What if the clinical and older adults' perspectives about frailty converge? A call for a mixed conceptual model of frailty:A traditional literature review

Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults' perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled "by others" as compared to "self-labeling", from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term "frail", and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult's perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it

Portuguese version of the Tilburg Frailty Indicator: Transcultural adaptation and psychometric validation

Artigo científico disponível actualmente em Early View (Online Version of Record published before inclusion in an issue)Aim To present the translation and validation process of the Portuguese version of the Tilburg Frailty Indicator (TFI). Methods A cross-sectional study was designed using a non-probability sample of 252 community-dwelling older adults. Preliminary studies were carried out for face and content validity assessment. Internal consistency, test–retest reliability, construct (convergent/divergent) and criterion validity were subsequently analyzed. Results The sample was mainly women (75.8%), with a mean age of 79.2 ± 7.3 years. TFI internal consistency was good (KR-20 = 0.78). Test–retest reliability for the total was also good (r = 0.91), with kappa coefficients showing substantial agreement for most items. TFI physical and social domains correlated as expected with concurrent measures, whereas the TFI psychological domain showed similar correlations with other psychological and physical measures. The TFI showed a good to excellent discrimination ability in regard to frailty criteria, and fair to good ability to predict adverse outcomes. Conclusions The psychometric properties of the TFI seem to be consistently good. These findings provide initial evidence that the Portuguese version is a valid and reliable measure for assessing frailty in the elderly

Disability transitions in Dutch community-dwelling older people aged 75 years or older

BackgroundRecent world population predictions show that the world population aged >=65 years will increase from 10% in 2022 to 16% in 2050. Population aging is accompanied by an increase in people with disability. It is important to pay special attention to people with disability, as these people are at high risk of adverse outcomes. Our study aimed to investigate the transitions of disability among Dutch community-dwelling older people aged 75 years or older, using a follow-up of nine years. We used socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events to predict the disability transitions over time.MethodsWe used a sample of 484 people that was randomly drawn from the municipality of Roosendaal (the Netherlands), a municipality with 78,000 inhabitants. A subset of people who completed part A of the Tilburg Frailty Indicator (TFI) at baseline and the Groningen Activity Restriction Scale (GARS) questionnaires was used with a nine-year follow-up. Paired Wilcoxon tests were used to compare the consecutive measurements. Socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events were included to predict the disability transitions over time. For the univariable and multivariable analysis of the measurements over time with the predictor variables, we used generalized estimation equations (GEE). A p-value <0.05 was considered significant. R version 3.4.4 was used for all analyses.ResultsOf the participants, 65% were younger than 80 years, 50% were married or cohabiting, 87% reported a healthy lifestyle, and 63% had no diseases or chronic disorders. Each year, more participants changed from status not disabled to disabled than vice versa. The GEE analyses showed that lifestyle (‘not healthy’) and diseases or chronic disorders (‘two or more’) were significant in the multivariable analysis for the disability score and only diseases or chronic disorders (‘two or more’) was significant in the multivariable analysis for the dichotomous disability score.ConclusionsThe transition of the disability score is strongly influenced by lifestyle and diseases or disorders. This applies to a lesser extent to the dichotomous disability score. There, only diseases or disorders are an important predictor. For health care professionals our study provides starting points for interventions focused on the prevention of worsening disability and for community-dwelling older people >= 75, the most important recommendation is: live healthy

Prediction of COVID-19 Infections for Municipalities in the Netherlands:Algorithm Development and Interpretation

BACKGROUND: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. OBJECTIVE: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. METHODS: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. RESULTS: The final prediction model had an R(2) of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. CONCLUSIONS: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared

Determinants of frailty: the added value of assessing medication

This study aims to analyze which determinants predict frailty in general and each frailty domain (physical, psychological, and social), considering the integral conceptual model of frailty, and particularly to examine the contribution of medication in this prediction. A cross-sectional study was designed using a non-probabilistic sample of 252 community-dwelling elderly from three Portuguese cities. Frailty and determinants of frailty were assessed with the Tilburg Frailty Indicator. The amount and type of different daily-consumed medication were also examined. Hierarchical regression analysis were conducted. The mean age of the participants was 79.2 years (±7.3), and most of them were women (75.8%), widowed (55.6%) and with a low educational level (0-4 years: 63.9%). In this study, determinants explained 46% of the variance of total frailty, and 39.8%, 25.3%, and 27.7% of physical, psychological, and social frailty respectively. Age, gender, income, death of a loved one in the past year, lifestyle, satisfaction with living environment and self-reported comorbidity predicted total frailty, while each frailty domain was associated with a different set of determinants. The number of medications independently predicted an additional 2.5% of total frailty and 5.3% of physical frailty. The adverse effects of polymedication and its direct link with the amount of comorbidities could explain the independent contribution of this variable to frailty prediction. Furthermore, the consumption of drugs for cardiovascular diseases was particularly important for the prediction of frailty and of its physical domain. In the present study, a significant part of frailty was predicted, and the different contributions of each determinant to frailty domains provided additional evidence of the integral model of frailty’s relevance. The added value of a simple assessment of medication was considerable, and it should be taken into account for effective identification of frailty

Guideline delirium adults and older adults

Een nieuwe richtlijn ondersteunt verzorgenden, verpleegkundigen en verpleegkundig specialisten bij de preventie, herkenning en diagnostiek van een delier bij volwassenen en ouderen in het verpleeghuis en de thuissituati