Prevalence of Mild Cognitive Impairment Among Older People in Kazakhstan and Potential Risk Factors

2019 ◽  
Vol 33 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Radmila T. Tsoy ◽  
Saule T. Turuspekova ◽  
Nina K. Klipitskaya ◽  
Alibek Mereke ◽  
Robert G. Cumming
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Older People ◽  
Potential Risk ◽  
Potential Risk Factors

scholarly journals Prevalence of Mild Cognitive Impairment in Rural Thai Older People, Associated Risk Factors and their Cognitive Characteristics

2020 ◽  
Vol 10 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Jiranan Griffiths ◽  
Lakkana Thaikruea ◽  
Nahathai Wongpakaran ◽  
Peeraya Munkhetvit
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Older People ◽  
Significant Risk ◽  
Depression Scale ◽  
Cross Sectional ◽  
Factors Associated ◽  
Low Education ◽  
Rural Thailand

Introduction: Mild cognitive impairment (MCI) is a transitional stage between normal cognition and dementia. A review showed that 10–15% of those with MCI annually progressed to Alzheimer’s disease. Objective: This study aimed to investigate the prevalence and risk factors associated with MCI as well as the characteristics of cognitive deficits among older people in rural Thailand. Methods: A cross-sectional study in 482 people who were 60 years old and over was conducted in northern Thailand. The assessments were administered by trained occupational therapists using demographic and health characteristics, Mental Status Examination Thai 10, Activities of Daily Living – Thai Assessment Scale, 15-item Geriatric Depression Scale and the Montreal Cognitive Assessment-Basic (MoCA-B, Thai version). Results: The mean age of MCI was 68.3 ± 6.82 years, and most had an education ≤4 years. The prevalence of MCI in older people was 71.4% (344 out of 482), and it increased with age. Low education and diabetes mellitus (DM) were the significant risk factors associated with cognitive decline. Older people with MCI were more likely to have an education ≤4 years (RR 1.74, 95% CI 1.21–2.51) and DM (RR 1.19, 95% CI 1.04–1.36) than those who did not. The 3 most common cognitive impairments according to MoCA-B were executive function (86%), alternating attention (33.1%) and delayed recall (31.1%). Conclusion: The prevalence of MCI in older Thai people in a rural area is high compared with that in other countries. The explanation might be due to low education and underlying disease associated with MCI. A suitable program that can reduce the prospects of MCI in rural Thailand is needed.

One size fits all? Why we need more sophisticated analytical methods in the explanation of trajectories of cognition in older age and their potential risk factors

2009 ◽  
Vol 22 (2) ◽  
pp. 291-299 ◽  
Author(s):  
Graciela Muniz Terrera ◽  
Carol Brayne ◽  
Fiona Matthews ◽  
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Potential Risk ◽  
Analytical Methods ◽  
Cognitive Change ◽  
Population Based ◽  
Growth Mixture ◽  
Potential Risk Factors ◽  
Rate Of Decline

ABSTRACTBackground: Cognitive decline in old age varies among individuals. The identification of groups of individuals with similar patterns of cognitive change over time may improve our ability to see whether the effect of risk factors is consistent across groups.Methods: Whilst accounting for the missing data, growth mixture models (GMM) were fitted to data from four interview waves of a population-based longitudinal study of aging, the Cambridge City over 75 Cohort Study (CC75C). At all interviews global cognition was assessed using the Mini-mental State Examination (MMSE).Results: Three patterns were identified: a slow decline with age from a baseline of cognitive ability (41% of sample), an accelerating decline from a baseline of cognitive impairment (54% of sample) and a steep constant decline also from a baseline of cognitive impairment (5% of sample). Lower cognitive scores in those with less education were seen at baseline for the first two groups. Only in those with good performance and steady decline was the effect of education strong, with an increased rate of decline associated with poor education. Good mobility was associated with higher initial score in the group with accelerating change but not with rate of decline.Conclusion: Using these analytical methods it is possible to detect different patterns of cognitive change with age. In this investigation the effect of education differs with group. To understand the relationship of potential risk factors for cognitive decline, careful attention to dropout and appropriate analytical methods, in addition to long-term detailed studies of the population points, are required.

Premature Arterial Disease Associated with Familial Antithrombin III Deficiency

1990 ◽  
Vol 63 (01) ◽  
pp. 013-015 ◽  
Author(s):  
E J Johnson ◽  
C R M Prentice ◽  
L A Parapia
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
Potential Risk ◽  
Antithrombin Iii ◽  
Arterial Disease ◽  
Coagulation System ◽  
Early Age ◽  
Arterial Thromboembolism ◽  
Potential Risk Factors ◽  
Antithrombin Iii Deficiency

SummaryAntithrombin III (ATIII) deficiency is one of the few known abnormalities of the coagulation system known to predispose to venous thromboembolism but its relation to arterial disease is not established. We describe two related patients with this disorder, both of whom suffered arterial thrombotic events, at an early age. Both patients had other potential risk factors, though these would normally be considered unlikely to lead to such catastrophic events at such an age. Thrombosis due to ATIII deficiency is potentially preventable, and this diagnosis should be sought more frequently in patients with arterial thromboembolism, particularly if occurring at a young age. In addition, in patients with known ATIII deficiency, other risk factors for arterial disease should be eliminated, if possible. In particular, these patients should be counselled against smoking.

Classification of Potential Risk Factors through HIRARC Method in Assessing Indoor Environment of Museums

2018 ◽  
Vol 10 (8) ◽  
Author(s):  
Syahrun Neizam Mohd Dzulkifli ◽  
◽  
Abd Halid Abdullah ◽  
Yee Yong Lee ◽  
Mohd Mahathir Suhaimi Shamsuri ◽  
...  
Keyword(s):  
Risk Factors ◽  
Potential Risk ◽  
Indoor Environment ◽  
Potential Risk Factors
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