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

Integrale of simpele frailty meting: de context moet leidend zijn

Dr. Gobbens en zijn promotor en co-promotoren kunnen om meerdere redenen gefeliciteerd worden met het proefschrift over een integrale benadering van het begrip frailty. De auteur en zijn begeleider hebben een serieuze inspanning geleverd om het begrip frailty vanuit een andere context te bezien, namelijk die van het integrale menselijke functioneren. Dat dit vanuit de Universiteit Tilburg gebeurt, met een rijke traditie in de sociale wetenschappen, lijkt haast vanzelfsprekend, maar is toch een belangrijke toevoeging. De context is immers in hoge mate bepalend voor het onderzoek aan het brede begrip frailty. Het kon eigenlijk dan ook niet anders dan dat een Tilburgs frailty proefschrift de sociale en psychische dimensie zou toevoegen aan het fysieke domein, dat tot nu toe dominant was in de frailty concepten. Deze aanpak past bovendien uitstekend bij de brede oriëntatie van de promotor, prof dr. Jos Schols. Aan het werk herkent men de meester

Associations between lifestyle factors and multidimensional frailty:A cross-sectional study among community-dwelling older people

BACKGROUND: Multidimensional frailty, including physical, psychological, and social components, is associated to disability, lower quality of life, increased healthcare utilization, and mortality. In order to prevent or delay frailty, more knowledge of its determinants is necessary; one of these determinants is lifestyle. The aim of this study is to determine the association between lifestyle factors smoking, alcohol use, nutrition, physical activity, and multidimensional frailty. METHODS: This cross-sectional study was conducted in two samples comprising in total 45,336 Dutch community-dwelling individuals aged 65 years or older. These samples completed a questionnaire including questions about smoking, alcohol use, physical activity, sociodemographic factors (both samples), and nutrition (one sample). Multidimensional frailty was assessed with the Tilburg Frailty Indicator (TFI). RESULTS: Higher alcohol consumption, physical activity, healthy nutrition, and less smoking were associated with less total, physical, psychological and social frailty after controlling for effects of other lifestyle factors and sociodemographic characteristics of the participants (age, gender, marital status, education, income). Effects of physical activity on total and physical frailty were up to considerable, whereas the effects of other lifestyle factors on frailty were small. CONCLUSIONS: The four lifestyle factors were not only associated with physical frailty but also with psychological and social frailty. The different associations of frailty domains with lifestyle factors emphasize the importance of assessing frailty broadly and thus to pay attention to the multidimensional nature of this concept. The findings offer healthcare professionals starting points for interventions with the purpose to prevent or delay the onset of frailty, so community-dwelling older people have the possibility to aging in place accompanied by a good quality of life

A comparison between uni- and multidimensional frailty measures:Prevalence, functional status, and relationships with disability

Background: Over the years, a plethora of frailty assessment tools has been developed. These instruments can be basically grouped into two types of conceptualizations – unidimensional, based on the physical–biological dimension – and multidimensional, based on the connections among the physical, psychological, and social domains. At present, studies on the comparison between uni- and multidimensional frailty measures are limited. Objective: The aims of this paper were: 1) to compare the prevalence of frailty obtained using a uni- and a multidimensional measure; 2) to analyze differences in the functional status among individuals captured as frail or robust by the two measures; and 3) to investigate relations between the two frailty measures and disability. Methods: Two hundred and sixty-seven community-dwelling older adults (73.4±6 years old, 59.9% of women) participated in this cross-sectional study. The Cardiovascular Health Study (CHS) index and the Tilburg Frailty Indicator (TFI) were used to measure frailty in a uni- and multidimensional way, respectively. The International Physical Activity Questionnaire, the Center of Epidemiologic Studies Depression scale, and the Loneliness Scale were administered to evaluate the functional status. Disability was assessed using the Groningen Activity Restriction Scale. Data were treated with descriptive statistics, one-way analysis of variance, correlations, and receiver operating characteristic analyses through the evaluation of the areas under the curve. Results: Results showed that frailty prevalence rate is strictly dependent on the index used (CHS =12.7%; TFI =44.6%). Furthermore, frail individuals presented differences in terms of functional status in all the domains. Frailty measures were significantly correlated with each other (r=0.483), and with disability (CHS: r=0.423; TFI: r=0.475). Finally, the area under the curve of the TFI (0.833) for disability was higher with respect to the one of CHS (0.770). Conclusion: Data reported here confirm that different instruments capture different frail individuals. Clinicians and researchers have to consider the different abilities of the two measures to detect frail individuals

Frailty in Community-Dwelling Older People

With a growing aging population around the world [...

Multidimensional frailty and its determinants among acutely admitted older people:a cross-sectional study using the Tilburg Frailty Indicator

Abstract Purpose: This study aimed to establish which determinants had an effect on frailty among acutely admitted patients, where frailty was identified at discharge. In particular, our study focused on associations of sex with frailty. Methods: A cross-sectional study was designed using a sample of 1267 people aged 65 years or older. The Tilburg Frailty Indicator (TFI), a user-friendly self-report questionnaire was used to measure multidimensional frailty (physical, psychological, social) and determinants of frailty (sex, age, marital status, education, income, lifestyle, life events, multimorbidity). Results: The mean age of the participants was 76.8 years (SD 7.5; range 65-100). The bivariate regression analyses showed that all determinants were associated with total and physical frailty, and six determinants were associated with psychological and social frailty. Using multiple linear regression analyses, the explained variances differed from 3.5% (psychological frailty) to 20.1% (social frailty), with p values < 0.001. Of the independent variables age, income, lifestyle, life events, and multimorbidity were associated with three frailty variables, after controlling for all the other variables in the model. At the level of both frailty domains and components, females appeared to be more frail than men. Conclusion: The present study showed that sociodemographic characteristics (sex, age, marital status, education, income), lifestyle, life events, and multimorbidity had a different effect on total frailty and its domains (physical, psychological, social) in a sample of acute admitted patients