# A Method for Screening Climate Change-Sensitive Infectious Diseases

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

Group | Disease | Disease | Total |
---|---|---|---|

Yes | No | ||

Exposure group | A | B | A+B |

Non-Exposure group | C | D | C+D |

Total | A+C | B+D | A+B+ C+D |

_{i}

^{mean}) and the standard deviation (HC

_{i}

^{std}) of a climate variable (C) for the ith month (calculated from a 30 year historical dataset in this study) were used to define a threshold for dividing the exposure and non-exposure groups. For a study area, if the difference of the climate variable between the interested month (PC

_{i}) during the study period and the corresponding historical mean value (HC

_{i}

^{mean}) is larger than the standard deviation (HC

_{i}

^{std}) of the climate variable for the same month, the month is identified as an exposure month. Otherwise, the month will be identified as a non-exposure month (Figure 2a) (Equations (2) and (3)):

_{1}and M

_{2}indicate linear regression models of the exposure and non-exposure groups (Figure 2b,c), I stands for disease incidence, C stands for a climate variable, and a

_{1}, a

_{2}are the linear sensitivities of the exposure and non-exposure groups, respectively, which can be estimated using the least square method.

_{1}, a

_{2}are slopes of different linear models, a hypothesis test can be carried out to examine whether these two linear models are statistically “parallel” (i.e., a

_{1}= a

_{2}). The null hypothesis for the test is that the sensitivity of the exposure group is statistically the same as the sensitivity of the non-exposure group (i.e., a

_{1}= a

_{2}), while the alternative hypothesis is that a

_{1}is significantly different from a

_{2}(i.e., a

_{1}≠ a

_{2}):

_{H}

_{0}and RSS

_{H1}are the residual sums of squares under the null hypothesis (H

_{0}) and the alternative hypothesis (H

_{1}) respectively, n

_{M}is the number of models (i.e., 2 in this research), while n

_{D}is the number of data for all models. The f statistic denotes an F-distribution with the degree of freedom of n

_{M}−1 and n

_{D}−n

_{M}(i.e., F(n

_{M}−1, n

_{D}−n

_{M})). Given a confidence level of 0.05 (i.e., α = 0.05), the rejection criteria can be obtained as follows:

_{α}

_{/2}(n

_{M}−1, n

_{D}−n

_{M}) is the α/2-quantile for the previously mentioned F-distribution. If the f statistic meets the rejection criteria, the p-value for the test is less than α (i.e., p < 0.05 in this study), which means that the null hypothesis can be rejected (i.e., a

_{1}is significantly different from a

_{2}). Otherwise, we do not have enough evidence to reject the null hypothesis, which means that a

_{1}is not significantly different from a

_{2}.

**Figure 2.**Schematic figure for calculating the Relative Sensitivity: (

**a**) identification of the exposure and non-exposure groups; (

**b**) linear model of the exposure group; (

**c**) linear model of the non-exposure group.

## 3. Application

#### 3.1. Study Area and Data Processing

_{i}

^{mean}) and the standard deviation (HC

_{i}

^{std}) of the three climate variables from 1973 to 2003 (30 years) were also calculated for identifying the exposure and non-exposure groups. Considering the statistical requirements of the linear regression method and the hypothesis test, only counties with at least 20 months of data concerning infectious disease, of which more than 5 and 7 months are in exposure and non-exposure groups, respectively, were regarded as the effective counties to be analyzed. The calculations were carried out using MATLAB 7.10.0 (R2010a).

#### 3.2. Results

#### 3.2.1. Characteristics of Climate Variables in Exposure and Non-exposure Group

**Table 2.**Descriptive statistics for the climate variables in the exposure and non-exposure groups in Anhui province.

Climate Variables | Groups | |
PC_{i} − HC_{i}^{mean}| | |||
---|---|---|---|---|---|

Average | Maximum | Minimum | Standard Deviation | ||

Temperature (°C) | Exposure | 2.11 | 4.57 | 0.75 | 0.76 |

Non-exposure | 0.53 | 1.63 | 0.0001 | 0.34 | |

Precipitation (m) | Exposure | 5.85 | 40.47 | 0.15 | 4.63 |

Non-exposure | 1.84 | 17.56 | 0.0008 | 1.96 | |

Absolute Humidity (mg/L) | Exposure | 115.24 | 253.05 | 34.65 | 44.69 |

Non-exposure | 38.30 | 115.15 | 0.002 | 25.35 |

#### 3.2.2. Screening Results for Climate Change-Sensitive Disease

**Table 3.**The screening results for climate change-sensitive disease using RS indicator in Anhui province.

Diseases | Monthly Average Temperature | Monthly Accumulated Precipitation | Monthly Average Absolute Humidity | |||
---|---|---|---|---|---|---|

Effective Counties | Counties with RS ≠ 0 ^{*} (Proportion) | Effective Counties | Counties with RS ≠ 0 ^{*} (Proportion) | Effective Counties | Counties with RS ≠ 0 ^{*} (Proportion) | |

Dysentery | 77 | 35 (45%) | 77 | 51 (66%) | 77 | 73 (95%) |

Hand, foot and mouth | 76 | 48 (63%) | 76 | 33 (43%) | 76 | 72 (95%) |

Hepatitis A | 69 | 33 (48%) | 68 | 40 (59%) | 68 | 37 (54%) |

Malaria | 36 | 21 (59%) | 36 | 10 (28%) | 36 | 30 (83%) |

Influenza | 29 | 20 (69%) | 29 | 10 (34%) | 29 | 18 (62%) |

Typhoid fever | 16 | 10 (63%) | 15 | 9 (60%) | 16 | 10 (63%) |

Hemorrhagic fever | 10 | 1 (10%) | 10 | 2 (20%) | 10 | 6 (60%) |

Meningitis | 8 | 4(50%) | 8 | 3 (38%) | 8 | 7 (88%) |

Schistosomiasis | 9 | 6 (67%) | 7 | 4 (57%) | 9 | 5 (56%) |

*****p-value is less than 0.05.

#### 3.2.3. Spatial Distribution for RS of the Climate Change-Sensitive Disease

**Figure 4.**The spatial distribution of RS for HFM in Anhui Province: (

**a**) RS to temperature; (

**b**) RS to precipitation; (

**c**) RS to absolute humidity.

**Figure 5.**The spatial distribution of RS for dysentery in Anhui Province: (

**a**) RS to temperature; (

**b**) RS to precipitation; (

**c**) RS to absolute humidity.

#### 3.3. Discussions

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- IPCC. Climate Change 2013: The Physical Science Basis; Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK and New York, NY, USA, 2013. [Google Scholar]
- Kovats, R.S.; Menne, B.; McMichael, A.J.; Corvalan, C.; Bertollini, R. Climate Change and Human Health: Impact and Adaptation; World Health Organization: Geneva, Switzerland, 2000. [Google Scholar]
- Altizer, S.; Ostfeld, R.S.; Johnson, P.T.; Kutz, S.; Harvell, C.D. Climate change and infectious diseases: From evidence to a predictive framework. Science
**2013**, 341, 514–519. [Google Scholar] [CrossRef] [PubMed] - Epstein, P.R. Climate change and emerging infectious diseases. Microbes Infect.
**2001**, 3, 747–754. [Google Scholar] [CrossRef] [PubMed] - Kuhn, K.; Campbell–Lendrum, D.; Haines, A.; Cox, J. Using Climate to Predict Infectious Disease Epidemics; World Health Organization: Geneva, Switzerland, 2005. [Google Scholar]
- Wu, X.; Tian, H.; Zhou, S.; Chen, L.; Xu, B. Impact of global change on transmission of human infectious diseases. Sci. China. Earth. Sci.
**2014**, 57, 189–203. [Google Scholar] [CrossRef] - Leeson, H. Longevity of anopheles maculipennis race atroparvus, van thiel, at controlled temperature and humidity after one blood meal. Bull. Entomol. Res.
**1939**, 30, 103–301. [Google Scholar] [CrossRef] - Patz, J.A.; Epstein, P.R.; Burke, T.A.; Balbus, J.M. Global climate change and emerging infectious diseases. JAMA
**1996**, 275, 217–223. [Google Scholar] [CrossRef] [PubMed] - Beck-Johnson, L.M.; Nelson, W.A.; Paaijmans, K.P.; Read, A.F.; Thomas, M.B. The effect of temperature on anopheles mosquito population dynamics and the potential for malaria transmission. PLoS One
**2013**, 8. [Google Scholar] [CrossRef] [PubMed] - Bunyavanich, S.; Landrigan, C.P.; McMichael, A.J.; Epstein, P.R. The impact of climate change on child health. Ambul. Pediatr.
**2003**, 3, 44–52. [Google Scholar] [CrossRef] [PubMed] - Zhou, Y.; Zhuang, J.; Yang, M.; Zhang, Z.; Wei, J.; Peng, W.; Jiang, Q. Effects of low temperature on the schistosome-transmitting snail Oncomelania hupensis and the implications of global climate change. Molluscan. Res.
**2010**, 30, 102–108. [Google Scholar] - Lloyd, J.S.; Kovats, R.S.; Armstrong, B.G. Global diarrhoea morbidity, weather and climate. Climate Res.
**2007**, 34, 119–127. [Google Scholar] [CrossRef] - Chou, W.; Wu, J.; Wang, Y.; Huang, H.; Sung, F.; Chuang, C. Modeling the impact of climate variability on diarrhea-associated diseases in Taiwan (1996–2007). Sci. Total. Environ.
**2010**, 409, 43–51. [Google Scholar] [CrossRef] - Stenseth, N.C.; Mysterud, A. Climate, changing phenology, and other life history traits: Nonlinearity and match-mismatch to the environment. Proc. Natl. Acad. Sci.
**2002**, 99, 13379–13381. [Google Scholar] [CrossRef] [PubMed] - Iler, A.M.; Høye, T.T.; Inouye, D.W.; Schmidt, N.M. Nonlinear flowering responses to climate: Are species approaching their limits of phenological change? Philos. Trans. R. Soc. Lond. B. Biol. Sci.
**2013**, 368. [Google Scholar] [CrossRef] [PubMed] - Woolf, R. On estimating the relation between blood group and disease. Ann. Hum. Genet.
**1955**, 19, 251–253. [Google Scholar] [CrossRef] [PubMed] - Falk, C.T.; Rubinstein, P. Haplotype relative risks: An easy reliable way to construct a proper control sample for risk calculations. Ann. Hum. Genet.
**1987**, 51, 227–233. [Google Scholar] [CrossRef] [PubMed] - Zhang, J.; Yu, K.F. What’s relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA
**1998**, 280, 1690–1691. [Google Scholar] [CrossRef] [PubMed] - Last, J.M. A Dictionary of Epidemiology,, 4th ed.; Oxford University Press/International Epidemiological Association: New York, NY, USA, 2001. [Google Scholar]
- WikiHow. http://www.wikihow.com/Calculate-Relative-Risk (accessed on 22 May 2013).
- Seber, G.A.F.; Lee, A.J. Linear Regression Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2012; pp. 154–159. [Google Scholar]
- Hu, M.; Li, Z.; Wang, J.; Jia, L.; Liao, Y. Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models. PLoS One
**2012**, 7. [Google Scholar] [CrossRef] [PubMed] - Li, T.; Zheng, X.; Dai, Y.; Yang, C.; Chen, Z.; Zhang, S.; Wu, G.; Wang, Z.; Huang, C.; Shen, Y.; Liao, R. Mapping near-surface air temperature, pressure, relative humidity and wind speed over mainland China with high spatiotemporal resolution. Adv. Atmos. Sci.
**2014**, 31, 1127–1135. [Google Scholar] [CrossRef] - Zhang, Y.; Bi, P.; Hiller, J.E.; Sun, Y.; Ryan, P. Climate variations and bacillary dysentery in northern and southern cities of China. J. Infect.
**2007**, 55, 194–200. [Google Scholar] [CrossRef] [PubMed] - Wang, J.; Guo, Y.; Christakos, G.; Yang, W.; Liao, Y.; Li, Z.; Li, X.; Lai, S.; Chen, H. Hand, foot and mouth disease: Spatiotemporal transmission and climate. Int. J. Health. Geogr.
**2011**, 25, 1–10. [Google Scholar] - Hii, Y.L.; Rock lÖv, J.; Ng, N. Short term effects of weather on hand, foot and mouth disease. PLoS One
**2011**, 6. [Google Scholar] [CrossRef] [PubMed] - Ma, E.; Lam, T.; Wong, C.; Chuang, S.K. Is hand, foot and mouth disease associated with meteorological parameters? Epidemiol. Infect.
**2010**, 138, 1779–1788. [Google Scholar] [CrossRef] [PubMed] - Onozuka, D.; Hashizume, M.; Hagihara, A. Effects of weather variability on infectious gastroenteritis. Epidemiol. Infect.
**2010**, 138, 236–243. [Google Scholar] [CrossRef] [PubMed] - Zhang, Y.; Liu, Q.; Luan, R.; Liu, X.; Zhou, G.; Jiang, J.; Li, H.; Li, Z. Spatial-temporal analysis of malaria and the effect of environmental factors on its incidence in Yongcheng, China, 2006–2010. BMC Public Health
**2012**, 544, 1–13. [Google Scholar] - Parham, P.E.; Michael, E. Modeling climate change and malaria transmission. Adv. Exp. Med. Biol.
**2010**, 673, 184–199. [Google Scholar] [PubMed] - Barreca, A.I.; Shimshack, J.P. Absolute humidity, temperature, and influenza mortality: 30 years of county-level evidence from the United States. Am. J. Epidemiol.
**2012**, 176, 114–122. [Google Scholar] [CrossRef] - Jaakkola, K.; Saukkoriipi, A.; Jokelainen, J.; Juvonen, R.; Kauppila, J.; Vainio, O.; Ziegler, T.; Rönkkö, E.; Jaakkola Jouni, J.K.; Ikäheimo, T.M.; KIAS-Study Group. Decline in temperature and humidity increases the occurrence of influenza in cold climate. Environ. Health
**2014**, 22, 1–8. [Google Scholar] - Zhang, K.; Huang, S.; Shi, C. Effect of meteorological and geological factors on epidemic of typhoid fever/paratyphoid fever in Guilin. Chin. J. Dis. Control Prevent.
**2009**, 5, 520–523. [Google Scholar] - Qu, B.; Huang, D.; Guo, H.; Zhou, B.; Dong, C.; Lu, J. The model of back-propagation neural network about meteorological factors and Typhoid Fever, Paratyphoid Fever in a drought area. Chin. Health Stat.
**2004**, 11, 333–337. [Google Scholar] - Abdussalam, A.F.; Monaghan, A.J.; Steinhoff, D.F.; Dukic, V.M.; Hayden, M.H.; Hopson, T.M.; Thornes, J.E.; Leckebusch, G.C. The impact of climate change on meningitis in Northwest Nigeria: An assessment using CMIP5 climate model simulations. Wea. Climate. Soc.
**2014**, 6, 371–379. [Google Scholar] [CrossRef] - Yang, G.; Vounatsou, P.; Zhou, X.; Tanner, M.; Utzinger, J. A potential impact of climate change and water resource development on the transmission of Schistosoma japonicum in China. Parassitologia
**2005**, 47, 127–134. [Google Scholar] [CrossRef] [PubMed] - Ding, G.; Zhang, Y.; Gao, L.; Ma, W.; Li, X. Quantitative analysis of burden of infectious diarrhea associated with floods in northwest of Anhui province, China: A mixed method evaluation. PLoS One
**2013**, 8, 1–8. [Google Scholar] - Li, Z.; Wang, L.; Sun, W.; Hou, X.; Yang, H.; Sun, L.; Xu, S.; Sun, Q.; Zhang, J.; Song, H.; Lin, H. Identifying high-risk areas of bacillary dysentery and associated meteorological factors in Wuhan, China. Sci. Rep.
**2013**, 21, 1–5. [Google Scholar] - Morand, S.; Owers, K.A.; Waret-Szkuta, A.; McIntyre, K.M.; Baylis, M. Climate variability and outbreaks of infectious diseases in Europe. Sci. Rep.
**2013**, 3, 1–6. [Google Scholar] [CrossRef] [Green Version] - Huang, Y.; Ren, Z.; Hang, D.; Hong, Q.; Gao, Y.; Guo, J.; Sun, D.; Zuo, Y. Potential effect of climate changes on schistosomiasis japonica transmission in east route of South-to-North Water Diversion Project. Chin. J. Schist. Control
**2009**, 21, 197–204. [Google Scholar] - Oregon State University. http://oregonstate.edu/ua/ncs/archives/2010/feb/absolute-humidity-temperature-anomalies-tied-seasonal-outbreaks-influenza-0 (accessed on 5 May 2014).
- Tunde, A.M.; Adeleke, E.A.; Adeniyi, E.E. Impact of climate variability on human health in Ilorin, Nigeria. Environ. Natl. Resour. Res.
**2013**, 3, 127–134. [Google Scholar] - Shaman, J.; Kohn, M. Absolute humidity modulates influenza survival, transmission, and seasonality. Proc. Natl. Acad. Sci. USA
**2009**, 106, 3243–3248. [Google Scholar] [CrossRef] [PubMed] - Shaman, J.; Pitzer, V.E.; Viboud, C.; Grenfell, B.T.; Lipsitch, M. Absolute humidity and the seasonal onset of influenza in the continental United States. PLoS Biol.
**2010**, 8, 1–13. [Google Scholar] - Shaman, J.; Goldstein, E.; Lipsitch, M. Absolute humidity and pandemic versus epidemic influenza. Am. J. Epidemiol.
**2011**, 173, 127–135. [Google Scholar] [CrossRef] [PubMed] - Kurane, I. The effect of global warming on infectious diseases. Public Health Res. Perspect.
**2010**, 1, 4–9. [Google Scholar] [CrossRef] - Adu-Prah, S.; Tetteh, E.K. Spatiotemporal analysis of climate variability impacts on malaria prevalence in Ghana. Appl. Geogr.
**2014**, 12, 1–8. [Google Scholar] - Yeager, J.G.; O’Brien, R.T. Enterovirus inactivation in soil. Appl. Environ. Microbiol.
**1979**, 38, 694–701. [Google Scholar] [PubMed] - Kung, C.; King, C.; Lee, C.; Huang, L.; Lee, P. Differences in replication capacity between enterovirus 71 isolates obtained from patients with encephalitis and those obtained from patients with herpangina in Taiwan. J. Med. Virol.
**2007**, 79, 60–68. [Google Scholar] [CrossRef] [PubMed] - Onozuka, D.; Hashizume, M. The influence of temperature and humidity on the incidence of hand, foot, and mouth disease in Japan. Sci. Total Environ.
**2011**, 410–411, 119–125. [Google Scholar] [CrossRef] [PubMed] - Huang, Y.; Deng, T.; Yu, S.; Gu, J.; Huang, C.; Xiao, G. Effect of meteorological variables on the incidence of hand, foot, and mouth disease in children: a time-series analysis in Guangzhou, China. BMC Infect. Dis.
**2013**, 13. [Google Scholar] [CrossRef] [PubMed] - Chen, C.; Lin, H.; Li, X.; Lang, L.; Xiao, X.; Ding, P. Short-term effects of meteorological factors on children hand, foot and mouth disease in Guangzhou, China. Int. J. Biometeorol.
**2014**, 58, 1605–1614. [Google Scholar] [CrossRef] [PubMed] - Anhui Statistical Bureau. Available online: http://www.ahtjj.gov.cn/tjj/web/tjnjview.jsp?strColId=13787135717978521&_index=1 (accessed on 20 March 2014).

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**MDPI and ACS Style**

Wang, Y.; Rao, Y.; Wu, X.; Zhao, H.; Chen, J.
A Method for Screening Climate Change-Sensitive Infectious Diseases. *Int. J. Environ. Res. Public Health* **2015**, *12*, 767-783.
https://doi.org/10.3390/ijerph120100767

**AMA Style**

Wang Y, Rao Y, Wu X, Zhao H, Chen J.
A Method for Screening Climate Change-Sensitive Infectious Diseases. *International Journal of Environmental Research and Public Health*. 2015; 12(1):767-783.
https://doi.org/10.3390/ijerph120100767

**Chicago/Turabian Style**

Wang, Yunjing, Yuhan Rao, Xiaoxu Wu, Hainan Zhao, and Jin Chen.
2015. "A Method for Screening Climate Change-Sensitive Infectious Diseases" *International Journal of Environmental Research and Public Health* 12, no. 1: 767-783.
https://doi.org/10.3390/ijerph120100767