Unbiased Analysis of Today's Healthcare Issues

Do Incentives for Healthy Behaviors Work? A Case Study of Medicaid in Iowa

Written By: Jason Shafrin - May• 09•17

One of the primary changes in the healthcare system directed by the Affordable Care Act was providing funding for states to expand Medicaid.  Many states did so; many others did not.  Some states expanded Medicaid but put additional hurdles in front of beneficiaries to receive this coverage at no cost.  Consider the case of the State of Iowa.   A paper by Akelson et al. (2017) writes:

the State of Iowa used a section 1115 waiver to expand Medicaid by establishing its own Iowa Health and Wellness Plan (IHAWP)…the state also created the Healthy Behaviors Program…[which] was designed with four components: a wellness exam and health risk assessment for members, incentives for providers who assist members with completing their health risk assessment, incentives for members’ healthy activities and behaviors, and a premium to be paid by members who did not complete the wellness exam and health risk assessment in the first twelve months of their coverage. To meet the wellness exam requirement, members had to have an annual preventive exam from any plan-enrolled physician…The health risk assessment component required the completion of a standardized instrument…[that]…assessed members’ health and their experiences with receiving health services…”

Some would argue that this is a positive development.  If the government is providing health insurnce to individuals and is responsible for most of their health care costs, the government should incentivize individuals to engage in healthy behaviors, both to improve their health and save on the government’s bottom line.  This type of policy could be considered a productive “nudge”.  Others would claim that this just one instance of the nanny state telling people what to do.  Forcing individuals to undergo wellness visits and standardized instruments for which they may or may not what to complete is in essence devaluing the time of people on Medicaid.

Regardless of what you think of this program, there is one question we can answer: did people actually get the wellness examine and fill out the risk assessment.

The study by Akelson et al. (2017) used qualitative interviews with Iowa staff, IHAWP patients and providers, and quantitatve analysis using claims and enrollment data.

Even though completing both items was a requirement for getting free Medicaid insurance, they found that:

Overall, we found that the proportion of members who completed a health risk assessment or wellness exam was very low. Even more important, the proportion who completed both, which was required for the member to avoid paying a monthly premium in the following year, was extremely low: Approximately 83 percent of Wellness Plan members and 92 percent of Marketplace Choice members in our sample failed to complete both activities in 2014

Why did so few people do the wellness exam and assessment?  Based on interviews, most people were not aware of this program and those who were did not understand that they needed to complete these tasks to get free insurance. Further, clinicians and the managers at clinician’s facilities also often did not fully understand this program.

In short, did the Healthy Behaviors Program not incentivize healthy behaviors, it may have made people less healthy if they later disenrolled in Medicaid due to receiving bills for unexpected premiums.

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The Case For Patient-Centered Assessment Of Value

Written By: Jason Shafrin - May• 08•17

Value assessments are all the rage these days. From ASCO to ESMO, from MSKCC to AHA/ACC, from AMCP to ICER, there are a variety of value frameworks (and acronyms) out there. In the Health Affairs blog today, Alan Balch and Darius Lakdawalla make the case that treatment value should be measured from a patient-centered approach. Although patient-centered measures of value clearly are sensible–as patients are the end consumers of medical goods and services–there are good reasons why value does not always take the patient’s perspective.

Economics, the science of measuring value, holds that the value of any good rests in the eye of its consumers. In health care, this has meant that value is defined by how patients perceive it, rather than by how much they actually pay for the services they receive. Unlike other markets, health care features payment arrangements that separate consumers from payers

Payers often restrict patient choice to reduce the risk of excessive pharmacy or medical cost and also to ensure that prescribing patterns do not deviate from clinical guidelines.  The solution to balancing patient choice with cost control likely isn’t either imposing narrow restrictions or allowing patients 100% free choice, but a middle ground.  The authors propose that:

A range of therapies might be clinically indicated for a given patient, but they may vary on non-clinical dimensions that matter to them. For instance, some treatment options may require less travel, such as an oral medication versus radiation treatment or surgical treatment versus continuing medical management. Others might possess a lower toxicity profile and fewer side effects, such as fatigue, nausea, pain, or neuropathy. And, some treatments might offer more certain short-term survival benefits, while others offer a riskier bet on the possibility of a longer-term gain. A flexible formulary might allow patients and their physicians to choose among these clinically valid therapies that nonetheless differ in ways that matter to the patient. In this way, the “guard rails” stay up around evidence-based care but become more aligned with the patient experience.

Please read the whole article. Interesting throughout.

Effect of Medicare Part D on Mortality

Written By: Jason Shafrin - May• 07•17

Huh and Reif (2017) have an interesting study of the effect of Medicare Part D on mortality.  The abstract is below.

We investigate the implementation of Medicare Part D and estimate that this prescription drug benefit program reduced elderly mortality by 2.2% annually. This was driven primarily by a reduction in cardiovascular mortality, the leading cause of death for the elderly. There was no effect on deaths due to cancer, a condition whose drug treatments are covered under Medicare Part B. We validate these results by demonstrating that the changes in drug utilization following the implementation of Medicare Part D match the mortality patterns we observe. We calculate that the value of the mortality reduction is equal to $5 billion per year.

Previous studies have shown that increases in Medicare Part D spending decrease medical spending (i.e., Part A and B). The CBO estimates that a 10% increase in Medicare Part D is correlated with a 2% decrease in medical care spending.

Adding in the results from Huh and Reif, we see that Part D drugs also reduce mortality as well.

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Mid-week Links

Written By: Jason Shafrin - May• 03•17

 

Trump’s First 100 Days: A Healthcare Review

Written By: Jason Shafrin - May• 02•17

The Order to begin the ACA Repeal.
Appointing Neil Gorsuch to the Supreme Court.
Appointing Tom Price to run HHS.
Proposing the American Health Care Act (AHCA) to replace Obamacare.
Confirming Seema Verma to run CMS.
Delayed implementation of bundeled payments.
Trump decides to continue Obamacare insurance subsidies.

All this and more happened in Donald Trump’s first 100 days. The American Journal of Managed Care Pharmacy has a nice infographic that summarizes how the Trump administration is reshaping health care in the U.S.

Does the hedonic treadmill work in reverse?

Written By: Jason Shafrin - May• 01•17

The hedonic treadmill is a concept that people acclimate to changes in their life and improvements in quality of life. For instance, people who buy a new car have a brief short-term burst of happiness, but after people acclimate to having the car, their quality of life returns to the previous level. This concept can be true of most new purchases (e.g., clothes, technology, housing) in the developed world. Going from lacking shelter or food are a few of the exceptions where material gains affect quality of life for a sustained period.

One question is whether this concept works in reverse with respect to health. In other words, when previously healthy individuals have a new disease or ailment, do they eventually acclimate to their new quality of life and return to the status quo quality of life?

Some previous research indicates that patients “tend to self‐ report better subjective health over the disease trajectory, even if more objective health measures suggest that their condition is not improving.” Additionally, patient reference points may change when a person has a disease. For instance, in some clinical trials for cancer treatments, the patient subjective quality of life among cancer patients who had recently come off treatment and were progression-free was higher than for healthy individuals in the general public.

Cubí‐Mollá, Jofre‐Bonet, and Serra‐Sastre (2017) examine these questions in more detail. The authors analyzed the issue of adaptation by estimating the effect of the presence of a long‐ standing illness and the time since diagnosis on the construct of subjective self‐ assessed health.  The authors use data from the the British Cohort Study (BCS70), a longitudinal survey dataset of 17,287 individuals born in 1970 in England, Wales, and Scotland that captures patient measures of quality of life.   The surveys were administered when the individuals were aged 5, 10, 16, 26, 30, 34, 38, and 42.   The authors measure changes in self-assessed health (Excellent, Good, Fair or Poor) as a function of the duration one of the long-standing illnesses studied. The long-standing illnesses include: diabetes; depression; anxiety; epilepsy; high blood pressure (HBP); migraine; hay fever, rhinitis, and other diseases of the upper respiratory tract (URT); asthma; cancer; ulcer; Crohn’s disease; eczema; psoriasis; and back problems.

Using this approach, the author find that:

Despite the negative impact of suffering from an LSI, individuals are likely to report better health states the longer they experience a chronic condition. In particular, the APEs [average partial effects] for each of the SAH [subjective assessment of health] categories reveal that differences in the effect of the morbidity and duration variables arise between excellent and all other SAH categories (good, fair , and poor ). Suffering from a chronic illness decreases the likelihood of reporting excellent health, but longer durations counterbalance this effect; that is, duration increases the probability of reporting SAH as excellent.

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Will the UK become a “desert for healthcare innovation”?

Written By: Jason Shafrin - Apr• 30•17

That is the claim made by the Association of the British Pharmaceutical Industry (ABPI).  Lisa Anson, who took over as ABPI president last week, told The Times that the financial squeeze on the NHS threatened the whole of Britain’s £30 billion life sciences sector as firms would reconsider working in the UK.  ABPI asked for the UK’s National Health Service (NHS) to increase spending by £20 billion per year to keep up with demand.

Would pharmaceutical companies really leave the UK?  Well, not entirely.  Let’s look at Anson’s argument, point by point.

Without higher health spending, companies would delay launching medicines in Britain as they were unlikely to be approved, she said. Nor would they be able to do clinical trials because they could not evaluate potential new drugs against existing best treatments if UK patients were not getting them

Will drug companies delay introduction of medicines?  Ms. Anson says drug companies are re-thinking entering the UK.  However, most medicines have very high fixed costs (i.e., RD) and very low variable costs (e.g., the cost of production).  Thus, even if the price the NHS pays declines, in the short-run countries will still find it profitable to sell in the UK, even at a lower price.  However, they may de-prioritize securing NHS coverage in the UK if this market is less lucrative than others; thus access to new medications may be slightly slower, but this impact is likely to be modest.   More importantly, decreased coverage of new treatments has a negative long-run effect on investment in innovation..  If coverage for new therapies in the UK becomes increasingly strict , that policy will decrease incentives for life sciences companies to invest in R&D to create new medicines.

On the second point, Anson is certainly right in many cases.  If the standard of care is not typically provided in the UK, pharmaceutical companies will be likely to conduct clinical trials in other countries.  This is problematic not only due to the extra jobs the clinical trials create, but for some disease–such as potentially terminal diseases like cancer–the ability to join a clinical trial is highly valued by patients.

Is the £20 billion per year spending increase the right number?  As I am not an expert on the NHS system, I am not able to weigh in on whether this figure is too high or too low. However, it is clear that the NHS is not prioritizing access to innovative treatments.

Simon Stevens, head of NHS England, signalled last month that new medicines would have to take a back seat to spending on GPs and mental health because the NHS could not afford everything patients wanted.

Although I agree with Mr. Stevens that patients can’t get everything they want, access to new innovative treatments is often at the top of their list.

 

Measuring the effect of multiple comorbidities on health care costs: Q&A Style

Written By: Jason Shafrin - Apr• 27•17

Q: What is the effect of the number of patient comorbidities on health care spending?

A: This is a simple analysis to do. One could just run a regression with cost as the dependent variable and the number of comorbidities as an independent.

 

Q: What if the effect of the number of comorbitiides is non-lineaer. For instance, going from 1 to 2 comorbidieis may not increase health care cost as much as going from 2 to 3 comorbidities and so on.

A: Good point. In that case, you could just include a series of indicator variabiles for the number of indicator variables. In other words, you would have an indicator for the patient having exactly 1 comorbidiity, exactly 2 comorbidity, exactly 3 comorbidities, and so on.

 

Q: This is a nice, elegant solution. One drawback, however, is that it treats all comorbidities as equivalent. For instance, consider two sets of individuals. The first set of individuals has diabetes and hypertension; the second set of individuals have diabetes and schizophrenia. In both cases the number of comorbidities is 2 but the latter group is likely to have higher cost.

A: Well in this case, you could simply include indicator variables for your disease combinations of interest. For instance, if you are interested in (D)iabetes, (H)ypertension, and (S) chizophrenia, the indicators would be D only, H only, S only, DH, DS, HS and DHS where D is diabetes.

 

Q: That works well in your simple example.   In practice, however, most researchers focus on a large number of disease. In that case, the number of disease interactions can get very large.

A: Excellent point. In addition to the large number of covariates, there typically are very few observations for any given disease combination. A study by Eckardt et al. (2016) had a sample of 1050 patients between ages 65 and 85 and there were 1047 unique combintations of comorbidities.

 

Q: Wow! That is a big problem. So, what is the solution?

A: One approach would use a hierarchical model to reduce the dimensionality. However, there is not a clear nesting structure when discussing patient comorbidities so this approach generally will not work in this case.

 

Q: Any other suggestions

A; Well, Eckhardt and co-authors use a finite mixture model. Finite mixture models include models you may be more familiar with including latent class models.

 

Q: Can you describe what they did?

A: Below is a quote from their paper:

We used a finite mixture of generalized regression techniques in order to statistically learn from data. Thus, opposite to mixtures of densities, we applied an extended finite mixture of regressions— also known as clusterwise regression— to capture unobserved heterogeneity of the regression coefficients. Thus, instead of taking specific patterns of single disease combinations into account, the aim of our study was to detect components of diseases within arbitrary morbidity patterns that influence healthcare costs in elderly patients with multiple chronic conditions.

After doing Akaike information criterion (AIC) test, they were left with a 4 component mixture model, where each component used a different gamma distribution.

 

Q: And what did they find?

A: Again, I quote from their study.

As expected, mean costs tend to increase with an increasing number of comorbidities in case of component 1 (group 1). A similar but less pronounced tendency is shown by component 4 (group 4). On the contrary, in component 3 (group 3) the mean costs tend to decrease with the number of comorbidities. This might be a hint that component 3 (group 3) captured the most expensive single disease leading to high mean costs with low numbers of comorbidities, while the mean costs in component 1 (group 1) and component 4 (group 4) were mainly caused by cumulative effects. Especially in component 1 each additional disease caused an increase in mean costs. In contrast, mean costs within component 4 are at a significantly lower level.

 

Q: I’m confused. Is this at all useful.

A: I agree that the summary statistics are not all that useful. However, you can combine the components to get results of interest. For instance, the authors found that “diagnosed obesity, osteoporosis and cerebral ischemia/chronic stroke cause positive effects on costs.”  Additionally, for any component, you can see which components factor most for each disease.

For example, while asthma/COPD is ranked 16 (17%) in component 1, it is ranked 9 in all other components (24% to 26%). Similarly, prostatic hyperplasia achieved rank 22 (11%) in component 1 in contrast to the ranks 10 (component 2, 24%), 12 (component 3, 22%) and 14 (component 4, 18%).

 

Q: Interesting stuff, isn’t it?

A: I agree!

Pay caps on nurses in the UK

Written By: Jason Shafrin - Apr• 26•17

I’m finishing my time in the UK today and head back home tomorrow.  The British Society for Rheumatology 2017 conference was interesting, on the news the key headlines were pay for National Health Service employees, nurses in particular.

Due to budget shortfalls, the NHS froze nurse pay between 2010 and 2012.  Beginning in 2012, pay raises resumed but were capped at 1% per year.  Because of these austerity measures, the Royal College of Nursing claims that real wages–i.e., the differences between nominal wage increases and increases to the cost of living–for nurses have actually fallen by 14%.

Economists would predict that when prices or wages fall below market rate, there will be shortages.  Sure enough, that is the case in the UK.  There is an estimated 50,000 person labor shortfall for NHS, equivalent to 6% of the current NHS workforce.

The Labor party is trying to address this situation. They are campaigning on lifting the the 1% cap on nurse wages and instituting mandatory staffing minimums.  The cost of this proposal, however, would be £350m a year (or £500m if increases to physician wages were also included).

This has lead some commentators to claim that the NHS reimbursement system is “complicated, unfair and frankly absurd.”

While I would like to claim that nothing of this sort happens in the U.S., that is not entirely true.  Medicare tried to artificially hold down physician reimbursement using the sustainable growth rate (SGR) policy.  The physicians, however, were too politically powerful for this type of budget cuts and the SGR was reversed through the annual Doc Fix every year until the SGR was repealed in 2015.

Single payer systems may seem good to those who focus on equality, but quality of care can suffer sue to staffing and treatment shortages due to centralized price setting and budget pressures.  For those interested in moving to a single payer system, take the NHS example as a cautionary tale.

The Voice of the Patient

Written By: Jason Shafrin - Apr• 25•17

Did you ever wonder what is is like having lung cancer?  Or narcolepsy?    What factors are most important to patients when receiving treatment for these diseases?

The FDA is working to collect these answers to help guide their drug approval process.  The FDA’s “Voice of the Patient” aims to “…more systematically gather patients’ perspectives on their condition and available therapies to treat their condition.”  This initiative is part of the FDA’s program of incorporating Enhancing Benefit-Risk Assessment in Regulatory Decision-Making.

For instance, did you know that excessive daytime sleepiness was the most significant symptom affecting patients with nacrolepsy’s  daily lives?  Probably so.  You may not know, however, that…

the effects of EDS go far beyond feeling tired or falling asleep during the day. Because of EDS, they constantly battle “brain fog” and other cognitive impairments, automatic behaviors, and chronic sleeping deprivation.

While EDS is the most significant and well known symptom, other symptoms include cataplexy, hallucinations or sleep paralysis.  For patients, these symptoms are often uncontrollable and/or  unpredictable and the loss of control can be terrifying to patients. Other side effects include insomnia, weight gain, mood fluctuations, and depression, all of which have a significant impact on the lives of patients with narcolepsy.

The next Voice of the Patient Meeting is in May and covers autism.  The reports are available here.