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. An ESRC pathfinder project

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.

 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;61:802-809 ©2007 by BMJ Publishing Group Ltd

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

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

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

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.

Dimensions of spatial segregation

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. 121-162, 2004

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.

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)

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

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 10-15 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: 2001 census (urban ward level) Mortality data: District Level Household and Facilities Survey 2002-04 and 2007-08 (Individual, neighbourhood and urban ward level)

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

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

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

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

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 0.23. 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)

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.

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< 0.0001) Distribution of income of the head of the household by district, Porto Alegre, 2000. Source: IBGE

AGE AND SEX ADJUSTED MORTALITY RATE Moran I Index: 0.34 ( ρ< 0.0001) 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, 2000. Source: DATASUS-SIM

CARDIOVASCULAR DISEASES MORTALITY 45-64 YEARS CVD Deaths by 100,000 Moran I Index: 0.52 ( ρ< 0.0001) 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, 2000-2004. Sources: IBGE and SIM * results after smoothing

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 For futher information: http://www.ccsr.ac.uk/staff/tc.htm http://www.ccsr.ac.uk/staff/tc.htm An ESRC pathfinder project