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- D’Aeth, Josh C., et al.
(författare)
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Optimal hospital care scheduling during the SARS-CoV-2 pandemic
- 2023
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Ingår i: Management science. - : Institute for Operations Research and the Management Sciences (INFORMS). - 0025-1909 .- 1526-5501. ; 69:10, s. 5695-6415
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer.
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| 2. |
- D’Aeth, Josh C., et al.
(författare)
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Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic
- 2021
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Ingår i: Nature Computational Science. - : Springer Nature. - 2662-8457. ; 1:8, s. 521-531
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Tidskriftsartikel (refereegranskat)abstract
- In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750–5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.
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| 3. |
- D’Aeth, Josh, et al.
(författare)
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Report 40 : Optimal scheduling rules for elective care to minimize years of life lost during the SARS-CoV-2 pandemic: an application to England
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Countries have deployed a wide range of policies to prioritize patients to hospital care to address unprecedent surges in demand during the course of the pandemic. Those policies included postponing planned hospital care for non-emergency cases and rationing critical care.We developed a model to optimally schedule elective hospitalizations and allocate hospital general and critical care beds to planned and emergency patients in England during the pandemic. We apply the model to NHS England data and show that optimized scheduling leads to lower years of life lost and costs than policies that reflect those implemented in England during the pandemic. Overall across all disease areas the model enables an extra 50,750-5,891,608 years of life gained when compared to standard policies, depending on the scenarios. Especially large gains in years of life are seen for neoplasms, diseases of the digestive system, and injuries &poisoning.
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| 4. |
- Forchini, Giovanni, et al.
(författare)
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A conditional approach to panel data models with common shocks
- 2016
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Ingår i: Econometrics. - : MDPI AG. - 2225-1146. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the effects of common shocks on the OLS estimators of the slopes' parameters in linear panel data models. The shocks are assumed to affect both the errors and some of the explanatory variables. In contrast to existing approaches, which rely on using results on martingale difference sequences, our method relies on conditional strong laws of large numbers and conditional central limit theorems for conditionally-heterogeneous random variables.
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| 5. |
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| 6. |
- Forchini, Giovanni, et al.
(författare)
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Fragility of identification in panel binary response models
- 2019
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Ingår i: Econometrics Journal. - : OXFORD UNIV PRESS. - 1368-4221 .- 1368-423X. ; 22:3, s. 282-291
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Tidskriftsartikel (refereegranskat)abstract
- The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible-including in the logistic case-to find a distribution of the random effects given the exogenous variables, such that the slopes' parameters are arbitrarily different, but the joint distributions of the binary response variables are arbitrarily close.
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| 7. |
- Forchini, Giovanni, et al.
(författare)
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Instrumental Variables Estimation in Large Heterogeneous Panels with Multifactor Structure
- 2020
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Ingår i: Journal of Econometric Methods. - : Walter de Gruyter. - 2156-6674 .- 2194-6345. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- The paper proposes new instrumental variables estimators for the slope parameters of a panel data model with classical endogeneity in which all the observables-including the instruments-may have a common factors structure. These estimators are shown to be consistent and asymptotically normal under weak regularity conditions. A small Monte Carlo simulation shows that these estimators compare favourably to existing estimators.
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| 8. |
- Forchini, Giovanni, et al.
(författare)
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Modelling Multivariate Durations
- 2016
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Ingår i: International Journal of Statistics & Economic. - 0975-556X. ; 17:1, s. 82-93
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Tidskriftsartikel (refereegranskat)abstract
- We propose a simple procedure to model multivariate durations. This is done by specifying two conditional models: anautoregressive conditional durationmodel for on a pooled series of durationsand a logit model for the type marks. We estimate the model by maximising a pseudo-likelihood which is equivalent to estimating the autoregressive conditional duration model and the logit model separately. We illustrate this methodology by modelling the joint dynamics of the trade shares of Tabcorp Holdings Limited and Tatts group Limited in the Australian financial market between January 15 and January 31 2009.
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| 9. |
- Forchini, Giovanni, et al.
(författare)
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Modified first-difference estimator in a panel data model with unobservable factors both in the errors and the regressors when the time dimension is small
- 2017
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Ingår i: Communications in Statistics - Theory and Methods. - : Taylor & Francis. - 0361-0926 .- 1532-415X. ; 46:24, s. 12226-12239
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Tidskriftsartikel (refereegranskat)abstract
- Panel data models with factor structures in both the errors and the regressors have received considerable attention recently. In these models, the errors and the regressors are correlated and the standard estimators are inconsistent. This paper shows that, for such models, a modified first-difference estimator (in which the time and the cross-sectional dimensions are interchanged) is consistent as the cross-sectional dimension grows but the time dimension is small. Although the estimator has a non standard asymptotic distribution, t and F tests have standard asymptotic distribution under the null hypothesis.
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| 10. |
- Forchini, Giovanni, et al.
(författare)
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Report 28 : Excess non-COVID-19 deaths in England and Wales between 29th February and 5th June 2020
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- There were 189,403 deaths from any cause reported in England from 29th February to 5th June 2020 inclusive, and 11,278 all-cause deaths in Wales over the same period. Of those deaths, 44,736 (23.6%) registered COVID-19 on the death certificate in England, and 2,294 (20.3%) in Wales, while 144,667 (76.4%) were not recorded as having been due to COVID-19 in England, and 8,984 (79.7%) in Wales. However, it could be that some of the ‘non-COVID-19’ deaths have in fact also been caused by COVID-19, either as the direct cause of death, or indirectly through provisions for the pandemic impeding access to care for other conditions. There is uncertainty in how many of the non-COVID-19 deaths were directly or indirectly caused by the pandemic. We estimated the excess deaths that were not recorded as associated with COVID-19 in the death certificate (excess non-COVID-19 deaths) as the deaths for which COVID-19 was not reported as the cause, compared to those we would have expected to occur had the pandemic not happened. Expected deaths were forecast with an analysis of historic trends in deaths between 2010 and April 2020 using data by the Office of National Statistics and a statistical time series model.According to the model, we expected 136,294 (95% CI 133,882 - 138,696) deaths in England, and 8,983 (CI 8,051 - 9,904) in Wales over this period, significantly fewer than the number of deaths reported. This means that there were 8,983 (95% CI 5,971 - 10,785) total excess non-COVID-19 deaths in England. For every 100 COVID-19 deaths during the period from 29th February to 5th June 2020 there were 19 (95% CI 13 – 24) cumulative excess non-COVID-19 deaths. The proportion of cumulative excess non-COVID-19 deaths of all reported deaths during this period was 4.4% (95% CI 3.2% - 5.7%) in England, with small regional variations. Excess deaths were highest in the South East at 2,213 (95% CI 327 - 4,047) and in London at 1,937 (95% CI 896 - 3,010), respectively. There is no evidence of non-COVID-19 excess deaths in Wales. Excess non-COVID-19 deaths are occurring in individuals aged 85+ and 75-84, and those aged 45-64. For those aged 85+, excess non-COVID-19 deaths are driven by females, with 6,115 (95% CI 206 – 11,795) deaths in total but no significant findings for males of those ages. For ages 75-84, excess non-COVID-19 deaths are nearly double for females at 2,070 (95% CI 393 – 3,887) than for males at 1,336 (95% CI 938 – 1,710), while for ages 45-64, excess non-COVID-19 deaths for females are at 347 (95% CI 90 – 603), almost half those of males at 681 (95% CI 282 – 1,091). There is no evidence of excess non-COVID-19 deaths for ages 65-74, and those below 45.Excess non-COVID-19 deaths could be due to non-reporting of COVID-19 on the death certificate or an increase in mortality for non-COVID-19 conditions. Severely ill patients may have been unable to access life-saving emergency treatment because of constraints in healthcare provision, or because they avoided seeking care due to concern over hospital-acquired infection, or to avoid burdening healthcare providers. Further research into reasons for excess non-COVID-19 deaths is warranted.This report accompanies the weekly update of excess death estimates on the Github website of the Abdul Latif Jameel Institute of Disease and Emergency Analytics (J-IDEA) (https://j-idea.github.io/ONSdeaths/) which has been set up to be regularly updated until June 2022.
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