Junkfood Science: June 2009

June 28, 2009

Even obesity paradoxes can’t “excuse” fatness

I wasn’t going to even cover this study looking for correlations between obesity and risks of dying. The predominance of evidence and carefully-designed studies have repeatedly failed to support BMI as a measure of health or predictive of our risks for dying. The value of null studies is wasted with yet more research in that direction. But this study has been so widely misrepresented in the media, that a quick look at what the data actually revealed may be helpful and clear up some myths being floated about the obesity paradox.

Reporting research finding anything positive about fat is accompanied by disclaimers, caveats and every effort to minimize its significance. It’s even called an obesity paradox, perhaps hoping we’ll think it an anomaly, rather than where the strength of the evidence lies. You’ve probably caught the news stories about a Canadian study reportedly showing that people with “a few pounds,” who are “slightly overweight,” are carrying “a little extra weight,” have “excess pounds, but not too many,” and are “overweight but not obese” will “actually live longer than those of normal weight.” But that isn’t what this latest study found. It’s what the press release said it found.

What they did

This study, led by Heather M. Orpana, Ph.D., from Statistics Canada, set out to estimate the relationship between BMI and all-cause mortality in a nationally representative sample of Canadian adults, weighted to represent the total population of the ten Canadian provinces. The database they used to look for these correlations, using Cox proportional hazards computer modeling, was of 11,834 adults (25+ years old) who had participated in the National Population Health Survey in 1994-1995 conducted by Statistics Canada. The data was matched to the Canadian Deaths Database through 2005. During those 12 years of follow-up, 1,929 people had died. The authors adjusted for age, gender, smoking status, physical activity and alcohol consumption in their computer model. They didn’t factor in social and economic status.

Because the BMI data was self-reported, they also used a correction factor they had developed, which showed the same pattern of results. As they pointed out, self-reported height and weight “are considered valid for identifying relationships in epidemiological studies, with self-reported values being strongly correlated with measured values.”

What they found

The results of this epidemiological study were published in Obesity, the journal of the Obesity Society. This study found that none of the relative risks associated with mortality they examined were tenable [explained here], except for one. Age. At age 65, the relative risks of dying rose to 44.35 times compared to age 25; and by age 75, relative risks are 119-fold. We should stop right there, as tenable correlations are the only ones that deserve our focus. But that wouldn’t have made a news story, so what followed was splitting hairs among the rest.

Looking at corrected BMIs, according to the breakdowns adopted by the world’s governments, the authors found that compared to ‘normal’ BMIs (18.5 up to 25):

● being overweight (BMI 25 up to 30) was associated with a 25% lower risk of dying

● being obese (BMI 30 up to 35, which includes about 80% of all obese people) was associated with a 12% lower risk of dying.

● And the risks associated with the most ‘morbidly obese’ (BMIs 35+) — the uppermost 3% of this Canadian cohort— were statistically the same as those with ‘normal’ BMIs. [RR=1.09 (0.86-1.39, 95% CI) versus RR=1.0.]

Because physical activity could be a confounding factor, and also associated with age and health problems, they analyzed the data using models that excluded and included physical activity. Physical activity had no statistical effect on their findings.

They also analyzed the data to adjust for health by excluding the first four years of follow-up to account for possible reverse causation, where pre-existing illness and poor health could lead to lower BMI and earlier mortality. The results, again, were not statistically affected.

As the authors broke down the data into a multitude of variables looking to parse out correlations, the most significant relative risk they found was among underweight men (BMI less than 18.5) associated with a 2.5 relative risk of mortality, while higher weights were associated with no greater risks for men until those with the very highest BMIs — although the 72% risk was still untenable (attributable to random chance or confounding factors).

In contrast, the authors noted that among women, who comprise most of those at the uppermost extremes of obesity, even the most ‘obese’ (obesity class II and higher) was associated with no higher mortality risks, while being overweight and obese up to class II were associated with a 23%-19% lower risk.

Putting it into perspective: Rather than a population-wide weight problem, the vast majority of Canadians, according to the government’s Community Health Survey: Nutrition, have BMIs of 18.5 to 35. In other words, nearly every Canadian is at a "healthy weight" range — with no association to higher risks for death and even associated with lower risks. Far from a crisis of people at extremes of weight, only about 2% of Canadians have BMIs under 18.5 and 2.7% have BMIs 40+.

As Statistics Canada reports, overweight and obesity are most strongly correlated with rising age among the population. Countering another popular myth about fat people, its Community Health Survey found that 58.2% of overweight and obese Canadians are eating 5+ servings of fruits and vegetables a day, significantly higher than those of ‘normal’ weight.

The authors then reanalyzed the data by breaking the BMI ranges down into nine categories, and compared mortality risks to those in the upper half of the ‘normal’ range (BMI 22.5-<25). It was then obvious that being fat, whether overweight or obese, is mostly associated with a lower risk of death and that it doesn’t matter much how fat one is. [Remember, we’re still splitting hairs here.]

BMIs 27.5-<30, for example, were associated with a 13% lower risk, while BMIs 30-<32.5 were associated with a 8% higher risk, and BMIs of 32.5-<35 had an 8% lower mortality risk. There was no dose-response, lending additional strength to higher weights not being the actual cause for differences in mortality among people.

Risks rose steadily with BMIs under 22.5 (18% higher risk with BMIs 20-<22.5; 23% higher risk associated with BMIs 18.5-<20; and 89% higher mortality associated with BMIs under 18.5), yet there are no governments and industries devoted to massive public campaigns against slenderness.

The value of this study is that it is a null study, finding no strong association between overweight-obesity and mortality — meaning overweight-obesity itself can’t have a causal role in mortality. Blaming weight for differences in mortality isn’t scientifically supportable. But, like countless other null studies, how likely will this one be used to redirect massive programs and medical interventions and research into other directions with more fruitful potentials to help people?

Putting it into Perspective: This data from the Canadian government fails to support its vast obesity network of industry, stakeholder associations, government, obesity societies and other stakeholder interests in an imperative to enact a national policy to eradicate obesity. The Canadian Obesity Network (CON), supported by the government of Canada ($5.6 million NCE funding this year), said in its 2009 Progress Report that the “global health calamity” is emerging as a priority for researchers, health practitioners and policy makers,” adding:

It’s easy to see why the alarm bells are ringing. One in 10 premature deaths among Canadian adults aged 20–64 years is directly attributable to overweight and obesity. Notably, obesity is a significant risk factor for at least five of the top 10 leading causes of death in this country… As much work remains to be done for our vision to fully come to fruition for Canadians, this is also our call for increased government funding towards efforts to better understand the causes of obesity and identify effective treatments, as well as a call for a renewed commitment by all stakeholders who can and should play a role in that process. Solutions require urgent action on many levels, with broad stakeholder involvement matched by political will and grassroots community engagement.

Notice how correlations are made into causations and then used to support government action and medical interventions. Yet, other Statistics Canada data provides no support for claims of a population-wide health calamity, either. It has been reporting that life expectancy at birth among Canadians has been soaring steadily for the past century and has reached all-time highs. Just since 1980, for example, life expectancy among men has risen from 72 years to 77, and among women from 79 years to 82. According to Statistics Canada, age-adjusted mortality rates for all causes dropped 6 percent between 2001 and 2005, going from 6/1,000 to 5.63/1,000 of the population. Deaths from chronic diseases popularly associated with obesity are all dropping, too. Cancers down 4.7%, heart disease down 15%, cerebrovascular disease down 20% and diabetes down 1%. Even Statistics Canada acknowledged that it’s difficult to enumerate changes in obesity rates because of the differing methods that have been used to gather information on height and weight among Canadians.

What was reported they found

So, according to the authors' findings, compared to ‘normal’ BMIs, ‘overweight’ (BMI 25-<30) and ‘obese’ (BMI 30 up to 35, which includes about 80% of all obese people) are associated with a 25% to 12% lower risk of dying. And the risks associated with the ‘morbidly obese’ (BMIs 35+) are statistically the same as those with ‘normal’ BMIs. These findings coincide with other population studies we've examined.

We read a very different spin the media. It showed how afraid people are to be seen “excusing” obesity. The New York Times, for instance, reported:

Excess Pounds, but Not Too Many, May Lead to Longer Life

…The report, published online last week in the journal Obesity, found that overall, people who were overweight but not obese — defined as a body mass index of 25 to 29.9 — were actually less likely to die than people of normal weight, defined as a B.M.I. of 18.5 to 24.9…

False. The study found obesity (BMIs 30-<35) were also less likely to die than people of a “normal” weight, and that the highest BMIs had statistically the same mortality risks as “normal” weight people.

“Overweight may not be the problem we thought it was,” said Dr. David H. Feeny, a senior investigator at Kaiser Permanente Center for Health Research in Portland, Ore., and one of the authors of the study. “Overweight was protective.”

So was obesity. The relative risks for mortality associated with the corrected BMIs were 25% to 16% lower among the overweight and obese (BMI 30-<35), respectively, compared to “normal weight.” And the risks associated with the most “morbidly obese” — the highest 3% of the population — were effectively the same as those with “normal” BMIs (18.5-<25).

He said the finding may be due to the fact that a little excess weight is protective for the elderly, who are at greatest risk for dying…

While overweight was protective for people from age 60, associated with a 19% lower mortality risk, their data found that being obese (BMI 30-<35) was also protective. But, obesity (BMI 30-<35) was even more strongly associated with reduced mortality in younger adults (25-59 years of age) than among those 60 years and older (11% and 6%, respectively).

It is difficult for the public to realize that what seems intuitively correct about the dangers of being fat, and our diets and lifestyles, is not grounded in science, but in what is currently socially desirable, in vogue and what we hear EVERYWHERE we turn. Marketing and entertainment, packaged as news or information, however, is not science…regardless of the prestige or popularity of the source.

While, in reality, our body shapes and sizes are primarily the result of genetics and age, weight has long been a marker of social class, celebrity status, and an outward sign of adhering to advantageous ideologies. Once, fat was seen as healthful and desirable. There was no obesity paradox. Today, fat is out and thin is in. Even our celebrities keep getting thinner.

While fads and fashions may be entertaining and make some people loads of money, their danger comes when people believe they are much more than that. It can put lives and livelihoods at risk. People deserve public health policies and medical care based on sound evidence-based science, not beliefs or correlations. And changing the definition of “evidence-based” medicine to now mean computer modeling to identify correlations doesn’t count.

© 2009 Sandy Szwarc

See Sidebar for other articles in Obesity Paradox series.

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June 27, 2009

The Figure-Flaw Paradox: Does it really matter how your body measures up? Part 2

The “figure flaw paradox” is really a retake on the obesity paradox. As obesity has proven to be a poor measure of health or mortality risk, new renditions are being proposed. But the fallacies are the same.

We’ve encountered all sorts of spins trying to preserve the myths of the deadliness of fat — from claims that the studies only show a paradox with really old people to that being overweight might be okay but not obese — hoping we won’t actually read the studies to see that that’s not what they found. It’s unpopular to spread the news that most fat — most overweight, as well as most obese — people have lower risks for mortality than those with “healthy” weights; or that thin people, regardless of their age, fair the worst. Some discount the better outcomes among obese people by saying they get better healthcare than thinner people — something completely opposite of decades of documented discrimination against obese people in healthcare.

The suggestion we most hear trying to negate the obesity paradox is that BMI doesn’t differentiate fat (“bad”) from muscle (“good”), the assumption being that fat is bad. Increasingly trendy measurements to identify “unhealthy fat” have been proposed, including percentage body fat, waist circumference and waist-to-hip ratio (“belly fat”). Savvy readers could simply take all of the flawed studies and staticulations behind obesity and step and repeat for each new variation.

As with using weight or BMI, the same misuses of correlations in poorly controlled studies have been used to point to abdominal fat as a cause of ill health and higher mortality. Where our bodies store fat, as well as our body shapes and types, is largely genetically determined. But weight gain, especially in the mid-section, is a marker for age among the general population. Studies of populations consistently find that people who are heavier, especially in their mid-section, are older. [They’re also more apt to be yo-yo dieters and lower social class.] And age is the biggest risk for dying and diseases of old age. That’s why well-controlled and properly designed studies that remove biases such as these, are so important. A null study in epidemiology is most important to us. If a strong correlation can’t even be found, then that measurement can’t possibly be a credible cause for us to focus on.

Two independent* groups of researchers wanted to see if obesity or where fat is distributed on our bodies can predict our risks for premature death. They went to the most reliable and objective data available on Americans: actual measurements taken by specially-trained technicians on a large representative sample of the population and actual mortality data. The Third National Health and Nutrition Examination Survey (NHANES III, 1988–1994) conducted the most precise measurements of body size, measurements and composition available on a large representative sample of the U.S. population (including BMI; body fat percentage measured by bioelectrical impedance; skinfold thickness; circumferences of waist, hip and arm; waist-hip ratio; and waist-height ratio). National death certificate data in the national Death Index linked mortality data through 2000 to these detailed measurements in NHANES III.

We recently reviewed the findings from senior scientists at the National Center for Health Statistics at the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute. In a nutshell, the data showed that no higher measurement of body shape or size, or body composition, is predictive of higher risks of dying from all causes, compared to people with “healthy” numbers and model figures. Nor was there a net benefit of using BMI versus another measurement. The data also found that NONE of the 21 diseases popularly attributed to obesity — those “obesity-related” diseases, including: cardiovascular disease, cancers (colon cancer, breast cancer, esophageal cancer, uterine cancer, ovarian cancer, kidney cancer, or pancreatic cancer) and diabetes or kidney disease — are actually associated with excess deaths at any BMI category, including obese.

Another group of researchers, led by professor Jared P. Reis, Ph.D., with Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, used the same nationally representative data on adults aged 30 years and older, and applied different statistical modeling to investigate the associations between BM, body fat, and various body measurements to mortality. They approached it from a different perspective regarding hypotheses about causal relationships to obesity-related conditions, noting: “We did not adjust for physical activity or possible mediators of obesity effects, such as diabetes, hypertension, or high cholesterol, since doing so would have resulted in over adjustment because of their position on the causal pathway.”

Did they show the same null findings as the CDC scientists? It turns out “yes,” but you have to look closely to realize that.

Their data showed

Their findings, published in Obesity, the journal of the Obesity Society, weren’t widely published in mainstream media. And those who glanced only at the abstract missed the results demonstrated in the data itself.

First, not surprisingly, most predictive of mortality was age. The men and women who died during the 12 years of follow-up were significantly older (20.7 years older among the men and 21.3 years among women). Among the younger men (30-64 years), only 7.5% died over 12 years of follow-up compared to 48.5% of older men (65+ years); and only 4.9% of the younger women (30-64 years) died during those 12 years, compared to 36.76% of the older women.

In examining the risks associated with BMI and the various body measurements and ratios, they adjusted for baseline age, race/ethnicity, education, smoking status, alcohol use, heart disease, stroke, respiratory disease and cancer (except nonmelanoma skin cancer). Their findings revealed:

● Among younger men, there was no statistically significant correlation between BMI, waist circumference or waist-hip ratio and risks for death from all causes.

● Among older men, mortality risks were inversely associated with each measurement — risks of death were lower the higher the BMI and waist circumference — with the fattest men associated with a 30-40% lower mortality risk compared to those in the ‘normal’ range. Even the men with the highest waist-hip ratios were associated with nearly half the risk of death of lean men.

● Among the younger women, there was no statistical difference in BMIs or waist circumference and risks for dying. So few young women died in each quintile over the 12 years (0.4% a year in the national sample, 18 women), that looking at hazard ratios alone exaggerates perceptions of actual risks, anyway.

● Among the older women, BMI was inversely associated with mortality. The highest risk for death was among lean women with low BMIs. Fat women of all sizes were associated with lower risks. The most “obese” women with the highest BMIs had a 23% lower risk for mortality compared to “normal” weight women. There was no correlation between waist circumference or waist-hip ratios and the women’s risk for death, although all of the larger sizes had lower risks than thin women.

To limit potential confounding influence from existing or subclinical diseases, the authors also adjusted for “clinically manifest disease at baseline and excluded deaths within the first 5 years of follow-up” and the results were the same.

Do you see the grossly higher risks for death among obese people, according to their BMI, waist-hip ratio or waist-thigh ratio? If you can’t, it’s because they aren’t there, of course. The risks of adiposity have been exceedingly oversold to the public.

Abdominal fat, or central adiposity, has been a topic of frequent interest and some have suggested it be used rather than BMI to evaluate mortality, under the belief that unhealthy visceral fat is the underlying reason for morbidity and mortality associated with overall obesity, wrote Dr. Reis and colleagues. In their review of the literature, they noted that waist circumference has been more highly correlated with visceral fat than waist-hip ratio or waist-thigh ratio. However, studies to date have not consistently supported a correlation between abdominal fat or body fat distribution as predictive of mortality. Nor has waist circumference been consistently shown to be more strongly associated with type 2 diabetes, cardiovascular disease or mortality than waist-hip ratio, they said. “Therefore, it is clear that waist-hip ratio and waist-thigh ratio do not reflect variation in visceral fat accumulation,” they wrote. [We’ll look soon at the definitive clinical trial that disproved the visceral fat hypothesis.]

The only barely statistical correlation they found was waist-thigh ratio in young men and elderly women. While this may be of interest to those still selling those thigh masters made popular by Suzanne Somers, the notion of spot reducing our way to better health or longevity was debunked long ago. A biologically plausible explanation for continuing down the body to look at other measurements is so far removed from science — we’re sooo not going to go there. Of course, that hasn’t stopped people from marketing weight loss programs to treat “fat ankles” and surgeons even treating “toebesity” with surgery to slenderize generously-proportioned digits.

The belief in the unhealthiness of body fat is so ingrained that it can sadly lead anyone to be unable to grasp the reality of the evidence. Dr. Reis wrote that the findings in their study suggests “that it is not only important to have a low BMI but also a low amount of abdominal adiposity to lower your risk of death.” Dr. Reis and colleagues wrote in their concluding paragraph:

In conclusion, we provided evidence on the relative importance of well-defined measures of overall obesity and abdominal adiposity or body fat distribution in assessing risk of total and cardiovascular disease mortality in a nationally representative sample of US adults. Despite their limitations, ratio measures of body fat distribution were strongly and positively associated with risk of mortality in middle-aged adults. In addition, WHR and WTR increased prediction of mortality among normal weight and obese middle-aged adults. Among the elderly, a higher BMI in both sexes and WC in men were associated with a lower risk of mortality, while indicators of body fat distribution increased survival or did not influence risk of mortality. These results suggest that ratio measures of body fat distribution carry important information for identifying middle-aged adults at increased risk of mortality and therefore should not be abandoned in research or practice.

Are those the conclusions you would have reached after an objective examination of the findings? The importance of independently thinking and looking at what a study found, rather than taking abstracts and authors’ interpretations of the findings as the same thing, are especially evident when it comes to the obesity paradox.

© 2009 Sandy Szwarc

* In a private email from Dr. Reis, he indicated that they hadn't known that Dr. Katherine Flegal, Ph.D., and colleagues at the CDC had also examined body shapes, measurements and composition. “The major difference between our study and those of Katherine Flegal is that we added information regarding central obesity or body shape including waist circumference, waist-to-hip ratio and waist-to-thigh ratio,” he wrote.

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June 25, 2009

Real life evidence — government funded healthcare

Yesterday’s news provided updates on two healthcare stories we’ve been following, so here’s a quick update.

The Massachusetts Experiment

Readers will remember when Massachusetts signed the nation’s first state universal health insurance program into law. This program was to be the test ground to see how universal health coverage by the government would work here in the United States. “This is the healthcare plan seen by many as a model that could be replicated around the country,” the Boston Globe noted yesterday.

As is known, it made the purchase of state-approved health insurance mandatory for every resident, but within weeks of the law’s deadline for people to have purchased insurance, the state was scrambling. It hadn’t accurately budgeted for the program, and without enough doctors, 95% of doctors weren’t accepting more patients. Rationing of services, reductions in benefits and growing waits for care began. In efforts to keep the program solvent, Massachusetts cut payments to doctors and hospitals, reduced choices and benefits for patients, and was looking at increasing out of pocket expenses for patients.

By February of last year, the state was asking the Federal government and America’s taxpayers to bail it out and cover half of the program’s costs from 2009 through 2011. According to the Boston Globe, the program will cost taxpayers $1.35 billion annually by June 2011.

Yesterday, according to the Boston Globe, the near bankrupt Massachusetts healthcare program cut $115 million — 12 percent of its budget — from Commonwealth Care, which subsidies premiums for the poorest residents. The savings is planned to come from eliminating dental coverage for them; reducing enrollment of poor residents by no longer automatically enrolling those who forget to designate a health plan; and the bulk of the remaining “savings,” $32 million, will come from slowing payments to the managed-care health insurance companies with the plan. The board of the Connector Authority also dropped coverage for 28,000 legal immigrants beginning July 1st.

“There’s no other place to go for money,” Lindsey Tucker, the organization’s healthcare reform manager, told the Globe. The Massachusetts Legislature just passed $1 billion in tax increases, in part trying to keep it afloat. State Treasurer Timothy P. Cahill came out strongly against the tax increases and proposed deep cuts in the state’s universal healthcare plan, calling it a luxury taxpayers can no longer afford, said the Boston Globe.

The plan had been sold to the public as helping to pay for itself by shifting more people into managed care. “We’re all still waiting for the savings,” Cahill said. “Universal healthcare was supposed to eventually save us money.” Instead, the Massachusetts Taxpayers Foundation, a business-funded group that had advocated for the healthcare law, has found that state spending on the healthcare plan has increased by about $88 million a year since it was implemented. Healthcare accounts for about a third of the state budget, although it is difficult to know precisely how much of that is attributed just to the state’s new healthplan.

“Nobody asked the tough questions. It was expensive, even in good times,” said Cahill. “In tough times, it just doesn’t seem doable… It’s a warning for the federal government as it looks to do something similar,” Cahill added.

The VA Example

The American Legion, which visits and inspects Veteran’s Administration health centers, reported that doctors at a facility in Pennsylvania gave 92 veterans incorrect radiation doses for treatment of prostate cancer, and that 53 veterans were possibly infected with hepatitis and HIV from unsterilized equipment at three VA health centers in Florida, Tennessee and Georgia.

At a hearing yesterday of the Senate Committee on Veterans Affairs, it was learned that staff from the Veteran’s Administration in Washington, DC, has been making unannounced visits to every VA health facility in the country and has found that fewer than half could provide any evidence of proper procedures and training for sterilizing of colonoscopy and endoscopy equipment, even after having received repeated alerts from the VA about the problem. The hearing revealed that improper sterilization had put more than 200 veterans at risk for HIV and hepatitis B infections after colonoscopies and other screening tests. As Associated Press reports, at the Senate hearing, it was learned that six patients have tested positive for HIV and 46 have tested positive for hepatitis after having colonoscopies or endoscopies.

More astounding, according to the Philadelphia Inquirer, soon after officials closed the prostate cancer program at the Philadelphia VA Medical Center for botching 92 out of 114 prostate cancer radiation treatments — in some cases causing grave injury by placing radioactive pellets in other organs and even giving too low of doses and/or unknown doses because they were “flying blind” because the equipment didn’t work — the medical center had been accredited by the Joint Commission, the main group that assures “quality” at our nation’s hospitals.

As we’ve covered, not only does the Veterans Administration offer insight into how well government-provided healthcare works in reality, the Veterans Administration has taken the lead on computerizing its entire medical record system throughout its 155 hospitals, 881 clinics, 135 nursing homes and 45 rehabilitation centers. It’s been held up as an example of the benefits possible with nationalized integrated health IT for reducing medical errors.

Continuing software glitches since August 2008 hadn’t been disclosed to the public until the Associated Press obtained internal documents under the Freedom of Information Act. These revealed that VA patients around the country have been being given the wrong doses of medications and exposed to medical errors due to electronic medical records, some of which might have been life-threatening.

As Fox News reported, veterans groups and lawmakers say that VA hospitals are underfunded and short staffed, leaving doctors overworked and largely unable to provide the best care, facilities and equipment are old and broken down, and the system is showing signs of an institutional breakdown. Joe Wilson, deputy director of the Veterans Affairs and Rehabilitation Commission for the American Legion, who testified before Congress, described disturbing problems.

This is how government-funded healthcare cares for our poorest citizens and those men and women who served our country. How well will it care for the rest of us?

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June 23, 2009

Comparative effective research — what it means for us

This past week, when speaking to doctors about healthcare reform and the steps needed to reduce healthcare spending, the President answered a rhetorical question recently posed here about comparative effective research. JFS readers may find his answers interesting. His speech, however, didn’t receive widespread mainstream media coverage, at least in a form we would recognize. Before we look at what he said, it might be helpful to sort through some popular misconceptions about what comparative effectiveness research is and isn’t.

The American Recovery and Reinvestment Act (known as the stimulus package) expanded the Agency for Healthcare Research and Quality and increased its funding with $700 million for comparative clinical effectiveness research, with an additional $400 million to be allocated at the discretion of the Secretary of HHS. A new Federal Coordinating Council for Comparative Clinical Effectiveness Research was created to compare the merits of various competing medical treatments and strategies and make its recommendations to the Congress and Secretary of Health and Human Services about what medical interventions are effective, cost-effective and appropriate for the prevention, diagnosis and treatment of diseases and other health conditions.

That objective might sound rather benign: as if experts will be making sure our tax dollars are prioritized so they’ll go towards advancing health interventions that offer us the best quality of care.

But, that is not what it’s saying.

Reading comprehension

Follow along as we re-read what the text on pages 806-26 of the Stimulus Bill really says. The $1.1 billion funding will first encourage the adoption and use of electronic health registries and databases that the government can use to generate outcome data — in other words, to compile observational correlations, not to conduct randomized, controlled intervention trials that are the gold standards of evidence-based medicine. Note that electronic medical records are also included as a way to disseminate the government’s comparative effective research — that’s referring to those electronic prompts that tell healthcare professionals what tests, drugs and treatments they should order and records their performance.

The legislation says that about $1.2 million is to be given to the private organization, Institutes of Medicine, for it to recommend the national priorities that will be supported or funded by the government. The IOM Committee on Comparative Effectiveness Research Prioritization began work in March.* It held a meeting on March 19, largely closed to the public except for a presentation by the AHRQ and Congress, and invited stakeholders to present their requests on March 20th. It will issue its final report this month (in little more than three months) — making its recommendations for what treatments and strategies will be supported, the IOM says, to enable “doctors and patients to make smart health decisions.”

The text of the stimulus bill goes on to specifically say that the funds must consider input from stakeholders and ensure the optimum coordination with research that’s supported or conducted by Federal agencies and departments. The Federal Coordinating Council will then consider this to make its final recommendations to the HHS Secretary and President. Yet, the members of the Council are all to be political appointees from senior management of each of those federal agencies responsible for the government’s health programs, appointed by the President and who work under the HHS Secretary.

Now, in reality, how likely will political appointees of Federal agencies, with huge programs and budgets under their control, and who are answerable to the President and HHS Secretary, be to recommend their programs be defunded or to go against the healthcare reforms outlined by the President and HHS Secretary?

Meaning and consequences

The ramifications are clear to medical professionals, even if they may not be to the public. The availability of health interventions that aren’t determined to be optimum, effective or consistent with national priorities will die from lack of funding. We might want to believe that this plan won’t interfere with the care that our individual doctors provide and that is will merely offer “information and guidance about best practices.” But doctors, who’ve had years of experience with Pay-for-Performance measures know the reality of third-party payer clinical guidelines.

They are concerned that their clinical judgment and knowledge about what care might be best for their individual patients, as well as consistent with the wishes of their patients, will be replaced by the determinations of a government agency. These aren’t clinical guidelines and recommendations for medical practitioners in their care of patients — instead, like other pay-for-performance measures, they will have the force of regulation and power of law in compelling compliance. Any healthcare professional whose practice fails to comply with what the government determines is effective, quality care will find himself uncompensated, as well as demoted as a “quality provider” and vulnerable to malpractice lawsuits. Doctors won’t, can’t, risk providing care outside the line — and insurers won’t cover care that isn’t government-approved — out of fear of liability or financial demise.

Understandably, medical researchers also won’t want to pursue costly research on innovative and potentially life-saving drugs, treatments and medical devices that aren’t in line with government supported programs. The dangers are evident: when government is given a greater role in determining what research is funded and what findings are endorsed and adopted, riskier research or research for rarer conditions or less politically popular issues could be jeopardized — and medical advancements could be held back.

History lessons

Experienced medical professionals also know the importance of anticipating the consequences of interventions that might sound good at the moment and of looking at what history and experience can teach us. They remember when Medicare was established as a Title XVIII amendment to the Social Security Act in 1965. It had promised consumers and medical professionals:

Sec. 1801. [42 U.S.C. 1395] Nothing in this title shall be construed to authorize any Federal officer or employee to exercise any supervision or control over the practice of medicine or the manner in which medical services are provided, or over the selection, tenure, or compensation of any officer or employee of any institution, agency, or person providing health services; or to exercise any supervision or control over the administration or operation of any such institution, agency, or person.

Today, there are 153 P4P (“quality”) measures that Medicare lists as necessary, based on comparative effectiveness research. Doctors who don’t comply with these P4P measures, and who fail to use electronic medical records integrated with government databases, see their reimbursements cuts, receive bad quality ratings and find their patient referrals slashed. That is most certainly governmental supervision and control over the practice of medicine and the manner in which medical services are provided, isn’t it? Pay-for-performance measures are certainly governmental supervision or control over compensation of a person providing health services, isn’t it? The undeniable fact is that whoever pays calls the shots over the care we receive — not our doctors and not us.

Medicare recipients had also originally been assured that this program was voluntary and merely a safety net to cover major medical expenses for poor seniors and Congress had promised that the program (Section 1803) would not interfere with people’s ability to purchase private health insurance. Today, every senior who wants to receive the social security benefits they’ve paid into their entire working lives, is automatically enrolled into Medicare Part A. The only way to opt out of the government managed healthcare program and its mandatory government oversight is to sign away their monthly social security benefits. Did you know that?

More importantly, think about what is really being discussed here: putting the government in charge of deciding where limited healthcare dollars and resources will go; what therapies are appropriate for the government to pay for and not; and who gets life-saving drugs and medical care, and who should go without — this is inevitably making life-and-death decisions about how medical care should be rationed.

Other examples of comparative effective research in action

This effectiveness research model is similar to other programs used to establish government healthcare spending. In Britain, for example, the National Health Service uses the National Institute for Clinical Excellence (NICE) to determine which treatments it will offer. We would all like to believe that health care available to people would be free from influence by special interests or profit, and based on the soundest medical evidence. But as we’ve seen with the NICE guidelines, that isn’t what happens. Lots of things are supported and authorized by government committees that have failed to demonstrate that they improve health or clinical outcomes, reduce mortality, or reduce healthcare costs or hospitalizations; things that are even potentially harmful for significant numbers of people.

We’d also like to believe that human compassion and medical ethics would prohibit medical professionals from having to deny care to individual patients who are seen by the government as undeserving or not worth saving, or be forced to act in ways that may not be in the best interests of their individual patients. But last year when NICE released its Social Value Judgments, outlining its guiding principles for allocating National Health Services resources, the realities of the value of life versus the interests of the state and stakeholders were brought home.

Will you be denied knee replacement for a shattered knee because you are fat and “noncompliant” with the government’s weight loss advice? Will your grandmother, who could live another 20 active years, be denied a heart operation because she’s too old per government guidelines? Will a Down’s child be seen as a life not worth saving when he needs a life-saving surgery? Will you be denied medical care if you refuse to take statins, enroll in a weight loss program or get a mammogram the government says you must do?

It feels far more reassuring to believe that the government or a third-party payer will make the wisest, most evidence-based decisions about what health care is best for us and will take care of us, that we’ll all have access to the care we want, and that rationing will never happen. But that is not reality. There isn’t enough healthcare monies and resources. Cost-effectiveness analysis means looking at the most effective ways to divide a pot of money. It does not mean the inventions save healthcare costs, as is popularly believed, explained professor Louise B. Russell, Ph.D., at the Institute for Health, Health Care Policy and Aging Research at Rutgers University. Cost effective research means budgeting and making decisions about where to cut costs.

But will those very difficult decisions be made objectively, based on the best evidence, and grounded by medical ethics, or will they be politicized and money disproportionately going towards things with the most powerful lobbies and popular causes?

The evidence: government priorities

We don’t have to look at Britain to see how government comparative effective research will play out here and how politicians and government agencies prioritize healthcare spending. We already have an example right here at home.

The Oregon Legislature began the first and only project in our country to develop a policy for setting health care priorities for the state’s health plan. In 1989, it created the Health Services Commission to develop a prioritized list of health services ranked in order of importance to the entire population to be covered. The unpopular results were adjusted based on their relative importance as gauged by public input and the Commission’s judgment and individual condition/treatment criteria were prioritized according to their impact on health, comparative effectiveness, and (as a tie-breaker) cost.

“The resulting prioritized list is used by the Legislature to allocate funding for Medicaid and SCHIP,” according to the Oregon Health Services Commission. “The benefits based on the prioritized list are administered primarily through managed care plans, and approximately 1.5 million Oregonians have gained health coverage due to the expanded access made possible by explicitly prioritizing health services.”

Confusing health coverage with health care, and confusing quality with better health outcomes for people, misleads consumers every time.

The Commission’s prioritization report just released to the 75th Oregon Legislature explains how government funding and oversight is irrevocably tied to compulsory clinical guidelines, those pay-for-performance measures, enforced through third party payer managed care plans:

As state resources continue to be stretched by competing demands, the Commission is constantly looking for ways to control costs to the Oregon Health Plan so that the largest number of people can be served. Practice guidelines are becoming an increasingly important mechanism in striving towards this goal. Sixteen new guidelines were developed over the past two years and seventeen previously existing guidelines were modified…

The first prioritized list of health services was released in 1993 and has been updated every two years, as part of the State’s budget process. The priority list for 2010-2011 is nearly identical to those currently in effect. Reading through the priority lists, you see a dramatic change in the conditions’ rankings over this decade. Life-saving medical care that used to be ranked high in 2000-2002 — such as head trauma, type 1 diabetes, peritonitis, acute kidney infection, pneumothorax and hemothorax, hernia and/or gangrene, etc. — has been shifted down. Meanwhile, interventions with poorer evidence for effectiveness or for reducing healthcare costs rank near the top, such as preventive services, substance abuse and smoking treatment, contraceptives and sterilization, obesity, and depression.

Interventions the government now prioritizes for funding and policy support are those of popular political causes and heavily lobbied financial interests, not those that are the most evidence-based.

For example, obesity treatment (including nutritional and lifestyle coaching) had been ranked at #645 in 2002. Today, obesity treatment ranks 8th. Chronic diseases of aging and conditions that most affect older and disabled people, such as chronic obstructive pulmonary disease (now ranking #305), cataracts (now #320), cirrhosis of liver (now #332), life threatening cardiac arrhythmias (now #303), knee surgeries (#448 and #618) and cancers have all been shifted significantly lower on the list. [It feels more palatable when people believe that diseases are victims' own fault and the result of failing to adopt healthy lifestyles. See how easily discrimination and eugenics can come to feel acceptable to a populace, especially when it rejects science?] Treating high cholesterol is ranked higher than appendicitis with abscess; intoxication is ranked higher than acute bacterial meningitis; dental cleanings is ranked more important than treating diabetic retinopathy, strokes or heart failure; treating attention deficit (ADHD) is ranked more important than treating cervical cancer, malaria or a ruptured spleen; and sleep apnea is higher than treating Parkinson’s Disease or a ruptured aortic aneurysm. And preventive wellness, smoking cessation and treating obesity is prioritized above all of these medical conditions — familiar stakeholder agendas, but offer little medical care when you need it, nor do they reduce medical costs.

Today’s preventive health strategies promote certain diet and lifestyle behaviors; as well as screenings, tests and treatments of health risk factors; with little credible evidence they improve outcomes for most people, no matter how intuitively correct they may sound. Prevention is not the slam dunk being marketed to consumers. More misleading, prevention, even disease management, has been sold to the public as a way to cut costs and has become the foundation of healthcare reform proposals. “If it costs $5,000 to save one year of life with smoking cessation programs, and $200,000 to save one year of life with statins, then we say smoking cessation is more cost effective than statins,” said Dr. Russell. “But neither one saves money.”

“We need to realize that prevention is not going to help reduce the growth of medical spending,” said Dr. Russell. “It’s touted as one [a panacea] but it is not. In fact, prevention has contributed to our rising medical costs.”

Most people don’t understand prevention or cost effective analysis, she explained. Prevention rarely saves money when studies examine actually costs. People don’t realize that studies claiming savings aren’t usually looking at medical costs and savings, she said. “You will see studies claiming that a preventive intervention saves five dollars for every one dollar spent,” she said. “What they are doing is valuing every life saved at the future earnings of the person and including those dollars along with medical costs and savings.”

Comparative effectiveness analysis means that the return on investment will always be lower among older people.

Comparative effectiveness analysis is not the same as evidence-based medical care, or that it’s been shown to be effective for improving the lives of people and reducing their risks of dying.

Comparative effectiveness analysis is not about reducing medical costs.

Back to that rhetorical question

Earlier this year, we asked if comparative effective research will mean the government will shut down its own programs, even though its own evidential reviews have shown them to be ineffective in improving health outcomes for people, reducing mortality or reducing healthcare costs —

● such as the CDC’s massive Division of Nutrition and Physical Activity and its war on obesity focused on diet and exercise

● such as the Centers for Medicare and Medicaid Services pay-for-performance measures

● such as preventive wellness and other alternative modalities

Or, is this not really about science and helping people, but about advancing ideologies and profiting stakeholders? Is the new Federal Coordinating Council for Comparative Clinical Effectiveness Research more about finding support for the government’s national healthcare reform agenda?

Here’s what the President said to doctors at the Annual Conference of the American Medical Association on June 15th: [A Google Search tool on the right-hand sidebar is available to search by any issue if you need decoded information.]

Make no mistake: The cost of our health care is a threat to our economy. It's an escalating burden on our families and businesses. It's a ticking time bomb for the federal budget… So to say it as plainly as I can, health care is the single most important thing we can do for America's long-term fiscal health… How do we permanently bring down costs and make quality, affordable health care available to every single American? That's what I've come to talk about today. We know the moment is right for health care reform…

First, we need to upgrade our medical records by switching from a paper to an electronic system of record keeping. And we've already begun to do this with an investment we made as part of our Recovery Act…And that will not only mean less paper-pushing and lower administrative costs, saving taxpayers billions of dollars; it will also mean all of you physicians will have an easier time doing your jobs…It will prevent the wrong dosages from going to a patient. It will reduce medical errors, it's estimated, that lead to 100,000 lives lost unnecessarily in our hospitals every year.

No one stood up to show the President the medical literature disproving these claims.

The second step that we can all agree on is to invest more in preventive care so we can avoid illness and disease in the first place…It means quitting smoking. It means going in for that mammogram or colon cancer screening. It means going for a run or hitting the gym, and raising our children to step away from the video games and spend more time playing outside. It also means cutting down on all the junk food that's fueling an epidemic of obesity which puts far too many Americans, young and old, at greater risk of costly, chronic conditions

Building a health care system that promotes prevention rather than just managing diseases will require all of us to do our parts. It will take doctors telling us what risk factors we should avoid and what preventive measures we should pursue. It will take employers following the example of places like Safeway that is rewarding workers for taking better care of their health while reducing health care costs in the process. If you're one of three-quarters of Safeway workers enrolled in their "Healthy Measures" program, you can get screened for problems like high cholesterol or high blood pressure. And if you score well, you can pay lower premiums; you get more money in your paycheck…

No one stood up to show the President the medical literature disproving these claims. Not one doctor explained risk factors or spoke out against discriminating against people based on numbers that are poor measures of anything but age, hereditary and social class.

Our federal government also has to step up its efforts to advance the cause of healthy living. Five of the costliest illnesses and conditions — cancer, cardiovascular disease, diabetes, lung disease, and strokes — can be prevented.

No one stood up to show the President the science disproving these beliefs.

Now, [reforming healthcare delivery] starts with reforming the way we compensate our providers — doctors and hospitals. We need to bundle payments so you aren't paid for every single treatment you offer a patient with a chronic condition like diabetes, but instead paid well for how you treat the overall disease… We need to give doctors bonuses for good health outcomes, so we're not promoting just more treatment, but better care…

With doctors paid more for treating healthier people with good numbers than older, sicker people with chronic diseases of aging, what do you think is going to happen?

So one thing we need to do is to figure out what works, and encourage rapid implementation of what works into your practices. That's why we're making a major investment in research to identify the best treatments for a variety of ailments and conditions… So replicating best practices, incentivizing excellence, closing cost disparities — any legislation sent to my desk that does not these — does not achieve these goals in my mind does not earn the title of reform.

So, will comparative effective research mean the government will shut down its own programs that have been found to be unsupported by the preponderance of sound evidence? Clearly not.

Politicians are not scientists and economists are not medical professionals, yet do we want politicians and economists making healthcare decisions for us and deciding what’s best? Will enough Americans see what is happening and where they are leading us?

© 2009 Sandy Szwarc

* The IOM’s Roundtable on Evidence-Based Medicine workshop entitled “Redesigning the Clinical Effectiveness Research Paradigm” was held on December 12-13, 2007. It offers an important glimpse at how what is considered evidence for the clinical effectiveness of drugs and medical interventions is being redefined, with stakeholders writing the script. As the published report of this Roundtable began:

In the face of this changing terrain, the IOM Roundtable on Evidence-Based Medicine has been convened to marshal senior national leadership from key sectors to explore a wholly different approach to the development and application of evidence for health care. Evidence-based medicine (EBM) emerged in the twentieth century as a methodology for improving care by emphasizing the integration of individual clinical expertise with the best available external evidence and serves as a necessary and valuable foundation for future progress. EBM has resulted in many advances in health care by highlighting the importance of a rigorous scientific base for practice and the important role of physician judgment in delivering individual patient care. However, the increased complexity of health care requires a deepened commitment by all stakeholders to develop a healthcare system engaged in producing the kinds of evidence needed at the point of care for the treatment of individual patients.

Chapter 2 “The Evolving Evidence Base” claimed that medical advancements of drugs and devices were happening too rapidly to make randomized clinical trials practical:

The latter emphasizes the need for improved statistical approaches and techniques to learn about the safety and effectiveness of medical device interventions in an efficient way… the utilization of Bayesian analysis to accelerate the approval process of medical devices…use of mathematical models is a promising approach to help answer clinical questions, particularly to fill the gaps in empirical evidence… Access to needed data will increase with the spread of electronic health records (EHRs) as long as person-specific data from existing trials are widely accessible…

To improve the specificity of treatment recommendations, Greenfield suggests that prevailing approaches to study design and data analysis in clinical research must change. The authors propose two major strategies to decrease the impact of HTE in clinical research: (1) the use of composite risk scores derived from multivariate models should be considered in both the design of a priori risk stratification groups and data analysis of clinical research studies; and (2) the full range of sources of HTE, many of which arise for members of the general population not eligible for trails, should be addressed by integrating the multiple existing phases of clinical research, both before and after an RCT.

The Roundtable went on to talk about the promising opportunities of genomics and pharmacogenetics and then “expanding sources of evidence, such as those related to the interoperability of electronic health records, expanding post-market surveillance and the use of registries, and mediating an appropriate balance between patient privacy and access to clinical data.”

IOM Roundtable Disclosures: “This project was supported by the contracts between the National Academy of Sciences and Agency for Healthcare Research and Quality, America’s Health Insurance Plans, AstraZeneca, Blue Shield of California Foundation, Burroughs Wellcome Fund, California Health Care Foundation, Centers for Medicare and Medicaid Services, Department of Veterans Affairs, Food and Drug Administration, Johnson & Johnson, sanofi-aventis, and Stryker.”

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June 21, 2009

The importance of sound data — managing your healthcare costs

This week, Attorney General Andrew Cuomo announced that Health Net, Inc., a managed care company covering more than two million Californians and nearly a quarter million New Yorkers, had agreed to end its relationship with Ingenix and pay $1.6 million towards the creation of an independent database. This was Cuomo’s twelfth settlement against a network of health insurers across the country (including Aetna, MVP Health Care, Cigna, Wellpoint and Excellus Health Plan) using the Ingenix database, which he charged was a “conflict-of-interest-ridden system” with manipulated data and behind industry-wide consumer fraud and corrupt out-of-network reimbursement schemes.

Ingenix is owned by one of the nation’s largest insurers, UnitedHealth Group, Inc., and used by the nation’s largest managed care companies to determine reimbursement policies. Ingenix ran afoul of the Federal Trade Commission, which issued consent orders (Docket No. C-4214) against Milliman, Inc. and Ingenix, Inc. for engaging in “unfair and deceptive acts and practices.” Cuomo said Ingenix was “a conduit for rigged data” for the country’s largest third party payers, private and federal health plans and has intentionally skewed healthcare costs to bilk millions of consumers with higher out-of-pocket insurance bills and short change medical providers for expenses claimed to be over “usual and customary costs.”

Earlier this year, UnitedHealth Group had agreed to shut down Ingenix and pay $50 million towards creating an independent database, along with $350 million in class-action lawsuits.

But this story has far greater significance than is being recognized. It’s much bigger than insurance reimbursements and care management. The Ingenix database has also been used in medical research reaching spurious conclusions and supporting medical interventions, as well as public policies and healthcare reform calling for third-party payer oversight, that consistently benefit sponsoring stakeholder interests. Preserving the integrity of medical research has critical importance for the healthcare we receive and affects the lives and welfare of each one of us. The potential misuse of research to sell anything — including political agendas, healthcare reform or medical interventions — should concern everyone.

Ingenix, which says it’s a leader in health information solutions, also sells electronic medical records, and its consulting clients include more than 300 national and regional and Medicaid/Medicare managed health plans; more than 100 federal and state government agencies, and more than 50 healthcare delivery systems, including pharmacy benefit managers (PBMs). Ingenix benefited by the stimulus package, with the government making the adoption of national electronic medical records and preventive wellness programs key parts of organized efforts towards healthcare reform. Tom Daschle — former Senate Majority Leader and the President’s originally designated Health and Human Services Secretary and designer of the administration’s healthcare reform — was a special policy advisor, personally representing UnitedHealth Group, and keynote speaker for Ingenix.

Most importantly, Ingenix data has been used in published medical research — albeit terribly flawed — to convince Americans of skyrocketing costs of obesity, diabetes, depression, high blood pressure, high cholesterol and unhealthy lifestyles.

● The database was used, for instance, in a recent study (covered here) that claimed that bariatric surgeries save healthcare costs and has been used to coerce private and government payers to cover the surgeries. Will this flawed research be retracted and coverage for these surgeries be withdrawn? Not likely. A search of the American Journal of Managed Care, where the research was published, finds no retraction of the study or mention that its claims were based on discredited data.

● The database was used in a recently published study in Population Health Management commissioned by the National Changing Diabetes® Program, a lobbying organization of Novo Nordisk, Inc., to claim that undiagnosed diabetes cost the United States $18 billion in 2007 and that diabetes and pre-diabetes had cost the country about $218 billion in higher medical costs and lost productivity. This study remains prominent on the homepage of the organization’s website, with no mention it was based on discredited data.

● In fact, this database has been used in comparative effectiveness research of nearly every Pay-for-Performance measure (called “quality” measures); of electronic medical records; and of disease management, employer wellness and preventive health programs, claiming “to contain rising medical costs.”

● It’s been used in claims that compliance with P4P measures — for treating obesity, hypertension, hyperlipidemia, diabetes, depression, etc. — are “evidence-based medicine” and save healthcare costs.

● It’s been used to convince the public that managed care, electronic medical records, preventive wellness and fitness, employer wellness programs and compliance with health indices save healthcare costs and that noncompliance is costing everyone.

Credible scientists knew the studies weren’t convincing, even before hearing that the Ingenix data used was faulty. But, sadly, a lot of people readily believe the claims from anything said to come from a study or research, don’t realize that all studies are not created equal, and don’t examine the studies for themselves or recognize those that were never designed to be fair tests of anything to begin with.

If understanding the differing types of studies and clinical trials feels too overwhelming, here’s a helpful rule of thumb: Anytime you hear claims of body counts — a condition or behavior kills some huge number of people a year — or price tags — a condition or behavior costs society some huge amount in healthcare costs — you’re seeing statistical manipulations based on associations (attributable risk fractions calculated from relative risks and turning them into causations). Cost estimates are most used to sell a crisis, point blame on socially undesirable lifestyles or physical features, and convince the public of the need to “do something”... under the guise of public health and the common good.

If you’ve examined the various “costs of obesity” studies purportedly showing skyrocketing healthcare costs associated with obesity, for example, you’ve caught things like:

● failing to account for age or socioeconomic status;

● tallying any condition that’s ever been “associated” with obesity, and even others that aren’t (like dental services and eye glasses);

● double counting of the same conditions (with the same health risk factor used as the “cause” for as many as four different “obesity-related” diseases);

● incorrectly defining obesity to overstate the numbers of people crossing the threshold of “too fat”;

● piling on fallacious estimates of lost productivity and work hours;

● citing high healthcare costs among fat people, while failing to reveal they were lower than thin people;

● not factoring for the costs of weight loss pills, extra tests and interventions imposed on fat people and on children in mandatory obesity guidelines;

● blaming fat people for the adverse effects of weight loss treatments imposed on them;

● claiming fat workers cost employers more but failing to control for age, hours worked, and type of manual labor (which would reveal that fat workers actually file fewer worker’s comp claims per hour worked, have lower rates of lost workdays and lower medical claim costs than the general population of employees);

● and failing to reveal that healthcare costs aren’t rising in numbers of cases being treated as much as rising costs per treatment — 70% of costs due to more expensive drugs and technological interventions.

No matter how well conducted a study might be, however, when the data used is false or faulty, then the conclusions reached are unsupportable. Yet, how likely will the Ingenix scandal lead to calls for re-evaluating the premises behind healthcare reform with its managed care and third party oversight and interventions to reduce the costs of obesity, diabetes, depression, high blood pressure, high cholesterol and unhealthy lifestyles?

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