Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

Affordable Access

Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

Publisher
BioMed Central
Publication Date
Source
PMC
Keywords
Disciplines
  • Medicine
License
Unknown

Abstract

1476-072X-4-31.fm ral International Journal of Health ss BioMed CentGeographics Open AcceMethodology Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging Pierre Goovaerts* Address: BioMedware, Inc., Ann Arbor, MI, USA Email: Pierre Goovaerts* - [email protected] * Corresponding author Abstract Background: Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided. Results: The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothing techniques. Because of the small weight assigned to the rate observed over the entity being smoothed (kernel weight), the population-weighted average leads to risk maps that show little variability. Other techniques assign larger and similar kernel weights but they use a different piece of auxiliary information in the prediction: global or local means for global or local empirical Bayes smoothers, and spatial combination of surrounding rates for the geostatistical estimator. Simulation studies indicated that Poisson kriging outperforms other approaches for most scenarios, with a clear benefit when t

Statistics

Seen <100 times