Unbiased Analysis of Today's Healthcare Issues

Will Better Care deliver better care?

Written By: Jason Shafrin - Jun• 23•17

The Senate’s new health care bill, the Better Care Reconciliation Act of 2017, proposes a number of changes to the Affordable Care Act.  The Kaiser Family Foundation has a detailed breakdown of the bill and compares it with the Affordable Care Act that President Obama passed and the American Health Care Act that was proposed by the House of Representatives. How should we evaluate these changes?

From an economist’s point of view, there are generally two dimensions over which one evaluates economic policy: efficiency and equity.   Efficiency asks whether the bill will increase market efficiency and improve overall social welfare.  Efficiency is not everything.  As a society we value providing support for the least fortunate among us.  Whereas maximizing efficacy is generally a good thing, equity depends on societal preferences.  No redistribution would provide little support to the poor, complete equity would lead to a communist society with little incentives for innovation or hard work.

Now back to the Better Care bill.

In terms of efficiency of health insurance markets, there is little to like.  The bill keeps in place premium subsidies for the working poor, but the value of these subsidies drops from 70% of the plan’s actuarial value to 58%.  Additionally, provisions to reduce cost sharing provisions for the poor  have been stripped.  Thus, fewer people are likely to buy insurance, particularly healthy people.  Second, the bill eliminates the individual mandate.  Thus, fewer people are likely to buy insurance, particularly healthy people.  Third, the employer mandate will be repealed meaning that fewer employers will offer insurance.   Many people may claim that the Obamacare health insurance exchanges were suboptimal.  For instance, community rating gives health insurers an incentive to provide poor care to the sickest patients to get them off their plan.  Nevertheless, a suboptimal but functional insurance market is better than one likely to go into a premium death spiral, which is likely what we have with the Better Care plan.

In terms of overall societal efficiency–taking into account things besides health care–there are some small things to like.  Taxes are lowered, which is a positive due to the deadweight loss taxes create.  For instance, distortionary taxes on specific services (e.g., health insurers, pharmaceuticals, medical devices) are dropped.  Allowing individuals to buy health insurance at 58% of actuarial value will allow more people to buy insurance, and still have funds left over for other expenses (even though some of those expenses will inevitably be out-of-pocket health care costs not covered by insurance).

In terms of equity, the plan is certainly bad for the poor.  Medicaid expansions will be rolled back over time (although not as quickly as in the AHCA).  As described above, premium and cost sharing support will decrease.   As described by Axios, the biggest winners of the bill are young healthy people who either no longer need to buy health insurance or who can buy less generous but less expensive (58% actuarial value) coverage.  The biggest losers are the poor and elderly, who will face some combination of higher premiums, or more cost sharing.  Better Care does provide some funds to States to set up high risk pools–which could be separate from the general health insurance exchanges and thus could drive down premiums–through the State Stability and Innovation Program.

In short, despite some tax cuts, there is a serious risk that the health insurance exchanges  collapse and poor and near elderly individuals are left with either no insurance or much less generous insurance.

Politically, this was much less of a repeal and replace, rather just more of the same.  According to Megan McArdle:

I called the House health care bill “Obamacare Lite,” but compared to the Senate bill, the House was offering a radical new taste sensation. The Senate bill touches very little of the underlying architecture of Obamacare; all it does is eliminate the insurance mandates, cut spending and give states somewhat more autonomy in how those dollars are spent. Repeal Obamacare, you say? They’re barely even worrying it.

 

Health Wonk Review is up

Written By: Jason Shafrin - Jun• 23•17

Joe Paduda has posted  this week’s version of the Health Wonk Review (HWR) – The double edition at Managed Care Matters.  Check it out.

I also found this very honest discussions of effects of concussions in the NFL from former player and Hall of Famer Warren Sapp.  The video is below and the article here.

Mid-week Links

Written By: Jason Shafrin - Jun• 21•17

Can physician quality be captured by a single composite measure?

Written By: Jason Shafrin - Jun• 20•17

Value-based payment for providers is often predicated on being able to measure physician quality with a single composite measures.  For instance, Medicare s Value-Based Payment Modifier (Value Modifier) combines a variety of individual quality metrics across domains to create a single quality score.  Payment to physicians is adjusted based on a combination of physician quality and resource use.

The question remains, however, whether these composite scores do a good job of measuring quality.  Martsolf, Carle, and Scanlon (2017) notes that this may not always be the case.

However, the creation of such global composite measures is not without risk. When multiple indicators measuring distinct aspects of quality are inappropriately combined into a single measure, the resulting composite measure is not useful or even completely uninterpretable. For example, when indicators measuring unrelated constructs are included in a single score, the high score on some indicators could hide low scores on other indicators or vice versa. In this case, the composite measure does not provide a clear quality signal. Inclusion of invalid composite measures could actually hurt quality reporting by leading to physician practice misclassification.”

To take a simple example, Physician A could be excellent at diagnosing a condition but poor at treatment and Physician B could be excellent at treatment but poor at diagnosis.  If this information where known to patients, and all patients went to Physician A for diagnosis and Physician B for treatment, they would both be excellent at treating the patients they do even though a composite score could rank both physicians as average. This example captures cases where quality is multidimensional.  Quality metrics also must be reliable as well and accurately capture underlying physician quality when measured across a reasonable sample size of patients.

While this argument is theoretical, the Martsolf, Carle, and Scanlon (2017) paper examines whether “HEDIS process indicators [can] be used to measure a single construct for the purpose of creating an internally valid global composite measure of physician practice quality.”  The authors use physician quality scores from the Puget Sound Health Alliance’s (PSHA) Community Checkup scorecard.  Their analytical approach was as follows:

We used measurement models (e.g., confirmatory factor analysis) to investigate the dimensionality of 19 specific physician practice quality indicators. In this case, dimensionality refers to the extent to which multiple indicators can be used to assess a single construct or multiple constructs. Specifically, the measurement model approach is used to assess the extent to which a single factor accounts for the observed covariance among indicators. Models that “fit well do a good job of reproducing the observed covariance matrix.

Using this approach, the authors’ results “…did not support the psychometric validity of a single unidimensional composite.”  The implications of these results are very interesting.  Although many payers and researchers have argued for a single quality measure for physicians and other providers, in practice this single measure may work poorly.  Thus, tying reimbursement to quality–if quality is measured using HEDIS process measures–is problematic.  In the words of the authors:

Our results may call into question efforts to create and use single unidimensional measures of physician practice quality, as using such measures can lead to spurious conclusions about quality by hiding important aspects of quality and to increased physician misclassification by exacerbating the measurement error inherent in any given measure. Particularly, performance on an invalid global measure of physician practice quality may obscure practices performance on more specific areas of clinical care.

For more details, do read the whole article.

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The Future of Oncology Treatment and Value Assessment

Written By: Jason Shafrin - Jun• 19•17

Value frameworks are all the rage of late.  But are payers really using them?

According to my colleague Jeremy Schafer, the answer is yes.  From an article in Drug Topics:

“Value assessment may become more important as the health-care market shifts to outcomes and value-based reimbursement models,” said Jeremy Schafer, PharmD, MBA, Senior Vice President and Director for Payer Access Solutions at Precision for Value… “Our research has also found that payers currently not using value frameworks either anticipate doing so in the future or are relying on internal cost-effective analyses within their organizations…The most common way payers are using value frameworks is in choosing preferred therapies, comparing products within a class, and policy/pathway development.”

The articles continues to notes a number of limitations of the current value framework, such as their ability to accurately capture a treatment’s effect on patient quality of life as well as the medication’s convenience factor (e.g., injection vs. oral).  Schafer even cites the work of the Innovation and Value Initiative, where I serve as the Director of Research.

New initiatives around value, such as one currently under development from the Innovation and Value Initiative, may also help to add transparency by allowing pharmacists and other stakeholders to measure value from a variety of perspectives within a single tool. They may also allow value measurement to be adjusted based on different methodological assumptions.

Do read the whole article.

Does more spending improve outcomes?

Written By: Jason Shafrin - Jun• 18•17

A number of studies have claimed that increased health expenditures may result in no better, or even worse outcomes.  For instance, a paper by Fisher et al. (2003) looking at patients with acute myocardial infarction, colorectal cancer, or hip fracture finds that “Quality of care in higher-spending regions was no better on most measures and was worse for several preventive care measures.”  This analysis, however, examined variation in spending and quality by hospital referral region (HRR) and could suffer from the ecological fallacy.

A recent paper by Watson et al. (2017) examines the effect of increased spending out outcomes at the individual level among patients in neonatal wards.  The authors use the National Neonatal Research Database (NNRD).  The data contain information from infants treated in neonatal units in England between 2009 and 2013.  Costs were estimated based on Helathcare Reseource Group (HRG) codes and costs per intensive care cot day were estimated.  The key outcome variable was overall mortality and a secondary outcomes were in-hospital mortality and moribidity free survival.  The key explanatory variable cost per intensive care cot day.  The regression also controlled for the baby’s gender, gestational age, birth weight, and mother’s receipt of steroids as well as year, ward, and ward-year fixed effects. To control for the potential endogeneity, the authors also ran a fixed-effects instrumental variables (FE-IV) regression using patient distance to the nearest hospital as a instrument for the unit providing the treatment.

Using this approach, they found:

…a £100 increase in the cost per intensive care cot day (sample average cost: £1,127) is estimated to reduce the risk of mortality of 0.38 percentage points (sample average mortality: 11.0%) in neonatal intensive care. This translates into a cost per life saved in neonatal intensive care of approximately £420,000.

Their estimates for their secondary outcome also suggest that “reductions in the mortality rate are accompanied by equivalent rises in morbidity.”

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Quotation of the day: Health as capital

Written By: Jason Shafrin - Jun• 15•17

The thought that health is a form of capital goes way back to the 19th century.  Max von Pettenkofer compares health and economics and health states with capital in the following quotation:

Just as the effort to obtain greater profits, an not merely fear of losses, is the driving force in economics, so too it must be in hygiene as a doctrine of health.  Hygiene (as a subject) must establish and investigate all the influences exerted on the organism by its natural and artificial environment, in order to increase its well-being through this knowledge.  Health really is a form of property or capital, which is to be sure usually inherited, but which must also be acquired by its owner and can be increased as well as reduced.

This is quoted  from Death in Hamburg by Richard J. Evans, p. 242.

Pettenkofer–a Bavarian innovator– even called the subject of hygiene “health economics” to make the parallel even more explicit.

Can financial incentives increase the effectiveness of weight loss programs?

Written By: Jason Shafrin - Jun• 14•17

As an economist, I would say “of course”!  Increasing the price (the reward for weight loss) generally leads to an increase in supply (of efforts to lose weight).  However, there is evidence that in some cases, adding a financial incentive can actually reduce effort.  For instance, Uri Gneezy and Aldo Rustichini (2000) found that adding a financial penalty when parents were late to pick up their children at daycare lead to an increase in the number of parents who arrived late to pick up their children.  As summarized by the Freakonomics podcast, some incentives were “…completely incompatible with money. Like, for example, avoiding the guilt of inconveniencing the day care workers.”

So, does paying for weight loss fall in the traditional economic realm where paying more leads to more effort or the Israeli daycare phenomenon?  According to a study by Finkelstein et al. (2017), the answer is the former.

We conducted a parallel-group randomized controlled trial from October 2012 to October 2015 with 161 overweight or obese individuals randomized to either control or reward arm in a 1:2 ratio. Control and reward arm participants received a four month weight loss program at the LIFE (Lifestyle Improvement and Fitness Enhancement) Centre at Singapore General Hospital. Those in the reward arm paid a fee of S$165.00 (1US$ = 1.35S$) to access a program that provided rewards of up to S$660 for meeting weight loss and physical activity goals. Participants could choose to receive rewards as guaranteed cash payments or a lottery ticket with a 1 in 10 chance of winning but with the same expected value. The primary outcome was weight loss at months 4, 8, and 12. 161 participants were randomized to control (n = 54) or reward (n = 107) arms. Average weight loss was more than twice as great in the reward arm compared to the control arm at month 4 when the program concluded (3.4 kg vs 1.4 kg, p < 0.01), month 8 when rewards concluded (3.3 kg vs 1.8 kg, p < 0.05), and at month 12 (2.3 kg vs 0.8 kg, p < 0.05). These results reveal that a payment/rewards program can be used to improve weight loss and weight loss maintenance when combined with an evidence-based weight loss program.

The world of sports also finds that financial incentives lead to weight loss.

The Finkelstein et al. 2017 study did find that weight loss was more persistent after the incentives ended (month 12), but it would be interesting to see if the financial incentives also lead to long-run weight loss multiple months after the incentives had ended.   If that was the case, then clearly neoclassical economics could not purely explain the result.  If that was the case, once the financial incentives were taken away individuals should go back to their preferred weight.  There may, however, be a status quo bias.  Getting people to change behavior may be costly and thus financial rewards are needed.  Once this behavior change has been made and turned into habit, however, perhaps the cost of continuing the healthier behaviors is lower and this a new optimal weight (which would likely fall between the weight with  financial incentives and pre-intervention weight) could be reached.

HT: Marginal Revolution.

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Mid-week Links

Written By: Jason Shafrin - Jun• 13•17

What can you learn from quality or cost outliers?

Written By: Jason Shafrin - Jun• 13•17

Many researchers have pointed to (positive) cost or quality outliers and made the claims that if only all physicians, or hospitals, or regions could be like these high quality or low cost providers/regions, then the health care system would be much more efficient.  Research teams such as the Dartmouth Atlas are famous for finding these conclusions.  My own research has also investigated these questions (here, here and here).

Describing variation in quality or cost, however, does not mean that the solution is to make everyone like the best providers.  For instance, the Mayo clinic has some of the best physicians in the world.  It is not possible for all hospitals to hire the best physicians, since clearly there is a limited number of these “best” physicians.  However, oftentimes, you can learn from these outliers using qualitative data collection methodologies.

What people can learn from so-called “positive deviance” analysis is exactly the topic of a recent HSR study by Rose and McCullough (2017). Their study describes how to sample sites for positive deviance analysis, how many sites to examine, how to collect data, and many other practical challenges. For instance, the authors provide an example why surveying both positive and negative deviants is important.

…we had expected to find that the staff at the best-performing sites would be distinguished by their willingness to go “above and beyond” for their patients, but in fact, we found that this was equally true at the high- and the low-outlier sites…meaning that simply exhorting providers to go the extra mile for their patients would not be an effective approach.

On the other hand, if there are specific processes or organizational structures that can be identified by positive deviants, these could potentially be adapted by both negative deviants as well as the typical organizations. When idiosyncratic factors, talent, or effort are key drivers of variability, then policy and management interventions to improve outcomes and reduce cost at negative deviant sites are less likely to succeed.

The authors do a good job of showing both the utility of positive deviant analysis (e.g., looking for potential causes for improved outcomes) as well as its limitations (e.g., negative deviants may face resource, organizational or legal constraints and thus may not be able to implement the practices of positive deviants).

For more information on studies using the positive deviance approach, check out the Positive Deviance Initiative.

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