Evidence map›Paper›PMID 40414720›Full record

ArticleIndustrial health2025

Association of severity of premenstrual disorders with absenteeism and presenteeism among Japanese working women: a cross-sectional study.

Miho Iida, Mira Namba, Kyoko Nomura

Abstract read
In one paragraph

Article in Industrial health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Miho IidaDepartment of Preventive Medicine and Public Health, School of Medicine, Keio University, Japan.
Mira NambaSchool of Medicine, Keio University, Japan.
Kyoko NomuraDepartment of Environmental Health Science and Public Health, Graduate School of Medicine, Akita University, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Premenstrual disorders (PMDs) in working women are reported to be associated with absenteeism and presenteeism, but few studies have considered their severity from a broad range of manifestations and investigated its impact on absenteeism and presenteeism simultaneously within the same population consisting of various occupations. Thus, we conducted a cross-sectional study of 2,987 premenopausal women working across Japan using a self-administered survey that included items on absenteeism and presenteeism due to premenstrual symptoms and symptom severity. Multivariate analyses were used to examine the association between PMD severity, absenteeism (being away from work) and presenteeism (decreased work performance). Results demonstrated that PMD severity was strongly associated with both absenteeism and presenteeism irrespective of age, socioeconomic background, and work-related factors. Those with moderate to severe forms of PMDs had a two-fold risk of absenteeism and 18-30% worse presenteeism compared to a milder group. Furthermore, two-thirds of women with severe PMDs reported ≥30% performance reduction without absenteeism, indicating that the impact of PMDs was more significant for presenteeism than absenteeism. Our study results suggest the importance of widely promoting education and treatment of PMDs regardless of sociodemographic factors and working conditions for securing the workforce through the advancement of working women.

Indexed as

AbsenteeismPremenstrual SyndromePresenteeismWomen, WorkingAdultCross-Sectional StudiesEast Asian PeopleFemaleHumansJapanMiddle AgedSeverity of Illness IndexSurveys and QuestionnairesYoung AdultAbsenteeismOccupational healthPremenstrual syndromePresenteeismWomen’s health

Identifiers

PMID40414720
PMCPMC12657055

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.