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Question: What is the cost to the US economy of socioeconomic health inequities?
Meaning: The costs of socioeconomic health inequities are unacceptably high and warrant societal investments in policies and interventions to promote health equity.
The study used cross-sectional analysis using nationally representative data to estimate the economic burden of socioeconomic health inequities in the US. The data included 2016-2019 data from the Medical Expenditure Panel Survey (MEPS), state-level Behavior Risk Factor Surveillance Survey (BRFSS), 2016-2018 mortality data from the National Vital Statistics System (NVSS), and 2018 IPUMS American Community Survey (ACS).
87,855 survey respondents to MEPS, 1,792,023 survey respondents to the BRFSS, and 8,416,203 death records from the NVSS were examined to determine the sum of excess medical care costs, lost labor market productivity, and excess premature death costs.
Life expectancy for adults without a four-year college degree has grown from 2.6 to 6.3 years over the last three decades. Educational attainment is associated with healthier lifestyle decisions and higher levels of health literacy, providing individuals with access to higher income and wages, better employment, and greater wealth. Persistent wage stagnation and the erosion of satisfactory jobs for high school-educated adults reduce their ability to access resources that are a prerequisite for people to achieve high levels of health and wellness.
We established the 90th percentile for the prevalence rates of health conditions and the 10th percentile for crude death rates as health equity goals. The prevalence models for each were established using the MEPS adult sample, with the dependent variable being the dichotomous variable indicating whether the respondent had been diagnosed with the condition. The independent variables included age, sex, marital status, educational attainment, poverty status, health insurance status, and region of the country.
We estimated each respondent’s risk for 13 health conditions: fair/poor health, diabetes, joint pain/arthritis, depression, limitation of activities, hypertension, high cholesterol, stroke, heart attack, coronary heart disease/angina, asthma, emphysema/chronic bronchitis, and cancer. Next, we divided the sample into 13 cohorts based on sex and age. Our age groups included 25-34, 35-44, 45-54, 55-64, 65-74, and 75 and over.
To compute years of life lost due to premature death, we used the death rate of the state at the 10th percentile for six age groups: 25-29, 30-39, 40-49, 50-59, 60-69, and 70-77 as the targeted crude death rate. We ordered from lowest to highest crude death rate for each age group and used that information as the health equity goal for that age category.
We used the same methodology to estimate the burden of racial and ethnic inequities to estimate excess medical care costs, lost labor market productivity, and excess premature death costs. To estimate excess medical care costs, we built a regression model that used data from the 2018 MEPS and created a two-part estimation technique. The first part is a logistic regression model, which estimates the impact of health conditions on the probability of having any type of medical care expenditures. Second is a generalized linear model (GLM), which estimates the impact of health conditions on levels of expenditures for individuals with positive expenditures. The dependent variable is the total medical expenditures. The predictor variables are whether respondents had the following health conditions listed above and included the same independent variables.
To compute the value of lost productivity, we estimated the impact of health conditions on disability and illness on sick days, annual hours of work, and wages for working-age adults ages 25-64. Similar to the above, we used a two-part technique. The first part estimates the impact of health conditions on the probability of having a nonzero sick day, annual hours, and wages. The second part estimates the impact of health conditions on the number of sick days, hours worked, and hourly wages, with the key predictor variables being health conditions.
We also simulated medical care costs and labor market outcomes using the reported health conditions. Then, we simulated the medical care cost and labor market outcome by assigning each group the target prevalence rate for each condition within the age/sex cohort. The respondents in each cohort were randomly assigned health conditions using a uniform distribution so the prevalence rate for the condition would be at the 90th percentile. We computed the cost of inequity and the predicted values for medical care costs using Monte Carlo simulations for the education groups. We randomly chose 1,000 samples to get “one” predicted probability and “one” predicted mean for the models. This exercise was repeated 1,000 times to get 1,000 predicted probabilities, and 1,000 predicted means by the education group.
Research suggests that society is willing to pay from $100,000 to $264,000 for a year of life, which provides a way to value the loss of life due to premature death we defined as any death occurring prior to age 78. We used data from NVSS to obtain the number of deaths by education and age for 2016-2018. The age groups comprised 25-29, 30-39, 40-49, 50-59, 60-69, and 70-77. We also used data from the 2018 American Community Survey five-year estimates to obtain popular size estimates for each subgroup based on age and the highest level of education in each state. We suppressed all counts under ten observations per our data use agreement with the NCHS to improve the reliability of our crude death rate estimates.
Next, we estimated the number of deaths that would have occurred for each education group if every group’s death rate were equal to the health equity target death rate within each age category. The difference between the actual number of deaths and the expected deaths represents “excess premature deaths.” Then we computed the number of years of life lost in each education group by assuming all people would live to 78, which we did by using the difference between 78 and the midpoint in each age group.
We modified the methodology we used to estimate the economic burden when estimating the burdens for individual states. MEPS data could not be used since the sample is too small and not designed for state-level analyses. Instead, we utilized data from BRFSS, but the data were too small, so we pooled four years of data to compute prevalence rates for each education category in each state. We then compared these rates to the 90th percentile target for the entire adult population and used the regression models to simulate their medical care costs and labor market outcomes. Then we compared these costs and results based on the actual prevalence rates to the target prevalence rates. Finally, we re-weighted estimates to match the size of less than high school, high school/GED, and some college populations in each state. We pooled data across three years for premature death costs to get stable estimates in each category.
BRFSS data could not be broken into different age-sex cohorts because they were too small. Therefore to produce consistent estimates between the national estimates that relief on the MEPS and the state-level BRFSS estimates, we employed a “calibration” approach that consisted of using 2016-2019 BRFSS national data to compute socio-demographic proportions and health conditions for 12 different age-sex cohorts to estimate the medical care costs and labor market estimates for eight different age-sex cohorts. The MEPS population numbers from 2018 were also used for each age-sex cohort. Using these results, we produced a series of adjustment factors by SES category, which accounted for the differences between estimates.
The study found that the estimated cost of socioeconomic health inequities in 2018 was up to $978 billion and up to 4.69% of the GDP. 65.4% of those costs were incurred by adults with a high school diploma or a GED. However, the study also found that while adults without a high school diploma are only 9% of the population, they were responsible for 26% of the costs.
Given the growing gap in life expectancy and the persistent education-health gradient, the economic costs of SES health inequity will grow over time. The nation needs to address the increasing inequity of social justice and economic reasons through a plan that would parallel address racial and ethnic inequities. In the study, we observed higher rates of fair/poor health, depression, and other health, depression, and other health conditions for female cohorts in the BRFSS data compared to the MEPS data, which was due to the differences in the prevalence of the health status and conditions between the data sets. Also, crude deaths for adults with some college education vary across states. Some have crude death rates for adults, with some college education exceeding the health equity target by more than the national rate.
This study’s findings highlight the importance of addressing the health inequities faced by adults who are not college-educated. Not only are these health inequities devastating to the mortality and morbidity of the US population, but also the US economy. The study estimates that the economic burden of SES health inequities is up to $978 billion, which is over 4% of the annual GDP. Policymakers should consider considerable measures to improve the health status of Americans who are not college educated.
Table 3. Economic Burden of Excess Medical Care Expenditures, Loss of Productivity, and Premature Death Attributed to Education-Related Health Inequities for the Nation ($ Billions)
Cost, $ in billions
Less than high school
High school / GED
Total health inequities for adults with <4-year college degree
4-year college degree or more
MEPS and national NVSS estimates
Excess medical care costs
Lost labor market productivity
BRFSS and state NVSS estimates
Excess medical care costs
Lost labor market productivity
Table 4. Economic Burden per Capita for Education-Related Health Inequities for Each State and the District of Columbia in 2018
|State||Total Economic Burden|
|State||Less than High School|
|State||High School / GED|
|Location||Less than high school||High school / GED||Some college|
|Mean (SD) across states||11,525 (4,843)||20,371 (3,363)||2,229 (1,113)|
Table 5. Economic Burden of Racial and Ethnic and Education-Related Health Inequities
Table 5 compares data from both this report and the Racial and Ethnic Health Report.
|Racial and ethnic health inequities||Education-related health inequities|
|Location||Total, $ in million||Share of gross domestic product, %||Total, $ in million||Share of gross domestic product, %|