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Students' knowledge on sickle cell disease in Kisangani, Democratic Republic of the Congo.

Paul Kambale-KombiRoland Marini Djang'eing'aJean-Pierre Alworong'a OparaSerge Tonen-WolyecCharles Kayembe TshilumbaSalomon Batina-Agasa
Published in: Hematology (Amsterdam, Netherlands) (2020)
Background: Education is needed as an action to reduce morbidity and mortality from sickle cell disease (SCD), an important but largely neglected risk to child survival in most African countries as Democratic Republic of Congo (DRC).Objective: To assess the knowledge of Kisangani University students in DRC regarding SCD.Methods: In this non-experimental, cross-sectional study, a validated questionnaire was used to assess the knowledge of 2 112 Kisangani University students in DRC and data were analyzed using SPSS version 20.Results: Most participants, 92.9% (95% confidence interval [CI]: 91.7-93.9) were knowledgeable about SCD and have heard about it through schools and/or universities (46.3%), followed by family (34.5%) and health-care workers (23.5%). Nine hundred and seventy-three (46.1%; 95% CI: 44.0-48.2) and 37.9% (95% CI: 35.9-40.0) subjects indicated, respectively, that SCD is an acquired and hereditary disease. Moreover, 53.6% (95% CI: 51.5-55.7) said that the diagnosis of SCD is made by blood tests, while 46.2% (95% CI: 44.1-48.3) talked about urine tests. About 85.6% were unaware of the risk of children becoming sickle cell patients when both parents have SCD. To prevent SCD, pre-marital screening was cited by only 7.7% (95% CI: 6.6-8.9) of subjects and no measure was known by 25.4% (95% CI: 23.6-27.3). However, 79.6% (95% CI: 77.8-81.3) approved the need of pre-marital screening of SCD.Discussion: This study highlighted that the Kisangani university students' knowledge regarding SCD is poor and needs to be improved; education programs and motivational campaigns to be enhanced.
Keyphrases
  • sickle cell disease
  • healthcare
  • public health
  • young adults
  • mental health
  • quality improvement
  • machine learning
  • electronic health record
  • prognostic factors
  • deep learning
  • peritoneal dialysis