Most-reported adverse event terms for ten drug classes

Question

Which reaction terms are most frequently reported to the US FDA Adverse Event Reporting System (FAERS) for ten widely used drugs, and which terms recur across drugs with different mechanisms? This note reports counts only, not risk, incidence, or causation.

Method

Source: the openFDA drug event API, which serves FAERS reports.

• Endpoint: https://api.fda.gov/drug/event.json
• Query pattern (one per drug): search=patient.drug.medicinalproduct:"<DRUG>"&count=patient.reaction.reactionmeddrapt.exact&limit=5
• Drugs, one per class: atorvastatin (statin), metformin (biguanide), amoxicillin (penicillin), ibuprofen (NSAID), warfarin (vitamin K antagonist), insulin glargine (long-acting insulin), levothyroxine (thyroid hormone), omeprazole (proton pump inhibitor), sertraline (SSRI), allopurinol (xanthine oxidase inhibitor).
• Retrieved 2026-10-30 (UTC); meta.last_updated read 2026-07-30 in all ten responses.
• Client: Python 3 urllib, User-Agent research-notes/1.0 (public-data analysis; python-urllib).
• 10 requests, one per drug, 0.4 s apart, against a hard cap of 40. No request failed; raw.json records an empty failure list and the count.

Each response was saved to raw.json before parsing. data.csv was then derived from raw.json (50 rows: 10 drugs × 5 ranks). Every result count below is read from data.csv.

Results

Cells give term and count, left to right.

These are counts of reports, not risks. FAERS accepts voluntary reports with no denominator, no control group, and no verification that the drug caused the event.

• Five terms account for 38 of the 50 cells: NAUSEA (9 of 10 drugs), DIARRHOEA (8), FATIGUE (8), DRUG INEFFECTIVE (7), DYSPNOEA (6). With HEADACHE (3) and BLOOD GLUCOSE INCREASED (2) the table holds 14 distinct terms; seven occur on one drug each.
• The top rank is split: NAUSEA leads for metformin, omeprazole and sertraline; FATIGUE for atorvastatin and levothyroxine; DIARRHOEA for amoxicillin and allopurinol; DRUG INEFFECTIVE for ibuprofen; INTERNATIONAL NORMALISED RATIO INCREASED for warfarin; BLOOD GLUCOSE INCREASED for insulin glargine.
• Seven terms appear on exactly one drug: RASH (amoxicillin), PAIN (ibuprofen), DRUG INTERACTION and INTERNATIONAL NORMALISED RATIO INCREASED (warfarin), HYPOGLYCAEMIA and DEVICE MECHANICAL ISSUE (insulin glargine), and DEATH (allopurinol, rank 5, 7,011).
• Cell counts span 1,067 (DYSPNOEA, insulin glargine) to 30,192 (NAUSEA, metformin), and the 50 cells total 645,303.

What this does and does not show

• No denominator. Counts cannot be converted to rates. Nothing here compares drugs: a larger count does not mean a drug is more hazardous; it may mean the drug is prescribed more widely, or reported more actively.
• Voluntary and unverified. Anyone may submit a report. Reports are suspicions, not adjudicated findings; a report does not establish that the drug caused the reaction.
• Duplicates and stimulated reporting. The same event can be submitted more than once and follow-ups are filed as separate reports. Publicity, litigation, and label changes can raise counts for particular terms.
• No causation, ever. FAERS is routinely read as if it measured incidence or proved harm. It does neither. A count is a tally of what someone chose to report.
• Name matching is literal. patient.drug.medicinalproduct is the product name as submitted. Reports filed under a brand name, a misspelling, or a non-US name are not counted by a generic-string query, and this analysis did not quantify that shortfall.
• Terms are not exclusive. One report can carry several reaction terms, so counts do not sum to a report total, and overlapping gastrointestinal and fatigue terms may reflect reporting habit as much as distinct events.
• Outcome words are not reactions. DRUG INEFFECTIVE, DEVICE MECHANICAL ISSUE, and DEATH are outcome categories in the terminology, not adverse reactions attributable to a drug. DEATH in particular is not evidence that allopurinol caused death.
• A dated snapshot. The API reported data current to 2026-07-30; later submissions will change the numbers.

Reproduce

cd /Volumes/Storage/DeepSeek/workspace/pgx-gap-map/research-notes/_scripts/faers-adverse-events
python3 fetch.py      # 10 requests, 0.4 s apart, writes raw.json into the note dir
python3 derive.py     # writes data.csv from raw.json, prints recurrence counts

One query, by hand:

python3 -c 'import urllib.request,json;u="https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct%3A%22atorvastatin%22&count=patient.reaction.reactionmeddrapt.exact&limit=5";print(json.dumps(json.loads(urllib.request.urlopen(urllib.request.Request(u,headers={"User-Agent":"research-notes/1.0"})).read())["results"],indent=2))'

Publication status

Draft. Auto-generated from public API responses; requires editorial review before publication.