Trajectories of frailty and related factors of elderly people in Mexico.
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ABSTRACT: Introduction
Frailty is an indicator of health status in old age and a common clinical syndrome in older adults that carries an increased risk of poor health outcomes, including falls, incidents of disability, hospitalization, and mortality. This study aimed to identify the different trajectories of frailty and the factors related to frailty among Mexican older adults over time.Methods
Data are from a four-wave panel composed of older Mexican adults from 2001 to 2015 of the Mexican Health and Aging Study (MHAS). Frailty is the accumulation of deficits using a frailty index. A multilevel analysis, using hierarchical models, was applied to know the changes of frailty trajectories and what factors are related to it.Results
Being female, older, being widowhood, and having a lower level of education were risk factors for having a high frailty index and lower financial satisfaction doing activities at home have adverse effects.Conclusion
The findings of this work present information about the prevalence of frailty considering the proportion of deficits that individuals possess and their main associated components in older Mexican adults. It is necessary to improve socioeconomic health conditions in phases before old age to avoid developing frailty in the future.
SUBMITTER: Rojas Huerta AV
PROVIDER: S-EPMC9286518 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundInfluenza is an important cause of morbidity and mortality among older adults. Even so, effectiveness of influenza vaccine for older adults has been reported to be lower than for younger adults, and the impact of frailty on vaccine effectiveness (VE) and outcomes is uncertain. We aimed to study VE against influenza hospitalization in older adults, focusing on the impact of frailty.MethodsWe report VE of trivalent influenza vaccine (TIV) in people ≥65 years of age hospitalized during the 2011-2012 influenza season using a multicenter, prospective, test-negative case-control design. A validated frailty index (FI) was used to measure frailty.ResultsThree hundred twenty cases and 564 controls (mean age, 80.6 and 78.7 years, respectively) were enrolled. Cases had higher baseline frailty than controls (P = .006). In the fully adjusted model, VE against influenza hospitalization was 58.0% (95% confidence interval [CI], 34.2%-73.2%). The contribution of frailty was important; adjusting for frailty alone yielded a VE estimate of 58.7% (95% CI, 36.2%-73.2%). VE was 77.6% among nonfrail older adults and declined as frailty increased.ConclusionsDespite commonly held views that VE is poor in older adults, we found that TIV provided good protection against influenza hospitalization in older adults who were not frail, though VE diminished as frailty increased.Clinical trials registrationNCT01517191.
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Project description:BACKGROUND:Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings. OBJECTIVE:The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors. METHODS:An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms - Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) - was carried out. The performance of each model was evaluated using a separate unseen dataset. RESULTS:Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics. CONCLUSIONS:We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults.
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Project description:BACKGROUND AND OBJECTIVES:Frailty describes an increased vulnerability to adverse events such as disease or injury. Combatting this state remains a major challenge for geriatric research. By exploring how and why frailty changes throughout later life we will be better positioned to improve ways of identifying and treating those at high risk. RESEARCH DESIGN AND METHODS:We systematically reviewed publications that captured rate of frailty progression over time and established any associated risk or protective factors that affected this progression. We included longitudinal observational studies which quantified frailty trajectories in adults aged 50+ using any validated continuous frailty measurement tool. RESULTS:After screening 8,318 publications, 25 met our criteria. Findings show that despite a great degree of heterogeneity in the literature, progression of frailty is unquestionably affected by numerous risk and protective factors, with particular influence exhibited by social demographics, brain pathology, and physical co-morbidities. DISCUSSION AND IMPLICATIONS:Findings that the gradient of frailty progression is affected by various influencing factors are valuable to clinicians and policymakers as will help identify those at highest frailty risk and inform prevention strategies. However, the heterogeneous methodological approaches of the publications included in this review highlights the need for consensus within the field to promote more coordinated research. Improved consistency of methods will enable further data synthesis and facilitate a greater understanding of the shape of frailty over time and the influencing factors contributing to change, the results of which could have crucial implications for frailty risk reduction.
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Project description:The level of frailty in the older population across age cohorts and how this changes is a factor in determining future care costs and may also influence the extent of socioeconomic and gender inequalities in frailty.We model cohort-specific trajectories in frailty among the community dwelling population older than 50?years, using five waves (2002-2010) of the English Longitudinal Study of Ageing. We stratify our analysis by wealth and gender and use a frailty index, based on accumulation of 'deficits'.For males and females between the ages of 50 and 70 in 2002, frailty trajectories for adjacent age cohorts converge. However, levels of frailty are higher in recent compared with earlier cohorts at the older ages (for cohorts aged over 70 in 2002). These cohort differences are largest in the poorest wealth group, while for the most affluent, frailty trajectories overlap across all adjacent cohorts suggesting no change across cohorts.A key driver of the cohort differences in frailty that we observe is likely to be increased survival of frail individuals. Importantly, this paper illustrates that the social conditions experienced across the wealth distribution impacts on the rate of deficit accumulation in older populations. Our results on trajectories of frailty between 2002 and 2010 are pessimistic and, in the context of rising life expectancies, suggest that poorer older people in particular spend additional years of life in a frail state.
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Project description:Frailty is a geriatric syndrome, characterized by a loss in functional reserve with an increase in morbidity and mortality. There are no reports that link the genetic polymorphisms between interleukin 10 (IL10) and frailty; for this reason, our objective was used to analyze the role of the polymorphisms of IL10 (rs1800896, rs1800871) in the susceptibility to frailty in a Mexican population. Our study included 984 participants divided into 368 nonfrail, 309 prefrail, and 307 frail. The models for the polymorphisms rs1800896 and rs1800871 were recessive models in association with frailty (OR = 2.3, CI 95% = 1.6-3.2; OR = 1.53, CI 95% = 1.0-2.6), respectively. Two risk haplotypes were identified: ACG and CCG (p < .0001), and three protective haplotypes were identified: ACA, ATG, and ATA (p < .05). This study evaluated the relationship between IL10 and the three subtypes of this geriatric syndrome (frail, prefrail, and nonfrail). These results support a greater susceptibility to frailty for the minor alleles of rs1800871 and rs1800896. In addition, we found two risk haplotypes supporting the participation of the IL10 in the susceptibility for frailty in the Mexican population.
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Project description:To investigate associations between baseline frailty status and subsequent changes in QOL over time among community-dwelling older people.Among 363 community-dwelling older people ?65 years, frailty was measured using Frailty Index (FI) constructed from 40 deficits at baseline. QOL was measured using Older People's Quality of Life Questionnaire (OPQOL) six times over 2.5 years. Two-level hierarchical linear models were employed to predict QOL changes over time according to baseline frailty.At baseline, mean age was 73.1 (range 65-90) and 62.0 % were women. Mean FI was 0.17 (range 0.00-0.66), and mean OPQOL was 130.80 (range 93-163). The hierarchical linear model adjusted for age, gender, ethnicity, education, and enrollment site predicted that those with higher FI at baseline have lower QOL than those with lower FI (regression coefficient = -47.64, p < 0.0001) and that QOL changes linearly over time with slopes ranging from 0.80 (FI = 0.00) to -1.15 (FI = 0.66) as the FI increases. A FI of 0.27 is the cutoff point at which improvements in QOL over time change to declines in QOL.Frailty was associated with lower QOL among British community-dwelling older people. While less frail participants had higher QOL at baseline and QOL improved over time, QOL of frailer participants was lower at baseline and declined.
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