Decreased Antibiotic Consumption Coincided with Reduction in Bacteremia Caused by Bacterial Species with Respiratory Transmission Potential during the COVID-19 Pandemic

Nonpharmaceutical interventions implemented during the COVID-19 pandemic (2020–2021) have provided a unique opportunity to understand their impact on the wholesale supply of antibiotics and incidences of infections represented by bacteremia due to common bacterial species in Hong Kong. The wholesale antibiotic supply data (surrogate indicator of antibiotic consumption) and notifications of scarlet fever, chickenpox, and tuberculosis collected by the Centre for Health Protection, and the data of blood cultures of patients admitted to public hospitals in Hong Kong collected by the Hospital Authority for the last 10 years, were tabulated and analyzed. A reduction in the wholesale supply of antibiotics was observed. This decrease coincided with a significant reduction in the incidence of community-onset bacteremia due to Streptococcus pyogenes, Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis, which are encapsulated bacteria with respiratory transmission potential. This reduction was sustained during two pandemic years (period 2: 2020–2021), compared with eight pre-pandemic years (period 1: 2012–2019). Although the mean number of patient admissions per year (1,704,079 vs. 1,702,484, p = 0.985) and blood culture requests per 1000 patient admissions (149.0 vs. 158.3, p = 0.132) were not significantly different between periods 1 and 2, a significant reduction in community-onset bacteremia due to encapsulated bacteria was observed in terms of the mean number of episodes per year (257 vs. 58, p p p Staphylococcus aureus or Escherichia coli. Sustained implementation of non-pharmaceutical interventions against respiratory microbes may reduce the overall consumption of antibiotics, which may have a consequential impact on antimicrobial resistance. Rebound of conventional respiratory microbial infections is likely with the relaxation of these interventions

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting

ObjectivesTo develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.MethodsPatients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).ResultsA total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.ConclusionTwo simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation