Evidence map›Paper›PMID 35246240›Full record

ArticleBMC research notes2022

Accurate categorisation of menopausal status for research studies: a step-by-step guide and detailed algorithm considering age, self-reported menopause and factors potentially masking the occurrence of menopause.

Sarsha Yap, Amy Vassallo, David E Goldsbury, Usha Salagame, Louiza Velentzis, Emily Banks, Dianne L O'Connell, Karen Canfell, Julia Steinberg

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sarsha YapThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia. sarshay@nswcc.org.au.ORCID http://orcid.org/0000-0002-2049-6352
Amy VassalloCancer Council NSW, Sydney, NSW, Australia.
David E GoldsburyThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia.
Usha SalagameCentre for Health Record Linkage, Centre of Epidemiology and Evidence, NSW Health, Sydney, Australia.
Louiza VelentzisThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia.
Emily BanksNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
Dianne L O'ConnellThe Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia.
Karen Canfell *The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia.ORCID http://orcid.org/0000-0002-6443-6618
Julia Steinberg *The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia.ORCID http://orcid.org/0000-0002-0585-2312

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMenopausal status impacts risk for many health outcomes. However, factors including hysterectomy without oophorectomy and Menopausal Hormone Therapy (MHT) can mask menopause, affecting reliability of self-reported menopausal status in surveys. We describe a step-by-step algorithm for classifying menopausal status using: directly self-reported menopausal status; MHT use; hysterectomy; oophorectomy; intervention timing; and attained age. We illustrate this approach using the Australian 45 and Up Study cohort (142,973 women aged ≥ 45 years).

resultsWe derived a detailed seven-category menopausal status, able to be further consolidated into four categories ("pre-menopause"/"peri-menopause"/"post-menopause"/"unknown") accounting for participants' ages. 48.3% of women had potentially menopause-masking interventions. Overall, 93,107 (65.1%), 9076 (6.4%), 17,930 (12.5%) and 22,860 (16.0%) women had a directly self-reported "post-menopause", "peri-menopause", "pre-menopause" and "not sure"/missing status, respectively. 61,464 women with directly self-reported "post-menopause" status were assigned a "natural menopause" detailed derived status (menopause without MHT use/hysterectomy/oophorectomy). By accounting for participants' ages, 105,817 (74.0%) women were assigned a "post-menopause" consolidated derived status, including 15,009 of 22,860 women with "not sure"/missing directly self-reported status. Conversely, 3178 of women with directly self-reported "post-menopause" status were assigned "unknown" consolidated derived status. This algorithm is likely to improve the accuracy and reliability of studies examining outcomes impacted by menopausal status.

Indexed as

AlgorithmsMenopauseAustraliaFemaleHumansMiddle AgedReproducibility of ResultsSelf Report45 and Up StudyAlgorithmHysterectomyMenopausal hormone therapyMenopausal statusMenopauseOophorectomy

Identifiers

PMID35246240
PMCPMC8895593

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.