BMC public health · 2026
Identifying individuals at risk of cognitive decline: cross-sectional analysis of variability in neuropsychological test scores among community-dwelling older adults.
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Sáez-González Lucía, Martínez Luis A, García-García Cristina, Blázquez Abellán G, Carbajal de Lara José Antonio, Martinez-Garcia Rosa M, Castro-Vázquez Lucía
Аннотация
BACKGROUND: Cognitive impairment is a major public health concern due to its impact on functional independence and its risk of progression to dementia. Early detection is critical, but the estimated prevalence varies substantially depending on the screening tool used and the role of modifiable metabolic risk factors, in accelerating cognitive aging.
OBJECTIVE: This study aimed to describe the variability in cognitive performance and the prevalence of low scores across several brief screening tools and cut-offs, and to explore sociodemographic, functional and metabolic factors associated with lower cognitive performance in community-dwelling older adults.
METHODS: A cross-sectional study was conducted with N = 286 community-dwelling participants aged over 60 years, recruited from community pharmacies in Albacete, Spain. Cognitive status was assessed using the MoCA (cut-offs < 26 and < 21), the Short Portable Mental Status Questionnaire, the Memory Impairment Screen, and the Semantic Verbal Fluency Test (animals). Comorbidities were assessed using active medication prescriptions as proxy variables. Cohen's Kappa coefficients were computed to assess concordance, and a binary logistic regression was performed to identify potential predictors of cognitive impairment, defined as a MoCA score < 21.
RESULTS: The estimated prevalence of suspected cognitive impairment varied from 71.3% using the highest MoCA cut-off (< 26) to 25.2% using the more conservative MoCA < 21 threshold. Concordance analysis revealed low agreement between MoCA < 26 and the other instruments (Kappa < 0.08). However, using the MoCA < 21 cut-off, the observed agreement improved substantially to over 75% (all Kappa values statistically significant at p < 0.001). The adjusted binary logistic regression model demonstrated that older age significantly increased the odds of cognitive impairment (OR = 1.10, p < 0.001), whereas higher cognitive reserve was a protective factor (OR = 0.75, p < 0.001).
CONCLUSIONS: The estimated prevalence of suspected cognitive impairment is highly dependent on the screening instrument and threshold selected. The findings support the adoption of a more conservative MoCA cut-off < 21 to enhance agreement with other brief instruments and may reduce potential overestimation of impairment. Additionally, the associations observed between metabolic conditions and lower cognitive performance highlight the importance of integrated preventive strategies in primary care, combining sensitive cognitive screening with cardiometabolic risk management.
OBJECTIVE: This study aimed to describe the variability in cognitive performance and the prevalence of low scores across several brief screening tools and cut-offs, and to explore sociodemographic, functional and metabolic factors associated with lower cognitive performance in community-dwelling older adults.
METHODS: A cross-sectional study was conducted with N = 286 community-dwelling participants aged over 60 years, recruited from community pharmacies in Albacete, Spain. Cognitive status was assessed using the MoCA (cut-offs < 26 and < 21), the Short Portable Mental Status Questionnaire, the Memory Impairment Screen, and the Semantic Verbal Fluency Test (animals). Comorbidities were assessed using active medication prescriptions as proxy variables. Cohen's Kappa coefficients were computed to assess concordance, and a binary logistic regression was performed to identify potential predictors of cognitive impairment, defined as a MoCA score < 21.
RESULTS: The estimated prevalence of suspected cognitive impairment varied from 71.3% using the highest MoCA cut-off (< 26) to 25.2% using the more conservative MoCA < 21 threshold. Concordance analysis revealed low agreement between MoCA < 26 and the other instruments (Kappa < 0.08). However, using the MoCA < 21 cut-off, the observed agreement improved substantially to over 75% (all Kappa values statistically significant at p < 0.001). The adjusted binary logistic regression model demonstrated that older age significantly increased the odds of cognitive impairment (OR = 1.10, p < 0.001), whereas higher cognitive reserve was a protective factor (OR = 0.75, p < 0.001).
CONCLUSIONS: The estimated prevalence of suspected cognitive impairment is highly dependent on the screening instrument and threshold selected. The findings support the adoption of a more conservative MoCA cut-off < 21 to enhance agreement with other brief instruments and may reduce potential overestimation of impairment. Additionally, the associations observed between metabolic conditions and lower cognitive performance highlight the importance of integrated preventive strategies in primary care, combining sensitive cognitive screening with cardiometabolic risk management.