Understanding COVID-19 Vaccine Acceptance among Healthcare Workers in Indonesia: Lessons from Multi-Site Survey.

The COVID-19 pandemic presented an unprecedented challenge to public health as well as an extraordinary burden on health systems worldwide. COVID-19 vaccines were attributed as a key tool to control the pandemic, with healthcare workers (HCWs) as a priority group to receive the vaccine. Healthcare workers are considered one of the most trusted sources of information on vaccines and vaccination. This study was conducted to evaluate the acceptability of the COVID-19 vaccine among HCWs in four different provinces of Indonesia. An anonymous cross-sectional study was conducted online among HCWs between December 2020 and February 2021. Out of 2732 participants, 80.39% stated that they would accept the COVID-19 vaccine, while 19.61% were hesitant to receive the vaccine. Concerns about the safety profile of COVID-19 vaccines and potential side-effects after vaccination were the main reasons among the participants to refuse the vaccine. Male gender, single status, higher education level, and higher risk perception increased the acceptability of the COVID-19 vaccine. Other motivators of COVID-19 vaccine acceptance include a high level of trust in the government and increased confidence in vaccine safety and efficacy studies. Dissemination of information in a timely manner as well as training programs for HCWs are crucial to increasing confidence in the COVID-19 vaccination program.

COVID-19 Vaccine Acceptance among University Students and Lecturers in Different Provinces of Indonesia: A Cross-Sectional Study.

The coronavirus disease 2019 (COVID-19) pandemic imposed a pressing global threat. Vaccines against COVID-19 are a key tool to control the ongoing pandemic. The success of COVID-19 vaccination programs will largely depend on public willingness to receive the vaccine. This study aimed to evaluate the acceptability of COVID-19 vaccines among university students and lecturers in four different provinces of Indonesia. An anonymous, cross-sectional study was conducted online among university students and lecturers in Indonesia between 23 December 2020 and 15 February 2021. Of 3433 respondents, 50.3% stated that they would accept COVID-19 vaccination, while 10.7% expressed unwillingness and 39% were not sure about receiving the vaccine. Concern regarding the side effects after COVID-19 vaccination was the main reason among the participants for not willing to receive the vaccine. Being male, associated with the health sector, having a higher monthly expenditure and having health insurance could increase the acceptability of the COVID-19 vaccine. Low trust in the government and low confidence towards vaccine safety and efficacy could hinder participants' decision to be vaccinated. Simple, clear and fact-based information from trusted sources on a regular basis will be important for building confidence towards the COVID-19 vaccination program in Indonesia.

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients.

Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan's National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.

COVID-19 infection in the palatine tonsil tissue and detritus: the detection of the virus compartment with RT-PCR.

Two patients suffering from chronic recurrent tonsillitis were reported. The first patient was confirmed infected with COVID-19, 3 weeks prior to tonsillectomy. The detritus and tonsil specimen were further analysed through real-time PCR (RT-PCR) and revealed amplification of the fragment N and ORF1ab genes of SARS-CoV-2. The second patient had a negative IgM and positive IgG antibody for COVID-19; however, the nasopharyngeal swab indicated negative for SARS-CoV-2. Tonsillectomy was performed 2 weeks after the swab; the tonsil specimen was analysed through RT-PCR and revealed amplification of the N2 and RdRp gene of SARS-CoV-2. According to both results, the presence of the SARS-CoV-2 gene remains to be detected in tonsil and/or detritus after 2-3 weeks after recovery. Hence, it is suggested that it is necessary to use adequate protection when performing tonsillectomy on early recovered patients with COVID-19. Furthermore, tonsillectomy would be more advisable to be performed after the fourth week after recovery from COVID-19.