Practical Health Data Triangulation Project

Case study: The District Health Data Challenge

A fictional district health team is reviewing a child health programme. It has received three datasets. The facility and outreach reports count service contacts. The survey estimates unique children. The datasets have not yet been checked for overlap.

Dataset C: the community survey estimates that 620 unique children received the specified service during the defined period.

1. Task 1. Name the three data sources.
2. Task 2. What does each source measure?
3. Task 3. Why can we not automatically add the facility and outreach totals and compare the result directly with the survey estimate?
4. Task 4. List at least four factors that could explain differences between the routine reports and the survey estimate.
5. Task 5. Identify the information you would need before deciding whether the datasets can be compared.
6. Task 6. Write three actions that the district health team should take.
7. Task 7. Write approximately 150–200 words explaining the findings, limitations and recommended next steps.

Module 4 completion checklist

• Explain multi-source health data analysis.
• Define data triangulation.
• Identify common health data sources.
• Explain why different datasets may disagree.
• Check reporting periods and geographical coverage.
• Distinguish service contacts from unique people.
• Identify possible duplicate or overlapping records.
• Explain the importance of indicator definitions and denominators.
• Prepare a triangulation matrix.
• Write recommendations that reflect the available evidence.

You have learned how to compare information from different health data sources, investigate discrepancies and communicate findings responsibly. These concepts are relevant to health information systems, public health reporting, monitoring and evaluation, research and multi-source analysis.

Next: Module 5, Power BI for Health Data Visualisation and Dashboards. That module will look at how organised health data can be turned into clear visual reports that help decision-makers understand patterns, compare performance and identify questions requiring further investigation.