Results

Through the principal component analysis, the original seven socioeconomic variables were reduced to two new factors. The two factors accounted for 90% of the original variability in the data. Table 1 shows the description of the new components in terms of the original variable loads. The variables that most influenced the definition of factor 1 were all related to socioeconomic standards (income, education and income distribution), while factor 2 was basically defined by the “density of slum residents”.

The factor scores provided summary indices of the AR socioeconomic conditions by which to derive clusters of ARs. Three clusters were chosen as a natural grouping on the basis of the two new dimensions that accounted for almost all socioeconomic variation. Figure 1 shows the three selected clusters where each AR is represented by its corresponding number.

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Figure 1

Administrative Region Factor Scores. Municipality of Rio de Janeiro, 1991.  

Figure 2 shows the map of the municipality of Rio de Janeiro. The three selected clusters that reflect the socioeconomic variation of the AR data were represented by different gray tones. It is worth noting that the socioeconomic structure determined a clear geographical division in the city. The light gray area represents the wealthiest sector of the city. The districts that compose this cluster are located along the coast or nearby, so it was denominated as “Coastal Sector”. The dark gray cluster represents the worst sector of the city in terms of living conditions. It evidenced not only low socioeconomic standards (as measured by factor 1), but also the highest concentration of slum residents. This sector is composed of the harbour AR and six other ARs in the northern vicinity and was denominated “Harbour Sector”. The medium gray cluster borders other municipalities located in the “Metropolitan Belt” of the State of Rio de Janeiro. As it is situated in the west, this area was denominated as the “West Sector”. This cluster also concentrated low income population but did not show a high density of slum residents. The concentration of slum residents in the dark gray sector of the city can be better visualised by the two different layers of the map (selected clusters and locations of slums).

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Figure 2

Selected clusters and locations of slums. Municipality of Rio de Janeiro, 1991.

From the health indicator thematic maps, it can be observed that the geographical patterns of health inequality followed the same spatial pattern determined by the socioeconomic structure of the AR-based data. A cluster of unfavourable health conditions could be identified in the maps corresponding to the homicide rate, the standardised mortality rate and life expectancy at birth. As an example, the thematic map for the homicide rate is displayed in figure 3. The worst health situation was established precisely in the sector that concentrates very poor living standards and the highest density of slum residents (Harbour Sector).

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Figure 3

Geographical distribution of the homicide rate (per 100 000 population) among men aged 15–29 years. Municipality of Rio de Janeiro, 1991.

The infant mortality rate showed a somewhat different spatial pattern. Although the lowest values were concentrated in the wealthiest sector of the city, an apparently random distribution of high values was found among the ARs located in the West and Harbour sectors (fig4). The inadequacy of the newborn assistance care can be visualised by the second layer of the map, which represents the AR number of neonatal intensive care beds.

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Figure 4

Geographical distribution of the infant mortality rate (per 1000 live births) and number of neonatal intensive care beds. Municipality of Rio de Janeiro, 1991.

Descriptive statistics of all variables used in this study in each of the three clusters are summarised in table 2. Although the quantitative description confirms the geographical characterisation, some results should be highlighted. The Harbour Sector showed: the most unbalanced income distribution with an average income of the top 10% household heads 29 times higher than the bottom 40%; the worst health situation as measured by all health indicators; an extremely high value of the homicide rate, greater than 200 per 100 000 population; a nine year difference in the life expectancy at birth compared with the wealthiest sector.

Discussion

In the past few decades, Brazil has experienced a significant increase in income inequality. Actually, according to the World Bank Report,20 Brazil has one of the most unbalanced income distributions of the world.

Urbanisation along with rural migration and the rising income inequality have produced a geographical concentration of poverty, particularly in the metropolitan areas of the country. Specifically in São Paulo and Rio de Janeiro, the two main cities of Brazil, the number of slums has grown sharply over the past 20 years.

The geographical analysis presented in this paper identified a socioeconomic stratification of the city of Rio de Janeiro by place of residence. The socioeconomic structure, basically oriented by socioeconomic standards and concentration of low income communities, determined a clear geographical division in the city.

With respect to the geographical and socioeconomic stratification, some aspects should be considered particularly. Firstly, it is important to note that the principal component analysis distinguished “slum residence” from “socioeconomic status”, generating two independent factors despite the fact that slums are also composed of low income residents. Secondly, the worst health situation was found precisely in the sector with the greatest concentration of slums. Therefore, this study evidenced not only a pronounced class geographical segregation but also that the health differentials followed exactly the same segregation pattern.

Growing literature in recent years suggests that features of the environments in which people live may exert an important influence on health deterioration or health promotion.7 21-23 It is currently believed that the spatial health contrasts may not be simply attributable to the within area concentration of people with adverse socioeconomic conditions. That is, the residential environment cannot be simply considered as a compositional effect but rather as a contextual effect.24

The evidence of relevant health differences in a specific sector of the city of Rio de Janeiro supports the hypothesis that properties of the environment of residence may be contextually influencing health. We specifically note that the “Harbour” and “West” sectors are both composed of low income residents but have great variations in health indicators.

The deterioration of health conditions in the sector of Rio de Janeiro that aggregates the highest proportion of slum residents suggests that people living in situations characterised by social disorganisation and poverty are at increased risk. These situations have been described in terms of rupture of social cohesion and absence of resources available to people living in such environments.7 10 25 It is believed that social cohesion deteriorates within neighbourhoods according to their degree of relative deprivation in relation to the wider society.26 Relative deprivation results in frustration, stress, family and social disruption, which in turn increase violence, crime rates, drug misuse, alcoholism and other social outcomes likely to adversely affect health.10

There is a considerable literature linking social integration to health outcomes.25-28 Results have demonstrated that social and community ties may benefit the socialisation of urban low income community residents, bringing them in as participating members of the larger society.28 Sampson et al 11 have shown that the association between concentration disadvantage and violent crime may be mediated by the level of collective efficacy, measured by the resident willingness to intervene on common goods.

The growing number of homicides in the informal communities of the city of Rio de Janeiro is known to be linked to organised criminal activities related to the drug traffic. It is believed that low income youth are easily seduced by the immediate offers of money and leadership positions as a consequence of the increased levels of frustration brought about the deprivation of social goods and lack of opportunities of personal growth. Involved in the dispute of drug trafficking control points, they end up killing each other and dying very young.29

Regarding the infant mortality rate, a specific pattern was found, different from the other health indicators under analysis. The variations in the infant mortality geographical distribution are probably attributable to the different behaviours of its components, the neonatal (under 28 days) and postneonatal mortality (28 days to 1 year). Actually, the main component of the infant mortality in Rio de Janeiro is the neonatal mortality, which is related to broad socioeconomic conditions, but particularly to the access to health care facilities and to the quality of perinatal care.30

Some limitations of the present investigation should be pointed out. Although the completeness of death registration in the State of Rio de Janeiro is known to be approximately 97%,31 the study is based on secondary data, which may have problems of enumeration and classification. For instance, homicide rates are believed to be higher than the presented ones as homicide deaths for which intention cannot be proved are classified as “firearm injuries ignored if intentionally caused” (ICD E985). Even knowing that these misclassifications could affect the results, a proper assumption is that they occur mostly in the poorest areas. In this sense, eventual corrections of the data would probably intensify even more the contrasts depicted here.

Another limitation refers to the geographical unit of analysis—that is, the AR, which is known to be quite heterogeneous. None the less, it is the smallest area for which mortality data are available. Furthermore, the study must be complemented by longitudinal analyses, investigating the geographical expansion of low income communities in the late years, along with the time trend of the health indicators.

Conclusions

GIS is a powerful tool to assess multilevel phenomena because of their capacity to use different levels of data aggregation depicted as distinct graphic layers. As indicated by the WHO's Healthy Cities project and network,32 the integration of social and health information from different sources, especially when it includes the analysis of small areas indicators, constitutes a core strategy to provide sound data for health analysts and planners.

In this investigation, by explicitly linking health outcomes to sociophysical variables, the GIS allowed us to reorient our explanations for health differentials that the traditional statistical methods would not be capable of capturing. The geoepidemiological analysis provided evidence that advocates in favour of local programmes of health promotion, which consider features of the communities in addition to individual features.

Although the available slum programmes are clearly limited, as most of them are deficiently funded and staffed, some successful initiatives must be mentioned. Created in the beginning of 1994, the “Favela-Bairro Program” is a City Hall programme that aims to change the medium size “favelas” into low income districts with basic infrastructure services such as pluvial water drainage, lighting and public rubbish collection.33

However, the main problem of the “favelas” is the appropriation of these low income areas by drug traffic organisations. For the slum young residents, drug trafficking means a job and the only way of meeting some of their needs. Lack of formal employment opportunities for an unprepared youth certainly contributes to increase involvement of young slum residents in this activity.34

Professional education, health and other social programmes specifically targeting these youth, including programmes to reduce the harmful effects of relative deprivation and efforts to strengthen social ties, may have an important impact on underprivileged youth survival. A well established programme in the “Favela da Mangueira” engages children and teenagers in athletic activities, which promotes socialisation through education and entertainment.

The government, in its different levels and competence, the private companies, the community leaderships, and the society, in general, have their share in the pressing need of improvement of this unbearable social context. In this sense, strategies of shared management could enlarge the possibilities of execution of effective and enduring actions, mainly in regard to projects turned to social development and integration of the slums into the city and the society at large. The creation of urban ghettos can only contribute towards the intensification of mutual social mistrust, poor health conditions and violence.