Association between residential green space and menstrual cycle characteristics among North American women
Bibliographic record
Abstract
BACKGROUND AND AIM: Exposure to green space (natural vegetation) has been linked to improved reproductive outcomes, potentially through behavioral, psychological, and physiological mechanisms. However, there has been no study of the role of green space in gynecologic health. We examined the association between residential green space and menstrual cycle characteristics in a cohort of reproductive-aged women. METHODS: We performed a cross-sectional analysis within Pregnancy Study Online (PRESTO), a web-based prospective cohort study of women aged 21-45 years who reside in the United States or Canada. We included 7,733 women who enrolled during June 2013-April 2019. We geocoded their residential addresses and calculated annual maximum normalized difference vegetation index (NDVI) at 30 meter resolution within 250 meters around their residences to quantify green space exposure. Women reported on menstrual cycle regularity, cycle length, bleed length, heaviness of bleed, and intensity of menstrual pain. We used log-binomial regression models to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs), adjusting for sociodemographic, lifestyle, and neighborhood characteristics, including population density and census tract median household income. RESULTS:The median (interquartile range) NDVI within 250 meters was 0.64 (0.52, 0.74). Low residential green space was associated with a higher prevalence of long bleeds (≥6 days) and severe period pain (medication and bed rest required). The PRs comparing the lowest levels of green space (NDVI 0.2) with the highest levels of green space (NDVI ≥0.8) were 1.43 (95% CI: 0.99, 2.08) for long bleeds and 1.71 (95% CI: 1.01, 2.89) for severe period pain. However, there was little evidence of dose-response associations. Other menstrual cycle characteristics were not appreciably associated with NDVI. CONCLUSIONS:Women with the lowest residential green space were more likely to have long menstrual bleeds and severe menstrual pain, indicating a potential role for green space in menstrual health. KEYWORDS: reproductive outcomes, green space, built environment, epidemiology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".