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1.
Stat Methods Med Res ; 31(9): 1656-1674, 2022 09.
Article in English | MEDLINE | ID: covidwho-1932991

ABSTRACT

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.


Subject(s)
COVID-19 , Hospitalization , Hospitals , Humans , Intensive Care Units , Probability
2.
BMJ Open ; 12(3): e054859, 2022 03 24.
Article in English | MEDLINE | ID: covidwho-1765124

ABSTRACT

BACKGROUND: For people with symptomatic COVID-19, the relative risks of hospital admission, death without hospital admission and recovery without admission, and the times to those events, are not well understood. We describe how these quantities varied with individual characteristics, and through the first wave of the pandemic, in Milan, Italy. METHODS: A cohort study of 27 598 people with known COVID-19 symptom onset date in Milan, Italy, testing positive between February and June 2020 and followed up until 17 July 2020. The probabilities of different events, and the times to events, were estimated using a mixture multistate model. RESULTS: The risk of death without hospital admission was higher in March and April (for non-care home residents, 6%-8% compared with 2%-3% in other months) and substantially higher for care home residents (22%-29% in March). For all groups, the probabilities of hospitalisation decreased from February to June. The probabilities of hospitalisation also increased with age, and were higher for men, substantially lower for healthcare workers and care home residents, and higher for people with comorbidities. Times to hospitalisation and confirmed recovery also decreased throughout the first wave. Combining these results with our previously developed model for events following hospitalisation, the overall symptomatic case fatality risk was 15.8% (15.4%-16.2%). CONCLUSIONS: The highest risks of death before hospital admission coincided with periods of severe burden on the healthcare system in Lombardy. Outcomes for care home residents were particularly poor. Outcomes improved as the first wave waned, community healthcare resources were reinforced and testing became more widely available.


Subject(s)
COVID-19 , COVID-19/epidemiology , Cohort Studies , Comorbidity , Hospitalization , Humans , Male , Pandemics
3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-314715

ABSTRACT

During Covid-19 outbreaks, school closures are employed as part of governments' non-pharmaceutical interventions around the world to reduce the number of contacts and keep the reproduction number below 1. Yet, prolonged school closures have profound negative impact on the future opportunities of pupils, particularly from disadvantaged backgrounds, as well as additional economic and social impacts by preventing their parents from returning to work. Data on Covid-19 in children are sparse and policy frameworks are evolving quickly. We compare a set of potential policies to accompany the reopening of schools by means of an agent-based simulation tool. The policies and scenarios we model reflect the public discussion and government guidelines in early March 2021 in England before the planned nationwide reopening of schools on the 8th of March. A point of particular interest is the potential contribution of a more wide-spread use of screening tests based on lateral flow devices. We compare policies both with respect to their potential to contain new outbreaks of Covid-19 in schools and the proportion of schooldays lost due to isolation of pupils. We find that regular asymptomatic screening of the whole school as an addition to a policy built around isolation of symptomatic pupils and their closest contacts is beneficial across a wide range of scenarios, including when screening tests with relatively low test sensitivity are used. Multiple screening tests per week bring only small additional benefits in some scenarios. These findings remain valid when test compliance is not enforced although the effectiveness of outbreak control is reduced.

4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-311567

ABSTRACT

Background: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time;to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods: : This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date. Results: : The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%);and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June. Conclusions: : The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2.

6.
BMC Public Health ; 21(1): 1612, 2021 09 03.
Article in English | MEDLINE | ID: covidwho-1496155

ABSTRACT

BACKGROUND: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. METHODS: This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date. RESULTS: The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%); and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June. CONCLUSIONS: The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i.


Subject(s)
COVID-19 , Cohort Studies , Female , Hospitalization , Hospitals , Humans , Male , SARS-CoV-2
7.
BMC Infect Dis ; 21(1): 1041, 2021 Oct 07.
Article in English | MEDLINE | ID: covidwho-1455944

ABSTRACT

BACKGROUND: Understanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection. METHODS: An observational cohort study is performed, using data on all PCR-confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively. RESULTS: Risks of severe outcomes such as ICU admission and mortality have decreased with month of admission (for example, the odds ratio of ICU admission in June vs March is 0.247 [0.120-0.508]) and increased with age (odds ratio of ICU admission in 45-65 vs 65 + age group is 0.286 [0.201-0.406]). Care home residents aged 65 + are associated with increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective association with mortality risk (odds ratio of ICU mortality is 0.254 [0.143-0.453] relative to non-healthcare workers) and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors. CONCLUSIONS: Improvements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time.


Subject(s)
COVID-19 , Cohort Studies , Communicable Disease Control , Hospitals , Humans , Intensive Care Units , Length of Stay , Risk Factors , SARS-CoV-2
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