Identify three common data quality issues in surveillance data.

• A. Excess data, perfect accuracy, and immediate reporting.
• B. Missing data, duplicate records, and misclassification or delays in reporting.
• C. Only missing data, no duplicates, no delays.
• D. Data that's irrelevant, inconsistent measurement units, and missing data.

Answer: (B)

In surveillance data, having complete, correctly labeled, and timely information is essential for an accurate picture of health status. The three common data quality issues are missing data, duplicate records, and misclassification or delays in reporting.

Missing data creates gaps that can bias results and reduce the reliability of analyses if those gaps aren’t handled properly. Duplicate records inflate counts and can make trends look higher than they truly are unless duplicates are identified and removed. Misclassification means a record is labeled incorrectly (such as a non-case counted as a case or the wrong disease category), which distorts the true distribution and counts; delays in reporting reduce timeliness, causing underestimation of current activity and lag in response.

Other options describe ideal conditions or mix in less central problems, so they don’t capture the real, common data quality challenges in surveillance.