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.
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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
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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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Song, C., He, Y., Bo, Y. et al. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stoch Environ Res Risk Assess 33, 1815–1833 (2019). https://doi.org/10.1007/s00477-019-01728-5
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DOI: https://doi.org/10.1007/s00477-019-01728-5