OBJECTIVE: to develop an index of socioeconomic position based on the 2001 Census to characterise the census blocks in Rome. DESIGN AND SETTING: we considered the 4888 census blocks in Rome with at least 50 inhabitants (average population: 500 people). We considered census information that represented various dimensions of deprivation: education, occupation, housing tenure, family composition and immigration. We performed a factorial analysis, and created a composite index of socioeconomic position. We considered the quintiles of the distribution of census block in order to have a 5-level indicator. PARTICIPANTS: 2516666 subjects who were residents in Rome in October, 2001. RESULTS: four factors explained 84% of the variance. The first factor combined education, occupation and crowding and explained 49.5% of the variance. The second (14% of variance) represented immigration, the third (11.3%) family composition, and the fourth (9.1%) home ownership. High-level census blocks tended to have older residents, a higher employment rate, more students and retirees, and fewer part time employed residents than low census blocks. CONCLUSION: when individual measures of socioeconomic position are missing, small area indicators based on census data can be used as a proxy of individual indices. In addition, they describe all socioeconomic aspects of the area. This study provides an instrument that can be used in public health and epidemiology to evaluate socioeconomic differences in health.
OBJECTIVE: to develop an index of socioeconomic position based on the 2001 Census to characterise the census blocks in Rome. DESIGN AND SETTING: we considered the 4888 census blocks in Rome with at least 50 inhabitants (average population: 500 people). We considered census information that represented various dimensions of deprivation: education, occupation, housing tenure, family composition and immigration. We performed a factorial analysis, and created a composite index of socioeconomic position. We considered the quintiles of the distribution of census block in order to have a 5-level indicator. PARTICIPANTS: 2516666 subjects who were residents in Rome in October, 2001. RESULTS: four factors explained 84% of the variance. The first factor combined education, occupation and crowding and explained 49.5% of the variance. The second (14% of variance) represented immigration, the third (11.3%) family composition, and the fourth (9.1%) home ownership. High-level census blocks tended to have older residents, a higher employment rate, more students and retirees, and fewer part time employed residents than low census blocks. CONCLUSION: when individual measures of socioeconomic position are missing, small area indicators based on census data can be used as a proxy of individual indices. In addition, they describe all socioeconomic aspects of the area. This study provides an instrument that can be used in public health and epidemiology to evaluate socioeconomic differences in health.
Authors: Patrizia Schifano; Giovanna Cappai; Manuela De Sario; Paola Michelozzi; Claudia Marino; Anna Maria Bargagli; Carlo A Perucci Journal: Environ Health Date: 2009-11-12 Impact factor: 5.984
Authors: Paola Michelozzi; Francesca K de' Donato; Anna Maria Bargagli; Daniela D'Ippoliti; Manuela De Sario; Claudia Marino; Patrizia Schifano; Giovanna Cappai; Michela Leone; Ursula Kirchmayer; Martina Ventura; Marta di Gennaro; Marco Leonardi; Fabrizio Oleari; Annamaria De Martino; Carlo A Perucci Journal: Int J Environ Res Public Health Date: 2010-05-06 Impact factor: 3.390
Authors: Giulia Cesaroni; Chiara Badaloni; Valeria Romano; Eugenio Donato; Carlo A Perucci; Francesco Forastiere Journal: Environ Health Date: 2010-07-21 Impact factor: 5.984
Authors: Patrizia Schifano; Chiara Marinacci; Giulia Cesaroni; Valeria Belleudi; Nicola Caranci; Antonio Russo; Carlo A Perucci Journal: Int J Equity Health Date: 2009-09-17