Skip to main content

Table 2 Characteristics of studies on the association between climatic variables and malaria transmission

From: Climate change and mosquito-borne diseases in China: a review

Study & Language Study area & period Data Collection Statistical Methods Main findings Comments
   Risk factors Disease/vector    
Huang et al. (2011) English [19] Anhui, Henan, Hubei Provinces 1990-2009 Normalized annual temperature, relative humidity and rainfall Cases counts -Bayesian Poisson models -Rainfall played a more important role in malaria transmission than other meteorological factors. -Spatial-temporal models were developed
- GIS -Socioeconomic factors were not taken into account.
Huang et al. (2011) English [20] Motuo County, Tibet 1986-2009 Monthly average temperature, maximum temperature, minimum temperature, relative humidity and total amount of rainfall Monthly incidence of malaria -Spearman correlation analysis -Relative humidity was more sensitive to monthly malaria incidence. -Several statistical methods were applied
-Cross-correlation analysis -The relationship between malaria incidence and rainfall was not directly and linearly. -Only one county was considered
-SARIMA model
-Inter-annual analysis
Zhou et al. (2010) English [21] Huaiyuan County of Anhui and Tongbai County of Henan Province 1990-2006 Monthly and annual average temperature, maximum temperature, minimum temperature, relative humidity and rainfall Monthly and annual incidence of malaria Vectorial capacity -Spearman correlation -Temperature and rainfall were major determinants for malaria transmission. However, no relationship between malaria incidence and relative humidity was observed. -Entomological investigate was conducted to determine the vectorial effect of malaria re-emergency.
-Stepwise regression analysis
-Curve fitting
-Trend analysis -Only two counties were examined
- Entomological investigation
Zhang et al. (2010) English [22] Jinan city, Shangdong Province 1959-1979 Monthly average maximum temperature, minimum temperature, relative humidity and rainfall Cases counts -Spearman correlation -Temperature was greatest relative to the transmission of malaria, but rainfall and relative humidity were not. -Only one city was included
-Cross-correlation -Socioeconomic factors ware ignored.
-SARIMA model
Yang et al. (2010) English [23] The P.R. China 1981-1995 Yearly growing degree days (YGDD), annual rainfall and relative humidity Malaria-endemic strata -A Delphi approach -Relative humidity was found to be the most important environmental factor, followed by temperature and rainfall. However, temperature was the major contributor of malaria intensity in regions with relative humidity >60%, -National-level analysis
-Multiple logistical regression -Risk maps of malaria based on different climatic factors were developed
-GIS
-Annual indicators were used
Xiao et al. (2010) English [24] Main island of Hainan province 1995-2008 Monthly average temperature, maximum temperature, minimum temperature, relative humidity and accumulative rainfall Monthly incidence of malaria -Cross correlation and autocorrelation analysis - Temperature during the previous one and two months were observed as major predictors of malaria epidemics. -Spatial-temporal analysis
-Poission regression
-GIS -Countermeasure and socioeconomic circumstances ware not taken into account.
-It was not necessary to consider rainfall and relative humidity to make malaria epidemic predictions in the tropical province.
Hui et al. (2009) English [25] Yunnan Province 1995-2005 Monthly average temperature, maximum temperature, minimum temperature, relative humidity and rainfall Monthly incidence of P. vivax malaria Monthly incidence of P. falciparum malaria -Spearman correlation analysis -Obvious associations between both P. vivax and P. falciparum malaria and climatic factors with a clear 1-month lagged effect, especially in cluster areas. -Analysis of both P. vivax malaria and P. falciparum malaria
-Temporal distribute analysis
-Spatio-temporal analysis
-Spatial autocorrelation
-Minimum temperature was most closely correlated to malaria incidence
-Spatial cluster analysis
- GIS
Clements et al. (2009) English [26] Yunnan Province 1991-2006 Monthly average rainfall, maximum temperature and minimum temperature Monthly incidence of P. vivax malaria Monthly incidence of P. falciparum malaria -Corss-correlation -Significant positive relationships between malaria incidence and rainfall and maximum temperature for both P. vivax and P.falciparum malaria -Analysis of both P. vivax malaria and P. falciparum malaria
-Bayesian Poisson regression
-Spatial-temporal analysis
-GIS
-Socioeconomic factors were ignored.
-High-incidence clusters located adjacent the international borders were not explained by climate, but partly due to population migration.
Tian et al. (2008) English [27] Mengla County, Yunnan Province 1971-1999 Monthly rainfall, minimum temperature, maximum temperature, relative humidity, and fog day frequency Monthly incidence of malaria -ARIMA models -Temperature and fog day frequency were key predictors of monthly malaria incidence. However, relative humidity and rainfall were not. -Fog day frequency used -P. vivax malaria and P. falciparum malaria were pooled together when malaria incidence was calculated.
-The annual fog frequency was the only weather predictor of the annual incidence of malaria
Bi et al. (2005) English [28] Anhui province 1966-1987 Monthly EI-Nino Southern Oscillation Index (ENSO) Monthly malaria cases -Spearman correlation -A positive correlation between ENSO and the incidence of malaria with no lag effect was found. -The impact of ENSO on malaria was analysed -Other meteorological variables were not considered.
-Only used correlation method
Liu et al. (2006) English [29] Twenty-one townships of 10 counties in Yunnan province 1984-1993 Monthly minimum temperature, maximum temperature, rainfall, sunshine duration, NDVI. Monthly incidence of malaria and vector density. -Principle component analysis -Remote sensing NDVI and climatic variables had a good correlation with Anopheles density and malaria incidence rate. -Both environmental and vector factors were analysed.
-Factor analysis
-Grey correlation analysis
Bi et al. (2003) English [30] Sunchen County in Ahui Province 1980-1991 Monthly maximum temperature, minimum temperature, relative humidity and rainfall Monthly incidence of malaria -Spearman correlation -Monthly average minimum temperature and total monthly rainfall, at one-month lag were major determinants in the transmission of malaria. -Non-climatic factors were neglected
-Cross-correlation
-Only one county considered
-ARIMA models
Hu et al. (1998) English [31] Yunnan Province 1991-1997 Annual rainfall, annual mean temperature Annual incidence of malaria - Multiple regression -Malaria incidence rates are higher in areas with temperature above 18°C, rainfall of more than 1000 mm -Socioeconomic factors such as income of farmers were taken into account.
-GIS
-Every one degree increase in temperature corresponds to 1.2/10,000 higher malaria incidence and when rainfall increase by 100 mm, malaria will increase to 100.0/10,000 -Annual data were used
Liu et al. (2011) Chinese [32] Pizhou City, Jiangsu province 2001-2006 Monthly mean temperature, maximum temperature, minimum temperature, rainfall days, relative humidity, evaporation, total cloud cover, sunlight time and low cloud. Monthly incidence of malaria -Correlation analysis -The incidence of malaria was passive relative to temperature, rainfall, relative humidity, evaporation and total cloud cover, but no relation with low cloud and sunlight. -Various meteorological variables were considered
-Multiple regression
-Only one city was analysed based on a relative short study period
-The monthly minimum temperature and relative humidity were two major factors influencing malaria transmission.
Wu et al. (2011) Chinese [33] Dianjiang county, Chongqing 1957-2010 Monthly mean temperature, maximum temperature, minimum temperature, rainfall days, relative humidity, absolute humidity, duration of sunshine, air pressure and wind speed. Case counts -Principal Component Analysis -Significant associations between malaria incidence and monthly mean temperature, rainfall and duration of sunshine were observed. -Various meteorological variables were considered
-Multiple regression -Temperature was greatest relative to malaria transmission -Long-term data from a fifty-four-years period-Only one county considered
Huang et al. (2009) Chinese [34] Tongbai and Dabie mountain areas, Huibei Province 1990-2007 Monthly mean temperature, maximum temperature, minimum temperature, rainfall. Case counts Descriptive study -Temperature and rainfall were major determinants for malaria transmission and the yearly peak of cases occurred one month after the rainy season. -Not enough statistical methods
Wang et al. (2009) Chinese [35] Anhui Province 2004-2006 Annually mean temperature and rainfall NDVI and elevation. Cases counts -Principal Component Analysis -Malaria transmission intensity was positively associated with the NDVI, but negatively associated with minimum temperature, rainfall and elevation. -Annual indicators were used
-Logistic regression -A two-years short period of study.
-GIS
Wen et al. (2008) Chinese [36] Hainan Province May-Oct in 2002 Monthly mean temperature, maximum temperature, minimum temperature, rainfall, relative humidity, land use, land surface temperature (LST) and elevation. Monthly incidence of malaria -Spearman correlation -No associations between meteorological factors and malaria incidence were observed. However, land use, elevation and LST appeared to be good contributors of malaria transmission. -Various environmental variables were collected
-Negative binomial regression analysis
-A six-month short period of study.
Su et al. (2006) Chinese [37] Hainan Province 1995 Monthly mean temperature, maximum temperature, minimum temperature, rainfall, relative humidity and NDVI. Monthly incidence of malaria -Factor Analysis -Rainfall and the NDVI may be used to explain the malaria transmission and distribution. -A one-year short period of study.
-Principal Component Analysis
-Multiple liner regression analysis
Fan et al. (2005) Chinese [38] Ailao mountain of Yuxi city in Yunnan Province 1993-2002 Annual man temperature and rainfall Anopheles minimus density -Correlation analysis -Significant relationship between malaria incidence and abundance of Anopheles minimus. However, no significant correlations between abundance of Anopheles minimus and climatic variables. -No disease data
-Annual data used
Wen et al. (2005) Chinese [39] Hainan Province Feb 1995- Jan 1996 NDVI Monthly incidence of malaria -Spearman correlation -Malaria prevalence was highly associated with NDVI value which could be used for malaria surveillance in Hainan province. -A short study period
-GIS
-No other climatic indicators used
Huang et al. (2004) Chinese [40] Luodian county 1951–2000 Libo county 1958–2000 Sandu county 1960–2000 Pintang county 1961–2000 Dushan county1951-2000 Guizhou Province Monthly mean temperature, rainfall, relative humidity Monthly incidence of malaria -Correlation analysis -Significant relationship between malaria incidence and climatic factors, but the influences of different climatic variables were not consistent among the eight study counties. -Relative long study periods
-Path analysis -Direct and indirect effects of climate were analysed by Path analysis
-The influence of climate on malaria was greater in Libo, Sandu, Dushan counties than in Luodian and Pintang counties
Gao et al. (2003) Chinese [41] Yunnan Province 1994-1999 Monthly mean temperature, maximum temperature, minimum temperature, rainfall, relative humidity, rain day, evaporation and sunshine hours Monthly incidence of malaria -Back Propagation Network Model -The efficiency of malaria forecasting was 84. 85% based on meteorological variables. -Descriptions of associations between malaria and climate was inadequate
-A five-years short study period
Wen et al. (2003) Chinese [42] Hainan Province 1995-2000 Monthly average temperature, maximum temperature, minimum temperature, rainfall, relative humidity Monthly incidence of malaria -Correlation analysis -Temperature and rainfall were relative to malaria transmission with various lag times, but relative humidity was not. -Analysis of high epidemic area and the whole province -Social-economic factors were neglected
-Stepwise regression analysis
-The influence of climatic variables on malaria was more obvious in high epidemic area than that in the whole province
Huang et al. (2002) Chinese [43] Jiangsu Province 1973-1983 Monthly rainfall, rain days, relative humidity, evaporation and NDVI Monthly incidence of malaria -Correlation analysis -The NDVI positively correlated with precipitation and relative humidity. -No temperature data included
-GIS -Only correlation method used
-The NDVI may be a good indicator to predict the distribution and transmission of malaria.
Huang et al. (2001) Chinese [44] Gaoan city, Jiangxi Province 1962-1999 Annually average rainfall during April to June, annually average temperature during July to August, annual average rainfall and temperature Case counts -Circular distribution method -Malaria cases increased with increase of average temperature from July to August and rainfall from April to June. -Annual index were used
-Descriptive study
Kan et al. (1999) Chinese [45] Anhui Province 1969-1999 Annual temperature and rainfall Annual incidence of malaria -Descriptive study -Annual incidences of malaria in 1975, 1977, 1980 in Madian, Lixin County increased with increase of rainfall, while decreased in 1976, 1978, 1981 with decreased rainfall -Not enough explanation on effects of climate factors on malaria.
-No statistical methods used
Yu et al. (1995) Chinese [46] Libo County, Guizhou Province 1958-1993 Monthly average temperature, rainfall, relative humidity Monthly incidence of malaria -Correlation analysis -Positive associations between malaria incidence and climatic factors were observed. -Relative long study periods
-Path analysis
      -Direct and indirect effects of climate were analysed
      -Direct effect of relative humidity was greatest on malaria incidence compared with temperature and rainfall.