In America, your health care expenses are taken care of when you get older…right?  We have Medicare after all…shouldn’t that pay for all my healthcare expenses?

Not according to a recent article from Yahoo! Finance.  Here are some of the health care costs retirees face:

• Part B Premiums: For most people retiring in 2010, the Medicare Part B monthly premium is $110.50 per month.  Retirees who earn more than $85,000 annually ($170,000 for couples) pay higher premiums of up to $353.60 monthly.
• Part D Premiums: These average about $30 per month.
• Cost Sharing: Medicare enrollees pay a 20% coinsurance rate for physician and outpatient services.  Plus, there is no out of pocket maximum.  Hospital stays have a $1,100 deductible.  If the hospital stay is more than two months, beneficiaries must pay an additional $275 per day for days 61 through 90, $550 for days 91 to 150, and all costs after that.
• Uncovered Expenses: These include items such as dental care, eyeglasses, and hearing aids.
• Long Term Care: Medicare pays for a maximum of 100 days of nursing home care before retirees absorb the entire cost themselves. When nursing-home costs are included, the amount needed for a typical couple’s medical bills increases from $197,000 to $260,000 with a 5 percent risk of exceeding $570,000, according to Boston College estimates.  Only those dual-eligibles also covered by Medicaid will have their long-term care services covered for the most part.
• Healthcare Inflation: Median out-of-pocket costs for the typical senior are expected to rise from about $2,600 in 2010 to $6,200 in 2040 in constant 2008 dollars, according to a recent Urban Institute report.

The morale of the story is either be rich (and have lots of money stashed away) or be poor (and have Medicaid take care of your long-term care expenses).

From the CMS Office of the Actuary:

U.S. health care spending growth decelerated in 2008, increasing 4.4 percent compared to 6.0 percent in 2007, as spending growth slowed for nearly all health care goods and services, particularly for hospitals. Health spending growth for state and local and private sources of funds also slowed while federal health spending growth accelerated in 2008. Total health expenditures reached $2.3 trillion in 2008, which translates to $7,681 per person and 16.2 percent of the nation’s Gross Domestic Product (GDP). Despite slower growth in overall health expenditures, the share of GDP devoted to health care increased from 15.9 percent in 2007.

A detailed table of health expenditures by service type (e.g., hospital, physician services) can be found here.

Also interesting is the changes in spending by payor.  While overall Medicaid expenditure growth decelerated between 2007 and 2008, the composition of expenditures changed significantly.  Federal Medicaid spending increased by 8.4%, but state Medicaid spending actually decrease by 0.1%.  A table providing more information on the changes health expenditures by payer is available here.

America’s Health Ratings 2009 report ranks states according to overall healthiness.  Mississippi is the least healthy state and Vermont is the healthiest state.  The ranking methodology is available here.

The following states are the least healthy (starting with the least healthy):

1. Mississippi
2. Oklahoma
3. Alabama
4. Louisiana
5. South Carolina
6. Nevada
7. Tennessee
8. Georgia
9. West Virginia
10. Kentucky

The following states are the most healthy:

1. Vermont
2. Utah
3. Massachusetts
4. Hawaii
5. New Hampshire
6. Minnesota
7. Connecticut
8. Colorado
9. Maine
10. Rhode Island

Indonesia.  Indonesia has the fourth largest population in the world and is the country with the largest Muslim population in the world.  The island of Bali–where I went for my honeymoon–is the most famous island for tourists.  Unlike most islands, it is largely Hindu.  Java, where the capital of Jakarta is located, is the most populous island in the world.  While Indonesia most recently made it into the news a week ago for the 7.6 magnitude earthquake in Sumatra, Indonesia is a country on the rise.  Other interesting facts about Indonesia:

Health

• Government spending on health is less than 1% of GDP. 
• Indonesia has 21 doctors per 100,000 people [compared to 163 per 100,000 in the U.S.] 
• Data on life expectancy, infant mortality and maternal mortality compared to Indonesia’s peers is available here.
• According to the Economist, “a public health insurance scheme for the poor, known as Jamkesmas, now covers about 76m people, nearly one-third of the population.  But Ajriani Munthe Salak, of the Legal Aid Foundation for Health, says that the poor often have to pass through a bewildering series of bureaucratic hoops to receive treatment.”  The government plans to extend Jamkesmas to all individuals in 2012.  
• Half of all health spending in Indonesia is private.

Economy

• Indonesia has the 15th largest economy in the world at $914 billion.  However, GDP per capita is only $3900, which ranks 155th in the world.
• The median age is only 27.6 [compared to 36.7 in the U.S.] 
• Indonesia’s leading trading partners are: Japan 20.2%, US 9.5%, Singapore 9.4%, China 8.5%, South Korea 6.7%, India 5.2%, and Malaysia 4.7%.
• The national language in Indonesia is Bahasa Indonesia.  In Bali, most people speak Bahasa Bali.  Bahasa Indoneisa is “spoken as a mother tongue by very few Indonesianas at the time of independence, yet now [is] in use in almost every village.”

The healthiest 76% of Medicare beneficiaries consume only 14% of program expenditures. The sickest 15% consume 75% of Medicare expenditures.

• Hughes et al. (2004).

Robert Fogel has a very well-written, well thought-out piece in The American on the topic of Forecasting the Cost of U.S. Healthcare. The article breaks down the forces affecting the future cost of medicine into five categories. The section below list each category which Dr. Fogel’s arguments and my comments mixed in.

1. A likely continued downward trend in age-specific prevalence rates of chronic diseases and disabilities. According to the article “First, there is now convincing evidence that prevalence rates of chronic diseases declined during the 20th century. Second, the rate of decline in these prevalence rates has accelerated. In the American case, prevalence rates declined at a rate of about 1.0 percent per annum between 1910 and 1980. Between the early 1980s and 1989, they declined at about 1.2 percent per annum.” Most importantly, Fogel comments on how the disease burden will shift. As life expectancy increases, will cost burdens be similar but simple shifted later in life? Will healthcare utilization decrease at every age? How change in life expectancy affect health care utilization is difficult to predict.
2. The rate of change in the cost of treating these conditions. For me, this is the main cost driver. Technological change will improve the quality, but also increase the cost of treating each disease.
3. The increase in the number and proportion of the population that is elderly. Fogel claims that this will have a small affect on rising health spending, “on the order of 10 percent.”
4. Changes in the overall U.S. population. Changes in overall population will affect total spending levels; if the U.S. population doubles and nothing else changes, it is likely that GDP and health spending will double. Thus, population growth will affect nominal medical spending, but should have a small affect on per-capita medical expenses.
5. The rate of growth of per capita income. When a society gets richer, it can afford more things. One major item that societies tend to purchase more of is health care. How heathcare expenditures evolve alongside economic growth is captured in the concept of income elasticity (i.e., by what percentage will I increase spending if my income goes up by 10%?). Fogel claims that income elasticity for medical care is 1.6 (i.e., society will spend 16% more on health care when its income goes up by 10%). According to Borger et al. (2008), empirical income elasticity estimates range between 0 and 1.6. Thus, Fogel choose a figure on the high end. However, most empiricial estimates of income elasticity are above one, meaning that the share of GDP dedicated to health expenditures will increase as society gets richer.

Overall, Fogel claims that because income elasticity is high, one need not worry about rising health care costs. “Consequently, there is no need to suppress the demand for healthcare. Expenditures on healthcare are driven by demand, which is spurred by income and by advances in biotechnology that make health interventions increasingly effective.”

On the other hand, it is important that this money is used wisely. Sick patients who want a miracle will pay nearly any price for treatment. If the doctor offers a fancy, high-tech treatment at twice the cost of an equally effective basic treatment, the patient may decide to go for the high-tech apparatus. The thinking is, it’s my life and I want the best technology available. However, if this high-cost high-tech equipment was not made available as a choice–again, assuming it was not better than the basic treatment–I would guess that the patient would be perfectly happy with the effective basic treatment. This is the phenomenon of supplier-induced demand that Fogel ignores.

The Healthcare Economist’s Take: Increased medical spending is not a problem in and of itself as long as those additional dollars are going towards productive, cost-effective treatment.

From a NCPA report.

• From 1960 to 2006, GDP grew at 2.27% per year. Over this time period, GDP increased cumulatively by 174%.
• From 1960 to 2006, national health expenditures (NHE) grew at 4.79% per year. Over this time period, NHE increased cumulatively by 721%.

For more details, see this chart.

Scientific American has an article ranking countries based on how conducive they are to biotechnology innovation.  The criteria are based on what is best for biotech firms and not necessary what is best for society.  The rankings are based on the following metrics:

• Intellectual Property (IP) protection.  In this ranking, more IP protection is considered better.  For a firm’s point of view, better IP laws lead to more profits and a higher incentive to innovate.  However, IP also increases cost and may decrease innovation of biotech companies modifying existing patented compounds.  The U.S. ranked #1 in this category with Canada, Japan, and a number of EU nations tied for 2nd place.
• Intensity.  This looks at the how much money is directed towards R&D and how much drugs a country can afford.  Metrics include: public companies per capita, portion of overall R&D spending used for biotech.  Here, Iceland is by far the leader, the U.S. is 2nd, and New Zealand third.
• Enterprise Support.  Measures how “business friendly” a country was perceived to be and the availability of various forms of capital, which are essential to support the growth of emerging biotechnology firms.  Top 3 Countries: U.S., Singapore, Australia.
• Education Workforce. The more educated the workforce, the better the score.  Education was measured in the number of R&D and biotech workers as well as the number of published papers.  Top 3: Singapore, Switzerland, U.S.
• Foundations.  Includes broad measures of foundations for biotech innovation.  Top 3: Israel, Sweden, Finland.

Overall the Top 5 Countries for Biotechnology Innovation

1. U.S.
2. Singapore
3. Denmark
4. Israel
5. Sweden

You can see the full list of rankings here.  More discussion on these rankings can be found here.

One question that plagues health economists is estimating the price inflation of medical care.  The output measured in these indices should be “health,” but this is often difficult to measure. Typically, economists simply look at the price increase of different inputs (e.g., the cost of a doctor’s visit, the cost of pharmaceuticals, the cost of different procedures)

In a novel approach, Aizcorbe and Nestoriak (2008) use cost of a bundle of treatments for a given disease as the unit of measurement.  This way, the authors can track how the cost to treat a specific disease changes over time. Even if the input costs for treating a disease increase over time, the mix of inputs used to treat a disease may also shift.  Further, if a new treatment arises, this will be included in the treatment bundles, eliminating the “new goods problem.”  The authors find the following results:

“ While the price of treating diseases grew an average of 12% over this period [2003-2005], costs would have risen even faster, 17%, if the mix of treatments in 2005 had been the same as that in 2003…For example, in the treatment of depression, there  has been a shift away from talk-therapy and towards (the lower cost) drug therapy that has reduced the cost of treating depression…[also, there have been] shifts from care at hospitals towards care at ambulatory surgical centers for orthopedic and gastroenterological conditions…”

In order for this estimation to be valid, a few things must hold.  First, disease severity must be constant over time.   Second, treatment outcomes must be constant over time.  If increase spending leads to a better outcome, this may not be price inflation, but simply a better treatment bundle.  Bundling will not correct this problem.  Third, the period of observation is only over 3 years and the data lacks information on Medicare claims.  Nevertheless, the bundling of treatments provides an attractive option to constructing medical care price indices.

• Aizcorbe, Ana; Nestoriak, Nicole (2008). “The Importance of Pricing the Bundle of Treatments” BEA Working Paper.

Watch the relationship between GDP/capita and Life Expectancy evolve over time: Gapminder World.

“Although the evidence was mixed for the 1980s and it is difficult to pinpoint when in the 1990s the decline began, during the mid- and late 1990s, the panel found consistent declines on the order of  0-2.5% per year for two commonly used measures in the disability literature: difficulty with daily activities and help with daily activities.”

• Freedman et al. (Demography 2004)

From the quotation above, we see that disability trends have been decreasing over time.  The question is, why is this?  Was the decrease in disability cause by decreasing rates of chronic disease or were decreasing disability rates caused by decreased disability rates among those with chronic diseases?  This is the question Aranovich, Bhattacharya, Garber and MaCurdy attempt to answer in their recent working paper “Coping with Chronic Disease?”

Let us assume an individual has a disability in year t when D_t=1 and the person has a chronic disease when C_t=1. According to the equation below

• P[D_t]=P[D_t|C_t]*P[C_t=1] + disability from non-chronically ill population.

The authors calculate the probability an individual has a chronic disease P[C_t], using data from the National Health Interview Survey (NHIS).  The authors calculate [D_t|C_t] using Bayes rule as follows:

• P[D_t|C_t]=P[C_t|D_t]*P[D_t]/P[C_t]

The numerator is calculated using the National Long Term Care Survey (NLTCS) and the denominator we already calculated from the NHIS.  Now we can decompose the changes in disability rates into the following:

• ΔP[D_t] = ΔP[D_t|C_t=1]P[C_t=1] + P[D_t|C_t]ΔP[C_t=1] + change attributable to non-chronically ill pop.

This says that the change in disability is a mixture of the change in the prevalence of chronic disease and the change in th probability of being disabled given that you have a chronic disease.

Results

Disability can be defined in one of two ways: IADLs or ADLs.  ”IADLs include everyday behaviors such as grocery shopping, managing money, and preparing meals and are considered a measure of moderate disability.  The ADL measure, which encompasses more basic, mechanically-oriented  activities, including dressing, eating, and bathing, is considered a gauge of more severe forms of functional impairment.”

• Overall Disability.  Between 1982 and 1999, the authors found a decrease in IADL disability of 45% whereas ADL disability decrease by 9%.  
• Chronic Disease Rates P[C_t].  In general, the authors found increases in the age-adjusted prevalence of chronic diseases.   The prevalence of being overweight increased by 10.4 percentage points, arthritis rates increased 3.0 and diabetes prevalence increased by 1.1.  There were small increases in the prevalence of stroke, chronic obstructive pulmonary disease (COPD).  On the other hand the prevalence hypertension and heart disease decreased by 2.6 percentage points and 3.3 percentage point respectively.
• Probability of Disability for those with a Chronic disease: P[D_t|C_t]. Between 1982 and 1999, people with arthritis, hypertension, COPD, overweight and heart diseases all experienced about a 50% decline in IADL disability.  Disability of those with diabetes decreased by 25%.  ”Among the seven conditions evaluated, only overweight was associated with a statistically significant decline (p<.05) in ADL disability between 1982 and 1999, a decrease of about 20%.”  However, there were also smaller, non-statistically significant declines in ADL disability among those with heart disease, COPD and arthritis.

Conclusion

Overall, we see a trend of decreasing disability rates and increasing rates of chronic illness.  This means that disability levels have decreased for those who have chronic disease.  It does not seem to be the case that preventive care is decreasing the level of chronic illness.  It could be the case, however, that as more people live longer, observing more chronic illness is an improvement from the counterfactual of death rather than a counterfactual of no disability.  It is also important to note that IADL disability decreased more than ADL disability.  This could be explained by environmental factors.  For instance, “[i]nternet shopping, amplifying devices for phones, and street ramps” all would help to decrease IADL levels, but would have little effect on ADL levels.

• Gabriel Aranovich, Jay Bhattacharya, Alan M. Garber, Thomas E. MaCurdy (2009) “Coping with Chronic Disease? Chronic Disease and Disability in Elderly American Population 1982-1999” NBER Working Paper #14811.
• Vicki A. Freedman, Eileen Crimmins, Robert F. Schoeni, Brenda C. Spillman, Hakan Aykan, Ellen Kramarow, Kenneth Land, James Lubitz, Kenneth Manton, Linda G. Martin, Diane Shinberg and Timothy Waidmann (2004) “Resolving Inconsistencies in Trends in Old-Age Disability: Report from a Technical Working Group,” Demography, Vol. 41, No. 3 (Aug., 2004), pp. 417-441 

The California HealthCare Foundation has a great document summarizing many useful Health Care Cost Statistics (see full text or fact sheet).  Some of the highlights:

• Health Care spending as a percentage of GDP is projected to grow 16.0% of GDP in 2006 to 19.5% of GDP in 2017.
• Average health care spending per capita was $7026 in 2006 and will grow to $7868 in 2008.
• In 1962, Defense spending made up half of the federal budget, Social Security about 15% and Medicare had not yet been enacted.  By 2007, Defense spending is only 20% of the budget while Social Security makes up 21% and Medicare 16% and rising.
• The U.S. has by far the highest health care spending as a percentage of GDP.  This table compares health care spending across different countries.
• Hospitalization and Physician Services make up over 50% of health care spending.  This table gives the distribution of health care spending in the U.S. by type of service.
• What are the drivers of the cost increases?  Price inflation and a change in the volume and mix of services provided.

In her California Healthcare Foundation report, Katherine Wilson does a nice job describing the health insurance market in California.  A little over health of individuals received health care from their employer or themselves (56%), a quarter of individuals receive health insurance through public programs, and 19% of Californians are uninsured (see chart). 

The private health insurance market cannot be portrayed as a monopoly nor one that is truly competitive in the neoclassical sense.  A handful of insurance carriers dominate the market.  Kaiser, Blue Cross, HealthNet, Blue Shield, Pacificare, and Aetna control 77% of the market (see chart).  

Of the premiums insurers receive, the percentage of revenues going to medical care varies widely.  Kaiser spends 93% of revenue on medical expenses, but Blue Shield and Pacificare’s traditional insurance plans spend only 70% of revenues on medical expenses.  Administrative expense ratios are also much lower for Kaiser (3.6%) than for providers such as Blue Shield’s tradiational Insurance plan (22.7%). More details about load factors are available here (see chart).

The full CHCF report can be viewed here.

Everyone knows that health insurance is getting more and more expensive.  But how can we measure how expensive it is?  A paper by UC-San Diego professors Richard Kronick and Todd Gilmer creates an “affordability index” to measure this.  The affordability index is equal to the per-capita, non-elderly health spending divided by the median income.  In essence, it measures what percentage of our income is being dedicated to health expenses.

The affordability index worsened from 5.6 percent in 1989 to 10.9 percent to 2002.  This lack of affordability has accompanied the contemporaneous rise in the percentage of uninsured individuals.

• Gilmer T, Kronick R (2005) “It’s the Premiums Stupid: Projections of the Uninsured through 2013” Health Affairs, 5 April 2005, Web exclusive, WS143-WS151.

Since the early 1980s, age-adjusted cancer mortality rates have been falling over time. Is this due to better screening, better treatment, or healthier behavioral factors? Is this progress cost-effective? Are we really winning the war on cancer?

A paper by Culter (2008) tries to answer this question. First it is important to note that there are two types of cancer. Localized tumors are located only in the originating tissue and metastatic tumors have spread to other parts of the body. “Localized cancer isn’t fatal; metastatic cancer is both lethal and incurable—even with recent treatment advances.”

Falling Cancer Mortality Rates

Raw cancer mortality statistics mask the significant benefits of cancer treatment and screening.  Since the reduction in the number of deaths from cardiovascular disease has fallen since 1970, the concurrent decline of cancer deaths is even more impressive since individual who will not die of heart disease are more at risk now of dying of cancer.  Of the decline in cancer-related mortality, 78% of this decline is due to decrease mortality in 4 types of cancer: lung cancer, colorectal cancer, breast cancer, and prostate cancer.   The risk factors for each of these disease can be found in Table 1.

Explanations for the reduction in cancer mortality can be found in Table 2.  Of the reduction in cancer mortality 78% comes from improved mortality from lung, colorectal, breast, and prostate cancers.  Of this reduction, 71% is due to healthier behaviors and better screening; only 29% is due to improved treatments.

Cost Effectiveness

One question that remains was whether or not this reduction in mortality was worth the cost.  Many people will say that life is priceless and we can not measure the value of a year of life…but economists believe they can.  “The analysis of screening is complicated, because the cost-effectiveness of screening depends on how frequently it is performed: if screening is either too frequent or too infrequent, it will have high costs relative to benefits.”  Further, decreasing mortality, but ignoring side effects may not be rational.  “Treatment of prostate cancer, for example, may lead to greater survival, but it frequently leads to reduced quality of life—impotence and incontinence are common side effects. For cancers where treatment is not very effective or where cancer would often not be a cause of death, treatment may even reduce quality-adjusted life expectancy.”

Table 3 shows Cutler’s evaluation of the cost effectiveness of various cancer screening tests.  Breast cancer and colorectal cancer screening are highly cost effective.  On the other hand, lung cancer screening is not seen to be cost effective since the knowledge of the existence of lung cancer rarely alters the treatment.  Prostate cancer screening does improve longevity, but the cost effectiveness depends on how one values the potential side effects of impotence and incontinence.

Compared to screening or behavior changes, cancer therapies are much less cost effective (Table 4).  The author notes that “Spending on cancer is generally U-shaped with time from diagnosis. Costs are high immediately after diagnosis, decline as the cancer goes into remission (if it does), and then increase substantially at the end of life.”

More Cost Effectiveness

The cost of treatment is growing over time.  One example of this the price of cancer drugs (see Table 5).  In the U.S., drugs only have to meet a safety and a minimum efficacy requirement.  In the UK, NICE ensures that drugs are cost effective as well.  In fact, NICE does not cover Avastin or Erbitux because they are not deemed to be cost effective.  Another issue related to drug cost-effectiveness is that “Many of the new, expensive drugs are tested first in metastatic settings because that is where clinical trials are easiest to conduct. Only later are tests done in non-metastatic cases. It is possible—even likely—that the effectiveness of new medications will be greater in non-metastatic settings.”

• Cutler, David M (2008) “Are we finally winning the war on cancer?” Journal of Economic Perspectives, v22(4): 3-26.

Wisconsin’s Medicaid plan covers children from 0-5 years old whose parents have income below 150% of the poverty line.  Sixty percent of Massachusetts residence receive coverage through their employer compared to 53% nationwide.  Forty-six percent of Californian firms with less than 50 employees offer health insurance compared to the national average of 43%.

How did I know these facts?  Am I a genius?

No, I used the State Coverage Initiatives website provided by the Robert Wood Johnson Foundation.  The website has a nice summary of each state’s SCHIP, Medicaid eligibility rules and well as graphs showing where individuals do (or don’t) receive insurance coverage.

• State Coverage Initiatives: statecoverage.org.

Accidental injuries kill more than 2,000 children per day worldwide.  The December 22, 2008 edition of Time lists the leading causes of accident-related childhood deaths worldwide:

• Other unintentional: 31.1%
• Road-traffic injuries: 22.3%
• Drowning: 16.8%
• Fire-related injuries: 9.1%
• Homicide: 5.8%
• Self-inflicted injuries: 4.4%
• Falls: 4.2%
• Poisoning: 3.9%
• War: 2.3%

There are now 33 million people living with HIV, including 2 million children. About 2.7 million people became newly infected with the virus each year.   Combating this epidemic is one of the top priorities facing public health workers around the world.

With limited resources, what strategy should be pursued?  As of now, a vaccine for AIDS is years away from viability.  So should resources go towards improving the health of those with AIDS or should funds be used to reduce the incidence of the disease?  

The Economist (“The ideal and the good“) reports on the efforts of Dr. Reuben Granich to treat patients and reduce incidence using the same drugs.  Dr. Granich finds that although first-line antiretroviral drugs (ARVs) do not offer a cure, they are cheap, reduce the symptoms of HIV and reduce the level of the virus in the person to the point where they are unlikely to pass on the virus.  Second-line treatments can be used for the 3% of individuals who do not respond to first-line ARVs.  ”Employing the logic of vaccination using proven drugs may be an idea whose time has come.”

You can support the fight against AIDS by donating to one of these fine organizations:

• The International AIDS Vaccine Initiative.
• The Global Fund to fight AIDS, Tuberculosis, and Malaria.

Google searches as a public health resource:

Google.org has released Flu Trends, an online reporting tool for flu-related search activity. It’s long been theorized that Google’s search data would be useful to predict epidemics. This is the first time they’ve released a tool like this to the public. As they say on the main page:

We have found a close relationship between how many people search for flu-related topics and how many people actually have flu symptoms. Of course, not every person who searches for “flu” is actually sick, but a pattern emerges when all the flu-related search queries from each state and region are added together. We compared our query counts with data from a surveillance system managed by the U.S. Centers for Disease Control and Prevention (CDC) and discovered that some search queries tend to be popular exactly when flu season is happening. By counting how often we see these search queries, we can estimate how much flu is circulating in various regions of the United States.

This tool comes to us via Google.org’s Predict & Prevent initiative. You can download the data for your own analysis.

[Update, 14 May 2009: Google is refining their Flu Trends data by asking people questions about their flu searches.]

According to Health Affairs, the U.S. spent 16.0% of GDP on health expenditures in 2006.  What will we be spending on health care in 2040?

Robert Fogel takes a stab at answering this question.  He claims that by 2040, the U.S. will spend 29% of GDP on health care in 2040.  

There are four main avenues by which health care spending will change.  First, there will likely be a continued downward trend in the age-specific prevalence of chronic diseases.  The prevalance of age specific chronic diseases “declined at a rate of about 1 percent per annum between 1910 and 1980.”  Second, the cost of treating diseases will change by 2040.  In the future, will innovations create higher quality medical care at higher cost, or will these innovations decrease health care spending?  Thirdly, as life expectancy increases and fertility rates decrease, we will likely see an increase in the share of the population who is elderly.  This will of course lead to increased health care spending even if the prevalence of age-specific chronic diseases and treatment costs remain constant.  Fourth, health care is a luxury good.  As incomes rise, individuals general spend a higher share of their income on medical care.  Fogel (2000) estimates that the long-term income elasticity of health care consumption is 1.6.

Using these four assumptions, Fogel claims that the U.S. will spend 29% of GDP on health care.  U.S. GDP in 2007 was $13.84 trillion.  If we assume a 2.5% annual growth rate, 29% of GDP in 2040 means that the U.S. will spend $9.07 trillion on health care.  

In the future, the health care system may need a bail-out as well.

• Aaron Catlin, Cathy Cowan, Micah Hartman, Stephen Heffler the National Health Expenditure Accounts Team (2008) “National Health Spending In 2006: A Year Of Change For Prescription Drugs” Health Affairs vol. 27, no. 1: 14-29.
• Fogel, Robert W. (2008) “Forecasting the Cost of U.S. Health Care in 2040” NBER Working Paper No. 14361.

Jonathan Gruber (2008) has a nice review article in the most recent edition of the Journal of Economic Literature.  This article is especially good for those who are not schooled in the basic issues of the uninsured.  It is also useful for professors wishing to teach students about the uninsured in America.

The article reviews economic concepts such as adverse selection, moral hazard, crowd-out, and risk aversion.  Below I will highlight some other key concepts.

• Employers are the source of health insurance for 3 reasons: 1) risk pooling, 2) the tax deductibility of employer-provided health insurance, 3) lower administrative cost.
• The uninsured often receive uncompensated care.  ”Under federal law, any hospital that accepts reimbursement from Medicare must treat individuals who arrive in an emergent state, regardless of their ability to pay.”
• Martin Feldstein (1971) proposes a major risk insurance plan, in which individuals would face a 50% copayment on all medical care until they spent 10% of their income on medical care.  
• Does higher deductibles lead individuals to forego needed preventive or primary care and lead to more hospitalizations?  The RAND Health Insurance Experiment found that this was not the case, higher deductibles lead to lower medical expenditures and no change in hospitalizations.  However, a paper by Gruber (2006) found that low income, chronically ill patients will forego needed healthcare with high deductibles and hospitalizations increase.
• Why should we care about the uninsured?  Possible externalities from increased probabilities of communicable disease infection, “job lock”, paternalism, and redistributional reasons.  
• Administrative costs for private health insurance in the U.S. are 12% of of premiums compared to 1.3% in Canada.

• Gruber, Jonathan 2008. “Covering the Uninsured in the United States. “Journal of Economic Literature, 46(3): 571–606.

Economists generally define efficiency in two manners: productive efficiency and allocative efficiency.  Productive efficiency means producing a good or service using fewest inputs.  A car company who produces a car that costs $20,000 to manufacture is less efficient than a company that can produce that same car (at the same quality) at a cost of $15,000.  Allocative efficiency is more subtle.  Are we producing the right amount of cars compared to trucks?  As gas prices rose, allocative efficiency compelled many car makers to shift to smaller passanger cars and hybrids compared to trucks.

Alan Garber and Jonathan Skinner (2008) apply the dual concepts of productive and allocative efficiency.  They ask: is the American health care system efficient?  The authors find that the American health care system is inefficient in both a productive and allocative sense.  The health care provided in other countries, however, is also inefficient, often for different reasons.

Productive Efficiency

Not providing low cost, high quality care or prescribing unnecessary treatment both decrease efficiency.  “There are sins of omission–one recent U.S. study suggested just half of recommended care is provided in a typical primary car visit (McGlynn et al. 2003)– as well as sins of commission–the spinal fusion surgery that provides marginal relief and more complications compared to conservative management (Rivero-Arias et al. 2005).”

Table 1 from the Garber and Skinner paper compares some key healthcare statistics between the U.S., Canada, France, Germany, the Netherlands, U.K., and Japan.  Any evaluation of a health care system must take into account the health of individuals before they are treated by medical providers.  Americans have the highest levels of obesity and diabetes and lowest levels of smoking in the world.  Further, rates of motor vehicles accidents and homicide are high compared to those in the rest of the developing world.  After taking these baseline population characteristics into account, is the production of American medical care efficient?

Table 1 also show that the U.S. has low levels of EMR usage and high administrative cost.  Elderly influenza vaccination, however, is fairly high compared to other developed nations.

An interesting survey by the McKinsey Global Institute looks at the cost and outcomes for 3 procedures (gallstone disease, breast cancer and lung cancer) in Germany, the U.K. and the U.S.

“In each case the United Kingdom was more parsimonious in its use of resources for the management of each condition.  However, Germany, not the U.S., use the most resources in the three conditions in which it was included.

In the treatment of lung cancer, patients in the U.S. experienced better outcomes than those in Germany and far better than for patients in the United Kingdom.  For breast cancer, outcomes were slightly better in the U.S. while for gallstone removal, the United Kingdom had worse outcomes than the U.S. or Germany.  Germany in turn had slightly better outcomes than the U.S. but much greater resource use.”

Allocative Efficiency

Allocative Efficiency determines whether health care spending is at the correct level.  Should we increase health care spending or instead spend those resources on education, roads or R&D?

Table 1 shows some statistics to quantify the allocative efficiency of the U.S.  Physicians per capita in the U.S. is in line with that of other nations, but this does not reveal the U.S. preference towards utilizing more specialist physicians than generalists.  Hospital beds per person is fairly low in the U.S., but this statistic hides the fact that the U.S. uses more outpatient facilities and that hospital care in the U.S. is more resource intensive than is the case in other countries.  Surgery wait times in the U.S. are fairly low, but many 20% of Americans receive unnecessary medical care.  Further, the American reduction of preventable deaths was the lowest of any country in Table 1.

While the U.S. does have a famously high MRI rate of 26.5/million, Japan loves the MRI machine the most.  The Japanese MRI rate is 40.1/million.  “The cost structure of …[high-tech] treatment seems ideally suited to rapid diffusion in the U.S.: high fixed cost of installation, low marginal cost of operation, and reimbursement rates based on average rather than marginal cost.”

Conclusion

The Garber and Skinner paper provides a nice overview of the American health care system compared to those of other countries.  While the paper works mostly in generalization and country-level statistics, it does provide a nice framework for thinking about health care reform.  The American health care system is certainly inefficient, but so are the health care systems in other countries.  How inefficiency manifests itself depends on the health care system adopted by the country.  In the U.S., inefficiency is mostly due to the fact that “the U.S. typically does not consider effectiveness relative to its costs or to the costs of alternative treatments.”  Further, because of the fee-for-service compensation system, American patients have high quality care available to them, but at a high cost.  Further, fee-for-service compensation induces providers to recommended unnecessary or less cost-effective care to patients.

• Garber, Alan M. and Skinner, Jonathan (2008) “Is American Health Care Uniquely Inefficient?” NBER WP 14257.

According to the U.S. Census:

• Both the percentage and number of people without health insurance decreased in 2007. The percentage without health insurance was 15.3 percent in 2007, down from 15.8 percent in 2006, and the number of uninsured was 45.7 million, down from 47.0 million.
• The percentage of people covered by private health insurance was 67.5 percent, down from 67.9 percent in 2006.
• The percentage of people covered by government health insurance programs increased to 27.8 percent in 2007, from 27.0 percent in 2006.

See also a USA Today story concerning these Census numbers on health insurance as well as income and poverty.

• U.S. Census (2008) “Income, Poverty & Health Insurance Coverage in the United States, 2007” p60-235.

Below is a side-by-side comparison of health care and economic statistics from China and India.

• Winnie Yip and Ajay Mahal, (2008) “The Health Care Systems Of China And India: Performance And Future Challenges” Health Affairs, 27(4): 921-932.
• Jin Ma, Mingshan Lu and Hude Quan, “From A National, Centrally Planned Health System To A System Based On The Market: Lessons From China” Health Affairs, 27(4): 937-948.
• CIA World Factbook.

The Kaiser Fast Facts website is a useful tool for any health researchers who need basic statistical information regarding medical care in the U.S.  The numerous slides filled with information-filled charts and graphs.

For many years price increases in the medical sector has outpaced overall inflation by a significant amount. According to the Bureau of Labor Statistics, here is the increase in consumer prices over the last few years.

Medical inflation is outpacing general inflation by an average of 1.5% per year. But is this measure of medical inflation accurately measured? Not according to paper by Joseph Newhouse (1992). Here are 4 reasons why not.

1. Medical CPI measures input, not final goods. The CPI for medical services focuses on inputs such as physician visits or hospital days. However, the service the patient consumers is treatment for a specific disease. An increase or decrease in the requisite number of doctors visits is a change in the input towards treatment. A true measure of medical CPI would measure how the price to treat a disease changes over time.
2. Actual Prices not observed. Generally, statisticians use the list price as the price of medical services. However, very few people pay this list price. Most individuals have insurance and these insurance companies negotiate bulk discounts. Thus, the list price is not the relevant price for most individuals.
3. Quality changes. Even if one uses the same amount of inputs in treating a disease, the quality of medical care has likely increased over time. Of course, observing quality changes in medical care is extremely difficult.
4. Medical CPI weight out-of-pocket expenses. Medical CPI weighs the cost to consumers of medical spending. However, since most people have health insurance, items which are paid more frequently out of pocket receive a higher weight. For instance, dental care is more frequently paid out of pocket and thus receives a higher weight in the CPI. [I am not sure if this weighting has changed in more recent versions of the medical CPI].

• Joseph P. Newhouse (1992) “Medical Care Costs: How Much Welfare Loss?” The Journal of Economic Perspectives, Vol. 6, No. 3 (Summer, 1992), pp. 3-21

How much money do you need to save for retirement?  $100,000?  $500,000?  $1,000,000?   $5,000,000?

Well whatever your figure is, you need to tack on an extra quarter of a million dollars in order to cover your health care costs in retirement.  The Boston Globe (“Fidelity…“) reports that Fidelity now estimates that a couple will need to have saved $225,000 in order to pay for health care costs during retirement.  When Fidelity initially conducted this study in 2002, the figure was $160,000, which means necessary savings for health care in retirement has increased by almost 6% per year.
You may think that Fidelity’s numbers are biased upwards since Fidelity has an interest in making people save more money (they make a commission off their investments).  Boston College’s Center for Retirement Research also conducted a similar study, however, and found that  a couple will need to have saved at least $206,000.

These figures are extremely high, especially considering that they take into account the fact that all of these people will be covered by Medicare.  According to BC’s Center for Retirement Research currently 60% of older workers are “at risk” of being unable to maintain their standard of living in retirement.

[Hat tip to New Health Dialogue blog]

The simple answer for this is that calorie intake is higher than the number of calories burned. But why are people getting fatter? In which countries are people the fattest?

This is the questioned tackle in a working paper by Sara Bleich and colleagues “Why is the Developed World Obese?” Obesity is a serious disease:

Excess body weight is the fifth most important risk factor contributing to the burden of disease in developed countries. Rising body mass index steadily increases the risks of type 2 diabetes, hypertension, cardiovascular disease, and some cancers.

Unsurprisingly, the U.S. has the highest rates of obesity throughout the data collected, but the rate of increase is very similar across all countries. Recent news reports (“Over 20m Chinese suffer diabetes“) even show that developing countries such as China are starting to experience higher rates of diabetes and obesity.

The main cause of increased obesity rates is increased food consumption. This is mostly due to decreasing real food prices over the past century. Another factor is the increased female labor force participation rate. One story to go along with this is that as more women work, families cook less and instead eat out more. If dining out occurs in high calorie restaurants and fast food establishments, calorie intake will increase.

The other facto influencing obesity is decreased activity. The main societal reason for a decrease in physical activity is the decrease in the number of active jobs. As the number of manual labor and manufacturing jobs has dwindled in the later half of the twentieth century, many more people are working at desk jobs where there is little physical activity. Further, increasing urbanization rates over time has also lead to decreased physical activity. Perhaps surprisingly, the number of cars per capita or the internet usage rate has no effect on obesity.

The authors claim that a junk food tax may help to decrease obesity and improve societal health. I am skeptical of this solution. Which foods will be categorized as junk foods? Who will decide this? I would guess there will be much lobbying by large food companies to have their products labeled as non-junk foods. Further, a junk food tax would hit the poor more heavily than the rich.

• Sara Bleich, David Cutler, Christopher Murray, Alyce Adams (2007) “Why is the Developed World Obese” NBER WP #12954.

An amazing collection of data has been performed by van Doorslaer et al. in their 2007 Health Economics paper “Catastrophic Payments for Health Care in Asia.” For 14 Asian countries, the authors categorize all charged services, free services, and income-related fee waivers. Further the paper examines the frequency and magnitude of out-of-pocket payments for medical care in each nation. Just three of these statistics are described below.

A paper in Health Services Research by Davern et al. (2007) claims that the Current Population Survey (CPS) estimates overstate the true number of people uninsured.  The authors claims that the “…uninsured rate is approximately one percentage point higher than it should be for people under 65 (i.e., approximately 2.5 million more people are counted as uninsured…)”

Although this is an important finding, it will not likely change anyone’s policy prescriptions. There are still millions of uninsured regardless of whether or not the estimates are overstated and how (or if) this issue will be addressed is still a major policy issue.

Many times on this blog, I have commented about pay-for-performance (P4P) programs. A healthcare economist, physician, or health services researcher may wonder where they can get data regarding P4P. In truth, getting P4P data is difficult; this is reinforced by the fact that for most physicians, P4P incentives make up a small portion of their salary and thus it may be difficult to detect major behavioral changes empirically.

Nevertheless, the Alliance for Health Reform has written a 2006 issue brief on P4P and cites some of the major programs.

• Agency for Healthcare Research and Quality
• Ambulatory Care Quality Alliance
• Bridges to Excellence
• California Report Card
• Integrated Healthcare Association
• Leapfrog Group
• MedPAC
• National Committee for Quality Assurance
• National Quality Forum
• Premier Hospital Quality Incentive Demonstration
• Robert Wood Johnson Foundation.

New rankings are available detailing the quality of care received in different states. The Agency for Healthcare Quality and Research (AHQR) gives some State Snapshots based on National Healthcare Quality Report. My home state of Wisconsin is ranked #1 according to the Milwaukee Journal Sentinel (“State is No. 1…“). The Commonwealth Fund also has a report ranking each state. The Commonwealth Fund Rankings are as follows:

• Top 10: HI, IA, NH, VT, ME, RI, CT, MA, WI, SD.
• Bottom 10: OK, MS, TX, AR, NV, LA, KY, WV, FL, GA.

How seriously should these rankings be taken? In my opinion, not seriously at all.

Let us say that each American individual living in country X would have a health level of x. If an individual would move to another state j, then their health level would be (x + a_j). Ideally, states would by ranking by ordering them from the ones with the highest level of a_j to the lowest. Empirically, however, we only view (x + a_j). Thus, some states may have poorer, less educated, non-English speaking residents who would have worse health regardless of which state they are in. Thus, scoring well on the dimension “Cancer deaths per 100,000 population per year,” which is measured by the AHQR’s quality report , does not tell the reader whether treatment is of a high quality or if the state’s population is just healthier to begin with.

Let us look at the state GDP levels according to the Bureau of Economic Analysis’ (“Regional Economic Accounts“):

• Top 10: DE, CT, MA, NY, NJ, AK, CO, VI, CA, MN
• Bottom 10: ID, ME, KY, AL, SC, OK, MT, AR, WV, MI

We see that 4 of the states with the worst health rankings also placed in the bottom quintile in terms of state GDP. Only Maine ranks in the top 10 in the health rankings and has a low GDP. None of the richest states rank in the bottom 10 Commonwealth Fund health care rankings. Finding a correlation between wealth and health is not surprising, but the exact relationship between the two is difficult to pin down.

In order to try to counter this selection bias based on the initial health levels of residents living in each state, most rankings include process measures in addition to actual health outcomes.

For instance, looking at individuals who have acute myocardial infarctions, if a high percentage of these individuals receive beta blockers within 24 hours of hospital admission or are prescribed aspirin at discharge, then a state will rank highly on these dimensions. While many of these process measures seem to be a good reflection of medical care quality, they may also reflect the characteristics underlying population, as well. For instance, ranking states based on the percentage of women age 40 and over who report they had a mammogram within the past 2 years (as does the AHQR), may indicate good health care policies by providers and health plans, or may indicate that the state’s population is more concerned with preventative care than residents of another state.

Also, states with high levels of health insurance usually gain extra points in the ranking, but it has not been conclusively shown that high levels of health insurance increase health levels.

So what can we use these reports for? While the overall rankings are not too instructive, policy makers can look at specific dimension to help improve care. For instance, why does New Mexico rank below average for “Receipt of recommended care for acute heart failure among Medicare patients?” Policy Makers could try to improve care by increasing the percentage of “Heart failure patients with left ventricular systolic dysfunction prescribed ACE inhibitor/angiotensin receptor blocker at discharge.” Most of the quality rankings, however, only take into account simple procedures, likely are not an accurate representation of a state’s medical care quality.

Many politicians and economists worry over the growing wage inequality which has arisen in the last few decades in the United States. On Wednesday, the New York Times published a story on this very topic (“…Paychecks“); Greg Mankiw also commented on the subject as well.  Statistics show that real wages grew 30% for those in the 90th income percentile between 1973 and 2005 while real wages grew only 10% for those with the median income during the same time period. For those in the bottom lower decile, real wages only increased 5%. The Economist’s View blog reports that inequality is increasing based on the type of education received (i.e.: college graduates are earning more than ever before, while high school dropout’s real income has been declining for decades).

The Big Picture blog, however, notes that while real wage inequality may be increasing, inequality in total compensation may not be growing as fast. According to the Bureau of Labor Statistics (BLS) Consumer Price Index (CPI) website, health care costs were rising at 6.8% to 7% per year while total inflation for the entire economy was only 4.7% per year. This means that real medical care costs doubled [I defined the real medical care costs to be the increase in the cost of medical care over the relevant time period divided by the CPI increase over the same time period]. This fact is an important matter to take into consideration in the inequality debate since lower wage individuals have a higher percentage of their total compensation taken up in the form of health insurance paid by the employer. Let us look at an example:

Let us say that there are three individuals. In 1973, the Poor Paul earns $10,000, the Median Mike earns $30,000 and Rich Rich earns $100,000 (all figures are in 2005 dollars). Paul, Mike, and Rich represent individuals at the 10th, 50th and 90th percentiles. Each individual receives health insurance from their employer which is valued at $2000. Thus we have the following scenario.

We see that Poor Paul only earns 1/3 of the Median Mike’s wage, but his total compensation is 38% of Mike’s. Rich Rich earns 3.33 times as much as Mike, but only 3.19 as much once we take into account that they have the same insurance. According to the Economist View article, Poor Paul’s real income increased 5% between 1973 and 2005, Median Mike’s increased 10% and Rich Rich’s increased 30% over that time period. The real cost of medical care doubled in this period. Thus, in 2005 we have the following scenario:

When we look at simple wage data, it seems that Poor Paul is doing worse and Rich Rich is doing better compared to Median Mike. When we take into account total compensation, however, Poor Paul is actually doing better—compared to Mike—than he was doing in 1973. Rich Rich is still doing better than he was in 1973, but the differential is smaller when we use total compensation rather than wage compensation.

The point of this exercise is not to claim that inequality is not increasing; I believe it is. One must take into account, however, that as employers are paying more and more for health insurance and these expenses should be treated as implicit wages for workers. A side effect of the increase in medical care costs is that it has become increasingly likely that Poor Paul does not have any insurance, since the employer may not be able to afford to pay for these benefits. Still, wages alone do not adequately explain the entire inequality story.

The United Health Foundation has established a methodology of ranking each state by various health factors. The methodology examines health determinant measures such as: the prevalence of smoking, motor vehicle deaths, obesity rates, rates of high school graduation, health insurance coverage, and measures of violent crime and poverty. Health outcome measures are also employed. These outcome variables include: infant mortality, poor mental health days, cancer deaths, and cardiovascular deaths. (To see all variables included in the health measure, click here).

According to the 2006 America’s Health Rankings just published, the healthiest states were Minnesota, Vermont and New Hampshire. The least healthy states were Louisiana, Mississippi and South Carolina. Complete results are available at the United Health Foundation website.

The structure of hospital care has been changing rapidly over the past few decades. The first item to note is that the number of nonprofit hospitals is decreasing. For-profit hospitals are making up a larger percentage of all hospitals than in 1980. Below, table 1 shows the number of hospitals in the United States. I did not break out the government hospitals separately but they exhibit a similar declining trend like the nonprofits.
Table 1 – Number of Hospitals

If we examine the number of beds the trend remains strong.

Table 2 – Beds (in thousands)

While it is true that for-profits are taking up a larger share of the market, it is evident that the number of total hospitals and hospital beds is decreasing. Below, we can see that occupancy rates, admissions, and lengths of stay are all decreasing. Do people simply need fewer medical services in recent years? The obvious answer is: No. We see a trend in the last row of Table 3 that outpatient visits have increased dramatically. One may worry that the trend of hospital consolidation would increase the hospitals’ market power, but if each hospital faces competition from outpatient services, one should not worry that hospitals we monopolistically increase price.

Table 3

Data from: Folland, Goodman, and Stano, The Economics of Health and Health Care, 4th ed., Pearson Prentice Hall, 2004, p. 306.

The Healthy Policy blog has two posts (one for the insured and one for the uninsured) which offer a compendium of statistics of these two groups.  Some interesting statistics:

• The number of employers providing health insurance has dropped 9% in five years; from 69% in 2000 to 60% in 2005.
• The average insured person already pays 34% of their health costs out of pocket.
• Despite the expansion of Medicaid for children through the S-CHIP program, 20% of children lack health insurance.

After a large number of posts criticizing the American healthcare system on this very blog, it is now time to sing its praise. The San Diego Union Tribune reports today (“U.S. deaths dropped 2% during ‘04, report finds“) that the number of deaths decreased by 50,000 between 2003 and 2004. This is surprising. Despite medical advances, one would expect the number of deaths to increase due to: 1) population increase, 2) the higher proportion of elderly individuals in society and 3) the trend towards higher obesity rates.

The top three killers of Americans are heart disease, cancer, and strokes; the age adjusted death rate for each of these illnesses dropped in 2004. The heart disease rate declined more than 6 percent, the cancer rate about 3 percent and the stroke rate about 6.5 percent.

Congratulations to all those involved in improving the health care of Americans today!

The problems with the Medical Malpractice system in the US have been well-documented. President Bush has presented proposals to cap punitive damages in malpractice litigation. Other others have decried the fact that despite a large number of negligence cases each year, very few patients bring suit to court. Below are two studies which should give the reader a more informed perception of how malpractice law functions in the United States today.

This first study is by Brennan, Sox and Burstin (1996). These authors find that iatrogenic injuries in New York account for 3.7% of all hospitalizations and negligent iatrogenic injuries account for 1.0% of hospitalizations. Below is their data for the number of people filing suits:

A second report by Studdert, Mello and Brennan (2004) confirm some of the Brennan, et al.’s findings. Citing a Medical Insurance Feasibility Study, 4.6% of all hospitalizations in California involved iatrogenic injury and 0.8% of all hospitalizations involved negligent iatrogenic injuries. These numbers are similar to the 3.7% and 1.0% which Brennan, et. al. estimate.

There are three issues here which I would like to touch on.

The first is that despite conventional wisdom that physician are nearly infallible, 3-5% of all hospitalizations are due to doctor error. One of the greatest risks facing the American medical system is…well…the American medical system. The easiest and most effective way to decrease the error rate is to integrate Information Technology (IT) into the medical field. The Healthcare IT Guy has some good suggestions.

Malpractice suits are not common. Less than 3% of people who receive negligent physician care actually sue. One must note that it is difficult for a patient to determine if negligence has occurred. ‘Do I feel sick because the treatment is not working or is this the doctor’s fault?’

Although only one in a thousand people who receive no medically induced injury sue, these ‘no injury’ cases make up over half of the malpractice caseload.

Brennan, Sox, Burstin (1996) “Relation Between Negligent Adverse Events and the Outcomes of Medical-Malpractice Litigation” New England Journal of Medicine Vol 335 (26).

Large firms offer more generous health insurance to employees…right????
While it is true that large firms are much more likely to offer insurance to their employees, small firms are actually more likely to offer insurance in which they pay for 100% of the costs. A December 2005 paper by Zawacki and Taylor (“Contributions to health insurance premiums: When does the employer pay 100%”) uses data from the Medical Expenditure Panel Survey-Insurance Component to establish this result.

One explanation is that insurance companies often require 100% employee enrollment before they will insure a small company. This requirement attempts to prevent adverse selection, where the healthy individuals would opt out of the plan. In order to induce each employee to participate, small firms may have to pay 100% of the health insurance cost.

Firms who offer 100% payment of health insurance premiums tend to be:

1. ‘Younger’ firms (firms in existence for more than 20 years are less likely to offer 100% coverage).
2. Non-profits versus for-profits,
3. Firms with more high wage earners
4. More unionized.

I have recreated a table from the Zawacki and Taylor (2005) paper demonstrating the above findings. Column 1 gives the percentage of establishments offering one insurance plan that pays 100% of the cost for single coverage; Column 2 shows the percentage of establishments offering one insurance plan that pays 100% for family coverage.

As of 2002, approximately 41 million people were on Medicaid. That is approximately 14% of the population of the United States. Who are the people on Medicaid?

We can generally divide the people who receive Medicaid into four categories: 1) poor adults, 2) elderly who can not afford Medicare co-payments or deductibles 3) children and 4) the blind and disabled.

Low-income parents and children make up 73% of Medicaid recipients, but account for only 27.5% of Medicaid expenditures. On the other hand, the aged, disabled, and mentally retarded are only 27% of Medicaid recipients but account for 72.5% of all expenditures. Here is the breakdown by group for the year 2002.

Children have made up an increasing percentage of Medicaid cases since the 1997 Balanced Budget Act which enacted the Children’s Health Insurance Program (CHIP).

Medicaid participation rates vary by states. Some states set Medicaid eligibility upper limit at 100% of the federal poverty level, while others have established upper bounds of up to 200% of the federal poverty limit.

Much of the background for my analysis was found in Health Care Economics by Paul J. Feldstein, 6th edition; Thompson Delmar Learning, 2005.

The Kaiser Family Foundation has performed their 2005 Employer Health Benefits Annual Survey.  Some interesting trends:

We can see the trend that employees are offered conventional health less frequently over time.  On the other hand, four out of five employers have choosen to offer a PPO plan to in 2005.  Three out of five workers in the survey elected a PPO among the choices given at their workplace.  The popularity of the PPO plan may be due to the fact that:

1. Firms offer PPOs since they are less expensive than conventional insurance.
2. PPOs offer more consumer choice than HMOs.  Most consumers prefer to elect their own health care providers; PPOs allow them to do that (at a cost if the physician is not in the network) while still maintaining a competitive price compared to HMOs.
3. The backlash against HMOs in the late 90s may have hurt the HMO reputation somewhat.  Nevertheless, one in five individuals who have employer provided insurance have an HMO.

With President Bush’s HSA initiative, I would expect the number of people with conventional insurance will fall even more.

In the 1950’s and early 1960’s, the United States maintained a fairly constant ratio of 141 physicians/100,000 people. In the 60’s, however, politicians began to worry that the supply of doctors would decrease in the near future. In 1963, Congress passes the Health Professions Educational Assistance Act (HPEA) in 1963. Senator Yarborough stated that the reasons for passing the bill were that “it was when we were trying to give more American boys and girls a chance to a medical education so that we would not have to drain the help of other foreign countries.” Later he states, “to me it is just shocking that we do not give American boys and girls a chance to obtain a medical education so that they can serve their own people.”

How has this bill effected the number of physicians in the United States? In 1980, the ratio of physicians per 100,000 citizens increased to 200 from 141 in 1960. In 1990 the ratio increased again to 230 and in 2001 the ratio reached 285, double what it was in 1960.

With the increase in physicians, we would expect to see that prices would decrease due to increased competition. This has not been the case. Besides the HPEA, additional causes for the increase in the physician to population ratio have not been clearly identified. Some possible suspects are:

• the increased supply of physicians is simply a response to increased demand for health care,

• an aging of the population–thus increasing demand,

• an increase in the returns to becoming a physician, or

• supplier-induced demand may be keeping physicians salaries constant despite an increase in competition.

A recent Health Affairs article by Cynthia Smith, Cathy Cowan, Stephen Heffler and Aaron Catlin details the trends in health care spending over the last 25 years. I have compiled their results into a handy graph.

In addition the the general overall increase, there are other significant findings. Between 1970 and 2004, out-of-pocket payments decreased from 33.2% to 12.6% of total expenditures. This means that consumers are not going to be as price sensitive in regards to their election of medical treatment since they pay less then 15% of the total cost. It is no wonder that people are demanding more and more medical procedures.

Private Insurance has picked up much of the slack. In 1970 private insurers payed for 20.6% of all expenditures but currently they foot 35.1% of the bill.

Further, government payment for health care has increased from 37.7% of all U.S. expenditures in 1970 to 45.1% in 2004. The Medicare and Medicaid programs have made up between 60 and 70 percent of the total government expenditures in health care between 1980 and the present.

What is the the significance of these percentages? In general, end consumers are paying less and less for medical care out of their pocket. When third parties pay a larger percentage of the cost, we have the problem of moral hazard. President Bush is trying to make consumers more price sensitive by introducing Health Savings Accounts (HSA). Will this work? Look at my 26 January and 29 January posts to see some of what I think.

Cynthia Smith, Cathy Cowan, Stephen Heffler, Aaron Catlin;
“National Health Spending In 2004: Recent Slowdown Led By Prescription Drug Spending”
Health Affairs, January/February 2006; 25(1): 186-196.