Anderson Tracy M, Sachdev Perminder S, Brodaty Henry, Trollor Julian N, Andrews Gavin
School of Psychiatry, University of New South Wales, the Clinical Research Unit for Anxiety and Depression, Darlinghurst, New South Wales, Australia.
Am J Geriatr Psychiatry. 2007 Jun;15(6):467-76. doi: 10.1097/JGP.0b013e3180547053.
This article examines the influence of sociodemographic, biological, and health variables on Mini-Mental State Exam (MMSE) performance, and assesses how the diversity of the population should be reflected in the MMSE cutoff scores used for screening.
The sociodemographic profiles and MMSE scores of adults aged 65-years and over who participated in the Australian National Mental Health and Well-being Survey were assessed (N = 1,792).
The regression models showed that older age, education levels, language spoken at home and in country of birth, socioeconomic status (SES), occupation, sex, and presence of a mood disorder made significant and unique contributions to performance on the MMSE. The individual (univariate) influence of each factor ranged from -2.61 to 0.09 points, with non-English speaking background (NESB) making the biggest impact. Based on a MMSE score of < or =23 points, 7.7% of the Australian elderly population screened positive for cognitive impairment that may be indicative of dementia. In those scoring < or =23 points, the multivariate model accounted for 24.61% of the variance.
Many sociodemographic variables and the presence of a mood disorder influence MMSE performance. Using conventional cutoff scores for screening will lead to a high rate of false positives in older adults (75+ years), those with NESB, and those with low SES, and is insensitive for those with high education. The authors suggest simple rules for the correction of the impact of these variables.
本文探讨社会人口统计学、生物学和健康变量对简易精神状态检查表（MMSE）测试结果的影响，并评估在用于筛查的MMSE临界值分数中应如何体现人群的多样性。
对参与澳大利亚全国心理健康与幸福调查的65岁及以上成年人的社会人口统计学概况和MMSE分数进行评估（N = 1792）。
回归模型显示，年龄较大、教育水平、在家中和出生国所使用的语言、社会经济地位（SES）、职业、性别以及是否存在情绪障碍对MMSE测试结果有显著且独特的影响。每个因素的个体（单变量）影响范围为 -2.61至0.09分，其中非英语背景（NESB）的影响最大。基于MMSE分数≤23分，7.7%的澳大利亚老年人群筛查出可能提示痴呆的认知障碍阳性。在那些得分≤23分的人群中，多变量模型解释了24.61%的方差。
许多社会人口统计学变量以及情绪障碍的存在会影响MMSE测试结果。使用传统的临界值分数进行筛查会导致老年人（75岁以上）、非英语背景者和社会经济地位较低者出现较高的假阳性率，并且对高学历者不敏感。作者提出了纠正这些变量影响的简单规则。
