The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.
The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.
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改变条件数量和选择对估计多发病率的影响的影响：使用大型初级保健人群数据集的横断面研究。
DOI：
10.1371/journal.pmed.1004208
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发表时间：
2023-04
期刊：
影响因子：
15.8
通讯作者：
中科院分区：
文献类型：
--
作者：
根据发病率计数中考虑的条件，多发病率的患病率差异很大，但没有标准化的方法来确定要包括的条件的数量或选择。我们使用英国初级保健数据进行了一项横断面研究，共有1，168，260名参与者，他们都是活着的人，永久注册了149个包括全科诊所。研究的结局指标是当改变80种疾病的数量和选择时，多发病（定义为≥ 2种疾病）的患病率估计值。纳入研究中检查的9种已发表疾病列表中的≥ 1种疾病和/或英国健康数据研究（HDR-UK）表型库中的表型分型算法。首先，当考虑个体最常见的2种疾病、3种疾病等时，计算多发病率，多达80个条件。其次，使用已发表研究的9个条件列表计算患病率。分析按因变量年龄、社会经济地位和性别分层。仅考虑2种最常见疾病时的患病率为4.6%（95% CI [4.6，4.6] p <0.001），上升至29.5%（95% CI [29.5，29.6] p <0.001）考虑到10种最常见的，35.2%（95% CI [35.1，35.3] p <0.001），考虑到20种最常见的疾病，40.5%（95% CI [40.4，40.6] p <0.001），考虑到所有80种疾病。当考虑到所有80种疾病时，多发病率> 99%的疾病的阈值数量在整个人群中为52，但老年人较低（> 80岁为29），年轻人较高（0至9岁为71）。九个已发表的条件清单进行了检查，这些都被推荐用于测量多发病，在以前高度引用的多发病患病率的研究，或广泛应用的措施“合并症”。使用这些列表的多发病率从11.1%到36.4%不等。该研究的一个局限性是，并不总是使用与先前研究相同的确定规则来复制条件，以提高条件列表之间的可比性，但这突出了研究之间患病率估计的进一步变异性。在这项研究中，我们观察到，不同的条件的数量和选择导致非常大的差异，在多发病率，并需要不同数量的条件，以达到多发病率在某些人群的上限。这些发现意味着需要一种标准化的方法来定义多发病，为了促进这一点，研究人员可以使用与最高多发病率相关的现有条件列表。使用英国范围内的初级保健数据集，包括80多个指标条件，克莱尔麦克雷和同事报告多发性硬化症的患病率和患病率估计如何变化时，不同的数量和类型的指标条件。研究人员在测量多发病率时所考虑的条件多种多样。对2021年发表的566项研究进行的系统综述发现，在选择考虑的条件方面缺乏共识。在一半的研究中，只有8种疾病（糖尿病、中风、癌症、慢性阻塞性肺疾病、高血压、冠心病、慢性肾脏疾病和心力衰竭）被一致考虑，考虑的疾病数量从2到285不等（中位数17）。需要一种更一致的方法来测量多变量，以促进研究之间的可比性和普遍性。我们研究了在测量多发病率时考虑的不同条件的影响。我们结合了不同数量的条件（从80个列表中）和条件的选择（使用9个已发布的条件列表，用于定义和测量合并症，多发病及其患病率），以确定多发病患病率如何变化。所有条件都以相同的方式使用公开的代码列表进行计数。当考虑不同数量和条件选择时，患病率差异很大，范围为4.6%至40.5%。在考虑所有80种疾病时，年龄最大、生活在最贫困地区的人和男子需要考虑的疾病较少，才能接近多重死亡率（上限效应，即发病率接近研究中可能的发病率上限）。当使用Ho always + usually（来自最近的德尔菲共识研究）、巴内特（广泛用于测量多发病率）和Fortin（推荐用于测量多发病率）条件列表时，发现多发病率最高。在测量多发病率时需要标准化，以便研究结果具有可比性，并准确代表人口亚组。为了解决这个问题，研究人员可以考虑使用Ho always + usually、巴内特或福廷条件列表，这些列表报告了多发病率的最高和最稳定估计值（在这些列表中添加更多条件的影响非常小）。
Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of “comorbidity.” Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence. Using a UK wide primary-care dataset and including more than 80 index conditions, Clare MacRae and colleagues report multimorbidity prevalence and how prevalence estimates change when varying the number and type of index conditions. There is wide variety in the conditions considered by researchers when measuring multimorbidity prevalence. A systematic review of 566 studies, published in 2021, found lack of consensus in the selection of conditions considered. In half of studies only 8 conditions (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure) were consistently considered, and the number of conditions considered varied from 2 to 285 (median 17). A more consistent approach to measuring multimorbidity is needed to facilitate comparability and generalisability across studies. We examined the impact of varying the conditions considered when measuring multimorbidity prevalence. We combined different numbers of conditions (from a list of 80) and selections of conditions (using 9 published condition-lists used to define and measure comorbidity, multimorbidity, and its prevalence) to determine how multimorbidity prevalence changed. All conditions were counted in the same way using publicly available code lists. There are large differences in prevalence, a range of 4.6% to 40.5%, when different numbers and selections of conditions are considered. People who are the oldest, living in the most deprived areas, and men require fewer conditions to be considered to reach close to multimorbidity prevalence when considering all 80 conditions (the ceiling effect, where the prevalence approaches the upper limit of prevalence possible in the study). Highest multimorbidity prevalence was found when using the Ho always + usually (derived from a recent Delphi consensus study), Barnett (widely used to measure multimorbidity prevalence), and Fortin (recommended for use in measuring multimorbidity) condition-lists. There is a need for standardisation when measuring multimorbidity prevalence so that results across studies are comparable and population subgroups are accurately represented. To address this, researchers can consider using the Ho always + usually, Barnett, or Fortin condition-lists that report the highest and most stable estimates of multimorbidity prevalence (where adding further conditions to the count had very little impact).
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DOI：
10.1016/s0140-6736(21)01330-1
发表时间：
2021-09-11
期刊：
Lancet (London, England)
影响因子：
--
作者：
NCD Risk Factor Collaboration (NCD-RisC)
通讯作者：
NCD Risk Factor Collaboration (NCD-RisC)
影响因子：
2.2
作者：
N'Goran, Alexandra A.;Blaser, Jeremie;Herzig, Lilli
通讯作者：
Herzig, Lilli
影响因子：
3
作者：
Elixhauser, A;Steiner, C;Coffey, RN
通讯作者：
Coffey, RN
影响因子：
8.9
作者：
MacRae C;Whittaker H;Mukherjee M;Daines L;Morgan A;Iwundu C;Alsallakh M;Vasileiou E;O'Rourke E;Williams AT;Stone PW;Sheikh A;Quint JK
通讯作者：
Quint JK
多重疾病的负担和复杂性不断增加。
影响因子：
4.5
作者：
Pefoyo AJ;Bronskill SE;Gruneir A;Calzavara A;Thavorn K;Petrosyan Y;Maxwell CJ;Bai Y;Wodchis WP
通讯作者：
Wodchis WP