Spatially varying effects of measured confounding variables on disease risk

Abstract Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chih-Chieh Wu, Yun-Hsuan Chu, Sanjay Shete, Chien-Hsiun Chen
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Disease cluster Hierarchical disease cluster Spatial association Spatial scan statistic Spatially varying Sudden infant death syndrome Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/de9f6e8477e24f4788667921b03d5e54
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:de9f6e8477e24f4788667921b03d5e54
record_format	dspace
spelling	oai:doaj.org-article:de9f6e8477e24f4788667921b03d5e542021-11-14T12:27:31ZSpatially varying effects of measured confounding variables on disease risk10.1186/s12942-021-00298-61476-072Xhttps://doaj.org/article/de9f6e8477e24f4788667921b03d5e542021-11-01T00:00:00Zhttps://doi.org/10.1186/s12942-021-00298-6https://doaj.org/toc/1476-072XAbstract Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. Methods We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. Results The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. Conclusion The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.Chih-Chieh WuYun-Hsuan ChuSanjay SheteChien-Hsiun ChenBMCarticleDisease clusterHierarchical disease clusterSpatial associationSpatial scan statisticSpatially varyingSudden infant death syndromeComputer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Health Geographics, Vol 20, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Disease cluster Hierarchical disease cluster Spatial association Spatial scan statistic Spatially varying Sudden infant death syndrome Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Disease cluster Hierarchical disease cluster Spatial association Spatial scan statistic Spatially varying Sudden infant death syndrome Computer applications to medicine. Medical informatics R858-859.7 Chih-Chieh Wu Yun-Hsuan Chu Sanjay Shete Chien-Hsiun Chen Spatially varying effects of measured confounding variables on disease risk
description	Abstract Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. Methods We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. Results The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. Conclusion The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.
format	article
author	Chih-Chieh Wu Yun-Hsuan Chu Sanjay Shete Chien-Hsiun Chen
author_facet	Chih-Chieh Wu Yun-Hsuan Chu Sanjay Shete Chien-Hsiun Chen
author_sort	Chih-Chieh Wu
title	Spatially varying effects of measured confounding variables on disease risk
title_short	Spatially varying effects of measured confounding variables on disease risk
title_full	Spatially varying effects of measured confounding variables on disease risk
title_fullStr	Spatially varying effects of measured confounding variables on disease risk
title_full_unstemmed	Spatially varying effects of measured confounding variables on disease risk
title_sort	spatially varying effects of measured confounding variables on disease risk
publisher	BMC
publishDate	2021
url	https://doaj.org/article/de9f6e8477e24f4788667921b03d5e54
work_keys_str_mv	AT chihchiehwu spatiallyvaryingeffectsofmeasuredconfoundingvariablesondiseaserisk AT yunhsuanchu spatiallyvaryingeffectsofmeasuredconfoundingvariablesondiseaserisk AT sanjayshete spatiallyvaryingeffectsofmeasuredconfoundingvariablesondiseaserisk AT chienhsiunchen spatiallyvaryingeffectsofmeasuredconfoundingvariablesondiseaserisk
_version_	1718429260792725504

Spatially varying effects of measured confounding variables on disease risk

Ejemplares similares