Well-being and gross national happiness for policy

December 2, 2016July 13, 2018Sam Watson 3 Comments

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.

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

Yumi Kimura (CC-BY-SA-2.0)

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

October 26, 2016July 13, 2018Sam Watson 9 Comments

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.

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.*

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.

Fermi problems and public health

March 17, 2016July 13, 2018Sam Watson 5 Comments

Enrico Fermi was a physicist well known for his ability to make good approximations to difficult questions. A well-known Fermi problem is ‘how many piano tuners are there in Chicago?’ Answering these sorts of questions involves using estimates of simpler quantities that, if correct, yield the correct answer when combined. Indeed, we could consider these calculations as arising from a simple model. Many public health policies can be approached in this way and yet, despite large amounts of journal space devoted to these issues, the debate usually revolves around the quality of the evidence. So why aren’t these sorts of calculations commonplace as a start to informing the debate? Consider the following two examples.

E-cigarettes

As we’ve noted before, e-cigarettes are a contentious public health issue. But we don’t want to wade into the vagaries of this debate, rather we’d just like to consider the benefits of maintaining a regulated e-cigarette market versus prohibition of e-cigarettes. If we assume that under a prohibition, ex-smokers who use e-cigarettes would switch back to smoking, and that the costs of enforcing a prohibition are balanced by the gain in tax revenue. Then the costs of prohibition and the benefits of regulation could be equivalent, and equal to the product of:

The number of switchers from cigarettes to e-cigarettes. Approximately 850,000 people.
Relative risk reduction of major smoking related illness. Let’s say 95% reduction.
The risk of a premature death. About 1.1% of smokers die prematurely each year.

This gives around 9,000 premature deaths avoided each year. And similarly at a cost of about £350 per person per year (£3b annual NHS costs divided by 8.8m smokers), a saving of approximately £14.5m per year. As a further cost to a regulated market we could add uptake of e-cigarette use by never smokers – about 56,000 people. Taking this into account, we get 8,600 premature deaths avoided and £13.5m saved. But, the benefits are likely to rise as public information increases and more people aim to quit smoking, which already seems to be the case (figure, left). Smoking consumption among e-cigarette using smokers (1.3m in the UK) may also be declining (figure, right).

Figure. Left – smoking prevalence by year in the UK. Right – daily cigarette consumption among smokers in the UK. Source data.

Seven day NHS

Seven day NHS services are a key aspect of current government healthcare policy. The aim is to reduce the so-called weekend effect: the increased risk of mortality associated with weekend admission versus weekday admission. Rachel Meacock described her paper on this blog that attempted to value those ‘excess’ deaths to see if the seven day policy met typical cost-effectiveness standards (they did not). But, as they and others have noted, not all of these deaths are preventable. We can attempt to come up with an estimate of how many of those deaths are preventable based upon what we know. So, we multiply:

Number of emergency weekend admissions: 1.23 million
Risk of a preventable adverse event: 4.5%
Relative risk increase of an adverse event associated with weekend admission: 20%
Probability an adverse event lead to death: 7.4%

Which gives 820 preventable deaths. This is 15% of the total number of deaths associated with weekend admission, which would suggest that the benefits of preventing these deaths are 15% of the size estimated by Meacock et al.

Informing the debate

Certainly, these are not thorough statistical analyses and do not capture the uncertainties involved. They are Fermi Problems and they are a simple attempt to inform the debate. The quality of the evidence is of course key to ensuring the estimates informing the calculations are correct. For example, the 95% reduction in risk associated with e-cigarettes is disputed by some. But, that figure would have to be close to zero, or there would have to be some very large unaccounted-for cost to regulation in order to favour prohibition.

There are, of course, moral, political, and legal dimensions to policy formation. Policy analysis simply provides the figures to inform that debate. In some cases no further evidence might be forthcoming, leaving such calculations as the only option. For example, policy aimed at mitigating pandemic influenza requires studies from a pandemic, and it is too late when one occurs. Even a back of the envelope calculation like this is preferable to basing policy on intuition and whim alone.