# FDA FAERS Dataset

### About the Dataset

The FDA FAERS (FDA Adverse Event Reporting System) Dataset is a comprehensive data product providing normalized, cleaned, and analytically-ready access to pharmaceutical drug adverse event reports. This dataset encompasses over 18 million adverse event records, 65 million drug records, 54 million reaction records, and 18 million patient records, with advanced data quality enhancements and 4 pre-aggregated analytical reporting models for drug safety surveillance.

{% hint style="info" %}
**Get Full Access** | [Snowflake Marketplace](https://app.snowflake.com/marketplace/listing/GZT1Z125KFN/dataplex-consulting-data-products-fda-faers-dataset) | [Databricks](https://checkout.dataplex-consulting.com/b/eVqaEQcXFdGoeUocKgbQY03) | [Databricks Marketplace](https://dbc-57d84859-e152.cloud.databricks.com/marketplace/consumer/listings/f661ad57-f97a-4c89-89a5-0b78f778b3b7?o=3249760874003130) | [Free Trial](https://trial.dataplex-consulting.com)
{% endhint %}

### Quick Access

**Base Tables**: Adverse events, drugs involved, patient demographics, reported reactions, and report sources\
**Aggregate Models**: 4 pre-built analytical views for drug safety analysis, risk profiling, and trend monitoring\
**Update Frequency**: Quarterly from FDA FAERS database

## Overview

The FDA FAERS Dataset provides comprehensive access to pharmaceutical drug adverse event data including:

* **Core Event Data** (drug\_\_events) - Main table with cleaned adverse event reports
* **Drug Information** (drug\_\_events\_drugs) - Detailed information about drugs involved in events
* **Patient Demographics** (drug\_\_events\_patients) - Normalized patient data with age and demographic information
* **Adverse Reactions** (drug\_\_events\_reactions) - MedDRA-coded reactions and outcomes
* **Report Sources** (drug\_\_events\_report\_sources) - Information about who reported the events
* **Analytical Reports** - Pre-aggregated intelligence for drug safety analysis, demographic risk profiling, and trend monitoring

{% hint style="success" %}
**Ready to access FDA FAERS data?**
{% endhint %}

| Platform       | Action                                                                                                                                                  |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Snowflake**  | [Get on Marketplace →](https://app.snowflake.com/marketplace/listing/GZT1Z125KFN/dataplex-consulting-data-products-fda-faers-dataset)                   |
| **Databricks** | [Subscribe →](https://checkout.dataplex-consulting.com/b/eVqaEQcXFdGoeUocKgbQY03) \| [Start 14-Day Free Trial →](https://trial.dataplex-consulting.com) |

{% hint style="success" %}
Questions? [Contact our team](mailto:support@dataplex-consulting.com) for a walkthrough.
{% endhint %}

### Dataset Structure

The FDA FAERS dataset is organized around adverse event reports, with connected tables containing detailed information about the drugs involved, patient demographics, reported reactions, and report sources.

![FDA FAERS Dataset Structure](/files/YprlTDrEfzSTBnvAn8NF)

## Base Tables

### drug\_\_events (Main Events Table)

Core adverse event reports containing event-level information. Each record represents a single adverse event report identified by a unique safety\_report\_id.

**Key Features:**

* Over 18 million adverse event records
* Clean, standardized data ready for analysis
* Complete event details including dates, countries, and seriousness criteria
* Reporter organization information for source tracking

### drug\_\_events\_drugs (Drug Details Table)

Drug-specific information for each adverse event. Each record represents one drug involved in an adverse event report.

**Key Features:**

* Over 65 million drug records with detailed information
* Drug characterization (Suspect, Concomitant, Interacting)
* Dosage information and administration routes
* Indications for use and actions taken after the event

### drug\_\_events\_patients (Patient Information Table)

Patient demographic information with comprehensive data cleaning and normalization.

**Key Features:**

* Over 18 million patient records
* Standardized age data in years and FDA age group categories
* Weight data normalized to kilograms
* Age at onset information for temporal analysis

### drug\_\_events\_reactions (Adverse Reactions Table)

Reported adverse reactions and outcomes using standardized MedDRA terminology.

**Key Features:**

* Over 54 million reaction records
* MedDRA Preferred Terms and Lowest Level Terms
* Reaction outcomes (Recovered, Fatal, etc.)
* Links to specific patients and drugs through sequence numbers

### drug\_\_events\_report\_sources (Report Sources Table)

Information about the sources of adverse event reports.

**Key Features:**

* Reporter qualifications and credentials
* Primary source country information
* Report source types for quality assessment
* Links to main event records

## Aggregate/Reporting Models

### drug\_\_agg\_drug\_reaction\_associations

Pre-aggregated drug-reaction associations with statistical significance measures for safety signal detection.

**Use Cases:**

* Drug safety signal detection and monitoring
* Adverse reaction frequency analysis
* Comparative safety assessments across drugs
* Pharmacovigilance reporting and alerts

**Key Metrics:**

* Association strength scores between drugs and reactions
* Occurrence counts and statistical significance
* Serious event and death rates by drug-reaction pair
* Percentage of drug reports with specific reactions

### drug\_\_agg\_manufacturer\_adverse\_events

Manufacturer-level adverse event analytics with portfolio safety metrics and competitive benchmarks.

**Use Cases:**

* Manufacturer safety performance benchmarking
* Portfolio risk assessment across drug products
* Competitive intelligence for pharmaceutical companies
* Regulatory compliance monitoring

**Key Metrics:**

* Total reports by manufacturer with YoY comparisons
* Serious event and death rates by company
* Number of unique drugs with adverse events
* Trend analysis and growth metrics

### drug\_\_agg\_demographic\_risk\_profiles

Demographic risk analysis segmented by patient age groups and gender for targeted safety monitoring.

**Use Cases:**

* Identifying high-risk patient populations
* Age and gender-based safety signal detection
* Pediatric and geriatric drug safety monitoring
* Demographic-specific risk assessment

**Key Metrics:**

* Risk scores by demographic segments
* Event counts and rates by age group and gender
* Comparative risk levels across populations
* Statistical significance thresholds (10+ events)

### drug\_\_agg\_safety\_trends

Time-series safety trends with statistical trend detection and forecasting capabilities.

**Use Cases:**

* Safety signal trend monitoring over time
* Emerging risk detection and early warnings
* Seasonal pattern analysis in adverse events
* Post-market surveillance and monitoring

**Key Metrics:**

* Multiple time granularities (Daily, Weekly, Monthly, Quarterly)
* Moving averages and trend indicators
* Serious event and death rate trends
* Statistical trend direction classifications

## Data Quality Improvements

### Patient Information Standardization

**Age Normalization**

* All patient ages converted to years for consistent analysis
* FDA age group categories applied (Neonate, Infant, Child, Adolescent, Adult, Elderly)
* Handles various reporting formats and units

**Weight Standardization**

* All weights normalized to kilograms
* Automatic unit detection and conversion
* Validation for realistic weight ranges

**Demographic Enhancement**

* Gender categories standardized (Male, Female, NULL for unknown)
* Missing data clearly identified for transparency
* Age at onset calculations for temporal analysis

### Drug Information Enhancements

**Drug Name Standardization**

* Medicinal product names cleaned and normalized
* Brand names, generic names, and ingredients handled
* Consistent formatting for analysis

**Characterization Categories**

* Clear classification: Suspect, Concomitant, Interacting
* Action taken standardization (Withdrawn, Dose reduced, etc.)
* Route of administration normalization

### Reaction Coding

**MedDRA Standardization**

* Preferred Terms (PT) and Lowest Level Terms (LLT) included
* Hierarchical coding system for reaction analysis
* Outcome standardization for severity assessment

## Getting Started

### Platform Schema Reference

This dataset is available on both Snowflake and Databricks. The table names are the same, but the schema prefix differs:

| Platform       | Schema          | Example                      |
| -------------- | --------------- | ---------------------------- |
| **Snowflake**  | `dwv`           | `dwv.drug__events`           |
| **Databricks** | `fda_faers_dwv` | `fda_faers_dwv.drug__events` |

The examples below show queries for both platforms using tabs.

### Basic Query Examples

#### **Recent Adverse Events Overview**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Search for adverse events involving a specific drug
SELECT
    e.safety_report_id,
    e.receipt_date,
    e.occur_country,
    e.serious,
    d.medicinalproduct,
    d.drug_characterization,
    d.drug_indication,
    d.drug_dose_text
FROM dwv.drug__events e
INNER JOIN dwv.drug__events_drugs d
    ON e.safety_report_id = d.safety_report_id
WHERE d.medicinalproduct ILIKE '%ozempic%'
LIMIT 100;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Search for adverse events involving a specific drug
SELECT
    e.safety_report_id,
    e.receipt_date,
    e.occur_country,
    e.serious,
    d.medicinalproduct,
    d.drug_characterization,
    d.drug_indication,
    d.drug_dose_text
FROM fda_faers_dwv.drug__events e
INNER JOIN fda_faers_dwv.drug__events_drugs d
    ON e.safety_report_id = d.safety_report_id
WHERE d.medicinalproduct ILIKE '%ozempic%'
LIMIT 100;
```

{% endtab %}
{% endtabs %}

#### **Top Adverse Reactions Analysis**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Count top adverse reactions for a specific drug
SELECT
    r.reaction_meddra_pt as adverse_reaction,
    COUNT(*) as report_count,
    COUNT(CASE WHEN e.serious = true THEN 1 END) as serious_cases,
    ROUND(COUNT(CASE WHEN e.serious = true THEN 1 END) * 100.0 / COUNT(*), 2) as serious_percentage
FROM dwv.drug__events e
INNER JOIN dwv.drug__events_drugs d
    ON e.safety_report_id = d.safety_report_id
INNER JOIN dwv.drug__events_reactions r
    ON e.safety_report_id = r.safety_report_id
WHERE d.medicinalproduct ILIKE '%rinvoq%'
    AND d.drug_characterization = 'Suspect'
    AND r.reaction_meddra_pt IS NOT NULL
GROUP BY r.reaction_meddra_pt
ORDER BY report_count DESC
LIMIT 10;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Count top adverse reactions for a specific drug
SELECT
    r.reaction_meddra_pt as adverse_reaction,
    COUNT(*) as report_count,
    COUNT(CASE WHEN e.serious = true THEN 1 END) as serious_cases,
    ROUND(COUNT(CASE WHEN e.serious = true THEN 1 END) * 100.0 / COUNT(*), 2) as serious_percentage
FROM fda_faers_dwv.drug__events e
INNER JOIN fda_faers_dwv.drug__events_drugs d
    ON e.safety_report_id = d.safety_report_id
INNER JOIN fda_faers_dwv.drug__events_reactions r
    ON e.safety_report_id = r.safety_report_id
WHERE d.medicinalproduct ILIKE '%rinvoq%'
    AND d.drug_characterization = 'Suspect'
    AND r.reaction_meddra_pt IS NOT NULL
GROUP BY r.reaction_meddra_pt
ORDER BY report_count DESC
LIMIT 10;
```

{% endtab %}
{% endtabs %}

#### **Geographic Distribution Analysis**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Analyze adverse events by country and seriousness
SELECT
    occur_country,
    COUNT(*) as total_reports,
    COUNT(CASE WHEN serious = true THEN 1 END) as serious_reports,
    COUNT(CASE WHEN seriousness_death = true THEN 1 END) as fatal_reports,
    ROUND(COUNT(CASE WHEN serious = true THEN 1 END) * 100.0 / COUNT(*), 2) as serious_percentage
FROM dwv.drug__events
WHERE receipt_date >= CURRENT_DATE - INTERVAL '2 years'
    AND occur_country IS NOT NULL
GROUP BY occur_country
ORDER BY total_reports DESC
LIMIT 15;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Analyze adverse events by country and seriousness
SELECT
    occur_country,
    COUNT(*) as total_reports,
    COUNT(CASE WHEN serious = true THEN 1 END) as serious_reports,
    COUNT(CASE WHEN seriousness_death = true THEN 1 END) as fatal_reports,
    ROUND(COUNT(CASE WHEN serious = true THEN 1 END) * 100.0 / COUNT(*), 2) as serious_percentage
FROM fda_faers_dwv.drug__events
WHERE receipt_date >= CURRENT_DATE - INTERVAL '2 years'
    AND occur_country IS NOT NULL
GROUP BY occur_country
ORDER BY total_reports DESC
LIMIT 15;
```

{% endtab %}
{% endtabs %}

### Advanced Analytics Examples

#### **Demographic Risk Analysis**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Analyze patient demographics for serious adverse events
SELECT
    p.patient_sex,
    p.patient_age_group,
    COUNT(*) as patient_count,
    AVG(p.patient_age_years) as avg_age,
    COUNT(CASE WHEN e.seriousness_death = true THEN 1 END) as fatal_cases
FROM dwv.drug__events e
INNER JOIN dwv.drug__events_patients p
    ON e.safety_report_id = p.safety_report_id
WHERE e.serious = true
    AND e.receipt_date >= '2022-01-01'
    AND p.patient_sex IS NOT NULL
    AND p.patient_age_group IS NOT NULL
GROUP BY p.patient_sex, p.patient_age_group
ORDER BY patient_count DESC;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Analyze patient demographics for serious adverse events
SELECT
    p.patient_sex,
    p.patient_age_group,
    COUNT(*) as patient_count,
    AVG(p.patient_age_years) as avg_age,
    COUNT(CASE WHEN e.seriousness_death = true THEN 1 END) as fatal_cases
FROM fda_faers_dwv.drug__events e
INNER JOIN fda_faers_dwv.drug__events_patients p
    ON e.safety_report_id = p.safety_report_id
WHERE e.serious = true
    AND e.receipt_date >= '2022-01-01'
    AND p.patient_sex IS NOT NULL
    AND p.patient_age_group IS NOT NULL
GROUP BY p.patient_sex, p.patient_age_group
ORDER BY patient_count DESC;
```

{% endtab %}
{% endtabs %}

#### **Manufacturer Safety Performance**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Top pharmaceutical manufacturers by adverse event volume
SELECT
    e.sender_organization_name as manufacturer,
    COUNT(DISTINCT e.safety_report_id) as total_reports,
    COUNT(DISTINCT d.medicinalproduct) as unique_drugs,
    COUNT(CASE WHEN e.serious = true THEN 1 END) as serious_events,
    ROUND(COUNT(CASE WHEN e.serious = true THEN 1 END) * 100.0 / COUNT(*), 2) as serious_rate
FROM dwv.drug__events e
LEFT JOIN dwv.drug__events_drugs d
    ON e.safety_report_id = d.safety_report_id
WHERE e.receipt_date >= '2023-01-01'
    AND e.sender_organization_name IS NOT NULL
    AND e.sender_organization_name != ''
GROUP BY e.sender_organization_name
HAVING COUNT(DISTINCT e.safety_report_id) >= 100
ORDER BY total_reports DESC
LIMIT 20;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Top pharmaceutical manufacturers by adverse event volume
SELECT
    e.sender_organization_name as manufacturer,
    COUNT(DISTINCT e.safety_report_id) as total_reports,
    COUNT(DISTINCT d.medicinalproduct) as unique_drugs,
    COUNT(CASE WHEN e.serious = true THEN 1 END) as serious_events,
    ROUND(COUNT(CASE WHEN e.serious = true THEN 1 END) * 100.0 / COUNT(*), 2) as serious_rate
FROM fda_faers_dwv.drug__events e
LEFT JOIN fda_faers_dwv.drug__events_drugs d
    ON e.safety_report_id = d.safety_report_id
WHERE e.receipt_date >= '2023-01-01'
    AND e.sender_organization_name IS NOT NULL
    AND e.sender_organization_name != ''
GROUP BY e.sender_organization_name
HAVING COUNT(DISTINCT e.safety_report_id) >= 100
ORDER BY total_reports DESC
LIMIT 20;
```

{% endtab %}
{% endtabs %}

#### **Temporal Trend Analysis**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Monthly adverse event trends
SELECT
    DATE_TRUNC('month', receipt_date) as report_month,
    COUNT(*) as total_events,
    COUNT(CASE WHEN serious = true THEN 1 END) as serious_events,
    COUNT(DISTINCT sender_organization_name) as reporting_companies,
    COUNT(DISTINCT occur_country) as reporting_countries
FROM dwv.drug__events
WHERE receipt_date >= CURRENT_DATE - INTERVAL '2 years'
  AND receipt_date < CURRENT_DATE
GROUP BY DATE_TRUNC('month', receipt_date)
ORDER BY report_month DESC;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Monthly adverse event trends
SELECT
    DATE_TRUNC('month', receipt_date) as report_month,
    COUNT(*) as total_events,
    COUNT(CASE WHEN serious = true THEN 1 END) as serious_events,
    COUNT(DISTINCT sender_organization_name) as reporting_companies,
    COUNT(DISTINCT occur_country) as reporting_countries
FROM fda_faers_dwv.drug__events
WHERE receipt_date >= CURRENT_DATE - INTERVAL '2 years'
  AND receipt_date < CURRENT_DATE
GROUP BY DATE_TRUNC('month', receipt_date)
ORDER BY report_month DESC;
```

{% endtab %}
{% endtabs %}

### Using Pre-Aggregated Analytics

#### **Drug-Reaction Association Analysis**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Use pre-built drug-reaction associations for quick insights
SELECT
    drug_name,
    reaction,
    occurrence_count,
    serious_rate,
    death_rate,
    association_strength_score
FROM dwv.drug__agg_drug_reaction_associations
WHERE occurrence_count >= 1000  -- Focus on statistically significant associations
    AND serious_rate >= 50.0     -- High seriousness rate
ORDER BY association_strength_score DESC
LIMIT 25;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Use pre-built drug-reaction associations for quick insights
SELECT
    drug_name,
    reaction,
    occurrence_count,
    serious_rate,
    death_rate,
    association_strength_score
FROM fda_faers_dwv.drug__agg_drug_reaction_associations
WHERE occurrence_count >= 1000  -- Focus on statistically significant associations
    AND serious_rate >= 50.0     -- High seriousness rate
ORDER BY association_strength_score DESC
LIMIT 25;
```

{% endtab %}
{% endtabs %}

#### **Demographic Risk Profiling**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Identify high-risk demographics for specific drugs
SELECT
    drug_name,
    age_group,
    patient_sex,
    event_count,
    serious_rate,
    risk_score,
    risk_level
FROM dwv.drug__agg_demographic_risk_profiles
WHERE drug_name ILIKE '%metformin%'
    AND event_count >= 10  -- Statistical significance threshold
ORDER BY risk_score DESC;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Identify high-risk demographics for specific drugs
SELECT
    drug_name,
    age_group,
    patient_sex,
    event_count,
    serious_rate,
    risk_score,
    risk_level
FROM fda_faers_dwv.drug__agg_demographic_risk_profiles
WHERE drug_name ILIKE '%metformin%'
    AND event_count >= 10  -- Statistical significance threshold
ORDER BY risk_score DESC;
```

{% endtab %}
{% endtabs %}

#### **Safety Trend Monitoring**

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- Monitor monthly safety trends for a drug
SELECT
    drug_name,
    time_period,
    period_type,
    event_count,
    serious_rate,
    trend_direction,
    moving_avg_30d
FROM dwv.drug__agg_safety_trends
WHERE drug_name ILIKE '%humira%'
    AND period_type = 'Monthly'
    AND time_period >= '2023-01-01'
ORDER BY time_period DESC;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- Monitor monthly safety trends for a drug
SELECT
    drug_name,
    time_period,
    period_type,
    event_count,
    serious_rate,
    trend_direction,
    moving_avg_30d
FROM fda_faers_dwv.drug__agg_safety_trends
WHERE drug_name ILIKE '%humira%'
    AND period_type = 'Monthly'
    AND time_period >= '2023-01-01'
ORDER BY time_period DESC;
```

{% endtab %}
{% endtabs %}

## Why Choose This Dataset

* **Ready for Analysis**: Clean, normalized data with standardized terminology
* **Comprehensive Coverage**: Complete adverse event data with all related information connected
* **Pre-Built Intelligence**: Aggregated analytics for immediate insights without complex queries
* **Statistical Significance**: Built-in thresholds and association measures for reliable analysis
* **Performance Optimized**: Denormalized keys and pre-aggregations for fast query performance

## Data Update Frequency and Freshness

### Update Schedule

* **FDA Updates**: FDA releases new FAERS data quarterly
* **Corrections & Amendments**: FDA publishes corrections and amendments several times per week between quarterly releases
* **Our Updates**: We check for new data and corrections twice daily and load them immediately when available
* **Typical Data Lag**: Drug adverse event data is typically 3-6 months behind real-time due to FDA's quarterly release cycle

### Understanding Data Timeliness

The FDA FAERS data has a significant lag due to the quarterly reporting cycle:

* Adverse events are collected throughout each quarter
* FDA processes and validates reports after quarter end
* Data is typically released 3-4 months after the quarter closes

### What This Means for You

* Most recent adverse event data will be 3-6 months old
* This is inherent to FDA's quarterly release schedule for ensuring data quality

***

## Trial Access

The free trial provides a complete, joinable subset of the FDA FAERS dataset on both Snowflake and Databricks so you can evaluate the data with realistic queries before subscribing.

### What's Included in the Trial

**Event tables (parent-child linked):**

| Table                    | Trial Scope                               | Filter                          |
| ------------------------ | ----------------------------------------- | ------------------------------- |
| `drug__events`           | Full year of events (Jan 2023 - Dec 2023) | Date-filtered by `receipt_date` |
| `drug__events_drugs`     | All drugs for trial events                | FK-linked to trial events       |
| `drug__events_patients`  | All patients for trial events             | FK-linked to trial events       |
| `drug__events_reactions` | All reactions for trial events            | FK-linked to trial events       |

**Standalone tables:** `drug__events_report_sources` and all aggregate/reporting models are available with a representative sample.

### Why This Matters

Unlike a random row sample, the trial data preserves referential integrity across all tables. Every drug, patient, and reaction record in the trial corresponds to an actual event in the trial period. This means:

* **All JOINs return complete results** - no missing child records
* **Multi-table queries work exactly as they would on the full dataset**
* **Aggregate analyses over the trial period are accurate and representative**

### Trial Query Example

{% tabs %}
{% tab title="Snowflake" %}

```sql
-- This query works correctly on trial data - all joins return matching rows
SELECT
    e.safety_report_id,
    e.receipt_date,
    e.serious,
    d.medicinalproduct,
    d.drug_characterization,
    p.patient_sex,
    p.patient_age_years,
    r.reaction_meddra_pt
FROM dwv.drug__events e
JOIN dwv.drug__events_drugs d ON e.safety_report_id = d.safety_report_id
LEFT JOIN dwv.drug__events_patients p ON e.safety_report_id = p.safety_report_id
LEFT JOIN dwv.drug__events_reactions r ON e.safety_report_id = r.safety_report_id
WHERE d.drug_characterization = 'Suspect'
LIMIT 100;
```

{% endtab %}

{% tab title="Databricks" %}

```sql
-- This query works correctly on trial data - all joins return matching rows
SELECT
    e.safety_report_id,
    e.receipt_date,
    e.serious,
    d.medicinalproduct,
    d.drug_characterization,
    p.patient_sex,
    p.patient_age_years,
    r.reaction_meddra_pt
FROM fda_faers_dwv.drug__events e
JOIN fda_faers_dwv.drug__events_drugs d ON e.safety_report_id = d.safety_report_id
LEFT JOIN fda_faers_dwv.drug__events_patients p ON e.safety_report_id = p.safety_report_id
LEFT JOIN fda_faers_dwv.drug__events_reactions r ON e.safety_report_id = r.safety_report_id
WHERE d.drug_characterization = 'Suspect'
LIMIT 100;
```

{% endtab %}
{% endtabs %}

***

## Get Started

{% hint style="success" %}
**FDA FAERS Data Access**
{% endhint %}

|                  |                                                        |
| ---------------- | ------------------------------------------------------ |
| **Includes**     | All base tables, 4 aggregate models, quarterly updates |
| **Support**      | Email support included                                 |
| **Cancellation** | Cancel anytime, no long-term commitment                |

{% hint style="success" %}

#### Choose Your Platform

{% endhint %}

| Platform       | Get Access                                                                                                                                                                                                                                 | Free Trial                                                         |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------ |
| **Snowflake**  | [Get on Marketplace →](https://app.snowflake.com/marketplace/listing/GZT1Z125KFN/dataplex-consulting-data-products-fda-faers-dataset)                                                                                                      | Available via Marketplace                                          |
| **Databricks** | [Subscribe →](https://checkout.dataplex-consulting.com/b/eVqaEQcXFdGoeUocKgbQY03) or [Marketplace →](https://dbc-57d84859-e152.cloud.databricks.com/marketplace/consumer/listings/f661ad57-f97a-4c89-89a5-0b78f778b3b7?o=3249760874003130) | [Start 14-Day Free Trial →](https://trial.dataplex-consulting.com) |

## Important Data Interpretation Guidelines

* Adverse event reports are voluntary submissions and may not represent all occurrences
* Reports do not establish causation between drugs and adverse events
* Duplicate reports may exist despite FDA's deduplication efforts
* Reporting rates can be influenced by media attention, new drug launches, and other factors
* Statistical associations should be validated with clinical expertise

## FDA Documentation Resources

* [FDA Adverse Event Reporting System (FAERS)](https://www.fda.gov/drugs/surveillance/fda-adverse-event-reporting-system-faers)
* [FAERS Public Dashboard](https://www.fda.gov/drugs/fda-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard)
* [MedDRA (Medical Dictionary for Regulatory Activities)](https://www.meddra.org/)
* [Questions and Answers on FDA's AERS](https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-faers)
* [FAERS Quarterly Data Files](https://www.fda.gov/drugs/fda-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-quarterly-data-extract-files)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.dataplex-consulting.com/data-catalog/fda-faers-dataset.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.