November 2011

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The End of Health Reform Starts in Ohio?

November 9, 2011 in Health Reform | 2 comments

One of the key tenets of health reform is that insurers cannot charge different premiums to individuals based on their pre-existing conditions.  Under this type of system, the optimal strategy for many individuals is to not buy any health insurance until one gets sick.  Since insurers cannot charge these sicker people higher premiums based on these conditions, healthy individuals will end up heavily subsidizing the sick.  In fact, average premiums will increase for everyone.

To prevent this from happening, health reform instituting an individual mandate which requires every American to buy health insurance.  Without this requirement, health insurance prices could spiral out of control.

Ohio, however, has recently rejected the individual mandate.  The Cato institute that Ohioans came out against the individual mandate on a 2 to 1 basis.

Is this the beginning of the end of health reform?

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Wal-mart to be the biggest health retailer in the nation?

November 9, 2011 in Current Events, Nonphysician Clinicians | 1 comment

Hot off the press:

Walmart announced it would stop offering health insurance benefits to new part-time employees, the retailer sent out a request for information seeking partners to help it “dramatically … lower the cost of healthcare … by becoming the largest provider of primary healthcare services in the nation.”

Why would Wal-mart want to provide health care services?  Here are some reasons:

  • Many health care services are high margin.
  • With some exceptions (e.g., Kaiser), most current health care service providers do not take advantage of economies of scale, particularly with respect to information technology (IT) services.
  • Wal-mart could take advantage of their current IT infrastructure to readily create EHR.  In fact, Walmart has offered commercial EHR software & services to healthcare providers since 2009.
  • This effort builds on the success of walk-in clinics at stores like CVS (MinuteClinic).  These efforts increase brand loyalty (people usually have a good opinion of the places they get health care) and increases store traffic.  Further, between 2007 and 2009 retail clinic use increased 10-fold.
  • Wal-mart recently dropped health insurance for its employees.  This could be a public relations mechanism to provide some care to these employees.
  • Wal-mart recently dropped health insurance for its employees.  These people will need low cost primary medical care since insurance won’t cover these services.
  • It could create a service provider which is national in scope an already has an existing distribution network.  Wal-mart has 3,800 stores nationwide that it can use to house these clinical services.
  • Wal-mart already delivers prescriptions drugs through its low cost generics program and Medicare Part D drug plan.

The Wall Street Journal reports that Wal-mart is actively seeking partners for its health care expansion.  I would assume that Wal-mart with staff the clinics with low-cost nurse practitioners (NPs) and physician assistants (PAs).

Although some health reformers have aimed to bolster the role of primary care providers, Wal-mart’s actions may help NPs and PAs who provide primary care while putting competitive pressures on MDs who provide primary care.

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Does getting cancer incentivize individuals to switch from Medicare managed care to FFS?

November 8, 2011 in Cancer, Medicaid/Medicare, Medicare Advantage, Quality | No comments

Medicare beneficiaries have a choice: pick the standard Medicare fee-for-service (FFS) benefit or rely on managed care plans to supply their healthcare through the Medicare Advantage (MA) program.  Many Medicare beneficiaries prefer MA because it offers them lower out-of-pocket costs and provide benefits not available in the traditional FFS Medicare program. Other beneficiaries prefer the FFS benefit because MA plans typically restrict provider choice in an effort to control costs.

The quality of care in Medicare MA relative to FFS, however, has still not yet been consistently evaluated.  Because beneficiaries can switch from MA to FFS each year, if quality is low, healthy individuals may prefer MA to reap the reduced cost sharing benefits, but when they become sick they may switch to Medicare FFS.

A study by Elkin and co-authors evaluates whether or not this is the case for beneficiaries who get cancer.

Data and Methodology

“We identified Medicare managed care enrollees aged 65 years or older who were diagnosed with a first primary breast (n = 28 331), colorectal (n = 26 494), prostate (n = 29 046), or lung (n = 31 243) cancer from January 1, 1995, through December 31, 2002, in Surveillance, Epidemiology, and End Results (SEER) cancer registry records linked with Medicare enrollment files. Cancer patients were pair-matched to cancer-free enrollees by age, sex, race, and geographic location. We estimated rates of voluntary disenrollment to fee-for-service Medicare in the 2 years after each cancer patient ’ s diagnosis, adjusted for plan characteristics and Medicare managed care penetration, by use of Cox proportional hazards regression.”

Results

The authors find that MA beneficiaries with cancer are less likely to switch to FFS than a cancer-free beneficiary. The hazard ratios range from 0.78 for colorectal cancer to 0.86 for prostate cancer. The results were consistent across various age, sex, race, cancer stage and region strata.

The likely reason for this finding is that people who have a serious disease do not want to change coverage. Even if the FFS benefit offers improved access to better care, there are significant costs of switching coverage. The new FFS providers may have less knowledge of the individual beneficiary’s health condition and the change can be stressful for the beneficiary as well. A worthwhile analysis to confirm whether this is the case would be to examine whether FFS beneficiaries who contract cancer are more likely to switch to a MA plan after contracting cancer. If the transaction cost/care coordination is driving Elkin’s results, then FFS beneficiaries with cancer should also be less likely to switch to MA than cancer-free FFS beneficiaries.

It could also be the case that MA provides high quality care for the most prevalent cancers (i.e., prostate, lung, colorectal, and breast), but there is a significant improvement in quality when beneficiaries visit FFS providers when they have rarer diseases. To confirm whether or not this is the case, the authors examine whether beneficiaries with non-Hodgkin lymphoma, acute leukemia, and soft tissue sarcoma are more likely to switch to FFS. The authors found no effect of these cancer diagnoses on the likelihood of disenrollment from a managed care plan.

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Decisions States Face When Implementing Health Exchanges

November 7, 2011 in Uncategorized | No comments

States face a number of decisions regarding how to establish an Health Insurance Exchange as part of the Health Reform Bill.  The State Health Access Data Assistance Center (SHADAC) describes in detail these policy choices.  The decisions include:

  • Creating separate exchanges for individuals and small businesses or combining the nongroup and small group markets into a single exchange
  • Allow the federal government to operate an exchange on the state’s behalf
  • Create a single-state exchange, regional exchanges (which include more than one state), or subsidiary exchanges (which serve distinct geographic areas).
  • Selecting the Exchange administrator which could be: a federal agency (if states cede control over exchange design and implementation), a state government, a quasi-public agency, a private or a nonprofit entity.
  • Acting as a market organizer (serving as impartial information source that lists and compares all qualified health plans) or an active purchaser (using a bidding process, applying restrictive certification and reporting requirements, and/or negotiating with plans to identify and select high performers).
  • Establishing minimum certification requirements (e.g., quality measures, claims payment policies and practices, and financial disclosures as well as data requirements describing enrollment, disenrollment and denied claims.
  • Determining specifications to define which tier a plan fits into (bronze-level provides benefits equal to 60 percent of the actuarial value of plan benefits, the silver level covers 70 percent, the gold level covers 80 percent, and the platinum level covers 90 percent).
  • Funding Exchange operations through mechanisms such as general revenue or assessments on plans, employers, or individuals.
  • Restrictions on how qualified health plans (QHPs) operate outside the Exchange.

Current Exchanges already in operation include:

Source: State Health Access Data Assistance Center (SHADAC) “Health Insurance Exchanges: Implementation and Data Considerations for States and Existing Models for Comparison” October 2010.

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The Demand for Policy Interventions

November 4, 2011 in Quotation, Regulation | No comments

“Authors of economics books, essays, articles, and political platforms demand interventionist measures before they are taken, but once they have been imposed no one likes them. Then everyone—usually even the authorities responsible for them—call them insufficient and unsatisfactory. Generally the demand then arises for the replacement of unsatisfactory interventions by other, more suitable measures. And once the new demands have been met, the same scenario begins all over again.”

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Early November Links

November 3, 2011 in Current Events | No comments

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CoR

November 2, 2011 in Carnivals | No comments

Check out the latest edition of the Cavalcade of Risk (CoR for those in the know) at Workers’ Comp Insider.

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Kappa Statistic

November 2, 2011 in Econometrics | No comments

Many research studies aim to figure out if a physicians did a good job.  Many studies use administrative claims data to evaluate performance.  Other times, researchers use medical record review.

One problem with medical record review is that oftentimes experts will come up with differing opinions from reviewing the same medical record.  Thus, researchers often have at least two individuals review the medical record so that the results are not biased by a single person’t opinion.

A question of interest is how reliable are different evaluators of medical record.  Cohen’s kappa can provide a quantitative estimate of inter-rater reliability.  The formula is the following:

  • [P(a)-P(e)]/[1-P(e)]
Where P(a) is the observed level of agreement and P(e) is the expected level of agreement from pure chance.  In essence, the kappa measurement compares the observed level of inter-rater agreement against the level of agreement that would be expected by pure chance.  

To give an example, consider the situation where two raters rate 10 blogs and can give them a rating of an A, B, or C. These data are available here.  You can see that Tester 1 is more likely to give positive ratings and Tester 2 is more likely to give negative ratings.  In this example, the value of Kappa is 0.44.

A general rule of thumb to follow is values < 0 as indicating no agreement, 0–.20 as slight, .21–.40 as fair, .41–.60 as moderate, .61–.80 as substantial, and .81–1 as almost perfect agreement.

 

Optimal Choices of Medicare Part D Plans

November 1, 2011 in Medicare, Medicare Part D | 4 comments

Are elderly Medicare beneficiaries able to choose Part D health plans optimally?  Many researchers may believe the answer is no.  Certain elderly individuals  (e.g., those with Alzheimer’s) may be cognitively impaired.  Inertia is also a problem; switching plans is mentally taxing and involves a spending a significant amount of time researching plan alternatives.

Nevertheless, a paper by Ketcham et al. finds that Medicare beneficiaries do learn from their mistakes and can decrease over spending over time.  The Medicare Part D program began in 2006.  The authors estimate that in that year, individual overspending was $547 (overspending is the difference in beneficiary out-of-pocket payments and premiums between their current plan and the lowest cost plan).  By 2007, overspending dropped by about $298 to $248.6.  Further, whereas 9.8% of the sample had overspending levels of more than $1,000 in 2006, only 1.7% of the cohort reached these high levels in 2007.

A portion of this decrease was due to certain high-cost plans changing their benefit structure, but much of the change was due to beneficiaries switching plans.  Specifically, individuals with overspending levels of more than $1,000 were not only more likely to switch plans, but also more likely reduce the levels of overspending by more than individuals with lower levels of overspending.  This may be a regression to the mean phenomenon, or it could be the case that it takes a high level of overspending for individuals to spend the time researching plans to switch their PDP.

How did this reduction in overspending occur?  CMS’s planfinder website may have improved the information available to beneficiaries.  The site itself may have improved or more beneficiaries may have been made aware of it.  Also, the children of Medicare beneficiaries may have been more active in choosing plans for their parents.  For instance, individuals newly diagnosed with Alzheimer’s saw a decrease in overspending; this result is likely due to children helping their parents choose better PDP.

Additionally, high spending rates may provide the impetus to change plan.  Consider a model where individuals do not change plans unless their premium + OOP spending exceeds a certain threshold.  Once this threshold is met (which could differ by individual), they search for lower cost plans.  If the threshold were not met, individuals would decide that searching for a new plan is not worth the smaller savings.  In this model of behavior, one question is whether switchers (who generally have higher initial levels of overspending) tend to choose average plans (which would reduce overspending) or one of the best plans (which would decrease overspending even more).  The quantitative results of the paper seem to indicate the latter.

The conclusion of this paper: markets may not work perfectly—especially at first—but over time learning occurs and individual self-interest can more markets towards a more efficient equilibrium.

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