Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.
Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.
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DOI：
10.1371/journal.pmed.1004310
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发表时间：
2023-11
期刊：
影响因子：
15.8
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中科院分区：
文献类型：
--
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以个人多种慢性病并存为特征的多发病是一个日益严重的公共卫生问题。虽然现有的大部分研究都集中在多发病的横断面模式上，但仍有必要更好地了解疾病的纵向积累。这包括检查重要的社会人口学特征与慢性病进展速度之间的关系。我们利用了来自英国1348万参与者的电子初级保健记录，这些记录来自临床实践研究数据链接(CPRD Aurum)，时间跨度从2005年到2020年，平均随访时间为4.71年(IQR：1.78，11.28)。这项研究聚焦于5种重要的慢性疾病：心血管疾病(CVD)、2型糖尿病(T2D)、慢性肾脏疾病(CKD)、心力衰竭(HF)和精神健康(MH)。考虑的关键社会人口特征包括种族、社会和物质匮乏、性别和年龄。我们采用了一种灵活的基于样条法的参数多态模型来研究这些社会人口学特征与多发病发展过程中不同疾病转移率之间的关系。我们的发现揭示了不同疾病过渡类型之间的不同关联模式。与种族差异相比，剥夺、性别和年龄通常与疾病诊断有更强的相关性。值得注意的是，这些因素的影响随着先前存在的疾病数量的增加而减弱，特别是在剥夺、性别和年龄方面。例如，对于没有既往疾病的人，剥夺与T2D诊断相关的风险比(95%CI；p值)为1.76([1.74，1.78]；p<；0.001)，而在4种既往疾病的情况下，风险比降至0.95([0.75，1.21]；p=0.69)。此外，当从MH条件过渡时，剥夺、性别和年龄的影响通常更明显。例如，当从MH过渡时，剥夺与T2D诊断相关的HR(95%CI；p值)为2.03([1.95，2.12]，p<；0.001)，而从CVD1.50([1.43，1.58]，p<；0.001)，CKD 1.37([1.30，1.44]，p<；0.001)和HF 1.55([1.34，1.79]，p<；0.001)过渡。我们研究的一个主要局限性是，初级保健记录中潜在的诊断错误，如诊断不足、过度诊断或慢性疾病的确证偏差，可能会影响我们的结果。我们的结果表明，多发病发展的早期阶段可能需要更多的关注。强调了及早发现和干预慢性病的潜在重要性，特别是对MH病症和高危人群。这些见解可能对多发性疾病的管理具有重要的意义。在这项多状态建模分析中，Sida Chen及其同事使用英格兰初级保健记录中的数据研究了社会人口学特征和多病的纵向进展。在老龄化社会中，一个人存在两种或两种以上的慢性病是一个日益令人担忧的问题。更好地了解这些情况是如何发展和进展的，以及与这一过程相关的因素，对于更有效的管理和治疗非常重要。以前的研究已经分析了某些社会经济和行为因素与疾病随时间发展的速度之间的联系。然而，这些研究通常集中在有限数量的条件下，很少考虑所有可能的组合。此外，他们的分析通常依赖于相对较小的数据集。我们对社会人口特征--如种族、贫困、年龄和性别--对多种慢性病进展的影响的详细理解存在差距。我们分析了2005年至2020年英国1300多万参与者的健康记录，重点关注种族、贫困、性别和年龄等因素与5种常见疾病的累积之间的关系：心血管疾病(CVD)、2型糖尿病(T2D)、慢性肾脏疾病(CKD)、心力衰竭(HF)和精神健康(MH)。我们发现，与种族相比，剥夺、年龄和性别等因素通常与这些疾病的诊断有更强的联系。此外，剥夺、年龄和性别的影响往往随着一个人先前存在的疾病数量的增加而减弱。特别是，当一个人已经患有MH疾病时，如果他们是年龄较大、男性或来自更贫穷的群体，与涉及其他先前存在的疾病的情况相比，他们预计会更快地发展成其他疾病，如CVD、T2D和HF。我们的发现表明，当人们开始出现多种健康问题的早期阶段，特别是当MH问题首次被诊断出来和在高危人群中时，可能需要更多地关注改善患者护理和医疗保健策略。我们的结果强调了调查和更好地了解影响多发病进展的不同生物、心理和社会因素的必要性。请注意，我们的分析是基于健康记录的，这些记录可能包含不完整或不准确的信息，包括条件诊断中的潜在不准确。这些限制可能会对我们的结果产生影响。
Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity. In this multistate modelling analysis, Sida Chen and colleagues examine sociodemographic characteristics and longitudinal progression of multimorbidity using data from primary care records in England Multimorbidity, the presence of 2 or more chronic conditions in an individual, is a growing concern in ageing societies. A better understanding of how these conditions develop and progress over time, and the factors associated with this process, is important for more effective management and treatment. Previous research has analysed the association between certain socioeconomic and behavioural factors and the rate of disease progression over time. However, these studies typically focused on a limited number of conditions and rarely considered all possible combinations. Furthermore, their analyses often rely on relatively small datasets. There is a gap in our detailed understanding of the impact of sociodemographic characteristics—such as ethnicity, deprivation, age, and gender—on the progression of multiple chronic conditions. We analysed the health records of over 13 million participants in England from 2005 to 2020, focusing on how factors like ethnicity, deprivation, gender, and age are associated with the accumulation of 5 common conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. We found that factors like deprivation, age, and gender generally have a stronger link to the diagnosis of these conditions compared to ethnicity. Moreover, the impact of deprivation, age, and gender tend to be weakened as the number of preexisting conditions a person has increases. In particular, when an individual already has an MH condition, and if they were older, male, or from more deprived groups, they were expected to develop other conditions like CVD, T2D, and HF more quickly compared to scenarios involving other preexisting conditions. Our findings suggest that early stages, when people are starting to develop multiple health issues, especially when MH problems are first diagnosed and in high-risk groups, may require more attention for improved patient care and healthcare strategies. Our results underscore the need to investigate and better understand the different biological, psychological, and societal factors that influence the progression to multimorbidity. Note that our analysis is based on health records, which may have incomplete or inaccurate information, including potential inaccuracies in condition diagnosis. These limitations may have an influence on our results.
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DOI：
10.1016/j.lanepe.2021.100047
发表时间：
2021-04
期刊：
The Lancet regional health. Europe
影响因子：
--
作者：
Bisquera A;Gulliford M;Dodhia H;Ledwaba-Chapman L;Durbaba S;Soley-Bori M;Fox-Rushby J;Ashworth M;Wang Y
通讯作者：
Wang Y
影响因子：
2.9
作者：
Ashworth, Mark;Durbaba, Stevo;Dodhia, Hiten
通讯作者：
Dodhia, Hiten
影响因子：
7.7
作者：
Chan, Mei Sum;van den Hout, Ardo;Bajekal, Madhavi
通讯作者：
Bajekal, Madhavi
影响因子：
5.9
作者：
Hirst, Jennifer A.;Hill, Nathan;Hobbs, F. D. Richard
通讯作者：
Hobbs, F. D. Richard
影响因子：
2.9
作者：
Feakins, Benjamin;Oke, Jason;Perera, Rafael
通讯作者：
Perera, Rafael