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Social and Environmental Influences on Child Mortality in Brazil: Logistic Regression Analysis of Data from Census Files

Published online by Cambridge University Press:  31 July 2008

Cesar G. Victora
Affiliation:
Evaluation and Planning Centre, London School of Hygiene and Tropical Medicine Department of Social Medicine, Federal University of Pelotas, Brazil
Peter G. Smith
Affiliation:
Tropical Epidemiology Unit, London School of Hygiene and Tropical Medicine
J. Patrick Vaughan
Affiliation:
Evaluation and Planning Centre, London School of Hygiene and Tropical Medicine

Summary

Census data were used to investigate the influences of socioeconomic and environmental variables on child mortality rates in southern Brazil. By multivariate logistic regression analysis the effects of correlated factors were distinguished, after adjustment for maternal age and parity. Low family income and, to a lesser degree, low employment status of the head of the family were associated with high child mortality levels. Place of residence, education of the mother and of the head of the family, availability of piped water in the home, access to a toilet and type of housing were all associated with childhood mortality variation, even after allowing for the effects of income and employment. The contributions of the source of the water supply and type of sanitation facilities, however, were less clear and tended to become unimportant after controlling for the socioeconomic variables. There was also no apparent advantage in being covered by government health insurance.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1986

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References

Baker, R.G. & Nelder, J.A. (1978) The GLIM System. Release 3, pp. 7.114.7. Numerical Algorithms Group, Oxford.Google Scholar
Behm, H. (1979) Socioeconomic determinants of mortality in Latin America. In: Proceedings of the Meeting on Socioeconomic Determinants and Consequences of Mortality, pp. 140165. United Nations, New York.Google Scholar
Brass, W. (1975) Methods for Estimating Fertility and Mortality from Limited and Defective Data, pp. 5059. Poplab, Chapel Hill, NC.Google Scholar
Breslow, N.E. & Day, N.E. (1980) The Analysis of Case Control Studies, pp. 192247. (Statistical Methods in Cancer Research, Vol. 1). International Agency for Research on Cancer, Lyon.Google Scholar
Carvalho, J.A.M. & Wood, C.H. (1978) Mortality, income distribution, and rural-urban residence in Brazil. Popul. Dev. Rev. 4, 405.CrossRefGoogle Scholar
IBGE (1980) Amostra de 1% dos Registros do Censo Demografico de 1970. Manual do Usuario, pp. 178. Serie Estudos e Pesquisas no. 5. IBGE, Rio de Janeiro.Google Scholar
IBGE (1981) O Quadro da Mortalidade por Classes de Renda. Um Estudo de Diferenciais nas Regioes Metropolitanas, pp. 154. Serie Ustudos e Pesquisas no. 9. IBGE, Rio de Janeiro.Google Scholar
IBGE (1982) Censo Demografico de 1980. Amostra das Tabulacoes Avancadas. Manual do Usuario, pp. 180. IBGE, Rio de Janeiro.Google Scholar
Schlesselman, J.J. (1982) Case-control Studies. Design, Conduct, Analysis, pp. 227290. Oxford University Press, New York.Google Scholar
Vetter, D.M. & Simoes, CCS. (1980) Acesso a infraestrutura de saneamento basico e mortalidade. Boletim Demografico IBGE, 10, 4.Google Scholar
Victora, C.G. (1983) The Epidemiology of Child Health in Southern Brazil. The Relationships Between Mortality, Malnutrition, Health Care and Agricultural Development. PhD thesis, University of London.Google Scholar