Predicting regional COVID-19 hospital admissions in Sweden using mobility data

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.

A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand

The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage—not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand.

Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation

One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed.

The Relationship between Chinese Outpatients’ Characteristics and Healthcare-Seeking Behavioral Decision-Making: Decision Tree and Linear Regression Analyses Based on Survey Data in Jiaxing

Background Chinese patients generally experience difficulties and high costs when obtaining medical services. One consensus reason for these difficulties is imbalances in hospitals’ medical standards and resources, which cannot be changed in the short term. This article explores the most perplexing healthcare-seeking problems in China by considering the laws pertaining to how different patients may see a doctor. Methods Data mining and analysis can characterize a person and explore the heuristics underlying his or her decisions using a combination of comprehensive feature data. Accordingly, a questionnaire was designed that probed numerous variables of relevance to decision-making and we analyzed the survey data using a decision tree and linear regression. The decision tree facilitated observation of the healthcare-seeking decision-making routes of different patients. In addition, linear regression analysis revealed that patients tended to choose different hospitals with different features.Results This article primarily argues that in China, having medical insurance and a profession that represents economic strength and social class are the most important factors that guide the outpatient's patterns of seeing a doctor. Further, outpatients who live far from hospitals and recognize they have a serious disease would choose different hospitals than those on the contrary. Conclusion Using a decision tree and line regression, we drew the portraits and decision-making routes behind various outpatients’ characteristics when he or she saw a doctor. It provides direction about the type and location about future hospital construction in this area, and it addresses how to avoid overcrowding at large hospitals and how to provide enhanced possibilities for diversion to smaller hospitals.

Ethical Considerations for Surgical Planners from the Lessons Learned on USNS COMFORT (T-AH 20) Deployment 2019

Ethical issues can arise when planning for direct patient care surgical missions. Based on the lessons learned from the USNS COMFORT Deployment 2019, the authors present concise considerations and recommendations for future hospital ship surgical mission planning.

Sedentary Behaviour in Hospitalised Older People: A Scoping Review

Background: Sedentary behaviour (SB) can delay hospitalised older adults’ recovery from acute illness and injuries. Currently, there is no synthesis of evidence on SB among hospitalised older people. This scoping review aimed to identify and map existing literature on key aspects of SB among hospitalised older adults, including the prevalence, measurement and intervention strategies for SB and sedentary behaviour bouts (SBBs) as well as healthcare professionals, patients and carers’ perspectives on interventions. Methods and analysis: Several electronic databases were searched between January 2001 and September 2020. The Joanna Briggs Institute (JBI) framework was used to conduct this scoping review. Results: Out of 1824 articles, 21 were included comprising 16 observational studies, 3 randomised controlled trials, 1 comparative study, and 1 phase-1 dose-response study. The sample size ranged from 13 to 393, with all 1435 participants community-dwelling before hospitalisation. Only two studies focused on measuring SB and SBBs as a primary outcome, with others (n = 19) reporting SB and SBB as a sub-set of physical activity (PA). Older adults spent an average of 86.5%/day (20.8 h) sedentary. Most studies (n = 15 out of 21) measured SB and SBB using objective tools. Conclusion: Hospitalised older people spent most of their waking hours sedentary. Studies explicitly focused on SB and SBB are lacking, and the perspectives of patients, carers and healthcare professionals are not clarified. Future hospital-based studies should focus on interventions to reduce SB and SBB, and the perspectives of healthcare professionals, patients and carers’ taken into account.