Spatial segregation and socioeconomic inequalities

in health in major Brazilian cities

An ESRC pathfinder project

Income per head and life-expectancy: rich & poor countries

Source: Wilkinson & Pickett, The Spirit Level (2009)

Male mortality (25-64 yrs) and income inequality in US states and

Canadian provinces.

Source: Ross NA, Wolfson MC, Dunn JR, Berthelot JM, Kaplan GA, Lynch JW. British

Medical Journal 2000;320:898-902

Life expectancy and income inequality: Brazil, 2000

Increasing urbanisation in developing countries

http://filipspagnoli.wordpress.com/stats-on-human-rights/statistics-on-

poverty/statistics-on-poverty-urbanization-and-slums/

Share of slum population in urban areas in selected Asian and Pacific countries

1990 and 2001

http://filipspagnoli.wordpress.com/stats-on-human-rights/statistics-on-

poverty/statistics-on-poverty-urbanization-and-slums/

In most parts of the world, the proportion of urban populations living in slums

has decreased

At the same time, the absolute number of slum dwellers around the world

is still rising

http://filipspagnoli.wordpress.com/stats-on-human-rights/statistics-on-

poverty/statistics-on-poverty-urbanization-and-slums/

Most slum households in Brazil live in slum neighbourhoods

The Human Opportunity Index (HOI) measures the likelihood that children from different

backgrounds, or in different combinations of circumstances, will be able to access the basic

services they need. Barros et al. (2006)

Brazil has a low Human Opportunity Index compared to other Latin

American countries with similar GDP per capita

Spatial Inequalities and Development

Despite having a relatively high GDP per capita, Brazilian cities are highly

unequal

- urbanisation and concentration of economic activity

- spatial concentration of affluence reproduces privileges of the rich

- spatial concentration of poverty results in segregation, involuntary

clustering in ghettos

Effects on population health and premature mortality/morbidity?

“Triple health jeopardy: being poor in a poor neighbourhood that is spatially

isolated from life-enhancing opportunities…” Nancy A Ross

Socioeconomic segregation and the Spatial poverty trap

- Severe job restriction

- Gender disparities

- Worsening living conditions

- Social exclusion and marginalisation

- Lack of social interaction

- High incidence of crime

Dimensions of segregation

Evenness: the unequal distribution of social groups across areal units of an urban

area. Index of Dissimilarity

Exposure: the degree of potential contact between groups within neighborhoods

of a city. Index of Isolation and Exposure

Clustering: extent to which areas inhabited by minority members adjoin one

another in space. Index of clustering

Centralization: the degree to which a group is located near the centre of an urban

area. Index of centralisation

Concentration: the relative amount of physical space occupied by a minority

group in the urban environment. Index of concentration

However, these indices are aspatial measures.

Dimensions of spatial segregation

Sean F. Reardon & David O'Sullivan. “Measures of Spatial Segregation” Sociological Methodology.

V. 34, n.1, p. 121-162, 2004

EVENNESS

CLUSTERING

EXPOSURE ISOLATION

CLUSTERING

EXPOSURE

EVENNESS

ISOLATION

Adapted from Bell, 2006 and from Reardon & O'Sullivan, 2004

Dimensions of spatial segregation

SPATIAL EXPOSURE INDEX

SPATIAL ISOLATION INDEX

EXPOSURE/ISOLATION DIMENSION

Feitosa, F. F.; Câmara, G.;Monteiro, A. M. V.; Koschitzki, T.; Silva, M. P. S., Global and local spatial indices of urban segregation.

International Journal of Geographical Information Science; Mar2007, Vol. 21 Issue 3, p299-323,

Transform aspatial segregation measures into spatial

measures

Localities: An urban area has different localities where people live and exchange

experiences with their neighbours. Measure the intensity of these exchanges by

assuming this intensity varies by the spatial distance between population groups.

Each locality has a core: geometrical centroid of an areal unit. The population

characteristics of the locality are expressed by its local population intensity. Use a

kernel function and a bandwidth parameter to estimate this local population

intensity.

Spatial clustering index:

-The percentage of the low income census tracts within a district that are

surrounded by other low income census tracts.

-The index varies from 0% to 100%

-0%: there are no low income census tracts surrounded by other low income

census tracts in the district

-100%: all the census tracts in the district are low income census tracts surrounded

by other low income census tracts

South

Southeast

Northeast

North

Central-West

Porto Alegre

Curitiba

Rio de Janeiro

Aracaju

Recife

João Pessoa

Natal Teresina

Brasília

Campo Grande

Brazilian regions, states and selected cities

Income groups

Spatial Isolation Index Is

ola

tion

In

de

x

Dimensions of spatial segregation

EVENNESS

CLUSTERING

EXPOSURE ISOLATION

Spatial CLUSTERING INDEX

Moran Scatter Plot

SLOPE OF THE REGRESSION LINE

Sp

atia

lly lagge

d v

aria

ble

Variable to be lagged, standardized

Moran Cluster Map

Spatial CLUSTERING INDEX

Within each district, the

Spatial Clustering Index

is the proportion of

census tracts that are

low income tracts and

are surrounded by other

low income tracts.

Dimensions of spatial segregation

EVENNESS

CLUSTERING

EXPOSURE ISOLATION

Local

Spatial Isolation Indexes

Income Groups

BW:400m

ms: minimum salaries

>20 ms 10-20 ms

5-10 ms <2ms 2-5 ms

INCOME

Moran I Index: 0.65 ( ρ< 0.0001)

Distribution of income of the head of the household by district, Porto Alegre, 2000. Source: IBGE

Outcome variable: Standardised Mortality Rates (age and

sex adjusted) for 861 districts within 15 Brazilian cities

Scatterplot of Standardised Mortality Rates by Poverty Rate

in Brazilian Districts

Scatterplot of Spatial Clustering by Poverty Rate in Brazilian

Districts

Scatterplot of Spatial Isolation of the poor by Poverty Rate in

Brazilian Districts

Multiple membership models

These are models where each level 1 unit is a

member of more than one higher level unit.

For example,

• Pupils change schools/classes and each

school/class has an effect on pupil outcomes.

• Patients are seen by more than one nurse

during the course of their treatment.

• Counties are bordered by more than one other

neighbouring county

Multilevel Multiple Membership Model of Mortality

Rate: Variance Components Model

SMR

Spatial clustering Index

South/South East

and Central West

Regions

North East Region

Northern Region

Predicted SMR by Spatial Clustering Index and Region

Restinga, Porto

Alegre

Ilha Joana Bezerra,

Recife

Paracuri, Belém

Adjusted for Population Size and Poverty Rate in the District

Discussion:

-“Triple health jeopardy”- revisited?

Living in a poor neighbourhood that is spatially segregated, in a developing

city

- Living in a rich city is not protective (of mortality risk) if you live in a

spatially segregated neighbourhood

- Implications for urban development and slum resettlement in other

developing countries

Slum clearances in Rio de Janeiro in preparation for the Olympics

Summary

- Districts in Brazil with higher poverty rates have higher mortality rates

- Districts where the poor are clustered also have higher mortality rates

- Interaction between Region and Spatial Clustering: The association of

clustering with mortality is strongest in cities in the richest (Southern)

regions

- Increasing the spatial isolation of the poor within rich cities could result in

poorer health and lower life expectancy.