Local and Individualized SDoH Data Is Critical to Advancing Health Equity

Published on September 15, 2021

In the U.S., health outcomes vary significantly among groups, regions, communities and individuals. Achieving health equity – a critical goal for many government and healthcare organizations – starts with addressing the root causes of health disparities, primarily social determinants of health (SDoH). To do so, insurers, health systems and others first need to define and document the barriers to health that exist within their member and patient populations. This requires robust, comprehensive data that can then be used to target the right audiences with specific health interventions and to deploy broader population health strategies.

What is health equity?

Health equity means providing all people with equal opportunity to achieve optimal health regardless of who they are, where they live or the resources at their disposal. Traditionally, health plans and providers have sought to improve health equity by providing better clinical care or more access to care services. With the advent of value-based care, they increasingly recognize the need to address underlying factors that drive such disparities, specifically SDoH.

What are some examples of disparities preventing health equity in the U.S.?

Disparities in life expectancy, health status, quality of life and susceptibility to illness and disease are pervasive and growing across the United States. These gaps can be observed broadly across populations, regions, communities, ages, races, ethnicities, genders, sexual identities and socioeconomic status, among other groupings.

For example, here’s what the data shows about health disparities related to zip code, race/ethnicity and sexual identity/orientation:

  • Life expectancy and geography – Life expectancy differs by as much as 20 years between the shortest- and longest-lived counties in the United States. Perhaps more alarming, significant disparities in life expectancy can also be observed within local communities. In New Orleans, according to the Robert Wood Johnson Foundation, the life expectancy of a child can vary by 25 years in neighborhoods just a few miles apart. In Boston, life expectancy in the Roxbury neighborhood is 58.9 years, yet residents just across Massachusetts Avenue have a life expectancy of 84.2 years.
  • Health disparities and race/ethnicity Non-elderly Hispanic, Black, American Indian and Alaska Native adults are more likely than non-elderly whites to delay or go without needed care. Blacks, American Indians and Alaska Natives are more likely than whites to report a range of health conditions, including asthma and diabetes. American Indians and Alaska Natives also have higher rates of heart disease compared to whites. Infant mortality rates are higher for Blacks and American Indians and Alaska Natives compared to whites, and Black males have the shortest life expectancy compared to other groups.
  • Health disparities and the LGBT community – In 2017, according to Kaiser Family Foundation, an estimated 4.5%, or nearly 15 million people, of the U.S. adult population identified as lesbian, gay, bisexual or transgender. Research suggests that some subgroups of the LGBT community have more chronic conditions, as well as higher prevalence and earlier onset of disabilities than heterosexuals.

How can we achieve health equity?

The root causes of SDoH (ranging from lack of care resources to the need for affordable housing and the health impacts of systemic racism) are largely beyond the healthcare system’s capacity to address. Yet, targeted interventions and population health strategies from healthcare organizations can have a significant impact on health outcomes – including mortality, morbidity, life expectancy, healthcare expenditures and health status – that stem from SDoH.

This is a formidable challenge. Health disparities not only exist between populations, neighborhoods, regions, races and classes, but within those groups as well. To be effective and efficient, health equity interventions must address health needs at the individual and local level.

Although more needs to be done, government entities and healthcare organizations are taking action. Here are just a few examples:

  • Department of Health and Human Services (HHS) –In 2011, HHS developed an action plan for eliminating racial and ethnic health disparities, building on the Healthy People 2020 goal to achieve health equity and eliminate disparities.
  • The Affordable Care Act (ACA) – The ACA’s broad coverage expansions and increased funding for community health centers improved access to coverage and care for many groups facing disparities.
  • Centers for Medicare and Medicaid Services (CMS) – In 2013, CMS released an equity plan for improving quality in Medicare. In 2018, it released a new rural health strategy.
  • Medicaid Managed Care Organizations (MCOs) – More than 20% of states require their MCOs to stratify quality measures by race, ethnicity and language preference. Nearly as many require Medicaid MCOs to analyze data, including SDoH.
  • State and local governments, non-profits and health providers – Twenty-three states or territories have strategic plans addressing minority health or health equity. Many states now collect and analyze data to support efforts that can alleviate health disparities.
  • Healthcare organizations – Health plans and health systems are also looking to address health disparities and close the health equity gap; Chief Equity Officers are becoming critical members of the C-Suite.

Where are organizations currently getting data on health disparities?

Traditionally, healthcare organizations collect social, economic, behavioral and environmental risk data by (1) administering surveys to a segment of the population or (2) using publicly available data at a geographic level:

  • Health plans typically use surveys to collect data on members’ SDoH. This data is used to conduct research, augment the consumer profile in the EMR or care management system and inform point-of-care interactions between healthcare practitioners and consumers. That data is limited, however, by selection bias, response bias, high administration costs, lack of timely updating, and other challenges.
  • Healthcare organizations also use public datasets like the CDC’s Social Vulnerability Index (SVI), University of Wisconsin’s Area Deprivation Index (ADI), and University of Wisconsin’s County Health Rankings (CHR) to understand aggregated SDoH in their respective communities.

Aggregated, out-of-date or biased data prevents healthcare organizations from addressing health disparities meaningfully. To be useful, SDoH must be examined at an individual consumer level. Failing to do so distorts and obscures understanding.

Health plans and providers also need to fill gaps in the traditional clinical risk modeling approaches used for health program identification and stratification. Because the models rely on historical claims history, they often project that next year’s high-cost consumers will be the same as last year’s high-cost consumers. In addition, they don’t take into account a member’s specific barriers to health. Incorporating SDoH data and consumer analytics improves the accuracy of traditional clinical risk models and fills the gap for new member risk assessment and rising risk member identification.

What additional data do organizations need?

Publicly available demographic data is too broad to identify variations within groups or areas. SDoH data is typically aggregated across regions or populations and not localized or individualized. Clinical and claims data only provides information on patients who’ve received care and fails to account for people who may need it but not receive it or predict those who will in the future. Surveys only reach those who are willing and able to respond and may overlook those with the highest need.

In contrast, consumer data enables plans and providers to assess SDoH at the local and individual level and predict the risk of adverse future health outcomes. Combined with clinical and claims data, demographic data and survey data, sophisticated analytics can produce insights that are robust and nuanced enough to address health disparities while improving health outcomes and reducing the total cost of care. Bringing consumer, demographic, survey and clinical data down to the individual level allows plans and providers to bolster research initiatives, shape population health interventions or predict future risk in members and populations. This allows the organization to immediately identify disparities across all segments of their population, including age, gender, ethnicity, income bracket, geographic designation and more. The healthcare organization can then select the top health behaviors and health outcome disparities in those groups or across the full population and make informed decisions regarding strategies, interventions and the direction of resources.

How does Carrot Health data contribute to organizations’ ability to advance health equity?

Carrot Health combines data from more than 100 sources to create a comprehensive view of each consumer across the U.S. With the largest consumer dataset in the healthcare industry, Carrot Health gives healthcare organizations the capabilities to identify, understand and measure health disparities in their populations.

Specifically, the Carrot Health Social Risk Grouper™ (SRG) classifies and organizes SDoH to help health plans and organizations understand, identify, measure and quantify the social barriers and circumstances in which people live. The SRG is a composite score driven by four components: behavioral, social, economic and environmental. Within these components are 11 social risk categories, including loneliness, housing instability, health literacy, food insecurity, financial insecurity, discord at home, unemployed, uninsured, low socioeconomic status, transportation needs and unacculturation. This data is used to conduct research, segment populations into social risk strata and inform program and community planning.

Understanding disparities at an individual and local level can have a significant impact on population risk scoring and future risk identification/assessment as well. Future risk of disease progression, inappropriate utilization of the health system, and other adverse health outcomes are largely shaped by individual-level social determinants of health. For example, when controlling for age, gender, and region across the Carrot Health portfolio of healthcare customers, a 10-unit increase in SRG equates to a 9% increase in total cost of care. This increase is more than 2.8x greater than a comparable approach using a community-based risk score like SVI.

For more information about advancing health equity and addressing disparities in member and patient populations, download “Closing the Health Equity Gap: Why Local and Individualized Data Is Critical” today!

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