Chris Sampson’s journal round-up for 24th 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.

Should health economic evaluations undertaken from a societal perspective include net government spending multiplier effects? Applied Health Economics and Health Policy [PubMed] Published 4th February 2020

Any mention of “macroeconomics” usually causes my eyes to glaze over and my mind to return to 2007 and to ECN202. With some luck, I might scrape through the conversation as I did my second year of undergrad. Perhaps other health economists share my affliction, and that’s why fiscal multipliers haven’t been paid much attention. This paper seeks to redress the balance by considering the possible importance of accounting for the general effects of health expenditure on the economy.

The title of the paper raises the question of ‘should‘, but it’s really more about the ‘could‘. The authors start out by reminding us what a fiscal multiplier is and how it is commonly used and estimated. In short, it’s the amount of expansion we might expect to see in an economy relative to government expenditure. So, with a fiscal multiplier of 1.5, the government spending £1 would expand the economy by £1.50. This study is conducted in the Australian context, so the authors do a bit of groundwork to identify a multiplier for Australia of 1.1. Then, the authors proceed to use this multiplier for domestic expenditure in health care, to demonstrate the potential importance of its use in estimating the societal benefits of health care expenditure.

Two previously conducted economic evaluations are used as test cases. One case study is for a pharmaceutical intervention and the other is for physiotherapy. The key difference between the two – as far as this analysis is concerned – is that the pharmaceutical is produced outside of Australia, with 77% of the expenditure falling outside of the country. Physiotherapy is largely based in expenditure on domestic labour, with only 3% of expenditure falling outside of Australia. What this means is that the effective cost-effectiveness of the pharmaceutical is reduced, while the cost-effectiveness of the physiotherapy is increased. The difference in the case of the pharmaceutical is quite large, with the incremental cost-effectiveness ratio shifting from $31,244 to $47,311 per QALY. Clearly, this could affect decision-making, with implications for cost-effectiveness thresholds.

Personally, I’m a societal perspective sceptic. So, from my position, the main value of considering fiscal multipliers is not from using them to estimate the cost-effectiveness of individual interventions, but rather to help to determine industrial policy. As the authors explain, this approach can be used to identify the value of having health care industries base their operations within a country. Perhaps we could get to the point where a pharmaceutical manufacturer enjoys a higher threshold in a given country if they also pay more taxes. From a (national) societal perspective, there’s a satisfying logic in that.

Whom should we ask? A systematic literature review of the arguments regarding the most accurate source of information for valuation of health states. Quality of Life Research [PubMed] Published 3rd February 2020

For many years, there has been debate about ‘patient’ vs ‘public’ values when it comes to preferences for health states. Gradually, researchers are realising that this is not a useful distinction, as all patients are members of the public and the public are all past and future patients. Nevertheless, there is a useful distinction between valuing a health state based on one’s own current experience and valuing a hypothetical health state. Which should be used to generate quality-adjusted life years (QALYs) to inform resource allocation decisions? In this paper, the authors review the arguments that have been made in the literature.

A literature search was conducted and studies were included in the review if they contained arguments about the source of health state valuations, with 82 articles included. The authors conducted a descriptive qualitative content analysis, grouping arguments into themes at three levels of granularity. The arguments favouring ‘patient’ values related to issues such as the superiority of patient knowledge, adaptation, and patient interests. Arguments in favour of ‘public’ values related to issues such as valuation difficulties for patients, socially directed values, and practical advantages.

This study provides a valuable review of a complex literature, but I believe it is flawed in its conception. There is no such thing as “the most accurate source of information” in this context. For this to be the case, there would need to be some true value that we were seeking to identify. But the value that is most close to the truth depends on our conception of value. Therefore, the answer to the question “which is most accurate?” and “whom should we ask?” are one in the same. This is easily demonstrated by the unanswerable question “how do we define accuracy?” The authors’ attempt to separate the two inevitably fails. Arguments that they frame as ‘irrelevant’ are only irrelevant if we accept the premise that value and decision-making imperatives are separable. The authors’ concluding argument in favour of patient values is both pre-determined by their conception of ‘accuracy’ and groundless.

Nevertheless, I like the paper a lot, as it digests a large amount of information and provides a taxonomy of quotations that will be extremely valuable to researchers developing ideas in this context. My only complaint is that they did not include blog posts in their review!

Health-related quality of life in neonates and infants: a conceptual framework. Quality of Life Research [PubMed] Published 29th January 2020

I’m working on an evaluative study in the context of newborns, and it’s the first time I’ve worked on an intervention where consideration of health-related quality of life (in the short term) – let alone QALYs – is barely even on the table. Yet many interventions and support services for newborns might reasonably be expected to have huge impacts on their quality of life. This study attempts to bring us closer to a world where we can identify QALYs for newborns and infants.

A qualitative study was conducted in a Toronto hospital with two focus groups and five interviews with caregivers of children with intestinal failure and with 14 health care professionals. The focus groups and interviews used open-ended questioning, allowing participants to state what they saw as the most important factors. Fourteen different factors arose, including things like sleep, feeding needs, safety, hygiene, and physical abilities. The authors arranged these into four levels, relating to i) basic needs, ii) non-basic needs, iii) caregivers and family, and iv) society and community.

Much the same dimensions were identified for neonates (0-28 days) and infants (up to one year), but the weighting attributed to the dimensions differed. The importance of non-basic needs increased with age. One implication of this is that time in hospital, where basic needs can be met but other aspects of HRQoL may be limited, may be very good for a neonate but not so good for an older infant. As a result of this, the authors suggest that a weighting algorithm according to age or developmental status may be appropriate. An interesting characterisation of HRQoL that comes out of the study is as a surrogate for the effort required by caregivers to obtain some degree of normalcy in the child’s life. The HRQoL of the individual and of the caregiver(s) is clearly inseparable. But the authors aren’t able to offer much guidance on how this affects measurement.

The authors set out from the position that measuring health-related quality of life in this cohort is important. I don’t know the ethics literature well enough to disagree, so I have to take their word for it. But it does seem that it might be OK for clinical decision-making in this context to be guided by a very different set of criteria to those used in older populations. Whatever the right solution, it will be guided by this important study.

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

Chris Sampson’s journal round-up for 16th December 2019

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.

MCDA-based deliberation to value health states: lessons learned from a pilot study. Health and Quality of Life Outcomes [PubMed] Published 1st July 2019

The rejection of the EQ-5D-5L value set for England indicates something of a crisis in health state valuation. Evidently, there is a lack of trust in the quantitative data and methods used. This is despite decades of methodological development. Perhaps we need a completely different approach. Could we instead develop a value set using qualitative methods?

A value set based on qualitative research aligns with an idea forwarded by Daniel Hausman, who has argued for the use of deliberative approaches. This could circumvent the problems associated with asking people to give instant (and possibly ill-thought-out) responses to preference elicitation surveys. The authors of this study report on the first ever (pilot) attempt to develop a consensus value set using methods of multi-criteria decision analysis (MCDA) and deliberation. The study attempts to identify a German value set for the SF-6D.

The study included 34 students in a one-day conference setting. A two-step process was followed for the MCDA using MACBETH (the Measuring Attractiveness by a Categorical Based Evaluation Technique), which uses pairwise comparisons to derive numerical scales without quantitative assessments. First, a scoring procedure was conducted for each of the six dimensions. Second, a weighting was identified for each dimension. After an introductory session, participants were allocated into groups of five or six and each group was tasked with scoring one SF-6D dimension. Within each group, consensus was achieved. After these group sessions, all participants were brought together to present and validate the results. In this deliberation process, consensus was achieved for all domains except pain. Then the weighting session took place, but resulted in no consensus. Subsequent to the one-day conference, a series of semi-structured interviews were conducted with moderators. All the sessions and interviews were recorded, transcribed, and analysed qualitatively.

In short, the study failed. A consensus value set could not be identified. Part of the problem was probably in the SF-6D descriptive system, particularly in relation to pain, which was interpreted differently by different people. But the main issue was that people had different opinions and didn’t seem willing to move towards consensus with a societal perspective in mind. Participants broadly fell into three groups – one in favour of prioritising pain and mental health, one opposed to trading-off SF-6D dimensions and favouring equal weights, and another group that was not willing to accept any trade-offs.

Despite its apparent failure, this seems like an extremely useful and important study. The authors provide a huge amount of detail regarding what they did, what went well, and what might be done differently next time. I’m not sure it will ever be possible to get a group of people to reach a consensus on a value set. The whole point of preference-based measures is surely that different people have different priorities, and they should be expected to disagree. But I think we should expect that the future of health state valuation lies in mixed methods. There might be more success in a qualitative and deliberative approach to scoring combined with a quantitative approach to weighting, or perhaps a qualitative approach informed by quantitative data that demands trade-offs. Whatever the future holds, this study will be a valuable guide.

Preference-based health-related quality of life outcomes associated with preterm birth: a systematic review and meta-analysis. PharmacoEconomics [PubMed] Published 9th December 2019

Premature and low birth weight babies can experience a whole host of negative health outcomes. Most studies in this context look at short-term biomedical assessments or behavioural and neurodevelopmental indicators. But some studies have sought to identify the long-term consequences on health-related quality of life by identifying health state utility values. This study provides us with a review and meta-analysis of such values.

The authors screened 2,139 articles from their search and included 20 in the review. Lots of data were extracted from the articles, which is helpfully tabulated in the paper. The majority of the studies included adolescents and focussed on children born very preterm or at very low birth weight.

For the meta-analysis, the authors employed a linear mixed-effects meta-regression, which is an increasingly routine approach in this context. The models were used to estimate the decrement in utility values associated with preterm birth or low birth weight, compared with matched controls. Conveniently, all but one of the studies used a measure other than the HUI2 or HUI3, so the analysis was restricted to these two measures. Preterm birth was associated with an average decrement of 0.066 and extremely low birth weight with a decrement of 0.068. The mean estimated utility scores for the study groups was 0.838, compared with 0.919 for the control groups.

Reviews of utility values are valuable as they provide modellers with a catalogue of potential parameters that can be selected in a meaningful and transparent way. Even though this is a thorough and well-reported study, it’s a bit harder to see how its findings will be used. Most reviews of utility values relate to a particular disease, which might be prevented or ameliorated by treatment, and the value of this treatment depends on the utility values selected. But how will these utility values be used? The avoidance of preterm or low-weight birth is not the subject of most evaluations in the neonatal setting. Even if it was, how valuable are estimates from a single point in adolescence? The authors suggest that future research should seek to identify a trajectory of utility values over the life course. But, even if we could achieve this, it’s not clear to me how this should complement utility values identified in relation to the specific health problems experienced by these people.

The new and non-transparent Cancer Drugs Fund. PharmacoEconomics [PubMed] Published 12th December 2019

Not many (any?) health economists liked the Cancer Drugs Fund (CDF). It was set-up to give special treatment to cancer drugs, which weren’t assessed on the same basis as other drugs being assessed by NICE. In 2016, the CDF was brought within NICE’s remit, with medicines available through the CDF requiring a managed access agreement. This includes agreements on data collection and on payments by the NHS during the period. In this article, the authors contend that the new CDF process is not sufficiently transparent.

Three main issued are raised: i) lack of transparency relating to the value of CDF drugs, ii) lack of transparency relating to the cost of CDF drugs, and iii) the amount of time that medicines remain on the CDF. The authors tabulate the reporting of ICERs according to the decisions made, showing that the majority of treatment comparisons do not report ICERs. Similarly, the time in the CDF is tabulated, with many indications being in the CDF for an unknown amount of time. In short, we don’t know much about medicines going through the CDF, except that they’re probably costing a lot.

I’m a fan of transparency, in almost all contexts. I think it is inherently valuable to share information widely. It seems that the authors of this paper do too. A lack of transparency in NICE decision-making is a broader problem that arises from the need to protect commercially sensitive pricing agreements. But what this paper doesn’t manage to do is to articulate why anybody who doesn’t support transparency in principle should care about the CDF in particular. Part of the authors’ argument is that the lack of transparency prevents independent scrutiny. But surely NICE is the independent scrutiny? The authors argue that it is a problem that commissioners and the public cannot assess the value of the medicines, but it isn’t clear why that should be a problem if they are not the arbiters of value. The CDF has quite rightly faced criticism over the years, but I’m not convinced that its lack of transparency is its main problem.

Credits