| Literature DB >> 35336744 |
Yurong Wu1,2, Cunrui Huang1,2,3.
Abstract
Vector-borne diseases have posed a heavy threat to public health, especially in the context of climate change. Currently, there is no comprehensive review of the impact of meteorological factors on all types of vector-borne diseases in China. Through a systematic review of literature between 2000 and 2021, this study summarizes the relationship between climate factors and vector-borne diseases and potential mechanisms of climate change affecting vector-borne diseases. It further examines the regional differences of climate impact. A total of 131 studies in both Chinese and English on 10 vector-borne diseases were included. The number of publications on mosquito-borne diseases is the largest and is increasing, while the number of studies on rodent-borne diseases has been decreasing in the past two decades. Temperature, precipitation, and humidity are the main parameters contributing to the transmission of vector-borne diseases. Both the association and mechanism show vast differences between northern and southern China resulting from nature and social factors. We recommend that more future research should focus on the effect of meteorological factors on mosquito-borne diseases in the era of climate change. Such information will be crucial in facilitating a multi-sectorial response to climate-sensitive diseases in China.Entities:
Keywords: climate change; meteorological factor; regional differentiation; vector-borne disease
Year: 2022 PMID: 35336744 PMCID: PMC8945209 DOI: 10.3390/biology11030370
Source DB: PubMed Journal: Biology (Basel) ISSN: 2079-7737
Figure 1Flow chart of the selection process to retrieve all relevant studies.
Figure 2The number of reviewed publications during 2000–2021. (a) The temporal trend of publications on vector-borne diseases, JE (Japanese encephalitis), ST (scrub typhus), TGR (typhus group rickettsiosis), SFTS (severe fever with thrombocytopenia syndrome), VL (visceral leishmaniasis), HFRS (hemorrhagic fever with renal syndrome); (b) The number of studies on different vector-borne diseases; (c) The geographical distribution of the studies.
Summary characteristics of research on meteorological factors and vector-borne diseases in China.
| Vector-Borne Disease | Vector | Study Area | Meteorological Factors | Outcome Metrics | Main Findings |
|---|---|---|---|---|---|
| Malaria | Mosquito | Shandong, Henan, Anhui, Jiangsu, Hubei, Sichuan, Chongqing, Guizhou, Yunnan, Guangdong, Hainan | Temperature, precipitation, humidity, air pressure, | Incidence, number of cases, detection rate | The association between meteorological factors and insect-borne diseases was nonlinear, consisting of reverse U-type and J-type shapes. The effects of rising temperature, rainfall, and humidity were beneficial to insect-borne disease transmission with lag effects. The correlations between wind speed, sunshine duration, air pressure, and insect-borne infectious diseases were negative. However, these correlations were different in some areas in China (see in |
| Dengue | Mosquito | Guangdong, Fujian, Guangxi, Yunnan | Temperature, precipitation, humidity, | Incidence, number of cases | |
| Japanese encephalitis | Mosquito | Shandong, Shaanxi, Hunan, Sichuan, Chongqing | Temperature, precipitation, humidity, air pressure, sunshine | Incidence, number of cases | |
| Scrub typhus | Mites | Shandong, Anhui, Jiangsu, Guangdong | Temperature, precipitation, humidity, air pressure, sunshine, wind speed, evaporation | Incidence, number of cases | |
| Typhus | Fleas | Liaoning, Yunnan | Temperature, precipitation, humidity | Number of cases | |
| SFTS | Ticks | Jiangsu | Temperature, humidity, wind speed | Incidence | |
| Leishmaniasis | Sandflies | Xinjiang | Temperature, precipitation, humidity | Number of cases | |
| Plague | Rodent | Gansu, Qinghai, Sichuan, Yunnan, Guizhou, Guangxi | Temperature, precipitation, humidity, Southern Oscillation Index, equatorial sea surface temperature in the eastern Pacific Ocean | Incidence, number of cases, bacteriological positive rate of plague, intensity of the outbreak, spread rate | The positive association between temperature, precipitation, and humidity and rodent-borne diseases was nonlinear with lag effects. Wind speed was negatively correlated with rodent-borne diseases. However, the results varied in different regions (see in |
| HFRS | Rodent | Liaoning, Shandong, Anhui | Temperature, precipitation, humidity, air pressure, wind speed, sunshine, Southern Osm index | Incidence, number of cases | |
| Schistosomiasis | Snails | Anhui, Jiangsu, Jiangxi | Temperature, precipitation, humidity, sunshine | Incidence, infection rate, number of cases, acute schistosomiasis detectable rate | The association between schistosomiasis and temperature was negative, while the rainfall and humidity associations were positive. However, the results varied in different regions (see in |
Figure 3The main pathway of climate change impact on the risk of vector-borne diseases.
The relationships between meteorological factors and vector-borne diseases according to the classification of different administrative regions in China.
| Disease | Area | Time Period | Meteorological Factors | ||
|---|---|---|---|---|---|
| Temperature | Precipitation | Humidity | |||
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| Jinan City | 1959–1979 | Max T (+) ** | P (+) | H (+) * | |
| Min T (+) ** | |||||
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| Yongcheng County | 2006–2010 | Monthly avg max T (+) *** | - | Monthly avg H (+) ** | |
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| 1990–2009 | Monthly avg T(+) * | Monthly avg P (+) ** | Monthly avg RH (+) * | |
| Shuchen County | 1980–1991 | Monthly avg max T (+) *** | Monthly P (+) *** | Monthly avg RH (+) *** | |
| Hefei city | 1999–2009 | Monthly avg T (+) | P (+) * | H (+) *** | |
| Hefei City | 1990–2011 | Monthly min T (+) *** | P | RH (+) *** | |
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| Mengla County | 1971–1999 | Monthly max T (+) * | Monthly P (−) | Monthly RH (−) | |
| 125 counties | 2012 | Yearly avg T (+) ** | Yearly P (+) ** | ||
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| 2005–2013 | High T (+) | P (J) | - | |
| Guangzhou city | 2006–2012 | Daily avg T (+) * | - | Daily RH (+) * | |
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| 1995–2008 | Monthly avg T (+) * | Monthly total P (+) * | - | |
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| Guangzhou City | 2006–2015 | Extremely high T (+) * | Extremely high P (+) * | Extremely high H (+) * | |
| Guangzhou City | 2005–2015 | Monthly avg max T (+) ** | Monthly total P (+) ** | - | |
| Guangzhou City | 2007–2012 | Monthly avg T (+) ** | - | Monthly avg RH (+) ** | |
| Guangzhou City | 2001–2006 | Min T (+) *** | Monthly total P (+) | Min H (+) | |
| Guangzhou City | 2000–2012 | Monthly avg min T (+) * | Monthly total P (+) * | Monthly avg RH (+) * | |
| Guangzhou City | 2005–2011 | Daily avg T (+) * | Daily P (+) | Daily H (+) | |
| Zhongshan City | 2001–2013 | Monthly max T (+) * | - | Monthly avg RH (+) * | |
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| 1978–2017 | Monthly avg T (+) * | Monthly total P (+) * | - | |
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| 1978–2017 | Monthly avg T (+) * | Monthly total P (+) * | - | |
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| 1978–2017 | Monthly avg T (+) * | Monthly total P (+) * | - | |
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| Jinan City | 1959–1979 | Monthly avg max T (+) *** | Monthly total P (+) * | Monthly avg RH (+) *** | |
| Linyi City | 1956–2004 | Monthly min T (+) ** | - | Monthly avg RH (+) * | |
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| 2006–2014 | Monthly min T (−) | Monthly P (+) | - | |
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| Jieshou County | 1980–1996 | Monthly avg max T (+) * | Monthly total P (+) ** | - | |
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| Changsha city | 2004–2009 | Monthly avg max T (+) * | Monthly total P (+) * | Monthly avg AH (+) * | |
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| Nanchong City | 2007–2012 | Daily avg T (+) * | - | Daily avg RH (+) * | |
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| 12 counties along the Yangtze River | 1997–2008 | Monthly avg T (+) *** | Monthly total P (−) *** | - | |
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| 2006–2013 | Monthly avg T (reversed U) *** | Monthly total P (−) *** | Monthly avg RH (−) *** |
| Laiwu City | 2006–2012 | Monthly avg T (+) ** | Monthly avg P (+) ** | Monthly avg RH (+) ** | |
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| 2006–2013 | Monthly avg T (reversed U) *** | Monthly total P (−) *** | Monthly avg RH (+) *** | |
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| 2006–2013 | Monthly avg T (reversed U) *** | Monthly total P (−) *** | Monthly avg RH (+) *** | |
| Yancheng City | 2005–2014 | Monthly avg min T (+) *** | Monthly total P (+) *** | Monthly avg RH (−) *** | |
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| Guangzhou City | 2006–2012 | Daily avg T (+) ** | Daily P (+) ** | Daily avg RH (−) * | |
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| Xishuangbanna | 2005–2017 | Weekly avg T (J) * | Weekly avg P (reversed U) * | - | |
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| 2010–2016 | Max T in warmest month (+) * | P in warmest month (+) * | |
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| Jiashi County | 2005–2015 | Monthly avg T (+) ** | Monthly total P | Monthly avg RH (−) ** | |
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| Sunan County, Subei County | 1973–2016 | Monthly avg T (+) * | Monthly avg P (+) * | Monthly avg RH (−) * | |
| Yunnan | 1982–2013 | Extreme max T (−) ** | - | Avg RH (+) ** | |
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| 1982–2013 | Extreme max T (−) ** | - | Avg RH (+) ** |
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| 1982–2013 | Extreme max T (−) ** | - | Avg RH (+) ** | |
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| 2005–2014 | Weekly max T (+) * | Weekly P (+) * | Weekly avg RH (+) * | |
| Shenyang City | 2004–2009 | Monthly avg T (−) * | Monthly total P (−) * | Monthly avg RH (−) * | |
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| 2005–2014 | Weekly max T (+) * | Weekly P (+) * | Weekly avg RH (+) * | |
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| 2005–2014 | Weekly max T (+) * | Weekly P (+) * | Weekly avg RH (+) * | |
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| 1976–1989 | Avg T in July (−) * | Avg P in July (−) * | - |
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| 1997–2010 | Monthly avg T (−) * | Monthly total P (−) * | ||
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| 2008 | - | Monthly min P (−) ** | ||
The bold fonts in the region column are provinces in China. Max (maximum), Min (minimum), Avg (average), T (temperature), DTR (the difference between the maximum and the minimum daily temperature), P (precipitation), H (humidity), RH (relative humidity), AH (absolute humidity). “*” (The result is significant at level of α = 0.05), “**” (The result is significant at level of α = 0.01), “***” (The result is significant at level of α = 0.001).