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Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping
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 di...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966783/ https://www.ncbi.nlm.nih.gov/pubmed/27473270 http://dx.doi.org/10.1186/s12942-016-0055-7 |
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author | Huque, Md. Hamidul Anderson, Craig Walton, Richard Ryan, Louise |
author_facet | Huque, Md. Hamidul Anderson, Craig Walton, Richard Ryan, Louise |
author_sort | Huque, Md. Hamidul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4966783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49667832016-07-30 Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping Huque, Md. Hamidul Anderson, Craig Walton, Richard Ryan, Louise Int J Health Geogr Methodology 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. BioMed Central 2016-07-29 /pmc/articles/PMC4966783/ /pubmed/27473270 http://dx.doi.org/10.1186/s12942-016-0055-7 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Huque, Md. Hamidul Anderson, Craig Walton, Richard Ryan, Louise Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping |
title | Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping |
title_full | Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping |
title_fullStr | Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping |
title_full_unstemmed | Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping |
title_short | Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping |
title_sort | individual level covariate adjusted conditional autoregressive (indicar) model for disease mapping |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966783/ https://www.ncbi.nlm.nih.gov/pubmed/27473270 http://dx.doi.org/10.1186/s12942-016-0055-7 |
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