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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Chris Sampson’s journal round-up for 14th August 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.

Does paying service providers by results improve recovery outcomes for drug misusers in treatment in England? Addiction [PubMedPublished 10th August 2017

‘Getting what you pay for’ is a fundamentally attractive funding model, which is why we see lots of pay for performance (P4P) initiatives cropping up in the NHS. But P4P plans can go awry. This study considers an experimental setting in which 8 areas participated in P4P pilots for drug misuse treatment, from 2012-2014. Payments were aligned with 3 national priorities: i) abstinence, ii) reduced offending and iii) improved health and well-being. The participating areas allocated differing proportions of payments to the P4P model, between 10% and 100%. Data were drawn from the National Drug Treatment Monitoring System, which includes information on drug use, assessment and interventions received. Other national sources were used to identify criminal activity and mortality rates. Drug misusers attending treatment services during the 2 years before and after the introduction of the P4P scheme were included in the study. Using a difference-in-differences analysis, the researchers compared outcomes in the 8 participating areas with those in 143 non-participating areas. Separate multilevel regression models were used for a set of outcomes, each controlling for a variety of individual-level characteristics. The authors analysed ‘treatment journeys’, of which there were around 20,000 for those in participating areas and 280,000 for those in non-participating areas; roughly half before the introduction and half after. The results don’t look good for P4P. Use of opiates, crack cocaine and injecting increased. Treatment initiation increased in non-participating areas but decreased in participating areas. Moreover, longer waiting times were observed in participating areas as well as more unplanned discharges. P4P was associated with people being less likely to successfully complete treatment within 12 months. In P4P’s favour, there was evidence that abstinence increased. I’d’ve liked to have seen some attempt at matching between the areas, given that there was an element of self-selection into the scheme. Or at least, better control for the characteristics of the areas before P4P was introduced. This paper isn’t quite the final nail in the coffin. I don’t see P4P disappearing anytime soon. There’s a lot to be learnt from the paper’s discussion, which outlines some of the likely reasons and mechanisms underlying the findings. Commissioners should take note.

The short- and long-run effects of smoking cessation on alcohol consumption. International Journal of Health Economics and Management [PubMedPublished 7th August 2017

Anecdotally, it seems as if smoking and drinking are complementary behaviours. Generally, the evidence suggests that this is true. Smoking cessation programmes may, therefore, have value in their ability to reduce alcohol consumption (and vice versa). But only if the relationship is causal. This study seeks to add to that causal evidence. Using data from 5887 individuals in the Lung Health Study, the author runs a two-stage least squares estimation, with randomisation to smoking cessation treatment as an instrumental variable for smoking status. In the short term, there is some evidence that smokers tend to drink more (especially men). But findings in the longer term, up to 5 years, are more persuasive. It’s unfortunate that the (largely incoherent) rational addiction theory makes an appearance and that the findings are presented as supportive of it. A stopped clock is right twice a day. In line with rational addiction theory, the long-term relationship is measured in terms of a ‘smoking stock’, which is an aggregate measure of smoking behaviour over the 5 year period. Smoking and drinking are found to be complementary in the long term. Crucially, the extent of their complementarity is associated with particular factors. For example, people who smoke more cigarettes or who abstain for longer exhibit larger reductions in alcohol consumption when they stop smoking. People who smoke relatively few cigarettes per day do not drink more alcohol. Those smoking 6-10 per day consume around 1 extra drink per week compared with non-smokers. Quitting for 5 years can reduce alcohol consumption by more than 50%. In the long run, the effect is more pronounced for women and for people who are married. This highlights important opportunities for targeted public policy, which could achieve a win-win in terms of reducing both cigarette and alcohol consumption.

Time for a change in how new antibiotics are reimbursed: development of an insurance framework for funding new antibiotics based on a policy of risk mitigation. Health Policy Published 5th August 2017

Antibiotics have become a key component of health care, but antimicrobial resistance threatens their usefulness and we don’t see new antibiotics in the pipeline to help overcome this. It’s a fundamentally difficult problem; we want new antibiotics but we want to use them as sparingly as possible. Antibiotic development is relatively unattractive (financially) to pharmaceutical companies. Provision of research funding and regulatory changes haven’t solved the problem to date. This paper considers why this might be the case, and explores 2 alternative approaches: a premium price model and an insurance-type model. Essentially, the authors conduct a spreadsheet analysis to compare the alternative models with a base case of no incentives. The expected net present value of the base case was negative (to the tune of about $1.5 billion), demonstrating why much-needed new antibiotics aren’t being developed. Current incentives – including public-private funding partnerships and market exclusivity – are also shown to fail to reach a positive net present value. The premium price model, whereby there is an enhanced price per unit, is not particularly attractive. The daily cost of the resulting antibiotics would likely be too high, and manufacturers’ pursuit of profit would be at odds with conservative prescribing. Furthermore, it exposes areas experiencing outbreaks to serious financial risk. The insurance model, which involved an annual fee paid by each healthcare system (to manufacturers), is more promising. Pharmaceutical companies would be insured against low prices and variable use and health systems would be insured against a lack of antibiotics and the risk of an infection outbreak. The key feature here is that manufacturers’ revenues are de-linked from sales volume. This is important when we consider the need for conservative prescribing. The authors estimate that the necessary fee (for the global market) would be around $262 million per year, or $114 million if combined with current funding and regulatory incentives. Of course, these findings are based on major assumptions about infection rates, research costs and plenty besides. A number of sensitivity analyses are conducted that highlight uncertainty about what the insurance fee might need to be in the future. I think this uncertainty is somewhat understated – there are far more sensitivity and scenario analyses that would be warranted if such a policy were being seriously considered. Nevertheless, pooling risk in an insurance model looks like a promising strategy that’s worthy of further investigation and piloting.

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Paul Mitchell’s journal round-up for 17th July 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.

What goes wrong with the allocation of domestic and international resources for HIV? Health Economics [PubMedPublished 7th July 2017

Investment in foreign aid is coming under considered scrutiny as a number of leading western economies re-evaluate their role in the world and their obligations to countries with developing economies. Therefore, it is important for those who believe in the benefits of such investments to show that they are being done efficiently. This paper looks at how funding for HIV is distributed both domestically and internationally across countries, using multivariate regression analysis with instruments to control for reverse causality between financing and HIV prevalence, and domestic and international financing. The author is also concerned about countries free riding on international aid and estimates how countries ought to be allocating national resources to HIV using quintile regression to estimate what countries have fiscal space for expanding their current spending domestically. The results of the study show that domestic expenditure relative to GDP per capita is almost unit elastic, whereas it is inelastic with regards to HIV prevalence. Government effectiveness (as defined by the World Bank indices) has a statistically significant effect on domestic expenditure, although it is nonlinear, with gains more likely when moving up from a lower level of government effectiveness. International expenditure is inversely related to GDP per capita and HIV prevalence, and positively with government effectiveness, albeit the regression models for international expenditure had poor explanatory power. Countries with higher GDP per capita tended to dedicate more money towards HIV, however, the author reckons there is $3bn of fiscal space in countries such as South Africa and Nigeria to contribute more to HIV, freeing up international aid for other countries such as Cameroon, Ghana, Thailand, Pakistan and Columbia. The author is concerned that countries with higher GDP should be able to allocate more to HIV, but feels there are improvements to be made in how international aid is distributed too. Although there is plenty of food for thought in this paper, I was left wondering how this analysis can help in aiding a better allocation of resources. The normative model of what funding for HIV ought to be is from the viewpoint that this is the sole objective of countries of allocating resources, which is clearly contestable (the author even casts doubt as to whether this is true for international funding of HIV). Perhaps the other demands faced by national governments (e.g. funding for other diseases, education etc.) can be better reflected in future research in this area.

Can pay-for-performance to primary care providers stimulate appropriate use of antibiotics? Health Economics [PubMed] [RePEcPublished 7th July 2017

Antibiotic resistance is one of the largest challenges facing global health this century. This study from Sweden looks to see whether pay for performance (P4P) can have a role in the prescription practices of GPs when it comes to treating children with respiratory tract infection. P4P was introduced on a staggered basis across a number of regions in Sweden to incentivise primary care to use narrow spectrum penicillin as a first line treatment, as it is said to have a smaller impact on resistance. Taking advantage of data from the Swedish Prescribed Drug Register between 2006-2013, the authors conducted a difference in difference regression analysis to show the effect P4P had on the share of the incentivised antibiotic. They find a positive main effect of P4P on drug prescribing of 1.1 percentage points, that is also statistically significant. Of interest, the P4P in Sweden under analysis here was not directly linked to salaries of GPs but the health care centre. Although there are a number of limitations with the study that the authors clearly highlight in the discussion, it is a good example of how to make the most of routinely available data. It also highlights that although the share of the less resistant antibiotic went up, the national picture of usage of antibiotics did not reduce in line with a national policy aimed at doing so during the same time period. Even though Sweden is reported to be one of the lower users of antibiotics in Europe, it highlights the need to carefully think through how targets are achieved and where incentives might help in some areas to meet such targets.

Econometric modelling of multiple self-reports of health states: the switch from EQ-5D-3L to EQ-5D-5L in evaluating drug therapies for rheumatoid arthritis. Journal of Health Economics Published 4th July 2017

The EQ-5D is the most frequently used health state descriptive system for the generation of utility values for quality-adjusted life years (QALYs) in economic evaluation. To improve sensitivity and reduce floor and ceiling effects, the EuroQol team developed a five level version (5L) compared to the previous three level (3L) version. This study adds to recent evidence in this area of the unforeseen consequences of making this change to the descriptive system and also the valuation system used for the 5L. Using data from the National Data Bank for Rheumatic Diseases, where both 3L and 5L versions were completed simultaneously alongside other clinical measures, the authors construct a mapping between both versions of EQ-5D, informed by the response levels and the valuation systems that have been developed in the UK for the measures. They also test their mapping estimates on a previous economic evaluation for rheumatoid arthritis treatments. The descriptive results show that although there is a high correlation between both versions, and the 5L version achieves its aim of greater sensitivity, there is a systematic difference in utility scores generated using both versions, with an average 87% of the score of the 3L recorded compared to the 5L. Not only are there differences highlighted between value sets for the 3L and 5L but also the responses to dimensions across measures, where the mobility and pain dimensions do not align as one would expect. The new mapping developed in this paper highlights some of the issues with previous mapping methods used in practice, including the assumption of independence of dimension levels from one another that was used while the new valuation for the 5L was being developed. Although the case study they use to demonstrate the effect of using the different approaches in practice did not result in a different cost-effectiveness result, the study does manage to highlight that the assumption of 3L and 5L versions being substitutes for one another, both in terms of descriptive systems and value sets, does not hold. Although the authors are keen to highlight the benefits of their new mapping that produces a smooth distribution from actual to predicted 5L, decision makers will need to be clear about what descriptive system they now want for the generation of QALYs, given the discrepancies between 3L and 5L versions of EQ-5D, so that consistent results are obtained from economic evaluations.

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