Methodology

A procedure to characterize geographic distributions of rare disorders in cohorts

Karla C Van Meter^1,2 , Lasse E Christiansen^2,3 , Irva Hertz-Picciotto^1,4 , Rahman Azari⁵ and Tim E Carpenter^2,6

¹Department of Public Health Sciences, School of Medicine, University of California, Davis, USA

²Center for Animal Disease Modeling and Surveillance, University of California, Davis, USA

³Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark

⁴Center for Children's Environmental Health, University of California, Davis, USA

⁵Department of Statistics, University of California, Davis, USA

⁶Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, USA

author email corresponding author email

International Journal of Health Geographics 2008, 7:26doi:10.1186/1476-072X-7-26

Published:	28 May 2008

Abstract

Background

Individual point data can be analyzed against an entire cohort instead of only sampled controls to accurately picture the geographic distribution of populations at risk for low prevalence diseases. Analyzed as individual points, many smaller clusters with high relative risks (RR) and low empirical p values are indistinguishable from a random distribution. When points are aggregated into areal units, small clusters may result in a larger cluster with a low RR or be lost if divided into pieces included in units of larger populations that show no increased prevalence. Previous simulation studies showed lowered validity of spatial scan tests for true clusters with low RR. Using simulations, this study explored the effects of low cluster RR and areal unit size on local area clustering test (LACT) results, proposing a procedure to improve accuracy of cohort spatial analysis for rare events.

Results

Our simulations demonstrated the relationship of true RR to observed RR and p values with various, randomly located, cluster shapes, areal unit sizes and scanning window shapes in a diverse population distribution. Clusters with RR < 1.7 had elevated observed RRs and high p values.

We propose a cluster identification procedure that applies parallel multiple LACTs, one on point data and three on two distinct sets of areal units created with varying population parameters that minimize the range of population sizes among units. By accepting only clusters identified by all LACTs, having a minimum population size, a minimum relative risk and a maximum p value, this procedure improves the specificity achieved by any one of these tests alone on a cohort study of low prevalence data while retaining sensitivity for small clusters. The procedure is demonstrated on two study regions, each with a five-year cohort of births and cases of a rare developmental disorder.

Conclusion

For truly exploratory research on a rare disorder, false positive clusters can cause costly diverted research efforts. By limiting false positives, this procedure identifies 'crude' clusters that can then be analyzed for known demographic risk factors to focus exploration for geographically-based environmental exposure on areas of otherwise unexplained raised incidence.