A hospital demand and capacity intervention approach for COVID-19

The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached

The effect of comorbidities on diagnostic interval for lung cancer in England: a cohort study using electronic health record data.

BackgroundComorbid conditions may delay lung cancer diagnosis by placing demand on general practioners’ time reducing the possibility of prompt cancer investigation (“competing demand conditions”), or by offering a plausible non-cancer explanation for signs/symptoms (“alternative explanation conditions”).MethodPatients in England born before 1955 and diagnosed with incident lung cancer between 1990 and 2019 were identified in the Clinical Practice Research Datalink and linked hospital admission and cancer registry data. Diagnostic interval was defined as time from first presentation in primary care with a relevant sign/symptom to the diagnosis date. 14 comorbidities were classified as ten “competing demand“ and four “alternative explanation” conditions. Associations with diagnostic interval were investigated using multivariable linear regression models.ResultsComplete data were available for 11870 lung cancer patients. In adjusted analyses diagnostic interval was longer for patients with “alternative explanation” conditions, by 31 and 74 days in patients with one and ≥2 conditions respectively versus those with none. Number of “competing demand” conditions did not remain in the final adjusted regression model for diagnostic interval.ConclusionsConditions offering alternative explanations for lung cancer symptoms are associated with increased diagnostic intervals. Clinical guidelines should incorporate the impact of alternative and competing causes upon delayed diagnosis.</p

Visible damp in a child’s bedroom is associated with increased respiratory morbidity in early life: a multicentre cohort study

ObjectiveHousehold damp exposure is an important public health issue. We aimed to assess the impact of the location of household damp on respiratory outcomes during early life.MethodsHousehold damp exposure was ascertained in children recruited to the GO-CHILD multicentre birth cohort study. The frequency of respiratory symptoms, infections, healthcare utilisation and medication prescription for wheezing were collected by postal questionnaires at 12 and 24 months. Log binomial and ordered logistic regression models were fitted to the data.ResultsFollow-up was obtained in 1344 children between August 2010 and January 2016. Visible damp was present in a quarter of households (25.3%) with 1 in 12 children’s bedrooms affected (8.3%). Damp in the bathroom, kitchen or living room was not associated with any respiratory or infection-related outcomes. Damp in the child’s bedroom was associated with an increased risk of dry cough (8.7% vs 5.7%) (adjusted relative risk 1.56, 95% CI 1.07 to 2.27; p=0.021) and odds of primary care attendance for cough and wheeze (7.6% vs 4.4%) (adjusted OR 1.37, 95% CI 1.07 to 1.76; p=0.009). There were also increased risk of inhaled corticosteroid (13.3% vs 5.9%) (adjusted RR 2.22, 95% CI 1.04 to 4.74; p=0.038) and reliever inhaler (8.3% vs 5.8%) (adjusted RR 2.01, 95% CI 1.21 to 2.79; p=0.018) prescription.ConclusionDamp in the child’s bedroom was associated with increased respiratory morbidity. In children presenting with recurrent respiratory symptoms, clinicians should enquire about both the existence and location of damp, the presence of which can help prioritise those families requiring urgent household damp assessment and remediation works.</p