CER

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Why does comparative effectiveness research fall on deaf ears?

March 7, 2012 in CER | 1 comment

Many of the best practices discovered in the research community are never implemented in practice.  Why is this the case?  One reason is that physicians are overloaded with information and it is costly to cull the literature for best practice information.  This is especially true when best practices are not clearly identified or change over time.

However, there is another reason why physicians would ignore best practices research: money.

“If you’re a radiologist and work in a hospital complex that can offer targeted proton beams from a $100 million cyclotron to treat prostate cancer (there’s more than a half dozen already up and operating in the U.S.), are you really interested in a scientific study that definitively determines whether that, drug therapy or simply ‘watchful waiting’ is the best course for treating that slow-growing tumor?”

The answer is ‘no’.  In the UK, National Institute for Health and Clinical Excellence (NICE) uses comparative effectiveness research to determine which treatments are covered by the public health insurance.  In the U.S., Patient-Centered Outcomes Research Institute (PCORI) aims to do the same thing but without the ability to affect payment policy.  Thus, PCORI is doomed to be a near useless body.

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Setting FDA Standards

July 5, 2011 in CER, Pharmaceuticals, Regulation | 1 comment

To what standard should the FDA hold new drugs?  The FDA has a number of choices.  Drugs companies could be required to prove that the drugs they make:

  • Do no harm.
  • Are more effective than placebos
  • Are more effective than existing drugs
  • Are more cost-effective than existing drugs, or
  • Are both more effective and more cost effective than existing drugs.

For my money, I believe the standard should be the first and second ones.  The drug company should simply have to show that the drug does no harm and is more effective than placebos.

Due to asymmetric information, however, the FDA could require the drug companies to compare their drug’s effectiveness against existing treatments or gauge the cost-effectiveness of the treatment.  Although these effectiveness and cost-effectiveness tests need not affect drug approval, insurance plans could use this information to determine if they should cover the drugs.

GoozNews has some interesting commentary regarding calls for the FDA to perform Stage III CER testing.

“I do not support the position of advocates like former New England Journal of Medicine editor Marcia Angell who think new drugs should have to be proven better than what exists before they are approved. If companies want to bring comparable therapies to market, that’s their business. It may even be the case that some me-too drugs work in some sub-populations, but not in others. So if one drug fails to achieve lower cholesterol, or offer arthritis pain relief, for instance, the doctor can switch her patient to the newer drug. But if the new drug is not proven to be better than what exists in a large Phase III trial, then physicians, patients and payers will have the information they need to insist that people start on the cheapest, comparably effective medicine that is available. For most drug classes, that will mean a generic.“

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Federal Medicaid Spending: The Worst is Yet to Come

June 9, 2011 in CER, CHIP, Medicaid | 1 comment

As the economy worsened, Medicaid enrollment has risen.  In the short-run, however, States have been able to avoid large increases in Medicaid spending because the Federal government has footed the tab.  According to the HHS:

“Over the past three years, despite rising enrollment due to the economic recession, nationwide State spending on the Medicaid program dropped by 13.2 percent (equivalent to a 10.3 percentage point decline in the State share of the total costs of the program) as a result of the added Federal support provided to State Medicaid programs through the American Recovery and Reinvestment Act of 2009 (the Recovery Act). In 2009 alone, due to this action, State Medicaid spending fell by 10 percent even though enrollment in Medicaid climbed by 7 percent due to the recession. However, this enhanced Federal Medical Assistance Percentage (FMAP) support is set to expire on June 30, 2011.”

Additionally, in 2014, States will need to expand Medicaid to cover adults and children with income up to 133 percent of the Federal Poverty Level (FPL) (currently about $27,000 income for a family of three).  Again, the federal government will cover most of these costs.

Can the federal government expand services, maintain quality, and slow the growth rate of healthcare spending costs?  Some options for doing this include:

  • Beneficiary cost sharing.  Although regulations limit to some degree the amount of cost Medicaid can charge beneficiaries, economists have long shown that cost sharing reduces utilization and decreases cost.  Will this cost sharing decrease the rate by which Medicaid beneficiaries receive unnecessary services or will patients forego necessary care? [Healthcare Economist: The answer is both].  If patients forego preventive services, will hospitalization rates increase and aggregate spending increase [Healthcare Economist: In some cases yes, but on aggregate costs will go down with more cost sharing, especially if inpatient cost sharing increases].
  • Benefits: States have the ability to cut optional benefits; change the amount duration or scope of the benefit; or offer “benchmark benefits.”  Benchmark benefit plans must provide equal coverage to one the following existing commercial plans: the standard Blue Cross/Blue Shield preferred provider plan offered to Federal employees; state employee health benefit, or the largest commercial Health maintenance organization (HMO).
  • Improve Efficiency.  Examples include:
    • “Money Follows the Person” demonstration grants to help transition people from costly nursing home settings to more integrated community settings
    • Change payment policies to reduce unnecessary cesarean deliveries.
    • Improve post-acute care benefit to reduce costly hospital re-hospitalizations.
    • ACOs.  I have written much about these in the past.
    • Health Information Technology and Electronic Medical Record initiatives.
    • Provide more integrated care models for dual-eligible beneficiaries.
    • Reduce fraud through initiatives such as the Medicaid Integrity Institute (provides free training to State Medicaid agency staff), better screening of providers (as mandated by the ACA), and synergies with Medicare fraud investigations.

Will any of these interventions work?  Reducing cost means either reducing prices or reducing utilization.  Since cutting Medicaid prices would drastically reduce poor individual’s access to quality medical care, the only solution is reducing utilization.  The key is identifying eliminating care that is either unnecessary not cost effective (e.g., an MRI for any minor injury), even if many beneficiaries like costly interventions.

Source: U.S. Department of Health and Human Services. Medicaid Cost-Savings Opportunities, February 3, 2011

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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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