# Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique

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## Abstract

**:**

## 1. Introduction

## 2. Data and Methods

#### 2.1. HFMD Data

_{i}) aged 0–9 years and the total child population number (P

_{i}) aged 0–9 years. However, the number of HFMD cases may be zero in some counties. Therefore, this may lead to deviation when dividing the cases by the total number of children in the population. We adopted the Hierarchical Bayesian model to reduce the spatial variance of CI [15,18]. We used log (λ

_{i}) to denote the logarithm of the expected CI in the i

^{th}county, and log (λ

_{i}) consists of three parts: the overall level of the disease risk α, the correlated heterogeneity u

_{i}and the uncorrelated heterogeneity v

_{i}, as following equation [11]:

_{i}) = α + u

_{i}+ v

_{i}

_{i}, spatial correlation is defined by the intrinsic Gaussian auto-regression model, while the uncorrelated heterogeneity v

_{i}is an independent normal variable with mean 0 and variance , as following equation [18,19].

_{i}~ N(0, τ

_{v}

^{2})

_{ij}is the spatial adjacent of the connectivity between counties. If the i

^{th}county and j

^{th}county is adjacent, then w

_{ij}= 1, otherwise w

_{ij}= 0. The variabilities of u

_{i}and v

_{i}are denoted by and , respectively. In this model, we use gamma distribution as prior distribution of the hyper-parameters: 1/τ

_{v}

^{2}~ Gamma(0.001, 0.001) and 1/τ

_{u}

^{2}~ Gamma(0.5, 0.0005) [11]. The Hierarchical Bayesian model is conducted by MCMC simulation in WinBUGS 1.4, and the length of burn-in sequence is 5,000 [11]. The incidence of children is shown in Figure 1 (after adjustment by the Hierarchical Bayesian model).

**Figure 1.**Spatial distribution of the incidence of children with HFMD who were aged between 0 and 9 years old in China in May 2008 after adjustment by the Hierarchical Bayesian model. Red represents a higher incidence of HFMD and green denotes a lower incidence.

#### 2.2. Determinants of HFMD and Their Proxies

#### 2.3. Geographical Detector

_{T}units, and the incidence of HFMD in each unit was denoted as H

_{i}(1 ≤ i ≤ N

_{T}). A risk factor, which may influence the incidence of HFMD, was denoted as D in the space, and this factor was divided into n

_{D}sub-regions in the geographical space. After intersecting the disease (H) and the risk factor (D), there were n

_{D}sub-regions in the whole geographical space. Every sub-region had n

_{D,Z}(1 ≤ z ≤ n

_{D}) grids and . The incidence of HFMD in every grid in the sub-region was defined as H

_{z,i}(1 ≤ z ≤ n

_{D}, 1 ≤ i ≤ n

_{D,Z}).

**Figure 3.**Distribution of the incidence of HFMD (H) and spatial patterns of potential factors C and D in the study area [15].

#### 2.3.1. Risk Detector

_{1}and z

_{2}, respectively. The average incidence of the two sub-regions should be:

_{z1}, H

_{z2}. If there are differences between H

_{z1}and H

_{z2}, then the incidence of HFMD in these two sub-regions may be different. We then determined whether the differences between H

_{z1}and H

_{z2}were significant using the t-test (Equation (10) [15]):

_{0}: H

_{z1}= H

_{z2}, we used the confidence level α (generally 5%). If , we can then reject H

_{0}, denoting that the incidence of HFMD in these two sub-regions was significantly different; otherwise the difference between them may be caused by error.

#### 2.3.2. Factor Detector

#### 2.3.3. Ecological Detector

_{1}and D

_{2}, the overall variance of these two risk factors is and , respectively. The total number of grids divided by these risk factors is n

_{T,D1}and n

_{T,D2}respectively. To compare the differences between and , the F test was used:

_{T,D}

_{1}− 1, n

_{T,D}

_{2}− 1) distribution, and the degree of freedom is df = (n

_{T,D}

_{1}, n

_{T,D}

_{2}) [24]. To test the null hypothesis , the confidence level was calculated (generally α = 5%). If H

_{0}was rejected under the confidence level α, this indicated that these two factors had a significant difference on the influence of HFMD.

#### 2.3.4. Interactive Detector

_{1}and D

_{2}, may be independent or have a combined effect on HFMD. If there is a combined effect, the effect of these factors on HFMD will be greater after intersecting. We used GIS software to stack the geographical layers D

_{1}and D

_{2}, and obtained a new geographical layer E. By comparing the value of PD of D

_{1}, D

_{2}and E, we were able to determine the influence of the intersection [15,16]. All four detectors were easily implemented using the new software Excel-GeogDetector, which can be freely downloaded at http://www.sssampling.org/Excel-GeoDetector.

## 3. Results

**Table 1.**Distribution of the incidence of HFMD, meteorological factors, population densities, and economic variables: AT: average temperature; RH: relative humidity; PD0_9: aged 0–9 years density; PupD: pupil density; MSD: middle school student density.

Variables | Mean | Min | 25% | 50% | 75% | Max |
---|---|---|---|---|---|---|

Incidence (cases/10^{5}) | 76.1 | 0.2 | 5.3 | 29.5 | 94.2 | 1,196.0 |

AT (℃) | 19.7 | −5.2 | 16.9 | 21.0 | 23.1 | 27.2 |

Rainfall (mm) | 99.6 | 0.1 | 38.9 | 83.7 | 135.8 | 497.9 |

RH (%) | 61.3 | 0.5 | 52.9 | 64.4 | 72.4 | 91.6 |

PD0_9 (person/ km^{2}) | 39.6 | 0.3 | 8.9 | 23.5 | 55.5 | 526.2 |

PupD (person/ km^{2}) | 29.1 | 0.1 | 6.9 | 17.7 | 37.9 | 468.5 |

MSD (person/ km^{2}) | 23.2 | 0.1 | 4.9 | 13.1 | 30.1 | 514.4 |

GDP (10^{8} CNY) | 153.6 | 0.8 | 22.9 | 55.1 | 115.4 | 15,541.0 |

FirstIndustry (10^{8} CNY) | 14.2 | 0.4 | 5.1 | 10.3 | 19.6 | 263.7 |

SecondIndustry (10^{8} CNY) | 74.5 | 1.5 | 8.0 | 23.1 | 58.9 | 6,167.3 |

ThirdIndustry (10^{8} CNY) | 57.2 | 0.6 | 7.2 | 16.5 | 35.1 | 7,599.3 |

^{2}, 29.1 persons/km

^{2}, and 23.2 persons/km

^{2}, respectively (Table 1). The spatial distribution of these three population densities was greatly different. In some counties, the minimal population density was less than 1 person/km

^{2}; while in other counties, it was more than 500 persons/km

^{2}. Regions with a higher population density were mainly distributed in Henan, the Yangtze River Delta, and the Pearl River Delta. However, in western and northern China, the population density was lower than other areas (Figure 5D–F). All counties within China have an average GDP of 15.36 billion Yuan, with a minimum of only 0.08 billion Yuan and a maximum of 1,554.1 billion Yuan. This indicates that the gap between the rich and the poor is large in China. Areas with a higher GDP were mainly located in the area of Beijing, the Yangtze River Delta, and the Pearl River Delta (Figure 5G). The average values of first industry, secondary industry, and tertiary industry were 1.42 billion Yuan, 7.45 billion Yuan, and 5.72 billion Yuan respectively. This finding indicated that that secondary industry comprised a maximum proportion of the economy in China. Areas with a strong first industry were mainly located in Sichuan, Shandong, Henan, and northeastern China (Figure 5H), while the coastal areas had developed secondary industry and tertiary industry, and they became less prevalent from east China to west China (Figure 5I,J).

**Figure 5.**Maps of meteorological factors, population densities, and economic factors of the incidence of HFMD in China. A: Average temperature; B: Relative humidity; C: GDP; D: Child density; E: Pupil density; F: Middle school student density; G: First industry; H: Secondary industry; I: Tertiary industry.

Stratum | <7 | 7–13 | 13–16 | 16–18 | 18–22 | 22–24 | >24 |

Incidence | 2.9 | 18.2 | 18.6 | 53.5 | 51.6 | 62.8 | 122.6 |

^{5}; average temperature: °C.

Stratum | <0.5 | 0.5–2.5 | 2.5–6.7 | 6.7–26.3 | 26.3–65.8 | 65.8–105.8 | >105.8 |

Incidence | 12.4 | 16.5 | 42.5 | 60.3 | 84.1 | 130.5 | 182.8 |

^{5}; 0–9 population density: 10

^{4}person/km

^{2}.

Risk factors | AT | RH | GDP | FI | TI | PD0_9 |
---|---|---|---|---|---|---|

RH | N | |||||

GDP | Y | Y | ||||

FI | N | N | Y | |||

TI | Y | Y | N | Y | ||

PD0_9 | Y | Y | N | Y | N | |

MSD | N | N | Y | Y | Y | Y |

Risk factors | AT | RH | GDP | FI | TI | PD0_9 |
---|---|---|---|---|---|---|

RH | 0.28 | |||||

GDP | 0.31 | 0.28 | ||||

FI | 0.17 | 0.16 | 0.24 | |||

TI | 0.31 | 0.32 | 0.34 | 0.26 | ||

PD0_9 | 0.31 | 0.33 | 0.35 | 0.30 | 0.42 | |

MSD | 0.23 | 0.21 | 0.29 | 0.19 | 0.26 | 0.27 |

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Huang, J.; Wang, J.; Bo, Y.; Xu, C.; Hu, M.; Huang, D.
Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique. *Int. J. Environ. Res. Public Health* **2014**, *11*, 3407-3423.
https://doi.org/10.3390/ijerph110303407

**AMA Style**

Huang J, Wang J, Bo Y, Xu C, Hu M, Huang D.
Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique. *International Journal of Environmental Research and Public Health*. 2014; 11(3):3407-3423.
https://doi.org/10.3390/ijerph110303407

**Chicago/Turabian Style**

Huang, Jixia, Jinfeng Wang, Yanchen Bo, Chengdong Xu, Maogui Hu, and Dacang Huang.
2014. "Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique" *International Journal of Environmental Research and Public Health* 11, no. 3: 3407-3423.
https://doi.org/10.3390/ijerph110303407