Do Mini-Mental State Examination and Montreal Cognitive Assessment predict high-cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

Objectives: 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. Results: 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. Conclusions: 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.

Automated summary

This study evaluated the predictive ability of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) for high costs in Ireland. The findings showed that MoCA had superior predictive accuracy for high costs compared to MMSE, but the two scores identified slightly different types of high-cost users. Combining these two approaches may improve efforts to identify high-cost users in advance.