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Abstract：
OBJECTIVE: To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.
METHODS: We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.
RESULTS: Five distinct clusters of accumulating LTC trajectories were identified and characterised as: \'no LTC\' (18.57%), \'single LTC\' (31.21%), \'evolving multimorbidity\' (25.82%), \'moderate multimorbidity\' (17.12%) and \'high multimorbidity\' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the \'high multimorbidity\' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the \'no LTC\' cluster.
CONCLUSIONS: The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
METHODS: We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.
RESULTS: Five distinct clusters of accumulating LTC trajectories were identified and characterised as: \'no LTC\' (18.57%), \'single LTC\' (31.21%), \'evolving multimorbidity\' (25.82%), \'moderate multimorbidity\' (17.12%) and \'high multimorbidity\' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the \'high multimorbidity\' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the \'no LTC\' cluster.
CONCLUSIONS: The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
摘要：
目的：根据累积的长期状况（LTC）作为轨迹，将老年人分类为簇，表征集群并量化它们与全因死亡率的关联。
方法：我们进行了一项纵向研究，使用英国纵向老龄化研究超过9年（n=15091，年龄在50岁及以上）。基于群体的轨迹建模用于基于随时间累积的LTC将人分类成群。派生聚类用于量化轨迹成员之间的关联，社会人口统计学特征和全因死亡率，通过回归模型。
结果：确定了五个不同的累积LTC轨迹簇，并将其表征为：\'无LTC\'（18.57％），“单一LTC”（31.21%），“不断发展的多发病率”（25.82%），“中度多浊度”（17.12%）和“高多浊度”（7.27%）。年龄的增长始终与更多的LTC相关。少数民族（校正后的OR=2.04；95%CI1.40至3.00）与“高多重性”群相关。随着时间的推移，高等教育和有偿就业与LTC数量增加的可能性较低相关。所有群集的全因死亡率均高于“无LTC”群集。
结论：随着时间的推移，多种疾病的发展遵循不同的轨迹。这些是由不可修改的(年龄，种族)和可改变的因素(教育和就业)。通过聚类对风险进行分层将使从业者能够识别出随着时间的推移LTC恶化的可能性较高的老年人，以制定有效的干预措施来预防死亡。
方法：我们进行了一项纵向研究，使用英国纵向老龄化研究超过9年（n=15091，年龄在50岁及以上）。基于群体的轨迹建模用于基于随时间累积的LTC将人分类成群。派生聚类用于量化轨迹成员之间的关联，社会人口统计学特征和全因死亡率，通过回归模型。
结果：确定了五个不同的累积LTC轨迹簇，并将其表征为：\'无LTC\'（18.57％），“单一LTC”（31.21%），“不断发展的多发病率”（25.82%），“中度多浊度”（17.12%）和“高多浊度”（7.27%）。年龄的增长始终与更多的LTC相关。少数民族（校正后的OR=2.04；95%CI1.40至3.00）与“高多重性”群相关。随着时间的推移，高等教育和有偿就业与LTC数量增加的可能性较低相关。所有群集的全因死亡率均高于“无LTC”群集。
结论：随着时间的推移，多种疾病的发展遵循不同的轨迹。这些是由不可修改的(年龄，种族)和可改变的因素(教育和就业)。通过聚类对风险进行分层将使从业者能够识别出随着时间的推移LTC恶化的可能性较高的老年人，以制定有效的干预措施来预防死亡。
