Menstrual health during COVID-19: How water, sanitation, and hygiene strategies can improve menstrual health and hygiene and foster gender equality
Bibliographic record
Abstract
Despite the fact that over 800 million women, girls, transgender, and gender nonbinary persons menstruate, menstrual health and hygiene (MHH) remains a taboo and under-resourced public health subject. Water, sanitation, and hygiene (WASH) insecurity, an issue made worse by the COVID-19 pandemic, plays a significant role in menstruators’ abilities to safely manage their periods. COVID-19 has further revealed how a continued lack of access to WASH resources hinders an individuals’ means to address their menstrual health with dignity. The impacts of COVID-19 have laid bare the many challenges associated with menstruation, intensifying existing inequalities for women, girls, and other menstruators. Mobility limitations and quarantining have created complex obstacles for menstruators while lockdown protocols, border closures, and panic buying have impacted the sanitary pad supply chain. Community and school closures have removed many of the routine ways in which menstruators access sanitary products. Our article seeks to understand how the pandemic and existing WASH systems exacerbated the ongoing challenges associated with MHH, highlighting opportunities for future crisis planning to advance MHH infrastructure, resources, and stigma reduction. A landscape analysis was used to understand how the pandemic affected those who menstruate. Academic, peer-reviewed literature as well as gray literature informed the authors’ understanding of the dynamic nature of COVID-19 and WASH infrastructure on menstruators. From these insights, the authors were able to identify emerging themes and trends that they have highlighted in this article. Water and sanitation programs have the power to improve gender equality and equity, health, and education by investing in menstrual hygiene infrastructure, awareness creation, and stigma reduction. The diversion of resources, and in particular WASH resources, toward pandemic-related hygiene supplies and mobility restrictions, however, have exposed menstruators to sexual exploitation and physical and mental strain. It is critical to understand and address the pandemic’s impacts on menstruators, drawing lessons from and creating opportunities to systematically advance MHH, thereby elevating gender equality.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".