Questions: Disease Surveillance Systems and Data Quality

Question 1

During a viral outbreak, testing capacity expands significantly — more people are tested, including many with mild symptoms. Reported cases double over two weeks. Which interpretation is most epidemiologically defensible?

• A. True incidence has doubled because the surveillance system is now capturing what was always there
• B. True incidence has doubled because more testing always reveals previously hidden cases with equal probability
• C. The observed increase may reflect expanded detection rather than increased true incidence — testing intensity changes the sample, not necessarily the population burden
• D. The increase confirms the surveillance system was previously functioning well, since it detected the doubling

Answer: (C)

When testing expands, more mild cases enter the reported data — cases that existed before but fell out of the detection chain. This is differential ascertainment, not necessarily a true increase in incidence. Before attributing the doubling to epidemiological change, an epidemiologist must track testing volume and test positivity rates. If testing doubles but positivity stays constant, the ascertainment fraction has increased but true incidence may not have. Option A conflates ascertainment with incidence; option B incorrectly assumes equal detection probability across all testing scenarios.

Question 2

A country uses passive surveillance to monitor influenza and consistently reports fewer cases than neighboring countries with active surveillance. What is the most likely explanation?

• A. The country has genuinely lower influenza burden due to better public health practices
• B. Passive surveillance has higher specificity, reducing false positives compared to active surveillance
• C. Passive surveillance systematically underestimates incidence because case identification depends on patients seeking care and clinicians reporting
• D. Active surveillance in neighboring countries overcounts through aggressive testing of mild cases

Answer: (C)

Passive surveillance depends on a chain: illness → care-seeking → clinical suspicion → testing → positive result → reporting. Cases fall out at every step, especially mild or asymptomatic infections that never prompt care-seeking. This is a predictable structural feature of passive systems, not a signal of lower burden. Active surveillance proactively finds cases, increasing sensitivity. Without additional evidence, option A is an unwarranted assumption; the structural difference in surveillance design is the parsimonious explanation.

Question 3

Underreporting in passive surveillance systems is a data quality failure that, with better training and incentives, could be eliminated mostly.

• T. True
• F. False

Answer: False

Underreporting is not primarily a data quality failure — it is a structural feature of passive surveillance. Even a perfectly compliant reporting system would miss cases where patients don't seek care, clinicians don't consider the diagnosis, or tests aren't available. Mild or asymptomatic infections will always be systematically underrepresented. Recognizing underreporting as a structural feature — rather than a correctable error — is essential for interpreting surveillance data correctly.

Question 4

A surveillance system with high sensitivity but poor timeliness may fail to prevent outbreaks even if it eventually detects all cases.

• T. True
• F. False

Answer: True

Timeliness is a critical performance attribute independent of sensitivity. A system that detects cases weeks after symptom onset provides little actionable information for outbreak control — by the time the signal is recognized, the window for early intervention has passed. Highly sensitive systems often require lab confirmation that introduces delays. Both sensitivity and timeliness must be adequate for surveillance to support a timely public health response; maximizing one often sacrifices the other.

Question 5

Why can an apparent increase in reported cases during an outbreak reflect changes in surveillance behavior rather than true increases in incidence, and how should an epidemiologist distinguish between the two?

Model answer: Surveillance data counts only cases that pass through the full detection chain. When testing expands or reporting incentives change, more mild cases are captured, inflating reported counts without any change in true incidence. An epidemiologist distinguishes these by tracking surveillance system attributes alongside case counts: testing volume, test positivity rates, case severity distribution, and reporting delays. If testing volume increases while positivity rates fall, the detection threshold has shifted toward milder cases. If severe cases remain constant while mild cases increase, ascertainment — not incidence — has changed.

This is the central interpretive challenge in surveillance epidemiology. Raw case counts are always a joint function of true epidemiology and surveillance behavior. Treating them as direct estimates of incidence leads to systematically wrong conclusions. The key habit is always to ask: could a change in detection explain this pattern before attributing it to a change in the pathogen or population?