Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies.
Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies.
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英国生物库和帆数据库的多种多发菌和不良健康结果之间的关联：纵向队列研究的比较。
DOI：
10.1371/journal.pmed.1003931
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发表时间：
2022-03
期刊：
影响因子：
15.8
通讯作者：
Mair FS
中科院分区：
文献类型：
--
作者：
Hanlon P;Jani BD;Nicholl B;Lewsey J;McAllister DA;Mair FS
英国生物银行等队列越来越多地用于研究多Mortocell;然而，人们担心缺乏代表性可能导致偏倚的结果。本研究旨在比较英国生物库和全国代表性样本中多种疾病与不良健康结果之间的关联。这些是对从英国生物库参与者的相关常规医疗保健数据中确定的队列的观察性分析（n = 211，597，来自英格兰、苏格兰和威尔士，有关联的初级保健数据，年龄40 - 70岁，平均年龄56.5岁，54.6%为女性，基线评估2006 - 2010年）和来自安全匿名信息链接（SAIL）数据库（n = 852，055，来自威尔士，年龄40 - 70岁，平均年龄54.2岁，50.0%为女性，基线2011年1月）。从初级保健Read代码中识别出多发性骨髓瘤（n = 40例长期疾病[LTC]），并使用简单计数和加权评分进行量化。还评估了单个LTC和LTC组合。使用Weibull或负二项模型评估与全因死亡率、计划外住院和主要不良心血管事件（MACE）的相关性，该模型根据年龄、性别和社会经济状况进行了调整，对两个数据集进行了7.5年以上的随访。在英国生物样本库中，多发性骨髓瘤的发生率低于SAIL（在英国生物样本库和SAIL中，≥2个LTC的发生率分别为26.9%和33.0%）。在按年龄、性别和社会经济地位进行标准化后，这种差异有所减弱，但仍然存在。在LTC计数≤3时，两个数据集之间的多瘤细胞计数增加与死亡率、住院和MACE之间的相关性相似;然而，在该水平以上，UK Biobank低估了与多瘤细胞相关的风险（例如，SAIL中2种LTC的死亡风险比为1.62（95%置信区间1.57至1.68），UK Biobank中为1.51（1.43至1.59），SAIL中5种LTC的风险比为3.46（3.31至3.61），UK Biobank中为2.88（2.63至3.15）。死亡率、住院率和MACE的绝对风险，在所有多发病水平下，UK Biobank均低于SAIL（调整年龄、性别和社会经济地位）。两个队列对一些LTC产生了相似的风险比（例如，高血压和冠心病），但英国生物银行低估了其他人的风险（例如，酒精相关疾病或精神健康状况）。一些LTC组合的风险比在队列之间相似（例如，心血管疾病）;然而，UK Biobank低估了包括其他疾病（例如，心理健康状况）。主要的局限性是SAIL数据库仅代表英国的一部分（仅威尔士），并且在两个队列中，我们缺乏关于LTC严重程度的数据。在本研究中，我们观察到UK Biobank准确估计了与LTC计数≤3相关的死亡率、计划外住院和MACE的相对风险。然而，对于计数≥4和某些LTC组合，UK生物样本库的相关性程度估计值可能是保守的。研究人员在进行和解释多变量分析时应注意英国生物库的这些局限性。尽管如此，英国生物库中丰富的数据确实为更好地理解多变量提供了机会，特别是在不太容易受到选择偏差影响的补充数据源可以用于为英国生物库的分析提供信息和资格的情况下。Peter Hanlon和他的同事比较了英国生物库和SAIL数据库中多Morphosis和不良健康结果之间的关联。多项长期疾病（LTC）的存在与一系列不良健康结果有关。英国生物库队列研究收集并链接了人口规模的遗传，身体和临床信息，为研究多Mortocell的影响提供了独特的机会。然而，英国生物样本库的参与者似乎平均比一般人群更健康（“健康志愿者偏倚”），目前尚不清楚这种选择偏倚是否会影响使用英国生物样本库对多重死亡影响的估计。我们比较了英国生物银行和来自英国威尔士的代表性样本（SAIL数据库）中多Mortocell的患病率以及多Mortocell对不良健康结果的影响。虽然多药耐药在英国生物库中不太常见，但在较低水平的多药耐药下，英国生物库和SAIL之间的LTC数量与死亡率、住院率和主要不良心血管事件（MACE）之间的关系相似（例如，2或3个LTC）和许多常见的LTC（例如，高血压、冠状动脉疾病和慢性阻塞性肺病）。然而，对于LTC计数较高的人（例如，4或以上），或特定的LTC，如精神健康状况，英国生物银行低估了死亡率，住院和MACE的风险。英国生物银行收集的广泛措施使其成为研究多Mortocell的宝贵资源，我们的研究表明，对中等水平的多Mortocell（如患有2或3个LTC的人）的分析可能会产生可靠的估计。然而，对于患有更多LTC或LTC（如精神健康状况，酒精相关疾病或成瘾）的人，与代表性样本相比，基于英国生物银行数据的估计可能是保守的。理想情况下，未来的LTC和多变量研究应该结合联合收割机的见解，从两个代表性的常规数据和信息丰富的研究队列，如英国生物银行。这些分析由于缺乏关于LTC严重程度的数据而受到限制。
Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample. These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included. In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank. Peter Hanlon and colleagues compare the associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank. Multimorbidity, the presence of multiple long-term conditions (LTCs), is associated with a range of adverse health outcomes. The UK Biobank cohort study has gathered and linked genetic, physical, and clinical information on a population scale providing unique opportunities to study the impact of multimorbidity. However, participants in UK Biobank appear on average to be healthier than the general population (“healthy volunteer bias”) and it is not clear if this selection bias affects estimates of the impact of multimorbidity using UK Biobank. We compared the prevalence of multimorbidity, and the impact of multimorbidity on adverse health outcomes, in UK Biobank and in a representative sample of people from Wales, UK (SAIL databank). While multimorbidity was less common in UK Biobank, the relationship between number of LTCs and mortality, hospital admissions, and major adverse cardiovascular events (MACEs) was similar between UK Biobank and SAIL at lower levels of multimorbidity (e.g., 2 or 3 LTCs) and for many common LTCs (e.g., hypertension, coronary artery disease, and chronic obstructive pulmonary disease). However, for people with higher LTC counts (e.g., 4 or more), or with specific LTCs such as mental health conditions, UK Biobank underestimates the risk of mortality, hospitalisation, and MACEs. The wide range of measures gathered by UK Biobank make it a valuable resource for studying multimorbidity, and our study suggests that analyses of modest levels of multimorbidity (such as people with 2 or 3 LTCs) are likely to yield reliable estimates. However, for people with a higher number of LTCs or with LTCs such as mental health conditions, alcohol-related disorders, or addiction, estimates based on UK Biobank data are likely to be conservative compared to a representative sample. Ideally, future LTC and multimorbidity research should combine insights from both representative routine data and information rich research cohorts such as UK Biobank. These analyses are limited by a lack of data on the severity of LTCs.
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