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Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China

  • Chao Song
  • Yaqian He
  • Yanchen BoEmail author
  • Jinfeng Wang
  • Zhoupeng Ren
  • Jiangang Guo
  • Huibin Yang
Original Paper
  • 7 Downloads

Abstract

Given the limitations of current approaches for disease relative risk mapping, it is necessary to develop a comprehensive mapping method not only to simultaneously downscale various epidemiologic indicators, but also to be suitable for different disease outcomes. We proposed a three-step progressive statistical method, named disease relative risk downscaling (DRRD) model, to localize different spatial epidemiologic relative risk indicators for disease mapping, and applied it to the real world hand, foot, and mouth disease (HFMD) occurrence data over Mainland China. First, to generate a spatially complete crude risk map for disease binary variable, we employed ordinary and spatial logistic regression models under Bayesian hierarchical modeling framework to estimate county-level HFMD occurrence probabilities. Cross-validation showed that spatial logistic regression (average prediction accuracy: 80.68%) outperformed ordinary logistic regression (69.75%), indicating the effectiveness of incorporating spatial autocorrelation effect in modeling. Second, for the sake of designing a suitable spatial case–control study, we took spatial stratified heterogeneity impact expressed as Chinese seven geographical divisions into consideration. Third, for generating different types of disease relative risk maps, we proposed local-scale formulas for calculating three spatial epidemiologic indicators, i.e., spatial odds ratio, spatial risk ratio, and spatial attributable risk. The immediate achievement of this study is constructing a series of national disease relative risk maps for China’s county-level HFMD interventions. The new DRRD model provides a more convenient and easily extended way for assessing local-scale relative risks in spatial and environmental epidemiology, as well as broader risk assessment sciences.

Keywords

Disease mapping modeling Local relative risk assessment Spatial odds ratio Spatial risk ratio Spatial attributable risk Hand, foot, and mouth disease 

Abbreviations

AR

Attributable risk

BHM

Bayesian hierarchical modeling

CAR

Conditional autoregressive

CI

Confidence interval

CISDCP

System for Disease Control and Prevention

CMDC

China Meteorological Data Service Center

DIC

Deviance information criterion

DRRD

Disease relative risk downscaling

EM

Expectation maximum

GDP

Gross domestic product

GIS

Geographic information science

HFMD

Hand, foot, and mouth disease

INLA

Integrated nested Laplace approximation

kNN

k-nearest neighbors

LS

Logarithmic score

OR

Odds ratio

PA

Prediction accuracy

PST

Progressive spatiotemporal

RF

Random forest

RR

Risk ratio or relative risk

SAR

Spatial attributable risk

SD

Standard deviation

SMR

Standardized mortality ratio

SOR

Spatial odds ratio

SRR

Spatial risk ratio

SSH

Spatial stratified heterogeneity

STVC

Spatiotemporally varying coefficients

SVD

Singular value decomposition

WAIC

Watanabe Akaike information criterion

Notes

Acknowledgements

The authors appreciate A-Xing Zhu (University of Wisconsin–Madison, US) and Xun Shi (Dartmouth College, US) for improving the quality of our article, Henry Chung (Michigan State University, US) for proofreading our paper carefully, María Dolores Ugarte (Public University of Navarre, Spain) for her help with coding. We would like to thank the editor and anonymous reviewers for their constructive comments and valuable suggestions on improving this manuscript. The work was jointly supported by the National Natural Science Foundation of China (No. 41701448), a grant from State Key Laboratory of Resources and Environmental Information System (No. 201811), the State Key Laboratory of Remote Sensing Science, the Young Scholars Development Fund of Southwest Petroleum University (No. 201699010064), the Technology Project of the Sichuan Bureau of Surveying, Mapping and Geoinformation (No. J2017ZC05), and the Science and Technology Strategy School Cooperation Projects of the Nanchong City Science and Technology Bureau (No. NC17SY4016, 18SXHZ0025).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and TechnologySouthwest Petroleum UniversityChengduChina
  4. 4.Department of GeographyDartmouth CollegeHanoverUSA
  5. 5.University of Chinese Academy of SciencesBeijingChina

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