Which statement best describes data validation in surveillance?

• A. Data validation checks data accuracy and consistency; for example, verifying that onset date precedes report date.
• B. Data validation means collecting more data, regardless of quality.
• C. Data validation is optional and rarely used.
• D. Data validation automatically corrects all errors.

Answer: (A)

In surveillance, data validation is about making sure the information you’re using is accurate and consistent before you analyze it. This means checking that values make sense, are plausible, and fit together logically. A classic example is ensuring the onset date of illness occurs before the report date; if the onset appears after the report date, that flags a possible data entry or recording error that needs to be resolved before analysis. Validation also covers other checks like plausible age ranges, valid geographic locations, consistent case classifications, and ensuring required fields aren’t missing. By catching these issues early, you protect the integrity of indicators like incidence trends and outbreak signals. It isn’t about collecting more data, it isn’t optional, and it doesn’t automatically fix every error—validation identifies problems so they can be corrected or excluded as appropriate, often followed by data cleaning to improve overall quality.