ABSTRACT
[This corrects the article DOI: 10.1371/journal.pone.0249394.].
ABSTRACT
INTRODUCTION: The reporting of Coronavirus Disease 19 (COVID-19) mortality among healthcare workers highlights their vulnerability in managing the COVID-19 pandemic. Some low- and middle-income countries have highlighted the challenges with COVID-19 testing, such as inadequate capacity, untrained laboratory personnel, and inadequate funding. This article describes the components and implementation of a healthcare worker surveillance programme in a designated COVID-19 teaching hospital in Malaysia. In addition, the distribution and characteristics of healthcare workers placed under surveillance are described. MATERIAL AND METHODS: A COVID-19 healthcare worker surveillance programme was implemented in University Malaya Medical Centre. The programme involved four teams: contact tracing, risk assessment, surveillance and outbreak investigation. Daily symptom surveillance was conducted over fourteen days for healthcare workers who were assessed to have low-, moderate- and high-risk of contracting COVID-19. A cross-sectional analysis was conducted for data collected over 24 weeks, from the 6th of March 2020 to the 20th of August 2020. RESULTS: A total of 1,174 healthcare workers were placed under surveillance. The majority were females (71.6%), aged between 25 and 34 years old (64.7%), were nursing staff (46.9%) and had no comorbidities (88.8%). A total of 70.9% were categorised as low-risk, 25.7% were moderate-risk, and 3.4% were at high risk of contracting COVID-19. One-third (35.2%) were symptomatic, with the sore throat (23.6%), cough (19.8%) and fever (5.0%) being the most commonly reported symptoms. A total of 17 healthcare workers tested positive for COVID-19, with a prevalence of 0.3% among all the healthcare workers. Risk category and presence of symptoms were associated with a positive COVID-19 test (p<0.001). Fever (p<0.001), cough (p = 0.003), shortness of breath (p = 0.015) and sore throat (p = 0.002) were associated with case positivity. CONCLUSION: COVID-19 symptom surveillance and risk-based assessment have merits to be included in a healthcare worker surveillance programme to safeguard the health of the workforce.
Subject(s)
COVID-19 Testing/methods , COVID-19/prevention & control , Disease Outbreaks/prevention & control , Adult , COVID-19/diagnosis , COVID-19 Testing/trends , Comorbidity , Contact Tracing/methods , Cross-Sectional Studies , Epidemiological Monitoring , Female , Health Personnel , Hospitals, Teaching , Humans , Malaysia/epidemiology , Male , Pandemics , SARS-CoV-2/isolation & purificationABSTRACT
The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.
