Monthly Archives: October 2014

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

Drs don’t care for interoperability

 

http://www.politico.com/story/2014/10/health-care-data-records-112039.html?hp=l13

Few motives to fix busted health data

 

 

A doctor is shown. | Getty

  • The records are difficult to use, reduce interaction with patients and cost a fortune. | Getty

  • By ARTHUR ALLEN | 10/20/14 1:32 PM EDT Updated: 10/21/14 3:00 PM EDT

  • Someday, doctors will have our data at their fingertips and will use it to prevent drug reactions, nip diabetes and cancers in the bud and lengthen our lives while preventing unpleasant and costly hospital stays.

    But for most doctors, that free-flowing information highway is a beautiful dream that doesn’t pay the bills.

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    Many hospitals don’t have any incentive to improve the clunky $30 billion federal electronic health records program: They still make most of their money by filling beds. Most doctors still get paid through procedures and visits.

    (Also on POLITICO: Weiner: Done with politics, not life)

    So a new 10-year plan for fixing the system from the Office of the National Coordinator for Health IT may have a hard time getting off the ground.

    “From the business perspective, there’s no financial benefit for the majority of hospitals and physicians to be interoperable,” says Steve Waldren, director of the Alliance for eHealth Innovation at the American Academy of Family Physicians. “If we don’t change the business end of it … it’s just checking a box.”

    Lobbying efforts have yielded a bill that would slow down federal requirements — and patient-centered care in the process. It could be taken up in the lame-duck session or added to another bill that directly addresses the interoperability issue next year, Hill staff say.

    (Also on POLITICO: Super PAC fundraising soars for conservatives)

    “It may already be too late,” says Thomson Kuhn, a researcher with the American College of Physicians. “Short of some crazy scheme to make a change at the end of the year I just don’t see how we’re going to get what we need. And at that point they’ve killed the program.”

    To address that issue, the Office of the National Coordinator’s plan is likely to require EHR vendors to include software interfaces that will make it easier for smooth communication across health care systems. But it may not work unless the economics of medicine shift in a way that forces doctors to require shared information to function.

    The HITECH Act of 2009 was designed to let health care professionals use information to improve care and reduce spending and shifting their economic motivation to keeping people healthy, rather than charging for individual treatments.

    (Also on POLITICO: Battle begins for NRSC chair)

    Five years later, most doctors have electronic health records (EHRs) — that’s where most of the money was spent — but doctors and nurses are unhappy with the time-consuming clumsiness of the software, much of which wasn’t ready for the medical profession.

    The records are difficult to use, reduce interaction with patients and cost a fortune. And for the most part, they haven’t made information sharing easier.

    There are some places — a growing number, in fact — where interoperability makes business sense. In Massachusetts, for example, many health systems such as Partners HealthCare have more than a third of their patients in value-based care systems built on the state’s network of health information.

    (Also on POLITICO: Tillis claims ‘momentum’ in N.C.)

    Some patients in these systems already are benefiting from “wrap-around” care that relies on good data, shared among medical professionals.

    Such success stories have popped up around the country in tandem with growing consolidation of health networks and the Affordable Care Act-fostered creation of accountable care organizations — in which doctors are nudged toward focusing on patients, rather than procedures.

    In such organizations, information sharing among doctors and hospitals is vital. Shifts in the Medicare payment schemes for doctors next year could also force the medical profession into more reliance on sharing.

    “Value-based care may not succeed with good information, but it can’t succeed without it,” says Josh Seidman, a former HHS health IT official who is now a vice president at Avalere Health.

    To date, though, most hospitals and provider networks aren’t financially motivated to freely share their patients and data with others. It will cost money to get their computer to share data. Providers, having already spent plenty, aren’t enthusiastic about spending more to meet the demands of the federal incentive program.

  • For some, accepting a penalty will beat buying new software, which they will need to upgrade again for the next stage of the program in a few years, says Rob Tennant of the Medical Group Management Association.
  • A retreat from the beautiful dream: It’s enough to make some health visionaries sick. One of them, a former official who was involved in creating the incentive program, sniffs that providers have themselves to blame.

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  • “A lot of people made bad choices,” he said, speaking on condition of anonymity. “They didn’t do half as much research before buying their EHRs as they would getting a new car, and they’re paying for it.
  • “The people complaining now are the ones at the trough — the vendors and doctors and hospitals. They gladly took the money, but when it comes time to step up or take the penalty, they say, ‘Let’s drop out of the program.’ It’s always, ‘Slow it down, dumb it down, give me money, don’t take anything away.’”
  • The biggest provider complaint about the current stage of the incentive program is its requirements that doctors share summary-of-care documents and get a percentage of their patients to receive or send health data through secure portals.
  • They often must shell out tens of thousands of dollars to software vendors to set up portals to transmit data across health care networks or even outside their offices. Their patients aren’t interested in accessing their records, they add — how can the government penalize them for what their patients won’t do?
  • Republican members of Congress are skeptical of the federal program’s top-down requirements, and equally incensed by its slow advance.
  • Rep. Phil Gingrey (R-Ga.) epitomizes this contradiction. This summer he threatened to investigate Epic, the leading EHR vendor, because he said it had taken billions in federal money to create information silos. A month later, he supported a bill put forward by Renee Ellmers (R-N.C.) that would slow down the meaningful use program, giving providers — and vendors — more time to learn how to share.
  • Some Hill staff who track the issue think the answer is to relax federal standards while focusing narrowly on freeing the information — a position that federal health IT advisory committees share.
  • “It’s hard to argue with physicians who say their systems aren’t ready, so they shouldn’t be penalized,” said a Hill staffer. “But the administration has to make this happen. … Every year we don’t have interoperability, public health issues go unaddressed and that’s unfortunate.”
  • A parade of witnesses appearing before the House Energy and Commerce Committee this year as part of its 21st Century Cures Initiative have stressed the importance of free-flowing health data — not only for patient care but also for research leading to cures.
  • “If we had a truly networked health care system, it would allow us to gather data on patients treated with drugs on and off label to see what kinds of adverse events are occurring,” said William Hanlon of drug testing company Covance, in testimony last month. “It could have huge benefits for clinical trials.”
  • Energy and Commerce staff say that information sharing will be part of legislation they expect to put out early next year.
  • The Health and Human Services Department is feeling pressure from Congress — in multiple directions. CMS has delayed the incentive program twice already in response to provider unhappiness. At the same time, ONC has taken a harder line on enforcing interoperability.
  • Earlier this month, HHS official Kelly Cronin noted that some EHR vendors were charging up to $20,000 to create computer interfaces for users to access out-of-network laboratories. She suggested her office might pass along such information to the Federal Trade Commission, to investigate as obstructions to free trade.
  • HHS’s health IT coordinator, Karen DeSalvo, has indicated that her agency may toughen its stance.
  • “[The Office of the National Coordinator] believes the market has not solved this problem on behalf of the American people,” she said in an interview. “Patient data is a public good so there has to be a public involvement in making sure everyone … has the data available in an appropriate way.”
  • The version of this story has corrected the spelling of Thomson Kuhn’s name.
  • Read more: http://www.politico.com/story/2014/10/health-care-data-records-112039.html#ixzz3HN1pB9eO
data saving lives, health market quality, healthcare, policy, rapid learning health systems

Why Big Health Insurance is pouring money into startups

 

http://fortune.com/2014/09/24/health-insurance-invest-startups/

TECH HEALTHCARE

Why Big Health Insurance is pouring money into startups

A part of the Affordable Care Act forces insurers to redeploy capital rather than distribute it to shareholders. And that’s where things get interesting.

One effect of the Affordable Care Act, a.k.a. Obamacare? A spike in venture interest in health care startups. Digital health care companies raised $2.3 billion in the first half of 2014, which surpasses the total raised in all of 2013, according Rock Health, a health care-focused seed fund. That figure is impressive given that 2013 was already a record year at $1.9 billion raised. (That’s a 39% increase year over year, even as biotech investment grew just 8% and medical devices investment fell by 17%.)

Funding rounds that engaged traditional venture capital firms grabbed most of the headlines: Flatiron Health raised $130 million in May, Doxmity raised $54 million in April, Zenefits raised $66 million in June, Teledoc raised $50 million this week. Look past the big VC firms, though, and you may notice that non-traditional investors—in particular, large health insurance companies—are increasingly participating and investing in the startup ecosystem.

In August, Blue Cross and Blue Shield of Florida, a Jacksonville-based insurer better known as Florida Blue, established an accelerator for health care startups based in Jacksonville. The program is run by Healthbox, an accelerator company that has invested in at least 47 health care startups—and, on its own behalf, raised seed funding from Blue Cross Blue Shield Venture Partners. BCBSVP has also funded its own incubator, called Sandbox Industries, that invests in startups. Lemhi Ventures, which invests in health care services companies, is said to be backed by UnitedHealth Group UNH -2.49% , though the firm declined to comment on its limited partners. The firms have been around for some time—Sandbox was founded in 2003, Lemhi was founded in 2006—but ramped up deal activity in the last year.

On Tuesday, Horizon Healthcare Services, a New Jersey insurance provider with 3.7 million members, announced it invested $3.7 million into Cota, a big data company focused on oncology. Glenn Pomerantz, Horizon’s chief medical officer, said the company has been investing in startups for the past three years. (He refused to disclose how many startups the company has backed.) The investments are part of the health insurance industry’s transition to a more “value-based” system, he says. “This is a dawn of a new day for us. You have the state’s largest insurance company taking an equity interest in the supply side of technology . . . That is a real shift.”

Insurers are interested in startup investments for the same reason venture capitalists are: because the Affordable Care Act is shaking things up.

For insurance companies, an esoteric part of the Affordable Care Act mandates that insurers spend somewhere between 80% and 85% of the premiums they collect from patients on quality and efficiency measures. Called the medical loss ratio rule, it forces insurers to redeploy capital rather then distribute it to shareholders. Josh Kaye, a partner at the multinational law firm DLA Piper, believes the rule has been a big driver of venture deals for insurance companies. “They’re mandated by law to use these funds in a variety of different ways,” he says. “Why not use it in a way that could result in additional returns while at the same time meeting the requirements?”

The ACA’s emphasis on efficiency measures is intended to lower the cost of health care by removing incentives to conduct unnecessary tests and procedures. In turn, health care providers are looking to big data and data analytics technologies to create best practices, make better decisions, and get better outcomes.

In this way, the Affordable Care Act blurs the categorical line between insurance company and health care provider by making insurers more responsible for measuring the quality and efficiency of providers. Meanwhile, providers take on more risk by conducting fewer tests and procedures, even if they’re deemed unnecessary. “Payers and providers who learn to work as if they are owned by a single entity are going to drive the marketplace,” Pomerantz says.

Corporate venture investors tend to have a bad reputation in the startup and venture capital world. At best, they are a potential acquirer of the startups they invest in. At worst, they cull ideas and strategies from the startups in which they invest, only to compete with them down the road. (In the words of prominent venture investor Fred Wilson, “They suck” because they may not be interested in the success of the company or of the entrepreneur. He later clarified his position by stating that some corporate VC firms are better than others.) With health insurance companies serving as investors, they have the chance to help ambitious but possibly naïve startups figure out the country’s complicated healthcare system.

data saving lives, policy, rapid learning health systems

data mining health records

“The goal in health care is not to protect privacy, the goal is to save lives. We need to have that as starting point,” said David Castro, director of the Center for Data Innovation. “Is the doctor treating me based on the last couple patients he saw, or is he treating me based on the rigorous analysis of millions of patents and finding the 5,000 that are actually just like me, and treating me in a much more accurate way?”

For data mining to succeed would also require recruiting top data scientists to health care, which isn’t easy given the demand in the hot field.

http://www.washingtonpost.com/blogs/innovations/wp/2014/10/01/the-incredible-potential-and-dangers-of-data-mining-health-records/

The incredible potential and dangers of data mining health records

By Matt McFarland October 1
Using the world’s best computers to analyze our health data could revolutionize how we live. (Connie Zhou/Google)

Imagine if your doctor could compare your physical health, diet and lifestyle to a thousand Americans with similar characteristics, and realize that you need treatment to prevent heart failure next month.

What if an analysis of your genome could help a physician give you a customized cancer treatment that saves your life?

Unleashing the modern power of computers, data crunching and artificial intelligence could revolutionize health care, improving and extending lives.

It’s the kind of potential Google chief executive Larry Page hinted at when he told the New York Times earlier this year that “we’d probably save 100,000 lives next year,” if we data mined health care data.

“Imagine you had the ability to search people’s medical records in the U.S.,” Page said in another interview this summer. “I imagine that would save 10,000 lives in the first year.”

Page’s numbers sound impressive, but are speculative and unfounded, according to many in the medical industry.

Interviews with more than a dozen health care professionals and data scientists found no evidence backing Page’s specific claims. While they universally agree that data mining — the examination and analysis of huge batches of information – could invigorate health care, they caution that any sort of accurate estimate would be impossible.

“Usually when I see someone put a number on it and throw around saving lives it usually means one, they aren’t usually a clinician or someone who provides care, or No. 2 it’s someone who really knows better, but is trying to grab a headline,” said Nicholas Marko, the department head of data science at the Geisinger Medical Center.

A Google spokeswoman declined to offer an explanation of Page’s numbers, or make him available for comment.

In one other instance where Page has used an unsubstantiated health care statistic, he told Time Magazine  last year that solving cancer would only “add about three years to people’s average life expectancy.” That’s a figure the American Cancer Society and National Cancer Institute had never heard of before. A Google spokeswoman didn’t have an answer when asked for an explanation.

To a cynic, Page is a shrewd businessman twisting facts to shape the national dialogue so that he can profit from absorbing our health data into the Google cloud, where his world-class engineers will find ways to make money off all of that information.

An optimist might remember Page’s assertion that Google is a company devoted to solving “huge problems for hundreds of millions of people,” and offer him the benefit of the doubt.

“Health care has been pretty archaic. We’re pretty behind the curve on things,” said Lorren Pettit, a vice president for the Healthcare Information and Management Systems Society, which aims to improve health care through information technology.  “We need the innovation of people from outside health care to come in and take a look and challenge this industry, and yes with data mining there’s a great world of possibility.”

Shaking up industries is part of Google’s DNA. Its self-driving car project could in theory eliminate the 1.24 million fatalities a year on global roads. If Page can soften a country’s fears about sharing our health data — which ends up saving lives — does the end justifies his means of fuzzy math?

“There’s tremendous opportunity if we start taking individualized genomic data and health histories and assuming you can perfectly de-identify it, my gosh, if you can mine that and look for patterns between genomic sequences and types of illnesses and effects of treatment on those illnesses you could potentially do a tremendous amount for society and the health of our individuals,” said Christopher Jaeger, Sutter Health’s chief medical information officer.

The average person might spend a few hours a year with their physician, during which data about their health (blood pressure, alcohol consumption, weight, etc.) is written down. If a patient’s health data was tracked 24-7 — as devices such as smartwatches are making realistic — there would be an exponential leap in the amount of data about someone’s health. More information — and the comparison of that information to other patients — should lead to better treatments.

“It would be great if when the patient walked in our Bluetooth sensors picked up their phone and it pushed in all their exercise and diet history, and then there were analytics that were performed in real time,” said Thomas Graf, chief medical officer at Geisinger Health System. “When the doc walked in the room they can say ‘Oh, looks like you’re exercising at 80 percent of what we were talking about.’ ”

But fear of litigation, privacy concerns, regulations and the challenge of collecting and standardizing data all stand in the way of realizing this health care utopia. Still, there are some early examples that hint at what could be done.

Researchers at the University of Bern in Switzerland have built a computer program to better measure the size of brain tumors.

Traditionally radiologists look at MRI scans and measure in two dimensions the size of a tumor. The computer program — called BraTumIA — is capable of a 3D analysis of the tumor’s volume, which better measures whether it’s shrinking or growing. Getting measurements right is crucial as physicians determine the best treatment plan for a patient.

“If I ask two radiologists to do the same job, you will see differences,” said researcher Mauricio Reyes. “The computer has the ability to be more consistent and more objective over time. Even if you have an error in the computer this error is consistent over time. What really matters is the trend.”

Here’s how the program works. A set of annotated brain scans — in which different parts of a tumor are labeled — are preloaded into the program. The program uses those as a guide to teach itself to identify different parts of future brain scans as a tumor or not.


A screenshot of BraTumIA in action. (Mauricio Reyes)

The end result is being able to run a scan for five minutes on a laptop and having a better understanding of a tumor. If more medical images made their way into databases such as BraTumIA, those services would get even better.

But what if health data we think is anonymous gets identified or hacked? The threat of being sued deters health organizations from sharing data and embracing the full potential of data mining.

For example, MRI exams and CT scans of a patient’s head could be used to reconstruct a person’s face. A hacker with access to such a database could use face-detection software to crosscheck the scans with a Web site where users post photos of themselves.

“If the same person has a Facebook account there’s a good chance that you could identify this person. If I had access to such a database I could give you a list of people in Facebook with names of who has a brain tumor,” cautioned Bjoern Menze, a computer science professor at TU Munchen who researches medical imaging.

“There will be criminals. There will be people who are bad actors. At some point something is going to get out,” Graf said. “It’s not an irrational fear. At the same time, people die driving every year and we still choose to drive cars, or most of us do. It’s a risk every person has to decide where they fall on the line.”

Many of those I interviewed anticipated a situation where patients could decide whether to opt into data mining of their health records. A tax benefit might even be given to encourage involvement.

If health records are ever going to be data mined, it’ll happen when consumers are convinced the perks outweigh the costs. The world has already seen dramatic changes to privacy norms as services such as Facebook grow in popularity. It’s incredibly popular Newsfeed — which funnels the latest information about friends into a feed — was initially met with uproar by users concerned about their privacy.

But as users saw the utility of the feed, the tradeoff in privacy became acceptable.

“The goal in health care is not to protect privacy, the goal is to save lives. We need to have that as starting point,” said David Castro, director of the Center for Data Innovation. “Is the doctor treating me based on the last couple patients he saw, or is he treating me based on the rigorous analysis of millions of patents and finding the 5,000 that are actually just like me, and treating me in a much more accurate way?”

For data mining to succeed would also require recruiting top data scientists to health care, which isn’t easy given the demand in the hot field.

“It’s hard,” said John Weinstein, chair of bioinformatics and computational biology at MD Anderson Cancer Center. “You really have to battle with Silicon Valley and the Boston academic scene.”

“Why would someone who is really really good at analyzing data come to work for a health care organization and make X dollars when they could go to Google and make 10X dollars?” Marko added.

Matt McFarland is the editor of Innovations. He’s always looking for the next big thing. You can find him on Twitter and Facebook.
complex adaptive systems, data saving lives, healthcare, rapid learning health systems

NYT: Can Big Data Tell Us What Clinical Trials Don’t?

 

http://www.nytimes.com/2014/10/05/magazine/can-big-data-tell-us-what-clinical-trials-dont.html?src=twr

MAGAZINE

Can Big Data Tell Us What Clinical Trials Don’t?

Illustration by Christopher Brand

Eureka

By VERONIQUE GREENWOOD

When a helicopter rushed a 13-year-old girl showing symptoms suggestive of kidney failure to Stanford’s Packard Children’s Hospital, Jennifer Frankovich was the rheumatologist on call. She and a team of other doctors quickly diagnosed lupus, an autoimmune disease. But as they hurried to treat the girl, Frankovich thought that something about the patient’s particular combination of lupus symptoms — kidney problems, inflamed pancreas and blood vessels — rang a bell. In the past, she’d seen lupus patients with these symptoms develop life-threatening blood clots. Her colleagues in other specialties didn’t think there was cause to give the girl anti-clotting drugs, so Frankovich deferred to them. But she retained her suspicions. “I could not forget these cases,” she says.

Back in her office, she found that the scientific literature had no studies on patients like this to guide her. So she did something unusual: She searched a database of all the lupus patients the hospital had seen over the previous five years, singling out those whose symptoms matched her patient’s, and ran an analysis to see whether they had developed blood clots. “I did some very simple statistics and brought the data to everybody that I had met with that morning,” she says. The change in attitude was striking. “It was very clear, based on the database, that she could be at an increased risk for a clot.”

The girl was given the drug, and she did not develop a clot. “At the end of the day, we don’t know whether it was the right decision,” says Chris Longhurst, a pediatrician and the chief medical information officer at Stanford Children’s Health, who is a colleague of Frankovich’s. But they felt that it was the best they could do with the limited information they had.

A large, costly and time-consuming clinical trial with proper controls might someday prove Frankovich’s hypothesis correct. But large, costly and time-consuming clinical trials are rarely carried out for uncommon complications of this sort. In the absence of such focused research, doctors and scientists are increasingly dipping into enormous troves of data that already exist — namely the aggregated medical records of thousands or even millions of patients to uncover patterns that might help steer care.

The Tatonetti Laboratory at Columbia University is a nexus in this search for signal in the noise. There, Nicholas Tatonetti, an assistant professor of biomedical informatics — an interdisciplinary field that combines computer science and medicine — develops algorithms to trawl medical databases and turn up correlations. For his doctoral thesis, he mined the F.D.A.’s records of adverse drug reactions to identify pairs of medications that seemed to cause problems when taken together. He found an interaction between two very commonly prescribed drugs: The antidepressant paroxetine (marketed as Paxil) and the cholesterol-lowering medication pravastatin were connected to higher blood-sugar levels. Taken individually, the drugs didn’t affect glucose levels. But taken together, the side-effect was impossible to ignore. “Nobody had ever thought to look for it,” Tatonetti says, “and so nobody had ever found it.”

The potential for this practice extends far beyond drug interactions. In the past, researchers noticed that being born in certain months or seasons appears to be linked to a higher risk of some diseases. In the Northern Hemisphere, people with multiple sclerosis tend to be born in the spring, while in the Southern Hemisphere they tend to be born in November; people with schizophrenia tend to have been born during the winter. There are numerous correlations like this, and the reasons for them are still foggy — a problem Tatonetti and a graduate assistant, Mary Boland, hope to solve by parsing the data on a vast array of outside factors. Tatonetti describes it as a quest to figure out “how these diseases could be dependent on birth month in a way that’s not just astrology.” Other researchers think data-mining might also be particularly beneficial for cancer patients, because so few types of cancer are represented in clinical trials.

As with so much network-enabled data-tinkering, this research is freighted with serious privacy concerns. If these analyses are considered part of treatment, hospitals may allow them on the grounds of doing what is best for a patient. But if they are considered medical research, then everyone whose records are being used must give permission. In practice, the distinction can be fuzzy and often depends on the culture of the institution. After Frankovich wrote about her experience in The New England Journal of Medicine in 2011, her hospital warned her not to conduct such analyses again until a proper framework for using patient information was in place.

In the lab, ensuring that the data-mining conclusions hold water can also be tricky. By definition, a medical-records database contains information only on sick people who sought help, so it is inherently incomplete. Also, they lack the controls of a clinical study and are full of other confounding factors that might trip up unwary researchers. Daniel Rubin, a professor of bioinformatics at Stanford, also warns that there have been no studies of data-driven medicine to determine whether it leads to positive outcomes more often than not. Because historical evidence is of “inferior quality,” he says, it has the potential to lead care astray.

Yet despite the pitfalls, developing a “learning health system” — one that can incorporate lessons from its own activities in real time — remains tantalizing to researchers. Stefan Thurner, a professor of complexity studies at the Medical University of Vienna, and his researcher, Peter Klimek, are working with a database of millions of people’s health-insurance claims, building networks of relationships among diseases. As they fill in the network with known connections and new ones mined from the data, Thurner and Klimek hope to be able to predict the health of individuals or of a population over time. On the clinical side, Longhurst has been advocating for a button in electronic medical-record software that would allow doctors to run automated searches for patients like theirs when no other sources of information are available.

With time, and with some crucial refinements, this kind of medicine may eventually become mainstream. Frankovich recalls a conversation with an older colleague. “She told me, ‘Research this decade benefits the next decade,’ ” Frankovich says. “That was how it was. But I feel like it doesn’t have to be that way anymore.”

cool, healthy habits, nutrition, policy, rapid learning health systems

Advertising tells you how affluent your suburb is…

 

http://www.news.com.au/finance/work/how-suburban-commuters-are-coaxed-into-unhealthy-eating-habits/story-fnkgbb6w-1227089160388

How suburban commuters are coaxed into unhealthy eating habits

  • 3 DAYS AGO OCTOBER 13, 2014 5:33PM
If you’re surrounded by ice coffee ads, you’re probably in a poorer suburb. Real coffee o

If you’re surrounded by ice coffee ads, you’re probably in a poorer suburb. Real coffee on the other hand … well, you could be well off. Source: News Corp Australia

EVER wondered whether your suburb is well-off or disadvantaged? There’s a simple test you can use to find the answer as you head home from work this evening.

Just check out the food advertisements around your train station or bus stop.

If the ads encourage you to drink diet soft drink, tea or coffee, you reside in an area considered pretty plush.

But if a lot of ads push fast food restaurants, flavoured milk and fruit juice, there is a fair chance you can mark your suburb as “disadvantaged”.

These are the findings from research by Philippa J. Settle, Adrian J. Cameron and Lukar E. Thornton of Deakin University.

Their investigation of ads aimed at commuters in 20 Melbourne suburbs is published in the October issue of the Australian and New Zealand Journal of Public Health.

“This exploration of outdoor food advertising at Melbourne transit stops found 30 per cent displayed food advertisements, with those in more disadvantaged suburbs more frequently promoting chain-brand fast food and less frequently promoting diet varieties of soft drinks,” concluded the researchers.

“These findings may help raise awareness of unhealthy environmental exposures.”

The study reinforces the proposition there is a distinct difference in food eaten in various social-economic communities. And the lower the income, the higher the likelihood that unhealthy fast food will be promoted.

Kooyong station volunteer gardeners John Dale and Charlie Baxter were disappointed when n

Kooyong station volunteer gardeners John Dale and Charlie Baxter were disappointed when new billboards were installed at Kooyong Station in Melbourne. Source: News Limited

The researchers contend advertising influences the type of food we eat and that overseas studies have found that unhealthy foods are most likely to appear in these advertisements.

“This being the case, advertising is likely to have played a role in the current obesity epidemic,” write the researchers in their paper.

“Furthermore, targeted advertising of unhealthy foods may entrench and even increase existing socio-economic inequalities in the prevalence of obesity.”

So some advertising doesn’t just make you fat, it can keep you overweight.

Previous studies found ads at Sydney rail stations commonly advertised unhealthy snacks — although water was the most common beverage — while a Perth study found 23 per cent of commuter stops audited had ads for alcohol.

The Melbourne study is the first to cover all types of commuter public transport and to make socio-economic conclusions.

A total of 233 food advertisements were identified at the 558 public transit stops audited across the 20 sampled suburbs, the study reports.

If you’re seeing ads such as this at your local bus stop, you probably live in an affluen

If you’re seeing ads such as this at your local bus stop, you probably live in an affluent area. Picture: AP/PepsiCoSource: AP

Least-disadvantaged suburbs had a higher mean number of advertisements per suburb compared to the most-disadvantaged suburbs, although this difference was not statistically significant.

And it’s not just a matter of where you live which decides the exposure to food ads. It also depends on how you commute.

“… however, differences were observed by the type of stop. A higher proportion of train stations in the least-disadvantaged suburbs had at least one advertisement present (86 per cent v 42 per cent). Conversely, fewer tram shelters in the least-disadvantaged areas featured food (32 per cent v 50 per cent),” says the research.

“The proportion of bus stop shelters with food advertisements was similar in the least- and most-disadvantaged suburbs (22 per cent and 25 per cent).”

data saving lives, health market quality, healthcare, policy, politics

Health Data “Interoperability”: A $30 Billion Unicorn Hunt

too funny

http://www.forbes.com/sites/theapothecary/2014/09/03/health-data-interoperability-a-30-billion-unicorn-hunt/

Health Data “Interoperability”: A $30 Billion Unicorn Hunt

John R. GrahamJohn R. Graham , Contributor
Comment Now

Having cheered as $26 billion of taxpayers’ money has been spent since 2009 inducing hospitals and physicians to install electronic health records (EHRs), many champions of the effort are dismayed that the EHRs are not interoperable. That is, they cannot talk to each other – which was the whole point of subsidizing the exercise.

All this money has achieved a process goal: There has been a significant uptake of EHRs. According to a recent review, the proportion of physicians who have at least a basic EHR has increased from under 22 percent to 48 percent. Doctors were motivated by the bounty offered, plus the threat of having reimbursements being clawed back in 2015 if they have not adopted EHRs. The proportion of hospitals has similarly increased from 12 percent to 44 percent.

But these EHRs do not  talk to each other. According to the same review, “only 10 percent of ambulatory practices and 30 percent of hospitals were found to be participating in operational health information exchange efforts.”

All those billions of taxpayers’ dollars were paid out to providers who attest to “meaningful use” of EHRs. However, there are three stages of meaningful use.  Stage 1 was relatively simple. Stage 2 was originally supposed to be achieved by 2013, but that has been pushed back until 2016. The hang up is that stage 2 has a high hurdle for interoperability.

According to the final rule published in September 2012, requirements include “the expectation that providers will electronically transmit patient care summaries with each other and with the patient to support transitions in care. Increasingly robust expectations for health information exchange in Stage 2 and Stage 3 would support the goal that information follows the patient.”

Despite the delay, providers are still complaining that the requirements are too demanding. According to Russell Branzell, president and CEO of the College of Healthcare Information Management Executives: “Now the very future of Meaningful Use is in question.”

So it should be: Evidence from Congressional investigations suggests that meaningful-use bounties have encouraged the adoption of EHRs that are deliberately closed to exchange with other parties. The problem is that exchanging data with competitors is fundamentally against the self-interest of the party which created the data. Nobody would expect The U.S. Department of Transportation to set up a fund to incentivize car-markers to exchange data with each other, or the U.S. Department of Agriculture to set up a fund to incentivize grocery stores to exchange data with each other.

670px-obama_signing_health_care-201003231

That is not to say that there would be no value to such data exchange. IfSafeway were out of my favorite brand of breakfast cereal, I’d love for the clerk to tell me that Giant had plenty in stock just down the road, instead of selling me something similar. However, the amount of government funding required to overwhelm competitors’ resistance to doing this would surely not be worth it.

I’m sure readers can come up with many examples that would demonstrate the public benefit of competing hospital systems sharing data seamlessly. An epidemic or terrorist attack are easy ones. However, advocates of health information exchange emphasize how it would reduce friction in the day to day operations of our health system.

But at what cost? $26 billion has not done the trick. It is unlikely that the remaining $4 billion in the pot will get the job done. The Office of the National Coordinator of Health IT has been promoting a ten-year plan for more funding – even a trust fund like the Federal Highway Trust Fund!

When the Office of the National Coordinator of Health IT was established during the Bush Administration, its purpose was to “coordinate,” not underwrite nor regulate. Congress should be wary of appropriating yet more funding to hunt the unicorn of health data interoperability.

data saving lives, facts & data points, healthy habits, nutrition, policy

The Key to Changing Individual Health Behaviors: Change the Environments That Give Rise to Them

 

http://harvardpublichealthreview.org/the-key-to-changing-individual-health-behaviors-change-the-environments-that-give-rise-to-them/

The Key to Changing Individual Health Behaviors: Change the Environments That Give Rise to Them

PDF: HPHRv2-Stulberg

Over the past four decades, the United States has faced steadily rising rates of obesity and associated chronic conditions. Many of these chronic conditions are rooted in nutrition and physical activity behaviors, and are often referred to as lifestyle diseases. Historically, the prevention of lifestyle diseases has focused on changes in individual behavior and personal choices, and personal responsibilities. However, a growing body of research has demonstrated the strong influence of physical and social surroundings on individuals’ actions. The context in which options are presented can shape the decision-making processes that impact health. Altogether, the research suggests that altering environments may be an effective driver of behavior change. 1Intentionally designing environments to promote healthy behaviors holds promise to reverse the increase of lifestyle diseases.

The emerging field of behavioral science – which gathers insights from disciplines like behavioral economics, cognitive psychology, and social psychology – illustrates that while individuals retain “free choice,” their environment significantly influences the choices they make, and in some instances, may lead them to act in ways that are counter to their true preferences. 2 A few examples:

  • Individual preferences are often inconsistent over time, especially in situations where immediate pleasures carry long term consequences. In a study that asked [hypothetically] if people would prefer fruit or chocolate as a future snack, 74% chose fruit. But, when those same participants were presented with both fruit and chocolate in real-time, 70% selected chocolate. 3
  • A person’s actions can be dramatically influenced by related contextual features. For instance, research shows that kitchenware size significantly influences serving and eating behavior. In a series of studies, individuals who were given larger bowls served themselves between 28-32% more cereal than those given smaller bowls. Studies also report that people tend to eat 90-97% of what is on their plate, irrespective of plate size. 4
  • People tend to consent to the “default option.” This has been observed in numerous situations ranging from deciding whether or not to become an organ donor to making saving allocations for retirement. For example, organ donation rates are 4% in Denmark and 12% in Germany where the default option is “opt-in.” In contrast, the rates are 86% in Sweden and nearly 100% in Austria where the default option is “opt-out.” Cultural differences cannot explain the discrepancy. 5

When these behavioral science insights are applied in the context of health, the growth of lifestyle diseases is not surprising. This expanding body of research sheds light on the difficulties of healthy living when society is dominated by the marketing of unhealthy foods and unduly large portion sizes, and where sedentary behavior is often the default option.

The good news is that the same forces that currently promote unhealthy behaviors can be used to encourage healthy ones. In their bestselling book Nudge, Richard Thaler and Cass Sunstein described “choice architecture,” or the proactive designing of environments that “nudge” people to make healthier selections while still retaining freedom of choice. 6 There are many opportunities to apply this concept to promoting healthy behaviors. In particular, given their resources, broad reach, and financial and social incentives, both governments and employers are in a unique position to promote healthy behaviors in a way that would affect many lives.

Government food programs such as the Supplemental Nutrition Assistance Program (“SNAP”) and the school lunch program could be designed to make healthy selections more accessible, and in some cases, the default options. Those that oppose the trend toward encouraging healthier foods often cite added costs and waste, arguing that children don’t like healthy foods and will throw them away uneaten. But the data tell a different story. A recent study in Childhood Obesity found that a vast majority of middle-school and high-school students like the updated and significantly healthier school lunch that was introduced in 2012. 7

Nonetheless, making the change is not cost-free. A recent meta-analysis found that the healthiest diets cost $1.50 more per-person, per-day, which amounts to $550 per-person, per-year. 8 While this amount is not insignificant, it pales in comparison to the cost of treating most diet-related chronic conditions. Designing government food programs around the “healthiest diets” may yield a positive return on investment.

Even so, many individuals – including those who do qualify for SNAP, as well as those who do not qualify for SNAP (i.e. incomes just about the SNAP cut-off) – may still struggle with affordability and availability of healthy foods. Perhaps the most sustainable and far-reaching approach to making healthy foods more accessible is to change food policies (e.g., subsidies) that currently favor the production and systematic delivery of unhealthy foods to favor healthy ones. This would likely lead to higher volumes, more efficient delivery, and lower costs for nutritious foods.

The government can also promote healthier eating by improving nutrition labeling. While the FDA’s recent proposal to ensure that serving sizes listed on food products reflect actual average consumption (e.g., nutrition specifications would reflect an entire muffin, not one-third of a muffin) is a small step in the right direction, there is potential to go a lot further. Research suggests that catchier and simplified nutrition labels could have a much greater impact on consumer behavior. 9 For example, NuVal, an independently designed system that gives food items a single overall score based on more than 30 nutrient and nutrition factors, could be considered for more widespread adoption. 10 Not only does NuVal make for easier interpretation of a product’s nutrition profile, it also enables comparison shopping between options and encourages people to “trade-up” to healthier options. 11 An additional model to consider is a traffic-light rating system that marks foods with either a green, yellow, or red light. In instances where it has already been implemented (in some private organizations and outside the United States), the traffic-light model has increased consumer awareness of health and leads to healthier purchases. 12

In addition to promoting a healthy diet, government should play an active role in encouraging physical activity through the education system (e.g., ensure the existence of meaningful recess and gym programs), transportation system (e.g., create options for safe pedestrian/bike commuting), and by supporting relevant community resources (e.g., building, maintaining, and ensuring the safety of outdoor parks and recreation centers). When options for physical activity are easily accessible, people tend to be more active. For example, a recent study published in the American Journal of Public Health illustrated that the establishment of traffic-free cycling and walking routes increased overall physical activity among those that lived nearby. 13

Employers may have the ability and incentives to move faster than government in designing health promoting environments. A healthier workforce results in both reduced health care costs and absenteeism, and in increased productivity. Recent data from the Society of Human Resource Management’s annual Employee Benefits Survey shows that employers are taking notice and increasing their investment in workforce wellness programs. While these programs have traditionally focused on offering employees classes, counseling, and incentives for healthy behaviors such as discounts on insurance premiums, subtler tweaks to the workplace itself could prove just as, if not more effective.

An example of these subtler changes is happening at Google. There, company leaders have invested in promoting employee nutrition and health. Instead of relying solely on traditional programs such as nutrition counseling and weight-loss classes, Google redesigned cafeterias to encourage healthier eating. Now, the most nutritious options are positioned at the front of the cafeteria and unhealthy foods are hidden in corners and placed in opaque bowls. Smaller plates are the norm and marked with reminder messages that “bigger dishes prompt people to eat more.” Foods are tagged with either red “warning” stickers, or green stickers signifying healthy foods. Beverage coolers stock water at eye level, and relegate sweetened beverages to the bottom where they are not as easily seen or accessed. These changes – which notably do not restrict options, but simply rearrange the way options are presented – have led to dramatic reductions in candy and sugar-sweetened beverage consumption, and increases in the use of smaller plates. 14 15

To encourage physical activity, employers can adopt similar approaches to workplace design, such as centrally located staircases and ergonomically fit workstations. Further, similar to current LEED certifications for environmentally-friendly buildings, there could also be a meaningful certification for health-promoting buildings. In addition to the design of physical workplaces, the way that work itself is conducted can also be designed to promote health. For example, some employers have made “walking meetings” a cultural norm to build physical activity into otherwise sedentary jobs. 16

 

Other Considerations

While the value of these environmental interventions is promising, there is a need for additional research that focuses on cost effectiveness. This is especially true if we hope to see increased governmental action, where broad policy implementation almost always follows a positive cost/benefit analysis. That said, some of the ideas – such as using smaller plates in government cafeterias or simplifying nutrition labels – come at relatively little additional financial cost, and have already demonstrated health-promoting benefits. These ideas could be fast-tracked for more widespread adoption.

Another potential barrier that must be overcome is the political power of special interests groups that rely on built-environments conducive to unhealthy behaviors. For example, a large part of the reason that the migration to healthier school lunches has taken so long is because various food interests have launched strong lobbying campaigns against such changes. 17 In order to transition entrenched unhealthy built-environments to healthier ones, policymakers will need to prioritize the demands of public health against the backdrop of influential and longstanding special interests

A broader approach to designing environments that promote healthy behaviors must also account for additional barriers that individuals with lower socioeconomic status commonly face. The government cannot rely solely on the private sector to drive these changes since those who stand to benefit most may be unemployed or not working for progressive employers with the resources to launch effective health campaigns. Thus, focusing on government food programs and community-based approaches that effect a lower-income demographic is critical (e.g., sidewalk coverage and safe streets, eliminating food deserts, maintaining outdoor parks). In addition to these more specific interventions, the clear connection between environment and health should only bolster the case for expanding social service programs more broadly. Realizing and addressing the fact that so many of the outcomes that lie inside of health care are rooted in factors that lie outside of health care is thus critical to improving health.

 

If we want to avert a public health crisis at the hands of chronic lifestyle-driven diseases, we need not only focus on changing individual behaviors, but also on changing the environments that give rise to those behaviors. Governments and employers must recognize the overwhelming influence of context on action, and take advantage of their unique position to intentionally shape environments that promote healthy behaviors.
  1. Kahneman, D. Thinking fast and slow. New York: Farrar, Straus, and Giroux. (2011). ↩
  2. For more on Behavioral Science, see the Behavioral Science and Policy Association and its forthcoming journal Behavioral Science and Policy. ↩
  3. Read, D., & Van Leeuwen, B. Predicting hunger: the effects of appetite and delay on choice. Organizational Behavior and Human Decision Processes. 1998; 76 (2), 189-205. ↩
  4. Van Ittersum, K., & Wansink, B. Plate size and color suggestibility: the delboeuf illusion’s bias on serving and eating behavior. Journal of Consumer Research. 2012; 39 (2), 215-228. ↩
  5. Johnson, E. J., & Goldstein, D. Do defaults save lives? Science. 2003; 302, 1338-1339. ↩
  6. Thaler, R. H., & Sunstein, C. R. Nudge: Improving decisions about health, wealth, and happiness. New York: Penguin Books. (2009). ↩
  7. Turner, L., & Chaloukpa, F. J. Perceived reactions of elementary school students to changes in school lunches after implementation of the United States Department of Agriculture’s new meals standards: minimal backlash, but rural and socioeconomic disparities exist. Childhood Obesity. 2014; 10 (4), 349-356. ↩
  8. Rao, M., Afshin, A., Singh, G., & Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open. 2013; 3 (12). doi:10.1136/bmjopen-2013-004277. ↩
  9. Roberto, C. A., & Khandpur, N. Improving the design of nutrition labels to promote healthier food choices and reasonable portion sizes. International Journal of Obesity. 2014; 38, 525-533. ↩
  10. Nuval.com. Accessed August 12, 2014. ↩
  11. Nuval.com: Trading Up Tips. Accessed August 28, 2014. ↩
  12. Sonnenberg, L., Gelsomin, E., Levy, E. D., Riis, D., Barraclough, S., & Thorndike, A., N. A traffic light food labeling intervention increases consumer awareness of health and healthy choices at the point-of-purchase. Preventative Medicine. 2013; 57 (4), 253-257. ↩
  13. Freeland, A. L., Banerjee, S. N., Dannenberg, A., L & Wendel, A. M. Walking associated with public transit: moving toward increased physical activity in the United States. American Journal of Public Health. 2013; 103 (3), 536-542. ↩
  14. Kuang, C. 6 ways Google hacks its cafeterias so Googlers eat healthier. Fast Company. April 2012; (164). ↩
  15. Wacther, Luke. Personal Interview on July 20, 2014. ↩
  16. Walking meetings could make work healthier, happier. CBS News. 07, May 2014. ↩
  17. Nixon, R. Nutrition Group Lobbies Against Healthier School Meals it Sought, Citing Cost. New York Times. 01, July 2014. ↩
data saving lives, healthy habits

Anosmia predicts longevity…

 

http://www.medicalobserver.com.au/news/noses-know-about-longevity

Noses know about longevity

A A A
3rd Oct 2014

Rada Rouse   all articles by this author

AN ELDERLY person who cannot accurately distinguish the smell of peppermint or fish may be staring death in the face, research suggests.

A study among a nationally representative sample of adults aged 57–85 found those who lost their sense of smell and were already at high risk from medical conditions had more than double the risk of dying in the next five years.

The cohort of 3000 provided baseline data by trying to identify odours, which were, in order of increasing difficulty to pinpoint, peppermint, fish, orange, rose and leather.

Five years later, the researchers assessed which participants were still alive.

Some 430, or 12.5% of the original cohort, had died.

People noted as anosmic in the first survey had a threefold increased risk of death when other factors including age, race and health were taken into consideration, the researchers said.

They noted a “dose-dependent” relationship between sense of smell and risk of death, with anosmic individuals having a greatly increased risk compared to hyposmic individuals, and the latter being more likely to die than those with a normal or “healthy” sense of smell.

The study showed 39% of anosmic individuals identified in the first test had died before the second survey. 

This compared to 19% of hyposmic people and 10% of those with a normal sense of smell.

“We believe olfaction is the canary in the coalmine of human health, not that its decline directly causes death,” the researchers wrote.

Assessment of olfactory function may be useful to help identify patients at high risk of mortality, they said.

PLoS ONE 2014; online 1 Oct