The effects of an emergency nurse-led stress-reduction project during the first 120 days of the COVID-19 pandemic in Taiwan.

BACKGROUND: The COVID-19 frontline nurses' stress-reduction programme by the cooperation of manager with the nurses is not-well developed. AIM: This study aimed to examine the effect of an emergency nurse-led stress-reduction project on reducing stress levels during the COVID-19 pandemic. METHODS: The action research was conducted using online and person-to-person group brainstorming strategies. The online survey was used to evaluate emergency nurses' stress levels, causes of stress and needs at the 50th, 80th and 110th days of the pandemic from March to May 2020. RESULTS: The numbers of nurses participating in three-time survey were 160, 166 and 160, respectively. There was a decrease in the nurses' work-related stress after implementing the improvement strategies. Stress from personal protective equipment (PPE), information about infection control and family's worry about being infected reduced across 2 months. Needs regarding PPE, COVID-19 information and a forum for sharing experiences of COVID-19 care decreased whereas needs of allowing more days off increased. CONCLUSIONS: The stress-reduction project targeting at nurses' views of their needs can reduce their stress during the COVID-19 pandemic. IMPLICATIONS FOR NURSING MANAGEMENT: The online and person-to-person group brainstorming building a good partnership between nurses and managers can be an effective nursing management.

Seroprevalence Surveys for Anti-SARS-CoV-2 Antibody in Different Populations in Taiwan With Low Incidence of COVID-19 in 2020 and Severe Outbreaks of SARS in 2003.

Accurate detection of anti-SARS-CoV-2 antibodies provides a more accurate estimation of incident cases, epidemic dynamics, and risk of community transmission. We conducted a cross-sectional seroprevalence study specifically targeting different populations to examine the performance of pandemic control in Taiwan: symptomatic patients with epidemiological risk and negative qRT-PCR test (Group P), frontline healthcare workers (Group H), healthy adult citizens (Group C), and participants with prior virologically-confirmed severe acute respiratory syndrome (SARS) infection in 2003 (Group S). The presence of anti-SARS-CoV-2 total and IgG antibodies in all participants were determined by Roche Elecsys® Anti-SARS-CoV-2 test and Abbott SARS-CoV-2 IgG assay, respectively. Sera that showed positive results by the two chemiluminescent immunoassays were further tested by three anti-SARS-CoV-2 lateral flow immunoassays and line immunoassay (MIKROGEN recomLine SARS-CoV-2 IgG). Between June 29 and July 25, 2020, sera of 2,115 participates, including 499 Group P participants, 464 Group H participants, 1,142 Group C participants, and 10 Group S participants, were tested. After excluding six false-positive samples, SARS-CoV-2 seroprevalence were 0.4, 0, and 0% in Groups P, H, and C, respectively. Cross-reactivity with SARS-CoV-2 antibodies was observed in 80.0% of recovered SARS participants. Our study showed that rigorous exclusion of false-positive testing results is imperative for an accurate estimate of seroprevalence in countries with previous SARS outbreak and low COVID-19 prevalence. The overall SARS-CoV-2 seroprevalence was extremely low among populations of different exposure risk of contracting SARS-CoV-2 in Taiwan, supporting the importance of integrated countermeasures in containing the spread of SARS-CoV-2 before effective COVID-19 vaccines available.

Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission.

OBJECTIVE Isolation of multidrug-resistant gram-negative bacteria (MDR-GNB) from patients in the community has been increasingly observed. A prediction model for MDR-GNB colonization and infection risk stratification on hospital admission is needed to improve patient care. METHODS A 2-stage, prospective study was performed with 995 and 998 emergency department patients enrolled, respectively. MDR-GNB colonization was defined as isolates resistant to 3 or more classes of antibiotics, identified in either the surveillance or early (≤48 hours) clinical cultures. RESULTS A score-assigned MDR-GNB colonization prediction model was developed and validated using clinical and microbiological data from 995 patients enrolled in the first stage of the study; 122 of these patients (12.3%) were MDR-GNB colonized. We identified 5 independent predictors: age>70 years (odds ratio [OR], 1.84 [95% confidence interval (CI), 1.06-3.17]; 1 point), assigned point value in the model), residence in a long-term-care facility (OR, 3.64 [95% CI, 1.57-8.43); 3 points), history of cerebrovascular accidents (OR, 2.23 [95% CI, 1.24-4.01]; 2 points), hospitalization within 1 month (OR, 2.63 [95% CI, 1.39-4.96]; 2 points), and recent antibiotic exposure (OR, 2.18 [95% CI, 1.16-4.11]; 2 points). The model displayed good discrimination in the derivation and validation sets (area under ROC curve, 0.75 and 0.80, respectively) with the best cutoffs of<4 and ≥4 points for low- and high-risk MDR-GNB colonization, respectively. When applied to 998 patients in the second stage of the study, the model successfully stratified the risk of MDR-GNB infection during hospitalization between low- and high-risk groups (probability, 0.02 vs 0.12, respectively; log-rank test, P<.001). CONCLUSION A model was developed to optimize both the decision to initiate antimicrobial therapy and the infection control interventions to mitigate threats from MDR-GNB. Infect Control Hosp Epidemiol 2017;38:1216-1225.

A mobile indoor positioning system based on iBeacon technology.

To increase the efficiency in the emergency room, the goal of this research is to implement a mobile-based indoor positioning system using mobile applications (APP) with the iBeacon solution based on the Bluetooth Low Energy (BLE) technology. We use the Received Signal Strength (RSS) based localization method to estimate the patients' locations. Our positioning algorithm achieves 97.22% (95% Confidence Interval = 95.90% - 98.55%) accuracy of classification. As the result, our mechanism is reliable enough to satisfy the need for medical staff to track the locations of their patients.