Patients

Talking risk: how to communicate health outcomes to non-health economists

Mar 1, 2009
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By Julie Stauffer (julie.stauffer@rxcomms.com)

You’ve scrutinized the data, crunched the numbers and arrived at a solid risk assessment. But how do you communicate that information so that great-aunt Betty can decide whether to take your drug for her heart condition?

While other health economists understand you when you talk about the probabilities, costs and benefits of a particular outcome, it’s a different picture when it comes to a math-phobic general public.

So how do you get your message across to someone who may struggle to calculate the price of that shirt on sale for 30 per cent off? Keep the following tips in mind:

  1. Express risks in absolute terms rather than relative ones. If patients are told a particular drug doubles their probability of developing liver cancer, for example, many will decide the potential risks outweigh the benefits. But phrase it in absolute terms –2 people in 10,000 who take the drug will develop liver cancer, compared to 1 in 10,000 who don’t take it – and their decision might well change.
  2. When it comes to probabilities, frequencies are easier to grasp than percentages, so say “6 in 1,000 people will experience this side effect,” rather than “you have a 0.6% risk.” If you mention several different frequencies, keep the denominator consistent to avoid confusion.
  3. Include comparisons to other treatments and to no treatments to help patients make a well-informed decision. If taking a particular birth control pill carries a risk of dying from a blood clot, for example, compare that to the risk of side effects from other birth control measures or the risk of dying during childbirth.
  4. Different people absorb information more easily in different ways, so include diagrams to reach visual learners. A pictorial representation of the number of people affected – a Paling Palette© highlighting three human figures out of a thousand, for example – is an excellent way to convey percentages, while line graphs quickly convey trends and bar graphs make comparisons easier. (See issues 11, 12 and 13 of Heath Outcomes Communicator for tips on making the best use of graphics.)
  5. Avoid vague language that even other health economists or doctors will have trouble interpreting. How frequently does a “rare” side effect occur? How probable is a “moderate” probability?
  6. Similarly, avoid economic language that can convey a very different meaning to Joe Public. To you, “a significant risk” may mean a statistically significant risk, but a non-economist will interpret it to be a highly probable outcome or a risk that carries grave consequences. Similarly, a layperson might believe a “conservative estimate” actually underestimates the likelihood of a particular outcome, while a “positive trend” brings exclusively good news.

Ultimately, patients will evaluate risks and make health decisions based on a number of factors, including many that aren’t rational. That’s not going to change. But by presenting your information as clearly as possible, you can make sure they make a truly informed decision, not one based on misunderstanding or confusion.

How sick are you? It depends on what the definition of ‘sick’ is…

Oct 29, 2008
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By Mary Gabb (mary.gabb@rxcomms.com )

The publication several years ago of Selling Sickness: How the World’s Biggest Pharmaceutical Companies are Turning Us All into Patients by Ray Moynihan and Alan Cassels has brought the concept of disease-mongering into a global debate, and has introduced (or reintroduced) terms such as “medicalisation” and “lifestyle drugs” into today’s lexicon.

An entire issue of PLoS Medicine was devoted to the topic in 2006. The first international conference on disease-mongering took place in 2006 in Newcastle, Australia. Disease-mongering even has its own Wikipedia page.

Moynihan and colleagues define disease-mongering as “widening the boundaries of treatable illness to expand markets for those who sell and deliver treatments”. It “turns healthy people into patients, causes iatrogenic harm, and wastes precious resources”. In short, disease-mongering is purported to make us believe we are sick (or more sick than we really are) so that we will buy more drugs to cure what ails us (or at least make us happier). Iona Heath, MD* has said it exploits our deepest atavistic fears of suffering and death.

Possible disorders susceptible to disease-mongering
Attention deficit hyperactivity disorder
Bipolar disorders
Depression
Erectile dysfunction
Menopause
Obesity
Restless legs syndrome
Social anxiety disorder

Those who say that pharmaceutical companies promote disease-mongering point to the recent upsurge in diagnoses and treatments for numerous conditions (see right) , asking whether some of these are even real conditions, or whether the stated prevalence is as broad as commonly stated by both medical professionals and the media.

In fact, disease-mongering proponents lay the blame for this alleged deception on several participants with Pharma affiliations: medical professionals (who are duped by Pharma via pharma-sponsored continuing education, especially in the United States), patient advocacy groups (also influenced by Pharma sponsorship to further their cause by “raising disease awareness”), and the mass media (who have a propensity to exaggerate a problem to sell their product and rely on lazy journalism in which facts such as prevalence statistics are never questioned).

In fact, as Drs Steven Woloshin and Lisa M. Schwartz* note, disease-mongering stories have all the ingredients for what is considered “good journalism”: compelling personal anecdotes, public health crises, uncaring or ignorant doctors, and miracle cures. Much like the field of pharmacoeconomics, the pursuit of disease-mongering involves multiple disciplines (beyond medical science and economics) such as public health policy, sociology, psychology, anthropology, and patient advocacy.

The idea of questioning the diagnosis of conditions such as bipolar disorders, menopause, or erectile dysfunction (ED) is sure to ruffle some feathers. While the symptoms of menopause or ED may be a “troublesome inconvenience” for some, they can be debilitating and/or terribly embarrassing for others, affecting long-term personal relationships and self-esteem.

Is it disease mongering? NO

We live in an era in which each generation has lived longer and better than the preceding one. We expect the most out of quantity and quality of life, simply because it is possible. Is that wrong?

Second, let’s be honest – Pharma products are addressing consumer needs, not medical science. Push-pull marketing has been around for ages, long before direct-to-consumer advertising. However, the danger is that Pharma is one of the few industries where profit-making activities have ethical overtones.

Third, the search for a biologic basis of disease and a reconsideration of what is normal has also led to some conditions being de medicalised, such as homosexuality.

Fourth, disease-mongering is not universally defined. Moynihan admits that the first step to studying possible disease-mongering is to create an operational definition.

Is it disease mongering? YES

The availability of so many pills and potions to address every ache, pain, and risk factor (which is treated as a disease state) runs the risk of removing the patient’s responsibility for any lifestyle changes that would help to address the condition. The impetus to reduce risk factor exposure (such as stress, tobacco smoke) is removed so the condition may be treated but the underlying potential causes remain. And, as noted by Iona Heath, MD, the irony is that such profit-driven practices of Pharma marketing “poisons the present in the name of a better, or at least a longer, future”.

Note also the increasingly stringent definitions of what are considered to be optimal measures of health, for example blood pressure, cholesterol, and weight. Achieving these levels through diet and exercise alone may be possible only for the truly devoted, but “we do happen to have a pill that will reduce your______”.

As discussed by Dr Olavo B. Amaral**, the struggle over disease-mongering may force us (ie, those involved directly or indirectly in the practice of medicine and medical consumers) to consider diseases as spectra rather than binary states (i.e., sick or well), and patients can decide, by working with their physicians, if they are sick enough for treatment and whether the treatment is worth the risk of the adverse events.

*Drs Heath, Woloshin, and Schwartz were authors of two of the articles from the PLoS Medicine issue on disease-mongering. Dr Heath is a general practitioner in London , UK . Drs Woloshin and Schwartz are at the Veterans Affairs Outcomes Group in Vermont , USA , and the Center for the Evaluative Clinical Sciences at Dartmouth Medical School , New Hampshire , USA .

**In a Letter to the Editor in PLoS Medicine.

The mean versus the gene: will national healthcare systems ever adopt personalised medicine?

Jul 26, 2008
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By John F P Bridges

While outcomes for any given treatment differ significantly among patients, national healthcare systems continue to take a top down population perspective in reviewing not only epidemiologic data but to evaluating the effectiveness and cost-effectiveness of new medicines.

This top down approach, coupled with a growing need for cost containment, has recently caused many governments to institutionalise these practices through health technology assessment institutions. The purpose of these agencies is to promote better quality or value for money in the healthcare system; but this has led to medicines and technologies either being considered good (in other words, good for all) or they are deemed bad and are blacklisted.

Whether it be data from a randomised control trial, a comparative effectiveness study or a cost-effectiveness study, the focus is on the average patient’s health outcomes, where all individuals are treated equally (or to be more correct, identically). Variation is something that we consider only when it comes to statistical inference, viz. does the average effect differ from zero.

This is a major over simplification since patients do vary: their needs vary; their preferences vary; their circumstances vary and, most important, their outcomes vary. Even if you wanted to treat individuals equally on ideological grounds, these top down approaches ignore the risks and uncertainties in medical decision making. For example, rather than understanding risk in clinical trials, we attempt to make it go away by demanding larger and larger trials (a movement away from the individual, towards the population).

As technology progresses, we are increasingly aware that the variation in benefits and adverse consequences of many healthcare interventions are predictable. As our knowledge of genetics and proteinomics expands, our ability to predict these events grows exponentially. This has prompted many healthcare innovators to develop diagnostics to tailor medicines for particular patients – in what is often referred to as personalised medicine. Governments everywhere have been eager to support these start-ups, almost to the point of frenzy and with little accountability.

One key problem exists, however, in that these new technologies are incompatible with the fundamental principles of many national healthcare systems and with the top down evaluation that has been implemented. Hence a bottleneck is occurring – ironically with government playing a dual role of promoting and rationing medical technology. In order to alleviate this bottleneck, we either have to address the funding crisis through personalised approaches to healthcare finance (The Netherlands and Switzerland have attempted this) or we need to stop wasting money on research and development of personalised medicine technologies that are unlikely to be funded in the future.

Essentially this means that healthcare systems need to decide whether they want to focus on the mean (the average effects of medicine) or on the gene (by accommodating personalised medicine.

John Bridges, PhD, is Assistant Professor, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Senior Fellow at the Center for Medicine in the Public Interest and founding editor, The Patient: Patient-Centered Outcomes Research.

www.PatientsLikeMe.com – The power of patient-provided information

Jun 26, 2008
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By Julie Stauffer (julie.stauffer@rxcomms.com)

Pharmaceutical companies depend on data – and lots of it – to develop drugs and measure their effectiveness. And many patients are eager to supply that information if it might mean better treatments or better quality of life.

That’s the premise of PatientsLikeMe.com. Launched in 2006, it connects patients to other people dealing with the same life-altering disease. Not only does it allow users to share experiences and provide peer support, it also generates a goldmine of data by encouraging patients to track and quantify their symptoms, medications, and outcomes.

Today, the pharmaceutical industry relies on point-in-time, survey-driven information, explains David S. Williams III, the site’s co-founder and head of business development. In contrast, PatientsLikeMe provides a much wider range of data.

Pharmaceutical companies can monitor its forums for insight into patient perspectives or recruit its members for clinical trials. Most exciting, however, is the opportunity to examine real-world, long-term data on how patient attitudes, decisions, and outcomes change over the course of their disease.

You now have a different longitudinal way of looking at how patients interact with your medication that you never had before,” Williams says.

To date, nearly 20,000 people from around the world have joined PatientsLikeMe, which offers on-line communities for patients with ALS, multiple sclerosis, HIV, Parkinson’s, and a variety of mood conditions. Williams predicts those numbers will increase to one million patients and 200 communities by the end of 2011.

“The people we’re attracting are that 5-15% of people in every single disease who will share their information openly. We know that target group exists,” he says. Once PatientsLikeMe proves that sharing medical information provides more collective benefit than keeping it private – establishing best care practices and accelerating research into treatments – he anticipates more people will overcome their privacy concerns.

But even data from the 5-15% segment alone has tremendous value, especially for a prevalent condition like heart disease. “The Framingham heart study has 5,000 people,” Williams points out. “We’re talking about building 200 Framingham heart studies in 200 different diseases.”

How reliable is the information generated by PatientsLikeMe? Although patients misinterpreting or misreporting information is a very real possibility, the aggregated data is structured to make outliers evident. Because these charts and graphs are posted on the site, patients have an opportunity to spot and correct mistakes themselves. If a patient sees that she is the only person who reported taking a particular medication for a particular purpose, for example, she may realize that she’s misunderstood why her doctor prescribed it.

Unlike most “health 2.0” businesses, PatientsLikeMe forgoes advertising in order to maintain high credibility with its members. Instead, revenues come from selling data to carefully screened partners that develop or sell drugs, devices, insurance or medical services aligned to the needs of patients.

While it’s a model that promises profitability, the ultimate goal is to benefit the patients themselves. As the site’s founders explain, “When patients share real-world data, collaboration on a global scale becomes possible. New treatments become possible. Most importantly, change becomes possible.”

Letter to the editor

Nov 24, 2007
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Our HOC People person this month, Dennis M. Gross, PhD, is an associate dean at Thomas Jefferson University where he also teaches courses in pharmacology and toxicology. He emailed us these thoughts on non-adherence.

A factor that has confounded the issue of adherence or compliance for 40 years is the actual pharmaceutical and the disease being treated. Thirty years ago we had an education tape at Merck about this issue: Keeping the Hypertensive Patient in Compliance.

The biggest problem with hypertension and high cholesterol is simply these diseases are asymptomatic. The patient does not sense anything wrong. They are likely to think: “why should I continue to take a drug that makes me feel bad regardless of how much it costs, even if it is generic?”

The patient was probably feeling fine with their high cholesterol, osteoporosis or high blood pressure and now we prescribe them a drug that makes them feel worse and restricts their lifestyle.

For the drugs used to treat osteoporosis they cannot take the medication with food for at least 30–60 minutes; they can drink only water with the pill, and they cannot lie down and, by the way, the medication won’t really do anything for them for at least 9–10 months and we won’t really know what it is doing for them unless they fall and don’t break a hip.

Why should they continue to adhere to something with lots of side-effects and no visible positive outcome – getting heartburn every time they take their osteoporosis drug or postural hypotension from their high blood pressure medicine? Cleaner side-effect profiles may be the key to adherence – not price.

Making adherence work for your dollar (or pound, or euro…)

Nov 24, 2007
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By Mary Gabb (mary.gabb@rxcomms.com)

As we reported in last month’s HOC, the past few years have witnessed a surge of interest in the role of adherence in health economics research, highlighted by a recent report from the National Council on Patient Information and Education (NCPIE) in the United States, which revealed the high rates of non-adherence.

So, how is the issue of non-adherence being used in health economics research? JoAnne LaFleur, Research Assistant Professor at the Pharmacotherapy Outcomes Research Center, University of Utah College of Pharmacy (Salt Lake City, UT, USA) and a member of the ISPOR special interest group on adherence/compliance, says that when adherence is factored into cost-outcomes studies, it’s probably not done well and this has important public health policy implications.

She cites a recent example in which two cost-effectiveness studies in H pylori eradication (H pylori is an infectious agent thought to be associated with peptic ulcer) had made assumptions about poor patient adherence to the less costly regimen that resulted in poor cost-effectiveness outcomes with that regimen, and favourable cost-effectiveness outcomes for the more expensive regimens. As she explains, “those cost-effectiveness studies had been responsible for spurring treatment guidelines favouring the more expensive regimens. However, using a real-world dataset, the authors of this paper showed that the assumptions about patient adherence did not translate into diminished effectiveness for the less costly regimen, and in fact, previous decisions based on those cost-effectiveness studies were flawed.”

However, not all news is dire when health economics research is translated into public health policy. For example, a team at the University of Michigan, USA, has developed the concept of ‘benefit-based copay(ment)’ (lead author, Mark Fendrick, MD, Professor in the Department of Internal Medicine and the Department of Health Management and Policy and Co-Director of the Consortium for Health Outcomes Innovation and Cost Effectiveness Studies (CHOICES). David B. Nash, MD, MBA, Chairman of the Department of Health Policy at Jefferson Medical College (Philadelphia, PA, USA), says that the effect of copayment (or ‘cost sharing’) on adherence is of great relevance to health economists, although the effect of copays varies with many factors, such as the socioeconomic status of the patient or population, the level of copay, age of the patient, and the type of disease.

With ‘benefit-based copay’, the benefit refers to clinical benefit, and the copay is limited to those with less serious illness. In other words, copays would be based on the actual clinical benefit a medication can give an individual, and the copays are scaled based on the importance and prevalence of the disease in a population. So, for a disease state such as asthma or heart disease – very common with serious clinical outcomes – the copay should be reduced or eliminated, to improve adherence and outcomes. As Dr. Nash states, “you have to understand the psychology of the copayment”. Importantly, copays would also vary within these long-term diseases based on the patient’s severity of the disease, and the benefit-based copay system would only be applied to drugs and diseases for which there is solid evidence that the benefit differs between patients with greater or lesser illness severity.

As health economists gain a greater understanding of the ‘psychology of copays’ and other determinants of adherence, HE research and public health policy initiatives will benefit as the research is applied to real-world scenarios.

In-store health clinics: a new approach to primary care?

Oct 22, 2007
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By David Woods (david.woods@rxcomms.com)

Health clinics based in retail outlets, particularly drug stores, are changing the way primary care is delivered. Their proponents cite access, convenience, and price transparency as the major reasons for their popularity and growth. Further fueling this concept is the growing shortage of primary care doctors.

Staffed mainly by nurse practitioners, the clinics offer quick services for routine health conditions such as colds and sore throats that formerly would have taken patients to the office of a family physician or general internist.

The expansion of these clinics is nothing short of phenomenal. Their umbrella trade group, aptly titled the Convenient Care Association, estimates that there will be more than 700 of them by the end of this year, and some 2000 by the end of next year.

Initial concerns by medical associations that the clinics would be unregulated and provide mainly one-shot care other than continuity are largely dissipating.

94For example, Minneapolis-based MinuteClinic – a subsidiary of CVS Caremark Corporation and the largest provider of retail-based healthcare in the US – now has full accreditation from the Joint Commission on Accreditation of Healthcare Organizations (JCAHO).

Its president, Michael Howe (pictured left), a former executive with Procter & Gamble, told HOC: “Our services complement primary care providers and our nurse practitioners make it a point to stress the importance of a regular medical exam with every patient they see.” He adds that “waiting times, compared with a doctor’s office, typically are about 15 minutes… and the average cost is about $60.”

The American Academy of Family Physicians (AAFP), rather than opposing the concept, has issued a set of standards for in-store clinics… and a former AAFP president serves on the company’s Clinical Quality Advisory Council.

A by-product of the clinics is that they have an impact on employee productivity because of the time saved in comparison with time spent in doctors’ waiting rooms.

In the United Kingdom, plans are in motion to formalise minor ailment clinics where pharmacists and nurse prescribers can serve to direct patients away from busy doctors’ offices.

The major UK pharmacist chain, Boots, is establishing walk-in clinics in concert with major supermarkets. However, such clinics have been slow to take off because they are limited by the National Health Service Primary Care Trusts’ budgets, since both doctors and pharmacists operating in these clinics will be seeking reimbursement for their prescribing.

The “other drug costs” – Part 1: non-adherence

Oct 22, 2007
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By Mary Gabb (mary.gabb@rxcomms.com)

The term “adherence” (or sometimes “compliance”) – the extent to which patients take their treatment as prescribed – has been understood to be a nagging clinical issue, but is now seen as a major cost driver in many therapeutic areas.

A recent report from the National Council on Patient Information and Education (NCPIE) in the United States reveals depressing statistics: 49% of those polled had forgotten to take a prescribed medication, 31% had not filled a prescription they were given, 29% had stopped taking a medicine before the supply ran out, and 24% had taken less than the recommended dosage.

A recent report in the Archives of Internal Medicine documents our failed efforts to improve adherence. Only about half of randomised controlled trials designed to improve medication adherence showed any consistent improvement in patient adherence, and less than one third of the studies demonstrated improvement in at least one clinical outcome. Non-adherence has even been referred to as the “other drug problem”.

How does the issue of adherence/compliance affect the field of health economics? The International Society for Pharmacoeconomic Outcomes and Research (ISPOR) has created the ISPOR Medication Compliance and Persistence Special Interest Group to study non-adherence in pharmacoeconomic evaluations.

JoAnne LaFleur, Research Assistant Professor at the Pharmacotherapy Outcomes Research Center, University of Utah College of Pharmacy (Salt Lake City, UT, USA) is a member of the ISPOR special interest group on adherence/compliance. She says there has been a surge in the last five years in measuring the cost outcomes of poor adherence.

She explains that medication costs are often much less expensive than the surgical or hospitalisation costs if the medications are not taken as directed and the clinical sequelae manifest. LaFleur also says that, in the US, a consistent problem has been that, with a few exceptions, the myriad health insurance plans don’t invest in programmes to improve adherence, in part because their pharmacy budgets are separate from the medical budgets. In fact, management of pharmacy coverage is often outsourced to other companies called pharmacy benefit managers. Thus, each group (pharmacy versus medical benefits) is not considering the ramifications of their decisions on the total healthcare cost, but rather only on their specific budgets.

According to David Nash, MD, MBA, Chairman of the Department of Health Policy at Jefferson Medical College (Philadelphia, PA, USA), who recently reviewed the Archives study, the only techniques that seem to have any impact on improving adherence are “once-a-day dosing and a rigorous reminder system to patients”.

For health economists, Dr. Nash sees three main issues regarding adherence and health economics:

  • a lack of programmatic progress in improving adherence, as evidenced in the Archives-published study and the NCPIE report
  • a lack of understanding of the downstream costs of poor adherence, which are “formidable”
  • the effect of co-payment (or “cost sharing”) on adherence.

In our next HOC article on adherence, we will discuss some of the challenges to including adherence in cost outcomes analyses, including effect of co-payment on adherence, as well as the use of accurate models of adherence in cost outcomes analyses – how closely do they reflect real life data?

Simplifying drug labels

Aug 22, 2007
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By David Woods (david.woods@rxcomms.com)

84The Institute of Medicine, which made headlines some years ago when it estimated that medical mistakes kill as many as 98,000 patients a year, followed up more recently with a report on the major cause – medication errors.

The Institute believes that one way to prevent such errors is through computerised prescribing systems; another is to present drug labels in clear English. The Institute wants hospitals to computerise their prescribing systems by next year and to start using them by 2010. It also wants the drug industry and the FDA to avoid the confusion created by look-alike and sound-alike drug names – and to simplify labels and packages.

In a study published in the Annals of Internal Medicine, Terry C. Davis and colleagues asked patients at three primary care clinics to demonstrate their understanding of five common drug labels. Almost half of the patients misunderstood at least one of the five labels.

For example, the requirement that ‘medication should be taken with plenty of water’ raises the question about what exactly ‘plenty’ means. The exhortation to ‘avoid prolonged exposure or excessive exposure to direct and/or artificial sunlight while taking this medication’ was understood by fewer than 40% of patients with an 8th or 9th grade education in the Davis study, and by a mere 4% of those with a 6th grade level.

Patients also confused tablespoons with teaspoons and were less likely to understand multiple instructions such as ‘take one tablet by mouth twice daily for seven days.’

It is surely in the best interests of both the patient and the pharmaceutical industry that drug labels be worded in language that can be understood by anyone. Clear, simple, unambiguous, helpful … and, above all, not hazardous to health.

Risk/benefit decisions – who should be involved, and are there valid measurement methods?

Apr 17, 2007
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By Clare Gurton (cgurton@rxcomms.com)

Here we summarise a recent article from ISPOR Connections 2006; December 15: 3–5

Patients are generally not included in decision-making yet they are the group to whom the benefits and risk of treatment apply.

Treatment decision-making policy and treatment guidelines are led by objective evidence of benefit, that outweighs any risks, are collected from clinical studies under conditions of an experiment rather than clinical practice, and are designed to satisfy regulatory requirements prior to approval.

The question is: how to include end-users and to generate a valid way of assessing the preferences of a representative sample of the patient population and, further, to incorporate their preferences into risk/benefit decisions.

In a recent article, Hauber et al discuss two approaches for overcoming this problem. The incremental net health benefits (INHB) approach has been used in two recent studies. It quantifies the benefits and adverse events of a treatment and assigns weights to each outcome.

The difference between the sum of the weighted benefits and weighted risks represents the net health benefit and the INHB is calculated as the difference between the net health benefit of the treatment under assessment and a comparative treatment (most often standard care).

Another approach is to calculate the maximum acceptable risk (MAR). This estimates the maximum risk that patients are willing to tolerate in order to achieve the therapeutic benefits of a treatment that incorporates patient preferences over risk and benefit outcomes.

The MAR can then be compared with actual or expected risk to determine patient acceptability. MAR is calculated using choice experiment or conjoint-analysis methods and so includes patient’s subjective assessment of the risks versus benefits directly; MAR is the patients’ appraisal of the risk level at which the benefits are zero.

Both of these methods are relatively new and there is little data on their use in practice; however, they highlight the increasing importance of quantifiable patient preferences in risk/benefit analysis.

ISPOR 9th Annual European meeting at Copenhagen

Dec 16, 2006
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By Adelina Pagliocca

Rx Communications and Greenflint had great pleasure in attending the 9th annual European meeting of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), 28–31 October in Copenhagen.

46Representing Rx were marketing manager Chris Gardiner (pictured centre), editorial manager Adelina Pagliocca (left), and project manager Maggie Jones (right).

Our booth featured Rx sister company Greenflint, whose models are designed to help in early-stage decisions, cutting costs, making strategic decisions during development, and developing a promotional message.

45Modelling was the theme, with attendees invited to craft models out of red and green clay.

With red clay, attendees were asked to create a communication tool, and with the green clay, to model their favourite thing.

This creative challenge produced, amongst other entries, a dove carrying an olive branch, a football match between Copenhagen and England, ‘probably’ Denmark’s best export (the famous lager), and a Christmas tree.

All entries and winners for both competitions can be viewed at the Rx and Greenflint websites.

Asking critical questions

The theme of the congress was ‘Asking critical questions’. The first plenary session was introduced by Bengt Jønsson, PhD, who initiated the discussion on the use of health technology assessment (HTA) as a basis for reimbursement and priorities in health care.

Richard Bergström, MScPharm, presented the HTA principles adopted by EFPIA and industry’s perspective on promoting good use of HTA. Audun Hågå, MSc, discussed priority decisions from a government perspective focusing on Nordic countries. One key message was the need for increased communication between industry and payers, and a decision on the best practices for Europe.

The second plenary session was a debate between Professor Paul Glasziou, PhD, FRACGP, and Ivar Sonbo Kristiansen, MD, PhD, MPH, moderated by Kjeld Møller Pedersen. This session examined questions surrounding evidence based medicine (EBM).

Dr Kristiansen questioned the use of EBM in health policy; he urged the audience to be more humble in interpreting the results of randomised clinical trials (RCT), and suggested that EBM does not improve patients’ health.

The speaker questioned the Cochrane collaboration, suggesting it is an anti-industry movement that should be consulted with caution. Professor Glasziou retaliated by providing examples of when an RCT is required, although he agreed that RCTs are not always the best approach, suggesting that the best study design depends on the type of question one is attempting to answer.

With regards to the Cochrane collaboration, Professor Glasziou explained the background and stressed that the collaboration is not anti-industry, and when used properly is a good tool to aid physicians in choosing the correct medication for their patients.

The third plenary session addressed the societal value of the QALY. Professor Dorte Gyrd-Hansen, PhD, gave a critical view demonstrating that the task of performing a linear translation from QALYs to willingness to pay (WTP) is theoretically and empirically unattainable.

The speaker stressed that we need to think carefully about study design and interpretation of results. Professor Martin Buxton, BA (Soc Sci), argued that ‘social’ WTP values are interesting but of no immediate relevance.

The speaker stressed that a health system is responsible for the entire population and a WTP threshold needs to be set. This threshold should ensure that technologies adopted are always more valuable than other new alternative and any displaced activities.

In addition to these plenary sessions, there were 28 thought-provoking workshops, 6 issue panel presentations, 64 contributed presentations, and almost 600 posters.

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