Jason Shafrin’s journal round-up for 7th October 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.

Combined impact of future trends on healthcare utilisation of older people: a Delphi study. Health Policy [PubMed] [RePEc] Published October 2019

Governments need to plan for the future. This is particularly important in countries where the government pays for the lion’s share of health care expenditures. Predicting the future, however, is not an easy task. One could use quantitative approaches and simply extrapolate recent trends. One could attempt to consult with political experts to determine what policies are likely to be incurred. Another approach is to use a Delphi Panel to elicit expert opinions on future trends in health care utilization to help predict future health care needs. This approach was the one taken by Ravensbergen and co-authors in an attempt to predict trends in health care utilization among older adults in the Netherlands in 2040.

The Delphi Panel approach was applied in this study as follows. First, individuals received a questionnaire via email. Researchers presented the experts with trends from the Dutch Public Health Foresight Study (Volksgezondheid Toekomst Verkenning) to help ground all experts with the same baseline information. The data and questions largely asked separately about trends for either the old (65–80 years) or the oldest old (>80 years). After the responses from the first questionnaire were received, responses were summarized and provided back to each panelist in an anonymous manner. Panelists were then able to revise their views on a second questionnaire taking into account the feedback by the other panelists. Because the panelists did not meet in person, this approach should be considered a modified Delphi Panel.

The Delphi panel identified three broad trends: increased use of eHealth tools, less support, and change in health status. While the panel thought eHealth was important, experts rarely reached consensus how eHealth would affect healthcare utilization. The experts did find consensus, however, in believing that the the share of adults aged 50-64 will decline relative to the share of individuals aged ≥ 85 years, implying fewer caregivers will be available and more of the oldest old will be living independently (i.e. with less support). Because less informal care will be available, the Delphi believed that the demand for home care and general practitioner services will rise. The respondents also believed that in most cases changes in health status will increase health care utilization of general practitioner and specialist services. There was less agreement about trends in the need for long-term care or mental health services, however.

The Delphi Panel approach may be useful to help governments predict future demand for services. More rigorous approaches, such as betting markets, are likely not feasible since the payouts would take too long to generate much interest. Betting markets could be used to predict shorter-run trends in health care utilization. The risk with betting markets, however, is that some individuals could act strategically to drive up or down predictions to increase or decrease reimbursement for certain sectors.

In short, the Delphi Panel is likely a reasonable, low-cost approach for predicting trends in health care utilization. Future studies, however, should validate how good the predictions are from using this type of method.

The fold-in, fold-out design for DCE choice tasks: application to burden of disease. Medical Decision Making [PubMed] Published 29th May 2019

Discrete choice experiments (DCEs) are a useful way to determine what treatment attributes patients (or providers or caregivers) value. Respondents are presented with multiple treatment options and the options can be compared across a series of attributes. An attribute could be treatment efficacy, safety, dosing, cost, or a host of other attributes. One can use this approach to measure the marginal rate of substitution across attributes. If cost is one of the attributes, one can measure willingness to pay for specific attributes.

One of the key challenges of DCEs, however, is attribute selection. Most treatments differ across a range of attributes. Most published DCEs however have four, five, or at most seven attributes presented. Including more attributes makes comparisons too complicated for most respondents. Thus, researchers are left with a difficult choice: (i) a tractable but overly simplified survey, or (ii) a realistic, but overly complex survey unlikely to be comprehended by respondents.

One solution proposed by Lucas Goossens and co-authors is to use a Fold-in Fold-out (FiFo) approach. In this approach, related attributes may be grouped into domains. For some questions, all attributes within the same domain have the same attribute level (i.e., fold in); in other questions, attributes may vary within the domain (i.e., fold out).

To be concrete, in the Goossens paper, they examine treatments for chronic obstructive pulmonary disorder (COPD). They use 15 attributes divided into three domains plus two stand-alone attributes:

a respiratory symptoms domain (with four attributes: shortness of breath at rest, shortness of breath during physical activity, coughing, and sputum production), a limitations domain (four attributes: limitations in strenuous physical activities, limitations in moderate physical activities, limitations in daily activities, and limitations in social activities), a mental problems domain (five attributes: feeling depressed, fearing that breathing gets worse, worrying, listlessness, and tense feeling), a fatigue attribute, and an exacerbations attribute.

This creative approach simplifies the choice set for respondents, but allows for a large number of attributes. Using the data collected, the authors used a Bayesian mixed logit regression model to conduct the analysis. The utility function underlying this assumed domain-specific parameters, but also included parameters for within-domain attribute weights to vary in the questions where it was folded out.

One key challenge, however, is that the authors found that individuals placed more weight on attributes when their domains were folded out (i.e., attribute levels varied within domain) compared to when their domains were folded in (i.e., attribute levels were the same within the domain). Thus, I would say that if five, six or seven attributes can capture the lion’s share of differences in treatment attributes across treatments, use the standard approach; however, if more attributes are needed, the FiFo approach is an attractive option researchers should consider.

The health and cost burden of antibiotic resistant and susceptible Escherichia coli bacteraemia in the English hospital setting: a national retrospective cohort study. PLoS One [PubMed] Published 10th September 2019

Bacterial infections are bad. The good news is that we have antibiotics to treat them so they no longer are a worry, right? While conventional wisdom may believe that we have many antibiotics to treat these infections, in recent years antibiotic resistance has grown. If antibiotics no longer are effective, what is the cost to society?

One effort to quantify the economic burden of antibiotic resistance by Nichola Naylor and co-authors used national surveillance and administrative data from National Health Service (NHS) hospitals in England. They compared the cost for patients with similar observable characteristics with E. coli bacteraemia compared to those who did not have E. coli bacteraemia. Antibiotic resistance in the study was defined as E. coli bacteraemia using laboratory-based definitions of ‘resistant’ and ‘intermediate’ isolates. The antibiotics to which resistance was considered included ciprofloxacin, third generation cephalosporins (ceftazidime and/or cefotaxime), gentamicin, piperacillin/tazobactam and carbapenems (imipenem and/or meropenem).

The authors use an Aalen-Johansen estimator to measure cumulative incidence of in-hospital mortality and length of stay. Both approaches control for the patient’s age, sex, Elixhauser comorbidity index, and hospital trust type. It does not appear that the authors control for the reason for admission to the hospital nor do they propensity match people with those without antibiotic resistance. Thus, it is likely that significant unobserved heterogeneity across groups remains in the analysis.

Despite these limitations, the authors do have some interesting findings. First, bacterial infections are associated with increased risk of death. In-hospital mortality is 14.3% for individuals infected with E. Coli compared to 1.3% for those not infected. Accounting for covariates, the subdistribution hazard rate (SHR) for in-hospital mortality due to E. coli bacteraemia was 5.88. Second, E. coli bacteraemia was associated with 3.9 excess hospital days compared to patients who were not antibiotic resistance. These extra hospital days cost £1,020 per case of E. coli bacteraemia and the estimated annual cost of E. coli bacteraemia in England was £14.3m. If antibiotic resistance has increased in recent years, these estimates are likely to be conservative.

The issue of antibiotic resistance presents a conundrum for policymakers. If current antibiotics are effective, drug-makers will have no incentive to develop new antibiotics since the new treatments are unlikely to be prescribed. On the other hand, failing to identify new antibiotics in reserve means that as antibiotic resistance grows, there will be few treatment alternatives. To address this issue, the United Kingdom is considering a ‘subscription style‘ approach to pay for new antibiotics to incentivize the development of new treatments.

Nevertheless, the paper by Naylor and co-authors provides a useful data point on the cost of antibiotic resistance.

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Simon McNamara’s journal round-up for 24th June 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.

Manipulating the 5 dimensions of the EuroQoL instrument: the effects on self-reporting actual health and valuing hypothetical health states. Medical Decision Making [PubMed] Published 4th June 2019

EQ-5D is the Rocky Balboa of health economics. A left-hook here, a jab there, vicious undercuts straight to the chin – it takes the hits, it never stays down. Every man and his dog is ganging up on it, yet, it still stands, proudly resolute in its undefeated record.

When you are the champ” it thinks to itself, “everyone wants a piece you”. The door opens. Out the darkness emerges four mysterious figures. “No… not…”, the instrument stumbles over its words. A bead of sweat rolls slowly down its glistening forehead. Its thumping heartbeat pierces the silence like a drum being thrashed by spear-wielding members of an ancient tribe. “It can’t beNo.” A clear, precise, voice emerges from the darkness, “taken at face value” it states, “our results suggest that economic evaluations that use EQ-5D-5L are systematically biased.” EQ-5D stares blankly, its pupils dilated. It responds, “I’ve been waiting for you”. The gloom clears. Tsuchiya et al (2019) stand there proudly: “bring it on… punk”.

The first paper in this week’s round-up is a surgical probing of a sample of potential issues with EQ-5D. Whilst the above paragraph contains a fair amount of poetic license (read: this is the product of an author who would rather be writing dystopian health-economics short stories than doing their actual work), this paper by Tsuchiya et al. does seems to land a number of strong blows squarely on the chin of EQ-5D. The authors employ a large discrete choice experiment (n=2,494 members of the UK general public), in order to explore the impact of three issues on the way people both report and value health. Specifically: (1) the order the five dimensions are presented; (2) the use of composite dimensions (dimensions that pool two things – e.g. pain or discomfort) rather than separate dimensions; (3) “bolting-off” domains (the reverse of a bolt-on: removing domains from the EQ-5D).

If you are interested in these issues, I suggest you read the paper in full. In brief, the authors find that splitting anxiety/depression into two dimensions had a significant effect on the way people reported their health; that splitting level 5 of the pain/discomfort and anxiety/depression dimensions (e.g. I have extreme pain or discomfort) into individual dimensions significantly impacted the way people valued health; and, that “bolting off” dimensions impacted valuation of the remaining dimensions. Personally, I think the composite domain findings are most interesting here. The authors find that that extreme pain/discomfort is perceived as being a more severe state than extreme discomfort alone, and similarly, that being extremely depressed/anxious is perceived as a more severe state than simply being extremely anxious. The authors suggest this means the EQ-5D-5L may be systematically biased, as an individual who reports extreme discomfort (or anxiety) will have their health state valued based upon the composite domains for each of these, and subsequently have the severity of their health-state over-estimated.

I like this paper, and think it has a lot to contribute to the refinement of EQ-5D, and the development of new instruments. I suggest the champ uses Tsuchiya et al as a sparring partner, gets back to the gym and works on some new moves – I sense a training montage coming on.

Methods for public health economic evaluation: A Delphi survey of decision makers in English and Welsh local government. Health Economics [PubMed] Published 7th June 2019

Imagine the government in your local city is considering a major new public health initiative. Politicians plan to destroy a number of out of date social housing blocks in deprived communities, and building 10,000 new high-quality homes in their place. This will cost a significant amount of money and, as a result, you have been asked to do an economic evaluation of this intervention. How would you go about doing this?

This is clearly a complicated task. You are unlikely to find a randomised controlled trial on which to base your evaluation, the costs and benefits of the programme are likely to fall on multiple sectors, and you will likely have to balance health gains with a wide range of other non-health outcomes (e.g. reductions in crime). If you somehow managed to model the impact of the intervention perfectly, you would then be faced with the challenge of how to value these benefits. Equally, you would have to consider whether or not to weight the benefits of this programme more highly than programmes in alternative parts of the city, because it benefits people in deprived communities – note that inequalities in health seem to be a much larger issue in public health than in ‘normal health’ (e.g. the bread and butter of health economics evaluation). This complexity, and concern for inequalities, makes public health economic evaluation a completely different beast to traditional economic evaluation. This has led some to question the value of QALY-based cost-utility analysis in public health, and to calls for methods that better meet the needs of the field.  

The second paper in this week’s round-up contributes to the development of these methods, by providing information on what public health decision makers in England and Wales think about different economic evaluation methodologies. The authors fielded an online, two-round, Delphi-panel study featuring 26 to 36 statements (round 1 and 2 respectively). For each statement, participants were asked to rank their level of agreement with the statement on a five-point scale (e.g. 1 = strongly agree and 5 = strongly disagree). In the first round, participants (n=66) simply responded to the statements, and in the second, they (n=29) were presented with the median response from the prior round, and asked to consider their response in light of this feedback. The statements tested covered a wide range of issues, including: the role distributional concerns should play in public health economic evaluation (e.g. economic evaluation should formally weight outcomes by population subgroup); the type of outcomes considered (e.g. economic evidence should use a single outcome that captures length of life and quality of life); and, the budgets to be considered (e.g. economic evaluation should take account of multi-sectoral budgets available).

Interestingly, the decision-makers rejected the idea of focusing solely on maximising outcomes (the current norm for health economic evaluations), and supported placing an equal focus on minimising inequality and maximising outcomes. Furthermore, they supported formal weighting of outcomes by population subgroup, the use of multiple outcomes to capture health, wellbeing and broader outcomes, and failed to support use of a single outcome that captures well-being gain. These findings suggest cost-consequence analysis may provide a better fit to the needs of these decision makers than simply attempting to apply the QALY model in public health – particularly if augmented by some form of multi-criteria decision analysis (MCDA) that can reflect distributional concerns and allow comparison across outcome types. I think this is a great paper and expect to be citing it for years to come.

I AM IMMORTAL. Economic Enquiry [RePEc] Published 16th November 2016

I love this paper. It isn’t a recent one, but it hasn’t been covered in the AHE blog before, and I think everyone should know about it, so – luckily for you – it has made it in to this week’s round-up.

In this groundbreaking work, Riccardo Trezzi fits a series of “state of the art”, complex, econometric models to his own electrocardiogram (ECG) signal – a measure of the electrical function of the heart. He then compares these models, identifies the one that best fits his data, and uses the model to predict his future ECG signal, and subsequently his life expectancy. This provides an astonishing result  – “the n steps ahead forecast remains bounded and well above zero even after one googol period, implying that my life expectancy tends to infinite. I therefore conclude that I am immortal”.

I think this is genius. If you haven’t already realised the point of the paper by the time you have reached this part of my write-up, I suggest you think very carefully about the face-validity of this result. If you still don’t get it after that, have a look at the note on the front page – specifically the bit that says “this paper is intended to be a joke”. If you still don’t get it – the author measured their heart activity for 10 seconds, and then applied lots of complex statistical methods, which (obviously) when extrapolated suggested his heart would keep beating forever, and subsequently that he would live forever.

Whilst the paper is a parody, it makes an important point. If we fit models to data, and attempt to predict the future without considering external evidence, we may well make a hash of that prediction – despite the apparent sophistication of our econometric methods. This is clearly an extreme example, but resonates with me, because this is what many people continue to do when modelling oncology data. This is certainly less prevalent than it was a few years ago, and I expect it will become a thing of the past, but for now, whenever I meet someone who does this, I will be sure to send them a copy of this paper. That being said, as far as I am aware the author is still alive, so maybe he will have the last laugh – perhaps even the last laugh of all of humankind if his model is to be believed.

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Paul Mitchell’s journal round-up for 25th December 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.

Consensus-based cross-European recommendations for the identification, measurement and valuation of costs in health economic evaluations: a European Delphi study. European Journal of Health Economics [PubMedPublished 19th December 2017

The primary aim of this study was to develop guidelines for costing in economic evaluation studies conducted across more than one European country. The starting point of the societal perspective as the benchmark for costing was not entirely obvious from the abstract, where this broadest approach to costing is not recommended uniformly across all European countries. Recommendations following this starting point looked at the identification, measurement and valuation of resource use, discount rate and discounting of future costs. A three-step Delphi study was used to gain consensus on what should be included in an economic evaluation from a societal perspective, based initially on findings from a review of costing methodologies adopted across European country-specific guidelines. Consensus required at least two thirds (67%) agreement across those participating in the Delphi study at all 3 stages. Where no agreement was reached after the three stages, a panel of four of the co-authors made a final decision on what should be recommended. In total, 26 of the 110 invited to participate completed at least one Delphi round, with all Delphi rounds having at least 16 participants. It remains unclear to me if 16 for a Delphi round is sufficient to reach a European wide consensus on costing methodologies. There were a number of key areas where no consensus was reached (e.g. including costs unrelated to the intervention, measurement of resource use and absenteeism, and valuation of opportunity costs of patient time and informal care), so the four-strong author panel had a leading role on some of the main recommendations. Notwithstanding the limitations associated with the reference perspective taken and sample for the Delphi study and panel, the paper provides a useful illustration of the different approaches to costing across European countries. It also provides a good coverage of costing issues that need to be explained in detail in economic evaluations to allow for clear understanding of methods used and the underpinning rationale for those decisions where a choice is required on the costing methodology applied.

A (five-)level playing field for mental health conditions?: exploratory analysis of EQ-5D-5L derived utility values. Quality of Life Research [PubMedPublished 16th December 2017

The UK health economics community has been reeling from the decision made earlier this year by UK guidelines developer, the National Institute for Health and Care Excellence (NICE), who recommended to not adopt the new population values developed for the EQ-5D-5L version when calculating QALYs and instead rely on a crosswalk of the values developed over 20 years ago for the 3 level EQ-5D version. This paper provides a timely comparison of how these two value sets perform for the EQ-5D-5L descriptive system in patient groups with mental health conditions, groups often thought to be disadvantaged by the physical health functioning focus of the EQ-5D descriptive system. Using baseline data from three trials, the authors find that the new utility values produce a higher mean EQ-5D score of 0.08 compared to the old crosswalk values, with a 0.225 difference for those reporting extreme problems with the anxiety/depression dimension on EQ-5D. Although, the authors of this study highlight using these new values would increase cost per QALY results in this sample using scenario analysis, when improvements are in the depression/anxiety category only, such improvements are relatively better than across the whole EQ-5D-5L descriptive system due to the relative additional value placed on the anxiety/depression dimension in the new values. This paper makes for interesting reading and one that NICE should take into consideration when reviewing their decision on this issue next year. Although I would disagree with the authors when they state that this study would be a primary reason for revising the NICE cost-effectiveness threshold (more compelling arguments for this elsewhere in my view), it does clearly highlight the influence of the choice of descriptive system and the values used in the outcomes produced for economic analysis such as QALYs, even when the two descriptive systems in question (EQ-5D-3L and EQ-5D-5L) are roughly the same.

What characteristics of nursing homes are most valued by customers? A discrete choice experiment with residents and family members. Value in Health Published 1st December 2017

Our final paper for review in 2017 looks at the characteristics that are of most importance to individuals and their family members when it comes to nursing home provision. The authors conducted a valuation exercise using a discrete choice experiment (DCE) to calculate the relative importance of the attributes contained on the Consumer Choice Index-Six Dimension (CCI-6D), a measure developed to assess the quality of nursing home care across 3 levels on six domains: 1. level of time care staff spent with residents; 2. homeliness of shared spaces; 3. homeliness of room setup; 4. access to outside and garden; 5. frequency of meaningful activities; and 6. flexibility with care routines. Those who lived in a nursing home for at least a year with low levels of cognitive impairment completed the DCE themselves, whereas family members were asked to proxy for their close relative with more severe cognitive impairment. 126 residents and 416 family member proxies completed the DCE comparisons of nursing homes with different qualities in these six areas. The results of the DCE show differences in preferences across the two groups. Although similar importance is placed on some dimensions across both groups (i.e. “homeliness of room set up” ranked highly, whereas “frequency of meaningful activities” ranked lower), residents value access to outside and garden four times as much as the family proxies do (second most important dimension for residents, lowest for family proxies), family members value level of time care staff spent with residents twice as much as residents themselves (most important attribute for family proxies, third most important for residents). Although residents in both groups may have important differences in characteristics that might explain some of this difference, it is probably a good time of year to remember family preferences may be inconsistent with individuals within them, so make sure to take account of this variation when preparing those Christmas dinners.

Happy holidays all.

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