Patients from all racial and ethnic groups met or fell below the ADA treatment target of an A1c below 7.0% after 6 months. Some groups even reached A1c less than 6.5%.

Success Factors

Several aspects of Virta’s solution have made it successful in closing some health equity gaps that traditional care has struggled to address:

  1. Treatment is individualized to reflect the patient’s cultural, religious, and personal food preferences.
  2. In-depth resources are provided to empower patient success, including access to online support tools and a supportive patient community.
  3. Virtual access to their clinical team so patients can access care whether they are at work on a manufacturing floor, or in the comfort of their own home.

Learn More

Learn about Virta’s analysis of health equity gaps in type 2 diabetes care here.

Citations

  1. Northeast Business Group on Health. Obesity, Diabetes and Racial Health Equity – What Employers Can Do. https://online.flippingbook.com/view/644877113/. Accessed 8/3/22.
  2. Virta Health Registry, T2D Reversal Enterprise, Self-Pay, and Clinical Trial patients enrolled with ADI data available, data as of 7/11/2022
  3. Virta Health Registry, covariate adjusted mean A1c at baseline and 6 months among T2D patients retained ≥ 180 days (83% retention) with lab HbA1c at 6±3 months (Cohort of n=7031), data as of 7/11/2022
  4. Virta Health Registry, covariate adjusted mean A1c at baseline and 6 months among T2D patients retained ≥ 180 days (83% retention) with lab HbA1c at 6±3 months (Cohort of n=7031), data as of 7/11/2022
The editorial staff had no role in this post's creation.