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. 2014 Apr 14:14:358.
doi: 10.1186/1471-2458-14-358.

Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China

Yan-Chen Bo  1 , Chao Song, Jin-Feng Wang, Xiao-Wen Li
Affiliations

Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China

Yan-Chen Bo et al. BMC Public Health. .

Abstract

Background: There have been large-scale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socio-economic determinants of HFMD risk at a space scale.

Methods: HFMD records in Mainland China in May 2008 were collected. Both climate and socio-economic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk.

Results: Results showed that both climate and socio-economic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China.

Conclusions: The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD occurrences were found to be spatially heterogeneous over the Mainland China, which is related to both the climate and socio-economic variables. The combination of socio-economic and climate exposures can explain the HFMD occurrences more comprehensively and objectively than those with only climate exposures. The modeled probability of HFMD occurrence at the county level reveals not only the spatial trends, but also the local details of epidemic risk, even in the regions where there were no HFMD case records.

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Figures

Figure 1
Figure 1
The spatial distribution of HFMD occurrence at county level in Mainland China in May 2008.
Figure 2
Figure 2
Experiment flow chart.
Figure 3
Figure 3
The spatial distribution of selected exposure factors in the regression model in China in May 2008. (a) Monthly average temperature. (b) Monthly average precipitation. (c) Monthly average wind speed. (d) Population density. (e) Proportion of student population. (f) Number of industrial enterprises above designated size.
Figure 4
Figure 4
The spatial distribution of the autocorrelation term: the autocovariate variable.
Figure 5
Figure 5
The spatial distribution of the probability of disease occurrence in Mainland China.
Figure 6
Figure 6
The spatial distribution of the uncertainty of the predictions in the study area.
See this image and copyright information in PMC

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