ABSTRACT
Background The COVID-19 pandemic has had a significant impact on healthcare including increased awareness of infection prevention and control (IPC). The aim of this study was to explore if the heightened awareness of IPC measures implemented in response to the pandemic influenced the rates of healthcare associated infections (HAI) using positive bloodstream and urine cultures as a proxy measure.Methods A 3 year retrospective review of laboratory data from 5 hospitals (4 acute public, 1 private) from two states in Australia was undertaken. Monthly positive bloodstream culture data and urinary culture data were collected from January 2017 to March 2021. Occupied bed days (OBDs) were used to generate monthly HAI incidence per 10,000 OBDs. An interrupted time series analysis was undertaken to compare incidence pre and post February 2020 (the pre COVID-19 cohort and the COVID-19 cohort respectively). A HAI was assumed if positive cultures were obtained 48 hours after admission and met other criteria.Results A total of 1,988 bloodstream and 7,697 urine positive cultures were identified. The unadjusted incident rate was 25.5 /10,000 OBDs in the pre-COVID-19 cohort, and 25.1/10,000 OBDs in the COVID-19 cohort. The overall rate of HAI aggregated for all sites did not differ significantly between the two periods. The two hospitals in one state which experienced an earlier and larger outbreak demonstrated a significant downward trend in the COVID-19 cohort (p = 0.011).Conclusion These mixed findings reflect the uncertainty of the effect the pandemic has had on HAI’s. Factors to consider in this analysis include local epidemiology, differences between public and private sector facilities, changes in patient populations and profiles between hospitals, and timing of enhanced IPC interventions. Future studies which factor in these differences may provide further insight on the effect of COVID-19 on HAIs.
Subject(s)
COVID-19ABSTRACT
Background Non-pharmaceutical interventions (NPI) play a key role in managing epidemics, yet it is challenging to evaluate their impacts on disease spread and outcomes. Methods To estimate the effect of a mask-wearing intervention to mitigate the spread of SARS-CoV-2 on the island of Ireland, we focused on the potential for interindividual infectious contact over time as the outcome. This is difficult to measure directly; in a companion paper we estimated it using a multi-strain epidemiological model. We used data on mask-wearing and mobility in both Northern Ireland (NI) and the Republic of Ireland (ROI) to predict independently the estimated infectious contact over time. We made counterfactual predictions of infectious contact rates and hospitalisations under a hypothetical intervention where 90% of the population were wearing masks during early 2020, when in reality few people were wearing masks in public; this was mandated in both jurisdictions on 10th August 2020. Results There were 1601 hospitalisations with COVID-19 in NI between 12th March and 10th August 2020, and 1521 in ROI between 3rd April and 10th August 2020. Under the counterfactual mask-wearing scenario, we estimated 512 (95% CI 400, 730) hospitalisations in NI, and 344 (95% CI 266, 526) in ROI, during the same periods. Conclusions We have estimated a large effect of population mask-wearing on COVID-19 hospitalisations. This could be partly due to other factors that were also changing over time.
Subject(s)
COVID-19ABSTRACT
Mathematical modelling plays a key role in understanding and predicting the epidemiological dynamics of infectious diseases. We construct a flexible discrete-time model that incorporates multiple viral strains with different transmissibilities to estimate the changing infectious contact that generates new infections. Using a Bayesian approach, we fit the model to longitudinal data on hospitalisation with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in infectious contact in the context of governmentmandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We take advantage of the fitted model to conduct counterfactual analyses exploring the impact of lockdown timing and introducing a novel, more transmissible variant. We found substantial differences in infectious contact between the two jurisdictions during periods of varied restriction easing and December holidays. Our counterfactual analyses reveal that implementing lockdowns earlier would have decreased subsequent hospitalisation substantially in most, but not all cases, and that an introduction of a more transmissible variant - without necessarily being more severe - can cause a large impact on the health care burden.