Abolishing Mammography Screening Programs? A View from the Swiss Medical Board
Nikola Biller-Andorno, M.D., Ph.D., and Peter Jüni, M.D.
April 16, 2014DOI: 10.1056/NEJMp1401875
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In January 2013, the Swiss Medical Board, an independent health technology assessment initiative under the auspices of the Conference of Health Ministers of the Swiss Cantons, the Swiss Medical Association, and the Swiss Academy of Medical Sciences, was mandated to prepare a review of mammography screening. The two of us, a medical ethicist and a clinical epidemiologist, were members of the expert panel that appraised the evidence and its implications. The other members were a clinical pharmacologist, an oncologic surgeon, a nurse scientist, a lawyer, and a health economist. As we embarked on the project, we were aware of the controversies that have surrounded mammography screening for the past 10 to 15 years. When we reviewed the available evidence and contemplated its implications in detail, however, we became increasingly concerned.
First, we noticed that the ongoing debate was based on a series of reanalyses of the same, predominantly outdated trials. The first trial started more than 50 years ago in New York City and the last trial in 1991 in the United Kingdom.1 None of these trials were initiated in the era of modern breast-cancer treatment, which has dramatically improved the prognosis of women with breast cancer. Could the modest benefit of mammography screening in terms of breast-cancer mortality that was shown in trials initiated between 1963 and 1991 still be detected in a trial conducted today?
Second, we were struck by how nonobvious it was that the benefits of mammography screening outweighed the harms. The relative risk reduction of approximately 20% in breast-cancer mortality associated with mammography that is currently described by most expert panels2 came at the price of a considerable diagnostic cascade, with repeat mammography, subsequent biopsies, and overdiagnosis of breast cancers — cancers that would never have become clinically apparent. The recently published extended follow-up of the Canadian National Breast Screening Study is likely to provide reliable estimates of the extent of overdiagnosis. After 25 years of follow-up, it found that 106 of 484 screen-detected cancers (21.9%) were overdiagnosed.3 This means that 106 of the 44,925 healthy women in the screening group were diagnosed with and treated for breast cancer unnecessarily, which resulted in needless surgical interventions, radiotherapy, chemotherapy, or some combination of these therapies. In addition, a Cochrane review of 10 trials involving more than 600,000 women showed there was no evidence suggesting an effect of mammography screening on overall mortality.1 In the best case, the small reduction in breast-cancer deaths was attenuated by deaths from other causes. In the worst case, the reduction was canceled out by deaths caused by coexisting conditions or by the harms of screening and associated overtreatment. Did the available evidence, taken together, indicate that mammography screening indeed benefits women?
Third, we were disconcerted by the pronounced discrepancy between women’s perceptions of the benefits of mammography screening and the benefits to be expected in reality. The figureU.S. Women’s Perceptions of the Effects of Mammography Screening on Breast-Cancer Mortality as Compared with the Actual Effects. shows the numbers of 50-year-old women in the United States expected to be alive, to die from breast cancer, or to die from other causes if they are invited to undergo regular mammography every 2 years over a 10-year period, as compared with women who do not undergo mammography. The numbers in Panel A are derived from a survey about U.S. women’s perceptions,4 in which 717 of 1003 women (71.5%) said they believed that mammography reduced the risk of breast-cancer deaths by at least half, and 723 women (72.1%) thought that at least 80 deaths would be prevented per 1000 women who were invited for screening. The numbers in Panel B reflect the most likely scenarios according to available trials1-3: a relative risk reduction of 20% and prevention of 1 breast-cancer death. The data for Switzerland, reported in the same study, show similarly overly optimistic expectations. How can women make an informed decision if they overestimate the benefit of mammography so grossly?
The Swiss Medical Board’s report was made public on February 2, 2014 (www.medical-board.ch). It acknowledged that systematic mammography screening might prevent about one death attributed to breast cancer for every 1000 women screened, even though there was no evidence to suggest that overall mortality was affected. At the same time, it emphasized the harm — in particular, false positive test results and the risk of overdiagnosis. For every breast-cancer death prevented in U.S. women over a 10-year course of annual screening beginning at 50 years of age, 490 to 670 women are likely to have a false positive mammogram with repeat examination; 70 to 100, an unnecessary biopsy; and 3 to 14, an overdiagnosed breast cancer that would never have become clinically apparent.5 The board therefore recommended that no new systematic mammography screening programs be introduced and that a time limit be placed on existing programs. In addition, it stipulated that the quality of all forms of mammography screening should be evaluated and that clear and balanced information should be provided to women regarding the benefits and harms of screening.
The report caused an uproar and was emphatically rejected by a number of Swiss cancer experts and organizations, some of which called the conclusions “unethical.” One of the main arguments used against it was that it contradicted the global consensus of leading experts in the field — a criticism that made us appreciate our unprejudiced perspective resulting from our lack of exposure to past consensus-building efforts by specialists in breast-cancer screening. Another argument was that the report unsettled women, but we wonder how to avoid unsettling women, given the available evidence.
The Swiss Medical Board is nongovernmental, and its recommendations are not legally binding. Therefore, it is unclear whether the report will have any effect on the policies in our country. Although Switzerland is a small country, there are notable differences among regions, with the French- and Italian-speaking cantons being much more in favor of screening programs than the German-speaking cantons — a finding suggesting that cultural factors need to be taken into account. Eleven of the 26 Swiss cantons have systematic mammography screening programs for women 50 years of age or older; two of these programs were introduced only last year. One German-speaking canton, Uri, is reconsidering its decision to start a mammography screening program in light of the board’s recommendations. Participation in existing programs ranges from 30 to 60% — variation that can be partially explained by the coexistence of opportunistic screening offered by physicians in private practice. At least three quarters of all Swiss women 50 years of age or older have had a mammogram at least once in their life. Health insurers are required to cover mammography as part of systematic screening programs or within the framework of diagnostic workups of potential breast disease.
It is easy to promote mammography screening if the majority of women believe that it prevents or reduces the risk of getting breast cancer and saves many lives through early detection of aggressive tumors.4 We would be in favor of mammography screening if these beliefs were valid. Unfortunately, they are not, and we believe that women need to be told so. From an ethical perspective, a public health program that does not clearly produce more benefits than harms is hard to justify. Providing clear, unbiased information, promoting appropriate care, and preventing overdiagnosis and overtreatment would be a better choice.
The views expressed in this article are those of the authors and do not necessarily reflect those of all members of the expert panel of the Swiss Medical Board.
Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.
This article was published on April 16, 2014, at NEJM.org.
SOURCE INFORMATION
From the Institute of Biomedical Ethics, University of Zurich, Zurich (N.B.-A.), and the Institute of Social and Preventive Medicine and Clinical Trials Unit Bern, Department of Clinical Research, University of Bern, Bern (P.J.) — both in Switzerland; and the Division of Medical Ethics, Department of Global Health and Social Medicine, Harvard Medical School, Boston (N.B.-A.). Dr. Biller-Andorno is a member of the expert panel of the Swiss Medical Board; Dr. Jüni was a member of the panel until August 30, 2013.
Patient Centered Outcomes Research Institute (PCORI) NIH-funded 10 site data linking project hoping to develop a complete clinical picture of 26-30 million Americans
Do certain sets of behavioral interventions work better than others for weight control? Might certain antibiotics work better for cystic fibrosis patients, based on their genetic profile?
It’s described in the story as the holy grail of health-care research, but it doesn’t incorporate social determinants, so how can it be? (PN)
Inside an otherwise ordinary office building in lower Manhattan, government-funded scientists have begun collecting and connecting together terabytes of patient medical records in what may be one of the most radical projects in health care ever attempted.
The data — from every patient treated at one of New York’s major hospital centers over the past few years — include some of the most intimate details of a life. Vital signs. Diagnoses and conditions. Results of blood tests, X-rays, MRI scans. Surgeries. Insurance claims. And in some cases, links to genetic samples.
The effort is being duplicated at 10 other sites across the country using data from hospitals, academic research centers, community health clinics, insurers and other sources. If all goes well, by September 2015 they will be linked together to create a giant repository of medical information from 26 million to 30 million Americans.
Nothing of this scale has been built before, and researchers say the potential of the network to speed up research efforts and to answer questions that have long vexed scientists cannot be overstated. But the creation of the network presents tricky ethical questions about who owns and controls the data, how to protect patient privacy and how research questions will be prioritized.
“Both the opportunity and the anxiety are pretty electrifying,” Francis S. Collins, director of the National Institutes of Health, said in an interview.
The origins of the patient project lie in an obscure part of the 2010 Affordable Care Act. As part of the nation’s health-care overhaul, Congress created an independent nonprofit group to help patients and their doctors make better-informed decisions about care. Dubbed the Patient-Centered Outcomes Research Institute, or PCORI, and based in the District, the organization’s mandate is to launch, fund and coordinate research on “comparative effectiveness” — to find out which drugs, devices and treatment options are more effective than others.
Do certain sets of behavioral interventions work better than others for weight control, for example? Might certain antibiotics work better for cystic fibrosis patients, based on their genetic profile?
Such questions have been surprisingly difficult to answer, despite the thousands of clinical trials published every year.
Physicians have long grumbled that few studies can be translated into practical advice. Some studies are too small to draw any definitive conclusions. Others include patients diagnosed with a single condition, while most patients are more complicated — they suffer from multiple issues. It isn’t uncommon for studies to contradict each other, and there’s no way for clinicians to know which one is right, because they often use different methodologies.
“The whole idea was to create a way to do the kind of research that would inform the real world,” explained Eugene Rich, who researches health-care effectiveness for Mathematica Policy Research, based in Princeton, N.J.
The database — an idea that has been talked about for years by everyone from insurance companies to Google but has never been successfully executed — holds the hope that some of those obstacles can be overcome.
“We will be able to get answers with a degree of certainty that we’ve never had before,” said Joe V. Selby, PCORI’s executive director, who calls the patient records network “the holy grail” of health-care research.
Collins said the value of the network is that it gives scientists the ability to ask an endless number of questions about a massive patient population with great speed and little cost.
In the randomized trials that NIH typically supports, “you have to enroll patients from the very beginning, and that’s a big infrastructure-building process that can take quite some time. And once a trial has been conducted, the whole thing has to be taken down again,” Collins said.
“It’s a great way to answer one specific question, but it’s not an efficient way to ask lots of questions,” he explained.
Before PCORI’s vision can be realized, the project’s leaders must overcome numerous hurdles.
The technical challenges of the project are enormous. The specter of the botched launch of HealthCare.gov haunts anyone trying to get large numbers of separate computer systems to talk to each other. But it is the larger questions about governance that have triggered conflict and worry in the nation’s health-care community.
How will research questions be prioritized? How should disagreements be resolved?
Should pharmaceutical companies and insurers be able to access the records and, if so, under what circumstances? What about the Centers for Disease Control and Prevention? The information could help epidemiologists track outbreaks and clusters of disease in a way they have never done before.
And, critically important to the multibillion-dollar pharmaceutical industry, how will the Food and Drug Administration view this type of research when considering applications for new drugs or in recalling old ones?
PCORI was never imagined to be the custodian of this kind of data network. It was designed to launch and fund research in a manner similar to NIH and the National Science Foundation. But it did not get nearly the same kind of funding.
President Obama’s budget request for fiscal 2015 included $30.4 billion for NIH and nearly $7.3 billion for NSF. But PCORI, which is funded through several streams, gets only about $500 million annually. Large, randomized clinical trials such as the ones NIH does for important questions cost upwards of $150 million, meaning that PCORI has enough to fund only two or three a year.
“So about a year ago, PCORI started talking about whether there is another model that is different from the NIH model, which would be more about embedding clinical studies in the fabric of day-to-day care,” said Robert W. Dubois, chief science officer for the National Pharmaceutical Council, a health policy research group funded by the industry.
The PCORI network, which is being built at a cost of nearly $100 million, would also be a way to pinpoint patients with certain criteria who could be invited to join a clinical trial. As it stands today, identifying patients eligible for trials is often time-consuming, expensive and hit-or-miss. Researchers must use a variety of tools to get enough participants, including reaching out to a network of doctors who then contact their patients and the old-fashioned method of putting up fliers in places where people with the criteria they are looking for congregate.
The new national patient network will comprise 11 sub-networks that include records from New York and Chicago, children’s hospitals, Kaiser Permanente and other groups. Each participating organization retains all the personally identifiable data and would have the right to accept or decline a research proposal. If a research project is greenlighted, each of the smaller networks would analyze its own databases and return an anonymized, aggregated response to the researcher.
“The raw data is not what is being shared. That remains with the institution that the patient trusts,” said Devon McGraw, director of the health privacy project for the Center for Democracy and Technology and head of the data privacy task force for PCORI.
Privacy experts say the general consent forms that patients sign when they get treatment should allow the use of data already collected in the aggregate. Hospitals and other organizations participating in the project don’t have any plans to explicitly inform patients about this. If researchers wanted doctors to collect additional data as part of a clinical trial, the researchers would clearly have to get a patient’s consent. But what about projects that involve more detailed analysis of individual patient histories?
One aspect of the project that was highlighted by researchers involved is that it has included patients at every step. Patient panels have been convened to help suggest research questions, and patients serve on the committees looking at privacy and data security.
Rainu Kaushal, a researcher at Weill Cornell Medical College who heads the New York-based part of the project, said patients’ perspectives on studies are often very different than scientists’.
“As a provider, I may be interested in how a serum marker changes with a treatment. But patients may be more interested in how it affects how they feel, their ability to exercise and eat,” she said.
Brian Currie, vice president for medical research for the Montefiore Medical Center, the university hospital for the Albert Einstein College of Medicine in the Bronx, which is participating in the New York network, said the number of questions are understandable given the historical barriers between different types of institutions that have prevented this research in the past.
“It’s pushing all the fronts on how medical institutions do research,” Currie said.
People have more goals than they have willpower for. That’s just the way our ambition works. They give up, get distracted, or prioritize some other goal.
About a year ago, we ran a one-off research project into the Slow-Carb Diet™ that turned up surprisingly strong results. Over a four week period, people who stuck to the diet showed an 84% success rate and an average weight loss 0f 8.6lbs.
But are those results legit? If I picked a person at random out of a crowd, could they expect to see the same results? Almost immediately after publishing the results we started getting feedback about experimental bias.
This first study was biased, which means it doesn’t carry any scientific confidence. That’s a fixable problem, so we set off to redo the study in a bigger and more rigorous way.
That led to the Quantified Diet, our quest to verify and compare every popular diet. We now have initial results for ten diets. This is the story of our experiment and how we’re interpreting the diet data we’ve collected.
Understanding Bias
To understand bias, here’s quick alternative explanation for our initial Slow-Carb data: a group of highly motivated, very overweight people joined the diet and lost what, for them, is a very small amount of weight. In this alternative explanation, the results really are not very interesting and they definitely aren’t generalizable.
However, we had some advice from academics at Berkeley aimed specifically at overcoming the biases of the people who were self-selecting into our study. The keys: a control group following non-diet advice and randomized assignment into a comparative group of diets.
Our Experimental Design
The gist of our experimental design hinged on the following elements:
We were going to start by comparing ten approaches to diet: Slow-Carb, Paleo, Whole Foods, Vegetarian, Gluten-free, No sweets, DASH, Calorie Counting, Sleep More, Mindful Eating.
Lift wrote instructions for each diet, with the help of diet experts, and provided 28-day goals (with community support) for each diet inside our app.
We included two control groups, one with the task of reading more and the other with the task of flossing more.
Participants were going to choose which of the approaches they were willing to try and then we would randomly assign from within that group. Leaving some room for choice allowed people to maintain control over their health, while still giving us room to apply a statistically relevant analysis.
Participants who said they were willing to try a control group and at least two others were in the experiment. This is who we were studying.
A lot of people didn’t meet this criteria, or opted out at some point along the way. We have observational data on this group, but they can’t be considered scientifically valid results for the reasons around bias covered above.
Full writeup of the methodology coming.
Top Level Results
At the beginning of the study, everyone thought we were going to choose a winning diet. Which of the ten diets was the best?
Nine of the diets performed well as measured by weight loss. Here’s the ranking, with weight loss measured as a percentage of body weight. Slow-Carb, Paleo and DASH look like they led the pack (but keep reading because this chart absolutely does not tell the whole story).
Sleep, which never really had a strong weight loss hypotheses, lost. We ended up calling this a placebo control in order to bolster our statistical relevance.
Before moving on, lets just call out that people in the diets were losing 4-ish pounds over a one month period on average. That’s great given that our data set contains people who didn’t even follow their diet completely.
The Value of the Control
The control groups help us understand whether the experimental advice (to diet) is better than doing nothing. Maybe everyone loses weight no matter what they do?
This sounds unlikely, but we were all surprised to see that the control groups lost 1.1% of their body weight (just by sleeping, reading and flossing!)
Is that because they were monitoring their weight? Is it because the bulk of the study occurred in January, right after people finished holiday gorging? We don’t actually know why the control groups lost weight, but we do know that dieting was better than being in the control.
Here’s the weight-loss chart revised to show the difference between each diet and the control (this chart shows the experimental effect).
The Value of Randomized Assignment
Randomized assignment helps us feel confident that the weight loss is not specific to the fans of a particular diet.
Because of the randomization, we can ask the following question. For each diet, what happens if we assigned the person to a different diet?
This is an indicator of whether a diet is actually better or if the people who are attracted to a diet have some other characteristic that is effecting our observational results.
The obvious example of bias would be a skew toward male or female. Bigger people have more weight to lose (male), plus we observed that males tended to lose a higher percentage of their body weight (2.8% vs. 1.8%).
Comparing the diets this way adds another promising diet approach: no sweets. But let’s, be real, the differences between these diets are very small, less than half a pound over four weeks, as compared to doing any diet at all, five pounds over four weeks. Our advice is pick the diet that’s most appealing (rather than trying to optimize).
Soda is bad! And other Correlations.
What else leads to weight loss?
It helps if your existing diet is terrible (your new diet is even better in comparison). People who reported heavy pre-diet soda consumption lost an extra 0.6% body weight.
Giving up fast food was also good for an extra 0.6% (but probably not worth adding fast food just to give it up).
Men lost more weight (2.6% vs 1.8%).
Adherence mattered (duh). Here’s a chart with weight loss by adherence.
How much of the time did people follow the diet advice?
Choosing a Diet
Ok. Now I think I’ve explained enough that you could choose one of these diets. All of them are available via the Lift app available on the web, iPhoneand Android.
Given that all the diets work, the real question you should be asking yourself is which one do you most want to follow.
I can’t stress that enough. It’s not just about which had the most weight loss. Choose a diet you can stick to.
Let’s Talk Success Rate
Adherence matters. Even half-way adherence to a diet led to more than 1% weight loss (better than the control groups).
This brings up an interesting point. So far, our data is based on the people who made it all the way to the end of our study. This is the survivor bias. We don’t know what happened to the other people (hopefully the diets weren’t fatal).
In order to judge the success rate of dieting you’ll have to use some judgement. But we can give you the most optimistic and most pessimistic estimates. The truth is somewhere in between.
Of people who gave us all of their data over four weeks, 75% lost weight. Let’s call this the success rate ceiling. It includes many reasons for not losing weight, including low adherence. But at least they paid attention to the goal for the entire time. The weight loss averages are based on this group.
Of people who joined the study, only 16% completed the entire study (and 75% of those lost weight). So, merely joining a diet, with no other data about your commitment, has a success rate of 12%. Let’s call this thesuccess rate floor.
Read that floor as 12% of people who merely said that they were interested in doing a diet had definitely lost weight four weeks later. There’s no measure of commitment in that result. If we filter by even a simple commitment measure, such as the person fills out the first survey on day one, then the success rate jumps from 12% to 28%.
If you are making public policy, then maybe that 12% number looks important. People have more goals than they have willpower for. That’s just the way our ambition works. They give up, get distracted, or prioritize some other goal.
If you are an individual, I’d put more weight in the ceiling. You want to know that whatever path you choose has a chance of succeeding. 75% is a number that should give you confidence.
Losing Weight?
We’ve focused on losing weight for two reasons. One, it’s a very common goal. But, two, it’s also the strongest signal we got out of our data.
We also measured happiness and energy but the signal was weak. We didn’t measure any other markers of health. That’s important to note.
We are behavior designers, so we’re looking at the effectiveness of behavior change advice. You should still consult a nutritionist when it comes to the full scope of health impacts from a diet change. For example, you could work with our partner WellnessFX for a blood workup (and talk to their doctors).
Open Sourcing the Research
We’ve open sourced the research. You can grab the raw data and some example code to evaluate it from our GitHub repository.
All of the participants were expecting to have their data anonymized for the purposes of research. Take a look and please share your work back (it’s required by the CC and MIT licenses).
There was some lossiness in the anonymization process. We’ve stripped out personal information (of course), but also made sure that rows in the data set can’t be tied back to individual Lift accounts. For that reason some of the data is summarized. For example, weight is expressed as percentage weight loss and adherence is expressed on a 1-5 scale.
If you want to go digging around in the data, I would suggest starting by looking at our surveys where we got extra data about the participants: day 1,week 1, week 2, week 3, week 4.
Citizen Science or No Science
I’m expecting that our research will spark some debate about the validity of scientific research from non-traditional sources. I expect this because I’ve already been on the receiving end of this debate.
Here’s how we’re seeing it right now. I acknowledge that we already have a robust scientific process living in academia. And I acknowledge that the way we ran this research broke the norms of that process.
The closest parallel I can think of is the rise of citizen journalism (mostly through blogs) as a complement to traditional journalism. At the beginning there was a lot of criticism of the approach as dangerous and irresponsible. Now we know that the approach brought a lot of benefits, namely: breadth, analysis and speed.
That’s the same with citizen science. We studied these diets because we didn’t see anyone else doing it. And we’re continuing to do other research (for example: meditation) because we’re imagining a world where everything in the self-improvement space, from fitness to diet to self-help, is verifiably trustworthy.
Continuing Research
One of our core tenants with this research is that we can revise it. We didn’t have to write a grant proposal and it didn’t cost us anything to run the study. In fact, we’re already revising it.
To start with, we’re adding in one more diet: “Don’t Drink Sugar.”
We wrote this diet based on the study results and a belief in minimal effective interventions. So, if you’re at all interested in losing weight while contributing to science, please sign up for the Quantified Diet.
Army ants subscribe to a simple rule: follow the ant
in front. If the group gets lost each ant tracks
another, eventually forming a circle. According to
crowd theorist James Surowiecki, one circle 400m
in circumference marched for two days until they
all died.1
Humans are not ants, but we often trudge together
along the same trail, neglecting to look around for
alternatives. Mass delusions involve large groups
holding false or exaggerated beliefs for sustained periods.
Humanity has a long, sorry list of these shadowthe-
leader epidemics of collective consciousness which
appear obviously wrong only in hindsight. Some last
for centuries: early alchemists intent on transmuting
base metals into gold and the Christian Crusades of
Europe’s middle ages, for example. Others have correlates
which resurface decades or centuries later:
McCarthy’s persecution of alleged communists in
the 1950s harked back to the Salem Witch hunts of
16th century America just as the 2008 Global
Financial Crisis had much in common with the
‘South Sea Bubble’ which slashed 17th century
Britain’s GDP.
In the educated 21st century, too, we blithely trust
in economic and political systems which are stripping
the earth’s resources, altering the climate and facilitating
wars. Are we then similarly mistaken, en masse,
about the capabilities of the health system?
Most of us believe in the miracles of modern medicine.
We like to think that the health system is
increasingly effective, that we are implementing
better treatments and cures with rapid diffusion of
new practices and pharmaceuticals and that there is
always another scientific or technological breakthrough
just around the corner promising to save
even more lives; all at an affordable price.
We maintain the faith despite multiple contraindications.
Modern health systems consistently deliver
at least 10% iatrogenic harm.2 Despite very large
investments and intermittent but important interventional
successes, such as checklists in theatres3 and
clinical bundles in ICU,4 there is no study showing
a step-change reduction in this rate, systems-wide.
Only half of care delivered is in line with guidelines,5
one-third is thought to be waste,6 and much is not
evidence-based,7 notwithstanding concerted efforts to
optimise that evidence and incorporate it into routine
practice.8
The reality is that progress is slowing, and medicine
seems to be reaching the limits of its capacities.
The potentially disastrous problems of antibiotic
resistance, for example, are yet to play out. This is
only one point among many. New technologies such
as the enormously expensive human genome project
have provided only marginal benefits to date. We still
do not have the answers to fundamental questions
about the causes of common diseases and how to
cure them. Many doctors are dissatisfied and increasingly
pessimistic.9,10 It must also be remembered that
although death is no longer seen as natural in the
modern era, everyone must die. Yet, we inflict most
of our medical ‘miracles’ on people during their last
six months of life. Le Fanu describes this levelling off
and now falling away of health care progress in The
Rise and Fall of Modern Medicine.11
Every major group of stakeholders has its own
specific delusion which acts to augment the metalevel
medical miracles delusion. Thus, the overarching
delusion is buttressed by a set of related ‘viruses
of the mind’, to borrow Richard Dawkins’ evocative
phrase.12
Although politicians think and act as if they are
running things, modern health systems are so complex
and encompass so many competing interests that no
one is actually in charge. Then, bureaucrats – acting
under their own brand of ‘groupthink’ – assume their
rules and pronouncements provide top-down stimulus
for medical progress and improved clinical performance
on the ground. Yet coalface clinicians are relatively
autonomous agents, so there can only ever be
modest policy trickle down.13,14
Researchers, too, support the medical miracles
industrial complex. The electronic database
PubMed holds some 23 million articles and is growing
rapidly. Every author hopes it will be his or her
results that will make a difference, yet there is far less
take up than imagined and comparatively little
investment in the science of implementation8 – translating
evidence into real life enhancements.
Nor are clinicians or the patients they serve
immune. While frontline clinicians strive to provide
good care, many myopically assume their practice is
above average; the so-called Dunning-Kruger
effect.15,16 Of course, statistically, half of all care clinicians
provide is below average. And notwithstanding
decades of public awareness, patients believe modern
medicine can repair them after decades of alcohol,
drugs, sedentary lives and dietary-excesses, despite
evidence to the contrary.
Meanwhile, the media’s unremitting propensity to
lend credibility to controversial views and to hone in
on ‘gee whiz’ breakthroughs – while ignoring the
incremental and the routine – fuels unrealistic expectations
of what modern medicine can deliver.
Throughout history, mass delusions have been
aligned with mass desires for favourable outcomes.
In the pursuit of medical miracles all of our interests
line up in a perfect circle. We seem more like army
ants than we think.
Just as the Global Financial Crisis was a wake-up
call for the serious consequences of blind fiscal faith
we must begin to manage our expectations of the
health system. Progress is always in jeopardy when
the real problems are obscured.
The challenge is to harness the tough-minded
scepticism needed to tackle this widely held ‘received
wisdom’. One realistic way forward is to encourage
stakeholders – politicians, policymakers, journalists,
researchers, clinicians, patients – to first consider
that their own and others’ perspectives are simply not
logically sustainable. This may be achieved through
genuine inter-group discourse about the health
system, where it is at, and its limitations.
As is so often the case, Albert Einstein said it best,
in a typically neat aphorism: ‘The significant problems
we face cannot be solved at the same level of
thinking we were at when we created them’.17 If we
can humbly accept that we need new perspectives
for healthcare – and radically different ways of
thinking – we will be better placed to free ourselves
from the hold of these peculiar viruses of the mind.
British gerontologist Aubrey de Grey believes achieving human immortality is inevitable. Last October de Grey told the audience at a US technology conference that they could expect to live 1000 years, maybe longer.
If you were given the chance, would you choose to live forever, or another few hundred years? It may sound like the stuff of fantasy, but some very smart people are working to make death a thing of the past.
Scientists are working to stop the ageing process, and extend the living… Photo: Shutterstock
Nanobots in your blood stream, backing up your brain to a computer, swapping your fallible human form for a sophisticated holographic avatar – it might sound like science fiction, but these are just some of the ways that science is hoping to extend human life and inch us closer to living forever.
US futurist, inventor and Google’s head of engineering, Ray Kurzweil has predicted that by the end of the century humans and machines will merge to create super humans who may never face the prospect of death. And Kurzweil, 65, hopes to be among those kicking mortality to the curb.
Ray Kurzweil: Working to bring an end to death. Photo: Getty
“Twenty years from now, we will be adding more time than is going by to your remaining life expectancy,” Kurzweil told Forbes Magazine. “We’ve quadrupled life expectancy in the past 1000 years and doubled it in the past 200 years. We’re now able to reprogram health and medicine as software, and so that pace is only going to continue to accelerate.”
Kurzweil is no slouch when it comes to accurate predictions. In the 1980s he predicted the incredible rise of the internet, foresaw the fall of the Soviet Union and identified the year when computers would beat humans at chess.
His next predictions include the programming of nanobots to work from within the body to augment the immune system and fight pathogens. By 2045 he sees us backing up our minds to the cloud and downloading ourselves into robotic forms.
And he’s not the only scientist hoping to blow out hundreds of candles in the future.
Immortality: Not if, when
British gerontologist Aubrey de Grey believes achieving human immortality is inevitable. Last October de Grey told the audience at a US technology conference that they could expect to live 1000 years, maybe longer.
Ageing, he says, is a simple case of bad engineering, and once the human body’s kinks are ironed out we’ll be able to reverse its effects and put death on the back burner.
“My approach is to start from the straightforward principle that our body is a machine. A very complicated machine, but nonetheless a machine, and it can be subjected to maintenance and repair in the same way as a simple machine, like a car,” de Grey has said. “What I’m after is not living to 1000. I’m after letting people avoid death for as long as they want to.”
Google is on board
It’s a goal that even tech giant Google thinks is worth pursuing.
When Google entered the anti-ageing business last year, with the launch of its new biotechnology company Calico, it brought a new level of interest, respectability and crucially – funding – to the field.
Calico has poached some of the leading anti-ageing researchers from across the world to work on the challenge of extending life.
“I think that if Google succeeds, this would be their greatest gift to humanity,” said David Sinclair, an Australian professor of genetics at Harvard Medical School.
Professor Sinclair led a research team which last year announced it had reversed muscle ageing in mice, the results of which exceeded his expectations.
“We want immortality so badly that we’re always ready to be swept away into unthinkingness … Half in love with the impossible we’ve always wanted to conquer death.”
“I’ve been studying ageing at the molecular level now for nearly 20 years and I didn’t think I’d see a day when ageing could be reversed. I thought we’d be lucky to slow it down a little bit,” he was quoted as saying.
“There’s clearly much more work to be done here, but if those results stand, then aging may be a reversible condition, if it is caught early,” he said.
The research involved improving communication between a cell’s mitochondria and nucleus. Mitochondria are like a battery within a cell, powering important biological functions. When communication breaks down between this and the nucleus, the effects of ageing accelerate.
Human trials of the groundbreaking process are expected to start this year.
Buying life
It’s the sort of breakthrough that can’t come soon enough for several billionaires across the globe who are pouring their fortunes and hopes into immortality research.
Russian entrepreneur, Dmitry Itskov founded the 2045 Initiative in 2011 with the aim of thwarting human death within three decades. Itskov envisages ‘neo-humans’ who will relinquish clunky human forms and adopt sophisticated machine bodies. He claims humans will eventually download their minds into artificial brains, which will then be connected to humanoid robots he calls Avatars.
According to 2045.com: “Substance independent minds will receive new bodies with capabilities far exceeding those of ordinary humans … Humanity will make a fully managed evolutionary transition and eventually become a new species.”
PayPal co-founder Peter Thiel donated $US3.5 million to Aubrey de Grey’s not-for-profit research foundation, telling the New Yorker at the time that: “Probably the most extreme form of inequality is between people who are alive and people who are dead”.
Clearly Thiel would prefer to remain among the living and he’s prepared to pay for his pitch at immortality, most recently making a large donation to the Singularity Institute, which focuses on creating artificial intelligence that could see the rise of cyborgs (merged humans and machines).
Maximising life, minimising death
US entrepreneur turned science innovator, David Kekich, dedicated his life and impressive bank balance to reversing ageing after he was paralysed from a spinal cord injury in 1978. Kekich initially raised money for paralysis research but then switched to anti-ageing research. He founded the Maximum Life Foundation in 1999 and aims to reverse human ageing by 2033.
On his website Kekich writes: “We are moving from an era in which nothing could be done to defeat ageing into an era in which advancing biotechnology will give us the tools to do overcome it … Now, at the dawn of the biotechnology era, the inevitable is no longer inevitable. The research establishment – if sufficiently funded and motivated – could make spectacular inroads into repairing and preventing the root causes of ageing within our lifetime.”
But given that there are yet to be any proven means for extending human life, these billionaires may be motivated more by ego than altruism.
As US author Adam Leith Gollner writes in The Book of Immortality: the Science Belief and Magic Behind Living Forever (Sribner 2013): “We want immortality so badly that we’re always ready to be swept away into unthinkingness … Half in love with the impossible we’ve always wanted to conquer death.”
Yet he says all humans can really do to live longer is to eat well and exercise.
“We all have to go … whether dying in battle, tumbling off a horse, succumbing to pneumonia or being shivved by a lover. Maybe one day we just don’t wake up. However it happens, we enter the mystery.”
To improve health care, governments need to use the right data
DECIDING where to seek treatment might seem simple for a German diagnosed with prostate cancer. The five-year survival rate hardly varies from one clinic to the next: all bunch around the national average of 94%. Health-care providers in Germany, and elsewhere, have usually been judged only by broad outcomes such as mortality.
But to patients, good health means more than life or death. Thanks to a study in 2011 by Germany’s biggest insurer, a sufferer now knows that the national average rate of severe erectile dysfunction a year after removal of a cancerous prostate gland is 76%—but at the best clinic, just 17%. For incontinence, the average is 43%; the best, 9%. But such information is the exception in Germany and elsewhere, not the rule.
Doctors and administrators have long argued that tracking patients after treatment would be too difficult and costly, and unfair to providers lumbered with particularly unhealthy patients. But better sharing of medical records and a switch to holding them electronically mean that such arguments are now moot. Risk-adjustment tools cut the chances that providers are judged on the quality of their patients, not their care.
In theory, national health-care systems should find measuring outcomes easier. Britain’s National Health Service (NHS) compiles masses of data. But it stores most data by region or clinic, and rarely tracks individual patients as they progress through treatment. Sweden’s quality registries do better. They analyse long-term outcomes for patients with similar conditions, or who have undergone the same treatment. Some go back to the 1970s and one of the oldest keeps records of hip replacements, letting medics compare the long-term performance of procedures and implants. Sweden now has the world’s lowest failure rate for artificial hips.
Elsewhere, individual hospitals are blazing a trail. Germany’s Martini-Klinik uses records going back a decade to fine-tune its treatment for prostate problems. The Cleveland Clinic, a non-profit outfit specialising in cardiac surgery, publishes a wide range of outcome statistics; it now has America’s lowest mortality rate for cardiac patients. And though American politicians flinch at the phrase “cost-effectiveness”, some of the country’s private health firms have become statistical whizzes. Kaiser Permanente, which operates in nine states and Washington, DC, pools the medical records for all its centres and, according to McKinsey, a consultancy, has improved care and saved $1 billion as a result.
Such approaches are easiest in fields such as prostate care and cardiac surgery, where measures for quality-of-life are clear. But some clinics have started to track less obvious variables too, such as how soon after surgery patients get back to work. This is new ground for doctors, who have long focused on clinical outcomes such as infection and re-admission rates. But by thinking about what matters to patients, providers can improve care and lower costs at the same time.
