Comparative Effectiveness Research

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Why does cost effective care spread so slowly

May 27, 2011 in CER | No comments

According to Fuchs and Millstein, here’s why:

  • Insurers hesitation to standardize coverage.  Standardization of coverage would force insurance companies to compete primarily on the basis of price, which would put pressure on their profits.
  • Employers bear too much of the marginal cost from employees choosing expensive health plans.  Because companies wish to avoid alienating employees, only 20% of large employers require workers who choose more expensive plans to pay the marginal difference in cost.
  • The public does not understand why cost effectiveness is good for them.  The general public does not typically realize that higher health insurance cost are not paid by employers, but by the employees themselves through lower wages in the long run.
  • Legislators need money.  Legislators seek campaign contributions from health industry stakeholders who benefit from the current inefficient arrangements.
  • Hospitals fear cost-effectiveness means lower reimbursements.  Hospital administrators often resist efforts to reduce hospital occupancy for fear that decreases in revenue will jeopardize their ability to cover large fixed costs.
  • Physicians fear pay cuts and loss of professional autonomy.
  • Drug and device manufacturers will lose profits.  Although manufacturer with unique products can sell their goods for a high price, there are alternatives to most medical products.  In these cases, firms attempt to create the perception that their products are unique to justify high prices.  Marketing and lobbying are vital parts of these efforts.

Source: Victor R. Fuchs, Ph.D., and Arnold Milstein, M.D., M.P.H “The $640 Billion Question — Why Does Cost-Effective Care Diffuse So Slowly?” NEJM, May 18, 2011.

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Who’s running the Patient-Centered Outcomes Research Institute (PCORI)?

September 23, 2010 in CER, Health Reform | No comments

As part of Health Reform, the government created the a center to study clinical effectiveness of different health treatments.  This center, known as the Patient-Centered Outcomes Research Institute (PCORI), “does not have the power to mandate or even endorse coverage rules or reimbursement for any particular treatment.”  What leverage does the Institute have?  Not too much.  “Medicare may take the institute’s research into account when deciding what procedures it will cover, so long as the new research is not the sole justification and the agency allows for public input.”  Basically, PCORI is supposed to be like the UK’s NICE but without any teeth.

Who is running PCORI?  The answer was revealed today.  The PCORI Board of Governors includes:

  • Associate Executive Director for the Permanente Medical Group of Northern California,
  • CEO of Empower, LLC,
  • Chairperson  of Friends of Cancer Research,
  • Chief Medical Officer of the Pfizer Medical Division,
  • Chief Science and Technology Officer for Johnson & Johnson,
  • Director of Strategic Partnerships and Alliances at the Xerox Corporation,
  • Executive Vice President of The Regence Group
  • President and CEO of the American Association of People with Disabilities
  • President of BJC Health Care,
  • Program Director for the Health Technology Assessment program at the Washington State Health Care Authority.
  • Principal Deputy Under Secretary for Health and National Program Director for Cardiology, Department of Veterans Affairs
  • Researchers from the following universities: Dartmouth, Harvard, Mississippi, North Carolina, UCLA, and Yale.
  • Senior Vice President of Medtronic, Inc.

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Patient Heterogeneity and Comparative Effectiveness Research

August 9, 2010 in CER | No comments

According to Kathleen Lohr, the most pressing issues for comparative effectiveness research (CER) include: 1) how to conduct CER for heterogeneous patient populations and 2) ways to implement longitudinal investigations to capture long-term health outcomes.  Today I will focus on measuring patient heterogeneity.  Although it makes sense to take into account differences across patients, measuring this in practice can be difficult.

A paper by Kaplan et al. (2010) discusses a number of ways to measure patient heterogeneity in practice.  The article discusses six categories of patient characteristics.  Below I present each and–where appropriate–discuss how the authors attempt to measure differences in patient characteristics.

  • Immutable characteristics: These are intrinsic factors that the patient cannot change such as demographic characteristics or genetic factors.  The authors use age, gender and race as well as education in their study.  Although one could of course get more education, few elderly receive advanced degrees.  A more appropriate measure might be highest education reached by age 30 (which would not change over time after age 30), but because this measure would likely be very similar to education overall, current education can be used as a substitute.
  • Health Profile.  This category represents the patient’s current health condition.  This can be represented by factors such as a patient’s disease burden, mental/physicial functioning and other measures.  The authors use the patient’s Total Illness Burden Index (TIBI).  A paper by Willson et al (2000) uses a Comprehensive Severity Index (CSI) to measure patient illness severity over time.  To determine physician functioning, the authors use a 10 item physical function scale (PFI-10) on the Short Form 36 of the TIBI.  Other studies have used the Functional Independence Measure (FIM) to estimate patient physical functioning. To measure the patient’s mental health and depression, the authors used a modified version of the Center for Epidemiological Studies Depression Scale (CES-D).
  • Personality Profile Measures.  A patient’s personality can affect health outcomes as well.  In Kaplanet al.  (2010) study, the authors measure whether the patient has a passive orientation to health and health care according to the Provider Dependent Health Care Orientation (PDHCO) measure.  The scale measures whether patients take a passive approach to disease management and the author claims that this measure “has been linked with poor transitions in physical functioning over time.”
  • Behavioral Profile.  This includes disease management skills and health habits (e.g., smoking, diet, exercise).
  • Medical/Treatment Context.  The category measures differences across patients in the relationship with their physician, the setting where care is given, and continuity of care.
  • Life Context.  The goal of this final category is to measure whether the patient experience a stressful life event or whether they have a significant social support system.

In the paper, the authors examine whether diabetes treatment improves glycemic control (i.e., HbA1c levels).  Only the first three categories of patient information are included in the regressions.  Unsurprisingly, the strongest predictor of differential benefits from treatment is generally the health profile.  The TIBI index has the strongest effect on how treatment affects patient outcomes, but the PF-10 physical functioning disability measure is also influential in a statistically significant manner.   Patient adherence to drug regimens, unsurprisingly, also improves the affect of treatment.

Although the finding that individual patient characteristics affects treatment benefits is not surprising, Kaplan and co-authors present researchers with some tools to summarize these differences for a number of different patient characteristic categories.

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The Impact of Comparative Effectiveness Research on Health Care Spending

April 14, 2010 in Health Insurance, Public Policy, Quality | 3 comments

Comparative Effectiveness Research (CER), as it name suggests, compares how well different medicines treat a given disease.  Politicians claim that using CER findings can help improve quality and decrease cost.  If one treatment produces better health outcomes on average than another and also costs less, we should always make people use that treatment, right?

Not according to a working paper by Basu and Philipson (2010).  Let us assume that health outcomes for Drug A are better than the health outcomes for Drug B.  If the results from this research were released, the public’s demand for Drug A would increase and the demand for Drug B would decrease.  However, this may not save money.  The demand for Drug B will decrease since fewer people want to buy it; this will reduce expenditures.  As the demand for Drug A increases, however, the price and quantity purchased will increase.  Thus, the net effect on spending is indeterminate.

In addition, if insurance companies or the government decided to subsidize or cover the entire cost of treatment using Drug A, the demand will increase even more.

Basu and Philipson, however, assume that the marginal cost to produce a drug increases as the quantity rises (i.e., the supply curve is upward sloping).  However, because there are economies of scale in the production of pharmaceuticals, the price of Drug A could actually decrease (increasing returns to scale) or stay the same (constant returns to scale) as demand increased.

The key assumption in the above analysis is that it assumes that all people with a given disease respond in a homogeneous way to Drug A.  If two-thirds of people have better health outcomes when treated with Drug A and one-third have better health outcomes when treated with Drug B, then it may be suboptimal to cover Drug A, but not Drug B.

To prove this, Basu and Philipson look at the Clinical Antipsychotic Trials of Intervention Effectiveness project (CATIE).  They find that “if Medicaid would have eliminated coverage for the least cost-effective treatments of the CATIE trial then under homogeneous effects, it would save about 90% of the $1.3B Medicaid class sales annually in non-elderly adult patient with schizophrenia. However, taking into account the observed heterogeneity in treatment effects, it would incur a loss of health valued annually at about 98% of class spending and thus a net loss of about 8% of annual class spending.”

However, one of their key assumptions is that not covering the less effective medicines means that no patients will take the uncovered drug.  This may be a fair assumption for the Medicaid population, but not for the population at large.  There is a big distinction that needs to be made between not covering a drug (but allowing for the purchase to purchase it out of of their own pocket) and prohibiting the drug entirely.  Basu and Philipson are looking at the most extreme case where not covering the drug means, de facto, that the drug will not be taken, but this need not be the case.

What is important to take from this research, however, is that the drug that is most effective on average may not be the best drug for everyone.  One must take into account heterogeneous treatment effects when designing any insurance benefit plan.

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Comparative Effectiveness vs. Cost Effectiveness Research

March 11, 2010 in Public Policy, Quality | 6 comments

Comparative Effectiveness has been a hot topic in health services research. According to a recent article in the New England Journal of Medicine, “the American Recovery and Reinvestment Act of 2009 authorizes the expenditure of $1.1 billion to conduct research comparing ‘clinical outcomes, effectiveness, and appropriateness of items, services, and procedures that are used to prevent, diagnose, or treat diseases, disorders, and other health conditions.’”

Comparative effectiveness compares how effective various medical treatments improve health outcomes.  This sounds like the course we want to take.  Most policymakers laud the health benefits of comparative effectiveness research, but some people claim that comparative effectiveness research can also save cost.

This is most easily seen in the case where a treatment is completely ineffective.  If research can prove a treatment is ineffective, then insurers could save a lot of money by not covering this type of treatment.  This is especially true if the treatment is expensive.

However, comparative effectiveness treatment could also increase cost.  Assume that there are two treatment currently in use: Treatment A and Treatment B.  Let us say that treatment A costs $1,000 and has a 90% cure rate and Treatment B costs $10,000 and has a 95% cure rate.  According to comparative effectiveness research, we should always use Treatment B.  Yet this would significantly increase costs.

Most health economists argue that cost effectiveness research is provides a better way to improve health and decrease cost.  In the example above, should we cover Treatment B?  The answer is likely yes if this is a very serious disease (e.g., cancer) but likely not if the disease is less serious (e.g., the common cold).  Some readers may believe insurers should always cover Treatment B no matter what.  However, would you be willing to pay increased premiums that would occur if treatment B were covered?  Would you feel the same way if Treatment B cost $100,000?  or $10 million?  What if the cure rate was only 90.1%?

At some point, there must be a trade-off between cost and benefit.  Admittedly, these are very difficult decisions in practice, but because there are limited healthcare resources, we must ration care.  Yes, I said it, we must ration care.  I’ve said this before.  This rationing can take many forms: the scope of what your insurance company (or Medicare) will cover, waiting lines, or increased prices you must pay out of pocket for medical services.  The government wants to avoid making these tough choices because it is politically unpopular.  Politicians don’t want to be labelled  the sentator who “killed Grandma” or “instituted a death panel.”  But to truly decrease cost and improve quality, cost effectiveness rather than comparative effectiveness is the prescription we need.

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