Grossardt Brandon R, Chamberlain Alanna M, Boyd Cynthia M, Bobo William V, St Sauver Jennifer L, Rocca Walter A
Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
J Multimorb Comorb. 2023 Jan 2;13:26335565221150124. doi: 10.1177/26335565221150124. eCollection 2023 Jan-Dec.
To compare the agreement between percentile ranks from 4 multi-morbidity scores.
Population-based descriptive 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 calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs).
We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older.
The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures.
比较4种多重疾病评分的百分位数之间的一致性。
基于人群的描述性研究。
美国明尼苏达州奥尔姆斯特德县。
我们使用罗切斯特流行病学项目(REP;http://www.rochesterproject.org)的医疗记录链接系统,识别出2005年1月1日至2014年12月31日(10年)期间年满1岁及以上的明尼苏达州奥尔姆斯特德县所有居民。
对于每个人,我们使用美国卫生与公众服务部现成的诊断代码列表、临床分类软件和埃利克斯豪泽共病指数计算4种多重疾病评分。我们使用索引生日前5年收到的诊断代码计算评分,并通过年龄和性别分别拟合分位数回归模型,将未加权的疾病简单计数转换为与同年龄、同性别的同龄人相比的百分位数。我们使用组内相关系数(ICC)比较4种多重疾病评分的百分位数。
在研究期间,我们评估了181,553人的一致性,这些人在18岁至85岁之间共度过了1,075,433个生日。总体而言,在6次评分与评分的两两比较中,4种多重疾病评分的百分位数表现出高度一致性。所有两两比较的一致性都随着年龄的增长而增加,在50岁及以上年龄组中,ICC始终大于0.65。
百分位数的分配可能是一种简单直观的方法,用于评估使用不同测量方法的研究中多重疾病的潜在特征。