Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.

BACKGROUND: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. METHOD: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. RESULTS: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. CONCLUSIONS: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

Climate emotions, thoughts, and plans among US adolescents and young adults: a cross-sectional descriptive survey and analysis by political party identification and self-reported exposure to severe weather events.

BACKGROUND: Climate change has adverse effects on youth mental health and wellbeing, but limited large-scale data exist globally or in the USA. Understanding the patterns and consequences of climate-related distress among US youth can inform necessary responses at the individual, community, and policy level. METHODS: A cross-sectional descriptive online survey was done of US youth aged 16-25 years from all 50 states and Washington, DC, between July 20 and Nov 7, 2023, via the Cint digital survey marketplace. The survey assessed: climate-related emotions and thoughts, including indicators of mental health; relational aspects of climate-related emotions; beliefs about who or what has responsibility for causing and responding to climate change; desired and planned actions in response to climate change; and emotions and thoughts about the US Government response to climate change. Respondents were asked whether they had been affected by various severe weather events linked to climate change and for their political party identification. Sample percentages were weighted according to 2022 US census age, sex, and race estimates. To test the effects of political party identification and self-reported exposure to severe weather events on climate-related thoughts and beliefs we used linear and logistic regression models, which included terms for political party identification, the number of self-reported severe weather event types in respondents' area of residence in the past year, and demographic control variables. FINDINGS: We evaluated survey responses from 15 793 individuals (weighted proportions: 80·5% aged 18-25 years and 19·5% aged 16-17 years; 48·8% female and 51·2% male). Overall, 85·0% of respondents endorsed being at least moderately worried, and 57·9% very or extremely worried, about climate change and its impacts on people and the planet. 42·8% indicated an impact of climate change on self-reported mental health, and 38·3% indicated that their feelings about climate change negatively affect their daily life. Respondents reported negative thoughts about the future due to climate change and actions planned in response, including being likely to vote for political candidates who support aggressive climate policy (72·8%). In regression models, self-reported exposure to more types of severe weather events was significantly associated with stronger endorsement of climate-related distress and desire and plans for action. Political party identification as Democrat or as Independent or Other (vs Republican) was also significantly associated with stronger endorsement of distress and desire and plans for action, although a majority of self-identified Republicans reported at least moderate distress. For all survey outcomes assessed in the models, the effect of experiencing more types of severe weather events did not significantly differ by political party identification. INTERPRETATION: Climate change is causing widespread distress among US youth and affecting their beliefs and plans for the future. These effects may intensify, across the political spectrum, as exposure to climate-related severe weather events increases. FUNDING: Avaaz Foundation.

Length of Stay in ICU of Covid-19 patients in England, March - May 2020.

INTRODUCTION: Length of Stay (LoS) in Intensive Care Units (ICUs) is an important measure for planning beds capacity during the Covid-19 pandemic. However, as the pandemic progresses and we learn more about the disease, treatment and subsequent LoS in ICU may change. OBJECTIVES: To investigate the LoS in ICUs in England associated with Covid-19, correcting for censoring, and to evaluate the effect of known predictors of Covid-19 outcomes on ICU LoS. DATA SOURCES: We used retrospective data on Covid-19 patients, admitted to ICU between 6 March and 24 May, from the "Covid-19 Hospitalisation in England Surveillance System" (CHESS) database, collected daily from England's National Health Service, and collated by Public Health England. METHODS: We used Accelerated Failure Time survival models with Weibull and log-normal distributional assumptions to investigate the effect of predictors, which are known to be associated with poor Covid-19 outcomes, on the LoS in ICU. RESULTS: Patients admitted before 25 March had significantly longer LoS in ICU (mean = 18.4 days, median = 12), controlling for age, sex, whether the patient received Extracorporeal Membrane Oxygenation, and a co-morbid risk factors score, compared with the period after 7 April (mean = 15.4, median = 10). The periods of admission reflected the changes in the ICU admission policy in England. Patients aged 50-65 had the longest LoS, while higher co-morbid risk factors score led to shorter LoS. Sex and ethnicity were not associated with ICU LoS. CONCLUSIONS: The skew of the predicted LoS suggests that a mean LoS, as compared with median, might be better suited as a measure used to assess and plan ICU beds capacity. This is important for the ongoing second and any future waves of Covid-19 cases and potential pressure on the ICU resources. Also, changes in the ICU admission policy are likely to be confounded with improvements in clinical knowledge of Covid-19.

Longitudinal Auxological recovery in a cohort of children with Hyperinsulinaemic Hypoglycaemia.

BACKGROUND: Hypoglycaemia due to hyperinsulinism (HI) is the commonest cause of severe, recurrent hypoglycaemia in childhood. Cohort outcomes of HI remain to be described and whilst previous follow up studies have focused on neurodevelopmental outcomes, there is no information available on feeding and auxology. AIM: We aimed to describe HI outcomes for auxology, medications, feeding and neurodevelopmental in a cohort up to age 5 years. METHOD: We reviewed medical records for all patients with confirmed HI over a three-year period in a single centre to derive a longitudinal dataset. RESULTS: Seventy patients were recruited to the study. Mean weight at birth was - 1.0 standard deviation scores (SDS) for age and sex, while mean height at 3 months was - 1.5 SDS. Both weight and height trended to the population median over the follow up period. Feeding difficulties were noted in 17% of patients at 3 months and this reduced to 3% by 5 years. At age 5 years, 11 patients (15%) had neurodevelopmental delay and of these only one was severe. Resolution of disease was predicted by lower maximum early diazoxide dose (p = 0.007) and being born SGA (p = 0.009). CONCLUSION: In a three-year cohort of HI patients followed up for 5 years, in spite of feeding difficulties and carbohydrate loading in early life, auxology parameters are normal in follow up. A lower than expected rate of neurodevelopmental delay could be attributed to prompt early treatment.