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Menstrual cycle features in mothers and daughters in the Avon Longitudinal Study of Parents and Children (ALSPAC)
Problematic menstrual cycle features, including irregular periods, severe pain, heavy bleeding, absence of periods, frequent or infrequent cycles, and premenstrual symptoms, are experienced by high proportions of females and can have substantial impacts on their health and well-being. However, resea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665607/ https://www.ncbi.nlm.nih.gov/pubmed/37997583 http://dx.doi.org/10.12688/wellcomeopenres.19774.3 |
Sumario: | Problematic menstrual cycle features, including irregular periods, severe pain, heavy bleeding, absence of periods, frequent or infrequent cycles, and premenstrual symptoms, are experienced by high proportions of females and can have substantial impacts on their health and well-being. However, research aimed at identifying causes and risk factors associated with such menstrual cycle features is sparse and limited. This data note describes prospective, longitudinal data collected in a UK birth cohort, the Avon Longitudinal Study of Parents and Children (ALSPAC), on menstrual cycle features, which can be utilised to address the research gaps in this area. Data were collected across 21 timepoints (between the average age of 28.6 and 57.7 years) in mothers (G0) and 20 timepoints (between the average age of 8 and 24 years) in index daughters (G1) between 1991 and 2020. This data note details all available variables, proposes methods to derive comparable variables across data collection timepoints, and discusses important limitations specific to each menstrual cycle feature. Also, the data note identifies broader issues for researchers to consider when utilising the menstrual cycle feature data, such as hormonal contraception, pregnancy, breastfeeding, and menopause, as well as missing data and misclassification. |
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