Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
BMJ Open. 2021 Mar 19;11(3):e042633. doi: 10.1136/bmjopen-2020-042633.
To describe the percentile distribution of multimorbidity across age by sex, race and ethnicity, and to demonstrate the utility of multimorbidity percentiles to predict mortality.
Population-based descriptive study and cohort study.
Olmsted County, Minnesota (USA).
We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years).
For each person, we obtained the count of chronic conditions (out of 20 conditions) present on each birthday by extracting all of the diagnostic codes received in the 5 years before the index birthday from the electronic indexes of the REP. To compare each person's count to peers of same age, the counts were transformed into percentiles of the total population and displayed graphically across age by sex, race and ethnicity. In addition, quintiles 1, 2, 4 and 5 were compared with quintile 3 (reference) to predict the risk of death at 1 year, 5 years and through end of follow-up using time-to-event analyses. Follow-up was passive using the REP.
We identified 238 010 persons who experienced a total of 1 458 094 birthdays during the study period (median of 6 birthdays per person; IQR 3-10). The percentiles of multimorbidity across age did not vary noticeably by sex, race or ethnicity. In general, there was an increased risk of mortality at 1 and 5 years for quintiles 4 and 5 of multimorbidity. The risk of mortality for quintile 5 was greater for younger age groups and for women.
The assignment of multimorbidity percentiles to persons in a population may be a simple and intuitive tool to assess relative health status, and to predict short-term mortality, especially in younger persons and in women.
描述按性别、种族和民族划分的多种疾病在不同年龄段的百分位分布,并展示多种疾病百分位在预测死亡率方面的作用。
基于人群的描述性研究和队列研究。
美国明尼苏达州奥姆斯特德县。
我们使用罗切斯特流行病学项目(REP;http://www.rochesterproject.org)的病历链接系统,确定所有在 2005 年 1 月 1 日至 2014 年 12 月 31 日之间达到一个或多个生日的明尼苏达州奥姆斯特德县居民(10 年)。
对于每个人,我们通过从 REP 的电子索引中提取在索引生日前 5 年中收到的所有诊断代码,获得每个生日存在的慢性疾病数量(20 种疾病中的一种)。为了将每个人的数量与同年龄的同龄人进行比较,我们将数量转换为总人群的百分位数,并按性别、种族和民族显示图形,以显示随年龄的变化。此外,我们将五分位数 1、2、4 和 5 与五分位数 3(参考)进行比较,使用事件时间分析预测 1 年、5 年和随访结束时的死亡风险。使用 REP 进行被动随访。
我们确定了 238010 人,他们在研究期间共经历了 1458094 个生日(每人中位数为 6 个生日;IQR 3-10)。随着年龄的增长,多种疾病的百分位数没有明显的性别、种族或民族差异。一般来说,五分位数 4 和 5 的多种疾病与 1 年和 5 年的死亡风险增加有关。五分位数 5 的死亡风险在年龄较小的人群和女性中更高。
将多种疾病百分位分配给人群中的个体可能是一种简单直观的工具,可以评估相对健康状况,并预测短期死亡率,尤其是在年轻人群和女性中。