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Improving data use for decision making by neglected tropical disease program teams: eight use cases.

Liz Grubin1, Lakshmi Balachandran2, Sarah Bartlett3

  • 1Vital Wave, Palo Atlo, CA, USA.

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Summary

Strengthening data use in neglected tropical diseases (NTDs) programs is crucial for achieving global health goals. This analysis identifies eight key use cases for improving data quality and decision-making in NTD control programs.

Keywords:
Neglected tropical diseases; data systems; decision-making; use cases; data solutions

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Area of Science:

  • Public Health
  • Tropical Medicine
  • Data Science

Background:

  • Neglected tropical diseases (NTDs) program success relies on high-quality, timely data for effective decision-making and delivery.
  • This analysis focuses on routine data utilization within national NTD programs targeting lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiasis, and trachoma.

Purpose of the Study:

  • To conduct a use case analysis of routine data employed by national NTD programs.
  • To identify critical data-related needs, challenges, and solutions for improving program delivery and decision-making.

Main Methods:

  • A mixed-methods approach combining secondary and primary research.
  • Case studies in Burkina Faso, Ethiopia, and Tanzania, with stakeholder consultations to refine and prioritize use cases.

Main Results:

  • Eight distinct use cases were developed, including improving treatment register data quality, strengthening supervision during mass drug administration (MDA), generating accurate population data, managing drug inventory, meeting coverage targets, and utilizing data for program evaluation.
  • Each use case details key actors, data needs, challenges, current/desired states, and solution profiles (digital and non-digital).

Conclusions:

  • Systematic enhancement of data use in NTD programs is essential for achieving the 2030 Roadmap goals.
  • Implementing these use cases with appropriate solutions can ensure accurate, timely data availability and empower data-driven program decisions at all levels.