Sawyer Gemma, Howe Laura D, Fraser Abigail, Clayton Gemma, Lawlor Deborah A, Sharp Gemma C
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, England, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England, UK.
Wellcome Open Res. 2023 Nov 24;8:386. doi: 10.12688/wellcomeopenres.19774.2. eCollection 2023.
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.
许多女性都经历过月经周期问题，包括月经不规律、剧痛、大量出血、闭经、月经周期频繁或稀少以及经前症状等，这些问题会对她们的健康和幸福产生重大影响。然而，旨在确定与此类月经周期特征相关的原因和风险因素的研究却很少且有限。本数据说明描述了在英国一个出生队列“埃文父母与儿童纵向研究”（ALSPAC）中收集的关于月经周期特征的前瞻性纵向数据，这些数据可用于填补该领域的研究空白。数据是在1991年至2020年期间，在母亲（G0）的21个时间点（平均年龄在28.6岁至57.7岁之间）和指标女儿（G1）的20个时间点（平均年龄在8岁至24岁之间）收集的。本数据说明详细介绍了所有可用变量，提出了在不同数据收集时间点得出可比变量的方法，并讨论了每个月经周期特征特有的重要局限性。此外，本数据说明还确定了研究人员在使用月经周期特征数据时需要考虑的更广泛问题，如激素避孕、怀孕、哺乳和更年期，以及数据缺失和错误分类。
