Lutambi Angelina Mageni, Emidi Basiliana, Mbuya Fredrick George, Ryoba Michael, Sagamiko Thadei Damas, Hugo Alfred Kisuda, Mbalawata Isambi Sailon
Dodoma Medical Research Centre, National Institute for Medical Research, Dodoma, Tanzania.
Department of Mathematics, Physics and Informatics, Dar es Salaam University College of Education, University of Da es Salaam, Dar es Salaam, Tanzania.
PLOS Glob Public Health. 2025 Aug 20;5(8):e0005075. doi: 10.1371/journal.pgph.0005075. eCollection 2025.
Malaria remains a significant public health challenge, particularly among vulnerable populations in high-burden countries like Tanzania. Despite progress in reducing malaria incidence, climatic and environmental condition variability has led to uneven reductions, hindering the achievement of the WHO 2030 targets. We assessed the impact of climatic and environmental variables on malaria incidence to better understand spatial and temporal trends and their implications for the WHO targets. We utilized geo-covariate data from the Demographic and Health surveys (DHS) program, applying a Moran's I test for spatial autocorrelation, a geostatistical Bayesian-based model to predict malaria incidence at an unsampled locations, and calculated the percentage change in predicted incidence over a ten-year interval. The results showed that malaria incidence decreased with greater variance across Tanzania. Mean malaria incidence decreased from 0.347 (95% CI: 0.336, 0.357) in 2000 to 0.118 (95% CI: 0.114, 0.122) in 2020, relative to the increasing insecticide-treated bednets (ITNs) coverage (0.037; 95% CI: 0.036, 0.039 in 2000 to 0.496; 95% CI: 0.476, 0.517 in 2020). Malaria incidence was higher in the Lake, western, eastern and southern zones compared to others, with spatial clustering observed (Moran's I of 0.93 in 2000, 0.87 in 2010, and 0.74 in 2020). Higher temperatures increased malaria incidence (Odds ratio (OR): 1.06; 95% credible interval (CI):1.04,1.08 and 1.13;95% CI:1.10,1.16) in 2000 and 2010, respectively). Enhanced vegetation index increased the likelihood of malaria incidence (ORs ranging from 5.28; 95% CI: 4.96,5.61) in 2000 to 6.22; 95% CI: 5.91,6.55) in 2020 and higher aridity was associated with higher malaria incidence (ORs: 1.11; 95% CI: 1.10,1.13) in 2010 and 1.07; 95% CI: 1.06,1.07) in 2020). To achieve the WHO 2030 malaria reduction targets, fine-scale and region-specific interventions are essential to mitigate the impact of climate and environmental factors on malaria incidence.
疟疾仍然是一项重大的公共卫生挑战,在坦桑尼亚等疟疾高负担国家的弱势群体中尤为如此。尽管在降低疟疾发病率方面取得了进展,但气候和环境条件的变化导致降幅不均衡,阻碍了世卫组织2030年目标的实现。我们评估了气候和环境变量对疟疾发病率的影响,以更好地了解空间和时间趋势及其对世卫组织目标的影响。我们利用了人口与健康调查(DHS)项目的地理协变量数据,应用莫兰指数I检验进行空间自相关分析,采用基于地理统计贝叶斯的模型预测未抽样地点的疟疾发病率,并计算了十年间预测发病率的百分比变化。结果表明,坦桑尼亚各地疟疾发病率下降幅度差异较大。平均疟疾发病率从2000年的0.347(95%置信区间:0.336,0.357)降至2020年的0.118(95%置信区间:0.114,0.122),与此同时,经杀虫剂处理的蚊帐(ITN)覆盖率上升(从2000年的0.037;95%置信区间:0.036,0.039升至2020年的0.496;95%置信区间:0.476,0.517)。与其他地区相比,湖泊、西部、东部和南部地区的疟疾发病率较高,呈现出空间聚集现象(2000年莫兰指数I为0.93,2010年为0.87,2020年为0.74)。温度升高会增加疟疾发病率(2000年和2010年的优势比(OR)分别为:1.06;95%可信区间(CI):1.04,1.08和1.13;95% CI:1.10,1.16)。增强植被指数增加了疟疾发病的可能性(优势比从2000年的5.28;95% CI:4.96,5.61到2020年的6.22;95% CI:5.91,6.55),而更高的干旱程度与更高的疟疾发病率相关(2010年优势比:1.11;95% CI:1.10,1.13,2020年优势比:1.07;95% CI:1.06,1.07)。为实现世卫组织2030年疟疾减少目标,必须采取精细且针对特定区域的干预措施,以减轻气候和环境因素对疟疾发病率的影响。