1. Urbanisation and spatial inequalities in health in Brazil and India
Tarani Chandola University of Manchester
Sergio Bassanesi UFRGS - Universidade Federal
Sitamma Mikkilineni Indian Institute of Public Health, Souvik Bandyopadhyay Hyderabad
Anil Chandran
Presented at
World Congress of Epidemiology 7-11th August 2011. Edinburgh International Conference Centre, Edinburgh, Scotland.
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2. Life expectancy and income inequality: Brazil, 2000
 Plot showing the odds ratios (ORs) and 95% confidence interval (CI) for one-standard deviation change in Gini coefficient for the risk of being underweight, pre-overweight, overweight and obese.

3.  Plot showing the odds ratios (ORs) and 95% confidence interval (CI) for one-standard deviation change in Gini coefficient for the risk of being underweight, pre-overweight, overweight and obese.
 Plot showing the odds ratios (ORs) and 95% confidence interval (CI) for one-standard deviation change in Gini coefficient for the risk of being underweight, pre-overweight, overweight and obese.
Subramanian S V et al. J Epidemiol Community Health 2007;61:
©2007 by BMJ Publishing Group Ltd

4. Health is related to income differences within rich societies but not to those between them
Between (rich) societies
Within societies
Most
deprived
Source: Wilkinson & Pickett, The Spirit Level (2009)

5. Increasing income inequality in Brazil and India
Increasing spatial inequality in poverty and income
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 Individual and Population Health?
“Triple health jeopardy: being poor in a poor neighbourhood that is spatially isolated from life-enhancing opportunities…” Nancy A Ross

7. Calculating index of dissimilarity for a geographic area Suppose:
pi = the poor population of the ith areal unit, e.g. census tract
P = the total Poor population of the large geographic entity for which the index of
dissimilarity is being calculated.
ri = the rich population of the ith area unit, e. g. census tract
R = the total Rich population of the large geographic entity for which the index of
dissimilarity is being calculated
I.D. measuring the segregation of poor from rich= (1/2)* SUM |(pi /P – ri / R) |
Interpretation:
The proportion of the poor population that would have to move areas, to become distributed across the areas in the same way as the rich population

8. Calculating isolation index of segregation pi = the poor population of a component part, for example, census tracts, of the larger
geographic entity for which the isolation index is calculated.
ti= the total population of a component part of the larger geographic entity for which the
isolation index is calculated.
P = the total poor population of the larger geographic entity for which the isolation
index is being calculated.
Then the isolation index for poor groups= SUM(pi / P) * (pi / ti)
Interpretation:
The probability that a poor person will meet another poor person locally. This is equivalent to the probability that a poor person will not meet someone of another group.
However, the indices of dissimilarity and isolation are aspatial measures.

9. Dimensions of spatial segregation

10. Dimensions of spatial segregation
EVENNESS
ISOLATION
EXPOSURE
And the solutions starts here.
With this guy, Sean Reardon, a professor of Education at Stanford University.
First, he reduced the Massey`s five dimensions, to two of them.
The isolation and exposure dimension and the evenness-clustering dimension.
And he maked it clear that residential segregation, to be meaningful, must take into account the geography, the spatial distribution of the attributes.
And, if such kind of metrics does not exist, they should be created.
40s
CLUSTERING
Dimensions of spatial segregation
Sean F. Reardon & David O'Sullivan. “Measures of Spatial Segregation” Sociological Methodology. V. 34, n.1, p , 2004

11. 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.

12. EXPOSURE/ISOLATION DIMENSION
SPATIAL EXPOSURE INDEX
Average proportion of group n in the localities of each member of group m
SPATIAL ISOLATION INDEX
So, these are the Feitosa’s indexes in the exposure/isolation dimension.
The Spatial Exposure Index is the average proportion of group n in the localities of each member of group m. For example, could be the average proportion of poor people living in the areas where each rich person live. Or the contrary.
It may be understood as a proxi for potential social interaction between two different groups.
The Spatial Isolation Index is the average proportion of group m in the local environments of each member of group m. It is a special case of exposure index that measures the spatial exposure of group m to it self.
As you can see in the formulas, L, representing the population intensity, is present, and so preventing the checkerboard problem.
1 min,
Average proportion of group m in the local environments of each member of group m (spatial exposure of group m to itself)

13. EVENNESS/ CLUSTERING DIMENSION
SPATIAL NEIGHBOURHOOD SORTING INDEX
Proportion of the variance between the different localities that contributes to the total variance of the variable X in the city
GENERALIZED SPATIAL DISSIMILARITY INDEX
These other index, in the evenness/clustering dimension, we are not going to use in our work, so, lets move on. 7s
Average difference of the population composition of the localities from the population composition of the urban area as a whole 13

14. Key research question:
What is the evidence of a triple health jeopardy in relation to mortality in Brazil and India?
Methods:
Brazil Data (for the largest cities):
Demographic and Socioeconomic data: 2000 Census (census tract level)
Mortality data: SIM Mortality Information System (district level data)
India Data (for largest 50 cities):
Demographic and Socioeconomic data: census (urban ward level)
Mortality data: District Level Household and Facilities Survey and (Individual, neighbourhood and urban ward level)

15. Dimensions of spatial segregation
EVENNESS
ISOLATION
EXPOSURE
It was for this dimension that a new index was developed.
From those available from the software, one, the Neighbourhood Sorting Index was only global. And without the local indexes it was useless.
And the Spatial Dissimilarity Index was not linear, it was high both in the richest and the poorest areas, and could not be used in the regression analysis.
So, to measure the segregation in the evenness/clustering dimension we developed a new measure based on the Local Indices of Autocorrelation.
CLUSTERING
Dimensions of spatial segregation

16. Spatial CLUSTERING INDEX
Moran Cluster Map
Moran Scatter Plot
SLOPE OF THE REGRESSION LINE
Spatially lagged variable
At the level of the census tracts, The Moran Scatter plot and the Moran Cluster map were produced.
Each census tract was classified into four categories.
In blue are depicted the census tracts which mean income of the head of the households was below the global mean and were surrounded by other census tracts also with income below the global mean.
In red are the census tracts with high income and that are surrounded by other high income census tracts.
The others are the outliers: in light blue are the low income tracts that are surrounded by high income tracts, and in pink, are the high income tracts surrounded by low income census tracts.
This quadrant represents the low-low situation. Low income census tracts (on the left in the X axe) surrounded by low income neighbours, below, in the Y axe.
As each district has some 30 census tracts, for each district we calculated the proportion of low income census tracts that are surrounded by other low income census tracts.
2min
Variable to be lagged, standardized 16

17. 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.
And here are our final results for this new Spatial Clustering Index.
In brown are the districts where 100% of its the census tracts have income below the global mean and are surrounded by other low income census tracts.
In light yellow are the districts where none of its census tracts were low income tracts surrounded by other low income tracts.
We think that for the purpose of this study, this new index may be a reasonably good spatial measure in the evenness/clustering dimension. 17

18. Dimensions of spatial segregation
EVENNESS
ISOLATION
EXPOSURE
Lets see now what did we get in the Isolation/exposure dimension
CLUSTERING
Dimensions of spatial segregation

19. Spatial Isolation Index Income >20 ms BW:400m
GLOBAL
Ŏ>20=0.228
p<0.01
LOCAL
The Spatial isolation Index was calculated for each income group, for each census tract.
This slide shows the results for the income group that earns more than 20 minimum salaries per month.
The global index was This means that where each member of this income group lives, about 23% of the population belongs to the same income group. And this number is statistically significant.
Looking to the local indexes in the map, we can observe that the census tracts were these rich people are more isolated are clustered around the central area of the city. Following the same pattern observed in the previous index (the spatial clustering index)

20. Spatial Isolation Indexes
Local
Spatial Isolation Indexes
Income Groups
BW:400m
ms: minimum salaries
>20 ms
10-20 ms
<2ms
2-5 ms
5-10 ms
And Here are the maps of local Spatial Isolation Index.
The richest people are isolated in the central area of the city.
It looks like an onion, several layers, each one representing the area where one income group lives, isolated from the others. The richest in the centre and the poorest in the distant suburbs.

21. INCOME Moran I Index: 0.65 ( ρ< 0.0001)
This is the distribution of the mean income of the head of the households, by districts.
Again we can observe that there is a spatial pattern. The rich people clustered around the central area of the city.
And what the map suggests, the Moran I Index confirms. There is a significant spatial autocorrelation.
This means that there is here a high degree of income inequality.
1 minimum salary 100 pounds per month
Em 2000, o salário mínimo era de R$151,00
O Dolar custava 1,8 reais
Logo o salario minimo era 84 dolares ou cerca de 50 libras
Mas o poder de compra (purchase power parity)  US$130,00 ou cerca de 80 Libras.
Mas considerando a inflacao da Libra, hoje essas 80 libras valeriam umas 100 Libras.
Moran I Index: 0.65 ( ρ< )
Distribution of income of the head of the household by district, Porto Alegre, 2000.
Source: IBGE

22. AGE AND SEX ADJUSTED MORTALITY RATE
Moran I Index: 0.34 ( ρ< )
Relative Index of Inequality: 1.8
Slope Index of Inequality: - 4.6
10.0
The total mortality rate figures are quite similar to the cardiovascular mortality rate.
The same significant spatial pattern.
The lower rates occurs in the districts where the rich people live.
The estimated rate of the worst district (the relative index of inequality) s almost 2 times greatter then estimated rate of the best one.
5.4
Distribution of age and sex adjusted mortality rate by district, Porto Alegre, Source: DATASUS-SIM

23. CARDIOVASCULAR DISEASES MORTALITY
45-64 YEARS
CVD Deaths by 100,000
Moran I Index: 0.52 ( ρ< )
Surprise:
The distribution of the premature cardiovascular mortality follows the same significant spatial pattern observed when we looked to distribution of the segregation index and to the income distribution.
The death rates are the lowest where the rich people live, around downtown, and the highest rates occurs where the poor live.
Distribution of age specific cardiovascular diseases mortality coefficient* , adjusted for age and sex, by district. Porto Alegre, Sources: IBGE and SIM * results after smoothing

25. Conclusions:
- Evidence for “triple health jeopardy”
- Being poor in a poor neighbourhood that is spatially isolated from life-enhancing opportunities is associated with higher mortality
- Socioeconomic segregation is an important spatial dimension of inequalities in health
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