Regional Patterns of Access and Participation in Non-Formal Cultural Education in Germany

(1) Background: The equality of life chances in Germany is often assessed along the lines of a west/east and urban/rural differentiation in which the latter usually perform worse. One currently popular proposal for addressing these inequalities is to strengthen cultural and arts education. The question arises to what extent regional characteristics genuinely influence participation opportunities and to what extent individual resources still play a decisive role. (2) Methods: Using descriptive analyses and multilevel logistic regression modelling, we investigate the distribution of and participation in non-formal cultural education amongst German youth. (3) Results: We find that differences are more complex than a simple west/east or urban/rural divides. Rather, cultural activities must be considered in terms of their character in order to assess the mechanisms at play. There seem to be differences in the dependency on district funding between very peripheral and very central districts that frame the cultural infrastructure. (4) Conclusions: Regional discrepancies are not uniformly distributed across different fields of education or infrastructure. Simplifying statements that classify peripheral regions the general losers can be refuted here. Simultaneously, more comprehensive data could yield significantly more results than we are currently able to produce.

An evaluation of factors that may influence clinicians’ decisions not to enroll eligible patients into randomized trials in critical care

Objectives To determine the association between intensive care unit (ICU) characteristics and clinicians’ decision to decline eligible patients for randomization into a multicentered pragmatic comparative-effectiveness controlled trial. Methods Screening logs from the Adjunctive Glucocorticoid Therapy in Septic Shock Trial (ADRENAL) and site-level data from the College of Intensive Care Medicine and Australia New Zealand Intensive Care Society were examined. The effects of ICU characteristics such as tertiary academic status, research coordinator availability, number of admissions, and ICU affiliations on clinicians declining to randomize eligible patients were calculated using mixed effects logistic regression modelling. Results There were 21,818 patients screened for inclusion in the ADRENAL trial at 69 sites across five countries, out of which 5,501 were eligible, 3,800 were randomized and 659 eligible patients were declined for randomization by the treating clinician. The proportion of eligible patients declined by clinicians at individual ICUs ranged from 0 to41%. In the multivariable model, none of the ICU characteristics were significantly associated with higher clinician decline rate. Conclusions Neither tertiary academic status, nor other site-level variables were significantly associated with increased rate of clinicians declining eligible patients.

Telemedicine during the COVID-19 pandemic

Aims The COVID-19 pandemic led to a swift adoption of telehealth in orthopaedic surgery. This study aimed to analyze the satisfaction of patients and surgeons with the rapid expansion of telehealth at this time within the division of adult reconstructive surgery at a major urban academic tertiary hospital. Methods A total of 334 patients underging arthroplasty of the hip or knee who completed a telemedicine visit between 30 March and 30 April 2020 were sent a 14-question survey, scored on a five-point Likert scale. Eight adult reconstructive surgeons who used telemedicine during this time were sent a separate 14-question survey at the end of the study period. Factors influencing patient satisfaction were determined using univariate and multivariate ordinal logistic regression modelling. Results A total of 68 patients (20.4%) and 100% of the surgeons completed the surveys. Patients were “Satisfied” with their telemedicine visits (4.10/5.00 (SD 0.98)) and 19 (27.9%) would prefer telemedicine to in-person visits in the absence of COVID-19. Multivariate ordinal logistic regression modelling revealed that patients were more likely to be satisfied if their surgeon effectively responded to their questions or concerns (odds ratio (OR) 3.977; 95% confidence interval (CI) 1.260 to 13.190; p = 0.019) and if their visit had a high audiovisual quality (OR 2.46; 95% CI 1.052 to 6.219; p = 0.042). Surgeons were “Satisfied” with their telemedicine experience (3.63/5.00 (SD 0.92)) and were “Fairly Confident” (4.00/5.00 (SD 0.53)) in their diagnostic accuracy despite finding the physical examinations to be only “Slightly Effective” (1.88/5.00 (SD 0.99)). Most adult reconstructive surgeons, seven of eight (87.5%) would continue to use telemedicine in the future. Conclusion Telemedicine emerged as a valuable tool during the COVID-19 pandemic. Patients undergoing arthroplasty and their surgeons were satisfied with telemedicine and see a role for its use after the pandemic. The audiovisual quality and the responsiveness of physicians to the concerns of patients determine their satisfaction. Future investigations should focus on improving the physical examination of patients through telemedicine and strategies for its widespread implementation. Cite this article: Bone Joint J 2021;103-B(6 Supple A):196–204.

TeamMate: A Longitudinal Study of New Zealand Working Farm Dogs. III. Factors Affecting the Risk of Dogs Being Lost from the Workforce

Working farm dogs are essential to many livestock farmers. Little is known about factors that influence dogs’ risk of being lost from work. This paper explores risk factors for farm dogs being lost through death, euthanasia and retirement. All enrolled dogs were working and a minimum of 18 months old. Five data collection rounds were performed over four years. Data about dogs were collected from owners and dogs were given physical examinations by veterinarians. Dogs that were lost from work were counted and owner-reported reasons for loss were recorded. Multivariable logistic regression modelling was used to investigate risk factors for loss. Of 589 dogs, 81 were lost from work. Of these, 59 dogs died or were euthanized and 22 were retired. Farm dogs tended to reach advanced ages, with 38% being 10 years or older when last examined. Acute injury or illness was the most commonly owner-reported reason for loss. Age group (p < 0.0001) and lameness (p = 0.04, OR = 1.8) significantly affected dogs’ risk of being lost. These results expand our knowledge about factors that affect health, welfare and work in farm dogs. Further investigation into reasons for lameness may help improve health and welfare in working farm dogs.

Proportion and risk factors for death by euthanasia in dogs in the UK

The loss of a pet can be particularly distressing for owners, whether the method of death is euthanasia or is unassisted. Using primary-care clinical data, this study aimed to report the demographic and clinical factors associated with euthanasia, relative to unassisted death, in dogs. Method of death (euthanasia or unassisted) and clinical cause of death were extracted from a random sample of 29,865 dogs within the VetCompass Programme from a sampling frame of 905,544 dogs under UK veterinary care in 2016. Multivariable logistic regression modelling was used to evaluate associations between risk factors and method of death. Of the confirmed deaths, 26,676 (89.3%) were euthanased and 2,487 (8.3%) died unassisted. After accounting for confounding factors, 6 grouped-level disorders had higher odds in euthanased dogs (than dogs that died unassisted), using neoplasia as the baseline. The disorders with greatest odds included: poor quality of life (OR 16.28), undesirable behaviour (OR 11.36) and spinal cord disorder (OR 6.00). Breed, larger bodyweight and increasing age were additional risk factors for euthanasia. The results highlight that a large majority of owners will face euthanasia decisions and these findings can support veterinarians and owners to better prepare for such an eventuality.

Application of agglomerative hierarchical clustering and logistic model development for assessing solar energy acceptability as an alternate energy option

This paper is focused on the assessment of acceptability of solar energy as an alternate efficient energy management option using Agglomerative Hierarchy Cluster (AHC) and logistic regression modelling approach. The study population includes randomly selected shop-owners and residential occupants within the Port Harcourt city in Rivers State, Nigeria. The collected data sets were subjected to AHC analysis using a statistical package XLSTAT 2016 version 4.6. The central object identified from the application of AHC with respect to the sampled shop-owners and residential occupants as pertaining to the acceptability of solar energy as an alternate efficient energy management option was centered around the financial implication of energy generation and the political influence of the government solar energy policies for energy generation. Finally, logistic regression modelling approach was applied into developing a predictive model for the probability of general acceptance (variable ‘yes’) of solar energy as an effective energy management system. From the developed model the chance of acceptance of a solar energy management system is 1% with 59.5% rejection from the study population while it is 99% with an unawareness level of 40.51% from the study population.

TeamMate: A Longitudinal Study of Health in Working Farm Sogs. III. Factors Affecting the Risk of Death, Euthanasia or Retirement from Work

Working farm dogs are essential to many livestock farmers. Little is known about factors that influence dogs&rsquo; risk of being lost from work. This paper explores risk factors for farm dogs being lost through death, euthanasia and retirement. All enrolled dogs were working and minimum 18 months old. Five data collection rounds were done over four years. Data about dogs were collected from owners and dogs were given physical examinations by veterinarians. Dogs that were lost from work were counted and owner-reported reasons for loss were recorded. Multivariable logistic regression modelling was used to investigate risk factors for loss. Of 589 dogs, 81 were lost from work. Of these, 59 dogs died or were euthanized and 22 were retired. Farm dogs tended to reach high ages, with 38% being 10 years or older when last examined. Acute injury or illness was the most commonly owner-reported reason for loss. Age group (P &amp;lt; 0.0001) and lameness (P = 0.04, OR = 1.8) significantly affected dogs&rsquo; risk being lost. These results expand our knowledge about factors that affect health, welfare and work in farm dogs. Further investigation into reasons for lameness may help improve health and welfare in working farm dogs.

Who is most at risk of physical and sexual partner violence and coercive control during the COVID-19 pandemic?

In this study, we analysed data from a survey of Australian women (n=9,284) to identify women at the highest risk of physical and sexual violence and coercive control during the early stages of the COVID-19 pandemic. Logistic regression modelling identified that specific groups of women were more likely than the general population to have experienced physical and sexual violence in the past three months. These were Aboriginal and Torres Strait Islander women, women aged 18–24, women with a restrictive health condition, pregnant women and women in financial stress. Similar results were identified for coercive control, and the co-occurrence of both physical/sexual violence and coercive control. These results show that domestic violence during the early stages of the COVID-19 pandemic was not evenly distributed across the Australian community, but more likely to occur among particular groups.

Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.