1. Urbanisation and spatial inequalities in health in Brazil and India Tarani ChandolaUniversity of Manchester Sergio BassanesiUFRGS - Universidade Federal Sitamma MikkilineniIndian Institute of Public Health, Souvik BandyopadhyayHyderabad Anil Chandran

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

3. Life expectancy and income inequality: Brazil, 2000

4. 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

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

6. 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

7. SPATIAL EXPOSURE INDEX SPATIAL ISOLATION INDEX Average proportion of group n in the localities of each member of group m Average proportion of group m in the local environments of each member of group m (spatial exposure of group m to itself) EXPOSURE/ISOLATION DIMENSION

8. 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 EVENNESS/ CLUSTERING DIMENSION GENERALIZED SPATIAL DISSIMILARITY INDEX Average difference of the population composition of the localities from the population composition of the urban area as a whole

9. Key hypotheses: Districts, cities and states with less spatial socioeconomic inequalities have better population health than areas with greater spatial socioeconomic inequalities For a given level of income/socioeconomic position, people living in areas with less spatial socioeconomic inequalities have better health than those living in more segregated areas. Methods: Brazil Data (for the 25 largest cities): Demographic and Socioeconomic data: 2000 Census (census tract level) Mortality data: SIM Mortality Information System (district level data) India Data: Demographic and Socioeconomic data: 2001 census (sub-district Tehsil level) Mortality data: District Level Household and Facilities Survey 2002-04 and 2007-08 (Individual and district level)

10. Dimensions of spatial segregation EVENNESS CLUSTERING EXPOSURE ISOLATION

11. Spatial CLUSTERING INDEX Moran Scatter Plot SLOPE OF THE REGRESSION LINE Spatially lagged variable Variable to be lagged, standardized Moran Cluster Map

12. 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.

13. Dimensions of spatial segregation EVENNESS CLUSTERING EXPOSURE ISOLATION

14. Spatial Isolation Index Income >20 ms BW:400m LOCAL GLOBAL Ŏ >20 =0.228 p<0.01

15. Local Spatial Isolation Indexes Income Groups BW:400m ms: minimum salaries >20 ms 10-20 ms 5-10 ms<2ms 2-5 ms

16. 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

17. Distribution of age and sex adjusted mortality rate by district, Porto Alegre, 2000. Source: DATASUS-SIM 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 5.4 10.0

18. CARDIOVASCULAR DISEASES MORTALITY 45-64 YEARS CVD Deaths by 100,000 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 Moran I Index: 0.52 ( ρ< 0.0001)

19. Independent variables Dependent variables Standardized B coefficients and (R 2 ) Income groups Isolation indexes Total mortality Premature CV mortality External causes mortality Pulmonary tuberculosis incidence Without income0.28* (0.08) 0.26 * (0.07) 0.35* (0.12) 0.45** (0.20) With income to < 2 ms0.36 * (0.13) 0.37* (0.11) 0.42 ** (0.17) 0.52** (0.27) 2 to < 5 ms0.19 (0.04) 0.18 (0.03) 0.22 (0.05) 0.30* (0.09) 5 to < 10 ms- 0.16 (0.03) - 0.19 (0.04) - 0.21 (0.04) - 0.13 (0.02) 10 to < 20 ms- 0.41** (0.17) - 0.44** (0.19) - 0.46** (0.21) - 0.37* (0.13) 20 or more ms- 0.53** (0.28) - 0.52** (0.27) - 0.53** (0.28) - 0.47** (0.22) * Significant p<0.05 ** Significant p<0.001 ms: minimum salaries/month Band Width: 400 m Isolation indexes Simple Linear Regression

20. Independent variables Dependent variables Standardized B coefficients and (R 2 ) Income groups Exposure indexes Total mortality Premature CV mortality External causes mortality Pulmonary tuberculosis incidence >0 to <2 ms No income 0.31* (0.09) 0.29* (0.08) 0.38* (0.15) 0.49** (0.24) 2 to <5 ms < 2 ms 0.28* (0.08) 0.26* (0.07) 0.33* (0.11) 0.43** (0.19) 10 to <20 ms ≥ 20 ms- 0.52** (0.27) - 0.53** (0.28) - 0.54** (0.29) - 0.46** (0.21) 5 to <10 ms ≥ 10 ms- 0.41** (0.17) -0.44** (0.19) - 0.45** (0.21) - 0.36* (0.13) * Significant p≤0.05 ** Significant p ≤ 0.001 Band Width: 400 m Spearman Correlation Coefficient Exposure indexes Simple Linear Regression Tuberculosis Spatial Exposure Index >0 to <2 ms No income 2 to <5 ms < 2 ms 10 to <20 MS ≥ 20 ms 5 to <10 ms ≥ 10 ms 0.698** 0.679** -0.634** -0.488** Average proportion of group n in the localities of each member of group m Tuberculosis

21. Independent variable Dependent variables Spatial CLUSTERING INDEX Total mortality Premature CV mortality External causes mortality Pulmonary tuberculosis incidence Standardized B0.65**0.63**0.64**0.68** R 2 0.420.390.410.46 Scattergram ** Significant p ≤ 0.01 CLUSTERING INDEX Simple Linear Regression Clustering Index

22. Dependent variables Standardized B coefficients and R 2 Independent variables Total mortality Premature CV mortality External causes mortality Pulmonary tuberculosis incidence Mean Income - 0.40**- 0.30*- 0.31*- 0.33* Clustering Index 0.33*0.39*0.41**0.42** R 2 47.742.645.049,8 Mean Income - 0.54**- 0.46**- 0.47**- 0.59** Isolation Index 10 or more ms - 0.21- 0.26*- 0.27*- 0.12 R 2 46.541.543.844.1 Mean Income - 0.59**- 0.52**- 0.53**- 0.60** Exposition Index 5 to <10 ms ≥ 10 ms - 0.22*- 0.27*- 0.28**- 0.17 R 2 48.043.346.045,6 * Significant p<0.05 ** Significant p<0.01 ms: minimum salaries/month Linear Regression

23. Next steps: Brazil: Obtain and analyse data for other Brazilian cities India: Analyse DLHS-3 data in a multilevel and spatial context Workshops on Spatial and Multilevel Analysis: Brazil: May 18-20 2010 India: June 2-4 2010