Actionable Qualitative Analysis: Menstrual Health in Khulna

According to the July 24, 2026 PLOS One study, women and adolescent girls in Khulna Railway Slum experienced menstruation as a socially and structurally shaped hardship driven by inadequate WASH, stigma, and economic constraints (PLOS One). The PLOS One study collected 18 in-depth interviews and 5 key informant interviews between 01/09/2025 and 31/10/2025 and used thematic analysis to identify three core domains: structural-environmental constraints, socio-cultural stigma, and gendered inequities.

• 18 in-depth interviews and 5 KIIs were conducted between 01/09/2025 and 31/10/2025, according to the PLOS One article published on July 24, 2026.
• Khulna district has about 2.61 million residents and roughly 20% live in slums, as reported in the PLOS One study.
• By the fifteenth interview the authors reported thematic saturation and continued to 18 IDIs to confirm patterns, according to the Methods section of the PLOS One paper.
• Women described practical harms: water scarcity, shared toilets without doors, and hidden disposal practices that increase health risk, as quoted in the PLOS One findings.

The direct answer: the PLOS One study used an interpretivist qualitative design to document lived menstrual experiences in Khulna Railway Slum between September and October 2025, using thematic analysis guided by Feminist Political Ecology (PLOS One).

The PLOS One study recruited 18 women and adolescent girls aged 15–45 and stratified participants into adolescent (15–19) and adult (20–45) groups to capture age-sensitive differences, as described in the Methods section of the PLOS One article.

The PLOS One authors identified three interrelated domains shaping menstrual experiences: (1) structural-environmental constraints such as limited water and private toilets, (2) socio-cultural stigma that enforces concealment, and (3) gendered inequities limiting access to paid menstrual products, per the Findings and Discussion in PLOS One.

Representative verbatim evidence from the PLOS One interviews includes: "When water stops coming from the tap, I wait for hours. If it doesn’t come, I can’t clean myself properly, " attributed to IDI-08 (Housewife) in the PLOS One paper.

The PLOS One authors also recorded disposal-related secrecy: "I wrap the used cloth in paper and throw it away at night. I don’t want anyone to see it; people will gossip, " attributed to IDI-10 (Tailor) in the PLOS One findings.

The direct answer: qualitative researchers should combine rigorous close-reading with reproducible coding workflows so findings like those in the PLOS One study can inform targeted WASH and education programs.

The PLOS One study links environment, stigma, and poverty to menstrual outcomes, so program teams must plan integrated interventions that improve private sanitation, disposal systems, and community education rather than single-component distributions, as recommended in the Discussion and Policy Implications of the PLOS One paper.

Researchers replicating this study design should note the PLOS One procedural details: ethical approval (IRB Approval No. 473478-FY 2024–2025; Approval Date 10 August 2025), audio-recorded interviews conducted in Bengali with English translation and back-checking, and coding using NVivo v12 with independent coders and consensus meetings.

The PLOS One study (published July 24, 2026) documents how inadequate WASH, stigma, and poverty shape menstruation in Khulna slums and provides a clear, context-specific evidence base for integrated interventions.

AI-enabled qualitative analysis can reproduce the PLOS One thematic rigor while speeding coding, producing extractable quotes, and generating visual evidence for program design.

If your team analyzes interview transcripts, consider tools that support transcription, translation, thematic and cross-segment analysis to turn lived experience into policy actions; for global guidance see the UNICEF guidance on menstrual health and hygiene.

Ready to test an AI platform on interview transcripts and open-ended survey data? Try Evidano for free.