Which practice is essential for data quality assurance in field surveillance?

• A. Fabricating data
• B. Ignoring missing values
• C. Selective reporting
• D. Data validation and cleaning

Answer: (D)

In field surveillance, ensuring data quality hinges on validating and cleaning the data to ensure accuracy, completeness, and consistency. Validation involves checking data as it’s collected or entered against predefined rules and logical checks—making sure values are plausible, correctly formatted, and consistent with related fields (for example, age isn’t negative, dates are in a sensible range, and categories match standard definitions). Cleaning takes what validation flags and addresses issues like missing values, duplicates, inconsistent labels, and outliers—revising or imputing data so the dataset reflects a true picture of the situation.

This combination is essential because trustworthy data underpins reliable analyses, timely detection of outbreaks, and appropriate public health actions. Without validation and cleaning, errors propagate, leading to biased results, misinterpretation, and poor decisions.

Fabricating data misleads and corrupts the dataset. Ignoring missing values can bias findings or distort trends. Selective reporting introduces bias and undermines the usefulness of surveillance.