Journal ArticleDOI
Bayesian factor analysis to calculate a deprivation index and its uncertainty.
Marc Marí-DellʼOlmo,Miguel A. Martinez-Beneito,Carme Borrell,Oscar Zurriaga,Andreu Nolasco,M. Felicitas Domínguez-Berjón +5 more
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TLDR
This work applies a cross-sectional ecological design to analyze the census tracts of 3 Spanish cities to calculate the deprivation index, which was estimated by a Bayesian factor analysis using hierarchical models, which takes the spatial dependence of the study units into account.Abstract:
Background: Procedures for calculating deprivation indices in epidemiologic studies often show some common problems because the spatial dependence between units of analysis and uncertainty of the estimates is not usually accounted for. This work highlights these problems and illustrates how spatial factor Bayesian modeling could alleviate them. Methods: This study applies a cross-sectional ecological design to analyze the census tracts of 3 Spanish cities. To calculate the deprivation index, we used 5 socioeconomic indicators that comprise the deprivation index calculated in the MEDEA project. The deprivation index was estimated by a Bayesian factor analysis using hierarchical models, which takes the spatial dependence of the study units into account. We studied the relationship between this index and the one obtained using principal component analysis. Various analyses were carried out to assess the uncertainty obtained in the index. Results: A high correlation was observed between the index obtained and the non-Bayesian index, but this relationship is not linear and there is disagreement between the methods when the areas are grouped according to quantiles. When the deprivation index is calculated using summary statistics based on the posterior distributions, the uncertainty of the index in each census tract is not taken into account. Failure to take this uncertainty into account may result in misclassification bias in the census tracts when these are grouped according to quantiles of the deprivation index. Conclusions: Not taking uncertainty into account may result in misclassification bias in the census tracts. This bias could interfere in subsequent analyses that include the deprivation index. Our proposal provides another tool for identifying groups with greater deprivation and for improving decision-making for public policy planning.read more
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Journal ArticleDOI
Construction of an adaptable European transnational ecological deprivation index: the French version
Carole Pornet,Cyrille Delpierre,Olivier Dejardin,Olivier Dejardin,Pascale Grosclaude,Ludivine Launay,Ludivine Launay,Lydia Guittet,Lydia Guittet,Thierry Lang,Guy Launoy,Guy Launoy +11 more
TL;DR: A method for constructing a French European deprivation index, which will be replicable in several European countries and is related to an individual deprivation indicator constructed from a European survey specifically designed to study deprivation, which could be replicated in 25 other European countries, thereby allowing European comparisons.
Journal ArticleDOI
A Swiss neighbourhood index of socioeconomic position: development and association with mortality
Radoslaw Panczak,Bruna Galobardes,Marieke Voorpostel,Adrian Spoerri,Marcel Zwahlen,Matthias Egger,Matthias Egger +6 more
TL;DR: The Swiss-SEP index was strongly associated with household income and some causes of death, and was useful for clinical- and population-based studies, where individual-level socioeconomic data is often missing, and to investigate the effects on health of the socioeconomic characteristics of a place.
Journal ArticleDOI
Development of a cross-cultural deprivation index in five European countries
Elodie Guillaume,Carole Pornet,Olivier Dejardin,Ludivine Launay,Roberto Lillini,Marina Vercelli,Marc Marí-Dell’Olmo,Amanda Fernández Fontelo,Carme Borrell,Ana Isabel Ribeiro,Maria Fatima de Pina,Alexandra Mayer,Cyrille Delpierre,Bernard Rachet,Guy Launoy +14 more
TL;DR: The European Deprivation Index is extended to four other European countries—Italy, Portugal, Spain and England, using available 2001 and 1999 national census data and is a weighted combination of aggregated variables from the national census that are most highly correlated with a country-specific individual deprivation indicator.
Journal ArticleDOI
A statistical procedure to create a neighborhood socioeconomic index for health inequalities analysis.
Benoît Lalloué,Benoît Lalloué,Benoît Lalloué,Jean-Marie Monnez,Cindy Padilla,Cindy Padilla,Wahida Kihal,Nolwenn Le Meur,Nolwenn Le Meur,Denis Zmirou-Navier,Denis Zmirou-Navier,Séverine Deguen,Séverine Deguen +12 more
TL;DR: A statistical procedure to create a neighborhood socioeconomic index that can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies is proposed and the importance of the classification method is highlighted.
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