What is data linkage and how is it used in surveillance?

• A. Data linkage connects records from different sources (e.g., lab, clinical, vital records) using identifiers to create a complete case profile and enhance data quality.
• B. Data linkage merges datasets to create synthetic data for simulations.
• C. Data linkage anonymizes all data to prevent re-identification.
• D. Data linkage is used only within a single source.

Answer: (A)

Data linkage in surveillance means connecting records from multiple data sources—such as laboratory results, clinical care, and vital records—to form a single, fuller view of each case. By using identifiers or matching information, records that refer to the same person can be linked, either through exact matches or through probabilistic methods when exact identifiers are missing. This creates a complete case profile and helps improve data quality by reducing duplicates, filling missing details, and reconciling conflicting data.

In practical terms, linkage lets public health workers track an outbreak more accurately and monitor outcomes over time, because they have richer, more reliable information about each case. It enhances timeliness and completeness of surveillance data, supporting better decision-making and response.

The other ideas don’t capture what data linkage does in surveillance: merging datasets to create synthetic data is a separate approach used for simulations, not for linking real-world records; anonymizing all data is about privacy and re-identification risk, not about building linked case histories; and using linkage within a single source ignores the benefit of combining information across multiple data streams to improve surveillance insights.