Hospitals Know that P-O-V-E-R-T-Y is at the Core of the Problem

For more than a decade, a coalition of health care pundits associated with Dartmouth, the IOM, MedPAC, CMS, the Urban Institute, the GAO, the CBO and the Commonwealth Fund has been peddling a line about the fact that, after adjusting for age, sex and health status, there is a large residual of unexplained geographic variation in health care that it is not explained by differences in income or other demographic factors and therefore must be due to low-performing health care systems. In response to “some” who have claimed that poverty is at the core of the problem, they state emphatically that, after adjusting for health status, there is little evidence that poverty and related sociodemographic factors play a role. I am proud to be one of the “some.”

In a sense, they are right. Of course health status is a major predictor of need. It really is the only predictor. Health care is given to people who are sick. That’s why it’s called health care. But it is poor people have the poorest health status and continue to do so even after receiving health care. So there is very little left to adjust for once an adjustment is made for health status. In fact, if the adjustment sequence were reversed — if health care spending were first adjusted for income — health status would be found to exert little influence. Failing to find that poverty is an operative element is, well, disingenuous.

But if poverty explains it all, why does the D-I-M-C-U-G-C-CF crowd consistently find an unexplained residual. The answer is that they all use large units of analysis, such as (hospital referral regions (HRRs), hospital service areas (HSAs) metropolitan statistical areas (MSAs), and these all introduce the error of aggregation of non-linear elements. Let me explain. The relationship between health care spending and the levels of income or disability are curvilinear. Spending rises steeply at the low end of income or the high end of disability, with relatively little change along the path. Averaging is, by definition, a linear manipulation. It will always fail to account for the extremes of poverty or disability. But when the problem is approach in ways that avoid such statistics, income fully explains the geographic differences. Read our paper in the Journal of Urban Health.

Now, after years of being brain washed by the D-I-M-G-U-C-C-CF crowd, whose arcane incursions into the practice of medicine have pushed hospitals and physicians to the brink, hospitals are catching on. Socioeconomic differences are at the core of the problem of high utilization after all. Read “Getting to the Root of the Problem,” excerpted below from an article by Steven Ross Johnson in Modern Health Care. It describes the success of efforts to attack the sociodemographic roots of the problem. One example is Health Leads. With support from the Physicians Foundation, the Robert Wood Johnson Foundation and others, Health Leads enables healthcare providers to prescribe basic resources like food and heat just as they do medication. But solving Americas social problems cannot be left to the health care system. The entire nation has to wake up to the problem. Read on…..

Getting to the Root of the Problem (excerpted from Modern HealthCare)

Delores Banks is a 61-year-old diabetic with congestive heart failure who was hospitalized twice last year. She lives alone on the 15th floor of a senior public housing project in one of the poorest sections of Chicago. A recent two-week elevator outage stopped Banks from leaving her building on three occasions. She decided to simply stay in her apartment until it was fixed. That’s when the Sinai Health System’s disease-management team sprang into action. One member called the Chicago Housing Authority to expedite the repair work. “It just makes you feel like you’re not alone,” Banks said. “The elevator is working now, and I haven’t been stuck.”

Healthcare systems in impoverished areas are turning toward tackling the social conditions that lead to ill-health, but they may pay a financial penalty since payers still do not reimburse for those activities.

The emergency intervention was part of a Sinai program launched in 2011 to help patients better manage the chronic conditions that lead to frequent hospitalizations. But as the care coordination team members quickly discovered, their efforts had to reach well beyond phone calls to make sure Banks took her medicine or to remind her about doctor appointments. In addition to fixing elevators, they helped her pay for her drugs and assist her with transportation to see her doctor. Team members even guided Banks through the paperwork needed to move to another senior-living facility.

Sinai is one of a growing number of health systems across the country that have begun tackling the social, economic and environmental conditions in the communities they serve as part of their programs to reduce hospital readmissions and improve outcomes. They are responding to the well-documented association between poverty, joblessness, inadequate housing, poor nutrition and chronic stress and poor health outcomes. Only by addressing these social determinants of health, they say, will they be able to get better outcomes and improve the overall health of their local populations.

Income is the single largest social factor driving overall health. A recent report from the Robert Wood Johnson Foundation’s Commission to Build a Healthier America found that 23% of African-Americans who earned less than 100% of the federal poverty level had a health status that was “poor to fair” compared with 6.8% of blacks with incomes that were more than 400% of the poverty level.

Among whites, the disparity was even greater. Twenty-one percent of whites earning below 100% of poverty reportedly had “poor to fair” health compared with only 4% of whites making more than 400% of poverty. Education level is another indicator of health. According to the report, a 25-year-old college graduate can expect to live up to nine years longer than a 25-year-old who has not completed high school.

“Without a doubt, addressing the social determinants is a key part of improving health,” said David Williams, a professor of public health at Harvard University. “Imagine a mother bringing a child to the hospital who has asthma, and that asthma is driven by poor housing conditions. All the asthma medication in the world and the latest and best medicine in the world will not solve that child’s asthma problem if we treat the child and then send them back to live in the same conditions that made them sick in the first place.”

The evidence is overwhelming that poverty, homelessness, unemployment and hunger have a significant impact on the overall health of a population in communities where such conditions are prevalent. They have disproportionately higher rates of heart disease, diabetes, lung disease and cancer.

Not surprisingly, rates of hospitalization are higher, too. Near Sinai, the rate of hospitalization for those diagnosed with diabetes in 2010 was 35 for every 100,000, compared with 25 for every 100,000 in Chicago as a whole and 19 for every 100,000 nationally. In Banks’ neighborhood of East Garfield Park, the rate was nearly double the city average, where 50 out of every 100,000 residents are hospitalized because of the disease.

Providers seeking to address the social conditions of their most impoverished patients face a fiscal environment in Washington that is making their jobs more difficult. The Supplemental Nutrition Assistance Program, for instance, was slashed by another $8.6 billion over 10 years last week in the latest version of the Farm Bill. Such cuts have a direct impact on the health of the people who rely on food stamps. A recent study in Health Affairs found the risk for hospital admission for hypoglycemia in low-income patients with diabetes increased by 27% during the last week of the month—when food budgets are strapped and food stamps run out—compared with the first week of the month. The study found no such occurrence among populations with higher incomes.

“It is not reasonable to think that every healthcare provider has to become a social worker and solve all of these problems,” Williams said. “But we can put in place complementary resources where the provider simply has to refer that patient to someone who could connect them with resources to help solve the problem that is driving their underlining health conditions.”

Some health systems have begun addressing social issues with the help of third-party coordinators who focus on providing for a patient’s nonmedical needs. Health Leads of Boston, funded by the Robert Wood Johnson Foundation, helps healthcare providers obtain basic resources such as food, heat, electricity or housing for their patients. When a physician identifies patients struggling with basic needs, they’ll refer them to a Health Leads “clinic” in the healthcare facility. The advocate then helps the patients gain access to community resources that can help provide those services. “A patient can take a prescription for heat in the winter or to have their lights turned back on to a Health Leads desk to get it filled,” said Rebecca Onie, Health Leads CEO and co-founder.

But foundation-supported efforts such as Health Leads are far from ubiquitous in communities with the greatest needs. That forces providers such as Sinai to use their own dollars to help patients address the social conditions that may worsen their illnesses and make recovery from hospitalization more difficult. Even though such work can lower readmissions, no payer compensates them for paying to get a patient’s electricity turned back on or steady access to food.

Sinai is betting that participation in the CMS’ bundled-payment demonstration program may generate enough savings to help them finance such efforts. The program gives a set payment for care over a 90-day period for inpatients with either chronic obstructive pulmonary disease or congestive heart failure. If the cost of treating those patients comes under the target price, Sinai keeps the savings. If they go over the target amount, however, the difference must be paid back to Medicare.

Some advocates contend addressing the social determinants of health is the only economically viable solution for a system that spends more than $2.8 trillion annually on healthcare without producing the best outcomes. Clearly, investing solely in sick care isn’t getting results. A study published last December in the Journal of Public Health found that every $100 spent on healthcare in the U.S. increased a patient’s life expectancy by two weeks. In Germany, the same amount spent on healthcare increased life expectancy more than four months. According to the RAND Corp.’s Tamara Dubowitz, “on a population level, investing in social conditions as they pertain to patient health is certainly the most economically sound approach.”

National Income Inequality and Local Poverty: Correlates of Health Care Spending

I recently posted an essay on the Health Affairs blog entitled “Inequality is at the Core of High Health Care Spending: A View from the OECD.” It explores the relationship between GDP and health care spending in the OECD countries, adjusts spending for price differences and demonstrates that the residual excess spending in the US, which accounts for 31% of total US health care spending, can be explained by the high degree of income inequality in the US as compared with other countries. It notes further that, while the US spends more on health care, it spends less on social services, and concludes by saying, “It is difficult not to connect the dots from inadequate social spending to excess poverty and income inequality to more chronic illness and higher health care spending. These dots reside in the core of the OECD onion, and the failure to cope with them is placing an unsustainable burden on our health care system.”

John Goodman called attention to this blog post on his National Center for policy Analysis blog. In a follow-up blog, he posted a message from Angus Deaton, a distinguished professor at Princeton, authority on the relationship between income and health and author of a recent book, “The Great Escape: Health, Wealth, and the Origins of Inequality.” Professor Deaton took issue with my conclusion that income-inequality is at the core of health care spending, pointing out that in his studies of mortality in states and metropolitan statistical areas (MSAs) with Darren Lubotsky, the correlation between income inequality and mortality stems from the failure to adjust for the density of African Americans, and that “inequality and mortality are uncorrelated across space in other settings where race is not a salient factor.”

I believe that Professor Deaton was referring to settings in the US, such as states, cities and neighborhoods, which I shall return to. But there is no question that income-inequality correlates strongly with both maternal and child mortality among OECD countries, even after excluding the US, which is the only OECD country with substantial numbers of blacks. The studies of health care spending that I reported were at the level of OECD countries, and I stand firmly behind them. At the level of countries, income inequality is at the core of high health care spending.

What about states, cities and neighborhoods? A high density of blacks in these smaller units of analysis proves to be a surrogate for poverty and income inequality. For example, Deaton and Lubotsky’s least equal states were LA and MS, which have high densities of blacks, while the most equal were NH and VT, which have few. But there is more than the density of blacks differentiating them. Moreover, both blacks and whites are affected. Deaton and Lubotsky noted that “white mortality rates are higher in places where the fraction black is higher.” Similarly, in a study of poverty and health care utilization in Milwaukee and Los Angeles, my colleagues and I found that whites living in poor neighborhoods with a high density of blacks had the same high health care utilization as their black neighbors. A high density of blacks is a marker for concentrated poverty, and concentrated poverty is the strongest correlate of health care utilization at the local level. It can be measured by income and education and often by black race, but make no mistake. It is concentrated poverty that is the operative factor at the local level.

Richard Wilkinson and Kate Pickett addressed the differences between local and national measures in their remarkable book, “The Spirit Level: Why Greater Equality Makes Societies Stronger.” They noted that in their review of nearly 170 studies of income inequality and health, the units analyzed varied substantially, from neighborhoods to towns to states or regions and to countries. While there was overwhelming evidence that inequality was related to health at the level of whole countries, the results were mixed when the measures were of smaller areas. They point out that “what marks out neighborhoods with poor health is not the inequality within them. It is, instead that they are unequal in relation to the rest of society….We should perhaps regard the scale of material inequalities in a society as providing the skeleton round which cultural differences are formed.”

Thus, at an international level, the measure of societal inequality is income inequality, and it is a strong correlate of health and health care utilization. At the level of neighborhoods, the best correlate is concentrated poverty, and it is best measured by the income-race-education triad, but within that triad, income and education always trump race. Inconsistencies often occur when data are collected at the level of MSAs or states because they are simply aggregates of neighborhoods yet subject to the national skeleton, and, unfortunately, aggregation of neighborhoods into units varying as much as CA, with 40M people, and VT, with <1M, leads to marginally significant and often conflicting conclusions. National income inequality and local poverty are the best correlates of health care utilization.

Another Look at Jobs

In response to a recent blog about jobs (see below), Alan Maynard, the dean of UK health economists, asks whether the loss of health care jobs is a manifestation of expenditure control or efficiency. Here’s my answer.

If  hospitals and physician offices were bloated with employees, this decline in employment could simply be a move toward greater efficiency. But rare is the health care worker who is not stressed already. No, this is expenditure control or, more properly, it is cutting back on the payroll in response to relentless cuts in Medicare reimbursement, both directly and in the form of penalties for the failure to meet various quality and readmission standards now and in the future as the ACA is implemented. These job losses must be viewed in the context of what’s been happening to jobs over the past several decades.

The US has currently has about 1,225 fewer jobs per 100,000 of population than it did in 1990 – almost 3% less. The big losers have been manufacturing, mining and construction, which lost almost 4,000 jobs/100,000. Local governments and most services underwent little change, but together with retail, both federal and state governments and some other sectors, another 1,500 jobs/100,000 were lost. Even information technology lost jobs, as the internet gained but newspapers shrunk.

What made up for this deficit of 5,500 jobs/100,000? Only four sectors did. Together they added 4,300 jobs/100,000. Education accounted for 8%, leisure and hospitality 17%, professional and business services 35% and health care 40%. Yes, about 1/3 of the jobs deficit that resulted from a contraction of manufacturing and other slow or no-growth sectors was made up for by additional health care jobs. but this is threatened.

Meanwhile, there are enormous unmet health care needs, especially among the poor.

The problem is, of course, that health care, more than other sectors of the economy, requires cross subsidies from the rich to the poor. But the poor have been subsidizing the rich most of these past 35 years, so a change in direction might not be unwarranted. And a bit more investment in social infrastructure would be good, too. Together, these investments in health and social services would fuel the economy, employ the populace, enable healthier lives and strengthen our democracy.

But it can’t be done without physicians. Doctors are not everything in health care — there are only about 225 practicing physicians per 100,000. But they are the lynchpins of the health care system.

Or we could continue to cut health care spending, constrain the growth of physician supply and allow the nation’s major jobs engine to sputter. But why?

Squeezing Physicians is Not Good for Jobs Growth

Four years ago, as ObamaCare was being debated, and as action on expanding physician supply languished (as is still the case), I wrote on this blog, “More Jobs, But Not Without More Physicians.” Over the previous decade, with the boom of the housing bubble and the recession that followed, the US had added only 6 million jobs, and half of those jobs were in health care.

This engine of health care jobs continued in the years after 2009 at about 30,000 new jobs per month, but since the beginning of 2013, it has slowed. Since May, health care jobs have grown at only 2/3 the previous rate, and the number of jobs in physicians’ offices has declined from a previous average of 4,000 monthly to zero. In fact, last month there was a small loss of jobs in physicians’ offices. Physicians are being squeezed, and its effect is rippling through the job market.

The top figure shows the long-term trend of jobs in physicians’ offices from 2000 to today. Below it is a graph of the past 18 months, from Jan 2012 to today.Phys supply both

The nation’s greatest engine of jobs is sputtering. Res ipsa loquitur!

IOM says “Target Decision Making.” No. Target Poverty

The IOM’s new report on geographic variation in health care was released yesterday. It attempted to define the reasons for the variation that has been projected by the Dartmouth Atlas and has fed the frenzied war on waste.

The IOM’s approach was similar to Dartmouth’s but differed in important ways. Both measured Medicare spending that had been aggregated at the level of HRRs and both adjusted it for age and gender (which don’t vary) and black race, which does but is a poor proxy for the broad group of socioeconomic factors that matter. That left a lot unexplained by Dartmouth. But the IOM committee further adjusted for input prices, which markedly decreased variation, and it also looked at variation in commercial insurance costs.

A clue that aggregation might be a problem was the IOM’s observation that there was even more variation among the 3,000 hospital service areas (HSAs) than among the 300 HRRs. Had they gone the further step of examining the 30,000 Zip codes, they would have found still more variation. So the variation that they and Dartmouth studied was partially obfuscated by the “tyranny of aggregation.”

Among Medicare beneficiaries, almost half of the variation between the top and bottom deciles was explained by health status, 9% by race and 6% by income. Due to co-variances, the three together explained 43%. These factors also explained 10-20% of the variation for commercial insurance. That left about 65% of the variation in total health care spending unexplained. Of this, about one-fourth was attributable to variation in acute care (including procedures, diagnostic tests, prescriptions and ER visits), while three-fourths was attributable to post-acute care.

The fact that there was greater variation among HSAs than HRRs drew the IOM committee to recognize that there was a great deal of internal variation and that adjusting payments based on aggregate measures of geographic differences in HRRs would unfairly reward “low-value providers in high-value regions” and punish “high-value providers in low-value regions.” Therefore, it encouraged Congress not to adopt a geographically based value index for Medicare.

But despite the fact that, as mentioned, most of the variation was in the post-acute sector, the Committee’s major conclusion was that CMS should test payment reforms that incentivize health care systems to assume some or all of the risk of managing health care. Indeed, this conclusion was incorporated into the title of its report (“Target Decision Making, Not Geography”).

No study was necessary to reach that conclusion. Indeed, nothing in the report suggests that payment reform would be necessary. Very little of the variation had anything to do with acute-care, and little credence was given to any of the variation. It could not have been more clearly stated. As I said in the Washington Post on September 11, 2009, the Dartmouth Atlas is the wrong map for health care reform.

Had the Committee pursued the lead that variation increases as the unit of analysis shrinks from 300 HRRs to 3,000 HSAs, they might have done what my colleagues and I did, which was to move even lower to the level of the 30,000 ZIP codes, where virtually all of the variation in acute care is due to variation in income, education and the burden of disease.

So what the report really found, but did not say, was that poverty and its associated poor health status is the major cause of geographic variation in health care and its effect is far greater than apparent from studies of HRRs which, because of aggregation, obfuscate its true magnitude.

Rather than recommending practice incentives to take on risk, the report should have recommended social interventions that could reduce risk. By coincidence, that is what President Obama did at Knox College in Galesburg Illinois the very same day. He said, “this growing inequality — it’s not just morally wrong; it’s bad economics… and it undermines the very essence of America.” He could have added, and it raises health care costs. Moreover, because poverty is distributed unevenly, it is responsible for the geographic differences in health care that we had been told were due to clinical practice variation but now know are due to geographic differences in income-inequality.

Geographic Variation Is Explained by Disease

In a new paper in Medical Care Research and Review, Reschovsky, Hadley and Romano have shown quite conclusively that geographic variation in health care spending is related to the burden of illness and little else. Of course, the burden of illness is greatest among the poor, and the two are strongly correlated, so, indirectly, they also showed that geographic variation is related to poverty and little else.

Their study examined health care spending in 60 regions that have been studied for many years by the Center for Health Systems Change, shown below.Hadley Variation Map

The graph below summarizes the data. Medicare expenditures varied widely, as had been observed in studies using the Dartmouth Atlas. Dartmouth’s earlier studies (not shown) corrected for age, gender and black race but nothing more, believing that there was no need to correct for illness levels because the expenditures that Dartmouth studied were in the last two years of life, and since everyone was similarly dead, they all must have been similarly ill, a conclusion which, aside from being absurd, has been shown to be false, most completely in the Reschovsky study. Sutherland and coworkers from Dartmouth approached the question by studying patients in the Medicare Current Beneficiary Survey. After the standard adjustment for demographic factors, they adjusted the data using five disease parameters and found that such as adjustment explained 18% of the variation between the extremes of expenditure quintiles. Zuckerman and colleagues carried out an identical study but applied 12 disease parameters, and they explained 29% of the variation. Reschovsky et al utilized 70 disease parameters within the hierarchical condition category (HCC) model developed for the Centers for Medicaid and Medicare Services (CMS) and found that illness levels explained 93% of the variation. Even using a modified version to remove observer bias in charting illness, disease burden accounted for 85% of the variation.

Hadley VariationIsn’t it time to stop this foolishness about geographic variation being a manifestation of variation in practice? Wouldn’t it have been wonderful if that could have occurred before all of the foolish incentives and penalties were written into Obama-care? Shouldn’t someone be held accountable for deceiving congress, distorting the practice of medicine and bilking the profession?  Isn’t it time that the high health care costs of poverty became a focus of national attention? Don’t we owe our children a health care system that they can sustain? Won’t it take honest, critical research (like Reschovsky’s) to get us there?

More War on Waste: Do Hospitals Profit from Complications?

Gawande has struck again, concluding in his recent JAMA article that “some hospitals have the potential for adverse near-term financial consequences for decreasing post-surgical complications” (aka, they’ll make more profit if they let complications happen). In a follow-up article entitled “Hospital Profits Linked to Patients with Surgical Complications,” the Huffington Post rephrased it this way: “Patients who suffer complications after surgery are lucrative for hospitals.” How grotesque.

And in an accompanying editorial, Reinhardt, the economist who trumpeted the “physician surplus” 15 years ago when that was popular, quickly added that the fault may be the fee-for-service payment system that rewards volume rather than quality, today’s bandwagon. And everyone has jumped on the band wagon. But remember McAllen? Everyone jumped on that, too. It took a while to figure out that the McAllen story was poppycock.

First, what were these complications? More than half were MIs, cardiac arrests, pneumonias or strokes. Another 15% were use of a ventilator for more than 96 hours, which is never preventable (one wishes it could be). Only 10% were wound-related (a total incidence of 0.5%). None were events that physicians or hospitals want. The remarkable thing is how few of the complications were avoidable. How do you prevent a stroke? Nonetheless, the implication was that hospitals and their doctors egregiously and selfishly tolerate errors to increase profits and that the fee-for-service system enables such behavior. Once again, the public has been poorly informed.

So how did hospitals profit from all of this? Please bear with me because the numbers in the article are a little complicated. I will try to simplify them.

For uncomplicated patients, who length of stay averaged 3 days, the margin earned by hospitals, after paying for variable costs (nurses, medications, etc.), was a little over $2,500 per day, a total of $7,600 for a 3-day admission.

Complicated patients stayed an extra 11 days, and although the margin for them was less (about $750 per day), the extra 11 days yielded an extra $8,100. That is what led Gawande et al to claim that hospitals make more. An extra $8,100 per admission.

The problem is that hospitals don’t run on variable costs alone. There are fixed costs, like buildings, maintenance, equipment, administrative staff, etc. In the Gawande study, these were $2,200 per day. When these were also considered, uncomplicated patients produced a net profit of $300 per day for 3 days, yielding a total profit of $1,000 per admission. Complicated patients produced a loss of $765 per day for 11 days, or $8,400, which was offset by a surplus of $1,000 from the first 3 days to yield a net loss of $7,400 per admission.

How do complications make money for hospitals if complicated patients are a loss? Gawande’s answer is that the hospitals aren’t full anyway, so the fixed costs are there with nobody to pay for them, and whatever extra revenue can be derived from complications is simply gravy, even if it’s less than the revenue from uncomplicated patients.

The implication is that hospitals encourage or at least permit complications to keep their beds full and earn an extra $750 per day. But how do they promote MIs or pneumonia, and do they really keep people on ventilators needlessly for more than 96 hours? Unfortunately, no one will look at the details. They didn’t in McAllen, either. JAMAs editors must nor have looked at the details, either.

And so, the “War on Waste” continues. To win that war, it is said, the US must get rid of fee-for-service reimbursement, which, in Reinhardt’s words, “can tempt otherwise admirable people into dubious conduct.”

Yes, as the events in Boston vividly showed, they are admirable. They are professionals. They deal with patients who will assuredly have complications that no one wants nor can avoid, least of all the physicians who are caring for them and the hospitals in which they are receiving care.