Epidemiological dynamics and associated risk factors of S. haematobium in humans and its snail vectors in Nigeria: a meta-analysis (1983-2018).
Paul Olalekan OdeniranKehinde Foluke OmolabiIsaiah Oluwafemi AdemolaPublished in: Pathogens and global health (2020)
Schistosoma haematobium, a major pathogen of urogenital schistosomiasis, has been reported to be affecting an estimated 30 million people in Nigeria. Current national estimates of S. haematobium and its cercariae, in humans and snail vectors respectively, are lacking in Nigeria, hence systematic meta-analyses were conducted to understand the disease dynamics in the endemic country over a period of 35 years based on publications from five databases (AJOL, Ovid MEDLINE, Google Scholar, PubMed and Web of Science). The preferred reporting items for systematic reviews and meta-analyses (PRIMSA) checklist were used as the standard guide for the analyses. The prevalence of S. haematobium in human hosts in Nigeria using quality effects model was 32.1% (27.3-37.2), while schistosome cercariae were observed at 3.5% (0.0-11.9), 18.2% (4.7-36.8) and 18.7% (0.0-46.1) and for B. forskalii, Bulinus globosus and B. truncatus, respectively. The high report of schistosome cercariae indicates the continuous transmission of S. haematobium in humans especially with individuals who have frequent contact with freshwater. Heterogeneity of subgroup analyses (regions, zones, sex, age groups, diagnostic techniques) and risk factors (pathological signs, occupation, water sources, anthropogenic activities, treatment) were determined. The result showed prevalence of an endemic moderate class infection that has been linked to several risk factors. Therefore, there is need for increased awareness on the prevalence, transmission routes and treatment strategies to mitigate the disease in this endemic area.
Keyphrases
- risk factors
- meta analyses
- systematic review
- randomized controlled trial
- epithelial mesenchymal transition
- endothelial cells
- quality improvement
- public health
- emergency department
- single cell
- signaling pathway
- clinical trial
- machine learning
- study protocol
- gene therapy
- candida albicans
- deep learning
- smoking cessation