A procedure to characterize geographic distributions of rare disorders in cohorts View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-12

AUTHORS

Karla C Van Meter, Lasse E Christiansen, Irva Hertz-Picciotto, Rahman Azari, Tim E Carpenter

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. More... »

PAGES

26

References to SciGraph publications

  • 2006-12. A simulation study of three methods for detecting disease clusters in INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS
  • 2003-12. Local clustering in breast, lung and colorectal cancer in Long Island, New York in INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS
  • 2006-12. Spatial scan statistics using elliptic windows in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2005-12. Lumping or splitting: seeking the preferred areal unit for health geography studies in INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS
  • 2005-12. A flexibly shaped spatial scan statistic for detecting clusters in INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1476-072x-7-26

    DOI

    http://dx.doi.org/10.1186/1476-072x-7-26

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030931568

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/18507863


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