Besides state and higher-level health care expenditures, county level HCE are useful, integral really. For example, to promote the Triple Aim (the best care for the whole population at the lowest cost) you need per capita HCE. And knowing those costs at the county level would help a lot. However, county estimates generally don’t exist. They didn’t in Washington State until a client needed cost estimates for our 39 counties. To supply those estimates I used a regression approach resulting in this model:
percaphce = +0.1*percapinc + 247*pctage65 + 0.71*percapmedaid + 10.5*pctrural – 1349
Washington State Context
Before discussing model rationale and county HCE estimation, here’s some context about Washington State and its counties. You might view Washington as a microcosm of the nation. It has mountains, forests, deserts, rivers and lakes, vast rural areas, major cities, diverse populations and industries, and a varied climate. It is distinguished by active volcanoes and a coastal border. There is a wide range of political, social and economic clusters. In 2010 King County, where Seattle is located, median annual household income was about $67 thousand (the U.S. median was roughly $50 thousand) yet there are state counties where one in three children live in poverty. The total population is approximately 7 million with half of those people living in just three of the 39 counties.1 At the other end about a third of the counties have populations of 30 thousand or less.
An Aside about Seattle Weather
You may have been told that it rains all the time in Seattle. I live in Seattle and can tell you that’s a myth. Seattle’s average annual rainfall is less than New York City’s. However, during a good part of the non-summer months Seattle, and Puget Sound generally, is grey and cloudy. I once heard a story about the original settlers who landed in November, 1851, at Alki near present-day Seattle. The story is they were there for months before the weather finally cleared and they saw Mt. Rainier for the first time. I don’t know if that story is historically true, but as a Seattleite it’s believable. Regardless, Seattle is a summer paradise. Seattle summers, like most of Puget Sound, are characterized by pleasant sunny days, cool nights and no mosquitoes.
Background for the County HCE Estimates
Last year Empire Health Foundation of Spokane, Washington, asked me to estimate HCE for the 39 counties in the state. The purpose was for an upcoming meeting of policy types such as county commissioners, members of various health organizations, and other stake holders. A theme would be Donald Berwick’s Triple Aim, so cost estimates were wanted for benchmarks and context. The CMS2 Office of the Actuary had recently developed state HCE.3 If I could build a reasonable regression model on state-level data to predict state HCE, and there were similar variables at the county level, I could use the state model to estimate county HCE. That’s the approach I took. A caveat is my understanding was that acceptance—believability and reasonableness of the estimates to a lay audience—were as important as accuracy.
Continue reading “Letting the Data Speak: Estimating County Health Care Costs In Washington State”
Filed Under: Uncategorized
Tagged: Costs, Economics, Frank de Libero, health care expenditures, The States, Washington state
Nov 6, 2013
Robert Pear wrote in the Times that the refusal by “states to expand Medicaid will leave millions of poor people ineligible for government-subsidized health insurance…” Indeed, the refusals will do that, as well as worsen what instead should be remedied. In the following I present a graph of two chronic diseases over the 50 states. Those states which have opted out of the Medicaid expansion are identified. Additionally each state’s poverty rate is indicated. The take-away is that populations in greater need are being further disadvantaged. A conjecture is presented as to why.
Please understand that refusal to expand Medicaid is not about state expenditures. Over the ten years, 2013-2022, every state would gain far more than it would spend for expansion . Were all states to opt-in, the total ROI for the states combined would be almost 10,000% ($8 billion state expenditures in return for $800 billion federal).
Empirically health is associated with income, so if you’re poor you’ll likely have worse health. Also it’s well known that chronic conditions are often comorbid, that if you have a chronic disease, you probably have more than one. Additionally, chronic disease is a major contributor to total health care costs. Continue reading “Letting the Data Speak: On Refusing Medicaid Expansion”
Filed Under: THCB
Tagged: chronic disease, Frank de Libero, Medicaid Expansion, The ACA, The States
May 29, 2013
In this post I recast the visual display of international health care expenditures. For select OECD countries, this clearly shows the growth of average costs has been moderating while U.S. cost-growth has been accelerating. The graph methodology is discussed along with a caution about marginal thinking. A conjecture is presented as to why the OECD cost-growth is moderating followed by a couple thoughts for action.
What’s Usually Presented
There are many graphs published of international health care expenditures (HCE). Some remind me of multi-colored electric cables strung together, except for one cable that strays from the group. Others are simpler but their message is obscured with chart junk. Continue reading “Letting the Data Speak: A Fresh Look at Health Care Cost Growth”
Filed Under: OP-ED, THCB
Tagged: Costs, Frank de Libero, international health care expenditures, OECD cost growth
Apr 16, 2013
“We spend far more on health care than other peer countries yet have worse outcomes. Why is U.S. health care so expensive?” I’m sure you’ve encountered similar statements, maybe even expressed it yourself. It occurs often, including by knowledgeable people and health-related institutions. However, it’s a fallacy because it confuses health care with population health.
Health care is a proper subset of population health. For example, longevity is determined by more than just health care. Using a specific recent estimate (Appendix Exhibit A6 – gated), an average 20-year old U.S. white male who did not graduate high school will live 10.5 fewer years than a similar man with a college degree. That’s over ten years of life related to educational attainment. Sure, there are many reasons for the difference, and health care or the lack of it is only one of them.
Continue reading “Health Care Shibboleth”
Filed Under: OP-ED, THCB
Tagged: Art Kellermann, Costs, Frank de Libero, Health Affairs, Health care spending, health-care-is-health-fallacy, Institute of Medicine, Outcomes, Population Health, Tom Daschle
Mar 24, 2013