Towards a threshold climate for emergency lower respiratory hospital admissions

Identification of ‘cut-points’ or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a ‘Climate Threshold’ for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000–2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and ‘day of the week’ as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥≥27 °C), low relative humidity (≤≤ 40%), high Pm10 level (≥≥70-µg/m3), low wind speed (≤≤ 2 knots) and high rainfall (≥≥30 mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2–3 days after the climate shift in the Greater London. The approach will be useful to initiate ‘region and disease specific’ climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations

A structured review of long-term care demand modelling

Long-term care (LTC) represents a significant and substantial proportion of healthcare spends across the globe. Its main aim is to assist individuals suffering with more or more chronic illnesses, disabilities or cognitive impairments, to carry out activities associated with daily living. Shifts in several economic, demographic and social factors have raised concerns surrounding the sustainability of current systems of LTC. Substantial effort has been put into modelling the LTC demand process itself so as to increase understanding of the factors driving demand for LTC and its related services. Furthermore, such modeling efforts have also been used to plan the operation and future composition of the LTC system itself. The main aim of this paper is to provide a structured review of the literature surrounding LTC demand modeling and any such industrial application, whilst highlighting any potential direction for future researchers

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.

The Analyses of Individual Patient Pathways : Investigating Regional Variation in COPD readmissions

Regional variation for those treated for chronic obstructive pulmonary disease (COPD) has been studied amongst many public health issues. However, for those experiencing frequent readmissions were not considered. The objective is to determine the probability of a patient being in the high risk group in their next readmission, given patient and regional specific previous readmissions, i.e. capturing individual clinical pathways of patient's leading to multiple readmissions. Data on COPD patients were extracted from the national (England) hospital episodes statistics (HES) dataset. Individual patient pathways from initial admission through to more than four readmissions are captured. Here, patients are classified into the low risk of readmission group (LRRG) and the high risk of readmission group (HRRG). This classification is based on the definition of readmission, where we have previously developed a stochastic modelling approach to determine the appropriate width of a time window in defining readmission. Hence, using this classification as a response variable, a marginal continuation-ratio logit model is used to examine regional disparities in progressing through multiple readmissions. Significant regional differences were found in each readmission and classification groups (HRRG or LRRG). For example, COPD patients from the North West of England were found to be 16% more likely to experience multiple readmissions than Yorkshire and The Humber region

Application of job shop scheduling approach in green patient flow optimization using a hybrid swarm intelligence.

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities. [Abstract copyright: © 2022 The Authors.

A Grid implementation for profiling hospitals based on patient readmissions

Generally, high level of readmission is associated with poor patient care, hence, its relation to the quality of care is plausible. Frequent patient readmissions have personal, financial and organisational consequences. This has motivated healthcare commissioners in England to use emergency readmission as an indicator in the performance rating framework. A statistical model, known as the multilevel transition model was previously developed, where individual hospitals propensity for first readmission, second readmission, third (and so on) were con-sidered to be measures of performance. Using these measures, we defined a new performance index. During the period 1997 and 2004, the national (England) hos-pital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a statistical model using the complete population dataset could possibly take weeks to estimate the parameters. Moreover, it is not statistically sound to utilise the full population dataset. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Using a stand-alone computer, it took approximately 500 hours to estimate 1000 samples, whereas in the Grid implementation, the full 1000 samples were analysed in less than 24 hours. From the 167 National Health Service Acute and Foundation Trusts in England, 4 out of the 5 worst performing hospitals treating cancer patients were in London.Non peer reviewe

A loss network model with overflow for capacity planning of a neonatal unit

The main aim of this paper is to derive a solution to the capacity problem faced by many perinatal networks in the United Kingdom. We propose a queueing model to determine the number of cots at all care units for any desired overflow and rejection probability in a neonatal unit. The model formulation is developed, being motivated by overflow models in telecommunication systems. Exact expressions for the overflow and rejection probabilities are derived. The model is then applied to a neonatal unit of a perinatal network in the UK