The Short-Term and Long-Term Impacts of Health Care Access for Low Income Americans

Poor People Have Poor Health

Source: Chetty et al. JAMA 2016

Where should we intervene?

Is there a way to break this cycle by improving access to health care? Or should we focus our efforts elsewhere (on education, income supplements, job training)?

Health Investments

• Access to health care in the womb/infancy
• Access to health care in childhood
• Access to health care in adulthood

A variety of public policies have been targeted at:

What are the impacts of these policies on health and well-being?

Health in the Womb

Strong link between health at birth and adult achievement (see Almond and Currie 2011 for an overview).

• Many studies look at negative shocks occurring early in life (flu pandemics, famine, poor nutrition, exposure to pollution);
• Some evidence on policy-driven choices (food stamps, salt iodization).

 

Notoriously poor health outcomes for low income mothers and babies.

 

Could an early life health intervention break this cycle?

Coverage for Low Income Pregnant Women

"The Long-Term Effects of Early Life Medicaid Coverage" by Miller and Wherry, Journal of Human Resources

From 1979 to 1993, fraction of women eligible for Medicaid in the event of a pregnancy more than tripled.

Single largest effort to improve prenatal and birth outcomes in the United States.

Initial evidence (e.g. Currie and Gruber 1996) suggested moms used more prenatal care, had more health interventions at the hospital, and gave birth to healthier babies.

Did better health at birth lead to better adult outcomes for those who gained coverage "in utero"/as newborns?

Coverage for Low Income Pregnant Women

Use an instrumental variables model to separate policy-driven changes in eligibility from changes driven by (potentially endogenous) economic factors.

In Utero/First Year of Life Intervention

Those who gained coverage in utero/as newborns:

• Lower incidence of chronic disease in adulthood (particularly diabetes, high blood pressure).
• Fewer hospitalizations for these types of conditions.

 

More likely to graduate high school.

Higher incomes, lower food stamps receipt rates [suggestive].

 

Effects are not huge, but show that health interventions can change economic outcomes--there could be an "arrow" from health to income.

Childhood Intervention

We know from previous research that early childhood interventions can yield big payoffs--but what about later childhood?

Childhood Intervention

"Childhood Medicaid Coverage and Later Life Health Care Utilization" by Wherry et al., Review of Economics and Statistics

Childhood Intervention

Childhood Intervention

Hospitalizations for Chronic Illnesses

Childhood Intervention

At age 25, affected cohorts had fewer hospitalizations and ER visits, in particular, fewer hospitalizations and ER visits for  chronic illnesses.

• These are particularly pronounced for black patients and those living in low-income areas
• Possible they will be larger over time--these cohorts are still pretty young
  • Each year these effects are present offsets initial cost of Medicaid by 3%-5%

Big Picture Trends

Decreasing health inequality for children even as we have seen increasing health inequality among adults

Source: Currie and Schwandt Science 2018

But.. what about adults?

Many reasons to think kids can give us the biggest bang for our buck if we want to improve health outcomes among low-income populations and reduce disparities.

 

What about adults? Should we just give up?

Adult Health Inequality

High degree of inequality in health outcomes by income.
 

• 55-64 year olds in households earning under 138 FPL have annual mortality rate of 1.7pp. In households earning 400%FPL+: 0.4pp. 4x higher for low income households.
• 787% higher chance of dying from diabetes, 552% higher chance of dying from cardiovascular disease, 813% higher chance of dying from respiratory disease.
• Correlation between income and health exists in all countries, correlation is higher in the US than in other wealthy countries (Semyonov, Lewin-Epstein and Maskileyson 2013).

Overall Coverage Gains

The number of people with insurance increased by about 20 million

Source: Duggan, Goda, Jackson 2017 NTA

2014 Coverage Expansions

Eligibility for Medicaid extended to everyone with incomes below 138% of the Federal Poverty Level (effective Jan 2014 for most states.)

 

Originally intended to be implemented in all states, but due to a 2012 Supreme Court decision, this became optional.

 

Opted to Expand in 2014

Coverage Gains Largest in states that expanded Medicaid

Sample among low income adults, Miller and Wherry 2016 New England Journal of Medicine

Analysis of ACA Medicaid Expansions

• In joint work with Altekruse, Johnson, and Wherry...
• Take advantage of non-universal adoption of the Medicaid expansion that resulted from the 2012 Supreme Court decision.
• Use a "difference in differences" quasi-experimental empirical design

Difference in Differences

Let's go back to this figure:

Difference in Differences

Let's go back to this figure:

Data Challenge

Typically data on mortality does not contain information on socio-economic characteristics such as income or insurance coverage.

• Makes it difficult to analyze mortality impact of Medicaid because you don't know who would actually be eligible.

Mortality is not a very common outcome for the non-elderly (elderly are covered by Medicare already so not relevant).

• You need a really big sample to have enough statistical power.

Data Solution

Solution for this paper: link data from American Community Survey (ACS) to Social Security Administration mortality records

• ACS is large survey (4 to 4.5 million respondents per year) and has information on household income, eligibility for other programs, and other information relevant to determine who is most likely to gain eligibility
• We focus on individuals age 55-64, low income or disadvantaged (no high school degree), not otherwise Medicaid eligible, citizens.
• A large % of this group gained Medicaid eligibility.
• Follow this group across expansion and non-expansion states for 4 years after the ACA expansions.

How Many Gained Eligibility?

Nearly half of the group gained eligibility

How Many Enrolled?

About 10-11percent enrolled, possibly higher due to misreporting in survey

Mortality

Probability of mortality for all who were alive at the beginning of the year

Mortality Reduction from Medicaid

Is this big or small?

~3.7 million people meeting our sample criteria in expansion states: implies 19,200 deaths averted over the period we study

 

~3 million people meeting our sample criteria in non-expansion states: 

implies 15,600 excess deaths that would have been averted if the state had expanded.

Where to go from here?

There are strong health disparities across income groups.

• We see the gaps closing among children--will this continue as they grow up?
• Improving access to care for adults can improve their health--but these gaps are so big, there is still a huge gap left.

 

Where should we intervene?

Improving access to health care is an important piece of the puzzle, but not the only solution.

We may need to work on more arrow than one!