Per Capita Health Care Spending in States: Getting to Apples to Apples

Focus Area:
Sustainable Health Care Costs
Topic:
Peterson-Milbank Program for Sustainable Health Care Costs

When I worked at a Medicaid managed care plan, I used to meet with cohorts of medical students to give them a glimpse of the dark underbelly of health insurance companies. Part of my presentation was passing on the secret words to use when insurance companies confronted them with quality measurement results that seemed to indicate poor performance.

Just keep saying these words, I told them: “But you don’t understand, my patients are sicker.”

Indeed, adjusting performance measurement for population characteristics like health status is critical for any credible population health initiative. You have to compare apples to apples.

The same rule applies when looking at the total costs of health care in different states.

There is endless evidence that money spent on health care is not the most effective way to improve the health of populations or health equity. State policy has significant influence over how and how much money is spent on health care and social services, so it makes sense to look at per capita spending on health care by state. Lower per person spending and lower rates of increase in expenses may mean more money for other more productive purposes.

But what good does it do to say Massachusetts has higher health care spending per person than Oklahoma? Everything is more expensive in Massachusetts than in Oklahoma. And what if the Bay State’s population is older or sicker? Factors outside of health care can swamp the cost-effectiveness of any health system or public policy.

A new study from the Institute for Health Metrics and Evaluation (IHME), however, takes into account state-specific factors and makes it possible to compare apples to apples on per person state health care spending. The findings are inconclusive but illuminating.

IHME took 24 years of per capita health care spending data by state and payer type and developed estimates for the next five years. The researchers then looked at annual changes in spending since 2013, the year the Affordable Care Act was implemented (Exhibit 1).

According to the study, Alaska was the most expensive state for health care in the country in 2019 – spending twice as much per capita as Utah. Meanwhile, South Dakota suffered from the worst health care spending inflation in the country between 2013 and 2019 – its rate of inflation was four-and-half times the rate in the District of Columbia.

But Alaska has high costs for many goods and services – is that why health care is more expensive there? To figure out how much of this variation in per capita health care costs is due to factors outside the health care system, IHME identified what drove differences in health care spending among the states. Their analysis indicates that 77% of the variation in state spending could be attributed to identified factors. Personal income explained 25 percentage points – health care has become a consumer item and people with more income spend more money on health care. Regional price differences in the costs of goods and services in general accounted for another 22 points. Smaller percentages of the variation were attributed to population demographics, smoking rates, exercise rates, and population density.

IHME researchers then adjusted the cost figures for these factors. The results – seen in Exhibit 1 (standardized results column) – now reflect differences in per capita spending between the states that take into account their economies, cost of living, health status, demographics, and population densities. Here we are comparing apples to apples.

So once one takes the factors outside the health care system into account, what drives the remaining variation in state health care spending? Is there something about the way medical care is organized and delivered in a state, or its public policies, that make health care more or less expensive? Comparing standardized 2019 per capita spending to the 2013-2019 annualized rate of change reveals outlier states (Exhibit 2).

To the far left, Rhode Island, Vermont, Massachusetts, and the District of Columbia have achieved enviably low adjusted-spending growth. Accounting for non-health-care cost factors, Massachusetts drops from the seventh most expensive state for health care to the 32nd and Vermont from 9th to 35th. Tennessee and South Carolina in the lower left have lower adjusted costs and relatively low spending growth. At the upper right, Alaska, South Dakota, and West Virginia appear to suffer from both high adjusted costs and spending growth.

Are there lessons in the positive deviants? Rhode Island, Vermont, and Massachusetts have all implemented statewide efforts to limit cost growth over the last decade, but those initiatives have varied. Rhode Island began its cost control work in 2008 with aggressive commercial insurance regulation and now has a health care cost growth target. Vermont has a 15-year history of delivery system reform that includes primary care transformation (the Blueprint for Health), health insurer rate review, and hospital budget approval (The Green Mountain Care Board). Today, the state is working to align a single statewide accountable care organization with Medicare. Since 2012, the Massachusetts Health Policy Commission has set a per capita health care cost growth target, with some consequences for outlier performance for health providers and payers.

All three have market-dominant local Blue Cross and Blue Shield Plans. And in each case multistakeholder groups are adjusting and analyzing payer claims data to allow for similar apples-to-apples comparisons of health care spending across health providers.

One should be cautious about drawing conclusions, however. Neither South Carolina nor Tennessee has a longstanding, systemic state health cost containment effort. And two states that do – Maryland, which has an all-payer rate setting commission and hospital budgeting, and Oregon, which has a large public purchasing authority and a track record of Medicaid and primary care innovation – have middling performance in the IHME analysis.

Of course, systemwide health care costs and cost growth trends are not the only measure of state health system performance. Tennessee and South Carolina, having to date not elected to expand Medicaid, have high uninsured rates. Population-specific health issues like children’s behavioral health vary by state and merit special strategies. The lowest-cost growth trend states, however, all do relatively well on state health system performance reports, such as the Commonwealth Fund’s scorecard and America’s Health Rankings.

Like all good studies, this one raises additional questions. Will the benefits of lower costs and lower cost-growth trends (and the costs of the inverse) be reflected in the economic and population health of states? Will states with lower health care cost growth be able to maintain their performance, or will pressures increase to loosen the screws on providers, particularly if health care staffing shortages persist or health system finances weaken? These IHME estimates also do not reflect the effects of the pandemic – a major disruption in health care delivery and financing.

Still, by identifying and reliably adjusting for the major external factors that drive variations in state spending, this research allows policymakers to compare apples to apples. Some states appear to be attaining protracted lower overall rates of health care cost growth and per capita costs — without negatively affecting their population health measures or uninsured rates. Even if it’s still unclear which approach to systemic health care cost containment works best, this data suggests that long-term systematic and collective attention to the issue may be helping some states take a bite of the apple.