CBDB Research Schema - Table and Column Documentation

Overview

This document provides detailed column information for all tables in the research schema, designed for analysis planning and code development. The research schema contains deidentified views with dates converted to ages for privacy protection.

Note: This documentation excludes adrc_values table as that documentation has been provided separately.

Table 1: `research.person`

Purpose: Demographics, cohort information, and insurance data Row Level: One record per person

Column List:

Column Name	Data Type	Description
`person_id`	integer	Unique identifier for each person
`gender_concept_id`	integer	OMOP concept ID for gender
`year_of_birth`	integer	Year of birth for age calculations
`race_concept_id`	integer	OMOP concept ID for race
`ethnicity_concept_id`	integer	OMOP concept ID for ethnicity
`provider_id`	integer	Associated provider ID
`care_site_id`	integer	Associated care site ID
`gender_source_value`	varchar	Source system gender value
`gender_source_concept_id`	integer	Source system gender concept
`race_source_value`	varchar	Source system race value
`race_source_concept_id`	integer	Source system race concept
`ethnicity_source_value`	varchar	Source system ethnicity value
`ethnicity_source_concept_id`	integer	Source system ethnicity concept
`adrc_cohort`	integer	ADRC cohort flag (1=yes, 0=no)
`insurance`	varchar	Insurance information from EPIC (concatenated financial class names)
`first_condition_year_month`	varchar	First condition date as YYYY-MM format only
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Year of birth included for precise age calculations and birth cohort analysis
Use adrc_cohort = 1 to filter for main research population
Insurance data contains concatenated financial class names from EPIC (e.g., "Medicare", "Commercial Insurance; Medicare")
first_condition_year_month provides timeline context without exact dates
Insurance information is available for Clarity patients who have coverage data in EPIC

Table 2: `research.condition_occurrence`

Purpose: Diagnosis records with age-based temporal data Row Level: One record per condition occurrence

Column List:

Column Name	Data Type	Description
`condition_occurrence_id`	bigint	Unique identifier for each condition occurrence
`person_id`	integer	Reference to person table
`condition_concept_id`	integer	OMOP concept ID for the condition
`age_at_condition_start`	numeric	Age when condition was first diagnosed
`age_at_condition_end`	numeric	Age when condition ended (if applicable)
`condition_type_concept_id`	integer	Type of condition record (EHR, claim, etc.)
`stop_reason`	varchar	Reason condition was stopped
`provider_id`	integer	Provider who recorded the condition
`visit_occurrence_id`	bigint	Associated visit
`visit_detail_id`	bigint	Associated visit detail
`condition_source_value`	varchar	Original condition code from source
`condition_source_concept_id`	integer	Source concept for the condition
`condition_status_source_value`	varchar	Source value for condition status
`condition_status_concept_id`	integer	Concept for condition status
`facility_id`	integer	Facility where condition was recorded
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Age fields replace all date fields for deidentification
Link to research.concept using condition_concept_id for condition names
condition_source_value contains original ICD codes (type:value format)

Table 3: `research.drug_exposure`

Purpose: Medication records with age-based temporal data Row Level: One record per drug exposure

Column List:

Column Name	Data Type	Description
`drug_exposure_id`	bigint	Unique identifier for each drug exposure
`person_id`	integer	Reference to person table
`drug_concept_id`	integer	OMOP concept ID for the drug
`age_at_drug_start`	numeric	Age when drug exposure started
`age_at_drug_end`	numeric	Age when drug exposure ended
`age_at_verbatim_end`	numeric	Age at verbatim end date
`drug_type_concept_id`	integer	Type of drug record (prescription, OTC, etc.)
`stop_reason`	varchar	Reason drug was stopped
`refills`	integer	Number of refills
`quantity`	numeric	Quantity dispensed
`days_supply`	integer	Days supply
`sig`	varchar	Prescription instructions
`route_concept_id`	integer	Route of administration concept
`lot_number`	varchar	Drug lot number
`provider_id`	integer	Prescribing provider
`visit_occurrence_id`	bigint	Associated visit
`visit_detail_id`	bigint	Associated visit detail
`drug_source_value`	varchar	Original drug code from source
`drug_source_concept_id`	integer	Source concept for the drug
`route_source_value`	varchar	Source value for route
`dose_unit_source_value`	varchar	Source value for dose unit
`description`	varchar	Drug description
`facility_id`	integer	Facility where drug was prescribed
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Three age fields provide comprehensive temporal information
quantity, days_supply, and refills enable dosage analysis
drug_source_value contains original NDC/RxNorm codes

Table 4: `research.procedure_occurrence`

Purpose: Procedure records with age-based temporal data Row Level: One record per procedure

Column List:

Column Name	Data Type	Description
`procedure_occurrence_id`	bigint	Unique identifier for each procedure
`person_id`	integer	Reference to person table
`procedure_concept_id`	integer	OMOP concept ID for the procedure
`age_at_procedure`	numeric	Age when procedure was performed
`procedure_type_concept_id`	integer	Type of procedure record
`modifier_concept_id`	integer	Procedure modifier concept
`quantity`	integer	Quantity of procedures performed
`provider_id`	integer	Provider who performed procedure
`visit_occurrence_id`	bigint	Associated visit
`visit_detail_id`	bigint	Associated visit detail
`procedure_source_value`	varchar	Original procedure code from source
`procedure_source_concept_id`	integer	Source concept for procedure
`modifier_source_value`	varchar	Source value for modifier
`facility_id`	integer	Facility where procedure was performed
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

procedure_source_value contains original CPT codes (type:value format)
quantity field enables counting multiple procedures
Link to concept table for procedure descriptions

Table 5: `research.measurement`

Purpose: Lab results and clinical measurements with age-based temporal data Row Level: One record per measurement

Column List:

Column Name	Data Type	Description
`measurement_id`	bigint	Unique identifier for each measurement
`person_id`	integer	Reference to person table
`measurement_concept_id`	integer	OMOP concept ID for the measurement
`age_at_measurement`	numeric	Age when measurement was taken
`measurement_time`	varchar	Time of measurement (time only, no date)
`measurement_type_concept_id`	integer	Type of measurement record
`operator_concept_id`	integer	Operator concept (=, <, >, etc.)
`value_as_number`	numeric	Numeric measurement value
`value_as_concept_id`	integer	Categorical measurement value
`unit_concept_id`	integer	Unit of measurement concept
`range_low`	numeric	Normal range low value
`range_high`	numeric	Normal range high value
`provider_id`	integer	Provider who ordered measurement
`visit_occurrence_id`	bigint	Associated visit
`visit_detail_id`	bigint	Associated visit detail
`measurement_source_value`	varchar	Original measurement code from source
`measurement_source_concept_id`	integer	Source concept for measurement
`unit_source_value`	varchar	Source value for unit
`unit_source_concept_id`	integer	Source concept for unit
`value_source_value`	varchar	Source value as recorded
`measurement_event_id`	bigint	Associated measurement event
`meas_event_field_concept_id`	integer	Measurement event field concept
`description`	varchar	Measurement description
`facility_id`	integer	Facility where measurement was taken
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Rich measurement data with values, units, and normal ranges
value_as_number for quantitative labs, value_as_concept_id for categorical
range_low/range_high enable abnormal value detection

Table 6: `research.observation`

Purpose: Clinical observations and notes with age-based temporal data Row Level: One record per observation

Column List:

Column Name	Data Type	Description
`observation_id`	bigint	Unique identifier for each observation
`person_id`	integer	Reference to person table
`observation_concept_id`	integer	OMOP concept ID for the observation
`age_at_observation`	numeric	Age when observation was made
`observation_type_concept_id`	integer	Type of observation record
`value_as_number`	numeric	Numeric observation value
`value_as_string`	varchar	Text observation value
`value_as_concept_id`	integer	Categorical observation value
`qualifier_concept_id`	integer	Qualifier concept for observation
`unit_concept_id`	integer	Unit concept for numeric values
`provider_id`	integer	Provider who made observation
`visit_occurrence_id`	bigint	Associated visit
`visit_detail_id`	bigint	Associated visit detail
`observation_source_value`	varchar	Original observation code from source
`observation_source_concept_id`	integer	Source concept for observation
`unit_source_value`	varchar	Source value for unit
`qualifier_source_value`	varchar	Source value for qualifier
`value_source_value`	varchar	Source value as recorded
`observation_event_id`	bigint	Associated observation event
`obs_event_field_concept_id`	integer	Observation event field concept
`description`	varchar	Observation description
`facility_id`	integer	Facility where observation was made
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Multiple value formats: numeric, string, and concept-based
Includes clinical assessments, vital signs, and survey responses
qualifier_concept_id provides additional context

Table 7: `research.visit_occurrence`

Purpose: Healthcare visit records with age-based temporal data Row Level: One record per visit

Column List:

Column Name	Data Type	Description
`visit_occurrence_id`	bigint	Unique identifier for each visit
`person_id`	integer	Reference to person table
`visit_concept_id`	integer	OMOP concept ID for visit type
`age_at_visit_start`	numeric	Age at start of visit
`age_at_visit_end`	numeric	Age at end of visit
`visit_type_concept_id`	integer	Type of visit record
`provider_id`	integer	Primary provider for visit
`care_site_id`	integer	Care site where visit occurred
`visit_source_value`	varchar	Original visit code from source
`visit_source_concept_id`	integer	Source concept for visit
`admitted_from_concept_id`	integer	Where patient was admitted from
`admitted_from_source_value`	varchar	Source value for admission source
`discharged_to_concept_id`	integer	Where patient was discharged to
`discharged_to_source_value`	varchar	Source value for discharge destination
`preceding_visit_occurrence_id`	bigint	Previous visit in sequence
`facility_id`	integer	Facility where visit occurred
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Visit duration can be calculated from start/end ages
visit_concept_id distinguishes inpatient, outpatient, emergency, etc.
preceding_visit_occurrence_id enables visit sequence analysis

Table 8: `research.death`

Purpose: Death records with age-based temporal data Row Level: One record per person (if deceased)

Column List:

Column Name	Data Type	Description
`person_id`	integer	Reference to person table
`age_at_death`	numeric	Age at time of death
`death_type_concept_id`	integer	Type of death record
`cause_concept_id`	integer	Primary cause of death concept
`cause_source_value`	varchar	Original cause of death code
`cause_source_concept_id`	integer	Source concept for cause of death
`source_system`	varchar	Data source system identifier
`source_table`	varchar	Source table name
`source_recordid`	varchar	Source record identifier

Key Analysis Notes:

Mortality endpoint for survival analysis
cause_concept_id provides standardized cause of death
Link to person table to identify deceased patients

Table 9: `research.neuro_clinic_notes`

Purpose: Clinical notes from Columbia Neurology Aging and Dementia department Row Level: One record per clinical note

Column List:

Column Name	Data Type	Description
`person_id`	bigint	Reference to person table
`pat_enc_csn_id`	bigint	EPIC-specific encounter identifier (for internal tracking only)
`contact_date`	timestamp	Date and time of the clinical note/encounter
`note_id`	bigint	EPIC-specific note identifier (for internal tracking only)
`note_text`	text	Full text of the clinical note

Key Analysis Notes:

Contains unstructured clinical text from neurology encounters
Focus on patients with aging and dementia concerns
note_text field can be analyzed for symptoms, diagnoses, treatment plans
Useful for natural language processing (NLP) and text mining
Link to research.person via person_id for patient demographics
contact_date provides exact temporal context (not converted to age)
Note: pat_enc_csn_id and note_id are EPIC system identifiers; cannot be linked to OMOP tables

Clinical Note Content May Include:

Patient symptoms and complaints
Clinical assessments and findings
Medication prescriptions and changes
Treatment recommendations
Follow-up instructions
Provider observations

Table 10: `research.cbdb_notes`

Purpose: Post-2020 (Epic-era) clinical notes from all CBDB cohorts (ADRD, pre-AD, control) Row Level: One record per clinical note

Column List:

Column Name	Data Type	Description
`person_id`	bigint	Reference to person table
`pat_enc_csn_id`	bigint	EPIC-specific encounter identifier (for internal tracking only)
`cohort`	text	CBDB cohort of the patient: `adrd`, `pre_ad`, or `control`
`age_at_contact`	numeric	Age at the time of the clinical note (replaces contact_date for deidentification)
`note_id`	bigint	EPIC-specific note identifier (for internal tracking only)
`note_type`	text	Epic note type, e.g. `Progress Notes`, `Telephone Encounter`, `Discharge Summary`
`note_text`	text	Full text of the clinical note

Key Analysis Notes:

Contains unstructured clinical text from all three CBDB cohorts; covers all departments, not just neurology
Spans ≈2020 onward (Epic era). For 1985–2019 notes use research.cbdb_notes_pre2020 (Table 11)
cohort lets you filter/stratify by ADRD vs pre-AD vs control directly (no join to research.person needed)
note_type is the Epic document type — useful to target clinically rich notes (Progress Notes, H&P) vs administrative ones (Telephone Encounter, Patient Instructions)
age_at_contact replaces contact_date for deidentification (consistent with other research tables)
Useful for natural language processing (NLP) and text mining across the full cohort
Link to research.person via person_id for patient demographics
Note: pat_enc_csn_id and note_id are EPIC system identifiers; cannot be linked to OMOP tables

Clinical Note Content May Include:

Patient symptoms and complaints
Clinical assessments and findings
Medication prescriptions and changes
Treatment recommendations
Follow-up instructions
Provider observations

Table 11: `research.cbdb_notes_pre2020`

Purpose: Legacy (pre-Epic) clinical notes for all CBDB cohorts, dated 1985–2019 Row Level: One record per clinical note

Column List:

Column Name	Data Type	Description
`person_id`	bigint	Reference to person table
`cohort`	text	CBDB cohort of the patient: `adrd`, `pre_ad`, or `control`
`age_at_contact`	numeric	Age at the time of the clinical note (replaces contact_date for deidentification)
`note_type`	text	Legacy note category, e.g. `RADIOLOGY`, `CARDIOLOGY`, `PATHOLOGY`, `DRAFT DISCHARGE SUMMARIES`
`source_table`	text	Originating legacy source table
`organization`	integer	Organization identifier
`event_code`	integer	Event code
`n_sections`	integer	Number of sections detected in the note
`note_text`	text	Full raw text of the clinical note
`note_text_clean`	text	Cleaned/normalized note text (sparsely populated — prefer `note_text`)

Key Analysis Notes:

Pre-Epic legacy notes (1985–2019) — complements the post-2020 research.cbdb_notes (Table 10) for longitudinal text coverage
note_type vocabulary differs from cbdb_notes: legacy specialty/document categories (RADIOLOGY, CARDIOLOGY, …), not Epic note types — do not compare note_type across the two tables directly
Pre-2020 radiology reports live here under note_type = 'RADIOLOGY' (post-2020 radiology is in research.cbdb_imaging_reports, Table 12)
age_at_contact replaces contact_date for deidentification
Link to research.person via person_id for patient demographics

Table 12: `research.cbdb_imaging_reports`

Purpose: Post-2020 brain imaging radiology reports for the CBDB cohort Row Level: One record per imaging study/report

Column List:

Column Name	Data Type	Description
`person_id`	bigint	Reference to person table
`cohort`	text	CBDB cohort of the patient: `adrd`, `pre_ad`, or `control`
`accession_num`	text	Radiology accession number — traces the report to the imaging study / PACS
`order_proc_id`	bigint	EPIC imaging order identifier — links the report to the ordered procedure
`age_at_exam`	numeric	Age at the imaging order date (replaces date for deidentification)
`modality`	text	Imaging modality derived from the order: `MRI`, `PET`, `CT`, `NM`, or `OTHER`
`description`	text	Order description, e.g. `MR BRAIN WITHOUT IV CONTRAST`, `PET-CT BRAIN`
`impression_text`	text	Radiologist's impression / conclusion
`narrative_text`	text	Full radiology report body (technique, comparison, findings)

Key Analysis Notes:

Brain imaging only, 2020 onward (CT head, MR brain incl. ARIA protocols, brain PET/PET-MR, DaTscan/NM). ~24.5k reports across ~11.4k patients
Traceability: every report has both accession_num and order_proc_id (100% populated) — link a report back to the ordered imaging study
modality is derived from the order; description gives the specific protocol
narrative_text is the main report body; impression_text is the conclusion (present for ~98%)
Pre-2020 radiology is not here — see research.cbdb_notes_pre2020 (note_type='RADIOLOGY')
Link to research.person via person_id for patient demographics

Table 13: `research.cbdb_notes_imaging_post2020`

Purpose: Unified view of all post-2020 clinical text — notes + brain imaging reports — in one shape Row Level: One record per document (clinical note or imaging report)

Column List:

Column Name	Data Type	Description
`person_id`	bigint	Reference to person table
`cohort`	text	CBDB cohort: `adrd`, `pre_ad`, or `control`
`doc_source`	text	`clinical_note` (from `cbdb_notes`) or `imaging_report` (from `cbdb_imaging_reports`)
`doc_id`	text	Globally-unique document id, namespaced: `HNO:<note_id>` or `ORD:<order_proc_id>`
`accession_num`	text	Imaging accession number for imaging reports; NULL for clinical notes
`note_type`	text	Epic note type for notes; imaging modality (MRI/PET/CT/NM) for imaging reports
`age_at_event`	numeric	Age at the note contact date / imaging order date
`text`	text	Document text — note body for notes; narrative (or impression fallback) for imaging reports

Key Analysis Notes:

One-stop surface for all post-2020 free text across the cohort (does not include pre-2020 notes — use cbdb_notes_pre2020 for those)
Filter on doc_source to separate notes vs imaging; doc_id is unique across both sources
Convenient for cohort-wide NLP — feed the text column directly; note_type/modality and cohort support stratification
Built as a UNION of research.cbdb_notes (Table 10) + research.cbdb_imaging_reports (Table 12)
Link to research.person via person_id for patient demographics

Table 14: `research.concept_relationship`

Purpose: OMOP vocabulary relationships between concepts (e.g., Maps to, Subsumes, Has ingredient) Row Level: One record per concept relationship

Column List:

Column Name	Data Type	Description
`concept_id_1`	integer	Source concept ID in the relationship
`concept_id_2`	integer	Target concept ID in the relationship
`relationship_id`	varchar	Type of relationship (e.g., 'Maps to', 'Subsumes', 'RxNorm has ing')
`valid_start_date`	date	When relationship became valid
`valid_end_date`	date	When relationship became invalid
`invalid_reason`	varchar	Reason relationship was invalidated

Key Analysis Notes:

Essential for mapping between vocabularies (e.g., ICD10CM to SNOMED)
Common relationship types:
'Maps to': Maps source concept to standard concept
'Subsumes': Hierarchical parent-child relationship
'RxNorm has ing': Drug to ingredient mapping
'Has asso proc': Condition to associated procedure
Use to find standard concepts from source codes
Enables concept hierarchy navigation
Critical for drug ingredient analysis (rolling up specific drugs to ingredients)

Common Use Cases:

-- Map ICD10 codes to standard SNOMED concepts
SELECT 
    c1.concept_code as icd10_code,
    c1.concept_name as icd10_name,
    c2.concept_code as snomed_code,
    c2.concept_name as snomed_name
FROM research.concept_relationship cr
JOIN research.concept c1 ON cr.concept_id_1 = c1.concept_id
JOIN research.concept c2 ON cr.concept_id_2 = c2.concept_id
WHERE c1.vocabulary_id = 'ICD10CM'
  AND c2.vocabulary_id = 'SNOMED'
  AND cr.relationship_id = 'Maps to';

-- Find all ingredients in a drug
SELECT 
    c1.concept_name as drug_name,
    c2.concept_name as ingredient_name
FROM research.concept_relationship cr
JOIN research.concept c1 ON cr.concept_id_1 = c1.concept_id
JOIN research.concept c2 ON cr.concept_id_2 = c2.concept_id
WHERE cr.relationship_id = 'RxNorm has ing';

Table 15: `research.concept`

Purpose: OMOP vocabulary lookup table (no deidentification needed) Row Level: One record per concept

Column List:

Column Name	Data Type	Description
`concept_id`	integer	Unique concept identifier
`concept_name`	varchar	Human-readable concept name
`domain_id`	varchar	Domain (Condition, Drug, Procedure, etc.)
`vocabulary_id`	varchar	Vocabulary system (ICD10CM, RxNorm, CPT4, etc.)
`concept_class_id`	varchar	Classification within vocabulary
`standard_concept`	varchar	Standard concept flag (S=standard, C=classification)
`concept_code`	varchar	Original code from vocabulary
`valid_start_date`	date	When concept became valid
`valid_end_date`	date	When concept became invalid
`invalid_reason`	varchar	Reason concept was invalidated

Key Analysis Notes:

Essential lookup table for translating concept_ids to readable names
Filter by domain_id to find concepts for specific domains
Use vocabulary_id to identify code systems (ICD10CM, CPT4, RxNorm, etc.)
standard_concept = 'S' identifies preferred standard concepts

Cross-Table Analysis Patterns

Common Joins:

-- All clinical events for ADRC cohort with insurance data
SELECT p.person_id, p.adrc_cohort, p.insurance, c.age_at_condition_start
FROM research.person p
JOIN research.condition_occurrence c ON p.person_id = c.person_id
WHERE p.adrc_cohort = 1;

-- Analyze conditions by insurance type
SELECT 
    p.insurance,
    COUNT(DISTINCT p.person_id) as patient_count,
    COUNT(c.condition_occurrence_id) as condition_count
FROM research.person p
JOIN research.condition_occurrence c ON p.person_id = c.person_id
WHERE p.adrc_cohort = 1 AND p.insurance IS NOT NULL
GROUP BY p.insurance
ORDER BY patient_count DESC;

-- Translate concepts to names
SELECT co.person_id, co.age_at_condition_start, c.concept_name
FROM research.condition_occurrence co
JOIN research.concept c ON co.condition_concept_id = c.concept_id;

-- Map source codes to standard concepts using concept_relationship
SELECT 
    co.person_id,
    co.condition_source_value,
    c_source.concept_name as source_condition_name,
    c_standard.concept_name as standard_condition_name
FROM research.condition_occurrence co
JOIN research.concept c_source ON co.condition_source_concept_id = c_source.concept_id
JOIN research.concept_relationship cr ON c_source.concept_id = cr.concept_id_1
JOIN research.concept c_standard ON cr.concept_id_2 = c_standard.concept_id
WHERE cr.relationship_id = 'Maps to';

-- Neurology clinic notes with patient demographics
SELECT 
    n.person_id,
    n.contact_date,
    p.year_of_birth,
    p.gender_concept_id,
    p.adrc_cohort,
    n.note_text
FROM research.neuro_clinic_notes n
JOIN research.person p ON n.person_id = p.person_id
WHERE p.adrc_cohort = 1;

-- Count notes per patient in neurology clinic
SELECT 
    p.person_id,
    p.year_of_birth,
    COUNT(n.note_id) as note_count,
    MIN(n.contact_date) as first_note_date,
    MAX(n.contact_date) as last_note_date
FROM research.person p
JOIN research.neuro_clinic_notes n ON p.person_id = n.person_id
GROUP BY p.person_id, p.year_of_birth
ORDER BY note_count DESC;

Key Analysis Variables:

Temporal: All age_at_* fields for timeline analysis; contact_date for neurology notes
Clinical Values: value_as_number, value_as_concept_id in measurements/observations
Unstructured Text: note_text (neuro_clinic_notes, cbdb_notes, cbdb_notes_pre2020), narrative_text/impression_text (cbdb_imaging_reports), or the unified text column in cbdb_notes_imaging_post2020 — for NLP and text mining
Clinical-text cohort filter: cohort column (adrd/pre_ad/control) on cbdb_notes, cbdb_notes_pre2020, cbdb_imaging_reports, and cbdb_notes_imaging_post2020
Cohort: adrc_cohort flag in person table
Insurance: insurance field for healthcare access and utilization analysis
Birth Cohort: year_of_birth for generational and age-based analysis
Mortality: age_at_death for survival endpoints

Source documentation:

The OMOP Common Data Model, official OHDSI Common Data Model documentation.
For search of concepts, use Athena's data dictionary

CBDB Research Schema - Table and Column Documentation

Overview

Table 1: research.person

Column List:

Key Analysis Notes:

Table 2: research.condition_occurrence

Column List:

Key Analysis Notes:

Table 3: research.drug_exposure

Column List:

Key Analysis Notes:

Table 4: research.procedure_occurrence

Column List:

Key Analysis Notes:

Table 5: research.measurement

Column List:

Key Analysis Notes:

Table 6: research.observation

Column List:

Key Analysis Notes:

Table 7: research.visit_occurrence

Column List:

Key Analysis Notes:

Table 8: research.death

Column List:

Key Analysis Notes:

Table 9: research.neuro_clinic_notes

Column List:

Key Analysis Notes:

Clinical Note Content May Include:

Table 10: research.cbdb_notes

Column List:

Key Analysis Notes:

Clinical Note Content May Include:

Table 11: research.cbdb_notes_pre2020

Column List:

Key Analysis Notes:

Table 12: research.cbdb_imaging_reports

Column List:

Key Analysis Notes:

Table 13: research.cbdb_notes_imaging_post2020

Column List:

Key Analysis Notes:

Table 14: research.concept_relationship

Column List:

Key Analysis Notes:

Common Use Cases:

Table 15: research.concept

Column List:

Key Analysis Notes:

Cross-Table Analysis Patterns

Common Joins:

Key Analysis Variables:

Source documentation:

Table 1: `research.person`

Table 2: `research.condition_occurrence`

Table 3: `research.drug_exposure`

Table 4: `research.procedure_occurrence`

Table 5: `research.measurement`

Table 6: `research.observation`

Table 7: `research.visit_occurrence`

Table 8: `research.death`

Table 9: `research.neuro_clinic_notes`

Table 10: `research.cbdb_notes`

Table 11: `research.cbdb_notes_pre2020`

Table 12: `research.cbdb_imaging_reports`

Table 13: `research.cbdb_notes_imaging_post2020`

Table 14: `research.concept_relationship`

Table 15: `research.concept`