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The neglected role of Enterobius vermicularis in appendicitis: A systematic review and meta-analysis.

Ali TaghipourMeysam OlfatifarEhsan JavanmardMojtaba NorouziHamed MirjalaliMohammad Reza Zali
Published in: PloS one (2020)
Although the main cause of appendicitis is unclear, infection with Enterobius vermicularis is suggested as a neglected risk factor. Since, there is no comprehensive analysis to estimate the prevalence of E. vermicularis in appendicitis; therefore, we conducted a global-scale systematic review and meta-analysis study to estimate the prevalence of E. vermicularis infection in appendicitis cases. PubMed, Scopus, Web of Science and Google Scholar databases were systematically searched for relevant studies published until 15 August 2019. Pooled prevalence of E. vermicularis infection was estimated using the random effects model. Data were classified based on the continents and countries. Moreover, subgroup analyses regarding the gender, the human development index (HDI), and income level of countries were also performed. Fifty-nine studies involving 103195 appendix tissue samples belonging to the individuals of appendicitis were included. The pooled prevalence of E. vermicularis infection was (4%, 95%CI, 2-6%), with the highest prevalence (8%, 95% CI: 0-36%) and lowest prevalence (2%, 95% CI: 1-4%) in Africa and Americas continents, respectively. With respect to countries, the lowest and highest prevalence rates were reported from Venezuela (<1%, 95% CI: 0-1%) and Nigeria (33%, 95% CI: 17-52%), respectively. Indeed, a higher prevalence was observed in females, as well as in countries with lower levels of income and HDI. Our findings indicate the relatively high burden of E. vermicularis infection in appendicitis cases. However, our findings suggest the great need for more epidemiological studies to depth understand overlaps between E. vermicularis infection and appendicitis in countries with lower HDI and income levels.
Keyphrases
  • risk factors
  • mental health
  • physical activity
  • randomized controlled trial
  • public health
  • machine learning
  • deep learning
  • case control
  • neural network