Achieving health equity has been identified as a major international challenge since the 1978 declaration of Alma Ata. Disease risk maps provide important clues concerning many aspects of health equity, such as etiology risk factors involved by occupational and environmental exposures, as well as gender-related and socioeconomic inequalities. This explains why epidemiological disease investigation should always include an assessment of the spatial variation of disease risk, with the objective of producing a representation of important spatial effects while removing any noise. Bearing in mind this goal, this review covers basic and more advanced aspects of Bayesian models for disease mapping, and methods to analyze whether the spatial distribution of the disease risk closely follows that of underlying population at risk, or there exist some nonrandom local patterns (disease clusters) which may suggest a further explanation for disease etiology. We provide a practical illustration by analyzing the spatial distribution of liver cancer mortality in Apulia, Italy, during the 2000–2005 quinquennial.
Geographical disparities in mortality rates: spatial data mining and Bayesian hierarchical modeling
BILANCIA, Massimo;
2013-01-01
Abstract
Achieving health equity has been identified as a major international challenge since the 1978 declaration of Alma Ata. Disease risk maps provide important clues concerning many aspects of health equity, such as etiology risk factors involved by occupational and environmental exposures, as well as gender-related and socioeconomic inequalities. This explains why epidemiological disease investigation should always include an assessment of the spatial variation of disease risk, with the objective of producing a representation of important spatial effects while removing any noise. Bearing in mind this goal, this review covers basic and more advanced aspects of Bayesian models for disease mapping, and methods to analyze whether the spatial distribution of the disease risk closely follows that of underlying population at risk, or there exist some nonrandom local patterns (disease clusters) which may suggest a further explanation for disease etiology. We provide a practical illustration by analyzing the spatial distribution of liver cancer mortality in Apulia, Italy, during the 2000–2005 quinquennial.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
