Ian Cromwell’s journal round-up for 17th 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.

Does the use of health technology assessment have an impact on the utilisation of health care resources? Evidence from two European countries. European Journal of Health Economics [PubMed] Published 5th February 2020

The ostensible purpose of health technology assessment (HTA) is to provide health care decision-makers with the information they need when considering whether to change existing policies. One of the questions I’ve heard muttered sotto voce (and that I will admit to having asked myself in more cynical moments) is whether or not HTAs actually make a difference. We are generating lots of evidence, but does it have any real impact on decision making? Do the complex analyses health economists undertake make any impact on policy?

This paper used data from Catalonia and England to estimate the impact of a positive HTA recommendation from the regulatory bodies – the National Institute for Health and Care Excellence (NICE) in England and a collection of regional approval bodies in Catalonia and Spain – to assess trends in medical usage prior to and following the publication of HTA-guided recommendations for new cancer drugs between 2011 and the end of 2016. Utilization (volume of drugs dispensed) and expenditure were extracted from retrospective records. The authors built a Poisson regression model that allowed them to observe temporal effects of usage before and following a positive recommendation.

The authors noted that a lack of pre-recommendation utilization data made it difficult to compute a model of negative recommendations (which is the more cynical version of the question!), so it is important to recognize that as a limitation of the approach. They also note, however, that it is typically the case in the UK and Catalonia that approvals for new drugs are conditional on a positive recommendation. Spain has a different system in which medicines may still be available even if they are not recommended.

The results of the model are a bit more complex than is easy to fit into a blog post, but the bottom line is that a positive recommendation does produce an increase in utilization. What stuck out to me about the descriptive findings was the consistent presence of a trend toward increased usage happening before the recommendation was published. But the Poisson model found a significant effect of the recommendation even controlling for that temporal trend. The authors helpfully noted that the criteria going into a recommendation are different between England and Spain (cost per QALY in England, clinical effectiveness alone sometimes in Spain), which makes inter-country comparisons challenging.

Health‐related quality of life in oncology drug reimbursement submissions in Canada: a review of submissions to the pan‐Canadian Oncology Drug Review. Cancer [PubMed] Published 1st January 2020

In Canada, newly-developed cancer drugs undergo HTA through the pan-Canadian Oncology Drug Review (pCODR), a program run under the auspices of the Canadian Agency for Drugs and Technologies in Health (CADTH). Unlike NICE in the UK, the results of CADTH’s pCODR recommendations are not binding; they are intended instead to provide provincial decision-makers with expert evidence they can use when deciding whether or not to add drugs to their formulary.

This paper, written by researchers at the Canadian Centre for Applied Research in Cancer Control (ARCC), reviewed the publicly-available reports governing 43 pCODR recommendations between 2015 and 2018. The paper summarizes the findings of the cost-effectiveness analyses generated in each report, including incremental costs and incremental QALYs (incremental cost per QALY being the reference case used by CADTH). The authors also appraised the methods chosen within each submission, both in terms of decision model structure and data inputs.

Interestingly, and perhaps disconcertingly, the paper reports a notable discrepancy between the ICERs reported by the submitting manufacturer and those calculated by CADTH’s Economics Guidance Panel. This appeared to be largely driven by the kind of health-related quality of life (HRQoL) data used to generate the QALYs in each submission. The authors note that the majority (56%) of the submissions provided to pCODR didn’t collect HRQoL data alongside clinical trials, preferring instead to use values published in the literature. In the face of high levels of uncertainty and relatively small incremental benefits (the median change in QALYs was 0.86), it seems crucial to have reliable information about HRQoL for making these kinds of decisions.

Regulatory and advisory agencies like CADTH have a rather weighty responsibility, not only to help decision makers identify which new drugs and technologies the health care system should adopt, but also which ones they should reject. When manufacturers’ submissions rely on inappropriate data with high levels of uncertainty, this task becomes much more difficult. The authors suggest that manufacturers should be collecting their own HRQoL data in clinical trials they fund. After all, if we want HTAs to have an effect on policy-making, we should also make sure they’re having a positive effect.

The cost-effectiveness of limiting federal housing vouchers to use in low-poverty neighborhoods in the United States. Public Health [PubMed] Published January 2020

My undergraduate education was heavily steeped in discussions of the social determinants of health. Another cynical opinion I’ve heard (again sometimes from myself) is that health economics is disproportionately concerned with the adoption of new drugs that have a marginal effect on health, often at the expense of investment in the other non-health-care determinants. This is a particularly persuasive bit of cynicism when you consider cancer drugs like in our previous two examples, where the incremental benefits are typically modest and the costs typically high. That’s why I was especially excited to see this paper published by my friend Dr. Zafar Zafari, applying health economic analysis frameworks to something atypical: housing policy.

The authors evaluated a trial running alongside a program providing housing vouchers to 4600 low-income households. The experimental condition in this case was that the vouchers could only be used in well-off neighbourhoods (i.e., those with a low level of poverty). The authors considered the evidence showing a link between neighbourhood wealth and lowering rates of obesity-related health conditions like diabetes, and used that evidence to construct a Markov decision model to measure incremental cost per QALY over the length of the study (10-15 years). Cohort characteristics, relative clinical effectiveness, and costs of the voucher program were estimated from trial results, with other costs and probabilities derived from the literature.

Compared to the control group (public housing), use of the housing vouchers provided an additional 0.23 QALYs per person, at a lower cost (about $750 less per person). Importantly, these findings were highly robust to parameter uncertainty, with 99% of ICERs falling below a willingness-to-pay threshold of $20,000/QALY (>90% below a WTP threshold of $0/QALY). The model was highly sensitive to the discount rate, which makes sense considering that we would expect, for a chronic condition like diabetes and a distal relationship like housing, that all the incremental health gains would be occurring years after the initial intervention.

There are a lot of things to like about this paper, but the one that stands out to me is the way they’ve framed the question:

We seek to inform the policy debate over the wisdom of spending health dollars on non-health sectors of the economy by defining the trade-off, or ‘opportunity cost’ of such a decision.

The idea that “health funds” should be focussed on “health care” robs us of the opportunity to consider the health impact of interventions in other policy areas. By bringing something like housing explicitly into the realm of cost-per-QALY analysis, the authors invite us all to consider the kinds of trade-offs we make when we relegate our consideration of health only to the kinds of things that happen inside hospitals.

A multidimensional array representation of state-transition model dynamics. Medical Decision Making [PubMed] Published 28th January 2020

I’ve been building models in R for a few years now, and developed a method of my own more or less out of necessity. So I’ve always been impressed with and drawn to the work of the group Decision Analysis in R for Technologies in Health (the amazingly-named DARTH). I’ve had the opportunity to meet a couple of their scientists and have followed their work for a while, and so I was really pleased to see the publication of this paper, hot on the heels of another paper discussing a formalized approach to model construction in R, and timed to coincide with the publication of a step-by-step guidebook on how to build models according to the DARTH recipe.

The DARTH approach (and, as a happy coincidence, mine too) involves tapping into R’s powerful ability to organize data into multidimensional arrays. The paper talks in depth about how R arrays can be used to represent health states, and how to set up and program models of essentially any level of complexity using a set of basic R commands. As a bonus they include publicly-accessible sample code that you can follow along as you read (which is the best way to learn something like this).

The authors argue that the method they propose is ideal for capturing and reflecting “transition rewards” – that is, effects on the cohort that occur during transitions between health states – in addition to “state rewards” (effects that happen as a consequence of being within a state). The key to this Dynamics Array approach is the use of a three-dimensional array to store the transitions, with the third array representing the passage of time. After walking the reader through the theory, the authors present a sample three-state model and show that the new method is fast, efficient, and accurate.

I hope that I have been sufficiently clear that I am a big fan of DARTH and admire their work a great deal. Because there is one big criticism I have to level at them, which is that this paper (and the others I have cited) is not terribly easy to follow. It sort of presumes that you already understand a lot of the topics that are discussed, which I personally do not. And if I, someone who has built many array-based models in R, am having a tough time understanding the explanation of their approach then woe betide anyone else who is reading this paper without a firm grasp of R, decision modelling theory, matrix algebra, and a handful of the other topics required to benefit from this (truly excellent) work.

DARTH is laying down a well-thought-out path to revolutionizing the standard approach to model building, but they can only do that if people start adopting their approach. If I were a grad student hoping to build my first model, this paper would likely intimidate me enough to maybe go back to the default of building it in Excel. As a postdoc with my own way of doing things there is a big opportunity cost of switching, and part of that cost is feeling too dumb to follow the instructions. I know that DARTH has tutorials and courses and workshops to help people get up to speed, but I hope that they also have a plan to translate some of this knowledge into a form that is more accessible for casual coders, non-economists, and other people who need this info but who (like me) might find this format opaque.

Credits

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

Thesis Thursday: Feng-An Yang

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Feng-An Yang who has a PhD from Ohio State University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Three essays on access to health care in rural areas
Supervisors
Daeho Kim, Joyce Chen
Repository link
http://rave.ohiolink.edu/etdc/view?acc_num=osu152353045188255

What are the policy challenges for rural hospitals in the US?

Rural hospitals have been financially vulnerable, especially after the implementation of Medicare Prospective Payment System (PPS) in 1983, under which hospitals receive a predetermined, fixed reimbursement for their inpatient services. Under the PPS, they suffer from financial losses as their costs tend to exceed the reimbursement rate due to their smaller size and lower patient volume than their urban counterparts (Medicare Payment Advisory Commission, 2001 [PDF]). As a result, a noticeable number of rural hospitals have closed since the implementation of PPS (Congressional Budget Office, 1991 [PDF]).

This closure trend has slowed down thanks to public payment policies such as the Critical Access Hospitals (CAH) program, but rural hospitals are continuing to close their doors and a total of 107 rural hospitals have closed from 2010 to present according to the North Carolina Rural Health Research Program. This issue has raised public concern for rural residents’ access to health services and health status, and how to keep rural hospitals open has become an important policy priority.

Which data sources and models did you use to identify key events?

My dissertation investigated the impact of the CAH program and hospital closure by compiling data from various sources. The primary data come from the Medicare cost report, which contains detailed financial statements for nearly every U.S. hospital. Historical data on health care utilization at the county-level are obtained from the Area Health Resource File. County-level mortality rates are calculated from the national mortality files. Lastly, the list of CAHs and closed hospitals is obtained from the Flex Monitoring Team and American Hospital Association Annual Survey, respectively. This list contains information on the hospital identifier and year of event which is key to my empirical strategy.

To identify the impact of key events (i.e., CAH conversion and hospital closure), I use an event-study approach exploiting the variation in the timing of events. This approach estimates the changes in outcome for the time relative to the ‘event time’. A primary advantage of this approach is that it allows a visual examination of the evolution of changes in outcome before and after the event.

How can policies relating to rural hospitals benefit patients?

This question is not trivial because public payment policies are not directly linked to patients. The primary objective of these policies is to strengthen rural hospitals’ financial viability by providing them with enhanced reimbursement. As a result, it has been expected that, under these policies, rural hospitals will improve their financial conditions and stay open, thereby maintaining the access to health services for rural residents. Broadly speaking, public payment policies can lead to an increase in accessibility if we compare patient access to health services between counties with at least one hospital receiving financial support and counties without any hospitals receiving financial support.

I look at patient benefits from three aspects: accessibility, health care utilization, and mortality. My research shows that the CAH program has substantially improved CAHs’ financial conditions and as a result, some CAHs that otherwise would have been closed have stayed open. This in turn leads to an increase in rural residents’ access to and use of health services. We then provide suggestive evidence that the increased access to and use of health care services have improved patient health in rural areas.

Did you find any evidence that policies could have negative or unexpected consequences?

Certainly. The second chapter of my dissertation focused on skilled nursing care which can be provided in either swing beds (inpatient beds that can be used interchangeably for inpatient care or skilled nursing care) or hospital-based skilled nursing facilities (SNFs). Since the services provided in swing beds and SNFs are equivalent, differential payments, if present, may encourage hospitals to use one over the other.

While the CAH program provides enhanced reimbursement to rural hospitals, it also changes the swing bed reimbursement method such that swing bed payments are more favorable than SNF payments. As a result, CAHs may have a financial incentive to increase the use of swing beds over SNFs. By focusing on CAHs with a SNF, my research shows a remarkable increase in swing bed utilization and this increase is fully offset by the decrease in SNF utilization. These results suggest that CAHs substitute swing beds for SNFs in response to the change in swing bed reimbursement method.

Based on your research, what would be your key recommendations for policymakers?

Based on my research findings, I would make two recommendations for policymakers.

First, my research speaks to the ongoing debate over the elimination of CAH designation for certain hospitals. Loss of CAH designation could have serious financial consequences and subsequently have potentially adverse impacts on patient access to and use of health care. Therefore, I would recommend policymakers to maintain the CAH designation.

Second, while the CAH program has improved rural hospitals’ financial conditions, it has also created a financial incentive for hospitals to use the service with a higher reimbursement rate. Thus, my recommendation to policymakers would be to consider potentially substitutable health care services when designing reimbursement rates.