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  1. Mustaffa N, Lee SY, Mohd Nawi SN, Che Rahim MJ, Chee YC, Muhd Besari A, et al.
    J Glob Health, 2020 Dec;10(2):020370.
    PMID: 33214887 DOI: 10.7189/jogh.10.020370
    Matched MeSH terms: Frail Elderly/statistics & numerical data*
  2. Setiati S, Marsigit J
    Acta Med Indones, 2021 Jan;53(1):1-4.
    PMID: 33818400
    It has been a year since the Indonesian government announced its first COVID-19 identified in Jakarta. Since then, there have been more than 900,000 cases in Indonesia with case fatality rate (CFR) of 2.9%. The number of new cases per day is now ranging from 9,000 cases to almost 13,000 cases. Not only in Indonesia, but the number of new cases along with the mortality rate in other countries, such as Malaysia, Japan, United States, and Europe region also increased dramatically. COVID-19 vaccines are being investigated and the world hopes that vaccines will be the answer to tackle this pandemic. Is it really so? Immunization is an effort to induce immunity in individuals to prevent a disease or the complication related to the diseases that may be catastrophic. Immunization can be divided into passive, which is by giving certain type of antibody and active, which means that either we get the disease, or we get the antigen injected into our body.Having prior vaccination or past COVID-19 does not mean that someone is totally immune to COVID-19 as a recent study suggested that the antibody related to COVID-19 past infection is significantly decreasing after 3 months post-infection. Compliance to implementation of health protocol remained the most crucial strategy during this pandemic.
    Matched MeSH terms: Frail Elderly/statistics & numerical data
  3. Dent E, Lien C, Lim WS, Wong WC, Wong CH, Ng TP, et al.
    J Am Med Dir Assoc, 2017 Jul 01;18(7):564-575.
    PMID: 28648901 DOI: 10.1016/j.jamda.2017.04.018
    OBJECTIVE: To develop Clinical Practice Guidelines for the screening, assessment and management of the geriatric condition of frailty.

    METHODS: An adapted Grading of Recommendations, Assessment, Development, and Evaluation approach was used to develop the guidelines. This process involved detailed evaluation of the current scientific evidence paired with expert panel interpretation. Three categories of Clinical Practice Guidelines recommendations were developed: strong, conditional, and no recommendation.

    RECOMMENDATIONS: Strong recommendations were (1) use a validated measurement tool to identify frailty; (2) prescribe physical activity with a resistance training component; and (3) address polypharmacy by reducing or deprescribing any inappropriate/superfluous medications. Conditional recommendations were (1) screen for, and address modifiable causes of fatigue; (2) for persons exhibiting unintentional weight loss, screen for reversible causes and consider food fortification and protein/caloric supplementation; and (3) prescribe vitamin D for individuals deficient in vitamin D. No recommendation was given regarding the provision of a patient support and education plan.

    CONCLUSIONS: The recommendations provided herein are intended for use by healthcare providers in their management of older adults with frailty in the Asia Pacific region. It is proposed that regional guideline support committees be formed to help provide regular updates to these evidence-based guidelines.

    Matched MeSH terms: Frail Elderly/statistics & numerical data*
  4. Ahmad NS, Hairi NN, Said MA, Kamaruzzaman SB, Choo WY, Hairi F, et al.
    PLoS One, 2018;13(11):e0206445.
    PMID: 30395649 DOI: 10.1371/journal.pone.0206445
    OBJECTIVES: This study aims to describe the prevalence and transitions of frailty among rural-community dwelling older adults in Malaysia and to analyse factors associated with different states of frailty transition. Frailty was conceptualized using modified Fried phenotype from the Cardiovascular Health Study.

    DESIGN: This is a prospective longitudinal study with 12-months follow up among older adults in Malaysia.

    SETTING: Kuala Pilah, a district in Negeri Sembilan, which is one of the fourteen states in Malaysia.

    PARTICIPANTS: 2,324 community-dwelling older Malaysians aged 60 years and older.

    RESULTS: The overall prevalence of frailty in this study was 9.4% (95% CI 7.8-11.2). The prevalence increased at least three-fold with every 10 years of age. This increase was seen higher in women compared to men. Being frail was significantly associated with older age, women, and respondents with a higher number of chronic diseases, poor cognitive function and low socioeconomic status (p<0.05). During the 12-months follow-up, our study showed that the transition towards greater frailty states were more likely (22.9%) than transition toward lesser frailty states (19.9%) while majority (57.2%) remained unchanged. Multivariate logistic regression analysis showed that presence of low physical activity increased the likelihood of worsening transition towards greater frailty states by three times (OR 2.9, 95% CI 2.2-3.7) and lowered the likelihood of transition towards lesser frailty states (OR 0.3, 95% CI 0.2-0.4).

    CONCLUSION: Frailty is reported among one in every eleven older adults in this study. The prevalence increased across age groups and was higher among women than men. Frailty possesses a dynamic status due to its potential reversibility. This reversibility makes it a cornerstone to delay frailty progression. Our study noted that physical activity conferred the greatest benefit as a modifiable factor in frailty prevention.

    Matched MeSH terms: Frail Elderly/statistics & numerical data
  5. Sathasivam J, Kamaruzzaman SB, Hairi F, Ng CW, Chinna K
    Asia Pac J Public Health, 2015 Nov;27(8 Suppl):52S-61S.
    PMID: 25902935 DOI: 10.1177/1010539515583332
    In the past decade, the population in Malaysia has been rapidly ageing. This poses new challenges and issues that threaten the ability of the elderly to independently age in place. A multistage cross-sectional study on 789 community-dwelling elderly individuals aged 60 years and above was conducted in an urban district in Malaysia to assess the geriatric syndrome of frailty. Using a multidimensional frailty index, we detected 67.7% prefrail and 5.7% frail elders. Cognitive status was a significant correlate for frailty status among the respondents as well as those who perceived their health status as very poor or quite poor; but self-rated health was no longer significant when controlled for sociodemographic variables. Lower-body weakness and history of falls were associated with increasing frailty levels, and this association persisted in the multivariate model. This study offers support that physical disability, falls, and cognition are important determinants for frailty. This initial work on frailty among urban elders in Malaysia provides important correlations and identifies potential risk factors that can form the basis of information for targeted preventive measures for this vulnerable group in their prefrail state.
    Matched MeSH terms: Frail Elderly/statistics & numerical data*
  6. DaVanzo J, Chan A
    Demography, 1994 Feb;31(1):95-113.
    PMID: 8005345
    More than two-thirds of Malaysians age 60 or older coreside with an adult child. Data from the Senior sample of the Second Malaysian Family Life Survey (MFLS-2) are used to investigate which "seniors" (persons age 60 or older) live in this way. The analysis generally supports the notion that coresidence is influenced by the benefits, costs, opportunities, and preferences for coresidence versus separate living arrangements. For example, married seniors are more likely to coreside with adult children when housing costs are greater in their area or when the husband or wife is in poor health. This finding suggests that married parents and children live together to economize on living costs or to receive help with household services. Unmarried seniors who are better off economically are less likely to live with adult children, presumably because they use their higher incomes to "purchase privacy."
    Matched MeSH terms: Frail Elderly/statistics & numerical data*
  7. Teoh RJJ, Mat S, Khor HM, Kamaruzzaman SB, Tan MP
    Postgrad Med, 2021 Apr;133(3):351-356.
    PMID: 33143493 DOI: 10.1080/00325481.2020.1842026
    OBJECTIVES: While metabolic syndrome, falls, and frailty are common health issues among older adults which are likely to be related, the potential interplay between these three conditions has not previously been investigated. We investigated the relationship between metabolic syndrome with falls, and the role of frailty markers in this potential relationship, among community-dwelling older adults.

    METHODS: Data from the first wave Malaysian Elders Longitudinal Research (MELoR) study comprising urban dwellers aged 55 years and above were utilized. Twelve-month fall histories were established during home-based, computer-assisted interviews which physical performance, anthropometric and laboratory measures were obtained during a hospital-based health check. Gait speed, exhaustion, weakness, and weight loss were employed as frailty markers.

    RESULTS: Data were available for 1415 participants, mean age of 68.56 ± 7.26 years, 57.2% women. Falls and metabolic syndrome were present in 22.8% and 44.2%, respectively. After adjusting for age, sex, and multiple comorbidities, metabolic syndrome was significantly associated with falls in the sample population [odds ratio (OR): 1.33, 95% confidence interval (CI): 1.03; 1.72]. This relationship was attenuated by the presence of slow gait speed, but not exhaustion, weakness, or weight loss.

    CONCLUSION: Metabolic syndrome was independently associated with falls among older adults, and this relationship was accounted for by the presence of slow gait speed. Future studies should determine the value of screening for frailty and falls with gait speed in older adults with metabolic syndrome as a potential fall prevention measure.

    Matched MeSH terms: Frail Elderly/statistics & numerical data*
  8. Malek Rivan NF, Shahar S, Rajab NF, Singh DKA, Din NC, Hazlina M, et al.
    Clin Interv Aging, 2019;14:1343-1352.
    PMID: 31413555 DOI: 10.2147/CIA.S211027
    PURPOSE: This study was aimed at determining the presence of cognitive frailty and its associated factors among community-dwelling older adults from the "LRGS-Towards Useful Aging (TUA)" longitudinal study.

    PATIENTS AND METHODS: The available data related to cognitive frailty among a sub-sample of older adults aged 60 years and above (n=815) from two states in Malaysia were analysed. In the LRGS-TUA study, a comprehensive interview-based questionnaire was administered to obtain the socio-demographic information of the participants, followed by assessments to examine the cognitive function, functional status, dietary intake, lifestyle, psychosocial status and biomarkers associated with cognitive frailty. The factors associated with cognitive frailty were assessed using a bivariate logistic regression (BLR).

    RESULTS: The majority of the older adults were categorized as robust (68.4%), followed by cognitively pre-frail (37.4%) and cognitively frail (2.2%). The data on the cognitively frail and pre-frail groups were combined for comparison with the robust group. A hierarchical BLR indicated that advancing age (OR=1.04, 95% CI:1.01-1.08, p<0.05) and depression (OR=1.49, 95% CI:1.34-1.65, p<0.001) scored lower on the Activity of Daily Living (ADL) scale (OR=0.98, 95% CI:0.96-0.99, p<0.05), while low social support (OR=0.98, 95% CI:0.97-0.99, p<0.05) and low niacin intake (OR=0.94, 95% CI:0.89-0.99, p<0.05) were found to be significant factors for cognitive frailty. Higher oxidative stress (MDA) and lower telomerase activity were also associated with cognitive frailty (p<0.05).

    CONCLUSION: Older age, a lower niacin intake, lack of social support, depression and lower functional status were identified as significant factors associated with cognitive frailty among older Malaysian adults. MDA and telomerase activity can be used as potential biomarkers for the identification of cognitive frailty.

    Matched MeSH terms: Frail Elderly/statistics & numerical data*
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