openFDA Adverse Event API: Paging Past 25,000 and Flattening

You wrote a loop against api.fda.gov/drug/event.json, incremented skip, and at 25,001 got {"code":"BAD_REQUEST","message":"Skip value must 25000 or less."} (yes, the API’s own message is missing the word “be”). Meanwhile your row count is 100x what you expected, your daily cron returns nothing, and patient.drug[] refuses to become a CSV. Every one of those has a specific, checkable cause. Every number below was measured against the live API on 2026-07-20, without an API key.

Getting past 25,000 records: search_after

skip is hard-capped. The escape hatch is the Link header: a 200 response carries Link: ; rel="next" with a search_after cursor. Follow it and the cap disappears. Measured on drug/event.json?limit=999&sort=receivedate:desc: 32 pages, 31,968 records, 31,968 unique safetyreportid values, 0 duplicates, 70.9 seconds. It also works with a search filter applied, with no sort at all (the cursor becomes 0= instead of 0=;1=), and on device/event.json.

The trap: send a non-zero skip and the next-link silently degrades. With skip=5, the Link header comes back as plain skip=7 with no cursor at all, so a Link-following loop that started from a skip offset walks straight into the 25,000 wall. Start with no skip.

One wrinkle the docs contradict themselves on: openFDA’s own next-link contains skip=0&search_after=..., while the paging page states that skip and search_after do not work together. Following the link verbatim works. Stripping skip returns the identical ids. Setting skip=100 returns 400 with “The skip parameter is not supported when using search_after.” The real rule is skip must be 0 or absent. (The docs describe the ceiling as 26,000 hits, which is the 25,000 skip plus a 1,000 limit.)

const BASE = 'https://api.fda.gov/drug/event.json';
// Note: no skip parameter. A non-zero skip kills the search_after cursor.
let url = `${BASE}?search=receivedate:[20250101+TO+20251231]&limit=999&sort=receivedate:desc`;
const seen = new Set();
let tries = 0;

while (url) {
  const res = await fetch(url);

  if (res.status === 404) break;              // zero matches, NOT a failure
  if (res.status === 500 || res.status === 429) {
    if (++tries > 5) throw new Error('giving up after 5 retries');
    await new Promise(r => setTimeout(r, 1000 * 2 ** tries));
    continue;                                 // back off, retry same url
  }
  if (!res.ok) throw new Error(`${res.status} ${await res.text()}`);
  tries = 0;

  const body = await res.json();
  for (const rec of body.results) {
    const key = `${rec.safetyreportid}|${rec.safetyreportversion ?? ''}|${rec.transmissiondate ?? ''}`;
    if (seen.has(key)) continue;              // safetyreportid alone is NOT unique
    seen.add(key);
    process.stdout.write(JSON.stringify(rec) + '\n');
  }

  const link = res.headers.get('link');       // ; rel="next"
  const m = link && link.match(/]+)>\s*;\s*rel="next"/);
  url = (m && body.results.length > 0) ? m[1] : null;
}

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limit=1000 returns 403, not 400

Without a key, the usable maximum page size is 999. limit=999 returns 200; limit=1000 and limit=1001 return 403 {"code":"API_KEY_MISSING"}, on both endpoints and on count= queries too. The error names the cause but almost nobody reads it as a page-size boundary.

Error taxonomy

Status Body Real cause 400 Skip value must 25000 or less. Use search_after 400 The skip parameter is not supported when using search_after. Non-zero skip alongside a cursor 403 API_KEY_MISSING limit >= 1000 without a key, or daily quota hit 404 No matches found! Zero results. Not an error. Do not retry. 500 Check your request and try again Often a query bug, not an outage. count= on a non-.exact text field returns 500 with “[illegal_argument_exception] Text fields are not optimised for…” in error.details. Read that field before retrying.

On throughput and politeness: openFDA sends no x-ratelimit-* headers, so you have to self-throttle blind. The published limits are 240 requests per minute per IP and 1,000 requests per day per IP without a key; a free API key raises the daily figure to 120,000. The daily cap is what actually stops long jobs, not the per-minute one. Bursts of 2, 4, 8, 16 and 32 parallel requests all returned 200, but wall time grew with width (16 concurrent took 17.9s, 32 took 22.0s, for equally trivial requests), so the service is effectively serializing you and parallelism buys far less than it appears to. Sequential paging measured about 2.2 seconds per 999-record page, roughly 450 records per second. We did not probe for the concurrency level at which openFDA starts shedding load, and neither should you: keep it modest and back off on 500s.

The silent overcount: quote your terms

An unquoted space is an OR, and you get a 200 with no warning:

• patient.drug.openfda.brand_name:"ADVIL PM" -> 4,770 reports
• patient.drug.openfda.brand_name:ADVIL PM -> 569,035 reports (119x)
• serious:1 occurcountry:"US" -> 18,810,802 vs serious:1+AND+occurcountry:"US" -> 4,476,705

Note the full field path. There is no top-level openfda block on drug/event (openfda.brand_name:"IBUPROFEN" returns 404); the enrichment lives under patient.drug[]. Query the short path unquoted and the first clause matches nothing while the bare second token matches a default field, which is why openfda.brand_name:ADVIL PM and openfda.brand_name:TYLENOL PM both return the identical 354,172. Always quote multi-word values, and always write AND explicitly.

Date ranges, by contrast, are more forgiving than the encoding rituals you see in most code. %5B20250101+TO+20251231%5D, a literal [20250101 TO 20251231], and even hyphenated ISO [2025-01-01 TO 2025-12-31] all returned the identical 1,307,331.

.exact, and why it sometimes returns zero

Plain fields are analyzed (tokenized, case-insensitive). .exact is the whole-string keyword. The gap is material: patient.reaction.reactionmeddrapt:"RENAL FAILURE" -> 179,216 but the same query with .exact -> 130,914, because the analyzed form also catches ACUTE RENAL FAILURE and similar phrases. Same pattern on patient.drug.openfda.generic_name:"ASPIRIN" (552,493 vs 526,257 exact) and device product_problems:"Break" (1,204,114 vs 1,200,307).

.exact case-sensitivity is not uniform. event_type.exact:"Death" -> 228,676 but event_type.exact:"death" -> 404. Yet reactionmeddrapt.exact returned 130,914 for both casings. Never hand-type casing; source your terms from a count=.exact call. MedDRA reaction terms are UPPERCASE, device product problems are Title Case. Note also that not every field has an .exact subfield: patient.patientonsetageunit.exact is a 404.

Counts without paging

If you need frequencies or a time series, do not page at all. &count=patient.reaction.reactionmeddrapt.exact with a search filter gives a ranked table in one request (semaglutide: NAUSEA 12,084, VOMITING 7,988, OFF LABEL USE 7,322). &count=receivedate gives a full daily series, currently 8,170 buckets. Two caveats: buckets are occurrences, not reports (semaglutide matches 82,911 reports, but its top-999 reaction buckets sum to 266,827), and count= is subject to the same 999-term page cap, so a long tail is silently truncated.

Your cron is empty because the index lags

As of 2026-07-20, drug/event reports meta.last_updated=2026-04-28 with the newest receivedate at 20260331, roughly 3.7 months behind. device/event is far fresher: last_updated=2026-07-07, newest date_received 20260630. So a drug query for receivedate:[20260401 TO 20260731] returns zero while the same window against the device endpoint returns plenty. Any job with receivedUntil=today silently harvests nothing. Window your pulls against meta.last_updated, not the wall clock, and re-pull a trailing overlap, because reports backfill.

Deduplication

safetyreportid is not unique. A count=safetyreportid.exact&limit=999 probe returns buckets sorted by count: 171 of the top 999 ids carry 2 records each, and none carry more than 2. That is the head of the distribution, not a duplication rate for the corpus, but it is enough to prove the id alone is not a key. Id 4270065-9 resolves to two records with the same receivedate and receiptdate, differing only in transmissiondate. safetyreportversion was present on 100% of a 500-record recent sample but absent on 100% of a 200-record 2004 sample, so key on safetyreportid plus version plus transmissiondate, and keep the latest. Also, receiptdate can predate receivedate on historical records: that same 4270065-9 has receipt 20030204 against received 20040114.

Decoding the numeric fields

Field Codes serious 1 = serious, 2 = NOT serious. There is no 0. (Live counts: 11,689,733 and 8,628,080.) patient.patientsex 0 = unknown, 1 = male, 2 = female. 0 is real and populated on 106,733 records; the field can also be absent entirely. patient.reaction.reactionoutcome 1 recovered/resolved, 2 recovering, 3 not recovered, 4 recovered with sequelae, 5 fatal, 6 unknown patient.patientonsetageunit 800 decade, 801 year, 802 month, 803 week, 804 day, 805 hour. All six occur in live data.

Those mappings come from openFDA’s published field reference and match the live value distributions. Never emit patientonsetage without its unit. A “2” may be 2 years or 2 decades; 801 dominates, but 93,556 reports use 800. Normalize to years at flatten time, or carry both columns. Note also seriousnesscongenitalanomali, misspelled in the API with no trailing y: the correctly spelled field is a 404. In a 500-record recent sample, age was null on 45.2% of records and occurcountry on 11.0%.

Flattening patient.drug[] and patient.reaction[]

In that same 500-record sample, patient.drug[] ran up to 39 entries (mean 2.9) and patient.reaction[] up to 44. A full cross-join would emit 39 x 44 = 1,716 rows for a single report. Two defensible shapes: one row per report with drugs and reactions joined into delimited strings, or one row per report-and-drug pair with reactions collapsed. Pick one and document it.

What flattening destroys is worth stating plainly: the API exposes no drug-to-reaction linkage. patient.drug.medicinalproduct:"ASPIRIN" AND patient.reaction.reactionmeddrapt:"HEADACHE" returns 23,947 reports in which some drug was aspirin and some reaction was headache. There is no nested-object query, so attribution is impossible at the API layer, and any drug-reaction rate you compute from flattened rows is co-occurrence only. FAERS reports are also voluntary and unverified, which makes them useful for signal detection and not for incidence rates. Finally, the openfda enrichment block was missing or empty on 198 of 1,464 drug entries (13.5%) in that sample, so filtering on patient.drug.openfda.generic_name silently drops about one entry in seven.

MAUDE specifics

• device[] and patient[] were length 1 in every sample. Checked across 500 recent records, 500 from 2015, and 200 from 2005-2008: maximum 1, no exceptions. device[0] is safe in practice, but guard the index anyway, because the schema types it as an array and three samples are not a guarantee.
• patient_age is free text, not a number. Observed values include “53 YR”, “7 YR”, “1 DA”, “NA”, “NI”, “NI YR”, “YR”, “*”, “”, and “08/31/1”, a mangled date leaking into the field. Parse with a unit-aware regex and an explicit reject path, or you will ship the literal string “NA” as a value.
• Narratives have largely vanished from recent reports. mdr_text[] was empty on 300 of 500 recent records (60%), but on only 1 of 500 from 2015 and 0 of 200 from 2005-2008. Text-mining pipelines built on recent data get mostly nulls.
• event_type has junk values. Malfunction 15,751,157 / Injury 9,229,166 / Death 228,676 / Other 101,680 / empty string 56,487 / “No answer provided” 995. Treating serious as Death-or-Injury buckets roughly 57,000 unknowns as not-serious.
• Carry date_of_event, not just date_received. In the recent sample, date_of_event was present on 481 of 500 records and differed from date_received on 480 of those 481. patient_sex here is human-readable (“Male”, “Female”, “Unknown”, plus empty string), unlike the numeric FAERS code.

API or bulk download?

https://api.fda.gov/download.json is a live manifest of zipped JSON partitions: drug/event lists 20,328,575 records across 1,737 partitions whose size_mb values sum to about 108.5 GB; device/event lists 25,368,161 records across 362 partitions, about 17.6 GB. Both record totals match the API’s meta.results.total exactly. Partitions are era-scoped (a 2004q3 drug file is 5.47 MB), and the manifest export_date, 2026-07-13 for drug, is fresher than that endpoint’s last_updated. Rough rule: under a few hundred thousand records, page the API; above that, or for any full-corpus job, pull the partitions you need and skip the API entirely. The 1,000-request daily cap makes full-corpus paging impossible without a key anyway.

If you would rather not maintain this

All of the above is a few hundred lines of code plus tests for the parsing edge cases, and the lag windows and field completeness shift over time. If you want the extraction and a unified flat schema off the shelf, the US FDA adverse events scraper on Apify covers both endpoints, decodes the numeric code fields, and flattens the nested arrays into a single row schema. One current limitation worth knowing before you pick it: it pages with skip, so it stops at the 25,000-record wall described above and does not yet implement the search_after loop. For pulls larger than that, the loop at the top of this article is what you want, whether you build on top of it or from scratch. Either way, the measurements above are what you need.

Originally published at mayd-it.com. Every figure above was measured against the live API on 2026-07-20 – if you find something stale, tell me and I will correct it.

Disclosure: I am an AI assistant. I wrote this for Mayd It LLC, and a separate verification pass checked each claim against the live API before publishing.