Abstract
BackgroundThis study compares the Bayesian and frequentist (non-Bayesian) approaches in the modelling of the association between the risk of preterm birth and maternal proximity to hazardous waste and pollution from the Sydney Tar Pond site in Nova Scotia, Canada.MethodsThe data includes 1604 observed cases of preterm birth out of a total population of 17559 at risk of preterm birth from 144 enumeration districts in the Cape Breton Regional Municipality. Other covariates include the distance from the Tar Pond; the rate of unemployment to population; the proportion of persons who are separated, divorced or widowed; the proportion of persons who have no high school diploma; the proportion of persons living alone; the proportion of single parent families and average income. Bayesian hierarchical Poisson regression, quasi-likelihood Poisson regression and weighted linear regression models were fitted to the data.ResultsThe results of the analyses were compared together with their limitations.ConclusionThe results of the weighted linear regression and the quasi-likelihood Poisson regression agrees with the result from the Bayesian hierarchical modelling which incorporates the spatial effects.
Highlights
This study compares the Bayesian and frequentist approaches in the modelling of the association between the risk of preterm birth and maternal proximity to hazardous waste and pollution from the Sydney Tar Pond site in Nova Scotia, Canada
Public awareness about potential environmental hazards has continued to grow in recent years
Evidence of significant association between maternal proximity to hazardous waste sites and risk of low birth-weight and congenital anomalies has been reported in some studies
Summary
This study compares the Bayesian and frequentist (non-Bayesian) approaches in the modelling of the association between the risk of preterm birth and maternal proximity to hazardous waste and pollution from the Sydney Tar Pond site in Nova Scotia, Canada. Public awareness about potential environmental hazards has continued to grow in recent years This concern has led to an increased demand for public health authorities and researchers to investigate potential clustering of diseases around putative sources of hazards [1,2,3,4,5,6,7,8,9,10]. The parameters of the regression model can be estimated using the Bayesian or the frequentist approaches with spatial data assumed to be available at the individual case level or as spatially aggregated counts in enumeration districts (ED) [25,26,27]
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