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.

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