Category Archives: policy

data saving lives, healthcare, policy, rapid learning health systems

Building a bridge to the future with population health analytics…

  • Leading US providers are using analytics to bring a more intense focus on gaps in care, to discover cost outliers, and to put a magnifying glass on efficiency
  • “Unlike other industries that may be high users of data and very sophisticated, the healthcare industry is at a different point”
  • “A platform where we mesh both claims data and data out of our electronic health records allows a lot more to be learned. The type of intelligence that we can glean is at a much more informed level than if we’re just dealing with one of those data sets in isolation.”
  • At the heart of population health analytics is the concept of risk stratification: understanding, through various inputs such as claims data, surveys, and EHRs, which members of a given healthcare organization’s customer base represent a level of risk for which intervention offers the greatest possibility of preventing future hospital admissions, reducing readmissions, improving overall health, and lowering costs.
  • Cleveland Clinic’s Explorys pulls data from a variety of sources—multiple electronic health records, billing systems, claims data from CMS and other payers—and assimilates that all together to allow filtering, reporting, identify care gaps and registry functions
  • A variety of tools exist to help stratify risk:

> Some tools place members of a population on a scatter plot to make the identification of outliers easier
> Other tools organize a population into patient registries to track various diseases and treatments
> Still other tools use input gathered from patient surveys.

  • near-real time data is an important addition

 

http://www.healthleadersmedia.com/content/TEC-298525/How-Population-Health-Analytics-Opens-Opportunities-for-Better-Care

How Population Health Analytics Opens Opportunities for Better Care

Scott Mace, for HealthLeaders Media , November 20, 2013

Innovators are blending technology with new care models while targeting high-risk patients in a patient-centered strategy.

This article appears in the November issue of HealthLeaders magazine.

Without robust analytics technology, the goals of accountable care and population health cannot fully be achieved, good intentions notwithstanding. ACOs must correlate clinical data and claims data and use analytics technology to produce the actions needed to manage the health of a population. The data is there, but the healthcare industry does not have an evenly distributed knowledge of how to use it effectively.

With potential savings of up to $300 billion a year, according to the consulting firm McKinsey & Company, the upside of industrywide analytics to manage a population is considerable.

And, increasingly, providers have the raw data they need to feed an analytics system. But it is not as simple or quick as installing electronic health record technology—no small feat in itself for many organizations—and must be accompanied by solid governance and education, according to leading providers.

These providers are using analytics to bring a more intense focus on gaps in care, to discover cost outliers, and to put a magnifying glass on efficiency. But the use of such healthcare analytics has yet to reach maturity.

Early in the process

“Our organization is facing what most of the industry is facing, and that is the need to build a bridge to the future through analytics; so unlike some other industries that may be high users of data and very sophisticated, the healthcare industry is just in a different point,” says Aric Sharp, vice president of the accountable care organization at UnityPoint Health, a West Des Moines, Iowa–based integrated health system with 3,026 licensed beds across 15 hospitals and total operating revenue of $2.7 billion.

“We’re still in the process, as an industry, of going through implementing electronic health records and achieving meaningful use and those types of things. At the same time, with a lot of the new efforts around accountable care organizations, for one of the first times many providers have an opportunity to collect claims data by working with payers,” Sharp says. “We felt it necessary to build a platform where we can mesh together both claims data and data out of our electronic health records, because there’s a lot more that’s able to be learned in that type of an environment. The type of intelligence that we can glean is at a much more informed level than if we’re just dealing with one of those data sets in isolation.”

UnityPoint Health typifies numerous providers, having initiated analytics for its population health initiative only a couple of years ago. “The primary lesson is, this is really difficult, and there’s a lot to learn along the way,” Sharp says. “And yet, we can certainly see that as we continue to enhance the work, there’s more and more benefit with every step. The big learning is that there’s just a lot to be learned, and it’s exciting, because with every step of the process, we are better able to identify opportunities to improve care, and we’re able to become more efficient at this type of work.”

At the heart of population health analytics is the concept of risk stratification: understanding, through various inputs such as claims data, surveys, and EHRs, which members of a given healthcare organization’s customer base represent a level of risk for which intervention offers the greatest possibility of preventing future hospital admissions, reducing readmissions, improving overall health, and lowering costs.

UnityPoint Health selected analytics technology from Explorys, a data spinoff of Cleveland Clinic founded in 2009.

“Explorys is able to pull data from a variety of sources—multiple electronic health records, our own billing systems, claims data from CMS or other payers—and assimilate that all together,” Sharp says. “Explorys is really what sits on top of that and gives us an ability to slice and dice and analyze it and probe it and report quality metrics, identify gaps in care, and in the future even use that to do outreach to patients and do registry-type functions.”

UnityPoint Health still counts the time until the big payoff in years. “We’re not yet ready to say that it has an impact on our global per-member per-month spent,” says Vice President of Operations Kathleen Cunningham. “It will, but we are so early in our innovation that some of our results are really based on the pilot type of innovation programs that we’re working on.”

Starting with employee populations

In many healthcare systems, population health analytics success stories are just beginning to emerge, but some providers have used their own employee populations as a proof of concept for the effectiveness of the effort.

For the past 11 years, employees of Adventist HealthCare—a nonprofit network based in Gaithersburg, Md., with three acute care and three specialty hospitals, 6,263 employees, and 2012 revenue of $726 million—have been managed for risk by the self-insured provider.

“It got started with the idea that a healthier population is going to be a more effective employee population, and it’s going to also be a lower-cost population,” says Bill Robertson, president and CEO of Adventist HealthCare.

 

A decade ago, Adventist started working with InforMed Healthcare Solutions, since acquired by Conifer Health Solutions, to use InforMed’s set of data warehouse tools to improve its health plan design and determine where interventions were needed, Robertson says. Adventist and InforMed worked collaboratively to develop those tools and restructure the Adventist workflow to ramp up the effectiveness of the population health program.

As a result of population analytics, as well as other measures such as discouraging tobacco use and encouraging use of generic drugs, the inflation rate of Adventist’s employee health plan cost over the past nine years was half the national average, Robertson says.

A key development in the population health initiative came in 2005, when Adventist created personal health nurses as part of a pilot patient-centered medical home to work with the approximately 360 high-risk members of Adventist’s 6,600 employee-based covered lives identified by the InforMed data tools, Robertson says.

In a pilot, Adventist selected 27 of 50 high-risk patients (54%) and was able to move them out of the high-risk pool into moderate or low-risk pools, and it achieved a 35% reduction in the cost of care for that population, he says.

According to Adventist, the pilot project that achieved the 35% reduction did reduce health plan costs by $381,000 among the 27 patients who moved from the high-risk pool. The amount expended to achieve this 35% reduction was only $31,000, so every dollar spent returned approximately $12 in savings.

“It was actually so dramatic that it brought the inflation rate on our health plan to zero in that year,” Robertson says. “We were pretty pleased with that.” Overall, Adventist has saved “tens of millions of dollars” due to employee population health analytics to reshape the program and services for employees, he says.

Adventist then expanded this pilot PCMH to 5% of its employees (roughly 360 people), and continues to see the same kind of positive outcomes, Robertson says. Nurses make up the majority of InforMed users.

Three years ago, Adventist created ACES, which stands for Ambulatory Care EHR Support, an initiative to move its ambulatory physicians to use electronic medical records to expand its capacity to do population-focused care. By the end of 2013, more than 400 physicians will be using the ACES system. “So much of the job is how you integrate care across physicians and across the delivery system,” Robertson says. “When you have one person who’s seeing 15 physicians, but each physician thinks they’re the only one, you end up with different challenges than when you can see everything.”

All physicians who are participating providers in the Adventist HealthCare employee health benefit plan have access to the InforMed tools and analytics. Only a limited number directly access the information because the personal health nurses provide most of the ongoing care management, with the physicians serving more as the team captains, Robertson says.

The next step for Adventist IT is to tie analytics with the employee EHR. “What we’re morphing toward is linking all of this together with HIE infrastructure so that the information that is in the InforMed platform will be available in your EHR platform and vice versa through the information exchange,” Robertson says.

Adventist also created financial incentives that help its physicians spend “all the time it takes” to manage high-risk patients, Robertson says. “With an ACO, you don’t really get paid an incentive until you’ve been successful—at least after the first year you’ve demonstrated that things are working and that they’re [generating] shared savings,” he says. “So we’re still in the process of sorting out how we’ll make sure this infrastructure is utilized actively.”

Detailing the financial incentives, Robertson says the primary care physicians who participate in the patient-centered medical homes receive additional compensation, such as a monthly retainer or hourly incentive to compensate them for the additional time that is necessary to care for the high-risk patients in the PCMH.

Recent headlines have highlighted some fallout from the Pioneer ACO program. Fifteen charter members dropped out of the program after finding inadequate return on investment or improvement from their ACO initiative. To Robertson, this just highlights the importance of population health analytics in achieving ACO success. Had Adventist focused on no-risk or low-risk populations, it might not have achieved nearly the cost savings it had with its own proof of concept by targeting the high-risk pool of its self-insured employee-based covered lives, he says.

Now Adventist is forming an ACO for Medicare populations based on this same set of tools to track high-risk members of those populations. As time goes on, commercial-payer populations are also in Adventist’s sights. “We have a couple of pilots, like an apartment building that has a very large population of higher-risk individuals that we’re providing those types of services to, and it’s interesting to see when you focus on it what you achieve in terms of reduced consumption of healthcare services and increased health status,” Robertson says.

 

Leading the way to better patient care

At Virtua Health, population health analytics from Alere Analytics is being implemented to determine the highest-risk patients from a cohort of 12,000 attributed Medicare lives, says James Gamble, MD, chief medical information officer of the four-hospital, 885-staffed-bed integrated delivery network headquartered in Marlton, N.J.

Virtua became an ACO on January 1 and is preparing to add another 14,000 covered lives with a commercial insurer, says Alfred Campanella, Virtua’s executive vice president of strategic business growth and analytics.

“There are lots of different scenarios where action is needed to prevent an admission or to prevent a condition from getting worse,” Campanella says. Virtua is working with Alere to publish its alert lists via a Microsoft Dynamics customer relationship management platform. “That allows care nurses to take advantage of our Microsoft products like email and word processing,” he adds.

Virtua uses RNs to provide close case management of the high-risk population. Meanwhile, 80 Virtua-employed primary care doctors are kept updated via the workflow into the system’s electronic health record software. “That way that doctor doesn’t have to leave their EMR or jump around to see where things are going,” Campanella says.

“Our initial focus,” Gamble explains, “will be on these high-risk patients, so as we see it, these case managers’ day-to-day job will be: They’ll have a patient load, they will have care plans, they will have activities assigned to them for these patients.”

But the physician does not need to be the primary manager.

“As long as patients are following care plans, which are developed and approved by the providers, then the nurses will be managing them,” Gamble says. “Their communication will be more as updates. When an alert arises that the patient is at risk or in trouble, then obviously the nurse would directly communicate with the physician to try to intervene at any early stage before the patient’s health deteriorates or the patient ends up in the emergency room of the hospital.”

“What we’re seeing now is a more intense focus to try to fix those gaps in care and to identify patients who are at high risk for hospitalization or readmission or who need special attention,” Campanella says. “Technology gives you a greater magnifying glass in many respects for seeing the barriers to care and for creating efficiencies in care delivery. While all the analysis is not complete, early results for clinical and financial savings are promising.”

Support from top leadership has been crucial to Virtua’s transformational pivot toward analytics. “This whole idea of care coordination was approved at the board of trustees level,” Campanella says. “We’ve had tremendous support from our CEO, Richard Miller. One of our senior vice presidents, Stephen Kolesk, MD, doubles as the president of this subsidiary that is the ACO. He has a title of senior vice president for clinical integration, so it’s very tightly integrated with the physicians.”

Technical design of the Virtua analytics solution is close to completion. Parts of it will deploy before the end of 2013, and other parts will roll out in the first quarter of 2014, says Campanella. Also part of the project are an existing health information exchange and a new patient portal built on top of the HIE, he adds.

“Innovation does require some experimentation and risk,” Campanella says. “The ones who are leaders are taking on some risk and putting some investment in without fully understanding the full picture, but that’s what makes them leaders.

“It’s now the right way to care for patients, to have this high touch, high visibility into all the different domains of their care and the handoffs between those domains, and so even if the ACO concept from a regulatory standpoint goes away, it’s still the right way to care for patients,”
Campanella says.

Outside the hospital walls

Organizations beyond postacute hospitals are also engaging healthcare in a variety of ways that have broad implications for how analytics will be deployed in healthcare across the United States.

Brentwood, Tenn.–based Brookdale Senior Living owns and operates about 650 senior living communities in 36 states. In 2012, Brookdale, through a partnership with the University of North Texas Health Science Center and Florida Atlantic University, received $2.8 million of a $7.3 million Centers for Medicare & Medicaid Services Health Innovations Challenge grant for population health management. The program expects to save more than $9 million over a three-year period.

Initially, Brookdale is focusing on population health at 27 communities in Texas and Florida, but by the end of the three-year grant, it will involve 67 communities, says Kevin O’Neil, MD, chief medical officer of the organization.

The CMS grant sets a goal for Brookdale of reducing avoidable hospital readmissions by 11%, O’Neil says. “We know we’re going to be focusing on certain quality metrics in addition to readmissions,” he says. “We’ll focus on dehydration rates, as well as new incidents of pressure ulcers, some of the major problem areas in geriatric care, and then, based on the data that we receive from the analytics tool, it’ll help guide our quality improvement teams in terms of the type of improvement efforts that need to be initiated.”

 

A variety of tools exist to help stratify risk. Some tools place members of a population on a scatter plot to make the identification of outliers easier. Other tools organize a population into patient registries to track various diseases and treatments. Still other tools use input gathered from patient surveys. A recent study, however, reported that many of those tools had not performed very well.

At St. David’s Health System in Austin, which is working with Brookdale on the challenge grant, 60% of readmissions recently were measured as coming from low-risk groups. “To me [this] means either that people hadn’t been stratified properly, or that they were being sent home when they probably did need some kind of service or follow-up,” O’Neil says.

The biggest hurdle in O’Neil’s experience with population health analytics has been engaging with the hospital C-suite to craft the business associate agreements necessary to manage populations. “Once we’ve developed a relationship with one entity and had success, it’s much easier to engage other entities within that system.”

In dealing with the two universities, O’Neil says, “We had to resolve some issues related to intellectual property to incorporate INTERACT into electronic information systems,” he says. INTERACT is an acronym for Interventions to Reduce Acute Care Transfers, a free quality improvement program for which FAU holds the trademark and copyright. “This has been resolved through a licensing agreement—Loopback [a Dallas-based analytics platform vendor] also has a licensing agreement with FAU to bake INTERACT tools into software programs.”

Both Brookdale and its hospital partners are using a common population health analysis dashboard and software provided by Loopback Analytics. “As a geriatrician, this is the most exciting time in my career, because I’ve always felt that fee-for-service medicine was the bane of good geriatric care because it rewarded volume rather than quality,” O’Neil says. “Having that near-real-time data is really going to be extremely helpful to us.”

Analytics and meaningful use

Analytics tools produce the patient registries that identify gaps in care, not just to meet ACO objectives, but also to meet the requirements of meaningful use stage 2, which takes effect in 2014, says Gregory Spencer, MD, a practicing general internist and chief medical officer at Crystal Run Healthcare, a multispecialty practice with more than 300 physicians based in Middletown, N.Y.

“There are frequently registry functions within EHRs, but the EHR is set up at the patient level,” Spencer says. “It’s not optimized for reporting groups of patients, so to kind of get that rollup, you have to have another layer on top of that to gather it up.”

Thus, some sort of aggregator function is needed. “Usually that is not something that many EMRs do well,” Spencer says. “Registries are mostly condition- or disease-specific lists of patients who satisfy a certain criteria: diabetics, patients with vascular disease, kids with asthma. Care gaps look at all patients who have not had a certain recommended service. There is overlap with the registries, since a list of patients due for their colonoscopy is a kind of registry that needs to be ‘worked’ to get those patients compliant.”

Like numerous other healthcare organizations, Crystal Run’s first foray into population health analytics employed Microsoft Excel spreadsheets.

“The basics can be done with available tools,” Spencer says. “People shouldn’t wait for the killer app that’s out there that’s fancy and has a slick user interface. You can really do a lot with what you have, probably immediately.”

Since 1999, however, Crystal Run has incrementally left Excel behind and built population health analytics reporting tools on top of its NextGen electronic health record software, Spencer says. Crystal Run also adopted the Crimson Population Risk Management service from the Advisory Board Company, which incorporates technology from Milliman Inc. on the back end, he says.

Like other providers, Crystal Run saw the shift coming from fee-for-service to accountable care and took early opportunities to get its hands on claims data and learn how to work with it, Spencer says.

Other resources offering insight to accountable care analytics were the Group Practice Improvement Network and the American Medical Group Association, where Spencer has been able to network with peers who have been pursuing population health analytics longer than Crystal Run has.

The Crystal Run practice, formed in 1996, grew out of a single-specialty oncology practice and today has 1,700 employees. It is designated by the NCQA as a level 3 patient-centered medical home, and in 2012, Crystal Run became one of the first 27 Medicare Shared Savings ACOs.

Analytics have revealed “a lot of surprises at who you think has been getting most of their care from you,” he says. Snowbirds—typically someone from the Northeast, Midwest, or Pacific Northwest who spends substantial time in warmer states during the winter—are receiving significant amounts of care that had been outside of Crystal Run’s knowledge.

But with Medicare claims data examined through its analytics services, Crystal Run has had its eyes opened to previously unobserved cost centers. For instance, the No. 1 biller of pathology services for a 10,000-patient Crystal Run cohort was discovered to be a local dermatologist.

“What it’s all about is improving quality and eliminating waste,” Spencer says. “That waste is [in] tests that aren’t really required [and even some] visits that are [being required]. It’s your habit and custom to see people back at a certain frequency, but when you really start thinking about it, do you really need to see somebody back every three months who has stable blood pressure and has been rock solid? Well, probably not. And so you start doing things like that, and it adds up incrementally.”

 

Crystal Run is able to incorporate patients’ outside visits to providers, Spencer says, “but it’s not easy. We require source documentation to satisfy measures. For example, we scan outside mammogram results into a directory that we can then report against. We don’t take people’s word for dates. We need to have the document.”

Getting the initial claims data from CMS took three months, and then it takes another three or six months’ worth of that data for it to become actionable, Spencer says.

Claims data on any one patient is also plagued by incurred but not reported claims. Until IBNR claims get processed through Medicare or other payers, a true picture of a patient’s treatment is incomplete.

In light of this, it’s important for all concerned to have realistic expectations of what population health analytics can achieve and when, Spencer says.

“Cost is a practical concern we all face in our day-to-day lives,” he says. “You get more for more money, but as in all things, you have to be prudent. I don’t know how you will be able to do business in the very near future without using some form of analytics. How will your quality measures be good enough to meet the ‘gates’ required for contracts? How will you know where you are or if you can grow and how? It has cost a lot of money—money that’s been spent over a long period of time. The cost is into the low millions.

“That said,” Spencer adds, “we are able to take advantage of newer payment models that reward us not just for healthcare, but outcomes. We can potentially get paid for not doing anything—the PMPM that can be negotiated when you show you are doing a good job managing a population of patients.”

Analytics in the ambulatory practice

Gastroenterologist Tom M. Deas Jr., MD, practices as part of North Texas Specialty Physicians based in Fort Worth, an independent physician association comprising nearly 600 family and specialty doctors. NTSP has its own health plan and has been managing Medicare patients at risk for several years.

NTSP provided initial funding for a population health analytics firm, Sandlot Solutions, which has now been spun out as a separate company, although NTSP remains a part owner and Deas also serves as Sandlot’s chief medical officer. NTSP uses Sandlot’s analytics software to manage 80,000 at-risk lives, Deas says.

“Without some of the information technology to identify those patients based on their illnesses, comorbid illnesses, their severity of illness, who their physicians are, where they’ve been going to get their care, and being able to manage the whole spectrum of the care, you’re at a serious disadvantage,” Deas says.

Sandlot’s technology combines claims and clinical data into a robust patient data warehouse that helps meet some of the quality measures required to be an ACO, says Deas. “With the ACO, no matter how much money you save, you don’t get a dime of it if you haven’t met all the quality measures, so if we fall short in that area, it’s economically not good and it’s not good for the patients.”

By default, all Pioneer ACOs received three years of Medicare claims data. Getting the data into the warehouse requires overcoming some well-known healthcare IT issues, such as reconciling that claims data with an enterprise master-patient index, eliminating duplicates, and general patient-matching issues, Deas notes.

Once that was done, NTSP could concentrate on using Sandlot’s analytics to spot and eliminate wasteful services, as such home visits for patients lacking a medical necessity for such visits, Deas says. Analytics-driven interventions can manage a few hundred overutilizers of services as outpatients, focusing care management on them, he adds.

After a year’s effort, NTSP has bent its cost curve through these efforts to the tune of $50 per member per month, Deas says. “Now we’re not completely there,” he cautions. “It’s an incremental process, because you’re not only doing management, but you’re changing behaviors also. You’re trying to get patients aligned with the primary care physician, trying to move them from one source of care that was maybe excessive utilization to another.”

Deas says measuring the ROI of analytics technology remains elusive.

“A lot of people think they just buy an analytics tool and a data warehouse and an HIE and it’ll sit there and solve their problems,” he says. “That is not the case. You have to have human folks using that tool to manage the care of patients, to lower the cost and improve the quality. It’s like me asking you how much more efficient are you with a smartphone than you were five years ago with whatever version of phone you had then. You can’t answer that question. All you know, it’s just one part of what’s happened in the past five years to make you more efficient.”

It no doubt helps that NTSP’s executive director, Karen van Wagner, has a PhD in statistics, giving the organization added expertise to quantify results as they emerge.

Analytics technology is just beginning to make its impact felt in population health management. Careful consideration of products, objectives, workflows, and business conditions will steer providers through potential pitfalls, but the effort is considerable and the challenge to healthcare leadership is ongoing.

“Among the things that made these changes successful is an IT infrastructure that supports population health management and care management,” Deas says. “We still have to throw a fair amount of resources—human resources—at it to make it work.”

Reprint HLR1113-2

This article appears in the November issue of HealthLeaders magazine.

Scott Mace is senior technology editor at HealthLeaders Media. 

facts & data points, healthy habits, nutrition, policy, research methodology

A behavioural economist’s view on obesity…

This is a typically obtuse, academic view of obesity, breathlessly attempting to cite the immense complexity of the disease, capping it with a plea for more research dollars, or at least a reallocation of research dollars.

There are a couple of interesting snippets:

  • pets are also getting obese – 58.3% of cats were obese in 2012
  • lab animals too are getting obese – 11.8% per decade from 1982 to 2003
  • is this due to antibiotic-mediated changes to gut bacteria that not just change how we digest, but also how we behave?
  • socially mediated effects?

So surprising that a behavioral economist’s view could be so dismal.

 

Source: http://www.nytimes.com/2013/11/10/business/the-co-villains-behind-obesitys-rise.html?_r=2&

The Co-Villains Behind Obesity’s Rise

Waltraud Grubitzsch/European Pressphoto Agency

Researchers have compared tissue samples from obese mice with those of normal mice to try to determine which behavioral or biological factors might cause humans to gain weight. Here, a 2012 experiment in Leipzig, Germany.

By SENDHIL MULLAINATHAN
Published: November 9, 2013

Why is obesity soaring? The answer seems pretty clear. In 1955, a standard soda at McDonald’s was only seven ounces. Today, a medium is three times as large, and even a child’s-size version is 12 ounces. It’s a widely held view that obesity is a consequence of our behaviors, and that behavioral economics thus plays a central role in understanding it — with markets, preferences and choices taking center stage. As a behavioral economist, I subscribed to that view — until recently, when I began to question my thinking.

For many health problems, of course, behavior plays some role but biology is often a major villain. “Biology” here is my catchall term for the myriad bodily mechanics that are only weakly connected to our choices. A few studies have led me to wonder whether the same is true with obesity. Have I been the proverbial owner of a (behavioral) hammer, looking for (behavioral) nails everywhere? Have I failed to appreciate the role of biology?

A first warning sign comes from looking at other animals. Our pets have been getting fatter along with us. In 2012, some 58.3 percent of cats were, literally, fat cats. That is taken from a survey by the Association for Pet Obesity Prevention. (The very existence of this organization is telling.) Pet obesity, however, can easily be tied to human behavior: a culture that eats more probably feeds its animals more, too.

And yet, a study by a group of biostatisticians in the Proceedings of the Royal Society challenges this interpretation. They collected data from animals raised in captivity: macaques, marmosets, chimpanzees, vervets, lab rats and mice. The data came from labs and centers and spanned several decades. These captive animals are also becoming fatter: weight gain for female lab mice, for example, came out to 11.8 percent a decade from 1982 to 2003.

But this weight gain is harder to explain. Captive animals are fed carefully controlled diets, which the researchers argue have not changed for decades. Animal obesity cannot be explained through eating behavior alone. We must look to some other — biological — driver.

Fittingly, the study is titled “Canaries in the Coal Mine.” Could our inability to explain animal obesity with behavior be a warning sign? Perhaps we are also overlooking biological drivers for human obesity. But what might these culprits be?

A particularly interesting candidate resides in your gut. Your digestive system is actually a complex ecosystem, playing host to hundreds of species of bacteria that do things as diverse as fermenting undigestedcarbohydrates and providing vitamins. They also regulate how much fat your body stores.

Not everyone, however, has the same gut bacteria. And, interestingly, the composition of this bacteria correlates with obesity. Of course, this relationship could be simple: the obese eat differently, and therefore they have different bacteria.

But a recent study in the journal Science showed that cause and effect could go the other way as well. Researchers harvested bacteria from pairs of human twins, where one twin was obese and the other was not. Then they transplanted these bacteria into mice. The mice who received bacteria from the obese twin gained weight, while the others did not. The mice did not eat more: Their metabolism changed so that they put on more weight even with the same caloric input.

What, then, determines your gut bacteria? It could be antibiotics or environmental toxins or how processed your food is. Another possibility is raised by a study in The New England Journal of Medicine that shows that obesity seems to “spread” across social networks, with people infecting their friends and neighbors. I had always assumed that was because birds of a feather flock together — and that is surely part of the explanation. But because gut bacteria can also spread among people in close proximity, perhaps the obesity epidemic really is, well, an epidemic?

I’m not arguing that behavior does not matter. Biology and behavior often interact; the spread of flu depends on whether we wash our hands. Similarly, the bacteria study found that the “obese gut bacteria” had an impact only when the mice were fed diets heavy in saturated fats.

Perhaps most interestingly, changing biology may even be changing cravings. Some biologists have hypothesized that our gut bacteria actually drive cravings for certain unhealthy foods. A focus on biology doesn’t mean a reduced emphasis on behavior, just a richer understanding of it.

These and other studies raise important possibilities, which deserve more research and attention. At the very least, we should invest as many obesity research dollars in uncovering and understanding these biological channels as we do in understanding behavioral channels. And this is a behavioral economist talking!

After all, this could radically change the way we think about policies to curb obesity. As one newspaper editorial pronounced:

“A little town in Sweden has put a local tax on fat men. It is declared that ‘the fat man stands accused by the very fact of his too solid flesh’ (vide “Hamlet”) ‘of gluttony and laziness.’ Millions of fat men throughout the world may rise up and denounce as liars the town councillor who drew up this cruel indictment and those who voted for it, but the gentler way of reproving them would be to point out the tritely recognised danger of generalisation in almost any statement of supposed fact. Not all fat men are lazy and gluttonous. Obesity is in many a congenital habit of body; in others a disease.”

That editorial was written in 1923, for the paper known as The Paris Herald. Maybe the writer was on to something.

SENDHIL MULLAINATHAN is a professor of economics at Harvard.

This article has been revised to reflect the following correction:

Correction: November 17, 2013

Because of an editing error, the Economic View column last Sunday, about possible causes of obesity, misstated the source of bacteria that were transplanted into mice as part of an obesity study. The bacteria came from human twins, not from other mice.

 

A version of this article appears in print on November 10, 2013, on page BU6 of the New York edition with the headline: The Co-Villains Behind Obesity’s Rise.
complex adaptive systems, data saving lives, facts & data points, healthcare, policy, rapid learning health systems

Preventing medical error

  • diagnostic errors are the most preventable medical mistakes
  • Automation is part of the solution – sifting through medical records to look for potential bad calls, or to prompt doctors to follow up on red-flag test results.
  • Another component is devices and tests that help doctors identify diseases and conditions more accurately
  • online services that give doctors suggestions when they aren’t sure what they’re dealing with
  • changing medical culture is another approach

Source: http://online.wsj.com/news/articles/SB10001424052702304402104579151232421802264

The Biggest Mistake Doctors Make

Misdiagnoses are harmful and costly. But they’re often preventable

A patient with abdominal pain dies from a ruptured appendix after a doctor fails to do a complete physical exam. A biopsy comes back positive for prostate cancer, but no one follows up when the lab result gets misplaced. A child’s fever and rash are diagnosed as a viral illness, but they turn out to be a much more serious case of bacterial meningitis.

Such devastating errors lead to permanent damage or death for as many as 160,000 patients each year, according to researchers at Johns Hopkins University. Not only are diagnostic problems more common than other medical mistakes—and more likely to harm patients—but they’re also the leading cause of malpractice claims, accounting for 35% of nearly $39 billion in payouts in the U.S. from 1986 to 2010, measured in 2011 dollars, according to Johns Hopkins.

The good news is that diagnostic errors are more likely to be preventable than other medical mistakes. And now health-care providers are turning to a number of innovative strategies to fix the complex web of errors, biases and oversights that stymie the quest for the right diagnosis.

Part of the solution is automation—using computers to sift through medical records to look for potential bad calls, or to prompt doctors to follow up on red-flag test results. Another component is devices and tests that help doctors identify diseases and conditions more accurately, and online services that give doctors suggestions when they aren’t sure what they’re dealing with.

twisted_stethescope

Finally, there’s a push to change the very culture of medicine. Doctors are being trained not to latch onto one diagnosis and stick with it no matter what. Instead, they’re being taught to keep an open mind when confronted with conflicting evidence and opinion.

“Diagnostic error is probably the biggest patient-safety issue we face in health care, and it is finally getting on the radar of the patient quality and safety movement,” says Mark Graber, a longtime Veterans Administration physician and a fellow at the nonprofit research group RTI International.

Big Efforts Under Way

The effort will get a big boost under the new health-care law, which requires multiple providers to coordinate care—and help prevent key information like test results from slipping through the cracks and make sure that patients follow through with referrals to specialists.

There are other large-scale efforts in the works. The Institute of Medicine, a federal advisory body, has agreed to undertake a $1 million study of the impact of diagnostic errors on health care in the U.S.

In addition, the Society to Improve Diagnosis in Medicine, which Dr. Graber founded two years ago, is working with health-care accreditation groups and safety organizations to develop methods to identify and measure diagnostic errors, which often aren’t revealed unless there is a lawsuit. In addition, it’s developing a medical-school curriculum to help trainees improve diagnostic skills and assess their competency.

 

Robert Wachter, associate chairman of the department of medicine at the University of California, San Francisco, says defining and measuring diagnostic errors is an important step. “Right now, none of the incentives for improvement in health care are based on whether the doctor made the correct diagnosis,” Dr. Wachter says. But equally important, he adds, “we need to nurture bottom-up innovation.”

That’s already happening. Large health-care systems are mining their electronic records for missed signals. At the Southern California Permanente Medical Group, part of managed-care giant Kaiser Permanente, a “Safety Net” program periodically surveys its database of 3.6 million members to catch lab results and other data that might fall through the cracks.

In one of the first uses of the system, a case manager reviewed 8,076 patients with abnormal PSA test results for prostate cancer, and more than 2,200 patients had follow-up biopsies. From 2006 to 2009, 745 cancers were diagnosed among those patients—and Kaiser had no malpractice claims related to missed PSA tests.

The program is also being used to find patients with undiagnosed kidney disease, which is often found via an abnormal test result for creatinine, which should be repeated within 90 days. From 2007 to 2012, the system found 7,218 lab orders placed for patients with an abnormal test that had not been repeated. Of those, 3,465 were repeated within 90 days of a notice to patients that they needed a repeat test, and 1,768 showed abnormal results. The majority, 1,624, turned out to be new cases of the disease.

Michael Kanter, regional medical director of quality and clinical analysis, says the system enables clinicians to go back “as far as is feasible to find all of the errors that we can and fix them.”

Because the disease is slow moving, Dr. Kanter says, people with a five-year-old undiagnosed case may not have been harmed. Likewise, with many early prostate cancers, “in many of these cases it doesn’t mean harm would have reached the patient,” he says. “But we don’t want patients not to have the information they should have had through some kind of lapse in the system.”

Dealing With the Flood

Electronic records aren’t a panacea, of course, and can even lead to information overload. In a survey of Veterans Administration primary-care practitioners reported last March in JAMA Internal Medicine, more than two-thirds reported receiving more patient-care-related alerts than they could effectively manage—making it possible for them to miss abnormal test results.

Some researchers suggest the best solution isn’t to flood doctors with information but to provide a second set of eyes to find things they may have missed.

The focus now is preventing dangerous delays in follow-ups of abnormal test results. In a pilot program, researchers at the Houston VA developed “trigger” queries—a set of rules—to electronically identify medical records of patients with potential delays in prostate and colorectal cancer evaluation and diagnosis. Records included charts that had no documented follow-up for abnormal findings suspicious for cancer after a certain period, according to the research team’s leader, Hardeep Singh, chief of health policy and quality at Michael E. DeBakey VA Medical Center in Houston and an assistant professor of medicine at Baylor College of Medicine.

The queries were run on nearly 600,000 records of patients seen at one VA facility in 2009 and 2010. Dr. Singh says the use of triggers, which helped find abnormal PSA tests and positive fecal occult blood tests, could detect an estimated 1,048 instances of delayed or missed follow-up of abnormal findings annually and 47 high-grade cancers.

The VA has funded a randomized trial to test whether an automated surveillance system of triggers can improve timely diagnosis and follow-up for five common cancers.

“This program is like finding needles in a haystack, and we use information technology to make the haystack smaller and smaller so it’s easier to find the needles,” Dr. Singh says.

More health-care systems are also turning to electronic decision-support programs that help doctors rank possible diagnoses by likelihood based on symptoms and notes in the medical record. In a study of one such system, called Isabel, researchers led by Dr. Graber found that it provided the correct diagnosis 96% of the time when key clinical features from 50 challenging cases reported in the New England Journal of Medicine were entered into the system. The American Board of Internal Medicine is studying how Isabel could be linked to assessments of physician skill and knowledge.

Another system, DXplain, developed at Massachusetts General Hospital in Boston, was shown in a study last year to significantly improve diagnostic accuracy among first-year medical residents.

Edward Hoffer, associate clinical professor at Harvard and senior computer scientist at Mass General who leads the DXplain program, says the aim now is to have DXplain “push” diagnostic suggestions to physicians through an electronic-medical-records system rather than requiring doctors to initiate a query, which some are still reluctant to do. “We have to focus our attention on dealing with situations where doctors think they know what the diagnosis is, but they don’t,” Dr. Hoffer says.

Other Avenues

New devices also hold promise for confirming a diagnosis and avoiding unnecessary tests. A number of companies are rushing to provide aids such as portable diagnostic equipment and lab tests that can analyze tiny samples of blood and other bodily fluids quickly to detect disease.

Consider MelaFind, which came to market in the U.S. in 2011. The device allows dermatologists to noninvasively examine moles as deep as 2.5 millimeters beneath the surface to gauge the level of “disorganization,” an indicator of irregular growth patterns that are a sign of melanoma, among the deadliest cancers.

New York dermatologist Macrene Alexiades-Armenakas says she uses MelaFind to confirm that a mole is to be removed and prioritize the level of disorganization in multiple abnormal moles. In some cases, when another doctor or the patient has been concerned about a mole, MelaFind supported “clinical diagnosis of a benign mole, thereby sparing them a biopsy,” she says.

But such devices will never replace a thorough physical exam with a trained eye and careful follow-up, says Dr. Alexiades-Armenakas: “These diagnostic tools are aids to increase our accuracy and adjuncts to good physical diagnosis, not a substitute.”

Some efforts to cut down on errors take a different route altogether—and try to improve diagnoses by improving communication.

For instance, there’s a push to get patients more engaged in the diagnostic process, by encouraging them to speak up about their symptoms and ask the doctor, “What else could this be?” At Kaiser Permanente, a pilot program provides patients with a pamphlet that encourages them to think about and write down their symptoms and what concerns or fears they have, encouraging them to ask specific questions to be sure they understand their diagnosis and the next steps they must take.

Medical schools, meanwhile, are teaching doctors to be more receptive to patient input and avoid “anchoring,” the habit of focusing on one diagnosis and excluding other possible scenarios, and “premature closure,” not even considering the correct diagnosis as a possibility.

The Critical Thinking program at Dalhousie University in Halifax, Nova Scotia, established last year, aims to help trainees step back and examine how biases may affect their thinking. Developed by Pat Croskerry, a physician known for his research on the role of cognitive error in diagnosis, it uses a list of 50 different types of bias that may lead to diagnostic error.

The program is being integrated throughout four years of the medical school. Students study cases such as a psychiatric patient with shortness of breath who was assumed to be merely having an anxiety attack; doctors overlooked that she was a smoker on birth-control pills, a risk for the blood clot that later traveled to her lung and killed her.

“If we can teach physicians how to think more critically,” Dr. Croskerry says, “they would be more effective in delivering good care and arriving at the right diagnosis.”

Ms. Landro is an assistant managing editor for The Wall Street Journal and writes the paper’s Informed Patient column. She can be reached at laura.landro@wsj.com.

healthy habits, policy

fat getting fatter

  • eat less and exercise more
  • Jim Clifton is Chairman and CEO of Gallup

Source: http://www.linkedin.com/today/post/article/20131122150210-14634910-america-s-biggest-fiscal-problem-the-fat-are-getting-fatter

America’s Biggest Fiscal Problem: The Fat Are Getting Fatter

November 22, 2013

Much of U.S. politics focuses on the fact that the rich are getting richer and the poor, poorer. But does anyone care that the fat are getting fatter?

The U.S. adult obesity rate so far this year is on pace to surpass all annual average obesity rates since Gallup-Healthways began tracking it five years ago.

Health costs are going to bankrupt us. At the current annual 6% growth rate, our total healthcare bill will go from $2.5 trillion per year — which it is now — to almost exactly $4.5 trillion in 10 years. If you add the stubs of the increases over the 10-year period, above the running $2.5 trillion our debt-burdened nation can’t afford, it totals a staggering $10 trillion.

To put this in perspective, the sum of our coming healthcare costs are three times the size of the subprime meltdown that brought America and the world to its knees. While we survived the subprime mess, healthcare costs will honestly break the nation.

Things look even worse when you compare America’s per person healthcare spending to comparable societies. We spend more than $8,000 annually per person, where Canada and Germany each spends roughly $4,500 per person, and the United Kingdom spends about $3,500, according to the Organisation for Economic Co-Operation and Development — and residents of those countries all live longer.

So is our American healthcare system superior? You tell me.

Americans obviously understand that this is a huge problem. Nearly a quarter of us say cost is the most urgent health problem facing the U.S., surpassing healthcare access for the first time since 2006. Obesity remains the No. 1 health condition named.

Keep in mind that all of the hoopla about the Affordable Care Act (ACA), or Obamacare, has little to do with reducing the bloated and growing $2.5 trillion expense. Obamacare attempts to address the insurance issue — who pays for what — but it doesn’t go after the core problem: Americans are too fat and unhealthy, and the vast majority of our health problems arepreventable.

That’s right — the Centers for Disease Control concluded a few years ago that of all of America’s chronic health problems, a whopping 70%, are preventable. And what is the common thread among these chronic diseases, such as diabetes and heart disease? Being obese puts people at higher risk for developing all of them.

Rather than go on and on about whether the ACA website works or not, or who wins and loses politically in 2014 and 2016 because of a disastrous rollout, shouldn’t the media be trumpeting this headline: 70% of Health Problems in America Are Preventable?

I just figured the overall weight of Americans, and it’s right at about 56 billion pounds if I assume 180 pounds per person. As a nation, in my view if we collectively lost about 10 billion pounds of excess weight, we might reduce our healthcare costs by a third. And we wouldn’t need all of these wasted political conversations, because we could balance the budget. Even better, the fix would be free — it wouldn’t require a new law, sequestration, or a shutdown.

That’s because the real fix doesn’t lie within political battles over insurance coverage. It lies within a sudden new culture of American fitness — and that begins with eating less and exercising more.

*****

Jim Clifton is Chairman and CEO of Gallup. He is author of The Coming Jobs War (Gallup Press, 2011).

cool, data saving lives, entrepreneurship, healthcare, healthy habits, meaning, nutrition, policy, politics, quantified self, rapid learning health systems, research methodology, storytelling

Chronic Disease Fear Factor Ageing Messaging

Governments won’t be able to afford you if you are over 70 and can’t work
You will need to be productive
The current health market can only extend your life, but not your productive life
The new health system will have to do both if we are to preserve our standard of living
Sure, people will need to die sometime, but it’s the when, how and why they die that needs to evolve
This health system aims to deliver on this
Australia is well positioned to lead the world on this
Excitement

healthy habits, musings, nutrition, policy

Why diets fail…

That diets fail seems to be the only uncontested fact in the world of nutrition.

Why do you suppose that is?

Well the answer is pretty obvious when you think about it. Its because the idea of normal that people revert to after a diet is pathological.

The modern idea of a “normal” diet is actually sick. Too much food. And too much of the wrong, highly processed food.

The public health challenge is to change the idea of normal, because the current idea is killing us.

Eat only twice per day. No refined carbohydrates. Minimal meat consumption.

As Pollan says: Eat food, mainly plants, not too much.

Brilliant!

healthcare, meaning, policy

How Doctors Die: Showing Others the Way

Source: http://www.nytimes.com/2013/11/20/your-money/how-doctors-die.html?from=homepage&_r=0

November 19, 2013  By DAN GORENSTEIN

BRAVE. You hear that word a lot when people are sick. It’s all about the fight, the survival instinct, the courage. But when Dr. Elizabeth D. McKinley’s family and friends talk about bravery, it is not so much about the way Dr. McKinley, a 53-year-old internist from Cleveland, battled breast cancer for 17 years. It is about the courage she has shown in doing something so few of us are able to do: stop fighting.

This spring, after Dr. McKinley’s cancer found its way into her liver and lungs and the tissue surrounding her brain, she was told she had two options.

“You can put chemotherapy directly into your brain, or total brain radiation,” she recalled recently from her home in suburban Cleveland. “I’m looking at these drugs head-on and either one would change me significantly. I didn’t want that.” She also did not want to endure the side effects of radiation.

What Dr. McKinley wanted was time with her husband, a radiologist, and their two college-age children, and another summer to soak her feet in the Atlantic Ocean. But most of all, she wanted “a little more time being me and not being somebody else.” So, she turned down more treatment and began hospice care, the point at which the medical fight to extend life gives way to creating the best quality of life for the time that is left.

Dr. Robert Gilkeson, Dr. McKinley’s husband, remembers his mother-in-law, Alice McKinley, being unable to comprehend her daughter’s decision. “ ‘Isn’t there some treatment we could do here?’ she pleaded with me,” he recalled. “I almost had to bite my tongue, so I didn’t say, ‘Do you have any idea how much disease your daughter has?’ ” Dr. McKinley and her husband were looking at her disease as doctors, who know the limits of medicine; her mother was looking at her daughter’s cancer as a mother, clinging to the promise of medicine as limitless.

When it comes to dying, doctors, of course, are ultimately no different from the rest of us. And their emotional and physical struggles are surely every bit as wrenching. But they have a clear advantage over many of us. They have seen death up close. They understand their choices, and they have access to the best that medicine has to offer.

“You have a lot of knowledge, a lot of awareness of what’s likely to come,” said Dr. J. Andrew Billings from his home in Cambridge, Mass.

Dr. Billings, 68 and semi-retired, is an expert in palliative care, which can include managing pain, emotional support and end-of-life planning. He is also a cancer patient with a life-threatening form of lymphoma. Dr. Billings said that knowledge of what may be ahead can give doctors more control over their quality of life before they die — control that eludes many of us.

Research shows that most Americans do not die well, which is to say they do not die the way they say they want to — at home, surrounded by the people who love them.According to data from Medicare, only a third of patients die this way. More than 50 percent spend their final days in hospitals, often in intensive care units, tethered to machines and feeding tubes, or in nursing homes.

There is no statistical proof that doctors enjoy a better quality of life before death than the rest of us. But research indicates they are better planners. An often-cited study, published in 2003, of physicians who had been medical students at Johns Hopkins University found that they were more likely than the general public to have created advance directives, or living wills, which lay out specific plans for care if a patient is unable to make decisions. Of the 765 doctors studied, 64 percent had advance directives, compared with about 47 percent for American adults over 40.

Patients and families often pay a high price for difficult and unscripted deaths, psychologically and economically. The Dartmouth Atlas Project, which gathers and analyzes health care data, found that 17 percent of Medicare’s $550 billion annual budget is spent on patients’ last six months of life.

“We haven’t bent the cost curve on end-of-life care,” said Dr. David C. Goodman, a senior researcher for the project.

The amount spent in the intensive care unit is climbing. Between 2007 and 2010, Medicare spending on patients in the last two years of life jumped 13 percent, to nearly $70,000 per patient.

The evidence is clear, Dr. Goodman said, that things could change if doctors “respect patient preferences and provide fair information about their prognosis and treatment choices.”

Sometimes that can be easier said than done, even for doctors. One day last month, as he sat through the first of several hours of chemotherapy at the Dana-Farber Cancer Institute in Boston, Dr. Billings said he had looked at statistical survival curves for his form of lymphoma.

“There are some dots that are very, very soon, and there are some dots that are a long ways off, and I hope I’m one of those distant dots,” he said.

Dr. Billings knows how important it is to have that information. As a palliative care doctor, he has spent a lifetime helping people plan their final days. Also, he is married to a prominent palliative care doctor, Dr. Susan D. Block.

“As a doctor you know how to ask for things,” he said. But as a patient, Dr. Billings said he had learned how difficult it can be to push for all the information needed. “It’s hard to ask those questions,” he said. “It’s hard to get answers.”

There is a reason for that. In his book “Death Foretold,” Nicholas A. Christakis, a Yale sociologist, writes that few physicians even offer patients a prognosis, and when they do, they do not do a great job. Predictions, he argues, are often overly optimistic, with doctors being accurate just 20 percent of the time.

But without some basic understanding of the road ahead, Dr. Anthony L. Back, a University of Washington professor and palliative care specialist, said even sophisticated patients could end up where they least want to be: the I.C.U. “They haven’t realized the implications of saying: ‘Yeah, I’ll have that one more treatment. Yeah, I’ll have that chemotherapy,’ ” Dr. Back said.

In Raleigh, N.C., Dr. Kenneth D. Zeitler has practiced oncology for 30 years. The son of a doctor and the father of two doctors, he learned 18 years ago that he had a brain tumor, which was removed. When the tumor recurred in 2004, he took the conservative route and decided against an operation — the risk of paralysis was too great. Dr. Zeitler, his wife and their two children mapped out a clear medical path, or so they thought.

Then in June, he woke up with the left half of his body paralyzed, after a low-risk biopsy caused a hemorrhage in his brain. “As a physician myself, when treating patients, I listened to this inner voice,” he said, but now he was mad at himself. “Instead of just saying ‘No, I won’t do this biopsy,’ I didn’t follow my instincts.”

Dr. Zeitler realized after his biopsy that saying no can mean more than turning down a procedure. It can mean dealing with something much harder: his family’s expectations that he will do whatever it takes to live and remain with them.

As transparent as Dr. Zeitler was with his family about his clinical care, he had walled off his deepest fears about losing pleasure in his daily life. He has since regained most physical functions and says he has had another chance to talk to his family. “As much as they’ll cry about me at every bar mitzvah and every wedding, I don’t want to be there if I’m just completely miserable psychologically and physically,” he said. “I’ve seen that. I don’t need that.”

Dr. Joan Teno, an internist and a professor of medicine at Brown University, says that often, even families like the Zeitlers, avoid the difficult conversations they need to have together and with doctors about the emotional side of dying.

“We pay for another day in I.C.U.,” she said. “But we don’t pay for people to understand what their goals and values are. We don’t pay doctors to help patients think about their goals and values and then develop a plan.”

But the end-of-life choices Americans make are slowly shifting. Medicare figures show that fewer people are dying in the hospital — nearly a 10 percent dip in the last decade — and that there has been a modest increase in hospice care. At the same time, palliative care is being embraced on a broad scale, with most large hospitals offering services.

The Affordable Care Act could accelerate those trends. Ezekiel Emanuel, the former White House health policy adviser, has said he believes that new penalties for hospital readmissions under the law could improve end-of-life care, making it more likely “we make the patient’s passage much more comfortable and out of the hospital.”

Culturally there is movement too. For example, deathoverdinner.org, a website to help people hold end-of-life discussions, was started in August. The project’s founder, Michael Hebb, said more than 1,000 dinner parties had been held, including some at nursing homes.

The front door at Dr. McKinley‘s big house was wide open recently. Friends and caregivers came and went. Her hospice bed sat in the living room. Since she stopped treatment, she was spending her time writing, being with her family, gazing at her plants. Dr. McKinley knew she was going to die, and she knew how she wanted it to go.

“It’s not a decision I would change,” Dr. McKinley said. “If you asked me 700 times I wouldn’t change it, because it is the right one for me.”

Dr. McKinley died Nov. 9, at home, where she wanted to be.

healthy habits, nutrition, policy, politics

MedObs: Govt changed food label system after industry lobbying

It’s clear the algorithm that DoHA established was stuffed if what they say about a glass of water vs chicko rolls is true.

The fact that dairy has held sway indicates the project has been undermined.

Never mind if it ever gets adopted, which is unlikely given AFGC’s mutterings at the press club recently.

Then finally, how much of an impact will food labeling actually have, given all the other drivers of the problem of fundamental overeating. I suspect industry is using food labelling as a straw man to keep the bureaucrats and academics tied up while industry marches on its merry way.

This is a classic case of policy development driven by obsessions with process rather than focus on outcome.

Source: http://www.medicalobserver.com.au/news/govt-changed-food-label-system-after-industry-lobbying

Govt changed food label system after industry lobbying

THE Department of Health and Ageing has admitted it changed how it rated dairy products under a radical new food labelling scheme following lobbying from the industry – but staunchly defended its assessment methods.

In Senate estimates this week, department secretary Professor Jane Halton said the dairy industry had complained about how its products fared in the new star system designed to combat obesity.

“The concern that was raised in respect of the algorithm in respect of dairy was that it didn’t give dairy the right prominence,” she said.

“The [department’s] project group considered in great detail how dairy might be recalibrated. We’ve pulled dairy out and we’ve got different categories now.”

But Professor Halton rejected “in the strongest possible terms” suggestions the formulae were wrong after the Senate heard a glass of water would be rated as less healthy than some junk food products.

“It is highly robust and it has been tested across a large number of foods,” she said of the system.

Other industries had told the department they wanted their products rated “better” but she would not say which.

In a statement, Senator Bridget McKenzie said the new scheme risked sending the message that healthy products like milk and cheese were unhealthy.

“The fact that under this scheme a glass of water is less healthy than a Chiko Roll calls into question the whole basis of the front-of-pack labelling scheme,” she said.

Research showed healthy amounts of dairy were linked to reduced risk of several chronic diseases, including heart disease, hypertension, stroke and type 2 diabetes, she said.

The scheme is expected to have the star ratings on food packaging by mid-2014.