Brief · Use case · Pharmacovigilance

Safety Report Rise Diagnosis.

LiveDiagnosis

Find out whether a rise in adverse event reports is a true signal or a change in reporting, with what would confirm it.

ProblemA rise in reports of one event type

Explanations weighedAt least 3

ExpertHead of Pharmacovigilance

Not askedA decision on what to do

LengthComprehensive

Safety Report Rise Diagnosis is a ready-made use case for Pharmacovigilance. It ships under the Diagnosis template.

For pharmacovigilance leads who see a rise in reports. You get what the reports show and what they cannot show, a true signal weighed against reporting and population explanations, the likely cause with a confidence level and the checks that would refute it. Claims are cited or marked as gaps.

Add it to your library to reuse from New → From template. Edit the saved copy in your library on the service form.

The decision

Why have spontaneous adverse event reports of one event type risen for a marketed injectable over the last two quarters: a true safety signal, a change in reporting behavior, a change in the exposed population, or a change in coding?

The explanations weighed

A rise in reports is a measurement of reporting, not of harm. The diagnosis keeps the two apart and weighs four explanations against the record.

Explanation	What it would look like	What would confirm or refute it
A true safety signal	The product causes the event more often, or in a new group. Disproportionality methods can flag a pair reported more often than expected, but a flag is a screening result.^{1U.S. Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS).,2Evans, S. J. W., Waller, P. C. and Davis, S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety, 2001;10(6):483–486.}	Confirmed by clinical review of cases, a plausible mechanism and consistent findings in other sources. Refuted if the cases are explained by something else.
A change in reporting behavior	More reports because more people report, for example after publicity or a new reporting channel. FDA notes that the time a product has been marketed and publicity about an event influence whether it is reported.^{1U.S. Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS).}	Confirmed by a jump that follows an event such as a news item or a program, with no change in case severity. Refuted if the rise precedes any such event.
A change in the exposed population	More reports because more patients, or different patients, use the product.	Confirmed by sales or prescription data that move with the reports. Refuted if exposure is flat.
A change in coding	The same events are coded differently, for example under a new dictionary version or a new coding practice.	Confirmed by a shift in the terms used with no change in case content. Refuted if the case narratives match the new terms.

What the diagnosis does

What spontaneous reports can and cannot show. FDA states that the existence of a report in its system does not mean the product caused the event, that reports are not medically confirmed, and that the data cannot be used to calculate how often an event occurs in the population. The diagnosis builds on those limits and does not argue past them.^{1U.S. Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS).}

A flag is a screen. The proportional reporting ratio was proposed with criteria of three or more cases, a ratio of at least 2 and a chi-squared of at least 4. In the original evaluation about 70 percent of the signals it generated were known reactions, so a flag leaves most of the work to clinical review. The diagnosis reports the method and its published performance.^{2Evans, S. J. W., Waller, P. C. and Davis, S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety, 2001;10(6):483–486.}

The denominator problem. Without the number of people exposed, a rise in reports cannot be told from a rise in use. The diagnosis asks for exposure data and says what it would change.^{1U.S. Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS).}

How it could be wrong. The likely cause rests on an assumption, such as that exposure is flat or that coding did not change. The diagnosis names the assumption and the check that would break it.

Recommended length

Comprehensive

The question asks you to weigh at least three explanations. The matrix that does that, and the Pareto view of where the effect sits, are in the Comprehensive length. Standard names a cause without the side-by-side.

You can pick another length on the form. The other two stay available.

Assumptions the diagnosis checks

The diagnosis states the assumptions its likely cause rests on and sets what would break each one. Examples for this problem:

The report counts are for the same product, the same route and the same period in both quarters.
Exposure data are available and comparable across the two quarters.
The coding dictionary and practice did not change between the quarters.

Standards, guidance and sources

Limits of spontaneous reports

FDA, Questions and Answers on FDA’s Adverse Event Reporting System (FAERS): reports do not establish causation, cannot give incidence, and are influenced by marketing time and publicity.^{1U.S. Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS).}

Signal detection

Evans, Waller and Davis, Use of proportional reporting ratios for signal generation from spontaneous adverse drug reaction reports, 2001.^{2Evans, S. J. W., Waller, P. C. and Davis, S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety, 2001;10(6):483–486.}

The decision practice behind the memo itself is on the template page.

What gets flagged

Any conclusion about causation drawn from reports alone, any rate computed without exposure, and any change in coding or reporting channels the diagnosis could not rule out.

What arrives

The problem as stated
What is actually happening
Competing explanations
The likely cause
What would confirm it
Where the effect concentrates
What to rule out

What it will not do

It does not decide what to do about the cause. A Decision Memo takes the diagnosis as its starting point.

It is not a signal evaluation under pharmacovigilance rules and does not conclude that a signal is confirmed.

It does not prove the cause. It names the most likely cause, says how sure it is, and says what would show it to be wrong.

It does not assign blame. It looks for contributing causes in the system, in the way a blameless postmortem does.

It does not act. Deciding what to do about the cause is a different question; a Decision Memo is the template for it.

References

[1] U.S. Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS). The limits are as stated in FDA’s FAERS materials listed by search; the page was not opened from our tools.
[2] Evans, S. J. W., Waller, P. C. and Davis, S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety, 2001;10(6):483–486. Read from the abstract as summarized in search excerpts.

Links checked 9 October 2026

These are the sources the template drew on. None of their authors or issuers has reviewed it.

About the Brief service

A Brief is a written answer from one specialist, at the depth you asked for. It arrives with its sources, and it closes by saying plainly what would change the conclusion. Nothing is hidden behind a summary, and anything the expert could not source is marked as a gap rather than filled in.

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