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What’s the story with health care spending?

A look at per capita personal expenditures by state

The map above shows spending on health care per person in each U.S. state, with darker colors indicating higher amounts. Various factors in each state influence the composition of these expenditures: the age structure of the population, income level, level of competition among health care providers, and local policies and regulations. Thus, everyone can develop an interpretation of why some states spend more on health care based on, for example, older populations, higher incomes, greater market power of health care providers, and policies that lead to more spending.

As it turns out, the story hasn’t changed much over the past 20 years. The map below shows that expenditures in 1997 don’t look much different from expenditures in 2017. Relatively speaking, of course: Expenditures have at least doubled since then, but the fundamental forces that drive health care costs across states seem persistent. For example, New York, Pennsylvania, and New Jersey still spend more than Virginia, Kentucky, and Tennessee, which still spend more than Utah, Nevada, and Arizona.

How these maps were created: On GeoFRED, go to the state-level maps, open the cogwheel in the upper-left corner, and select the series “Per Capita Personal Consumption Expenditures: Services: Health Care.” For the second map, simply change the date.

Suggested by Christian Zimmermann.

Where health is lacking

Mapping public health issues with GeoFRED

GeoFRED maps can help us understand a lot of things, including trends in regional socioeconomic data, which could ultimately provide insights for policy recommendations. In this post, we look at two important indicators of health throughout the United States: premature deaths and preventable hospital admissions. High levels of premature deaths indicate issues with public health. (See a previous blog post for some background on this concept.) The South has a comparatively higher concentration of high rates in this area.

The maps show a correlation between areas that suffer from high rates of premature death and areas that have a high rate of preventable hospital admissions, which is defined as stays in acute-care hospitals that could have been taken care of in ambulatory or ordinary inpatient settings, adjusted for socioeconomic factors. Examples are pneumonia, diabetes, and dehydration. A high rate of these admissions indicates that more people are lacking appropriate health options, likely leading to more preventable deaths.

While regional trends and correlations do not indicate causation, a review of interconnected socioeconomic patterns over several years can be useful for understanding persistent problems in certain areas. Refer to GeoFRED for related maps on race, income inequality, homeownership, burdened homeowners, and disconnected youth.

How these maps were created: Premature Death: From GeoFRED, click on “Build New Map.” Under the “Tools” menu, select “County” in the region type search bar. For the first map, enter “premature death” in the data search bar and then select “Age-Adjusted Premature Death Rate”; for the second map, enter “preventable hospital admissions” in the data search bar and then select “Rate of Preventable Hospital Admissions.”

Suggested by Samantha Kiss and Christian Zimmermann.

A healthy appetite for health care?

How supply and demand may affect the costs and consumption of health care services

Health care has improved considerably in the past couple of decades, in terms of both quality and access. Yet, with health care costs on the rise in recent years, it’s also a topic of many heated discussions. Supply factors could be behind the increase in costs for health care services, but would also have a negative impact on their demand. On the other hand, higher demand for health care services would increase both the price and quantity consumed.

With FRED’s personal consumption expenditures price index data, we use the graph above to show the ratio of the price index for health care services to the overall price index for all goods and services in the economy. (The base year is set to 1999.) We can see that health care services are about 10 percent more expensive today, relative to all other goods and services, than they were 18 years ago.

The graph below shows, in billions of chained 2009 dollars, the amount spent on health care as a share of total consumption spending. (It’s important to keep in mind that the series displayed here mute the effect of changes in the price levels, as prices are “fixed” to the levels in 2009.) We can see an increasing trend for the past 18 years, indicating that the amount of health care consumed, as a share of total expenditures, has also been rising. This also implies that consumer spending on health care has been increasing more than consumer spending on other types of goods.

These graphs suggest that some demand factors could be behind the increased cost of health care, as both the price and the consumption of health care services, relative to other components of consumption, have increased. Some possible demand factors could be related to longer life spans, the demand for newer and more expensive procedures, and so on. Our analysis here is stylized, but further research should look at this issue more closely to try to illuminate the supply and demand factors behind the rising cost of health care.

How these graphs were created: For the first graph, search for “Personal Consumption Expenditures: Services: Health care (chain-type price index)” and select the quarterly, seasonally adjusted series. From the “Edit Graph” section, under “Units,” select “Index (Scale value to 100 for chosen date)” and set the date to 1999-01-01. Then use the “Add Line” option to add the quarterly and seasonally adjusted series for “Personal Consumption Expenditures (chain-type price index).” Apply the same adjustment to set the index to 100 for 1999-01-01. Then apply the formula a/b. Set the starting date for the graph to 1999-01-01. For the second graph, search for “Real Personal Consumption Expenditures: Services: Health care” and select the quarterly, seasonally adjusted series. Then, from the “Edit Graph” section, use the “Add Line” option to search for “Real Personal Consumption Expenditures,” quarterly, seasonally adjusted. Then apply the formula a/b.

Suggested by Maximiliano Dvorkin and Asha Bharadwaj.

View on FRED, series used in this post: DHLCRG3Q086SBEA, DHLCRX1Q020SBEA, PCEC96, PCECTPI


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