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Record W4410352765 · doi:10.1016/s2542-5196(25)00080-4

The effect of air pollution exposure on menstrual cycle health using self-reported data from a mobile health app: a prospective, observational study

2025· article· en· W4410352765 on OpenAlexaff
Priyanka deSouza, Amanda A. Shea, Virginia J. Vitzthum, Fábio Duarte, Claire Gorman Hanly, Meghan Timmons, Patricia Huguelet, Mary D. Sammel, Carlo Ratti, Danielle Braun, Rachel C. Nethery

Bibliographic record

VenueThe Lancet Planetary Health · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsBritish Columbia Centre of Excellence for Women's Health
Fundersnot available
KeywordsObservational studyEnvironmental healthMenstrual cycleHealth dataMedicinePolitical scienceHealth care

Abstract

fetched live from OpenAlex

Background Toxicological evidence suggests that ambient air pollution has endocrine-disrupting properties that can affect menstrual cycle functioning, which represents an important marker of women's reproductive health. We aimed to estimate the effect of short-term and long-term PM 2·5 exposure on menstrual cycle outcomes across the USA, Brazil, and Mexico using self-reported data from a mobile health app. Methods For this prospective observational study, we collected de-identified self-reported data from the Clue mobile health app, in which users self-tracked menstruation cycles. For the current study, eligible participants were aged 18–44 years, were not using hormonal birth control, and lived in one of 230 cities in the USA, Mexico, or Brazil. The primary outcome of interest at the city level was the proportion of menstrual cycles with abnormally short length (<24 days) and long length (>38 days) of all cycles recorded. The primary outcome at the cycle level was a binary indicator: abnormal cycle length (<24 days or >38 days) or not (normal cycle length). We used regression analyses to evaluate associations between long-term PM 2·5 concentrations (mean concentration between 2016 and 2020) and the city-level outcomes after controlling for potential confounders. Conditional logistic regression models were used to evaluate associations between cycle-specific PM 2·5 and if a cycle was of abnormal length within an individual in the dataset, after controlling for time-varying factors. Findings Between Jan 1, 2016 and Dec 31, 2020, 92 550 app users residing in 230 cities across the USA, Brazil, and Mexico provided data corresponding to 2 220 281 menstrual cycles, and were included in our main cohort. A significant association was observed between long-term PM 2·5 exposure and the proportion of menstrual cycles of abnormally long or short duration (odds ratio [OR] 1·023 [95% CI 1·013–1·033]) and the proportion of cycles that were specifically abnormally long (OR 1·036 [1·023–1·049]) for every 10 μg/m 3 increase in PM 2·5 . No associations were identified between short-term PM 2·5 concentrations and abnormal cycle length. Interpretation These findings suggest that PM 2·5 exposure affects menstrual cycle outcomes. More research is needed to better elucidate the biological mechanisms through which PM 2·5 affects the menstrual cycle. Funding None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.113
GPT teacher head0.384
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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