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1.
Trends psychiatry psychother. (Impr.) ; 43(1): 9-16, Jan.-Mar. 2021. tab
Article in English | LILACS | ID: biblio-1156993

ABSTRACT

Abstract Introduction Compared to other types of caregiver, spouse-caregivers tend to be closer to people with Alzheimer's disease (PwAD) because of their different position in the relationship. We designed this study to compare the differences in caregivers' quality of life (QoL) and domains of QoL according to the kinship relationship between the members of caregiving dyads. Methods We assessed QoL of 98 PwAD and their family caregivers (spouse-caregivers, n = 49; adult children, n = 43; and others, n = 6). The PwAD and their caregivers completed questionnaires about their QoL, awareness of disease, cognition, severity of dementia, depression, and burden of caring. Results The comparison between caregiver types showed that spouse-caregivers were older, with higher levels of burden and lower scores for cognition. Caregivers' total QoL scores were not significantly different according to type of kinship. However, there were significant differences in the domains physical health (p = 0.04, Cohen's d [d] = -0.42), marriage (p = 0.01, d = 1.31), and friends (p = 0.04, d = -0.41), and life as a whole showed a trend to difference (p = 0.08, d = -0.33). When QoL domains were analyzed within dyads, there were significant differences between members of spouse dyads in the domains energy (p = 0.01, d = -0.49), ability to do things for fun (p = 0.01, d = -0.48), and memory (p = 0.000, d = -1.07). For non-spouse dyads, there were significant differences between caregivers and PwAD for the QoL domains memory (p = 0.004, d = -0.63), marriage (p = 0.001, d = -0.72), friends (p = 0.001, d = -0.65), and ability to do chores (p = 0.000, d = -0.76). Conclusions Differences were only detected between spouse/non-spouse-caregivers when QoL was analyzed by domains. We speculate that spouse and non-spouse caregivers have distinct assessments and perceptions of what is important to their QoL.


Subject(s)
Humans , Child , Adult , Caregivers , Alzheimer Disease , Quality of Life , Activities of Daily Living , Surveys and Questionnaires , Spouses
2.
Journal of Forensic Medicine ; (6): 372-377, 2021.
Article in English | WPRIM | ID: wpr-985227

ABSTRACT

Objective To derive the probability distribution formula of combined identity by state (CIBS) score among individuals with different relationships based on population data of autosomal multiallelic genetic markers. Methods The probabilities of different identity by state (IBS) scores occurring at a single locus between two individuals with different relationships were derived based on the principle of ITO method. Then the distribution probability formula of CIBS score between two individuals with different relationships when a certain number of genetic markers were used for relationship identification was derived based on the multinomial distribution theory. The formula was compared with the CIBS probability distribution formula based on binomial distribution theory. Results Between individuals with a certain relationship, labelled as RS, the probabilities of IBS=2, 1 and 0 occurring at a certain autosomal genetic marker x (that is, p2(RSx), p1(RSx) and p0(RSx)), can be calculated based on the allele frequency data of that genetic marker and the probability of two individuals with the corresponding RS relationship sharing 0, 1 or 2 identity by descent (IBD) alleles (that is, φ0, φ1 and φ2). For a genotyping system with multiple independent genetic markers, the distribution of CIBS score between pairs of individuals with relationships other than parent-child can be deducted using the averages of the 3 probabilities of all genetic markers (that is, p2(RS), p1(RS) and p0(RS)), based on multinomial distribution theory. Conclusion The calculation of CIBS score distribution formula can be extended to all kinships and has great application value in case interpretation and system effectiveness evaluation. In most situations, the results based on binomial distribution formula are similar to those based on the formula derived in this study, thus, there is little difference between the two methods in actual work.


Subject(s)
Humans , Alleles , Gene Frequency , Genetic Markers , Genotype , Probability
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