1. Which single design limitation most threatens valid estimates of sensitivity and representativeness? How would you address it within six weeks?

The single design limitation that most threatens valid estimates of sensitivity and representativeness of the AEFI surveillance system is the limited geographic and facility coverage, specifically the under-reporting from private and rural health facilities. This may lead to weak data collection, incomplete data reporting, and a lack of representativeness for the coverage.

To address this within six weeks, we need to focus on integration and collaboration with private and rural health facilities for the AEFI surveillance system. Firstly, we need to organize short compulsory training sessions (1-2 days in week one) for key staff from the above health facilities that offer vaccinations. This training will ensure everyone understands the AEFI case definitions and knows exactly how to fill out the required reporting forms, addressing critical knowledge gaps. Secondly, we need to establish clear guidelines that every confirmed adverse event must be reported immediately. This will create a clear, fast channel for data flow and speed up the response (within the timeframe). Finally, we need to conduct review meetings on AEFI reporting, along with acknowledgment of the health facilities that report AEFI cases timely and consistently with good data quality (in week six). This would help the AEFI frontline staff stay motivated, feel recognized, and gain a sense of ownership.

2. Using the CDC surveillance attributes, propose one low-cost intervention to increase sensitivity. State the expected trade-offs, and list 2–3 indicators to detect impact from the intervention.

Since sensitivity is sub-optimal due to high under-reporting, delayed transmission, and low community awareness about AEFI and its reporting, a low-cost intervention would be to increase community awareness by conducting education and outreach activities using communication tools such as pamphlets, flyers, and community talks. This aims to empower the community to initiate passive reporting. At the same time, immunization staff will provide education during their visist for immunization, on how to report AEFI cases through simple method such as sending SMS messages for AEFI cases.

Expected trade-offs: Improved data quality and greater acceptability of the system by the community which will help address under-reporting, delayed transmission, and low public awareness.

Indicators to detect the impact of the intervention (based on simplicity and generalizability):
• Increased reported AEFI cases: This will be calculated as the ratio of AEFI reports per 100,000 surviving infants per year. Reported cases will be collected from all data sources. This indicator contributes to global AEFI reporting as part of the Global Vaccine Action Plan.
Source: WHO Global Advisory Committee on Vaccine Safety – Indicators
• Proportion of community-reported AEFI cases: This will be calculated as the ratio of AEFI cases reported by the community to the total AEFI cases reported. This indicator will track the percentage of total AEFI reports that come directly via SMS or community alerts compared to the overall AEFI cases reported.

3. For a newly introduced vaccine, should the AEFI case definition be temporarily broadened to maximize early signal detection?
– If yes, what trigger would you use to revert to the prior definition?
– If no, why should this change not be implemented?

Yes, the AEFI case definition should be temporarily broadened to maximize early signal detection for a newly introduced vaccine since it described that the AEFI surveillance system in Northern Nigeria is recognized as not robust enough to generate sufficient and convincing vaccine safety data, especially for new vaccines and those under emergency authorization use.

The trigger to revert to the prior definition would be used once the National Expert Committee confirms that the AEFI surveillance system has successfully generated sufficient vaccine safety data. This means the data must be robust enough to support accurate causality assessments with consistent reporting.