Jason Shafrin’s journal round-up for 3rd February 2020

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Capacity constraints and time allocation in public health clinics. Health Economics [PubMed] Published 14th January 2020

Capacity constraints are a key issue in many health care markets. Capacity constraints can be due to short-run fluctuations in available labor or long-term resource scarcity where available supply does not meet demand at prevailing prices. One key issue to understand is how providers respond when capacity constraints appear. There are a number of potential options for health care providers to address capacity constraints. The first would be to decrease the number of patients seen. A second option would be to decrease quality. A third option would be to access additional funding to increase capacity. A fourth option would be to demand workers to work longer hours at no additional pay, which would be an implicit hourly wage reduction (and may not be legal).

In an attempt to find out the answer to this question, Harris, Liu and McCarthy examined data from a clinic in Tennessee. One issue with capacity constraints is that workforce allocation may be endogenous. Harris and co-authors look at a case where two nurses were removed from the clinic on selected mornings to administer flu shots at local schools. They claim that the assignment of nurses to schools was done by the local health department and those decisions were plausibly exogenous to any clinic decisions on volume or quality of care. Further, these nurses were not replaced by staff from other clinics.

The authors use data covering 16 months (i.e. two flu seasons) of visits. They first conducted a nurse-level analysis to evaluate if nurses on flu days had lower productivity. ‘FluNurse’ and ‘FluDay’ indicator variables were used. The former was used to control for whether the nurses selected to administer the flu shots at the schools were systematically more or less productive than the other nurses; the latter was used to measure the impact of the actual day when nurses were assigned to the school. The authors also conduct a visit-level analysis to see how time spent in the clinic varies on the days when nurse capacity is reduced compared to when it is not.

The authors find that the extensive margin is most important. On days when nurses visited schools, capacity is reduced by about 17%. On these days, providers do see fewer patients overall and prioritize scheduled visits over walk-ins. On the intensive margin, providers do decrease time spent with patients (by about 7%), but this time reduction is largely achieved by reducing some of the administrative aspects of the visit (i.e. expedited check-out times). The authors conclude that “providers value spending sufficient time with patients over seeing as many patients as possible.”

It is unclear whether these results would translate to other settings/countries or cases where capacity constraints are more long-term. The study does not discuss in detail how the clinic is reimbursed. A fee-for-service reimbursement may incentivize prioritizing volume over quality/time spent whereas under capitated or salaried reimbursement they may prioritize quality. Since many of the services examined by the study were provided by nurses, and nurses are more likely to be paid a salary rather than compensated based on clinic volume, it is unclear whether these results would translate to capacity constraints involving physicians.

Outcome measures for oncology alternative payment models: practical considerations and recommendations. American Journal of Managed Care [PubMed] Published 11th December 2019

Value-based payment sounds great in theory. Step 1: measure health outcomes and cost. Step 2: risk adjust to control for variability in patient health status. Step 3: pay providers more who have better outcomes and lower cost; pay providers less who have worse outcomes and higher cost. Simple, right? The answer is ‘yes’ according to many payers. A number of alternative payment models are being adapted by payers in the U.S. In oncology, the Centers for Medicare and Medicaid Services (CMS) has implemented the Oncology Care Model (OCM) to reimburse providers based on quality and cost.

This approach, however, is only valid if payers and policymakers are able to adequately measure quality. How is quality currently captured for oncology patients? A paper by Hlávka, Lin, and Neumann provides an overview of existing quality measures. Specifically they review quality metrics from the following entities: (i) OCM, (ii) the Quality Oncology Practice Initiative by the American Society of Clinical Oncologists (ASCO), (iii) the Prospective Payment System–Exempt Cancer Hospital Quality Reporting Program by CMS, (iv) the Core Quality Measures Collaborative Core Sets by CMS and America’s Health Insurance Plans (AHIP), (v) the Oncology Medical Home program by the Community Oncology Alliance, (vi) the Osteoporosis Quality Improvement Registry by the National Osteoporosis Foundation and National Bone Health Alliance, and (vii) the Oncology Qualified Clinical Data Registry by the Oncology Nursing Society. So, how well do these entities measure health outcomes?

Well, in general, most are measuring quality of care processes rather than health outcomes. Of the 142 quality measures examined, only 28 (19.7%) were outcome measures; the rest were process measures. The outcome measures of interest fell into five categories: (1) hospital admissions or emergency department visits, (2) hospice care, (3) mortality, (4) patient reported outcomes (PROs), and (5) adverse events (AEs). The paper describes in more detail how/why these metrics are used (e.g. many hospital admissions are related to chemotherapy AEs, hospice care is underutilized).

While this paper is not a methodological advance, knowing what quality measures are available is extremely important. Further, the paper cites a number of limitations of these quality metrics. First, you need reliable data to measure these measures, even if patients move across health care systems. Additionally, administrative data (e.g. claims data) often appear with a lag. Second, risk adjustment is imperfect and applying a value-based payment may incentivize providers to select more low-risk patients and avoid the sickest cancer patients. Third, if we care about patients’ opinions—which we should!—PROs are important. Collecting PROs, however, is more expensive than using administrative data. Fourth, quality measurement in general takes time and effort. My own commentary in the Journal of Clinical Pathways argues that these limitations need to be taken seriously and these costs and accuracy issues may undermine the value of value-based payment in oncology if quality is measured poorly and is costly to collect.

Modeling the impact of patient treatment preference on health outcomes in relapsing-remitting multiple sclerosis. Journal of Medical Economics [PubMed] Published January 2020

We hear a lot about patient-centered care. Intuitively, it makes a lot of sense. Patients are the end users, the customers. So we should do our best to give them the treatments they want. At the same time, patients have imperfect information and rely on physicians to help guide their decisions. However, patients may value things that—as a society—we may not be willing to pay for. For instance, empirical research shows that patients place a high value on hospital amenities. Thus, one key question to answer is whether following patient preferences is likely to result in better health outcomes.

A paper by van Eijndhoven et al. (disclosure: I am an author on this paper) aims to answer this question for patients with relapsing-remitting multiple sclerosis. The first step of the paper is to measure patient preferences across disease modifying drugs (DMDs). This was parameterized based on a discrete choice experiment of patients with multiple sclerosis. Second, a Markov model was used to estimate the impact of each individual DMD on health outcomes (i.e., number of relapses, disability progression over time, QALYs). QALYs were estimated utilities by health states defined by the Expanded Disability Status Scale (EDSS), caregiver disutility in each state, disutility per relapse, and disutility due to adverse events from the literature. Third, the authors compared two states of the world: one based on patient-driven preferences (as estimated in Step 1) and another based on current prescribing practices in the United Kingdom.

Using this approach, patient-centered prescribing practices lead to 6.8% fewer relapses and a 4.6% increase in QALYs. Applied to the UK population level, this would result in almost 37,000 avoided relapses and over 44,000 discounted QALYs over a 50 year period. Additionally, disease progression was slowed. For the typical patients, EDSS was -0.16 less each year under the patient-centered prescribing compared to the current market shares.

There are a few reasons for this finding. First, under current treatment patterns, many patients are not treated. About 21% of patients with RRMS in the UK do not receive DMDs. Access to neurologist care is often difficult. Second, patients do have a strong preference for more effective, newer treatments. Previous research indicates that prescribers in England generally viewed NICE guidelines as mandatory criteria they were obligated to follow, whereas neurologists in Scotland and Wales were more varied.

This study did have a number of limitations. First, the study did not examine costs. Patients may prefer more effective treatments, but this may have cost implications for the UK health system. Second, the impact of treatments was based on clinical trial data. DMDs may be more or less effective in the real world, particularly if adherence is suboptimal. Third, once people reach high levels of disability (EDSS ≥7), the study assumed that they were treated with best supportive care. Thus, the estimates may be conservative if treatment is more aggressive. Fourth, the treatment options were based on currently approved DMDs. In the real-world, however, new treatments may become available and the actual health trajectories are likely to deviate from our model.

While this study does not answer what is the optimal treatment mix for a country, we do see evidence that patient-preferred treatments are highly related to the health benefits received. Thus—in the case of multiple sclerosis—physicians should not fear that shared decision-making with patients will result in worse outcomes. On the country, better health outcomes could be expected from shared decision-making.

Credits

Chris Sampson’s journal round-up for 29th May 2017

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

“Naming and framing”: The impact of labeling on health state values for multiple sclerosis. Medical Decision Making [PubMedPublished 21st May 2017

Tell someone that the health state that they’re valuing is actually related to cancer, and they’ll give you a different value than if you hadn’t mentioned cancer. A lower value, probably. There’s a growing amount of evidence that ‘labelling’ health state descriptions with the name of a particular disease can influence the resulting values. Generally, the evidence is that mentioning the disease will lower values, though that’s probably because researchers have been selecting diseases that they think will show this. (Has anyone tried it for hayfever?) The jury is out on whether labelling is a good thing or a bad thing, so in the meantime, we need evidence for particular diseases to help us understand what’s going on. This study looks at MS. Two UK-representative samples (n = 1576; n = 1641) completed an online TTO valuation task for states defined using the condition-specific preference-based MSIS-8D. Participants were first asked to complete the MSIS-8D to provide their own health state, and then to rank three MSIS-8D states and also complete a practice TTO task. For the preference elicitation proper, individuals were presented with a set of 5 MSIS-8D health states. One group were asked to imagine that they had MS and were provided with some information and a link to the NHS Choices website. The authors’ first analysis tests for a difference due to labelling. Their second analysis creates two alternative tariffs for the MSIS-8D based on the two surveys. People in the label group reported lower health state values on average. The size of this labelling-related decrement was greater for less severe health states. The creation of the tariffs seemed to show that labelling does not have a consistent impact across dimensions. This means that, in practice, the two tariffs could favour different types of interventions, depending on for which dimensions benefits might be observed. The tariff derived from the label group demonstrated slightly poorer predictive performance. This study tells us that label-or-not is a decision that will influence the relative cost-effectiveness of interventions for MS. But we still need a sound basis for making that choice.

Nudges in a post-truth world. Journal of Medical Ethics [PubMed] Published 19th May 2017

Not everyone likes the idea of nudges. They can be used to get people to behave in ways that are ‘better’… but who decides what is better? Truth, surely, we can all agree, is better. There are strong forces against the truth, whether they be our own cognitive biases, the mainstream media (FAKE NEWS!!!), or Nutella trying to tell us they offer a healthy breakfast option thanks to all that calcium. In this essay, the author outlines a special kind of nudge, which he refers to as a ‘nudge to reason’. The paper starts with a summary of the evidence regarding the failure of people to change their minds in response to evidence, and the backfire effect, whereby false beliefs become even more entrenched in light of conflicting evidence. Memory failures, and the ease with which people can handle the information, are identified as key reasons for perverse responses to evidence. The author then goes on to look at the evidence in relation to the conditions in which people do respond to evidence. In particular, where people get their evidence matters (we still trust academics, right?). The persuasiveness of evidence can be influenced by the way it is delivered. So why not nudge towards the truth? The author focuses on a key objection to nudges; that they do not protect freedom in a substantive sense because they bypass people’s capacities for deliberation. Nudges take advantage of non-rational features of human nature and fail to treat people as autonomous agents deserving of respect. One of the reasons I’ve never much like nudges is that they could promote ignorance and reinforce biases. Nudges to reason, on the other hand, influence behaviour indirectly via beliefs: changing behaviour by changing minds by improving responses to genuine evidence. The author argues that nudges to reason do not bypass the deliberative capacities of agents at all, but rather appeal to them, and are thus permissible. They operate by appealing to mechanisms that are partially constitutive of rationality and this is itself part of what defines our substantive freedom. We could also extend this to argue that we have a moral responsibility to frame arguments in a way that is truth-conducive, in order to show respect to individuals. I think health economists are in a great position to contribute to these debates. Our subfield exists principally because of uncertainty and asymmetry of information in health care. We’ve been studying these things for years. I’m convinced by the author’s arguments about the permissibility of nudges to reason. But they’d probably make for flaccid public policy. Nudges to reason would surely be dominated by nudges to ignorance. Either people need coercing towards the truth or those nudges to ignorance need to be shut down.

How should hospital reimbursement be refined to support concentration of complex care services? Health Economics [PubMed] Published 19th May 2017

Treating rare and complex conditions in specialist centres may be good for patients. We might expect these patients to be especially expensive to treat compared with people treated in general hospitals. Therefore, unless reimbursement mechanisms are able to account for this, specialist hospitals will be financially disadvantaged and concentration might not be sustainable. Healthcare Resource Groups (HRGs) – the basis for current payments – only work if variation in cost is not related to any differences in the types of patients treated at particular hospitals. This study looks at hospitals that might be at risk of financial disadvantage due to differences in casemix complexity. Individual-level Hospital Episode Statistics for 2013-14 were matched to hospital-level Reference Costs and a set of indicators for the use of specialist services were applied. The data included 12.4 million patients of whom 766,204 received complex care. The authors construct a random effects model estimating the cost difference associated with complex care, by modelling the impact of a set of complex care markers on individual-level cost estimates. The Gini coefficient is estimated to look at the concentration of complex care across hospitals. Most of the complex care markers were associated with significantly higher costs. 26 of 69 types of complex care were associated with costs more than 10% higher. What’s more, complex care was concentrated among relatively few hospitals with a mean Gini coefficient of 0.88. Two possible approaches to fixing the payment system are considered: i) recalculation of the HRG price to include a top-up or ii) a more complex refinement of the allocation of patients to different HRGs. The second option becomes less attractive as more HRGs are subject to this refinement as we could end up with just one hospital reporting all of the activity for a particular HRG. Based on the expected impact of these differences – in view of the size of the cost difference and the extent of distribution across different HRGs and hospitals – the authors are able to make recommendations about which HRGs might require refinement. The study also hints at an interesting challenge. Some of the complex care services were associated with lower costs where care was concentrated in very few centres, suggesting that concentration could give rise to cost savings. This could imply that some HRGs may need refining downwards with complexity, which feels a bit counterintuitive. My only criticism of the paper? The references include at least 3 web pages that are no longer there. Please use WebCite, people!

Credits

Rationing and deprivation in risk sharing schemes

Here in the UK, NICE sometimes advises against the provision of particular drugs, by the NHS, on the grounds that evidence does not indicate them to be cost-effective. In some cases it appears that these ‘rejections’ are the result of insufficient data rather than comprehensive data against the use of the drug. On occasion the Department of Health has followed such decisions with a trial-in-practice patient access scheme; what are known as Risk Sharing Schemes. These allow for the provision of the drug, following an agreement with the manufacturer, and the possibility for evidence development.

One well-publicised risk sharing scheme is the Multiple Sclerosis Risk Sharing Scheme. The outcomes of this trial-in-practice remain uncertain, but the scheme has been described as a “costly failure“. In a study of participant data, a recent article demonstrated that the likelihood of individuals being offered treatment, as a part of the MS Risk Sharing Scheme, was positively related to their socio-economic status. This raises questions about the value of the results from a ‘trial-in-practice’ such as this.

Rationing and deprivation

That individuals’ deprivation levels or economic status can be a determinant of prescribing decisions is a well-documented phenomenon. Evidence exists in relation to treatment for colorectal cancer, glaucomalung cancer and the prescription of statins and antidementia drugs. Health economists often suppose that the most deprived individuals are also those most in need of health care, but the evidence from all the studies mentioned above highlights deprivation level as being a negative predictor of the levels of care received. In some cases there is good reason for this; those who are more deprived can sometimes tend to present later when care would be less effective, and there may also be relevant issues surrounding health literacy. However, in other cases such an explanation is not so obvious.

Experimental evidence

Trudy Owens and co’s study shows that risk sharing schemes can demonstrate similar characteristics, which I believe to be a point of concern. I would suggest that such schemes as the MS Risk Sharing Scheme can only be justified if they seek to produce experimental evidence of the cost-effectiveness of the intervention. Without this they will be little more than a back door means of provision for drugs that have not been demonstrated to be cost-effective. It seems obvious to me, therefore, that this research and experimental evidence, and the schemes themselves, must conform to a good study design. One would not tolerate a study that allowed practitioners to pick and choose individuals for treatment based on their subjective expectation of success. While this may contribute to the manufacturer’s aims of finding a cost-effective use of their drug it is hardly good science. If such a drug were accepted on to formularies, would doctors continue to (inadvertently or otherwise) discriminate based on deprivation status? Quite possibly, but we’d certainly highlight this as a problematic issue and it would be one reinforced by the poor quality trial-in-practice of the risk sharing scheme.

While some papers offer advice on the design and administration of risk sharing and evidence development schemes, there appear to be no studies addressing the problems caused by discrimination and rationing. It seems to me that there is a substantial gap in the research if we are to prevent risk sharing schemes becoming publicly-funded bad science.

Do you see value in risk sharing schemes? Are they likely to be more representative of practice than randomised trials? Is deprivation a reasonable basis for rationing?