Analysis and Estimation of Length of In-Hospital Stay Using Demographic Data of COVID-19 Recovered Patients in Singapore
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Abstract
Singapore is one of the countries which has taken early, systematic, and rigorous nationwide responses to slowing the COVID-19 contagion down. By February 4, 2020, the government of Singapore restricted the mobility of the vulnerable age-groups. In the current study, we study the influence of the age-based vulnerability of the population with respect to COVID-19 conditions on the recovery of COVID-19 patients. We study 245 patients in Singapore recovered and discharged during January 23–April 01. We first study the descriptive statistics of the length of in-hospital stay (LOS) of the COVID-19 patients based on demographic variables, namely age, and gender. Then, we determine the distribution of LOS, using local and generalized linear regression models. We take the approach of periodization based on critical changes in the disease transmission model. Even though the overall recovery rate has reduced drastically after a sudden spike in daily confirmations, our analysis shows that there is a considerable shift in the COVID-19 confirmations to the population in the non-vulnerable age-groups. We show that the LOS of the non-vulnerable age group is considerably lower at 9 days, as opposed to 15 or 20 days in the existing literature.
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SciScore for 10.1101/2020.04.17.20069724: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, our work has the limitation of modeling recovery exclusively, without the interaction with the distribution of daily positive confirmations. With N = 1000 in Singapore by April 1, but Nr = 245, we have not analyzed the outcome of the remaining 755 patients. Our study can be enriched by doing a similar analysis on all the cases confirmed by April 1, closed by recovery and death. Our regression model can be improved to include …
SciScore for 10.1101/2020.04.17.20069724: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, our work has the limitation of modeling recovery exclusively, without the interaction with the distribution of daily positive confirmations. With N = 1000 in Singapore by April 1, but Nr = 245, we have not analyzed the outcome of the remaining 755 patients. Our study can be enriched by doing a similar analysis on all the cases confirmed by April 1, closed by recovery and death. Our regression model can be improved to include interaction effects, which could be effective if we have additional data on other variables, e.g., co-morbidities, whose data is not available in the public domain. Nevertheless, our estimated ΔtLOS based on the first 245 discharged patients is useful for assessing the hospital load in terms of required resources for the in-hospital stay of the COVID-19 cases.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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