By Ivy Xiwen Deng

Last November, the Centers for Medicare & Medicaid Services (CMS) announced the release of Medicaid and Children Insurance Program (CHIP) data through its Transformed Medicaid Statistical Information System (T-MSIS) analytical files. This release marks a historic step in CMS’ commitment towards increasing data transparency and enables healthcare professionals to identify waste, promote best practices, and optimize program performance.1

As one of the first organizations to apply for Medicaid data, CareJourney has now received access to this dataset. This data expansion doubles the number of lives in CareJourney’s data asset to over 125+ million, which strengthens CareJourney’s ability to provide data-driven and evidence-based analytics.

What is Medicaid?

Compared to Medicare, Medicaid covers populations of more diverse demographics, such as children, pregnant women, childless adults, parents, and seniors/people with disabilities. Federal Law mandates that states cover certain groups of people, for example, low-income families, individuals receiving Supplemental Security Income (SSI), and qualified pregnant women and children.2

Together with CHIP, Medicaid is a joint program administered by federal and state governments. In 2017, Medicaid/CHIP provided needed care to more than 70 million Americans, almost a quarter of the country’s total population, making it the largest single source of healthcare in the United States. Nationwide, Medicaid pays for 1 in 6 dollars in the healthcare system, and 1 in 2 dollars on long-term care services and supports.3

Addressing Social Determinants of Health Using Medicaid Data

As the COVID-19 pandemic evolves, we have seen certain populations are disproportionately affected by the disease. The need to understand the sociodemographic factors of these high impact populations has become more critical than ever.

Medicaid caters to low-income and vulnerable populations whose health is often conversely impacted by their lack of access to healthy food, stable and safe housing, and transportation. An increasing amount of research has shown that social determinants of health (SDOH), such as age, gender, race, ethnicity, language, educational level, and income can profoundly impact care access, utilization, and outcomes.

As early as 2003, a report by the Institute of Medicine (IOM) has addressed the roots of racial and ethnic health inequalities in the U.S. and recommends developing comprehensive strategies to tackle these challenges.4 A recent study claims that community economic distressed levels are associated with increased stroke severity.5 In a partnered research with Microsoft Health Next using Medicare Fee-for-Service (FFS) data, CareJourney finds that beneficiaries living in communities with higher levels of economic deprivation receive significantly less preventive care than those living in affluent neighborhoods.6

Thus, in order to deliver and advance value-based care, stakeholders must understand the socio-economic context of the community they are serving and address the social determinants of health that are associated with high costs and adverse health outcomes.7

Get Access

Overview of Sociodemographic Factors of Medicaid Beneficiaries

Fill out the form to access CareJourney’s first report on Medicaid data that examines the six main sociodemographic indicators of Medicaid beneficiaries.

Using the most recently released data, the following analysis examines six sociodemographic factors of Medicaid beneficiaries; gender, age, race, household size, primary language, and family income relative to the Federal Poverty Level (FPL).8 As eligibility criteria of CHIP vary significantly from state to state, we have limited our analysis to Medicaid beneficiaries only.

The national overview panel presents the distribution of all Medicaid beneficiaries. In the state overview panel, we selected a number of states and presented the patient breakdowns by the same six socio-demographic indicators. For the purpose of the analysis, we removed missing data.

As shown in the dashboard, almost half of Medicaid beneficiaries lived in single-person-household in 2016. 78.7% of beneficiaries’ primary language was English, and more than 10% spoke Spanish as their primary language. The following age groups, 1 to 18 years old and 19 to 44 years old, comprised the largest volume of Medicaid beneficiaries. More females were enrolled in Medicaid than males. Nearly half of all Medicaid beneficiaries were white; the second-largest race/ethnic group was Hispanic.

Income level is determined by household income relative to the Federal Poverty Level (FPL) for the year. For a family of four, the 2016 FPL is $ 24,300. Shown in the bar chart, more than 70% of beneficiaries in 2016 had family income under the FPL. Income is also a determining factor of one’s eligibility to enroll in Medicaid programs. Since the Affordable Care Act (ACA) went into effect, 35 states and Washington, DC have expanded their Medicaid eligibility criteria. Any American household earning less than 133% of FPL could qualify for Medicaid.9

Household Size

Number of People Percentage
1 46.37%
2 11.82%
3 12.48%
4 11.58%
5+ 11.58%

Primary Language

Language Percentage
English 78.70%
Spanish 13.09%
Chinese 0.89%
Vietnamese 0.49%
Other 6.82%

Income Relative to FPL

Income Level Percentage
100 – 200% FPL 25.59%
Under 100 % FPL 74.41%

Age

Age Group Percentage
1 – 18 yr 44.54%
19 – 44 yr 28.82%
44 – 64 yr 17.01%
65 + yr 9.63%

Gender

Gender Percentage
Male 44.64%
Female 55.36%

Race

Race Percentage
White 44.64%
Black 22.62%
Hispanic 24.18%
Other 8.56%

Explore with CareJourney

As CMS stated, the availability of the Medicaid data enables provider performance evaluation, and research on the variability of care delivered to various populations. Even though quality measurement of Medicaid has been established, few published works have assessed care performance using large-scale datasets.

With the addition of Medicaid data to CareJourney’s robust data asset, CareJourney plans to develop metrics evaluating maternity care delivery, additional patient segmentation to identify high-risk subpopulations and assess opportunities to improve care delivery to the dual-eligible patient population.

We look forward to working with you to implement, track, and share ways of identifying actionable cohorts of patients and the best clinical interventions to leverage in your pursuit of an optimal value-based patient journey for your Medicaid population. For more information, please reach out to your member services contact or email us at [email protected].

  1. Centers for Medicare & Medicaid Services. CMS Takes Historic Steps to Increase Public Access to Medicaid and CHIP Data | CMS. https://www.cms.gov/newsroom/press-releases/cms-takes-historic-steps-increase-public-access-medicaid-and-chip-data. Accessed April 5, 2020.
  2. Mandatory & Optional Medicaid Benefits | Medicaid. https://www.medicaid.gov/medicaid/benefits/mandatory-optional-medicaid-benefits/index.html Accessed April 10, 2020.
  3. Kaiser Family Foundation. Medicaid State Fact Sheets. The Henry J Kaiser Family Foundation. October 2019. https://www.kff.org/interactive/medicaid-state-fact-sheets/. Accessed April 5, 2020.
  4. Institute of Medicine (U.S.) Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. (Smedley BD, Stith AY, Nelson AR, eds.). Washington (D.C.): National Academies Press (U.S.); 2003. http://www.ncbi.nlm.nih.gov/books/NBK220358/. Accessed April 5, 2020.
  5. Kleindorfer Dawn, Lindsell Christopher, Alwell Kathleen A., et al. Patients Living in Impoverished Areas Have More Severe Ischemic Strokes. Stroke. 2012;43(8):2055-2059. doi:10.1161/STROKEAHA.111.649608
  6. Weeks WB, Cao SY, Lester CM, Weinstein JN, Morden NE. Association Between Community Economic Distress and Receipt of Recommended Services Among Medicare Fee-for-Service Enrollees. J GEN INTERN MED. 2019;34(12):2731-2732. doi:10.1007/s11606-019-05076-6
  7. DeSalvo K, Leavitt MO. For An Option To Address Social Determinants Of Health, Look To Medicaid | Health Affairs. https://www.healthaffairs.org/do/10.1377/hblog20190701.764626/full/. Accessed April 5, 2020.
  8. Federal Poverty Level (FPL) – HealthCare.gov Glossary. HealthCare.gov. https://www.healthcare.gov/glossary/federal-poverty-level-fpl/. Accessed April 5, 2020.
  9. Kaiser Family Foundation. Status of State Medicaid Expansion Decisions: Interactive Map. The Henry J Kaiser Family Foundation. March 2020. https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map/. Accessed April 5, 2020.