PolicyMap and Quantitative Innovations (QI) developed Medical Spending data by combining information on payments for medical care from the U.S. Department of Health and Human Services’ Agency for Healthcare Research and Quality (AHRQ) Medical Expenditure Panel Survey (MEPS) with demographic and health data from the U.S. Census American Community Survey (ACS).
Medical Spending data provides estimates of two main kinds of medical payments, aggregated to various geographies:
Expense categories include all medical costs, prescription medications, medical office visits, dental care, and eyeglasses/contact lenses:
About MEPS: MEPS is an annual survey administered by AHRQ to both households and healthcare providers. The household component covers health conditions of household members, healthcare spending, access to healthcare, and more. Estimates in this dataset are calculated using MEPS’ survey design, incorporating person-level sampling weights, primary sampling units, and strata, so that both the average spending figures and their margins of error properly reflect the survey’s national sample design rather than treating responses as a simple random sample.
About ACS: Conducted by the Census Bureau, it provides estimates for demographic, social, economic, and housing characteristics over a moving five-year period. ACS population and insurance-coverage estimates are used to weight the MEPS-based per-person spending figures up to each geography.
A note on comparing years: ACS 5-year estimates are built from overlapping annual samples, PolicyMap only publishes non-overlapping ACS vintages side by side so that year-over-year comparisons reflect real change rather than the statistical smoothing that comes from shared sample years. This follows the Census Bureau’s own guidance for comparing ACS period estimates.
Reliability: Spending estimates are broken out by age group (under 19, 19-44, 45-64, 65+) and insurance coverage type (private, public, uninsured). Because MEPS is a sample survey, some age-by-insurance combinations are based on relatively few respondents and carry wider margins of error, particularly uninsured populations, which make up a small share of the sample in most age groups.
Quantitative Innovations is a data strategy and applied analytics advisory firm that helps clients turn their data into strategic insights and actions.