This project explores the relationship between socioeconomic factors, health expenditure, and Medicaid/CHIP enrollment in the United States with a focus on Virginia. Despite being a developed country with significant wealth and resources, the disparities in health outcomes are extreme and they’re only exacerbated by policy, poverty, and limited access to healthcare services and so by taking a global approach I first understand how health spending has changed over time across different countries with different health infrastructure systems in place, then take a closer look in the United States by understanding Medicaid and CHIP enrollment and seeing how state-level policies impact the number of enrollees by eligibility group, and lastly honing in on Virginia to see how Virginia is being impacted with Medicaid expansion and coverage.

Being born in India and moving to the U.S. when I was young, I’ve seen firsthand how people in different areas have drastically different access to healthcare. I wanted to understand underlying factors that contribute to these disparities. Additionally, even though programs such as Medicaid do exist, I’ve seen people that don’t qualify despite needing coverage and even the people that have qualified, have varied benefits depending on their income level and other factors (i.e health conditions that aren’t treatable). Also, while people may qualify, the fact that they live in states that haven’t expanded Medicaid leaves them vulnerable and without access to a safety net. Medicaid is a great service and provides essential healthcare, but the true issue is much more complex and that’s what I wanted to understand.

Global Context

To better understand how much US divests into healthcare every year, I first made an interactive map that shows Current Health Expenditure per capita over time from 2008 to 2021. And sure enough the US leads in how much they spend on health, but what’s interesting is that some Asian countries faced larger increases in the past few years, while the US has remained constant. Even Canads increased how much they spend on healthcare. Now there’s more nuance to this as different countries have different healthcare policies and that’s what I wanted to discover next.

Global Healthcare Spending Over the Years

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The animated map above illustrates current health expenditure per capita from 2008 to 2021 across the globe. While the United States consistently leads in per-capita spending, several Asian and European countries have experienced steeper increases in recent years, highlighting divergent trajectories in national health investment.

Interactive Health Metrics Over Time

To explore multiple related indicators—such as health expenditure, GDP per capita, and life expectancy—over time and across countries, I created an interactive Shiny application.

This dashboard lets you select any health or economic metric and animate its evolution from 2008 onward. For example, comparing “Health Exp per Capita” against “Life Expectancy” reveals that although the U.S. spends substantially more per person than most high-income countries, it still falls behind in longevity, which is a sign of systemic inefficiencies compared to universal systems like the U.K.’s NHS.

Medicaid & CHIP Enrollment in the United States

This map shows state-level enrollment trends from 2018 to 2023, with expansion states leading growth. Enrollment in regards to Medicaid refers to the total number of individuals that are enrolled in a state’s Medicaid program. Expansion states, are states that under the Affordable Care Act, can cover all adults (19-64 years) that have incomes that are up to 138% of the federal-poverty level in Medicaid. As you can see states like Missouri and Oklahoma observed increase in enrollment once they expanded Medicaid in 2020. And you can also observe how Covid-19 affected these numbers as during the Public Health Emergency Congress prevetned states from removing anyone from Medicaid, and with the loss of jobs at this time, enrollment increased.

Medicaid Coverage in Virginia Counties

Being from Virginia, I wanted to better understand how different areas (rural vs. urban as rural counties usually have lower median household income rates compared to urban centers) differed in Medicaid coverage rates. And as you can see lower-income counties have higher Medicaid coverage rates, underscoring Medicaid’s critical role for economically vulnerable populations.

County Distribution by Income and Coverage

This bar chart shows the number of Virginia counties in each median household income bin, with fill indicating the average Medicaid coverage rate. Lower-income bins have both higher counts and higher average coverage rates, emphasizing that counties with lower incomes rely more heavily on Medicaid. Lower-income counties have higher Medicaid coverage rates, underscoring Medicaid’s critical role for economically vulnerable populations. Specificallly, looking at the bar for median household income of 40K-60K, many counties fall in this category and these counties most likely have expansion protections that cover even the working-class/lower middle-class brackets. Overall, there appears to be an inverse relationship between county-level median household income and average Medicaid coverage, as counties with median household incomes >100K, don’t have the need for Medicaid. Additionally, it speaks to Virginia as a whole, as it’s a wealthy state.

Impact of Medicaid Expansion on Maternal Health Outcomes

I now wanted to understand specific groups and how they’ve been affected with Medicaid expansion. I’m looking at women in particular, and ones during pregnancy as Medicaid pays for nearly half of all the births nationwide. And I wanted to see this trend in Virginia in particular across different races. And so with that being said the bar plot above compares key maternal health indicators by race group under Medicaid expansion. More specifically, the numbers are relative to women who had a birth within the period of analyssi within each race category and this data was collected between 2016 and early 2020. While overall enrollment in the first trimester is high across all groups, there are small but notable differences in prenatal care access and outcomes: Black mothers have slightly higher enrollment but similar prenatal visits compared to White and Hispanic mothers, and experience marginally higher preterm birth and cesarean delivery rates. This reveals racial disparities in pregnancy-related outcomes and care access under Medicaid expansion in Virginia. When I compared thse outcomes with other states, I noticed that Virginia’s expansion reduced the chances of a preterm delivery. Broadly speaking, Medicaid expansion has been shown to improve maternal health outcomes, but worsens existing barriers when it comes to obtaining care for Hispanic women, which means that more work still needs to be done to address this issue.

Medicaid Expansion Revenue and Hospital Assessments

This stacked area chart illustrates how different revenue streams and assessments have grown from 2019 to 2023. Revenue from Medicaid expansion and enhanced payment rates to hospitals have seen substantial increases, reflecting the financial impact of expanded coverage on hospital funding. This reveals Medicaid’s critical role in boosting hospital revenue via increased coverage assessments and Medicaid-driven income streams.

Conclusion

While the United States leads in per-capita healthcare spending, this investment does not guarantee the best outcomes. Global comparisons reveal that several nations have achieved longer lifespans with far lower spending. Within the U.S., socioeconomic status and state policy choices—particularly Medicaid expansion—play a pivotal role in shaping access to care and health outcomes. Virginia’s county-level analysis highlights that lower-income areas rely heavily on Medicaid, and expansion has measurably improved maternal care metrics while generating critical hospital revenue. Going forward, addressing systemic inefficiencies and expanding coverage to underserved regions remain essential for closing health equity gaps.

Sources

World Bank, Current Health Expenditure per Capita (SH.XPD.CHEX.PC.CD)

WHO, Global Health Expenditure Database

CMS, Medicaid & CHIP Enrollment Data

KFF, Medicaid Coverage Rates by County

VDH, County Health Rankings & Roadmaps

CMS, Hospital Financial Data on Medicaid Expansion