Associations of adolescent menstrual symptoms with school absences and educational attainment: analysis of a prospective cohort study.

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Overview

Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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Heavy Periods, Period Pain, and School: A Teen Girl Cohort Study

In a longitudinal study of ~2,700 adolescent girls in England, both heavy/prolonged bleeding and menstrual pain were associated with increased school absences and lower exam scores. Heavy bleeding had a stronger association with lower attainment than pain. More research is needed to understand underlying mechanisms and potential solutions.

Possible Conflicts of Interest

None identified.

Identified Weaknesses

Selection bias

While linked administrative data reduced missing data for the outcomes, it also introduced selection bias, as those participants without administrative data were excluded.

Limited sample size, despite imputation

The limited sample size, even with imputation, limits the generalizability of findings and may reduce statistical power, especially for subgroup analysis.

Self-reported menstrual symptoms

Menstrual symptoms were self-reported, which can be subject to recall bias and social desirability bias, especially about sensitive topics like heavy bleeding.

Limited detail in measurement of menstrual symptoms

The study did not collect detailed data on the frequency and severity of pain and bleeding which could have important implications for school functioning.

Limited considerations of other important factors

This study doesn't fully consider the influence of social factors, access to products, support from schools, or management strategies, which may impact attendance/attainment.

Rating Explanation

This is a well-conducted longitudinal study utilizing a valuable dataset with linked administrative outcomes. The analyses were robust and addressed potential confounders. The study acknowledges limitations, strengthening its conclusions. The sample size is a bit limited, dropping to ~2,700 after exclusions, but this is a common challenge in longitudinal cohorts, and they used imputation to address some missing confounder data. The lack of more specific data on symptoms and potential interventions is a limitation but leaves room for important future work.

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