OBJECTIVE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.
OBJECTIVE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.
Authors: Lori L Jervis; Alexandra Fickenscher; Janette Beals; C Munro Cullum; Douglas K Novins; Spero M Manson; David B Arciniegas Journal: J Neuropsychiatry Clin Neurosci Date: 2010 Impact factor: 2.198
Authors: Katherine Samaras; Helen L Lutgers; Nicole A Kochan; John D Crawford; Lesley V Campbell; Wei Wen; Melissa J Slavin; Bernard T Baune; Darren M Lipnicki; Henry Brodaty; Julian N Trollor; Perminder S Sachdev Journal: Age (Dordr) Date: 2014-01-09
Authors: Julian N Trollor; Evelyn Smith; Emmeline Agars; Stacey A Kuan; Bernhard T Baune; Lesley Campbell; Katherine Samaras; John Crawford; Ora Lux; Nicole A Kochan; Henry Brodaty; Perminder Sachdev Journal: Age (Dordr) Date: 2011-08-19