Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach

Lan Luo,Fahui Wang,Sara Mclafferty

doi:10.1186/1476-072x-9-51

Abstract

PurposeThis paper examines the effect of spatial aggregation error on statistical estimates of the association between spatial access to health care and late-stage cancer.MethodsMonte Carlo simulation was used to disaggregate cancer cases for two Illinois counties from zip code to census block in proportion to the age-race composition of the block population. After the disaggregation, a hierarchical logistic model was estimated examining the relationship between late-stage breast cancer and risk factors including travel distance to mammography, at both the zip code and census block levels. Model coefficients were compared between the two levels to assess the impact of spatial aggregation error.ResultsWe found that spatial aggregation error influences the coefficients of regression-type models at the zip code level, and this impact is highly dependent on the study area. In one study area (Kane County), block-level coefficients were very similar to those estimated on the basis of zip code data; whereas in the other study area (Peoria County), the two sets of coefficients differed substantially raising the possibility of drawing inaccurate inferences about the association between distance to mammography and late-stage cancer risk.ConclusionsSpatial aggregation error can significantly affect the coefficient values and inferences drawn from statistical models of the association between cancer outcomes and spatial and non-spatial variables. Relying on data at the zip code level may lead to inaccurate findings on health risk factors.

Highlights

Detecting and analyzing spatial aggregation error in large spatial data sets is an increasingly important topic in GIS and public health research [1,2,3,4,5]
This study develops a Monte Carlo simulation procedure for disaggregating cancer cases from larger to smaller study units in empirical simulations, and uses that procedure to examine the implications of spatial aggregation error for multilevel model coefficients
We examine the impact of spatial aggregation error on the coefficients of multilevel statistical models which analyze the associations between late-stage breast cancer, demographic variables and distance to mammography facilities

Summary

Introduction

Detecting and analyzing spatial aggregation error in large spatial data sets is an increasingly important topic in GIS and public health research [1,2,3,4,5]. In geography and public health, much data is publicly available to researchers for analysis in predefined areas (zones) with an arbitrary and modifiable boundary. These zones were not optimally designed to answer the research question, introducing geographical bias which affects subsequent statistical analyses based on such data. This is the well-known Modifiable Area Unit Problem (MAUP). As in earlier work on the ecological fallacy, they found that spatial aggregation tends to increase the magnitude of correlation coefficients, confirming that spatial aggregation error has an impact on statistical analysis. The three types of error are based on different geographical characteristics of origins and destinations and can result in under- or over-estimation of individuals’ actual travel distances

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Results

Conclusion