An Ecological Study to Identify Census Blocks Supporting a Higher Burden of Disease: Infant Mortality in the Lille Metropolitan Area, France
- 283 Downloads
In France, reducing social health inequalities has become an explicit goal of health policies over the past few years, one of its objectives is specifically the reduction of the perinatal mortality rate. This study investigates the association between infant mortality and social deprivation categories at a small area level in the Lille metropolitan area, in the north of France, to identify census blocks where public authorities should prioritize appropriate preventive actions. We used census data to establish a neighbourhood deprivation index whose multiple dimensions encompass socioeconomic characteristics. Infant mortality data were obtained from the Lille metropolitan area municipalities to estimate a death rate at the census tract level. We used Bayesian hierarchical models in order to reduce the extra variability when computing relative risks (RR) and to assess the associations between infant mortality and deprivation. Between 2000 and 2009, 668 cases of infant death occurred in the Lille metropolitan area (4.2 per 1,000 live births). The socioeconomic status is associated with infant mortality, with a clear gradient of risk from the most privileged census blocks to the most deprived ones (RR = 2.62, 95 % confidence interval [1.87; 3.70]). The latter have 24.6 % of families who were single parents and 29.9 % of unemployed people in the labor force versus 8.5 % and 7.7 % in the former. Our study reveals socio-spatial disparities in infant mortality in the Lille metropolitan area and highlights the census blocks most affected by the inequalities. Fine spatial analysis may help inform the design of preventive policies tailored to the characteristics of the local communities.
KeywordsInfant mortality Small-area analysis Social health inequalities Census tracts Deprivation index Bayesian models
The authors thank all scientific researchers of the Equit Area project, personnel of the local council of Lille Metropolitan area and Grégoire Rey of the CepiDC institute for their participation. Equit’Area project was supported by the French National Research Agency (ANR), the EHESP School of Public Health, the French Health General Directorate (DGS), the Environment and Energy Management Agency (ADEME) and the Nord-Pas de Calais region.
Conflict of interest
- 1.Organisation for Economic Co-operation and Development (OECD) (2008). Infant mortality rates in OCDE countries 2008. Available: http://www.oecd.org/dataoecd/30/54/43136879.xls; 20-6-2010.
- 2.The National Institute of Statistics and Economic Studies (INSEE). Infant mortality rate in France metropolitan. Available: http://www.indices.insee.fr/bsweb/servlet/bsweb?action=BS_SERIE&BS_IDBANK=000436398&BS_IDARBO=01000000000000; 2011.
- 3.Moser, K., Macfarlane, A., Chow, Y. H., Hilder, L., & Dattani, N. (2007). Introducing new data on gestation-specific infant mortality among babies born in 2005 in England and Wales. Health Statistics Quarterly, 35, 13–27.Google Scholar
- 4.Euro-peristat project. (2010). European perinatal health report 2008. Available: http://www.europeristat.com; 2010.
- 7.Gray, R., Bonellie, S. R., Chalmers, J., Greer, I., Jarvis, S., Kurinczuk, J. J., et al. (2009). Contribution of smoking during pregnancy to inequalities in stillbirth and infant death in Scotland 1994–2003: Retrospective population based study using hospital maternity records. British Medical Journal, 1(339), b3754. doi: 10.1136/bmj.b3754.CrossRefGoogle Scholar
- 11.Lopez-Azpiazu, I., Sanchez-Villegas, A., Johansson, L., Petkeviciene, J., Prattala, R., & Martinez-Gonzalez, M. A. (2003). Disparities in food habits in Europe: systematic review of educational and occupational differences in the intake of fat. Journal of Human Nutrition & Dietetics, 16(5), 349–364.CrossRefGoogle Scholar
- 12.Irala-Estevez, J. D., Groth, M., Johansson, L., Oltersdorf, U., Prattala, R., & Martinez-Gonzalez, M. A. (2000). A systematic review of socio-economic differences in food habits in Europe: consumption of fruit and vegetables. European Journal of Clinical Nutrition, 54(9), 706–714.CrossRefGoogle Scholar
- 16.Krieger, N., Chen, J. T., Waterman, P. D., Soobader, M. J., Subramanian, S. V., & Carson, R. (2003). Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: The public health disparities geocoding project (US). Journal of Epidemiology and Community Health, 57(3), 186–199.CrossRefGoogle Scholar
- 26.Lawson, A. (2006). Statistical methods in spatial epidemiology. (2nd edn). New Jersey.Google Scholar
- 28.Elliott, P., Wakefield, J., & Best, N. (2000). Spatial epidemiology: Methods and applications. Oxford: Oxford University Press ed.Google Scholar
- 30.Besag, J., York, J., Mollié, A. Bayesian image restoration, with two applications in spatial statistics.Google Scholar
- 32.Brooks, S., & Gelman, A. (1998). A alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434–455.Google Scholar
- 40.Nkansah-Amankra, S., Dhawain, A., Hussey, J. R., & Luchok, K. J. (2010). Maternal social support and neighborhood income inequality as predictors of low birth weight and preterm birth outcome disparities: Analysis of South Carolina pregnancy risk assessment and monitoring system survey, 2000–2003. Maternal and Child Health Journal, 14(5), 774–785.CrossRefGoogle Scholar