Agent relationships and information asymmetries in public health

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!

Well-being and gross national happiness for policy

In the early years of the coalition government, David Cameron lauded the measurement of happiness and well-being as an indicator of national performance. Data on life satisfaction have been collected and published by the Office for National Statistics every year since 2012. Despite this, very little is said about well-being. It is not discussed at spending or policy reviews and rarely in the media. Gross domestic product (GDP) continues to dominate the coverage of national performance and the potential impact of policies such as Brexit. Nevertheless, a precursory glance at the data can reveal an interesting picture of national well-being.

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Proportion of respondents reporting their life satisfaction to be ‘high’ or ‘very high’. [Data source: ONS; .csv data; R code]

The map above plots the proportion of people reporting their life satisfaction to be ‘high’ or ‘very high’ across England and Wales. This corresponds to a score of seven or more on a ten point scale in response to the question:

Overall, how satisfied are you with your life nowadays? Where 0 is ‘not at all satisfied’ and 10 is ‘completely satisfied’.

There are clearly variations across the country, with the most obvious being the urban/rural divide. The proportion of people reporting ‘high’ or ‘very high’ life satisfaction in the UK has also increased over time, from 76.1% to 81.2% between 2012/3 and 2015/6, corresponding to a mean life satisfaction rating rising from 7.42 to 7.65.

Well-being data can also be used to evaluate the impact of policies or interventions in a cost-benefit analysis. Typically an in-depth analysis may model the impact of a policy on household incomes. But, these changes in income are only valuable insofar as they are instrumental for changes in well-being or welfare. Hence the attraction of well-being data. To derive a monetary valuation of a change in life satisfaction economists consider either compensating surplus or equivalent surplus. The former is the amount of money that someone would need to pay or receive to return them to their initial welfare position following a change in life satisfaction; the latter is the amount they would need to move them to their subsequent welfare position in the absence of a change. For example, to estimate the compensating surplus for a change in life satisfaction, one could estimate the effect of an exogenous change in income on life satisfaction. Such an exogenous change could be a lottery win, which is exactly the approach used in this report valuing the benefits of cultural and sports events like the Olympics.

Health economists have been one of the pioneering groups in the development and valuation of measures of non-monetary benefits. The quality-adjusted life year (QALY) being a prime example. However, a common criticism of these measures is that they only capture health related quality of life, and are fairly insensitive to changes in other areas of well-being. As a result there have been a growing number of broader measures of well-being, such as WEMWBS, that can be used as well as the generic life satisfaction measures discussed above. Broader measures may be able to capture some of the effects of health care policies that QALYs do not. For example, centralisation of healthcare services increases travel time and time away from home for many relatives and carers; reduced staff to patient ratios and consultation time can impact on process of care and staff-patient relationships; or, other barriers to care, such as language difficulties, may cause distress and dissatisfaction.

There are clearly good arguments for the use of broad life satisfaction and well-being instruments and sound methods to value them. One of the major barriers to their adoption is a lack of good data. The other barrier is likely to be the political willingness to accept them as measures of national performance and policy impact.

Credits

The health of people who live in slums and the trouble with estimating neighbourhood effects

Slums are a large and growing feature of urban areas in low and middle income countries. But, despite the ease with which you might picture what such informal settlements look like, there is no consensus about what exactly defines a slum. UN-Habitat defines ‘slum’ at the household level as a dwelling that lacks a number of important amenities such as improved water and sanitation while UNESCO defines it in terms of an urban area with certain features. In a new series of two papers, we discuss slum health and interventions designed to improve the health and welfare of people who live in slums. We make the argument that the UN-Habitat definition of slums is inadequate as slums exhibit unique neighbourhood effects.

Neighbourhood effects are those effects caused by the shared physical and social environment in which a person lives. Identifying and measuring such effects are important for public and health policy. In the context of slums, we argue that such neighbourhood effects determine the effectiveness of an intervention. For example, the benefits of provision of water and sanitation facilities are dependent on the already existing infrastructure, density of housing, and social structure of the area. The intervention may therefore be effective in some places but not in others, or require a certain level of input to reach a ‘tipping point’. However, estimation of the causal effect of a neighbourhood on population health and well-being can be difficult.

For certain outcomes causal neighbourhood effects are fairly easy to discern. Consider John Snow’s map of the 1854 outbreak of cholera below. The plot of cholera cases enabled John Snow to identify the infamous water pump from which cases of cholera were being contracted. In this instance, the causal neighbourhood effect of the shared water pump is clear, and the effects simple to measure. It’s not as if people contracted cholera and then decided to move closer to the water pump.

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John Snow’s map of the 1854 cholera outbreak in London

A similar exercise can be conducted using survey data. The map below shows an estimate of the spatial distribution of cases of diarrhoea in the under 5s in Nairobi, Kenya, with notable slum areas marked by the letters A to E. There is clearly an strong correlation between slum areas and risk of diarrhoea. It would not be a strong assumption that there was a common cause of diarrhoea in slum areas rather than people who were more likely to get diarrhoea choose to move to those areas.*

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Estimation of the risk of diarrhoea in the under 5s in Nairobi, Kenya. Disease risk is estimated by applying a spatial filter across a regular lattice grid and then estimating a binomial model to predict disease risk at each point. Red areas indicate higher risk and turquoise areas lower risk. Blue lines indicate areas with a >80% probability of having higher risk than the city average.

Inference about the effects of higher or lower wealth or socioeconomic status or more ephemeral characteristics of neighbourhoods on health and outcomes is more difficult. It is generally not possible to randomise people to neighbourhoods; individuals of lower socioeconomic status are more likely to move to poorer neighbourhoods. The exception is the Moving to Opportunity experiments in the US, which showed that better neighbourhoods improved adult health and improved the health and economic outcomes of their children.

J. Michael Oakes has a detailed discussion of the issues involved in the estimation of causal neighbourhood effects. He identifies four key problems. Firstly, due to social stratification between neighbourhoods, the “selection” equation that sorts individuals into neighbourhoods is likely to be nearly identical for all people in the same neighbourhood. Modelling selection therefore removes most of the variation between neighbourhoods. Secondly, even if neighbourhood effects were emergent properties of the interactions between individuals, such as the epidemiology of infection, they would still not be necessarily identifiable as the expression of those emergent properties is dependent on the neighbourhood level variables. Oakes likens it to trying to estimate the incidence by controlling for prevalence. Thirdly, neighbourhood level effects are not likely to be exchangeable, an assumption widely used in statistical inference. And fourthly, neighbourhood effects are not likely to be static. Arguably, quasi-experiemental methods such as instrumental variable or regression discontinuity designs and more sophisticated models may help solve these issues, but convincing applications can still remain elusive.

The points above contribute to the argument that the effectiveness of a community-level intervention, even when measured in a randomised trials, depends on neighbourhood effects. As we have discussed in other contexts, development of a causal theory is clearly required for the appropriate design of studies and interpretation of evidence. From a health and public policy standpoint innovations in methods of causal inference for neighbourhood effects can only be a good thing.

 

*Of course, there are other factors that may explain the correlation. For example, slum areas were more likely to be sampled in the rainy season. We also therefore examined childhood stunting, which showed the same pattern. See the paper for more detail.