STUDY OBJECTIVES—To establish the geographical relation of health conditions to socioeconomic status in the city of Rio de Janeiro, Brazil.
DESIGN—All reported deaths in the municipality of Rio de Janeiro, from 1987 to 1995, obtained from the Mortality Information System, were considered in the study. The 24 "administrative regions" that compose the city were used as the geographical units. A geographical information system (GIS) was used to link mortality data and population census data, and allowed the authors to establish the geographical pattern of the health indicators considered in this study: "infant mortality rate"; "standardised mortality rate"; "life expectancy" and "homicide rate". Information on location of low income communities (slums) was also provided by the GIS. A varimax rotation principal component analysis combined information on socioeconomic conditions and provided a two dimension basis to assess contextual variation.
MAIN RESULTS—The 24 administrative regions were aggregated into three different clusters, identified as relevant to reflect the socioeconomic variation. Almost all health indicator thematic maps showed the same socioeconomic stratification pattern. The worst health situation was found in the cluster composed of the harbour area and northern vicinity, precisely in the sector where the highest concentration of slum residents are present. This sector of the city exhibited an extremely high homicide rate and a seven year lower life expectancy than the remainder of the city. The sector that concentrates affluence, composed of the geographical units located along the coast, showed the best health situation. Intermediate health conditions were found in the west area, which also has poor living standards but low concentration of slums.
CONCLUSIONS—The findings suggest that social and organisation characteristics of low income communities may have a relevant role in understanding health variations. Local health and other social programmes specifically targeting these communities are recommended.


Keywords: geographical information system; health conditions; low income communities