OBJECTIVETo evaluate the associations between malaria risk and meteorological factors.

METHODSA negative binomial distribution regression analysis was built between the temperature, relative humidity, rainfall capacity and the monthly incidence of malaria, based on the temperature information provided by Guangdong Meteorological Department and the malaria incidence information provided by Guangdong Center of Disease Prevention and Control during year 1980 to 2004, adopting the time-series analysis method and by distributed lag non-linear model, in order to analyze the immediate factors.

RESULTSThe number of monthly malaria cases in Guangdong province reached 4010 between year 1984 and 2004, while the monthly maximal temperature, minimal temperature, average temperature, relative humidity and average rainfall capacity was separately 26.3°C, 18.8°C, 21.9°C, 88.0% and 5.6 mm. The immediate effect of monthly maximal temperature on malaria incidence showed non-linear relationships. When the temperature reached 32.3°C, the risk was highest, the relative risk (RR) was 2.51 (95%CI: 1.99 - 3.16); when the relative humidity was 60.0%, the relative risk of malaria was highest as 1.19 (95%CI: 0.66 - 2.11) and then decreased gradually; and when the relative humidity was 86.6%, the risk of malaria was lowest at 0.51 (95%CI: 0.34 - 0.76). The risk of malaria increased while the rainfall capacity was 14.5 mm, the risk of malaria was the highest at 1.29 (95%CI: 0.87 - 1.93). Strongest delayed effects on malaria incidence was observed when the monthly maximal temperature reached 31.5°C at lagged 2 months, with the value of RR at 1.81 (95%CI: 1.02 - 3.22). When the monthly rainfall capacity was over 15.2 mm, the delayed effects was strong but short. When the monthly maximal temperature of 33.7°C, the excess risk of malaria was comparatively high, the excess risk was 92.2% (95%CI: 30.5% - 183.2%) when lagging one month. When the relative humidity was low, the delayed effect of malaria lasted for a long time, and the cumulative effect was huge. When the relative humidity reached 87.0%, the excess risk lagging 3 months was only -66.6% (95%CI: -86.4% - -17.7%). When the rainfall capacity was 15.5 mm, the cumulative effect on malaria reached the peak after 3 months, while the excess risk was 40.7% (95%CI: -30.0% - -182.6%); afterwards the cumulative effect gradually weakened. Positive and negative interaction effects were significant between malaria risk and maximal temperature and monthly rainfall capacity, and monthly rainfall capacity and relative humidity at lagged 2 months, respectively.

CONCLUSIONHigh temperature and large rainfall capacity might be the risk factors of malaria in Guangdong province, and there was an obvious interaction between the two factors.