Identification of high-risk symptom cluster burden group among midlife peri-menopausal and post-menopausal women with metabolic syndrome using latent class growth analysis.
Identification of high-risk symptom cluster burden group among midlife peri-menopausal and post-menopausal women with metabolic syndrome using latent class growth analysis.
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DOI：
10.1177/17455057231160955
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发表时间：
2023-01
期刊：
影响因子：
2.4
通讯作者：
Yang, Qing
中科院分区：
文献类型：
--
作者：
Min, Se Hee;Docherty, Sharron L.;Im, Eun-Ok;Hu, Xiao;Hatch, Daniel;Yang, Qing
中年围绝经期和绝经后代谢综合征妇女出现多种症状或症状群同时发生，往往导致显著的症状群负担。虽然她们是一个高风险的症状负担组，但没有研究集中在确定中年围绝经期和绝经后代谢综合征妇女的症状群轨迹。目的是根据不同的症状群负担轨迹，确定中年围绝经期和绝经后代谢综合征妇女的有意义的亚组，并描述不同症状群负担亚组的人口学、社会和临床特征。这是二次数据分析，使用的是全国妇女健康研究的纵向数据。采用潜在类别增长分析进行多轨迹分析，将不同症状群的发展轨迹结合起来，以确定有意义的亚组和高风险的亚组，随着时间的推移，症状群负担更大。然后，采用描述性统计解释各症状聚类轨迹亚组的人口学特征，并采用双变量分析检验各症状聚类轨迹亚组与人口学特征之间的相关性。共分为4类：1类（低症状群集负担）、2和3类（中度症状群集负担）和4类（高症状群集负担）。社会支持是高症状群负担亚组的显著预测因子，并强调提供常规评估的必要性。了解和欣赏不同的症状群轨迹亚组及其动态性质将有助于临床医生在临床环境中提供有针对性和常规的症状群评估和管理。
Midlife peri-menopausal and post-menopausal women with metabolic syndrome experience multiple co-occurring symptoms or symptom clusters, which often result in significant symptom cluster burden. While they are a high-risk symptom burden group, there are no studies that have focused on identifying symptom cluster trajectories in midlife peri-menopausal and post-menopausal women with metabolic syndrome. The objectives were to identify meaningful subgroups of midlife peri-menopausal and post-menopausal women with metabolic syndrome based on their distinct symptom cluster burden trajectories, and to describe the demographic, social, and clinical characteristics of different symptom cluster burden subgroups. This is a secondary data analysis using the longitudinal data from Study of Women’s Health Across the Nation. Multi-trajectory analysis using latent class growth analysis was conducted to join the different developmental trajectories of symptom clusters to identify meaningful subgroups and high-risk subgroup for greater symptom cluster burden over time. Then, descriptive statistics were used to explain the demographic characteristics of each symptom cluster trajectory subgroup, and bivariate analysis to examine the association between each symptom cluster trajectory subgroup and demographic characteristics. A total of four classes were identified: Class 1 (low symptom cluster burden), Classes 2 and 3 (moderate symptom cluster burden), and Class 4 (high symptom cluster burden). Social support was a significant predictor of high symptom cluster burden subgroup and highlights the need to provide routine assessment. An understanding and appreciation for the different symptom cluster trajectory subgroups and their dynamic nature will assist clinicians to offer targeted and routine symptom cluster assessment and management in clinical settings.
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影响因子：
6.6
作者：
Greene, T.;Gelkopf, M.;Lapid, L.
通讯作者：
Lapid, L.
影响因子：
5.7
作者：
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DOI：
10.1186/s40695-017-0021-y
发表时间：
2017-01-01
期刊：
Women's midlife health
影响因子：
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作者：
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作者：
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影响因子：
3.1
作者：
Lampousi AM;Möller J;Liang Y;Berglind D;Forsell Y
通讯作者：
Forsell Y