The prediction of mortality by quality of life assessed with the WHOQOL-BREF: a longitudinal analysis at the domain and item levels using a seven-year follow-up period.

PURPOSE: To determine the predictive value of quality of life for mortality at the domain and item levels. METHODS: This longitudinal study was carried out in a sample of 479 Dutch people aged 75 years or older living independently, using a follow-up of 7 years. Participants completed a self-report questionnaire. Quality of life was assessed with the WHOQOL-BREF, including four domains: physical health, psychological, social relationships, and environment. The municipality of Roosendaal (a town in the Netherlands) indicated the dates of death of the individuals. RESULTS: Based on mean, all quality of life domains predicted mortality adjusted for gender, age, marital status, education, and income. The hazard ratios ranged from 0.811 (psychological) to 0.933 (social relationships). The areas under the curve (AUCs) of the four domains were 0.730 (physical health), 0.723 (psychological), 0.693 (social relationships), and 0.700 (environment). In all quality of life domains, at least one item predicted mortality (adjusted). CONCLUSION: Our study showed that all four quality of life domains belonging to the WHOQOL-BREF predict mortality in a sample of Dutch community-dwelling older people using a follow-up period of 7 years. Two AUCs were above threshold (psychological, physical health). The findings offer health care and welfare professionals evidence for conducting interventions to reduce the risk of premature death.

Prolonging the duration of post-infusion scalp cooling in the prevention of anthracycline-induced alopecia: a randomised trial in patients with breast cancer treated with adjuvant chemotherapy.

PURPOSE: Scalp cooling as a method to reduce the incidence of chemotherapy-induced alopecia (CIA) is increasingly used in daily practice worldwide. However, in patients treated with 5-fluorouracil, epirubicin and cyclophosphamide (FEC), scalp cooling fails in 48-67% of patients. This study investigated the efficacy of extended duration of post-infusion scalp cooling in breast cancer patients treated with this regimen. METHODS: In this prospective multi-centre randomised study, 102 patients with early breast cancer treated with adjuvant FEC chemotherapy were randomly assigned in a 1:1 ratio to a post-infusion cooling time of 90 or 150 min. The primary endpoint was the need to wear a wig or other head covering to mask visible hair loss. RESULTS: Sixteen out of 48 patients (33%) treated with 90 min of post-infusion cooling did not need any head covering, compared with 21 out of 46 patients (45%) treated with 150 min of post-infusion cooling (p = 0.2). WHO grades 2-3 (moderate-complete) alopecia were reported more often in patients treated with 90-min post-infusion cooling time (n = 25/51 (49%) versus n = 17/51 (33%); p = 0,02). Scalp cooling was well-tolerated (mean Visual Analogue Score 7.4) and only three patients (3%) stopped due to intolerance during treatment. CONCLUSIONS: Extending the duration of 90-min post-infusion scalp cooling to 150 min in patients treated with adjuvant FEC chemotherapy was well-tolerated but did not significantly diminish the need for head covering. However, grades 2-3 alopecia was seen less often with prolonged post-infusion scalp cooling.

Single- and multiple viral respiratory infections in children: disease and management cannot be related to a specific pathogen.

BACKGROUND: The number of viral pathogens associated with pediatric acute respiratory tract infection (ARI) has grown since the introduction of reverse transcription real-time polymerase chain reaction (RT-PCR) assays. Multiple viruses are detected during a single ARI episode in approximately a quarter of all cases. The clinical relevance of these multiple detections is unclear, as is the role of the individual virus. We therefore investigated the correlation between clinical data and RT-PCR results in children with single- and multiple viral ARI. METHODS: Data from children with ARI were prospectively collected during two winter seasons. RT-PCR testing for 15 viruses was performed in 560 ARI episodes. In the patients with a single-viral etiology, clinical data, laboratory findings, patient management- and outcome data were compared between the different viruses. With this information, we compared data from children of whom RT-PCR data were negative, with children with single- and multiple viral positive results. RESULTS: The viral detection rate was 457/560 (81.6%) of which 331/560 (59.1%) were single infections and 126/560 (22.5%) were multiple infections. In single viral infections, some statistically significant differences in demographics, clinical findings, disease severity and outcome were found between children with different viral etiologies. However, no clinically recognizable pattern was established to be virus-specific. In a multivariate analysis, the only variables that were correlated with longer hospital stay were the use of oxygen and nebulizer therapy, irrespective of the viral pathogen. Children with RT-PCR positive test results had a significant higher disease severity, fever, length of hospital stay, days of extra oxygen supply, and days of antibiotic treatment than children with a negative RT-PCR test result. For children with single- versus children with multiple positive RT-PCR test results, these differences were not significant. CONCLUSIONS: Disease (severity), management and outcome in pediatric ARI are not associated with a specific virus. Single- and multiple viral ARI do not significantly differ with regard to clinical outcome and patient management. For general pediatrics, RT-PCR assays should be restricted to pathogens for which therapy is available or otherwise may have clinical consequences. Further research with an extended panel of RT-PCR assays and a larger number of inclusions is necessary to further validate our findings.

Results of 20- versus 45-min post-infusion scalp cooling time in the prevention of docetaxel-induced alopecia.

PURPOSE: For patients, chemotherapy-induced alopecia (CIA) is one of the most distressing side effects of treatment. Scalp cooling can prevent or minimise CIA; the results may depend on the duration of cooling. Since a previous study on post-infusion cooling time in patients treated with docetaxel chemotherapy found no difference between 90 and 45 min, we investigated whether hair-preserving results could be maintained with a shorter post-infusion cooling time. METHODS: In this prospective, multi-centre randomised study, 134 patients who started treatment with docetaxel 75-100 mg/m(2) in a 3-weekly schedule were randomly assigned in a 1:1 ratio to a post-infusion cooling time of 45 or 20 min. The primary end point was the need for a wig or other head covering as assessed by the patient. A visual analogue scale (VAS) with a range from 0 (not tolerable) to 10 (very tolerable) was used to measure tolerance. RESULTS: Scalp cooling results were similar for 45- and 20-min post-infusion cooling times. Thirty-three out of 45 patients (73 %) treated with 20 min of post-infusion cooling did not need a form of head covering, compared with 41 out of 52 patients (79 %) treated with 45 min of post-infusion cooling (p = 0.5). The procedure was well tolerated (mean visual analogue score 8.3). Six patients stopped due to intolerance during the first treatment cycle. CONCLUSIONS: A 20-min post-infusion cooling time is effective and tolerable for patients treated with scalp cooling to prevent docetaxel-induced alopecia. TRIAL REGISTRATION: Trialregister.nl Identifier, NTR 1856.

Perceived need for information among patients with a haematological malignancy: associations with information satisfaction and treatment decision-making preferences.

For patients with haematological malignancies, information on disease, prognosis, treatment and impact on quality of life is of the utmost importance. To gain insight into the perceived need for information in relation to sociodemographic and clinical parameters, comorbidity, quality of life (QoL) and information satisfaction, we compiled a questionnaire based on existing validated questionnaires. A total of 458 patients diagnosed with a haematological malignancy participated. The perceived need for information was moderate to high (40-70%). Multivariate regression analyses showed that a higher need for information was related to younger age, worse QoL, being member of a patient society and moderate comorbidity. The need for disease and treatment-related information was higher than the need for psychosocial information. A higher need for disease and treatment-related information was associated to being diagnosed with multiple myeloma. A higher need for psychosocial information was related to a lower educational level. The information provision could be improved according to 41% of the patients. Higher satisfaction with provided information was associated with better QoL. Most patients (62%) reported that they wanted to be fully informed about their illness and actively involved in treatment decision-making. The results contribute to improving patient-tailored information provision and shared decision-making in clinical practice.

Cognitive coping style (monitoring and blunting) and the need for information, information satisfaction and shared decision making among patients with haematological malignancies.

OBJECTIVE: A haematological malignancy is a serious, life-altering disease and may be characterised as an uncontrollable and unpredictable stress situation. In dealing with potentially threatening information, individuals generally utilise two main cognitive coping styles: monitoring (the tendency to seek threat-relevant information) and blunting (avoiding threatening information and seeking distraction). The aim of this study was to obtain insight into the association between cognitive coping style and (a) need for information, (b) satisfaction with information, (c) involvement in decision making, and (d) quality of life (QoL). METHODS: In this cross-sectional study, coping style was assessed among adult patients diagnosed with a haematological malignancy, using an adapted version of the Threatening Medical Situations Inventory. Information need, information satisfaction, decision-making preference and QoL were measured with validated questionnaires. RESULTS: In total, 458 patients returned the questionnaire (66%). A monitoring coping style was positively related to need for both general and specific information. Blunting was positively and QoL was negatively related to need for information. Monitoring was positively related to involvement in decision-making and negatively to information satisfaction. Using multivariate analysis, this relation between monitoring and information satisfaction disappeared, and for blunting, we found a negatively significant relation. QoL was not related to coping style. CONCLUSIONS: Among patients with haematological malignancies, coping style is related to a need for information, information satisfaction, and involvement in treatment decision-making. Therefore, it is important for health care professionals to be aware of individual differences in cognitive coping style.

Visceral obesity determined by CT scan and outcomes after colorectal surgery; a systematic review and meta-analysis.

BACKGROUND: Visceral obesity may affect outcome after colorectal surgery. The visceral fat area as determined by CT scanning is considered the standard in the detection of visceral obesity. METHOD: A systematic review was performed of trials investigating the effect of visceral obesity on outcomes of patients with colorectal cancer with no radiotherapy or chemotherapy and measured by CT scanning. The main endpoints were primary hospital stay, morbidity, operative time and blood loss. Quality assessment and data extraction were performed independently by two observers. RESULTS: Seven studies were eligible for analysis, including 1230 patients. Primary hospital stay (weighted mean difference 1.16 days, 95% CI 0.0.05 to 2.28 days, p = 0.04), morbidity rates (RR 0.15, 95% CI 0.10 to 0.21, p < 0.00001) and operative time (weighted mean difference 20.47 min, 95% CI 12.76 to 28.17 min, p < 0.00001) were significantly higher for visceral obese patients. No difference was found in blood loss. CONCLUSION: Visceral obesity leads to a longer hospital stay, higher morbidity and longer operative time after elective colon surgery. These findings show that the preoperative CT scan for detecting disseminated disease can be used to assess visceral obesity and helps in risk profiling patients undergoing elective colon surgery.

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints.

BACKGROUND: Modern modelling techniques may potentially provide more accurate predictions of binary outcomes than classical techniques. We aimed to study the predictive performance of different modelling techniques in relation to the effective sample size ("data hungriness"). METHODS: We performed simulation studies based on three clinical cohorts: 1282 patients with head and neck cancer (with 46.9% 5 year survival), 1731 patients with traumatic brain injury (22.3% 6 month mortality) and 3181 patients with minor head injury (7.6% with CT scan abnormalities). We compared three relatively modern modelling techniques: support vector machines (SVM), neural nets (NN), and random forests (RF) and two classical techniques: logistic regression (LR) and classification and regression trees (CART). We created three large artificial databases with 20 fold, 10 fold and 6 fold replication of subjects, where we generated dichotomous outcomes according to different underlying models. We applied each modelling technique to increasingly larger development parts (100 repetitions). The area under the ROC-curve (AUC) indicated the performance of each model in the development part and in an independent validation part. Data hungriness was defined by plateauing of AUC and small optimism (difference between the mean apparent AUC and the mean validated AUC <0.01). RESULTS: We found that a stable AUC was reached by LR at approximately 20 to 50 events per variable, followed by CART, SVM, NN and RF models. Optimism decreased with increasing sample sizes and the same ranking of techniques. The RF, SVM and NN models showed instability and a high optimism even with >200 events per variable. CONCLUSIONS: Modern modelling techniques such as SVM, NN and RF may need over 10 times as many events per variable to achieve a stable AUC and a small optimism than classical modelling techniques such as LR. This implies that such modern techniques should only be used in medical prediction problems if very large data sets are available.

Risk prediction with machine learning and regression methods.

This is a discussion of issues in risk prediction based on the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.

The Prediction of Quality of Life by Frailty and Disability among Dutch Community-Dwelling People Aged 75 Years or Older.

The present study aimed to examine the prediction of quality of life by frailty and disability in a baseline sample of 479 Dutch community-dwelling people aged 75 years or older using a follow-up period of 8 years. Regarding frailty, we distinguish between physical, psychological, and social frailty. Concerning physical disability, we distinguish between limitations in performing activities in daily living (ADL) and instrumental activities in daily living (IADL). The Tilburg Frailty Indicator (TFI) and the Groningen Activity Restriction Scale (GARS) were used to assess frailty domains and types of disability, respectively. Quality of life was determined by the WHOQOL-BREF containing physical, psychological, social, and environmental domains. In our study, 53.9% of participants were woman, and the mean age was 80.3 years (range 75-93). The study showed that psychological frailty predicted four domains of quality of life and physical frailty three. Social frailty was only found to be a significant predictor of social quality of life and environmental quality of life. ADL and IADL disability proved to be the worst predictors. It is recommended that primary healthcare professionals (e.g., general practitioners, district nurses) focus their interventions primarily on factors that can prevent or delay psychological and physical frailty, thereby ensuring that people's quality of life does not deteriorate.

Atrial fibrillation in cardiac resynchronization therapy with a defibrillator: a risk factor for mortality, appropriate and inappropriate shocks.

INTRODUCTION: Knowledge about predictive factors for mortality and (in)appropriate shocks in cardiac resynchronization therapy with a defibrillator (CRT-D) should be available and updated to predict clinical outcome. METHODS: We retrospectively analyzed 543 consecutive patients assigned to CRT-D in 2 tertiary medical centers. The aim of this study was to assess risk factors for all-cause mortality, appropriate and inappropriate shocks. RESULTS: Mean follow-up time was 3.2 (±1.8) years. A total of 110 (20%) patients died, 71 (13%) received ≥1 appropriate shocks, and 33 (6.1%) received ≥1 inappropriate shocks. No patients received a His bundle ablation and biventricular pacing percentage was not analyzed. Multivariable Cox regression analysis showed that a history of atrial fibrillation (AF) (HR 1.74 CI 1.06-2.86), higher creatinine (HR 1.12; CI 1.08-1.16), and a poorer left ventricular ejection fraction (LVEF) (HR 0.97; CI 0.94-1.01) independently predict all-cause mortality. In the entire cohort, history of AF and secondary prevention were independent predictors of appropriate shocks and variables associated with inappropriate shocks were history of AF and QRS ≥150 milliseconds. In primary prevention patients, history of AF also predicted appropriate shocks as did ischemic cardiomyopathy and poorer LVEF. History of AF, QRS ≥150 milliseconds, and lower creatinine were associated with inappropriate shocks in this subgroup. Appropriate shocks increased mortality risk, but inappropriate shocks did not. CONCLUSION: In symptomatic CHF patients treated with CRT-D, history of AF is an independent risk factor not only for mortality, but also for appropriate and inappropriate shocks. Further efforts in AF management may optimize the care in CRT-D patients.

Gender, age, and educational level attribute to blood alcohol concentration in hospitalized intoxicated adolescents; a cohort study.

BACKGROUND: The prevalence of adolescents hospitalized with acute alcohol intoxication, mainly because of severe reduced consciousness, is increasing. However, the characteristics of these adolescents are mainly unidentified. In this clinical research, we aimed to identify factors that attribute to higher ethanol concentration, on which targeted alcohol health interventions can be designed. METHODS: Since 2007, alcohol intoxication among adolescents has been one of the leading topics of the Dutch Pediatric Surveillance System. In the current study, we have analyzed which demographic characteristics, general alcohol use behaviors, and clinical intoxication data were related to the blood alcohol concentration (BAC) levels at hospital admittance. We included all adolescents aged <18 years, admitted with BAC >0.0 g/l, and reduced consciousness during the years 2007, 2008, 2009, and 2010. RESULTS: A total of 2,023 adolescents with alcohol intoxication were reported, and 1,618 questionnaires were returned, of which 1,350 met our inclusion criteria. In univariate analysis, age, gender, educational level, place of alcohol purchase, place of alcohol consumption, age of first drink, and regular alcohol use during the weekend correlated with higher BAC. After multivariate analysis, older adolescents, boys, and higher educational level significantly attributed to higher BAC at admittance. CONCLUSIONS: In alcohol-intoxicated adolescents with reduced consciousness, gender, age, and also educational level correlate with BAC at admittance. Explanatory factors could be found in sensitivity to alcohol, but also in socioeconomic factors, which influence availability. Intervention strategies could be targeted more specific now for the subgroups found in this study to decrease the growing burden of adolescent alcohol intoxication, both on the societal level and on the clinical level.

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands.

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

Predicting Disability Using a Nomogram of the Tilburg Frailty Indicator (TFI).

Disability is associated with lower quality of life and premature death in older people. Therefore, prevention and intervention targeting older people living with a disability is important. Frailty can be considered a major predictor of disability. In this study, we aimed to develop nomograms with items of the Tilburg Frailty Indicator (TFI) as predictors by using cross-sectional and longitudinal data (follow-up of five and nine years), focusing on the prediction of total disability, disability in activities of daily living (ADL), and disability in instrumental activities of daily living (IADL). At baseline, 479 Dutch community-dwelling people aged ≥75 years participated. They completed a questionnaire that included the TFI and the Groningen Activity Restriction Scale to assess the three disability variables. We showed that the TFI items scored different points, especially over time. Therefore, not every item was equally important in predicting disability. 'Difficulty in walking' and 'unexplained weight loss' appeared to be important predictors of disability. Healthcare professionals need to focus on these two items to prevent disability. We also conclude that the points given to frailty items differed between total, ADL, and IADL disability and also differed regarding years of follow-up. Creating one monogram that does justice to this seems impossible.

External Validation of Models for Predicting Disability in Community-Dwelling Older People in the Netherlands: A Comparative Study.

Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.

Frailty at Risk Scale (FARS): development and application.

The aim of this cross-sectional study was to develop a Frailty at Risk Scale (FARS) incorporating ten well-known determinants of frailty: age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. In addition, a second aim was to develop an online calculator that can easily support healthcare professionals in determining the risk of frailty among community-dwelling older people. The FARS was developed using data of 373 people aged ≥ 75 years. The Tilburg Frailty Indicator (TFI) was used for assessing frailty. Multivariate logistic regression analysis showed that the determinants multimorbidity, unhealthy lifestyle, and ethnicity (ethnic minority) were the most important predictors. The area under the curve (AUC) of the model was 0.811 (optimism 0.019, 95% bootstrap CI = -0.029; 0.064). The FARS is offered on a Web site, so that it can be easily used by healthcare professionals, allowing quick intervention in promoting quality of life among community-dwelling older people.

A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study.

BACKGROUND: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. OBJECTIVE: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. METHODS: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. RESULTS: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable "difficulty in walking" was important for all models. CONCLUSIONS: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of "gender" and "age" variables. External validation is a necessary step before applying the prediction models in a new setting.

Metadata Correction: A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study.

[This corrects the article DOI: 10.2196/31480.].

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 R2 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.

Nurses' Opinions on Frailty.

Nurses come into frequent contact with frail older people in all healthcare settings. However, few studies have specifically asked nurses about their views on frailty. The main aim of this study was to explore the opinions of nurses working with older people on the concept of frailty, regardless of the care setting. In addition, the associations between the background characteristics of nurses and their opinions about frailty were examined. In 2021, members of professional association of nurses and nursing assistants in the Netherlands (V&VN) received a digital questionnaire asking their opinions on frailty, and 251 individuals completed the questionnaire (response rate of 32.1%). The questionnaire contained seven topics: keywords of frailty, frailty domains, causes of frailty, consequences of frailty, reversing frailty, the prevention of frailty, and addressing frailty. Regarding frailty, nurses especially thought of physical deterioration and dementia. However, other domains of human functioning, such as the social and psychological domains, were often mentioned, pointing to a holistic approach to frailty. It also appears that nurses can identify many causes and consequences of frailty. They see opportunities to reverse frailty and an important role for themselves in this process.