Exploring the acceptance of COVID-19 vaccine among healthcare workers and general population using health belief model.

RATIONALE, AIMS AND OBJECTIVES: Little is known about hesitancy to receive the COVID-19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID-19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination. METHODS: This was a cross-sectional study based on electronic survey data that was collected in Iraq during December first-19th, 2020. The electronic survey was designed using Qualtrics. HBM was followed to develop the survey items. A regression analysis was used to identify factors influencing people accepting vaccination. Thematic analysis for participant comments to an open-ended question. RESULTS: A total of 1680 completed surveys were received. The mean age of 31.2 ± 9.9 years, 53.0% were female and 47.0% were male. The largest group was HCWs (45.7%), followed by general population (37.5%) and health college students (16.8%). Our findings expressed some hesitancy to receive the COVID-19 vaccine with the acceptance rate of 61.7%. The HCWs perceived significantly higher susceptibility and severity of the COVID-19 infection compared to the general population. The HCWs were significantly more likely than the general population to receive COVID-19 vaccine. Concerns with proper storage was the biggest barrier to vaccine receipt. The regression analysis indicated eight factors that were significantly associated with the willingness to receive COVID-19 vaccine: Preventive measures, perceived benefit, perceived barriers, cue to action, subjective norm, supportive of vaccination in general and having received a flu vaccine before. CONCLUSIONS: Awareness campaign can focus on enhancing the vaccine perceived benefit, debunking misconceptions, and increasing the disease perceived severity. Additionally, the public health leaders need to minimize the perceived barriers by providing the vaccines and appeasing people concerns about their storage, effectiveness, and adverse events.

Personalised Warfarin Dosing in Children Post-cardiac Surgery.

Warfarin dosing is challenging due to a multitude of factors affecting its pharmacokinetics (PK) and pharmacodynamics (PD). A novel personalised dosing algorithm predicated on a warfarin PK/PD model and incorporating CYP2C9 and VKORC1 genotype information has been developed for children. The present prospective, observational study aimed to compare the model with conventional weight-based dosing. The study involved two groups of children post-cardiac surgery: Group 1 were warfarin naïve, in whom loading and maintenance doses were estimated using the model over a 6-month duration and compared to historical case-matched controls. Group 2 were already established on maintenance therapy and randomised into a crossover study comparing the model with conventional maintenance dosing, over a 12-month period. Five patients enrolled in Group 1. Compared to the control group, the median time to achieve the first therapeutic INR was longer (5 vs. 2 days), to stable anticoagulation was shorter (29.0 vs. 96.5 days), to over-anticoagulation was longer (15.0 vs. 4.0 days). In addition, median percentage of INRs within the target range (%ITR) and percentage of time in therapeutic range (%TTR) was higher; 70% versus 47.4% and 83.4% versus 62.3%, respectively. Group 2 included 26 patients. No significant differences in INR control were found between model and conventional dosing phases; mean %ITR was 68.82% versus 67.9% (p = 0.84) and mean %TTR was 85.47% versus 80.2% (p = 0.09), respectively. The results suggest model-based dosing can improve anticoagulation control, particularly when initiating and stabilising warfarin dosing. Larger studies are needed to confirm these findings.

Personalised dosing of medicines for children.

OBJECTIVES: Doses for most drugs are determined from population-level information, resulting in a standard ?one-size-fits-all' dose range for all individuals. This review explores how doses can be personalised through the use of the individuals' pharmacokinetic (PK)-pharmacodynamic (PD) profile, its particular application in children, and therapy areas where such approaches have made inroads. KEY FINDINGS: The Bayesian forecasting approach, based on population PK/PD models that account for variability in exposure and response, is a potent method for personalising drug therapy. Its potential utility is even greater in young children where additional sources of variability are observed such as maturation of eliminating enzymes and organs. The benefits of personalised dosing are most easily demonstrated for drugs with narrow therapeutic ranges such as antibiotics and cytotoxics and limited studies have shown improved outcomes. However, for a variety of reasons the approach has struggled to make more widespread impact at the bedside: complex dosing algorithms, high level of technical skills required, lack of randomised controlled clinical trials and the need for regulatory approval. SUMMARY: Personalised dosing will be a necessary corollary of the new precision medicine initiative. However, it faces a number of challenges that need to be overcome before such an approach to dosing in children becomes the norm.