Prevalence of Mild Cognitive Impairment in Mexican Older Adults: Data from the Mexican Health and Aging Study (MHAS)
Arce Renteria, M.; Manly, J. J.; Vonk, J. M. J.; Mejia Arango, S.; Michaels Obregon, A.; Samper-Ternent, R.; Wong, R.; Mayeux, R.; Barral, S.; Tosto, G.
Show abstract
INTRODUCTIONWe estimated the prevalence and risk factors for mild cognitive impairment (MCI) and its subtypes in Mexican population using the cognitive aging ancillary study of the Mexican Health and Aging Study. METHODSUsing a robust norms approach and comprehensive neuropsychological criteria, we determined MCI in a sample of adult Mexicans (N=1,807;55-97years). Additionally, we determined prevalence rates using traditional criteria. RESULTSPrevalence of amnestic MCI was 5.9%. Other MCI subtypes ranged 4.3% to 7.7%. MCI with and without memory impairment was associated with older age and rurality. Depression, diabetes and low educational attainment were associated with MCI without memory impairment. Using traditional criteria, prevalence of MCI was lower (2.2% amnestic MCI, other subtypes ranged 1.3%-2.4%). DISCUSSIONOlder age, depression, low education, diabetes, and rurality were associated with increased risk of MCI among older adults in Mexico. Our findings suggest that the causes of cognitive impairment are likely multifactorial and may vary by MCI subtype. Research in ContextO_ST_ABSSystematic reviewC_ST_ABSWe reviewed the literature using Google Scholar and PubMed. Few studies have reported prevalence rates for mild cognitive impairment (MCI) in Mexican population. These studies have primarily relied on limited cognitive assessments, and diverse MCI criteria. Evaluating the prevalence of MCI with a robust neuropsychological approach can help understand the rates and risk factors associated with MCI across a large and representative sample of the aging Mexican population. InterpretationVarious sociodemographic and health factors such as older age, depression, low education, diabetes, and rurality were significant correlates of MCI and differed by MCI subtype. Future directionsLongitudinal studies will be needed to evaluate the diagnostic stability of MCI over time, and its association with incident dementia. Future work will evaluate the casual path of these sociodemographic and health factors on cognitive impairment to develop effective interventions.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Loneliness and Cognitive Decline Among U.S. Adults: A Stratified Analysis of the BRFSS 95%
- Impact of the COVID-19 pandemic on cognitive function in Japanese community-dwelling older adults in a class for preventing cognitive decline 95%
- Social Determinants of Healthy Aging: An Investigation using the All of Us Cohort 95%
Similar papers in this journal
- Cardiometabolic Indicators of Cognitive Impairment in The Cameron County Hispanic Cohort 97%
- Racial Composition in K-12 Schooling and Cognitive Health of Older Black Adults 95%
- Association between motor task acquisition and hippocampal atrophy across cognitively unimpaired, amnestic Mild Cognitive Impairment, and Alzheimer’s disease individuals 94%
Similar papers in this journal
Similar papers in this journal
- Enriching hippocampal memory function in older adults through real-world exploration 95%
- Everyday functioning in a community-based volunteer population: Differences between participant- and study partner-report 93%
- Differential patterns of gyral and sulcal morphological changes during normal aging process 92%
Similar papers in this journal
- Self-reported word-finding complaints are associated with cerebrospinal fluid beta-amyloid and atrophy in cognitively normal older adults 94%
- Association of Item-Level Responses to Cognitive Function Index with Tau Pathology and Hippocampal volume in The A4 Study 93%
- Delayed primacy recall performance predicts post mortem Alzheimers disease pathology from unimpaired ante mortem cognitive baseline 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.