---
title: 'SQL Scripts'
output: 
  html_document:
      toc: TRUE
      toc_depth: 5
      toc_float: TRUE
---

# **SQL Scripts**

These SQL scripts have been developed by the community to facilitate the Extract, Transform, and Load (ETL) process from source data to the OMOP CDM. 

## **Vocabulary Mapping**

### Source to Standard

This script will create a large table containing all source codes and the Standard Concepts they map to. By creating this table ahead of time and referencing it during ETL, you can save processing time than if you were to map source codes on the fly. It is important to note that this script will show ALL mappings, not just those to Standard, Valid Concepts. Be sure to add a filter that will only grab the correct Standard, Valid codes. 

```{sql eval=FALSE, echo=TRUE}
SELECT c.concept_code AS SOURCE_CODE, c.concept_id AS SOURCE_CONCEPT_ID, c.concept_name AS SOURCE_CODE_DESCRIPTION, c.vocabulary_id AS SOURCE_VOCABULARY_ID, c.domain_id AS SOURCE_DOMAIN_ID, c.CONCEPT_CLASS_ID AS SOURCE_CONCEPT_CLASS_ID, c.VALID_START_DATE AS SOURCE_VALID_START_DATE, c.VALID_END_DATE AS SOURCE_VALID_END_DATE, c.INVALID_REASON AS SOURCE_INVALID_REASON, c1.concept_id AS TARGET_CONCEPT_ID, c1.concept_name AS TARGET_CONCEPT_NAME, c1.VOCABULARY_ID AS TARGET_VOCABUALRY_ID, c1.domain_id AS TARGET_DOMAIN_ID, c1.concept_class_id AS TARGET_CONCEPT_CLASS_ID, c1.INVALID_REASON AS TARGET_INVALID_REASON, c1.standard_concept AS TARGET_STANDARD_CONCEPT
       FROM CONCEPT C
             JOIN CONCEPT_RELATIONSHIP CR
                        ON C.CONCEPT_ID = CR.CONCEPT_ID_1
                        AND CR.invalid_reason IS NULL
                        AND lower(cr.relationship_id) = 'maps to'
              JOIN CONCEPT C1
                        ON CR.CONCEPT_ID_2 = C1.CONCEPT_ID
                        AND C1.INVALID_REASON IS NULL
       UNION
       SELECT source_code, SOURCE_CONCEPT_ID, SOURCE_CODE_DESCRIPTION, source_vocabulary_id, c1.domain_id AS SOURCE_DOMAIN_ID, c2.CONCEPT_CLASS_ID AS SOURCE_CONCEPT_CLASS_ID, c1.VALID_START_DATE AS SOURCE_VALID_START_DATE, c1.VALID_END_DATE AS SOURCE_VALID_END_DATE, stcm.INVALID_REASON AS SOURCE_INVALID_REASON,target_concept_id, c2.CONCEPT_NAME AS TARGET_CONCEPT_NAME, target_vocabulary_id, c2.domain_id AS TARGET_DOMAIN_ID, c2.concept_class_id AS TARGET_CONCEPT_CLASS_ID, c2.INVALID_REASON AS TARGET_INVALID_REASON, c2.standard_concept AS TARGET_STANDARD_CONCEPT
       FROM source_to_concept_map stcm
              LEFT OUTER JOIN CONCEPT c1
                     ON c1.concept_id = stcm.source_concept_id
              LEFT OUTER JOIN CONCEPT c2
                     ON c2.CONCEPT_ID = stcm.target_concept_id
       WHERE stcm.INVALID_REASON IS NULL

```

### Source to Source

This script will create a large table containing all source codes and their referent Concepts. While you may be tempted to use the Source to Standard query to find these, it is important to use this one instead to populate *_SOURCE_CONCEPT_ID fields. Some source codes map to multiple Standard Concepts though they have only one source Concept. By using the Source to Standard query you may inadvertently create duplicate records in your CDM instance.  

```{sql eval=FALSE, echo=TRUE}
SELECT c.concept_code AS SOURCE_CODE, c.concept_id AS SOURCE_CONCEPT_ID, c.CONCEPT_NAME AS SOURCE_CODE_DESCRIPTION, c.vocabulary_id AS SOURCE_VOCABULARY_ID, c.domain_id AS SOURCE_DOMAIN_ID, c.concept_class_id AS SOURCE_CONCEPT_CLASS_ID, c.VALID_START_DATE AS SOURCE_VALID_START_DATE, c.VALID_END_DATE AS SOURCE_VALID_END_DATE, c.invalid_reason AS SOURCE_INVALID_REASON, c.concept_ID as TARGET_CONCEPT_ID, c.concept_name AS TARGET_CONCEPT_NAME, c.vocabulary_id AS TARGET_VOCABULARY_ID, c.domain_id AS TARGET_DOMAIN_ID, c.concept_class_id AS TARGET_CONCEPT_CLASS_ID, c.INVALID_REASON AS TARGET_INVALID_REASON, c.STANDARD_CONCEPT AS TARGET_STANDARD_CONCEPT
       FROM CONCEPT c
       UNION
       SELECT source_code, SOURCE_CONCEPT_ID, SOURCE_CODE_DESCRIPTION, source_vocabulary_id, c1.domain_id AS SOURCE_DOMAIN_ID, c2.CONCEPT_CLASS_ID AS SOURCE_CONCEPT_CLASS_ID, c1.VALID_START_DATE AS SOURCE_VALID_START_DATE, c1.VALID_END_DATE AS SOURCE_VALID_END_DATE,stcm.INVALID_REASON AS SOURCE_INVALID_REASON, target_concept_id, c2.CONCEPT_NAME AS TARGET_CONCEPT_NAME, target_vocabulary_id, c2.domain_id AS TARGET_DOMAIN_ID, c2.concept_class_id AS TARGET_CONCEPT_CLASS_ID, c2.INVALID_REASON AS TARGET_INVALID_REASON, c2.standard_concept AS TARGET_STANDARD_CONCEPT
       FROM source_to_concept_map stcm
              LEFT OUTER JOIN CONCEPT c1
                     ON c1.concept_id = stcm.source_concept_id
              LEFT OUTER JOIN CONCEPT c2
                     ON c2.CONCEPT_ID = stcm.target_concept_id
       WHERE stcm.INVALID_REASON IS NULL
```

## **Era Tables**

### Condition Eras

This script will insert values into the CONDITION_ERA table given that the CONDITION_OCCURRENCE table is populated. It will string together condition records that have <= 30 days between them into eras during which the Person is assumed to have the given condition. **NOTE** This query only works with 5.3 and below.

```{sql eval= FALSE, echo=TRUE}
/****************************************************
OHDSI-SQL File Instructions
-----------------------------
 1. Set parameter name of schema that contains CDMv4 instance
    (@SOURCE_CDMV4, @SOURCE_CDMV4_SCHEMA)
 2. Set parameter name of schema that contains CDMv5 instance
    (@TARGET_CDMV5, @TARGET_CDMV5_SCHEMA)
 3. Run this script through SqlRender to produce a script that will work in your
    source dialect. SqlRender can be found here: https://github.com/OHDSI/SqlRender
 4. Run the script produced by SQL Render on your target RDBDMS.
<RDBMS> File Instructions
-------------------------
 1. This script will hold a number of placeholders for your CDM V4 and CDMV5
    database/schema. In order to make this file work in your environment, you
	should plan to do a global "FIND AND REPLACE" on this file to fill in the
	file with values that pertain to your environment. The following are the
	tokens you should use when doing your "FIND AND REPLACE" operation:
	
     [CDM]
	 [CDM].[CDMSCHEMA]
	
*********************************************************************************/
/* SCRIPT PARAMETERS */

	
	{DEFAULT @TARGET_CDMV5 = '[CDM]' } -- The target CDMv5 database name
	{DEFAULT @TARGET_CDMV5_SCHEMA = '[CDM].[CDMSCHEMA]' } -- the target CDMv5 database plus schema

USE @TARGET_CDMV5;



/****
CONDITION ERA
Note: Eras derived from CONDITION_OCCURRENCE table, using 30d gap
 ****/
IF OBJECT_ID('tempdb..#condition_era_phase_1', 'U') IS NOT NULL
	DROP TABLE #condition_era_phase_1;

/* / */

IF OBJECT_ID('tempdb..#cteConditionTarget', 'U') IS NOT NULL
	DROP TABLE #cteConditionTarget;

/* / */

-- create base eras from the concepts found in condition_occurrence
SELECT co.PERSON_ID
	,co.condition_concept_id
	,co.CONDITION_START_DATE
	,COALESCE(co.CONDITION_END_DATE, DATEADD(day, 1, CONDITION_START_DATE)) AS CONDITION_END_DATE
INTO #cteConditionTarget
FROM @TARGET_CDMV5_SCHEMA.CONDITION_OCCURRENCE co;

/* / */

IF OBJECT_ID('tempdb..#cteCondEndDates', 'U') IS NOT NULL
	DROP TABLE #cteCondEndDates;

/* / */

SELECT PERSON_ID
	,CONDITION_CONCEPT_ID
	,DATEADD(day, - 30, EVENT_DATE) AS END_DATE -- unpad the end date
INTO #cteCondEndDates
FROM (
	SELECT E1.PERSON_ID
		,E1.CONDITION_CONCEPT_ID
		,E1.EVENT_DATE
		,COALESCE(E1.START_ORDINAL, MAX(E2.START_ORDINAL)) START_ORDINAL
		,E1.OVERALL_ORD
	FROM (
		SELECT PERSON_ID
			,CONDITION_CONCEPT_ID
			,EVENT_DATE
			,EVENT_TYPE
			,START_ORDINAL
			,ROW_NUMBER() OVER (
				PARTITION BY PERSON_ID
				,CONDITION_CONCEPT_ID ORDER BY EVENT_DATE
					,EVENT_TYPE
				) AS OVERALL_ORD -- this re-numbers the inner UNION so all rows are numbered ordered by the event date
		FROM (
			-- select the start dates, assigning a row number to each
			SELECT PERSON_ID
				,CONDITION_CONCEPT_ID
				,CONDITION_START_DATE AS EVENT_DATE
				,- 1 AS EVENT_TYPE
				,ROW_NUMBER() OVER (
					PARTITION BY PERSON_ID
					,CONDITION_CONCEPT_ID ORDER BY CONDITION_START_DATE
					) AS START_ORDINAL
			FROM #cteConditionTarget

			UNION ALL

			-- pad the end dates by 30 to allow a grace period for overlapping ranges.
			SELECT PERSON_ID
				,CONDITION_CONCEPT_ID
				,DATEADD(day, 30, CONDITION_END_DATE)
				,1 AS EVENT_TYPE
				,NULL
			FROM #cteConditionTarget
			) RAWDATA
		) E1
	INNER JOIN (
		SELECT PERSON_ID
			,CONDITION_CONCEPT_ID
			,CONDITION_START_DATE AS EVENT_DATE
			,ROW_NUMBER() OVER (
				PARTITION BY PERSON_ID
				,CONDITION_CONCEPT_ID ORDER BY CONDITION_START_DATE
				) AS START_ORDINAL
		FROM #cteConditionTarget
		) E2 ON E1.PERSON_ID = E2.PERSON_ID
		AND E1.CONDITION_CONCEPT_ID = E2.CONDITION_CONCEPT_ID
		AND E2.EVENT_DATE <= E1.EVENT_DATE
	GROUP BY E1.PERSON_ID
		,E1.CONDITION_CONCEPT_ID
		,E1.EVENT_DATE
		,E1.START_ORDINAL
		,E1.OVERALL_ORD
	) E
WHERE (2 * E.START_ORDINAL) - E.OVERALL_ORD = 0;

/* / */

IF OBJECT_ID('tempdb..#cteConditionEnds', 'U') IS NOT NULL
	DROP TABLE #cteConditionEnds;

/* / */

SELECT c.PERSON_ID
	,c.CONDITION_CONCEPT_ID
	,c.CONDITION_START_DATE
	,MIN(e.END_DATE) AS ERA_END_DATE
INTO #cteConditionEnds
FROM #cteConditionTarget c
INNER JOIN #cteCondEndDates e ON c.PERSON_ID = e.PERSON_ID
	AND c.CONDITION_CONCEPT_ID = e.CONDITION_CONCEPT_ID
	AND e.END_DATE >= c.CONDITION_START_DATE
GROUP BY c.PERSON_ID
	,c.CONDITION_CONCEPT_ID
	,c.CONDITION_START_DATE;

/* / */

INSERT INTO @TARGET_CDMV5_SCHEMA.condition_era (
	condition_era_id
	,person_id
	,condition_concept_id
	,condition_era_start_date
	,condition_era_end_date
	,condition_occurrence_count
	)
SELECT row_number() OVER (
		ORDER BY person_id
		) AS condition_era_id
	,person_id
	,CONDITION_CONCEPT_ID
	,min(CONDITION_START_DATE) AS CONDITION_ERA_START_DATE
	,ERA_END_DATE AS CONDITION_ERA_END_DATE
	,COUNT(*) AS CONDITION_OCCURRENCE_COUNT
FROM #cteConditionEnds
GROUP BY person_id
	,CONDITION_CONCEPT_ID
	,ERA_END_DATE;
```

### Drug Eras

If the DRUG_EXPOSURE table is populated this script will string together periods of time that a person is exposed to an active drug ingredient, allowing for 30 gaps in between. It will then insert the resulting era records into the DRUG_ERA table. **NOTE** This query only works with the **version 5** series and below.

```{sql eval = FALSE, echo = TRUE}

-- Code taken from:
-- https://github.com/OHDSI/ETL-CMS/blob/master/SQL/create_CDMv5_drug_era_non_stockpile.sql


if object_id('tempdb..#tmp_de', 'U') is not null drop table #tmp_de;

WITH
ctePreDrugTarget(drug_exposure_id, person_id, ingredient_concept_id, drug_exposure_start_date, days_supply, drug_exposure_end_date) AS
(-- Normalize DRUG_EXPOSURE_END_DATE to either the existing drug exposure end date, or add days supply, or add 1 day to the start date
	SELECT
		d.drug_exposure_id
		, d.person_id
		, c.concept_id AS ingredient_concept_id
		, d.drug_exposure_start_date AS drug_exposure_start_date
		, d.days_supply AS days_supply
		, COALESCE(
			---NULLIF returns NULL if both values are the same, otherwise it returns the first parameter
			NULLIF(drug_exposure_end_date, NULL),
			---If drug_exposure_end_date != NULL, return drug_exposure_end_date, otherwise go to next case
			NULLIF(dateadd(day,days_supply,drug_exposure_start_date), drug_exposure_start_date),
			---If days_supply != NULL or 0, return drug_exposure_start_date + days_supply, otherwise go to next case
			dateadd(day,1,drug_exposure_start_date)
			---Add 1 day to the drug_exposure_start_date since there is no end_date or INTERVAL for the days_supply
		) AS drug_exposure_end_date
	FROM @cdm_schema.drug_exposure d
		JOIN @cdm_schema.concept_ancestor ca ON ca.descendant_concept_id = d.drug_concept_id
		JOIN @cdm_schema.concept c ON ca.ancestor_concept_id = c.concept_id
		WHERE c.vocabulary_id = 'RxNorm' ---8 selects RxNorm from the vocabulary_id
		AND c.concept_class_id = 'Ingredient'
		AND d.drug_concept_id != 0 ---Our unmapped drug_concept_id's are set to 0, so we don't want different drugs wrapped up in the same era
		AND coalesce(d.days_supply,0) >= 0 ---We have cases where days_supply is negative, and this can set the end_date before the start_date, which we don't want. So we're just looking over those rows. This is a data-quality issue.
)

, cteSubExposureEndDates (person_id, ingredient_concept_id, end_date) AS --- A preliminary sorting that groups all of the overlapping exposures into one exposure so that we don't double-count non-gap-days
(
	SELECT person_id, ingredient_concept_id, event_date AS end_date
	FROM
	(
		SELECT person_id, ingredient_concept_id, event_date, event_type,
		MAX(start_ordinal) OVER (PARTITION BY person_id, ingredient_concept_id
			ORDER BY event_date, event_type ROWS unbounded preceding) AS start_ordinal,
		-- this pulls the current START down from the prior rows so that the NULLs
		-- from the END DATES will contain a value we can compare with
			ROW_NUMBER() OVER (PARTITION BY person_id, ingredient_concept_id
				ORDER BY event_date, event_type) AS overall_ord
			-- this re-numbers the inner UNION so all rows are numbered ordered by the event date
		FROM (
			-- select the start dates, assigning a row number to each
			SELECT person_id, ingredient_concept_id, drug_exposure_start_date AS event_date,
			-1 AS event_type,
			ROW_NUMBER() OVER (PARTITION BY person_id, ingredient_concept_id
				ORDER BY drug_exposure_start_date) AS start_ordinal
			FROM ctePreDrugTarget

			UNION ALL

			SELECT person_id, ingredient_concept_id, drug_exposure_end_date, 1 AS event_type, NULL
			FROM ctePreDrugTarget
		) RAWDATA
	) e
	WHERE (2 * e.start_ordinal) - e.overall_ord = 0
)

, cteDrugExposureEnds (person_id, drug_concept_id, drug_exposure_start_date, drug_sub_exposure_end_date) AS
(
SELECT
	dt.person_id
	, dt.ingredient_concept_id
	, dt.drug_exposure_start_date
	, MIN(e.end_date) AS drug_sub_exposure_end_date
FROM ctePreDrugTarget dt
JOIN cteSubExposureEndDates e ON dt.person_id = e.person_id AND dt.ingredient_concept_id = e.ingredient_concept_id AND e.end_date >= dt.drug_exposure_start_date
GROUP BY
      	dt.drug_exposure_id
      	, dt.person_id
	, dt.ingredient_concept_id
	, dt.drug_exposure_start_date
)
--------------------------------------------------------------------------------------------------------------
, cteSubExposures(row_number, person_id, drug_concept_id, drug_sub_exposure_start_date, drug_sub_exposure_end_date, drug_exposure_count) AS
(
	SELECT ROW_NUMBER() OVER (PARTITION BY person_id, drug_concept_id, drug_sub_exposure_end_date ORDER BY person_id)
		, person_id, drug_concept_id, MIN(drug_exposure_start_date) AS drug_sub_exposure_start_date, drug_sub_exposure_end_date, COUNT(*) AS drug_exposure_count
	FROM cteDrugExposureEnds
	GROUP BY person_id, drug_concept_id, drug_sub_exposure_end_date
	--ORDER BY person_id, drug_concept_id
)
--------------------------------------------------------------------------------------------------------------
/*Everything above grouped exposures into sub_exposures if there was overlap between exposures.
 *So there was no persistence window. Now we can add the persistence window to calculate eras.
 */
--------------------------------------------------------------------------------------------------------------
, cteFinalTarget(row_number, person_id, ingredient_concept_id, drug_sub_exposure_start_date, drug_sub_exposure_end_date, drug_exposure_count, days_exposed) AS
(
	SELECT row_number, person_id, drug_concept_id, drug_sub_exposure_start_date, drug_sub_exposure_end_date, drug_exposure_count
		, datediff(day,drug_sub_exposure_start_date,drug_sub_exposure_end_date) AS days_exposed
	FROM cteSubExposures
)
--------------------------------------------------------------------------------------------------------------
, cteEndDates (person_id, ingredient_concept_id, end_date) AS -- the magic
(
	SELECT person_id, ingredient_concept_id, dateadd(day,-30,event_date) AS end_date -- unpad the end date
	FROM
	(
		SELECT person_id, ingredient_concept_id, event_date, event_type,
		MAX(start_ordinal) OVER (PARTITION BY person_id, ingredient_concept_id
			ORDER BY event_date, event_type ROWS UNBOUNDED PRECEDING) AS start_ordinal,
		-- this pulls the current START down from the prior rows so that the NULLs
		-- from the END DATES will contain a value we can compare with
			ROW_NUMBER() OVER (PARTITION BY person_id, ingredient_concept_id
				ORDER BY event_date, event_type) AS overall_ord
			-- this re-numbers the inner UNION so all rows are numbered ordered by the event date
		FROM (
			-- select the start dates, assigning a row number to each
			SELECT person_id, ingredient_concept_id, drug_sub_exposure_start_date AS event_date,
			-1 AS event_type,
			ROW_NUMBER() OVER (PARTITION BY person_id, ingredient_concept_id
				ORDER BY drug_sub_exposure_start_date) AS start_ordinal
			FROM cteFinalTarget

			UNION ALL

			-- pad the end dates by 30 to allow a grace period for overlapping ranges.
			SELECT person_id, ingredient_concept_id, dateadd(day,30,drug_sub_exposure_end_date), 1 AS event_type, NULL
			FROM cteFinalTarget
		) RAWDATA
	) e
	WHERE (2 * e.start_ordinal) - e.overall_ord = 0

)
, cteDrugEraEnds (person_id, drug_concept_id, drug_sub_exposure_start_date, drug_era_end_date, drug_exposure_count, days_exposed) AS
(
SELECT
	ft.person_id
	, ft.ingredient_concept_id
	, ft.drug_sub_exposure_start_date
	, MIN(e.end_date) AS era_end_date
	, drug_exposure_count
	, days_exposed
FROM cteFinalTarget ft
JOIN cteEndDates e ON ft.person_id = e.person_id AND ft.ingredient_concept_id = e.ingredient_concept_id AND e.end_date >= ft.drug_sub_exposure_start_date
GROUP BY
      	ft.person_id
	, ft.ingredient_concept_id
	, ft.drug_sub_exposure_start_date
	, drug_exposure_count
	, days_exposed
)
SELECT
    row_number()over(order by person_id) drug_era_id
	, person_id
	, drug_concept_id
	, MIN(drug_sub_exposure_start_date) AS drug_era_start_date
	, drug_era_end_date
	, SUM(drug_exposure_count) AS drug_exposure_count
	, datediff(day,MIN(drug_sub_exposure_start_date),drug_era_end_date)-SUM(days_exposed) as gap_days
INTO #tmp_de
FROM cteDrugEraEnds dee
GROUP BY person_id, drug_concept_id, drug_era_end_date;

INSERT INTO @cdm_schema.drug_era(drug_era_id,person_id, drug_concept_id, drug_era_start_date, drug_era_end_date, drug_exposure_count, gap_days)
SELECT * FROM #tmp_de;
```

## **Example ETL Script**

### CDM_SOURCE Table

The script below is an example for how to fill in the CDM_SOURCE table.
This table is required for the [Data Quality Dashboard](https://github.com/OHDSI/DataQualityDashboard) package to run.

#### v5.3
This example was taken from the [ETL-Synthea](https://github.com/OHDSI/ETL-Synthea) package, 
a good example on how to write a complete extract-transform-load script from start to finish, including vocabulary import.

```{sql eval = FALSE, echo = TRUE}
INSERT INTO @cdm_schema.cdm_source (
	cdm_source_name,
	cdm_source_abbreviation,
	cdm_holder,
	source_description,
	source_documentation_reference,
	cdm_etl_reference,
	source_release_date,
	cdm_release_date,
	cdm_version,
	vocabulary_version
) 
SELECT
	'Synthea synthetic health database',
	'Synthea',
	'OHDSI Community',
	'SyntheaTM is a Synthetic Patient Population Simulator. The goal is to output synthetic, realistic (but not real), patient data and associated health records in a variety of formats.',
	'https://synthetichealth.github.io/synthea/',
	'https://github.com/OHDSI/ETL-Synthea',
	getdate(), -- NB: Set this value to the day the source data was pulled
	getdate(), 
	'v5.3',
	vocabulary_version
FROM @cdm_schema.vocabulary 
WHERE vocabulary_id = 'None';
```

#### v5.4
```{sql eval = FALSE, echo = TRUE}
INSERT INTO @cdm_schema.cdm_source (
	cdm_source_name,
	cdm_source_abbreviation,
	cdm_holder,
	source_description,
	source_documentation_reference,
	cdm_etl_reference,
	source_release_date,
	cdm_release_date,
	cdm_version,
	vocabulary_version,
	vocabulary_version_concept_id
) 
SELECT
	'<your_cdm_source_name>',
	'<your_cdm_source_abbreviation>',
	'<your_cdm_holder>',
	'<your_source_description>',
	'<your_source_documentation_reference>',  -- e.g. link to source data dictionary
	'<your_etl_reference>',  -- e.g. link to ETL documentation
	'<your_source_release_date>',  -- when the source data was pulled
	getdate(),  -- or the date of ETL run
	'v5.4',
	vocabulary_version,
	756265  -- 'OMOP CDM Version 5.4.0'
FROM @cdm_schema.vocabulary 
WHERE vocabulary_id = 'None';
```

## **Bonus Queries**

### Visit Concept Roll-up

The query below will utilize the Visit Concept hierarchy to find the highest-level ancestors. In the case that both the VISIT_OCCURRENCE and VISIT_DETAIL tables are populated, it is good practice (though not required) to use the highest-level ancestors as the VISIT_CONCEPT_IDs in the VISIT_OCCURRENCE table and their children as the VISIT_DETAIL_CONCEPT_IDs in the VISIT_DETAIL table. This relationship between the VISIT_OCCURRENCE and VISIT_DETAIL tables allow for standardized Visit logic to be written, building Visits from Visit Details. For more information on how this can be done, please see the [Optum Extended ETL documentation](https://ohdsi.github.io/ETL-LambdaBuilder/Optum%20Clinformatics/Optum_visit_occurrence.html).

```{sql eval=FALSE, echo=TRUE}
SELECT concept_id, concept_name
FROM concept 
LEFT JOIN concept_ancestor 
  ON concept_id=descendant_concept_id 
  AND ancestor_concept_id!=descendant_concept_id
WHERE domain_id='Visit' 
  AND standard_concept='S'
  AND ancestor_concept_id IS NULL
```