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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.

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urbanisation and spatial inequalities in health in brazil and india

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, SouvikBandyopadhyay Hyderabad

Anil Chandran

An ESRC pathfinder project

slide3

 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

slide4

Most

deprived

www.equalitytrust.org.uk

Health is related to income differences within rich societies but not to those between them

Between (rich) societies

Within societies

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

slide5

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
slide7

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

slide8

Calculating isolation index of segregationpi = 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.

slide10

EVENNESS

ISOLATION

EXPOSURE

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

slide11

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.

slide12

EXPOSURE/ISOLATION DIMENSION

SPATIAL EXPOSURE INDEX

Average proportion of group n in the localities of each member of group m

SPATIAL ISOLATION INDEX

Average proportion of group m in the local environments of each member of group m (spatial exposure of group m to itself)

slide13

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

Average difference of the population composition of the localities from the population composition of the urban area as a whole

slide14

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)
slide15

EVENNESS

EXPOSURE

ISOLATION

CLUSTERING

Dimensions of spatial segregation

slide16

SpatialCLUSTERING INDEX

Moran Cluster Map

Moran Scatter Plot

SLOPE OF THE REGRESSION LINE

Spatially lagged variable

Variable to be lagged, standardized

slide17

SpatialCLUSTERING 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.

slide18

EVENNESS

ISOLATION

EXPOSURE

CLUSTERING

Dimensions of spatial segregation

slide20

10-20 ms

Local

Spatial Isolation Indexes

Income Groups

BW:400m

ms: minimum salaries

>20 ms

2-5 ms

<2ms

5-10 ms

slide21

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

slide22

AGE AND SEX ADJUSTED MORTALITY RATE

10.0

5.4

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

slide23

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

slide25

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

An ESRC pathfinder project