OBJECTIVE: Compare the infant mortality rate due to congenital malformations ( IMRCM) and the percentage of deaths due to congenital malformations (%DCM) with sociodemographic and economic characteristics in Argentina. METHODS: The Argentine study population resided in 511 departments of 23 provinces, grouped into five geographic regions (Northwest, Northeast, Central, Cuyo, and Patagonia). The analyzed variables were the IMRCM and the %DCM calculated on the basis of births and deaths during 2002-2006 period. In addition, 21 variables were used from the 2001 Population and Housing Census (National Census and Statistics Institute of Argentina) to construct the Sociodemographic and Economic Indicator (SDEI) through the analysis of principal components. Comparison tests were carried out in order to assess the significant differences among the various regions and the correlations between indicators, and of these with the departmental latitudes and longitudes. RESULTS: There was no significant correlation between the IMRCM and the SDEI, nor with geographic coordinates. However, there was a significant positive correlation between the IMRCM and the SDEI (P < 0.05) at all levels of political organization. The SDEI explained 41% of the %DCM. CONCLUSIONS: The IMRCM was not significantly associated with the country's marked socioeconomic heterogeneity; the highest %DCM values, on the other hand, were observed in the populations of the central and southern areas of the country. Given the relationship between the %DCM and socioeconomic development of the population, use of this indicator as a proxy of well-being and quality of life is suggested.
OBJECTIVE: Compare the infant mortality rate due to congenital malformations ( IMRCM) and the percentage of deaths due to congenital malformations (%DCM) with sociodemographic and economic characteristics in Argentina. METHODS: The Argentine study population resided in 511 departments of 23 provinces, grouped into five geographic regions (Northwest, Northeast, Central, Cuyo, and Patagonia). The analyzed variables were the IMRCM and the %DCM calculated on the basis of births and deaths during 2002-2006 period. In addition, 21 variables were used from the 2001 Population and Housing Census (National Census and Statistics Institute of Argentina) to construct the Sociodemographic and Economic Indicator (SDEI) through the analysis of principal components. Comparison tests were carried out in order to assess the significant differences among the various regions and the correlations between indicators, and of these with the departmental latitudes and longitudes. RESULTS: There was no significant correlation between the IMRCM and the SDEI, nor with geographic coordinates. However, there was a significant positive correlation between the IMRCM and the SDEI (P < 0.05) at all levels of political organization. The SDEI explained 41% of the %DCM. CONCLUSIONS: The IMRCM was not significantly associated with the country's marked socioeconomic heterogeneity; the highest %DCM values, on the other hand, were observed in the populations of the central and southern areas of the country. Given the relationship between the %DCM and socioeconomic development of the population, use of this indicator as a proxy of well-being and quality of life is suggested.