by Cynthia Cox
Kaiser Family Foundation
(This post, from the Kaiser Family Foundation, is based on BEA’s new health care statistics released earlier this year.)
The Bureau of Economic Analysis (BEA) recently updated their disease-based health spending estimates with new data that allows users to examine national health spending trends by disease category from 2000 – 2012. The BEA satellite account differs from the official national health expenditure accounts, developed by the Centers for Medicare and Medicaid Services, which break out spending by type of service. This means that, in addition to knowing how much the U.S. spends on hospital care, for example, we can now also know how much the U.S. spends to treat different diseases like circulatory conditions and cancer. (Our blog post on the initial BEA release is here).
The updated BEA data shows that circulatory conditions had been the largest category of spending for at least a decade, until 2012 when they were surpassed by “ill-defined” conditions (a category including check-ups, follow-up appointments, preventive care, and treatment of minor conditions such as colds, flus, and allergies). From 2000 through 2012, ill-defined conditions grew faster than other major category, at an average annual rate of almost 10% per person. Circulatory condition spending per person, by contrast, grew at just 4% over this period.
From 2000 – 2012, treatments for musculoskeletal disorders (which include back problems and arthritis) and circulatory diseases were the second and third largest contributors to overall health services spending growth, following ill-defined conditions. Together, these three disease areas account for 36% of growth in disease-based spending over the period.
Using the BEA satellite account, spending growth can be broken into its components, the cost per case (price and service intensity) and the number of treated cases (utilization). Price indexes in the Health Care Satellite Account differ from official price indexes, such as the consumer price index, in that they are not only influenced by the price of a given treatment, but also by changes in treatment intensity per visits (e.g., getting greater or lesser amounts of care per visit) and shifts from lower-cost to higher-cost treatments for the same condition. For example, from 2000 – 2012, the cost per case grew fastest for infectious diseases (6.1%), nervous system conditions (5.3%), and ill-defined conditions (5.0%) annually while the number of treated cases grew fastest for endocrine (4.4% annually) and ill-defined conditions (4.4% annually).
In its previous release, spanning 2000 – 2010, the BEA estimated that 73% of the growth in per capita health services spending was attributable to increases in the cost per case (e.g. from higher prices or greater service intensity), with the remaining coming from non-price factors (primarily the number of treated cases). Using a similar method described by the BEA, we estimate that over the 2000 – 2012 period, about 65% of per capita spending growth can be attributed to increases in the cost per case, with the remaining 35% due to non-price factors. This estimate differs from that previously released by the BEA because the number of treated cases started to drive growth relatively more in the most recent years. In fact, 2012 was the first time in recent years that the number of treated cases grew faster than the cost per case.
What are we getting in return for these higher-cost treatments? By breaking spending into disease categories, the BEA satellite account brings researchers a step closer to answering this question. Assessing the value of health care spending is difficult for a number of reasons. We would ideally like to know whether the money invested in health care has led to improved health outcomes, but the limited availability of outcomes measures, along with differences in methodology and categorization in outcomes and spending data, and a wide range of factors outside of the health system (socioeconomic, environmental, and scientific) that contribute to health outcomes, all pose challenges to measuring value.
With all of these limitations in mind, one way to look at changes in health outcomes is to use a measure of disease burden called Disability Adjusted Life Years, or DALYs. This measure is useful because it takes into account both premature death and years lived with poor health, and can therefore be used to asses outcomes for leading causes of death (like circulatory disease and cancer), as well as diseases that cause suffering but are less likely to cause death (like musculoskeletal diseases and mental health conditions). Juxtaposing improvements or declines in disease burden with measures of spending by disease for the same diseases can give us an idea of whether we are getting good value in exchange for higher spending.
From 2005 – 2010 (the years of overlap between the DALY and BEA spending data), the increase in health services spending (24%) corresponded with an overall improvement in disease burden (14%). Differences in categorization between the two data sources as well as the limited number of years of data complicate direct comparisons across all disease categories, but a similar trend can be seen for both circulatory diseases and cancers (for which spending increased by 7% and 26% respectively and outcomes improved 21% and 12% respectively). A more comprehensive study by BEA researchers compared spending and outcomes across 30 chronic conditions from 1987 – 2010 and found that “overall gains in health outcomes for the population more than offset the increase in the average cost of treatment, suggesting a positive net value for medical spending.”