FDA recall enforcement velocity (monthly firm panel)

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Le jeu complet n’est pas encore publié — revenez après la prochaine actualisation.Télécharger l'exemple CSV Télécharger l'exemple JSONLignes d’exemple seulement (jusqu’à 20) — pas le jeu de données complet.

Firm x month panel of FDA recall enforcement velocity from the official keyless openFDA enforcement APIs (food, drug, device). Each firm's monthly recalls are canonicalized and scored with the documented 0-100 risk composite from the sibling record-level dataset (classification severity + distribution scope + reason severity + ongoing status); trailing-3-month severity volume is compared against the prior 3 months to measure acceleration. A documented 0-100 velocity_score blends recent severity, acceleration, 12-month persistence, recall volume, and new-entrant status, ranked deterministically as velocity_rank. The panel covers the 12 complete calendar months before the feed last_updated (a ~23-month sweep supplies the full lookback); only factual recall metadata is stored. Caveat: the feed lags the real enforcement calendar by ~10 days, and report_date is the FDA publication date, not the recall initiation date. Primary key: (firm_canonical, month). Cadence: monthly.

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: FDA recall enforcement velocity (agent-curated)
Lignes: 1 517
Colonnes: 24
Cadence de la source: Mensuelle
Dernière actualisation: 26 sept. 2026
Thème: health

Colonne	Type	Description
firm_canonical	string	Canonical firm key (corporate suffixes and location tails stripped); primary key part 1.
month	string	Panel month, YYYY-MM (complete calendar months only); primary key part 2. (unit: date)
firm_country_code	string	Modal firm country over the sweep, ISO alpha-3; null when unresolvable.
recall_count	integer	Recalls attributed to the firm this month. (unit: count)
class_i_count	integer	Class I recalls this month. (unit: count)
class_ii_count	integer	Class II recalls this month. (unit: count)
class_iii_count	integer	Class III recalls this month. (unit: count)
severity_volume	float	Sum of the 0-100 recall-risk composite over this month's recalls. (unit: 0-100)
severity_volume_t3	float	Severity volume over the trailing 3 months (m-2..m). (unit: 0-100)
severity_volume_p3	float	Severity volume over the prior 3 months (m-5..m-3). (unit: 0-100)
recall_count_t12	integer	Recall count over the trailing 12 months (m-11..m). (unit: count)
months_active_t12	integer	Distinct months with >= 1 recall over m-11..m. (unit: count)
repeat_offender	boolean	True when recall_count_t12 >= 3. (unit: boolean)
new_entrant	boolean	True when the firm's first recall in the sweep falls inside the trailing 3 months. (unit: boolean)
momentum	float	(sev_t3 - sev_p3) / (sev_t3 + sev_p3 + 1), in (-1, 1). (unit: ratio)
velocity_score	float	0-100 velocity composite: 35% recent severity volume (log-scaled), 25% positive momentum, 20% 12-month persistence, 15% recall volume (log-scaled), 5% new-entrant flag. (unit: 0-100)
velocity_trend	string	new / accelerating (momentum > 0.05) / decelerating (momentum < -0.05) / stable.
velocity_rank	integer	Deterministic rank: velocity_score desc, severity_volume_t3 desc, recall_count_t12 desc, firm_canonical asc. (unit: rank)
top_reason_category	string	Modal reason category this month (ties broken lexicographically).
reason_categories	string	Distinct reason categories this month, pipe-joined.
product_lines	string	Distinct enforcement lines this month (food/drug/device), pipe-joined.
openfda_url	string	openFDA sweep query URL(s) behind this firm-month, pipe-joined. (unit: URL)
asof_date	string	Sweep window end (feed last_updated). (unit: date)
row_hash	string	Deterministic 16-hex row hash.

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

firm_canonical	month	firm_country_code	recall_count	class_i_count	class_ii_count	severity_volume	severity_volume_t3	severity_volume_p3	recall_count_t12	months_active_t12	repeat_offender	new_entrant	momentum	velocity_score	velocity_trend	velocity_rank	top_reason_category	reason_categories	product_lines	openfda_url	asof_date	row_hash
Olympus	2026-04	USA	29	0	29	2 000	7 018	427	149	11	true	false	0,885	90,5	accelerating	1	manufacturing_quality_defect	manufacturing_quality_defect\|other_unspecified	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20260401+TO+20260430]&limit=1000	2026-09-16	48197065aee39645
ICU Medical	2026-07	USA	3	0	3	198	460	0	61	9	true	false	0,998	89,9	accelerating	2	other_unspecified	electrical_mechanical_hazard\|other_unspecified	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20260701+TO+20260731]&limit=1000	2026-09-16	a5004270aab747ed
GE Medical Systems	2026-05	USA	7	0	7	448	704	47	46	9	true	false	0,874	86,8	accelerating	3	other_unspecified	other_unspecified	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20260501+TO+20260531]&limit=1000	2026-09-16	85941eabccb95c1b
Zydus Pharmaceuticals (USA)	2025-11	USA	4	0	4	280	1 317	52	27	8	true	false	0,923	86,4	accelerating	4	manufacturing_quality_defect	manufacturing_quality_defect	drug	https://api.fda.gov/drug/enforcement.json?search=report_date:[20251101+TO+20251130]&limit=1000	2026-09-16	c9a978f4054bd43b
Siemens Healthcare Diagnostics	2026-05	USA	2	0	2	134	278	0	17	8	true	false	0,996	86,1	accelerating	5	manufacturing_quality_defect	manufacturing_quality_defect\|other_unspecified	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20260501+TO+20260531]&limit=1000	2026-09-16	0105bd64d87fec6a
ICU Medical	2025-11	USA	38	0	38	2 660	3 164	290	53	9	true	false	0,832	85,8	accelerating	6	electrical_mechanical_hazard	electrical_mechanical_hazard	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20251101+TO+20251130]&limit=1000	2026-09-16	f2d284e200f61c85
GE Medical Systems	2025-09	USA	2	0	2	140	1 804	94	48	8	true	false	0,9	85,8	accelerating	7	electrical_mechanical_hazard	electrical_mechanical_hazard	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20250901+TO+20250930]&limit=1000	2026-09-16	6456329cd74eb6ca
AVID Medical	2026-07	USA	2	0	2	142	1 212	75	51	8	true	false	0,883	85,4	accelerating	8	manufacturing_quality_defect	manufacturing_quality_defect\|potency_efficacy_failure	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20260701+TO+20260731]&limit=1000	2026-09-16	330ea5167d419940
Medline Industries	2025-12	USA	9	0	9	540	4 456	0	63	3	true	true	1	85	new	9	sterility_failure	manufacturing_quality_defect\|other_unspecified\|sterility_failure	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20251201+TO+20251231]&limit=1000	2026-09-16	d397d47872c07bc4
ARROW INTERNATIONAL	2026-06	USA	35	35	0	3 325	4 221	0	47	3	true	true	1	85	new	10	sterility_failure	sterility_failure	device	https://api.fda.gov/device/enforcement.json?search=report_date:[20260601+TO+20260630]&limit=1000	2026-09-16	c9fd7f55dbdd3eee

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/fda_enforcement_velocity_intel/fda_enforcement_velocity_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/fda_enforcement_velocity_intel/fda_enforcement_velocity_monthly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API : https://datazimuts.com/v1/datasets/fda_enforcement_velocity_intel/fda_enforcement_velocity_monthly

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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APA

FDA recall enforcement velocity (agent-curated). (2026). FDA recall enforcement velocity (monthly firm panel) [Data set, snapshot 20260926T175525Z-bbda7c76b82e, sha256 bbda7c76b82e]. Datazimuts. Retrieved 2026-09-26, from https://datazimuts.com/fr/datasets/fda_enforcement_velocity_intel/fda_enforcement_velocity_monthly?snapshot=20260926T175525Z-bbda7c76b82e

BibTeX

@misc{dz_fda_enforcement_velocity_intel_fda_enfor_bbda7c76,
  title        = {{FDA recall enforcement velocity (monthly firm panel)}},
  author       = {{FDA recall enforcement velocity (agent-curated)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/fda_enforcement_velocity_intel/fda_enforcement_velocity_monthly?snapshot=20260926T175525Z-bbda7c76b82e}},
  note         = {Snapshot 20260926T175525Z-bbda7c76b82e, sha256 bbda7c76b82e1ead0dad14ae53f184e11aba8e057dfe230e8a7dbcd8723fd069; accessed 2026-09-26}
}

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