Beaney Thomas, Clarke Jonathan, Woodcock Thomas, Majeed Azeem, Barahona Mauricio, Aylin Paul
Department of Primary Care and Public Health, Imperial College London, London, UK.
Department of Mathematics, Imperial College London, London, UK.
BMJ Med. 2024 Feb 13;3(1):e000474. doi: 10.1136/bmjmed-2022-000474. eCollection 2024.
To determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors.
Retrospective study of disease code frequency in primary care electronic health records.
Routinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used.
Adults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation.
9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 years 1-10% for ≥80 years), people of some minority ethnic groups (eg, people in the Other ethnic group had higher probability than the South Asian ethnic group), and people living in areas of lower socioeconomic deprivation were more likely to be re-classified as not multimorbid when using definitions requiring multiple codes.
Choice of timeframe to define long term conditions has a substantial effect on the prevalence of multimorbidity in this nationally representative sample. Different timeframes affect prevalence for some people more than others, highlighting the need to consider the impact of bias in the choice of method when defining multimorbidity.
确定用于定义长期病症的时间范围选择对多重疾病患病率的影响程度，以及这是否因社会人口学因素而异。
对初级保健电子健康记录中的疾病编码频率进行回顾性研究。
使用了来自临床实践研究数据链奥鲁姆（Clinical Practice Research Datalink Aurum）的常规收集的全科医疗电子健康记录数据。
纳入2020年1月1日在数据库中注册的英格兰成年人（≥18岁）。多重疾病被定义为在一组212种长期病症中存在两种或更多种病症。使用五种定义比较多重疾病患病率。电子健康记录中记录的212种病症的任何疾病编码用作参考定义。此外，对于41种需要多个编码（其中单个疾病编码可能表示急性病症）的病症或其余171种病症的单个编码的替代定义如下：两个编码间隔至少三个月；两个编码间隔至少12个月；在任何12个月期间内有三个编码；以及过去12个月内的任何编码。使用混合效应回归根据每种定义计算多重疾病状态和长期病症数量的预期变化，以及与患者年龄、性别、种族和社会经济剥夺的关联。
9718573人纳入研究，其中7183662人（73.9%）符合单一编码足以定义长期病症时的多重疾病定义。根据所使用的时间范围，患病率差异很大，从任何12个月期间内有三个编码时的41.4%（n = 4023023）到两个编码间隔至少三个月时的55.2%（n = 5366285）。年轻人（例如，18 - 29岁的概率为50 - 75%，≥80岁的概率为1 - 10%）、一些少数族裔群体的人（例如，其他族裔群体的人比南亚族裔群体的人概率更高）以及生活在社会经济剥夺程度较低地区的人在使用需要多个编码的定义时更有可能被重新分类为非多重疾病。
在这个具有全国代表性的样本中，定义长期病症的时间范围选择对多重疾病患病率有重大影响。不同的时间范围对某些人的患病率影响大于其他人，这凸显了在定义多重疾病时需要考虑方法选择中偏差的影响。
