Lingala Mercy A L
Environmental Epidemiology Division, National Institute of Malaria Research (ICMR), Sector-8, Dwarka, New Delhi 110077, India.
J Infect Public Health. 2017 Nov-Dec;10(6):875-880. doi: 10.1016/j.jiph.2017.02.007. Epub 2017 Mar 9.
Malaria is a public health problem caused by Plasmodium parasite and transmitted by anopheline mosquitoes. Arid and semi-arid regions of western India are prone to malaria outbreaks. Malaria outbreak prone districts viz. Bikaner, Barmer and Jodhpur were selected to study the effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria outbreaks for the period of 2009-2012.
The data of monthly malaria cases and meteorological variables was analysed using SPSS 20v. Spearman correlation analysis was conducted to examine the strength of the relationship between meteorological variables, P. vivax and P. falciparum malaria cases. Pearson's correlation analysis was carried out among the meteorological variables to observe the independent effect of each independent variable on the outcome.
Results indicate that malaria outbreaks have occurred in Bikaner and Barmer due to continuous rains for more than two months. Rainfall has shown to be an important predictor of malaria outbreaks in Rajasthan. P. vivax is more significantly correlated with rainfall, minimum temperature (P<0.01) and less significantly with relative humidity (P<0.05); whereas P. falciparum is significantly correlated with rainfall, relative humidity (P<0.01) and less significantly with temperature (P<0.05). The determination of the lag period for P. vivax is relative humidity and for P. falciparum is temperature. The lag period between malaria cases and rainfall is shorter for P. vivax than P. falciparum.
In conclusion, the knowledge generated is not only useful to take prompt malaria control interventions but also helpful to develop better forecasting model in outbreak prone regions.
疟疾是由疟原虫引起的公共卫生问题，通过按蚊传播。印度西部的干旱和半干旱地区容易爆发疟疾疫情。选择疟疾疫情易爆发地区，即比卡内尔、巴尔梅尔和焦特布尔，研究2009 - 2012年期间气象变量对间日疟原虫和恶性疟原虫疟疾疫情的影响。
使用SPSS 20v分析每月疟疾病例数据和气象变量。进行Spearman相关性分析，以检验气象变量与间日疟原虫和恶性疟原虫疟疾病例之间关系的强度。在气象变量之间进行Pearson相关性分析，以观察每个自变量对结果的独立影响。
结果表明，比卡内尔和巴尔梅尔因持续降雨超过两个月而爆发了疟疾疫情。降雨已被证明是拉贾斯坦邦疟疾疫情爆发的一个重要预测指标。间日疟原虫与降雨、最低温度显著相关（P<0.01），与相对湿度相关性较弱（P<0.05）；而恶性疟原虫与降雨、相对湿度显著相关（P<0.01），与温度相关性较弱（P<0.05）。间日疟原虫的滞后期决定因素是相对湿度，恶性疟原虫的是温度。间日疟原虫病例与降雨之间的滞后期比恶性疟原虫短。
总之，所获得的知识不仅有助于及时采取疟疾控制干预措施，而且有助于在疫情易爆发地区开发更好的预测模型。
