Author Bren Collins

Agent relationships and information asymmetries in public health

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The agent relationship and information asymmetry are two features of healthcare economics – but how do they apply to public health policy around processed foods?

Why is health different to other goods?

Arrow’s 1963 seminal paper helped lay the foundations for health economics as a discipline. The Nobel-winning economist talks about what makes healthcare different to other types of market goods. Two of the principal things are agent relationship – that a clinician often makes choices on behalf of a patient (Arrow calls them a “controlling agent”); and information asymmetry – that a clinician knows more than the patient (“informational inequality”). Whereas if someone is buying a new car, they make their own choices, and they might read up on the extensive information available so that they are reasonably knowledgeable about what to buy. These two factors have evolved and possibly diminished over time, especially among highly educated people in developed countries; people often have more choice over their treatment options, and some people have become ‘expert patients‘. Patients may no longer believe that the Götter in Weiß (Gods dressed in white) always know best.

Agent relationship and information asymmetry are features of healthcare economics but they also apply to public health economics. But where people accept clinicians as having more knowledge or acting as their agent, people don’t always accept advice on food from public health policy makers in the same way. People may think, “well I know how to buy a bottle of beer, or a can of coke, or a pizza”, and may not see any potential information asymmetry. Some of it might be ‘akrasia’ – they know that food is unhealthy, but they eat it anyway because it is delicious! However, few people may be aware that poor diet and obesity are the biggest risk factors for ill health and mortality in England.

People might ask “why should a nanny state agent make my food or drink decisions for me?” Of course, this is ignoring the fact that processed food companies might be making those decisions, and reinforcing them using huge marketing budgets. Consumers see government influences but they don’t always see the other information asymmetry and agent relationship; the latent power structures that drive their behaviours – from the food, drinks, alcohol industry, etc. Unsustainable food systems that promote obesity and poor health might be an example of market failure or a tragedy of the commons. The English food system has not moved on enough from post-world war 2 rationing, where food security was the major concern; it still has an objective to maximise calorie supply across the population, rather than maximise population health.

Some of the big UK misselling scandals like mortgage PPI are asymmetries. You could argue that processed foods (junk food high in salt, sugar and saturated fats) might be missold because producers try to misrepresent the true mix of ingredients – for example, many advertisements for processed foods try to misrepresent their products by showing lots of fresh fruit and vegetables. Even though processed foods might have ingredients listed, people have an information asymmetry (or at least, a deficit around information processing) around truly understanding the amount of hidden salt and sugars, because they may assume that the preparation process is similar to a familiar home cooked method. In the US there have been several lawsuits from consumers alleging that companies have misled them by promoting products as being wholesome and natural when they are in fact loaded with added sugars.

The agent relationship and information asymmetry as applied to food policy and health.

How acceptable are public health policies?

A 2012 UK poll carried out by YouGov, funded by the Adam Smith Institute (a right wing free market think tank), found that 22% of people in England thought that the government should tell people what to eat and drink, and 44% thought the government should not. Does this indicate a lack of respect for public health as a specialism? But telling people what to eat and drink is not the same as enacting structural policies to improve foods. Research has shown that interventions like reducing salt in processed foods in the UK or added sugar labelling in the US could be very cost effective. There has been some progress with US and UK programmes like the sugary drinks industry levy, which now has a good level of public support. But voluntary initiatives like the UK sugar reduction programme have been less effective, which may be because they are weakly enforced, and not ambitious enough.

A recent UK study used another YouGov survey to assess the public acceptability of behavioural ‘nudge’ interventions around tobacco, alcohol, and high-calorie snack foods. It compared four types of nudges: labelling (adding graphic warning labels to products); size (reducing pack size of snacks, serving size for alcohol, and number of cigarettes in packets for tobacco); tax (increasing the price to consumers); and availability (banning sales from corner shops). This study found that labelling was the most acceptable policy, then size, tax, and availability. It found that targeting tobacco use was more acceptable than targeting alcohol or food. Acceptability was lower in people who participated in the relevant behaviour regularly, i.e. smokers, heavy drinkers, frequent snackers.

What should public health experts do?

Perhaps public health experts need to do more to enhance their reputation with the public. But when they are competing with a partnership between right wing think tanks, the media and politicians, all funded by big food, tobacco and alcohol, it is difficult for public health experts to get their message out. Perhaps it falls to celebrities and TV chefs like Jamie Oliver and Hugh Fearnley-Whittingstall to push for healthy (and often more sustainable) food policy, or fiscal measures to internalise the externalities around unhealthy foods. The food industry falls back on saying that obesity is complex, exercise is important as well as diet, and more research is needed. They are right that obesity is complex, but there is enough evidence to act. There is good evidence for an ‘equity effectiveness hierarchy‘ where policy-level interventions are more effective at a population level, and more likely to reduce inequalities between rich and poor, than individual, agentic interventions. This means that individual education and promoting exercise may not be as effective as national policy interventions around food.

The answer to these issues may be in doing more to reduce information asymmetries by educating the public about what is in processed food, starting with schools. At the same time understanding that industries are not benevolent; they have an agent relationship in deciding what is in the foods that arrive at our tables, and the main objectives for their shareholders are that food is cheap, palatable, and with a long shelf life. Healthy comes lower on the list of priorities. Government action is needed to set standards for foods or make unhealthy foods more expensive and harder to buy on impulse, and restrict marketing, as previously done with other harmful commodities such as tobacco.

In conclusion, there are hidden agent relationships and information asymmetries around public health policies, for instance around healthy food and drinks. Public health can potentially learn from economic instruments that have been used in other industries to mitigate information asymmetries and agent relationships. If Government and the food industry had shared incentives to create a healthier population then good things might happen. I would be curious to know what others think about this!

Brendan Collins’s journal round-up for 22nd July 2019

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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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Brendan Collins’s journal round-up for 18th March 2019

Bren Collins 2 Comments

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.

Evaluation of intervention impact on health inequality for resource allocation. Medical Decision Making [PubMed] Published 28th February 2019

How should decision-makers factor equity impacts into economic decisions? Can we trade off an intervention’s cost-effectiveness with its impact on unfair health inequalities? Is a QALY just a QALY or should we weight it more if it is gained by someone from a disadvantaged group? Can we assume that, because people of lower socioeconomic position lose more QALYs through ill health, that most interventions should, by default, reduce inequalities?

I really like the health equity plane. This is where you show health impacts (usually including a summary measure of cost-effectiveness like net health benefit or net monetary benefit) and equity impacts (which might be a change in slope index of inequality [SII] or relative index of inequality) on the same plane. This enables decision-makers to identify potential trade-offs between interventions that produce a greater benefit, but have less impact on inequalities, and those that produce a smaller benefit, but increase equity. I think there has been a debate over whether the ‘win-win’ quadrant should be south-east (which would be consistent with the dominant quadrant of the cost-effectiveness plane) or north-east, which is what seems to have been adopted as the consensus and is used here.

This paper showcases a reproducible method to estimate the equity impact of interventions. It considers public health interventions recommended by NICE from 2006-2016, with equity impacts estimated based on whether they targeted specific diseases, risk factors or populations. The disease distributions were based on hospital episode statistics data by deprivation (IMD). The study used equity weights to convert QALYs gained to different social groups into net social welfare. In this case, valuing the most disadvantaged fifth of people’s health at around 6-7 times that of the least disadvantaged fifth. I think there might still be work to be done around reaching consensus for equity weights.

The total expected effect on inequalities is small – full implementation of all recommendations would produce a reduction of the quality-adjusted life expectancy gap between the healthiest and least healthy from 13.78 to 13.34 QALYs. But maybe this is to be expected; NICE does not typically look at vaccinations or screening and has not looked at large scale public health programmes like the Healthy Child Programme in the whole. Reassuringly, where recommended interventions were likely to increase inequality, the trade-off between efficiency and equity was within the social welfare function they had used. The increase in inequality might be acceptable because the interventions were cost-effective – producing 5.6million QALYs while increasing the SII by 0.005. If these interventions are buying health at a good price, then you would hope this might then release money for other interventions that would reduce inequalities.

I suspect that public health folks might not like equity trade-offs at all – trading off equity and cost-effectiveness might be the moral equivalent of trading off human rights – you can’t choose between them. But the reality is that these kinds of trade-offs do happen, and like a lot of economic methods, it is about revealing these implicit trade-offs so that they become explicit, and having ‘accountability for reasonableness‘.

Future unrelated medical costs need to be considered in cost effectiveness analysis. The European Journal of Health Economics [PubMed] [RePEc] Published February 2019

This editorial says that NICE should include unrelated future medical costs in its decision making. At the moment, if NICE looks at a cardiovascular disease (CVD) drug, it might look at future costs related to CVD but it won’t include changes in future costs of cancer, or dementia, which may occur because individuals live longer. But usually unrelated QALY gains will be implicitly included; so there is an inconsistency. If you are a health economic modeller, you know that including unrelated costs properly is technically difficult. You might weight average population costs by disease prevalence so you get a cost estimate for people with coronary heart disease, diabetes, and people without either disease. Or you might have a general healthcare running cost that you can apply to future years. But accounting for a full matrix of competing causes of morbidity and mortality is very tricky if not impossible. To help with this, this group of authors produced the excellent PAID tool, which helps with doing this for the Netherlands (can we have one for the UK please?).

To me, including unrelated future costs means that in some cases ICERs might be driven more by the ratio of future costs to QALYs gained. Whereas currently, ICERs are often driven by the ratio of the intervention costs to QALYs gained. So it might be that a lot of treatments that are currently cost-effective no longer are, or we need to judge all interventions with a higher ICER willingness to pay threshold or value of a QALY. The authors suggest that, although including unrelated medical costs usually pushes up the ICER, it should ultimately result in better decisions that increase health.

There are real ethical issues here. I worry that including future unrelated costs might be used for an integrated care agenda in the NHS, moving towards a capitation system where the total healthcare spend on any one individual is capped, which I don’t necessarily think should happen in a health insurance system. Future developments around big data mean we will be able to segment the population a lot better and estimate who will benefit from treatments. But I think if someone is unlucky enough to need a lot of healthcare spending, maybe they should have it. This is risk sharing and, without it, you may get the ‘double jeopardy‘ problem.

For health economic modellers and decision-makers, a compromise might be to present analyses with related and unrelated medical costs and to consider both for investment decisions.

Overview of cost-effectiveness analysis. JAMA [PubMed] Published 11th March 2019

This paper probably won’t offer anything new to academic health economists in terms of methods, but I think it might be a useful teaching resource. It gives an interesting example of a model of ovarian cancer screening in the US that was published in February 2018. There has been a large-scale trial of ovarian cancer screening in the UK (the UKCTOCS), which has been extended because the results have been promising but mortality reductions were not statistically significant. The model gives a central ICER estimate of $106,187/QALY (based on $100 per screen) which would probably not be considered cost-effective in the UK.

I would like to explore one statement that I found particularly interesting, around the willingness to pay threshold; “This willingness to pay is often represented by the largest ICER among all the interventions that were adopted before current resources were exhausted, because adoption of any new intervention would require removal of an existing intervention to free up resources.”

The Culyer bookshelf model is similar to this, although as well as the ICER you also need to consider the burden of disease or size of the investment. Displacing a $110,000/QALY intervention for 1000 people with a $109,000/QALY intervention for a million people will bust your budget.

This idea works intuitively – if Liverpool FC are signing a new player then I might hope they are better than all of the other players, or at least better than the average player. But actually, as long as they are better than the worst player then the team will be improved (leaving aside issues around different positions, how they play together, etc.).

However, I think that saying that the reference ICER should be the largest current ICER might be a bit dangerous. Leaving aside inefficient legacy interventions (like unnecessary tonsillectomies etc), it is likely that the intervention being considered for investment and the current maximum ICER intervention to be displaced may both be new, expensive immunotherapies. It might be last in, first out. But I can’t see this happening; people are loss averse, so decision-makers and patients might not accept what is seen as a fantastic new drug for pancreatic cancer being approved then quickly usurped by a fantastic new leukaemia drug.

There has been a lot of debate around what the threshold should be in the UK; in England NICE currently use £20,000 – £30,000, up to a hypothetical maximum £300,000/QALY in very specific circumstances. UK Treasury value QALYs at £60,000. Work by Karl Claxton and colleagues suggests that marginal productivity (the ‘shadow price’) in the NHS is nearer to £5,000 – £15,000 per QALY.

I don’t know what the answer to this is. I don’t think the willingness-to-pay threshold for a new treatment should be the maximum ICER of a current portfolio of interventions; maybe it should be the marginal health production cost in a health system, as might be inferred from the Claxton work. Of course, investment decisions are made on other factors, like impact on health inequalities, not just on the ICER.

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