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Implementation considerations in case-area targeted interventions to prevent cholera transmission in Northeast Nigeria: A qualitative analysis.

Gurpreet KaurLindsay Salem-BangoAna Leticia Melquiades Dos Santos NeryEmmanuel Chimda SolomonEmmanuel IhemezueChristine KellyChiara AltareAndrew S AzmanPaul B SpiegelDaniele Lantagne
Published in: PLoS neglected tropical diseases (2023)
Cholera outbreaks primarily occur in areas lacking adequate water, sanitation, and hygiene (WASH), and infection can cause severe dehydration and death. As individuals living near cholera cases are more likely to contract cholera, case-area targeted interventions (CATI), where a response team visits case and neighbor households and conducts WASH and/or epidemiological interventions, are increasingly implemented to interrupt cholera transmission. As part of a multi-pronged evaluation on whether CATIs reduce cholera transmission, we compared two organizations' standard operating procedures (SOPs) with information from key informant interviews with 26 staff at national/headquarters and field levels who implemented CATIs in Nigeria in 2021. While organizations generally adhered to SOPs during implementation, deviations related to accessing case household and neighbor household selection were made due to incomplete line lists, high population density, and insufficient staffing and materials. We recommend reducing the CATI radius, providing more explicit context-specific guidance in SOPs, adopting more measures to ensure sufficient staffing and supplies, improving surveillance and data management, and strengthening risk communication and community engagement. The qualitative results herein will inform future quantitative analysis to provide recommendations for overall CATI implementation in future cholera responses in fragile contexts.
Keyphrases
  • healthcare
  • quality improvement
  • primary care
  • physical activity
  • cancer therapy
  • public health
  • palliative care
  • current status
  • systematic review
  • machine learning
  • drinking water
  • big data