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 | |
|---|---|---|---|---|---|---|
| Â | Â | Risk factors | Disease/vector | Â | Â | Â |
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 | ||||||
| Â | Â | Â | Â | Â | -Direct and indirect effects of climate were analysed | |
| Â | Â | Â | Â | Â | -Direct effect of relative humidity was greatest on malaria incidence compared with temperature and rainfall. | Â |