Worked example · 01

Menopause in the OMOP vocabularies

If you already know menopause clinically, you can use it to learn your way around the OMOP vocabularies. Search it in ATHENA, find the same concepts in ATLAS, build a concept set and a cohort from them, then turn what you have found into a research question.

← Back to Learn Example 02: preterm labor → Open in ATHENA ↗ Open in ATLAS demo ↗

Current as of 1 August 2026. The counts and concept IDs below come from an ATHENA export taken that day, under vocabulary release v20260227. Vocabularies are updated a few times a year, so treat the numbers as something to reproduce rather than to cite.

What a vocabulary search returns

Type menopause into ATHENA and you get a long list. Notice first that the list holds many things that are not related to the study you are trying to design.

683

CONCEPTS RETURNED

STANDARD CONCEPTS

DOMAINS

You build with the 75 standard concepts. The rest are source codes from national coding systems, product labels, and terms that have been replaced over time. That ratio is normal for any clinical word, and reading the list mostly comes down to recognizing which kind of thing each row is.

What else the search picks up (7 kinds)

Kind of hit	Example	What it is
Over-the-counter products	163 product-label concepts, mostly homeopathic remedies named for menopause	The word appears on a package, not in a patient record
A drug for a different indication	Human menopausal gonadotrophin (Menopur), 2909978	Menotropins are named for the source they were first purified from and are given in fertility treatment
Family history	FH: Early menopause 4050823	Somebody else's menopause, recorded in a family history
Negation	Menopause absent 4128330	A record that a woman is not menopausal
Care process	Menopause education 4193571, Menopause monitoring 4152040	A conversation or a review happened, which can be before menopause
Qualifier values	Before menopause 4206716, After menopause 4144036	These describe a measurement result rather than forming a record of their own
A device	Menopause thermal therapy wristband 37165887	Equipment, not a diagnosis

Menopause is stored in more than one place

Every concept has a domain, and the domain decides which table its records go into. For menopause the useful split is that menopausal status is Observation while conditions attributed to menopause are Condition. That distinction determines the concept set you build next.

Concepts, their domain, and the table that stores them (13 rows)

Concept	ID	Domain	OMOP table
Menopause	4059477	Observation	`OBSERVATION`
Menopause finding	4172857	Observation	`OBSERVATION`
Menopause present	4128329	Observation	`OBSERVATION`
Premature menopause	198715	Observation	`OBSERVATION`
Age at menopause	36676381	Observation	`OBSERVATION`
Menopausal flushing	4113666	Observation	`OBSERVATION`
FH: Early menopause	4050823	Observation	`OBSERVATION`
Normal menopause	4034019	Condition	`CONDITION_OCCURRENCE`
Menopausal syndrome	439082	Condition	`CONDITION_OCCURRENCE`
Menopausal osteoporosis	4136988	Condition	`CONDITION_OCCURRENCE`
Genitourinary syndrome of menopause	37209648	Condition	`CONDITION_OCCURRENCE`
Postmenopausal bleeding	195321	Condition	`CONDITION_OCCURRENCE`
Menopause absent	4128330	Condition	`CONDITION_OCCURRENCE`

Two to look at closely. Menopausal flushing (4113666) is Observation even though its sibling symptoms for headache, sleeplessness and depression are Condition, so a Condition-only set for "menopausal symptoms" leaves out the most common one. Normal menopause (4034019) reads like a status concept but is Condition domain, so it does not belong in an Observation-domain set however sensible its name looks there.

The same search in ATLAS

ATLAS reads the same vocabularies, so the same search gives you the same concepts. Both tools give you filters for narrowing the list by domain, vocabulary and standard concept. What ATLAS adds is record counts, which tell you how much each concept is used.

ATLAS demo search results for menopause, showing facet filters for vocabulary, class, domain, standard concept and validity down the left side, and a results table of concept names, identifiers, class, record counts, domain and vocabulary. — **ATLAS → Search → "menopause."** Use the filters down the left to narrow to **Standard Concept** and to a **Domain** in one click, which takes you from a long list to the handful you want. The **RC, DRC, PC and DPC** columns are record and person counts, with and without descendant concepts, so a concept with a high DRC and a low RC is one whose children hold most of the data. Click a concept name to open it and read its hierarchy.

Record counts on this screen come from a network-wide reference set rather than from the data you are going to query. Use Data Sources to see what a specific data source holds.

ATLAS demo Data Sources report for SYNPUF 5 percent, Condition Occurrence, filtered to menopause, showing concept 198715 Premature menopause with a person count of 1,303 and 1.12 percent prevalence. — **ATLAS → Data Sources → pick a source → Condition Occurrence → filter.** This is the report to run against your own institution's data before you plan anything. It gives you person counts and prevalence per concept, so you can see which concepts are used where you work and which are not recorded at all.

On the numbers here. SYNPUF is synthetic data built from Medicare claims, so its population skews old and includes men. The 1.12% shown is against everybody in that file rather than against women of the relevant age, which is why a prevalence figure always needs its denominator checked before it is used or reported as fact.

Building a concept set

A concept set is the code list your study runs on. Build it from standard concepts, turn descendants on so the specific terms clinicians write are included, and write down what you excluded and why. Keep each set to one domain, because the domain decides which table you query it against.

Menopausal status

The record that a woman has reached menopause, however it was written. Every concept here is Observation domain, so this set is queried against OBSERVATION.

The concepts to include and exclude (5 rows)

Concept	ID	Domain	Descendants
Menopause finding	4172857	Observation	Yes
Menopause present	4128329	Observation	Yes
Menopause	4059477	Observation	Yes
Exclude: FH: Early menopause	4050823	Observation	Yes
Exclude: FH: Late menopause	4051695	Observation	Yes

The two family history concepts are standard, valid and Observation domain, which puts them in the same table as the rest of this set, so exclude them by name. Normal menopause (4034019) and Menopause absent (4128330) look like they belong here and do not, because both are Condition domain.

Conditions attributed to menopause

Clinical problems recorded as menopausal, which answers a different question from who has reached menopause. Every concept here is Condition domain, so this set is queried against CONDITION_OCCURRENCE.

The concepts to include and exclude (13 rows)

Concept	ID	Domain	Note
Menopausal syndrome	439082	Condition	The broad one
Genitourinary syndrome of menopause	37209648	Condition
Menopausal osteoporosis	4136988	Condition
Postmenopausal osteopenia	42536667	Condition
Menopausal depression	4223090	Condition
Menopausal sleeplessness	4113205	Condition
Menopausal headache	4113841	Condition
Menopausal hyperhidrosis	37165466	Condition
Postmenopausal bleeding	195321	Condition	Often recorded on its own, without a status concept
Postmenopausal urethral atrophy	4054881	Condition
Climacteric arthritis	79109	Condition
Exclude: Menopause absent	4128330	Condition	Records that a woman is not menopausal
Consider excluding: Disorder associated with menstruation AND/OR menopause	4024558	Condition	Its descendants reach well outside menopause

Menopausal flushing (4113666) belongs in this list clinically but cannot go in it, because it is Observation domain. If your protocol says "menopausal symptoms," you need both concept sets, and that limitation belongs in the protocol rather than in a footnote discovered at review.

From concept set to cohort

A concept set says which codes count. A cohort says which people count, and from when. Two to build from the sets above.

First recorded menopausal status

Every part of the definition (7 rows)

Part	Setting	Why
Entry event	Observation of any concept in the menopausal status set	The criterion is Observation, not Condition occurrence, because every concept in that set is Observation domain. Reaching for Condition out of habit returns nothing, and that looks like missing data
Initial event limit	Earliest event per person	Index is first recorded status, not most recent mention
Inclusion 1	Female at index	The concept does not imply sex, so the rule states it
Inclusion 2	At least 365 days continuous prior observation	Otherwise "first recorded" only means "first we were watching"
Inclusion 3	Age 35 or over at index	A floor rather than a definition, and a clinical judgment to name as one
Exit	End of continuous observation	Menopause does not resolve, so a fixed window would be a statement about follow-up
Censoring	None	Nothing here makes later follow-up uninformative

Then check it. The age distribution should sit in the late 40s to mid 50s, the index events should not be dominated by one source code, and incidence over time should not step at a coding-system change.

Premature or early menopause

Every part of the definition (6 rows)

Part	Setting	Why
Entry event	Observation of Premature menopause (198715) with descendants, or Condition occurrence of Menopause ovarian failure (4129547)	These two related concepts sit in different domains, so the entry event needs a criterion for each table
Initial event limit	Earliest event per person
Inclusion 1	Female, at least 365 days prior observation	As above
Inclusion 2	Age under 45 at index	The code does not enforce the age its name implies, so the rule does
Inclusion 3, for spontaneous cases only	No condition occurrence of postsurgical (4154697), post-hysterectomy (4175534) or radiation-induced (1076252, 1076253) menopause in the year before index. All four are Condition domain	Iatrogenic menopause is a different phenotype with a different cause
Exit	End of continuous observation

Run the attrition table and look at what the age rule removes. If it removes most of the cohort, the finding is not that premature menopause is rare but that the code is being used for something else, which is a real result in its own right.

Turning it into a research question

Once you have seen what the vocabulary holds and what your data source records, you are in a position to write a question and judge whether it can be answered. Fill this in, adjust the feasibility answers to match what you found, then download it and load it into the Analytic Use-Case Generator.

Notes and sources

If ATHENA and ATLAS do not quite agree. An ATLAS instance loads only the vocabularies its database needs, and it may be running an older release than ATHENA, so totals and occasionally a concept's domain can differ between the two. This is expected rather than a fault, and the practical rule is to check what your own instance holds, under Configuration, before comparing anything.

Read from an ATHENA search for menopause exported 1 August 2026, giving 683 rows (download the export), and two screenshots of the ATLAS public demo taken the same day.

ATHENA and the ATLAS public demo
The Book of OHDSI, chapters 5 and 10
OMOP CDM specification
OHDSI Forums, Vocabulary Users, if a concept looks wrong and you want to ask
Prevalence of premature ovarian insufficiency, quoted in the feasibility notes above, from Panay et al., Human Reproduction Open 2024;2024(4):hoae065

To re-date this page, re-run the ATHENA search and replace the counts, check the concept IDs for deprecation or domain change, retake the screenshots, and update the date at the top.

Corrections or suggestions welcome: danielle@boycedatascience.com