Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping

Huque, M. H. , Anderson, C. , Walton, R. and Ryan, L. (2016) Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping. International Journal of Health Geographics, 15(1), 25. (doi:10.1186/s12942-016-0055-7) (PMID:27473270) (PMCID:PMC4966783)

[img]
Preview
Text
147428.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

Abstract

Background: Mapping disease rates over a region provides a visual illustration of underlying geographical variation of the disease and can be useful to generate new hypotheses on the disease aetiology. However, methods to fit the popular and widely used conditional autoregressive (CAR) models for disease mapping are not feasible in many applications due to memory constraints, particularly when the sample size is large. We propose a new algorithm to fit a CAR model that can accommodate both individual and group level covariates while adjusting for spatial correlation in the disease rates, termed indiCAR. Our method scales well and works in very large datasets where other methods fail. Results: We evaluate the performance of the indiCAR method through simulation studies. Our simulation results indicate that the indiCAR provides reliable estimates of all the regression and random effect parameters. We also apply indiCAR to the analysis of data on neutropenia admissions in New South Wales (NSW), Australia. Our analyses reveal that lower rates of neutropenia admissions are significantly associated with individual level predictors including higher age, male gender, residence in an outer regional area and a group level predictor of social disadvantage, the socio-economic index for areas. A large value for the spatial dependence parameter is estimated after adjusting for individual and area level covariates. This suggests the presence of important variation in the management of cancer patients across NSW. Conclusions: Incorporating individual covariate data in disease mapping studies improves the estimation of fixed and random effect parameters by utilizing information from multiple sources. Health registries routinely collect individual and area level information and thus could benefit by using indiCAR for mapping disease rates. Moreover, the natural applicability of indiCAR in a distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. CI NSW Study Reference Number: 2012/07/410. Dated: July 2012.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anderson, Dr Craig
Authors: Huque, M. H., Anderson, C., Walton, R., and Ryan, L.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:International Journal of Health Geographics
Publisher:BioMed Central
ISSN:1476-072X
ISSN (Online):1476-072X
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in International Journal of Health Geographics 15(1):25
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record