May Peter, De Looze Céline, Feeney Joanne, Matthews Soraya, Kenny Rose Anne, Normand Charles
The Irish Longitudinal Study on Ageing, School of Medicine, Trinity College Dublin, Dublin, Ireland.
Centre for Health Policy and Management, Trinity College Dublin, Dublin, Ireland.
Int J Geriatr Psychiatry. 2022 Jul;37(7). doi: 10.1002/gps.5766.
Policymakers want to better identify in advance the 10% of people who account for approximately 75% of health care costs. We evaluated how well Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) predicted high costs in Ireland.
METHODS/DESIGN: We used five waves from The Irish Longitudinal Study on Ageing, a biennial population-representative survey of people aged 50+ (2010-2018). We used competing risks analysis where our outcome of interest was "high costs" (top 10% at any wave) and the competing outcome was dying or loss to follow-up without first having the high-cost outcome. Our binary predictors of interest were a 'low score' (bottom 10% in the sample) in MMSE (≤25 pts) and MoCA (≤19 pts) at baseline, and we calculated sub-hazard ratios after controlling for sociodemographic, clinical and functional factors.
Of 5856 participants, 1427 (24%) had the 'high cost' outcome; 1463 (25%) had a competing outcome; and 2966 (51%) completed eight years of follow-up without either outcome. In multivariable regressions a low MoCA score was associated with high costs (SHR: 1.38 (95% CI: 1.2-1.6) but a low MMSE score was not. Low MoCA score at baseline had a higher true positive rate (40%) than did low MMSE score (35%). The scores had similar association with exit from the study.
MoCA had superior predictive accuracy for high costs than MMSE but the two scores identify somewhat different types of high-cost user. Combining the approaches may improve efforts to identify in advance high-cost users.
政策制定者希望能提前更好地识别出占医疗保健费用约75%的那10%的人群。我们评估了简易精神状态检查表（MMSE）和蒙特利尔认知评估量表（MoCA）在爱尔兰预测高费用情况的效果。
方法/设计：我们使用了来自爱尔兰老龄化纵向研究的五轮数据，这是一项对50岁及以上人群进行的两年一次的具有人口代表性的调查（2010 - 2018年）。我们采用了竞争风险分析，其中我们感兴趣的结果是“高费用”（在任何一轮中处于前10%），竞争结果是死亡或失访且未先出现高费用结果。我们感兴趣的二元预测变量是基线时MMSE（≤25分）和MoCA（≤19分）的“低分”（样本中处于后10%），并且在控制了社会人口统计学、临床和功能因素后计算了亚风险比。
在5856名参与者中，1427人（24%）出现了“高费用”结果；1463人（25%）出现了竞争结果；2966人（51%）完成了八年的随访且未出现任何一种结果。在多变量回归中，MoCA低分与高费用相关（亚风险比：1.38（95%置信区间：1.2 - 1.6）），但MMSE低分则不然。基线时MoCA低分的真阳性率（40%）高于MMSE低分（35%）。这些分数与退出研究的关联相似。
MoCA对高费用的预测准确性优于MMSE，但这两个分数识别出的高费用使用者类型略有不同。结合这两种方法可能会改善提前识别高费用使用者的工作。
