Frailty and Its Associated Risk Factors: First Phase Analysis of Multicentre Indonesia Longitudinal Aging Study.

Background: National long-term care development requires updated epidemiological data related to frailty. We aimed to find the prevalence of frailty and its associated factors among Indonesian elderly. Methods: We conducted first-phase cross-sectional analysis of Indonesia Longitudinal Aging Study (INALAS) data collected from community-dwelling outpatients aged 60 years and older without acute illness in nine geriatric service care centres. Descriptive, bivariate and multivariate analyses were conducted. Results: Among 908 elderly in this study, 15.10% were robust, 66.20% were pre-frail, and 18.70% were frail. Functional dependence was associated with frailty among Indonesian elderly (OR 5.97, 95% CI 4.04-8.80). Being depressed and at risk for malnutrition were also associated with frailty with OR 2.54, 95% CI 1.56-4.12, and OR 2.56, 95% CI 1.68-3.90, respectively. Prior history of fall (OR 1.77, 95% CI 1.16-2.72) and hospitalization (OR 1.46, 95% CI 0.97-2.20) in the previous 12 months were associated with frailty. There is also significant association between poly pharmacy and frailty (OR 2.42, 95% CI 1.50-3.91). Conclusion: Approximately one in five Indonesian community-dwelling elderly was frail. Frailty is associated with functional dependence, being at risk for malnutrition or being malnourished, depression, history of fall, history of hospitalization, and poly pharmacy. There may be bidirectional relationships between the risk factors and frailty. The development of long-term care in Indonesia should be considered, without forcing the elderly who need it.

Frailty state among Indonesian elderly: prevalence, associated factors, and frailty state transition.

BACKGROUND: Information about frailty status and its transition is important to inform clinical decisions. Predicting frailty transition is beneficial for its prevention. While Indonesia is the 4th largest geriatric population in Asia, data about frailty transition is limited. This study aimed to obtain data on prevalence of frailty, its risk factors, frailty state transition and its prognostic factors, as well as to develop prognostic score for frailty state transition. METHODS: Multicenter study on subjects aged ≥60 years old was done to obtain the prevalence of frailty status and to identify risk factors of frailty. Prospective cohort over 12 months was done to obtain data on frailty state transition. Multiple logistic regression analysis was performed to identify its prognostic factors from several clinical data, which then were utilized to develop prognostic score for frailty state worsening. RESULTS: Cross-sectional data from 448 subjects showed that 25.2% of the subjects were frail based on Frailty index-40 items. Risk factors of frailty were age (OR 2.72; 95% CI 1.58-4.76), functional status (OR 2.89; 95% CI 1.79-4.67), and nutritional status (OR 3.75; 95% CI 2.29-6.13). Data from the 162 subjects who completed the cohort showed 27.2% of the cohort had frailty state worsening. Prognostic factors for frailty state worsening were being 70 years or older (OR 3.9; 95% CI 1.2-12.3, p < 0.05), negative QoL, i.e., fair and poor QoL (OR 2.5; 95% CI 1.1-5.9, p < 0.05), and slow gait speed (OR 2.8; 95% CI 1.3-6.4, p < 0.05). The internal validation of the prognostic score consisted of those three variables showed good performance. CONCLUSION: The prevalence of frailty in this study among Indonesian elderly in outpatient setting was 25.2%. The risk factors of frailty were age, functional status and nutritional status. The prognostic factors for frailty state worsening were being 70 years old or older, negative QoL (fair or poor quality of life), and slow gait speed. A prognostic score to predict frailty state worsening in 12 months had been developed.

Cut-off of anthropometry measurement and nutritional status among elderly outpatient in Indonesia: multi-centre study.

AIM: To obtain the cut-off value of anthropometric measurements and nutritional status of elderly in Indonesia. METHODS: A multicentre-cross sectional study was performed at 9 hospitals in Indonesia. The data collected comprises of samples characteristics, anthropometric measurements (weight, height, trisep, bisep, subscapular, suprailiac, and circumference of the hip, waist, arm, calf, and thigh), albumin value, MNA score and ADL Index of Barthel. RESULTS: A total of 702 subjects were collected. The average value of serum albumin is 4.28 g/dl, with 98% subjects had normal serum albumin (> 3.5 g/dl). The mean MNA score and BMI was 23.07 and 22.54 respectively. Most of subjects (56.70%) had risk of malnutrition based on MNA score, and 45.01% had normal nutritional status based on body mass index. Average value of several anthropometric measures (weight, stature, and body mass index; sub-scapular and supra-iliac skinfolds; thigh, calf, mid-arm, and waist circumferences) in various age groups in both groups of women and men were obtained. Cut-off values of various anthropometric indicators were also analyzed in this study with MNA as a gold standard. CONCLUSION: This study showed age related anthropometric measurement differences in both men and women aged 60 years and older.