Area disease estimation based on sentinel hospital records.

<h4>Background</h4>Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can le...

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Autores principales:	Jin-Feng Wang, Ben Y Reis, Mao-Gui Hu, George Christakos, Wei-Zhong Yang, Qiao Sun, Zhong-Jie Li, Xiao-Zhou Li, Sheng-Jie Lai, Hong-Yan Chen, Dao-Chen Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ec920534ad69477081e92589494f11b0
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spelling	oai:doaj.org-article:ec920534ad69477081e92589494f11b02021-11-18T06:47:29ZArea disease estimation based on sentinel hospital records.1932-620310.1371/journal.pone.0023428https://doaj.org/article/ec920534ad69477081e92589494f11b02011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21886791/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased.<h4>Methods and findings</h4>A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospital's records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators.<h4>Conclusions</h4>The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Kriging's inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimator's limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.Jin-Feng WangBen Y ReisMao-Gui HuGeorge ChristakosWei-Zhong YangQiao SunZhong-Jie LiXiao-Zhou LiSheng-Jie LaiHong-Yan ChenDao-Chen WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e23428 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jin-Feng Wang Ben Y Reis Mao-Gui Hu George Christakos Wei-Zhong Yang Qiao Sun Zhong-Jie Li Xiao-Zhou Li Sheng-Jie Lai Hong-Yan Chen Dao-Chen Wang Area disease estimation based on sentinel hospital records.
description	<h4>Background</h4>Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased.<h4>Methods and findings</h4>A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospital's records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators.<h4>Conclusions</h4>The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Kriging's inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimator's limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.
format	article
author	Jin-Feng Wang Ben Y Reis Mao-Gui Hu George Christakos Wei-Zhong Yang Qiao Sun Zhong-Jie Li Xiao-Zhou Li Sheng-Jie Lai Hong-Yan Chen Dao-Chen Wang
author_facet	Jin-Feng Wang Ben Y Reis Mao-Gui Hu George Christakos Wei-Zhong Yang Qiao Sun Zhong-Jie Li Xiao-Zhou Li Sheng-Jie Lai Hong-Yan Chen Dao-Chen Wang
author_sort	Jin-Feng Wang
title	Area disease estimation based on sentinel hospital records.
title_short	Area disease estimation based on sentinel hospital records.
title_full	Area disease estimation based on sentinel hospital records.
title_fullStr	Area disease estimation based on sentinel hospital records.
title_full_unstemmed	Area disease estimation based on sentinel hospital records.
title_sort	area disease estimation based on sentinel hospital records.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/ec920534ad69477081e92589494f11b0
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Area disease estimation based on sentinel hospital records.

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