Why is de-duplication essential when merging datasets from multiple sources?

• A. It prevents double-counting and misrepresentation of disease burden; ensures accurate estimates for decisions.
• B. It makes data harder to analyze.
• C. It reduces data quality by deleting duplicates randomly.
• D. It has no impact on data accuracy.

Answer: (A)

Removing duplicates when merging data from multiple sources is essential because it ensures that each case is counted only once. In disease surveillance, information comes from many places—hospitals, labs, clinics, and registries—and the same patient or event can appear in more than one system. If duplicates aren’t removed, the same case can be counted multiple times, inflating measures of disease incidence and prevalence and leading to overstated disease burden. That overestimation can mislead decisions about where and how to allocate resources, which interventions to implement, and how to interpret trends.

Deduplication uses matching techniques to link records that refer to the same case, creating a single, unified record with accurate counts. This improves the reliability of estimates used for public health decisions and helps ensure data quality. The other statements miss the mark because removing duplicates actually aids analysis, improves accuracy rather than reduces it, and does have a meaningful impact on data quality.