Brendan Collins’s journal round-up for 22nd July 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.

Making hard choices in local public health spending with a cost-benefit analysis approach. Frontiers in Public Health Published 29th June 2019

In this round-up I have chosen three papers which look broadly at public health economics.

While NHS healthcare funding has been relatively preserved in the UK (in financial terms at least, though not keeping up with demographic change), funding for local government public health departments has been cut. These departments commission early years services, smoking cessation, drug and alcohol treatment, sexual health, and lots of other services. A recent working paper suggests that marginal changes in Public Health funding produce a more favourable ICER than changes in NHS funding.

This is a neat paper looking at the cost-benefit for a subset of £14 million investment in public health programmes in Dorset, a county on the south coast of England, whose population is slightly older and more affluent than the England average. I try to go to Dorset every year, it has beautiful beaches with traditional Punch and Judy shows, and nice old towns where you can get out on a mackerel fishing trip.

This paper looks at the potential financial savings for each public health programme across different sectors of the economy. One of the big issues with public health as opposed to clinical interventions is the cross sector flow problem – you spend money on drug and alcohol treatment, but the majority of benefits are through prevented crime; or you prevent teenage pregnancy, and a lot of the benefits are to the welfare system (because women delay pregnancy until they are more likely to be in a stable relationship and working). This makes it hard when local councillors might say, ‘what’s in it for us?’

Figure 2 in this paper shows the cross sector flow issue clearly – the spend comes from local authority public health, but 94% of the financial benefits are in the NHS.

I think this study has a good blueprint that other local authorities could follow. The study applies an optimism bias reduction, so it is not just assuming that programmes will be as effective as the research evidence suggests. This is important as there may be a big drop off in effectiveness when something is implemented locally. Of course, sometimes local implementation might be more effective. But it would be nice to see this kind of study carried out with more real-world data. Although the optimism bias reduction makes it less likely to overestimate the cost-benefit, it doesn’t necessarily make the estimate any more precise. National outcomes data collection for public health programmes is weak or absent; better data collection might mean more evidence that prevention interventions provide value for money.

Impact of sugar‐sweetened beverage taxes on purchases and dietary intake: systematic review and meta‐analysis. Obesity Reviews [PubMed] Published 19th June 2019

A lot of health economics focuses on healthcare interventions. But, upstream, structural policy interventions have the capacity to be a lot more cost effective in preventing ill health. Sugary drinks (sugar sweetened beverages – SSBs) are a source of excess empty calories and increase the risk of cardiovascular disease, diabetes and early death. One of the first pieces of work I did as a grown-up academic was looking at a sugary drinks tax, which resulted in me getting up early one day and seeing this. At the time I thought it had roughly zero chance of being implemented. But the sugary drinks industry levy (SDIL) was implemented in the UK in April last year, and had a huge effect in terms of motivating the industry to reformulate below the thresholds of 5g and 8g of sugar per 100ml. Milk-based drinks like Frijj and Yazoo are exempt and still often have nearly 10g sugar per 100ml so there has been talk of extending the tax to these drinks. But Boris Johnson, the likely next UK Prime Minister, has come out against these ‘nanny state’ ‘sin taxes’ and said he will review them, seemingly despite there being a large scale evaluation of the SDIL, and a growing evidence base. There is a good twitter thread on this by Adam Briggs here.

Policies like the SDIL rely on price elasticity of demand (PED). But this PED varies depending, for instance, on how addictive something is and the availability of substitutes. For tobacco, because it is addictive, a 10% price increase might only produce a 5% reduction in demand.

This systematic review and meta-analysis looked at data from 17 studies in 6 jurisdictions and found that, on average, sugar consumption is unit elastic – a 10% price increase produces a 10% reduction in purchases. However, there was considerable variation between studies. The authors designed a bespoke risk of bias tool for this, as the traditional tools used for health interventions did not include all of the potential biases for an SSB tax evaluation; this checklist may be useful for future analyses of similar policies.

If the SSB duty produced a unit elastic response in the UK, it means that people aren’t spending more on SSBs, they are merely buying less of something that they don’t need and which damages their health. And maybe a few people, over many years, consume a bit less sugar, don’t get type 2 diabetes, don’t have to give up work, and are actually better off and can provide for their families for a bit longer. Of course, in the UK the picture is complex because of the different tiers of the duty, but reformulation has meant that people are consuming less sugar even if they don’t reduce their sugary drink consumption. Also, the revenue from the SDIL is spent on healthier schools, so it could be argued that the policy is a win-win.

The cost of not breastfeeding: global results from a new tool. Health Policy & Planning [PubMed] Published 24th June 2019

This study looks at the potential worldwide cost savings if breastfeeding rates were improved. Breastfeeding prevents cases of diarrhoea, obesity, maternal cancer, and other diseases and adverse outcomes. Low breastfeeding rates are a big problem in developing countries where formula costs a huge proportion of income (nearly 20% of average household income in India and Pakistan according to this paper) and water supplies may be contaminated. This study includes healthcare costs, and economic losses from early deaths and reduced IQ through sub-optimal breastfeeding, which total $341 billion per year worldwide.

The authors have said there is also going to be an online, and Excel-based, results tool.

I love reading such ambitious studies that cover the whole world. Producing worldwide estimates for costs is a difficult exercise and can have a danger of losing meaning. For instance, in developing countries, medical costs may be very low if health coverage is very sparse. If a country doesn’t spend anything on healthcare and you measure public health interventions in healthcare cost savings, then it looks like these public health interventions are not worth doing. That is why it is sometimes better to focus on DALYs (and potentially put a financial value on them, although this can be controversial) rather than financial costs. The study found the biggest absolute costs of not breastfeeding were in North America ($115bn), while biggest costs as a proportion of gross national income (GNI) were for sub-Saharan Africa, where not breastfeeding cost 2.6% of GNI.

It looks like two out of the three authors are men. Is there a problem with men being pro-breastfeeding? Why should a man tell women what to do with their bodies? Women shouldn’t feel stigmatised about their infant feeding choices. But for me it is not about telling women what to do. It is making sure the structures and social norms are there to support breastfeeding and that formula companies are regulated in how they market themselves and their products. Maybe men not caring enough about breastfeeding is what has got us to where we are now. 

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Thesis Thursday: Angela Devine

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 Angela Devine who has a PhD from The Open University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
The economics of vivax malaria treatment
Supervisors
Yoel Lubell, Ric Price, Ricardo Aguas, Shunmay Yeung
Repository link
https://thesiscommons.org/zsc6x/

What is vivax malaria and what are some of the key challenges that it presents for health economists?

One infectious bite from a mosquito carrying vivax malaria can lead to multiple episodes of malaria due to dormant liver parasites called hypnozoites. We can’t tell the difference between these relapse infections and new infections, which means that it’s challenging to model. Unlike falciparum malaria, which frequently results in severe outcomes and deaths, vivax malaria doesn’t often result in direct mortality. Instead, it likely causes indirect mortality through the malnutrition and anaemia that are caused by repeated malaria episodes. Unfortunately, the evidence of this is limited.

To prevent future relapses, patients need to be given a drug to treat the liver parasites (radical cure) in addition to treating the blood stage treatment. The only drug that is currently licensed for radical cure, primaquine, can cause potentially life-threatening haemolysis in individuals who have a genetic disorder called glucose-6-phosphate-dehydrogenase (G6PD) deficiency. While some countries are so concerned about haemolytic events that primaquine isn’t used at all, other settings prescribe primaquine to everyone. The evidence on the risk of primaquine-induced haemolysis and death is sparse, and expert opinion on this matter is fiercely divided.

How did you go about collecting the data needed for your study?

Not much has been done previously on vivax malaria costs, which meant that a lot of my work involved generating cost data. I started by analysing some fairly old data that my supervisors had from a study on treatment-seeking behaviour in Papua, Indonesia. The cost of illness study indicated that household costs were similar for both vivax and falciparum malaria in 2006. I also collected provider and patient-level cost data alongside a multi-country clinical trial on vivax malaria treatment. I wasn’t able to travel to the some of the study sites (e.g. Afghanistan) to collect the provider costs, so I had to create worksheets for local trial staff to fill out. It was an iterative process, particularly with the first site in Indonesia, but it got faster and easier to do each time. I’m very thankful that the local teams were enthusiastic about this work and patient with my many questions and requests.

What is the economic burden of vivax malaria and who bears the cost?

A lot of vivax malaria episodes occur in remote areas where access to care is limited. The highest incidence of the disease is in children, particularly those under the age of five. This often means that someone will need to take time off from usual activities, such as farming, attending school, or household chores, to care for the sick. I estimated the global economic burden to be US$330 million. These estimates don’t include mortality, malnutrition or anaemia. Since we know that repeated episodes can have a profound impact on a household’s income, I included productivity losses for those who were ill and their carers. We also know that malaria causes educational losses, so I included these productivity costs for children as well as adults to try to capture some of those losses. In total, productivity losses accounted for US$263 million, nearly 80% of the total costs. Since many who are affected by this disease aren’t paid for their work, I used one GDP per capita per day for every day lost to illness or caretaking. Other methods of valuing these losses would have a substantial impact on the total costs. While there’s a considerable amount of uncertainty around some of the numbers I used and assumptions that I made, my hope is that by identifying the issues, we will be able to generate the data needed for better estimates in the future.

What methods did you use to evaluate the cost-effectiveness of new treatment strategies?

Asia-Pacific malaria control programs stated that the cost of G6PD screening was an obstacle to its widespread use. My research addressed those concerns through a decision tree model in R that weighed up the costs, risks and benefits of screening using newly developed G6PD rapid diagnostic tests (RDTs) before prescribing primaquine. I wanted to make this work as relevant to policymakers as possible, so I did two separate comparisons. First I compared this strategy to not using primaquine, then I compared it to prescribing primaquine to everyone without screening. While this strayed from typical economic evaluation methods, it seemed unlikely that a setting where primaquine isn’t prescribed due to fear of haemolysis would switch to prescribing primaquine to everyone without screening, or that a setting where primaquine is prescribed to everyone would stop using it altogether.

As G6PD deficiency is X-linked, the risk of haemolysis varies by gender, so results need to be stratified by gender. The prevalence and severity of G6PD deficiency and the latency period and number of relapses for vivax malaria varies geographically. While I wanted to have more than one setting to explore how these might impact the results, four comparisons was already a lot of information to present. Instead, I used R-shiny with my model to create an interactive website where people can see how changes in the baseline model parameters impact the results. My goal was to provide a tool that policymakers could use to help make decisions about treatment strategies in their settings. This also provides an opportunity to explore the impact of parameter values that may be seen as contentious.

What are some of the issues you encountered in working with policymakers to ensure that cost-effective treatments become more widely used?

One issue is that patients, especially those who can afford to do so, seek treatment in the private sector, which is harder to control. Encouragingly, the follow-up survey in Papua, Indonesia indicated that changing treatment policy in the public sector also had an impact on how private sector providers diagnosed and treated malaria. As someone keen to influence policy, I benefited a lot from meetings with malaria control program officials from the Asia Pacific. These provided insights on the challenges that countries are facing. For example, the work I did on G6PD screening was aimed at addressing the cost issue that kept coming up in these meetings. Unfortunately, I’m not aware of settings where they have begun to routinely use G6PD RDTs. There are additional barriers, like getting the tests licensed so that malaria control programs can purchase them with a subsidy from The Global Fund. Another issue that I hadn’t fully appreciated before beginning my PhD is that funding for diseases like malaria is often siloed for specific purposes by the various donors. This can make it more challenging to ensure that countries are getting the best possible value for the money that is spent. There’s also been a lot of debate recently about what willingness to pay threshold should be used in poorly resourced settings. This is a debate that we need to have, but it also makes it more challenging to decide which treatments should be considered to be cost-effective.

Alastair Canaway’s journal round-up for 18th September 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.

Selection of key health domains from PROMIS® for a generic preference-based scoring system. Quality of Life Research [PubMedPublished 19th August 2017

The US Panel on Cost-Effectiveness recommends the use of QALYs. It doesn’t, however, instruct (unlike the UK) as to what measure should be used. This leaves the door ajar for both new and established measures. This paper sets about developing a new preference-based measure from the Patient-Reported Outcomes Measurement System (PROMIS). PROMIS is a US National Institutes of Health funded suite of person-centred measures of physical, mental, and social health. Across all the PROMIS measures there exist over 70 domains of health relevant to adult health. For all its promise, the PROMIS system does not produce a summary score amenable to the calculation of QALYs, nor for general descriptive purposes such as measuring HRQL over time. This study aimed to reduce the 70 items down to a number suitable for valuation. To do this, Delphi methods were used. The Delphi approach is something that seems to be increasing in popularity in the health economics world. For those unfamiliar, it essentially involves obtaining the opinions of experts independently and iteratively conducting rounds of questioning to reach a consensus (over two or more rounds). In this case nine health outcomes experts were recruited, they were presented with ‘all 37 domains’ (no mention is made of how they got from 70 to 37!) and asked to remove any domains that were not appropriate for inclusion in a general health utility measure or were redundant due to another PROMIS domain. If more than seven experts agreed, then the domain was removed. Responses were combined and presented until consensus was reached. This left 10 domains. They then used a community sample of 50 participants to test for independence of domains using a pairwise independence evaluation test. They were given the option of removing a domain they felt was not important to overall HRQL and asked to rate the importance of remaining domains using a VAS. These findings were used by the research team to whittle down from nine domains to seven. The final domains were: Cognitive function- abilities; Depression; Fatigue; Pain Interference; Physical Function; Ability to participate in social roles and activities; and Sleep disturbance. Many of these are common to existing measures but I did rather like the inclusion of cognitive function and fatigue – something that is missing in many, and to me appear important. The next step is valuation. Upon valuation, this is a promising candidate for use in economic evaluation – particularly in the US where the PROMIS measurement suite is already established.

Predictive validation and the re-analysis of cost-effectiveness: do we dare to tread? PharmacoEconomics [PubMedPublished 22nd August 2017

PharmacoEconomics treated us to a provocative editorial regarding predictive validation and re-analysis of cost-effectiveness models – a call to arms of sorts. For those (like me) who are not modelling experts, predictive validation (aka 4th order validation) refers to the comparison of model outputs with data that are collected after the initial analysis of the model. So essentially you’re comparing what you modelled would happen with what actually happened. The literature suggests that predictive validation is widely ignored. The importance of predictive validity is highlighted with a case study where predictive-validity was examined three years after the end of a trial – upon reanalysis the model was poor. This was then revised, which led to a much better fit of the prospective data. Predictive validation can, therefore, be used to identify sources of inaccuracies in models. If predictive validity was examined more commonly, improvements in model quality more generally are possible. Furthermore, it might be possible to identify specific contexts where poor predictive validity is prevalent and thus require further research. The authors highlight the field of advanced cancers as a particularly relevant context where uncertainty around survival curves is prevalent. By actively scheduling further data collection and updating the survival curves we can reduce the uncertainty surrounding the value of high-cost drugs. Predictive validation can also inform other aspects of the modelling process, such as the best choice of time point from which to extrapolate, or credible rates of change in predicted hazards. The authors suggest using expected value of information analysis to identify technologies with the largest costs of uncertainty to prioritise where predictive validity could be assessed. NICE and other reimbursement bodies require continued data collection for ‘some’ new technologies, the processes are therefore in place for future studies to be designed and implemented in a way to capture such data which allows later re-analysis. Assessing predictive validity seems eminently sensible, there are however barriers. Money is the obvious issue, extended prospective data collection and re-analysis of models requires resources. It does, however, have the potential to save money and improve health in the long run. The authors note how in a recent study they demonstrated that a drug for osteoporosis that had been recommended by Australia’s Pharmaceutical Benefits Advisory Committee was not actually cost-effective when further data were examined. There is clearly value to be achieved in predictive validation and re-analysis – it’s hard to disagree with the authors and we should probably be campaigning for longer term follow-ups, re-analysis and increased acknowledgement of the desirability of predictive validity.

How should cost-of-illness studies be interpreted? The Lancet Psychiatry [PubMed] Published 7th September 2017

It’s a good question – cost of illness studies are commonplace, but are they useful from a health economics perspective? A comment piece in The Lancet Psychiatry examines this issue using the case study of self-harm and suicide. It focuses on a recent publication by Tsiachristas et al, which examines the hospital resource use and care costs for all presentations of self-harm in a UK hospital. Each episode of self-harm cost £809, and when extrapolated to the UK cost £162 million. Over 30% of these costs were psychological assessments which despite being recommended by NICE only 75% of self-harming patients received. If all self-harming patients received assessments as recommended by NICE then another £51 million would be added to the bill. The author raises the question of how much use is this information for health economists. Nearly all cost of illness studies end up concluding that i) they cost a lot, and ii) money could be saved by reducing or ameliorating the underlying factors that cause the illness. Is this helpful? Well, not particularly, by focusing only on one illness there is no consideration of the opportunity cost: if you spend money preventing one condition then that money will be displacing resources elsewhere, likewise, resources spent reducing one illness will likely be balanced by increased spending on another illness. The author highlights this with a thought experiment: “imagine a world where a cost of illness study has been done for every possible diseases and that the total cost of illness was aggregated. The counterfactual from such an exercise is a world where nobody gets sick and everybody dies suddenly at some pre-determined age”. Another issue is that more often than not, cost of illness studies identify that more, not less should be spent on a problem, in the self-harm example it was that an extra £51 million should be spent on psychological assessments. Similarly, it highlights the extra cost of psychological assessments, rather than the glaring issue that 25% who attend hospital for self-harm are not getting the required psychological assessments. This very much links into the final point that cost of illness studies neglect the benefits being achieved. Now all the negatives are out the way, there are at least a couple of positives I can think of off the top of my head i) identification of key cost drivers, and ii) information for use in economic models. The take home message is that although there is some use to cost of illness studies, from a health economics perspective we (as a field) would be better off spending our time steering clear.

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