Spatial segregation and socioeconomic inequalities in health in major Brazilian cities. An ESRC pathfinder project

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1 Spatial segregation and socioeconomic inequalities in health in major Brazilian cities An ESRC pathfinder project

2 Income per head and life-expectancy: rich & poor countries Source: Wilkinson & Pickett, The Spirit Level (2009)

3 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:

4 Life expectancy and income inequality: Brazil, 2000

5 Increasing urbanisation in developing countries

6 Share of slum population in urban areas in selected Asian and Pacific countries 1990 and

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

8 At the same time, the absolute number of slum dwellers around the world is still rising

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10 Most slum households in Brazil live in slum neighbourhoods

11 Brazil has a low Human Opportunity Index compared to other Latin American countries with similar GDP per capita 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)

12 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

13 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

14 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.

15 EVENNESS ISOLATION EXPOSURE CLUSTERING Dimensions of spatial segregation Sean F. Reardon & David O'Sullivan. Measures of Spatial Segregation V. 34, n.1, p , 2004 Sociological Methodology.

16 Dimensions of spatial segregation EVENNESS ISOLATION EXPOSURE CLUSTERING

17 EXPOSURE/ISOLATION DIMENSION SPATIAL EXPOSURE INDEX SPATIAL ISOLATION INDEX 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, p ,

18 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.

19 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

20 Brazilian regions, states and selected cities North Teresina Northeast Natal João Pessoa Recife Aracaju Central-West Campo Grande Brasília Rio de Janeiro Curitiba Southeast Porto Alegre South

21 Isolation Index Spatial Isolation Index Income groups

22 EVENNESS ISOLATION EXPOSURE CLUSTERING Dimensions of spatial segregation

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

24 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.

25 EVENNESS ISOLATION EXPOSURE CLUSTERING Dimensions of spatial segregation

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

27 INCOME Moran I Index: 0.65 ( ρ< ) Distribution of income of the head of the household by district, Porto Alegre, Source: IBGE

28 Outcome variable: Standardised Mortality Rates (age and sex adjusted) for 861 districts within 15 Brazilian cities

29 Scatterplot of Standardised Mortality Rates by Poverty Rate in Brazilian Districts

30 Scatterplot of Spatial Clustering by Poverty Rate in Brazilian Districts

31 Scatterplot of Spatial Isolation of the poor by Poverty Rate in Brazilian Districts

32 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

33 Multilevel Multiple Membership Model of Mortality Rate: Variance Components Model

34 Predicted SMR by Spatial Clustering Index and Region South/South East and Central West Regions Restinga, Porto Alegre SMR North East Region Ilha Joana Bezerra, Recife Northern Region Paracuri, Belém Spatial clustering Index Adjusted for Population Size and Poverty Rate in the District

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38 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

39 Slum clearances in Rio de Janeiro in preparation for the Olympics

40 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.