Lemma Wossenseged
College of Medicine and Health Sciences, School of Biomedical and Laboratory Sciences, Department of Medical Parasitology, University of Gondar, Ethiopia.
Heliyon. 2021 Jul 27;7(8):e07653. doi: 10.1016/j.heliyon.2021.e07653. eCollection 2021 Aug.
Rainfall is one of the climate variables most studied as it affects malaria occurrence directly.
This study aimed to describe how monthly rainfall variability affects malaria incidence in different years.
A total of 7 years (2013/14-2019/20) retrospective confirmed and treated malaria cases in Gondar Zuria district were used for analysis in addition to five (2013/14-2017/18) years retrospective data from Dembia district.
The annual rainfalls in the study years showed no statistically significant difference (p = 0. 78). But, variations in rainfalls of the different months (p = 0.000) of the different years were the source of variations for malaria count (incidences) in the different years. Malaria was transmitted throughout the year with the highest peak in November (mean count = 1468.7 ± 697.8) and followed by May (mean count = 1253.4 ± 1391.8), after main Kiremt/Summer and minor Bulg/Spring rains respectively. The lowest transmission was occurred in February (338 ± 240.3) when the rivers were the only source of mosquito vectors. Year 2013/14 (RF = 2351.12 mm) and 2019/20 (RF = 2278.80 mm) with no statistically significant difference (p = 0.977) in annual rainfalls produced 10, 702 (49.2%) and 961 (20%) malaria counts for the Bulg (spring) season respectively due to 581.92 mm (24.8%) higher total Bulg/Spring rain in 2013/14 compared to 124.1 mm (5.45%) in 2019/20. Generally, above normal rainfalls in Bulg/Spring season increased malaria transmission by providing more aquatic habitats supporting the growth of the immature stages. But heavy rains in Summer/Kiremt produced low malaria counts due to the high intensity of the rainfalls which could kill the larvae and pupae. Spearman's correlation analysis indicated that the mean rainfalls of current month (RF) (0 lagged month) (P = 0.025), previous month (RF1) (1 month lagged) (p = 0.000), before previous months (RF2) (2 months lagged) (p = 0.001) and mean RF + RF1 + RF2 (P = 0.001) were positive significantly correlated with mean monthly malaria counts compared to negative significant correlations for temperature variables. Temperature variables negative correlations were interpreted as confounding effects because decreased malaria counts in dry months were due to a decrease in rainfalls. Conclusion: rainfall distribution in different months of a year affects malaria occurrences.
降雨是研究最多的气候变量之一，因为它直接影响疟疾的发生。
本研究旨在描述月降雨量变化如何影响不同年份的疟疾发病率。
除了来自登比亚区的五年（2013/14 - 2017/18）回顾性数据外，还使用了贡德尔祖里亚区7年（2013/14 - 2019/20）回顾性确诊和治疗的疟疾病例进行分析。
研究年份的年降雨量无统计学显著差异（p = 0.78）。但是，不同年份不同月份的降雨量变化（p = 0.000）是不同年份疟疾计数（发病率）变化的来源。疟疾全年传播，11月达到最高峰（平均计数 = 1468.7 ± 697.8），其次是5月（平均计数 = 1253.4 ± 1391.8），分别在主要的基尔梅特/夏季和次要的布尔格/春季降雨之后。传播率最低发生在2月（338 ± 240.3），此时河流是蚊虫媒介的唯一来源。2013/14年（降雨量 = 2351.12毫米）和2019/20年（降雨量 = 2278.80毫米）年降雨量无统计学显著差异（p = 0.977），但由于2013/14年布尔格/春季总降雨量比2019/20年高581.92毫米（24.8%），而2019/20年为124.1毫米（5.45%），因此2013/14年和2019/20年布尔格（春季）季节的疟疾病例数分别为10702例（49.2%）和961例（20%）。一般来说，布尔格/春季季节降雨量高于正常水平会增加疟疾传播，因为它提供了更多支持未成熟阶段生长的水生栖息地。但是夏季/基尔梅特的暴雨导致疟疾病例数较低，因为降雨强度大可能会杀死幼虫和蛹。斯皮尔曼相关性分析表明，当月平均降雨量（RF）（0个月滞后）（P = 0.025）、上月平均降雨量（RF1）（1个月滞后）（p = 0.000）、上上月平均降雨量（RF2）（2个月滞后）（p = 0.001）以及平均RF + RF1 + RF2（P = 0.001）与月平均疟疾病例数呈显著正相关，而温度变量呈显著负相关。温度变量的负相关被解释为混杂效应，因为干旱月份疟疾病例数减少是由于降雨量减少。结论：一年中不同月份的降雨分布会影响疟疾的发生。
