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Quality control in schistosomiasis programmes.

R A Braun-Munzinger1

  • 1Project Bilharziose (G.T.Z.), Mahajanga, Madagascar.

Tropical Medicine and Parasitology : Official Organ of Deutsche Tropenmedizinische Gesellschaft and of Deutsche Gesellschaft Fur Technische Zusammenarbeit (GTZ)
|June 1, 1989
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A new quality control method for schistosomiasis (a parasitic disease) monitoring was developed. This simple, effective technique improved technician performance and data accuracy in Madagascar.

Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Parasitology

Background:

  • Effective schistosomiasis control programs depend on accurate parasite prevalence and intensity measurements.
  • Traditional quality control methods for microscopy faced logistical challenges in Madagascar, including specimen preservation and transport issues.

Purpose of the Study:

  • To develop and evaluate a simple, effective, and acceptable quality control method for schistosomiasis microscopy in field settings.
  • To assess the performance of technicians and improve the reliability of infection data for control programs.

Main Methods:

  • A novel method using dried filters fixed on plastic sheets with adhesive tape was implemented for specimen transport.
  • Acceptable limits of error (ALE) were established through technician capability assessment and clinical chemistry quality control principles.

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  • Results were analyzed using 2x2 tables and visual charts.
  • Main Results:

    • Technician performance varied significantly, with higher false negative rates (FNR) than false positive rates (FPR).
    • Field egg counts by technicians were frequently lower than control counts.
    • The new quality control system proved valuable in motivating technicians and identifying those needing additional training.

    Conclusions:

    • The developed quality control method is simple, effective, and acceptable for monitoring schistosomiasis control programs.
    • Regular quality control is crucial for maintaining high work standards and improving the accuracy of prevalence data.
    • This approach enhances technician performance and supports targeted training initiatives.