The Impact of COVID-19 on Acute Surgical Admissions at the Sunshine Coast University Hospital.

Purpose It has been noted in international literature that acute surgical admissions and number of operations reduced as a result of coronavirus disease2019 (COVID-19). This study assesses the impact of the COVID-19 pandemic on the number of acute surgical admissions, operations, and length of stay (LoS) at the Sunshine Coast University Hospital (SCUH), Queensland, Australia. Methodology A retrospective study was conducted on patients admitted to the Acute Surgical Unit (ASU) during March and April for the years 2018, 2019, and 2020. Admission data for ASU patients in 2018 and 2019 were combined (pre-COVID) and compared with 2020 (COVID) to determine impact of the pandemic on presentations and procedures. Results ASU admissions reduced in 2020 (461 patients) compared with pre-COVID years (mean: 545 patients per year). There was an increase in the number (%) of operations performed in 2020, 175 patients (38%) compared with pre-COVID years, mean 158 patients (29%), p = 0.001. There was a significant decrease in the number (%) of functional presentations in 2020, 29 patients (6.3%) compared with pre-COVID years, mean 105 patients (9.6%), p = 0.04. LoS was not significantly different (52 hours vs. 54 hours, p = 0.11). Conclusion COVID-19 has reduced the absolute number of acute surgical admissions at SCUH. This effectively reduced triage workload. Contrary to the literature, this study did not demonstrate a reduction in the number of operations or change in LoS. These data could be used by health administrators to help with resource allocation during future pandemics.

Comparing hospital-resource utilization by an enhanced pneumonia surveillance programme for COVID-19 with pre-pandemic pneumonia admissions - a Singaporean hospital's experience.

Introduction. During the early days of coronavirus disease 2019 (COVID-19) in Singapore, Tan Tock Seng Hospital implemented an enhanced pneumonia surveillance (EPS) programme enrolling all patients who were admitted from the Emergency Department (ED) with a diagnosis of pneumonia but not meeting the prevalent COVID-19 suspect case definition.Hypothesis/Gap Statement. There is a paucity of data supporting the implementation of such a programme.Aims. To compare and contrast our hospital-resource utilization of an EPS programme for COVID-19 infection detection with a suitable comparison group.Methodology. We enrolled all patients admitted under the EPS programme from TTSH's ED from 7 February 2020 (date of EPS implementation) to 20 March 2020 (date of study ethics application) inclusive. We designated a comparison cohort over a similar duration the preceding year. Relevant demographic and clinical data were extracted from the electronic medical records.Results. There was a 3.2 times higher incidence of patients with an admitting diagnosis of pneumonia from the ED in the EPS cohort compared to the comparison cohort (P<0.001). However, there was no significant difference in the median length of stay of 7 days (P=0.160). Within the EPS cohort, stroke and fluid overload occur more frequently as alternative primary diagnoses.Conclusions. Our study successfully evaluated our hospital-resource utilization demanded by our EPS programme in relation to an appropriate comparison group. This helps to inform strategic use of hospital resources to meet the needs of both COVID-19 related services and essential 'peace-time' healthcare services concurrently.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.