Byles Julie E, D'Este Catherine, Parkinson Lynne, O'Connell Rachel, Treloar Carla
Centre for Research and Education in Ageing, The University of Newcastle, Level 2, David Maddison Clinical Sciences Building, Watt Street, Newcastle, NSW 2308, Australia.
J Clin Epidemiol. 2005 Oct;58(10):997-1005. doi: 10.1016/j.jclinepi.2005.02.025.
Measurement of multimorbidity and comorbidity is important in epidemiologic and health services research. The aim of this research was to derive a generic multimorbidity index based on patient self-report, incorporating severity, for predicting a range of outcomes.
The dataset was obtained from a trial including 1,541 Veterans and war widows aged 70 years and over. The survey included sociodemographics, hospital admissions, SF-36, and information on deaths was obtained. The methods of Charlson were used to derive Multimorbidity Indices.
All indices predicted quality of life, with decreasing quality of life for each increase in multimorbidity category. Multimorbidity scores incorporating severity significantly contributed to the prediction of mortality, hospital admission, and follow-up quality of life, regardless of adjustment for baseline quality of life.
Our results indicate that a single index cannot predict a variety of relevant outcomes. Consequently, research undertaken to assess the impact of intervention or illness on health outcomes should use an index that is valid for predicting the specific outcome of interest.
在流行病学和卫生服务研究中，测量多种疾病并存和共病情况很重要。本研究的目的是基于患者自我报告得出一个通用的多种疾病并存指数，纳入疾病严重程度，以预测一系列结果。
数据集来自一项包含1541名70岁及以上退伍军人和战争遗孀的试验。调查包括社会人口统计学信息、住院情况、SF-36量表，以及死亡信息。采用查尔森方法得出多种疾病并存指数。
所有指数都能预测生活质量，随着多种疾病并存类别每增加一级，生活质量下降。无论是否对基线生活质量进行调整，纳入疾病严重程度的多种疾病并存评分对死亡率、住院率和随访生活质量的预测都有显著贡献。
我们的结果表明，单一指数无法预测多种相关结果。因此，为评估干预或疾病对健康结果的影响而进行的研究应使用对预测感兴趣的特定结果有效的指数。
