Are COVID-19 systematic reviews up to date and can we tell? A cross-sectional study.

BACKGROUND: COVID-19 led to a rapid acceleration in the number of systematic reviews. Readers need to know how up to date evidence is when selecting reviews to inform decisions. This cross-sectional study aimed to evaluate how easily the currency of COVID-19 systematic reviews published early in the pandemic could be determined and how up to date these reviews were at the time of publication. METHODS: We searched for systematic reviews and meta-analyses relevant to COVID-19 added to PubMed in July 2020 and January 2021, including any that were first published as preprints. We extracted data on the date of search, number of included studies, and date first published online. For the search date, we noted the format of the date and where in the review this was reported. A sample of non-COVID-19 systematic reviews from November 2020 served as a comparator. RESULTS: We identified 246 systematic reviews on COVID-19. In the abstract of these reviews, just over half (57%) reported the search date (day/month/year or month/year) while 43% failed to report any date. When the full text was considered, the search date was missing from 6% of reviews. The median time from last search to publication online was 91 days (IQR 63-130). Time from search to publication was similar for the subset of 15 rapid or living reviews (92 days) but shorter for the 29 reviews published as preprints (37 days). The median number of studies or publications included per review was 23 (IQR 12-40). In the sample of 290 non-COVID SRs, around two-thirds (65%) reported the search date while a third (34%) did not include any date in the abstract. The median time from search to publication online was 253 days (IQR 153-381) and each review included a median of 12 studies (IQR 8-21). CONCLUSIONS: Despite the context of the pandemic and the need to easily ascertain the currency of systematic reviews, reporting of the search date information for COVID-19 reviews was inadequate. Adherence to reporting guidelines would improve the transparency and usefulness of systematic reviews to users.

Estimated Mask Use and Temporal Relationship to COVID-19 Epidemiology of Black Lives Matter Protests in 12 Cities.

BACKGROUND: There is an increased risk of SARS-CoV-2 transmission during mass gatherings and a risk of asymptomatic infection. We aimed to estimate the use of masks during Black Lives Matter (BLM) protests and whether these protests increased the risk of COVID-19. Two reviewers screened 496 protest images for mask use, with high inter-rater reliability. Protest intensity, use of tear gas, government control measures, and testing rates were estimated in 12 cities. A correlation analysis was conducted to assess the potential effect of mask use and other measures, adjusting for testing rates, on COVID-19 epidemiology 4 weeks (two incubation periods) post-protests. Mask use ranged from 69 to 96% across protests. There was no increase in the incidence of COVID-19 post-protest in 11 cities. After adjusting for testing rates, only Miami, which involved use of tear gas and had high protest intensity, showed a clear increase in COVID-19 after one incubation period post-protest. No significant correlation was found between incidence and protest factors. Our study showed that protests in most cities studied did not increase COVID-19 incidence in 2020, and a high level of mask use was seen. The absence of an epidemic surge within two incubation periods of a protest is indicative that the protests did not have a major influence on epidemic activity, except in Miami. With the globally circulating highly transmissible Alpha, Delta, and Omicron variants, layered interventions such as mandated mask use, physical distancing, testing, and vaccination should be applied for mass gatherings in the future.

Have Deaths of Despair Risen during the COVID-19 Pandemic? A Systematic Review.

This systematic review synthesized literature on potential impacts of protracted isolation and other disruptions during the COVID-19 pandemic on deaths of despair (suicide, overdoses, and drug-related liver diseases). Five electronic databases were searched yielding 70 eligible articles. Extant evidence mostly from high-income countries indicates COVID-19-related disruption may not have influenced suicide rates so far, but there have been reports of increased drug-related and liver disease mortality. Minority groups and women were more vulnerable, indicating the need for stronger equity focus on pandemic recovery and resilience strategies. Further high-quality studies with longer-term follow-up, especially from low-income countries, will inform these strategies.

Social media and COVID-19 vaccination hesitancy: mediating role of the COVID-19 vaccine perception.

Individuals' COVID-19 vaccination behaviors were examined when the government introduced a new vaccine into the immunization program. The purpose of this study is to thoroughly examine the effects of COVID-19 risk perception (CR), COVID-19 vaccination perception (VC), and Social Media (SO) on COVID-19 vaccine hesitancy (HE) in Vietnam. Three hundred fifty samples were collected regarding a reluctance to vaccinate against COVID-19 from 6/2021 to 7/2021. This is when immunizations are administered and injected in Vietnam; hence, hesitation regarding injection is rather prevalent. Multivariate regression analysis is conducted on a dataset of 350 Vietnamese respondents using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. The main results indicated that the Perception Vaccine functions as a link between VC and HE. CR has a positive effect on both HE and VC; whereas VC has a negative impact on HE. Simultaneously, the study illustrates the detrimental effect of SO on immunity by comparing it to the influence of social media. The study's findings also demonstrated the critical role of protection motivational theory (PMT) and information theory in promoting vaccination efforts in Vietnam.

A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry

Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries.

Spherical Fuzzy Decision-Making Approach Integrating Delphi and TOPSIS for Package Tour Provider Selection

The spherical fuzzy sets were recently developed among various fuzzy sets to handle the hesitancy representation issue in multiple criteria decision-making (MCDM) problems, where experts provide information about attributes in the form of spherical fuzzy numbers using linguistic variables. The main purpose of this study is to develop a novel approach integrating Delphi Technique for Order of Preference by Similarity to Ideal Solution based on spherical fuzzy sets (SF-Delphi and SF-TOPSIS). First, the SF-Delphi technique is suggested to derive a valid set of critical criteria based on qualitative information and linguistic preferences. Second, the SF-TOPSIS approach is utilized to rank alternatives based on different spherical fuzzy aggregating operators. Hence, to validate the effectiveness of the proposed methodology, an empirical case study of package tour provider selection is given. Seventeen critical criteria related to four main dimensions (price, service quality, information and technology, and location) were shortlisted and validated from literature and expert opinions. Ten potential package tour providers from Vietnam were ranked in this study. A comparative analysis was conducted to check the proposed methodology’s robustness and validity. The results indicated that the novel SF-Delphi technique may become very helpful for dealing with critical factors, and SF-TOPSIS could be applied to decision problems in uncertain data environments. Furthermore, this research’s findings imply that tour operators should emphasize the most critical attributes to increase the appeal and competitiveness of their package tour products.

A Cross-Country European Efficiency Measurement of Maritime Transport: A Data Envelopment Analysis Approach

Maritime transport, which includes shipping and port operations, is the fundamental basis of international trade and globalization. In transportation management, efficiency is critical for verifying performance and proposing the best countermeasure to meet predetermined goals. Various efforts in this field have been made to solve this problem satisfactorily. However, the significant proportion of conventional approaches are based on long-term observations and professional expertise, with only a few exceptions based on practice-based historical data. Data Envelopment Analysis (DEA) is a non-parametric technique for analyzing various output and input variables parallelly. The efficiency of maritime transport in European countries is explored using a two-stage DEA approach based on Malmquist and Epsilon-Based Measure (EBM). First, the Malmquist model analyses countries’ total productivity growth rates and their breakdown into technical efficiency (catch-up) and technology change (frontier-shift). Second, the EBM model is used to determine the efficiency and inefficiency of the maritime transportation systems in each European country. Apart from identifying the best-performing countries in specific areas over the study period (2016–2019), the results highlight that the gap in applying the EBM method to maritime transport has been successfully closed and that the emerging paradigm, when combined with the Malmquist model, can be a sustainable and appropriate evaluation model for other research areas.

Pacific Eclipse - A tabletop exercise on smallpox pandemic response.

BACKGROUND: In December 2019, we ran Pacific Eclipse, a pandemic tabletop exercise using smallpox originating in Fiji as a case study. Pacific Eclipse brought together international stakeholders from health, defence, law enforcement, emergency management and a range of other organisations. AIM: To review potential gaps in preparedness and identify modifiable factors which could prevent a pandemic or mitigate the impact of a pandemic. METHODS: Pacific Eclipse was held on December 9-10 in Washington DC, Phoenix and Honolulu simultaneously. The scenario began in Fiji and becomes a pandemic. Mathematical modelling of smallpox transmission was used to simulate the epidemic under different conditions and to test the effect of interventions. Live polling, using Poll Everywhere software that participants downloaded onto their smart phones, was used to gather participant decisions as the scenario unfolded. Stakeholders from state and federal government and non-government organisations from The United States, The United Kingdom, Australia, New Zealand, Canada, as well as industry and non-government organisations attended. RESULTS: The scenario progressed in three phases and participants were able to make decisions during each phase using live polling. The polling showed very diverse and sometimes conflicting decision making. Factors influential to pandemic severity were identified and categorised as modifiable or unmodifiable. A series of recommendations were made on the modifiable determinants of pandemic severity and how these can be incorporated into pandemic planning. These included preventing an attack through intelligence, law enforcement and legislation, improved speed of diagnosis, speed and completeness of case finding and case isolation, speed and security of vaccination response (including stockpiling), speed and completeness of contact tracing, protecting critical infrastructure and business continuity, non-pharmaceutical interventions (social distancing, PPE, border control) and protecting first responders. DISCUSSION: Pacific Eclipse illustrated the impact of a pandemic of smallpox under different response scenarios, which were validated to some extent by the COVID-19 pandemic. The framework developed from the scenario draws out modifiable determinants of pandemic severity which can inform pandemic planning for the ongoing COVID-19 pandemic and for future pandemics.

Two-Stage PLS-SEM and Fuzzy AHP Approach to Investigate Vietnamese SMEs’ Export Competitiveness

Vietnam is paying great to the seafood exporting sector, offering various significant production advantages, concluding that it is critical to understand the competitiveness of the target market and implement effective strategies. However, due to COVID 19, the value of Vietnamese pangasius exports resulted in low and unpredictable profits for pangasius farmers. It is obvious to recognize competitiveness as Multi-Criteria Decision Making (MCDM) problem in the uncertain business environment. Therefore, this study is the first to propose a two-staged Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Fuzzy Analytic Hierarchy Process (FAHP) analysis to identify potential criteria and comprehensively investigate the competitiveness of Vietnamese pangasius export Small and Medium-sized Enterprises (SMEs) in the context of China market. First, a dataset of 186 valid respondents from seafood export SMEs was collected through an online survey from June to December 2020. The PLS-SEM approach was applied to ascertain the positive impact of the proposed criteria contributing to the competitiveness of Vietnamese pangasius against China market. The PLS-SEM results showed that all criteria strongly correlated with the pangasius exporting competitiveness. Then, the FAHP method is employed to rank the subjective weights of mentioned criteria based on 12 experts’ judgments. Knowledge infrastructure (C3) has the highest rank of competitiveness criteria with the highest weight, followed by Product (C4). In contrast, Prices (C6) was indicated as the lowest rank at 0.107 regarding FAHP results. Furthermore, this study provides insights into stakeholders seeking to improve competitiveness performance.

Survey data on perceived COVID-19 risk, COVID-19 vaccine perception, and COVID-19 vaccination intention among Vietnamese.

In the context of the COVID-19 response, this study presents an illustrated dataset to examine trust, COVID-19 risk perception, COVID-19 vaccination perception, subject norms, social media and intention to vaccinate among Vietnamese. Our questionnaire was conducted in Vietnamese and then translated into English and distributed to respondents through email and Facebook from June to July 2021, gathering 329 responses. Participation was voluntary, and participants were allowed to withdraw from the survey at any time. Data analysis was carried out using the SPSS 24.0 and Smart PLS 3.0 software packages following data cleansing and coding. The data summarizes respondents' socio-economic and demographic characteristics, and Statistical techniques were deployed to assess the validity and reliability of scales relating to COVID-19 vaccination intention in Vietnam. Additionally, these data will contribute to the existing literature about COVID-19 vaccination perceptions and intention to vaccinate among Vietnamese.

How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews

Delivering high-quality service to passengers can be critical for an airport’s survival, competitiveness, profitability, and long-term growth in a highly competitive environment. The present study aims to examine the relationship between airport service attributes and passenger satisfaction. To this end, we conducted multi-method research consisting of symmetric (multiple regression analysis—MRA) and asymmetric (necessary condition analysis—NCA) approaches. The research data consists of 1463 valid online reviews (n = 1463) of the top 50 busiest airports in Europe retrieved from Skytrax. The MRA was employed to examine the net effect of the eight airport service attributes on passenger satisfaction, while the NCA was used to explore the necessary conditions and level of necessity to achieve passenger satisfaction. Using MRA, the findings reveal that airport staff is the most influential predictor of passenger satisfaction, whereas airport shopping and airport Wi-Fi connectivity do not have a significant effect on passenger satisfaction. Moreover, the NCA results found that six of the eight conditions are necessary to achieve passenger satisfaction at the airport. To complement and comprehend the findings, this study also sheds light on the antecedents underlying airport passenger satisfaction in the post-COVID-19 era using NCA.

COVID-19 outbreaks in aged-care facilities in Australia.

BACKGROUND: Aged-care facilities (ACF's) provide unique challenges when implementing infection control methods for respiratory outbreaks such as COVID-19. Research on this highly vulnerable setting is lacking and there was no national reporting data of COVID-19 cases in ACFs in Australia early in the pandemic. We aimed to estimate the burden of aged-care worker (ACW) infections and outbreaks of COVID-19 in Australian aged-care. METHODS: A line list of publicly available aged-care related COVID-19 reported cases from January 25 to June 10, 2020 was created and was enhanced by matching data extracted from media reports of aged-care related COVID-19 relevant outbreaks and reports. Rate ratios (RR) were used to predict risk of infection in ACW and aged-care residents, and were calculated independently, by comparing overall cases to ACW and aged-care residents' cases. RESULTS: A total of 14 ACFs with COVID-19 cases were recorded by June 2020 nationwide, with a high case fatality rate (CFR) of 50% (n = 34) and 100% (n = 3) seen in two ACFs. Analysis on the resident risk found that the COVID-19 risk is 1.27 times higher (unadjusted RR 1.27 95% confidence interval [CI] 1.00 to1.61; P = 0.047) as compared with the risk of infection in the general population. In over 60% of cases identified in ACFs, the source of infection in the index case was unknown. A total of 28 deaths associated within ACFs were reported, accounting for 54.9% of total deaths in New South Wales and 26.9% of total deaths in Australia. CONCLUSIONS: This high-risk population requires additional prevention and control measures, such as routine testing of all staff and patients regardless of symptoms. Prompt isolation and quarantine as soon as a case is confirmed within a facility is essential.

A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19

This study aims to identify the key factors affecting individuals’behavioral vaccination intention against COVID-19 in Vietnam through an online questionnaire survey. Differing from previous studies, a novel three-staged approach combining Spherical Fuzzy Analytic Hierarchy Process (SF-AHP), Partial Least Squares-Structural Equation Model (PLS-SEM), and Artificial Neural Network (ANN) is proposed. Five factors associated with individuals’behavioral vaccination intention (INT) based on 15 experts’opinions are considered in SF-AHP analysis, including Perceived Severity of COVID-19 (PSC), Perceived COVID-19 vaccines (PVC), Trust in government intervention strategies (TRS), Social Influence (SOI), and Social media (SOM). First, the results of SF-AHP indicated that all proposed factors correlate with INT. Second, the data of 474 valid respondents were collected and analyzed using PLS-SEM. The PLS-SEM results reported that INT was directly influenced by PVC and TRS. In contrast, SOI had no direct effect on INT. Further, PSC and SOM moderated the relationship between PVC, TRS and INT, respectively. The ANN was deployed to validate the previous stages and found that the best predictors of COVID-19 vaccination intention were PVC, TRS, and SOM. These results were consistent with the SF-AHP and PLS-SEM models. This research provides an innovative new approach employing quantitative and qualitative techniques to understand individuals’vaccination intention during the global pandemic. Furthermore, the proposed method can be used and expanded to assess the perceived efficacy of COVID-19 measures in other nations currently battling the COVID-19 outbreak.

A Hybrid Spherical Fuzzy MCDM Approach to Prioritize Governmental Intervention Strategies against the COVID-19 Pandemic: A Case Study from Vietnam

The unprecedented coronavirus pandemic (COVID-19) is fluctuating worldwide. Since the COVID-19 epidemic has a negative impact on all countries and has become a significant threat, it is necessary to determine the most effective strategy for governments by considering a variety of criteria;however, few studies in the literature can assist governments in this topic. Selective governmental intervention during the COVID-19 outbreak is considered a Multi-Criteria Decision-Making (MCDM) problem under a vague and uncertain environment when governments and medical communities adjust their priorities in response to rising issues and the efficacy of interventions applied in various nations. In this study, a novel hybrid Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS-F) model is proposed to help stakeholders such as governors and policymakers to prioritize governmental interventions for dealing with the COVID-19 outbreak. The SF-AHP is implemented to measure the significance of the criteria, while the WASPAS-F approach is deployed to rank intervention alternatives. An empirical case study is conducted in Vietnam. From the SF-AHP findings, the criteria of “effectiveness in preventing the spread of COVID-19”, “ease of implementation”, and “high acceptability to citizens” were recognized as the most important criteria. As for the ranking of strategies, “vaccinations”, “enhanced control of the country’s health resources”, “common health testing”, “formation of an emergency response team”, and “quarantining patients and those suspected of infection” are the top five strategies. Aside from that, the robustness of the approach was tested by performing a comparative analysis. The results illustrate that the applied methods reach the general best strategy rankings. The applied methodology and its analysis will provide insight to authorities for fighting against the severe pandemic in the long run. It may aid in solving many complicated challenges in government strategy selection and assessment. It is also a flexible design model for considering the evaluation criteria. Finally, this research provides valuable guidance for policymakers in other nations.

Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam

The Coronavirus (COVID-19) pandemic has had a significant impact on most countries’ social and economic perspectives worldwide. Unemployment has become a vital challenge for policymakers as a result of COVID-19′s negative impact. Because of the nonstationary and nonlinear nature of the dataset, researchers applied various time series models to forecast the unemployment rate. This study aims to ensure a better forecasting approach for predicting the unemployment rates with an uncertainty of insufficient knowledge and tiny data throughout Vietnam. The study proposes the Grey theory system-based GM (1,1), the Grey Verhulst Model (GVM), and the Autoregressive Integrated Moving Average (ARIMA) model that can more precisely predict unemployment rates. The model’s applications are shown using the Vietnamese unemployment rate at six different rural and urban areas with data sets from 2014–2019. The results indicate that the lower Mean Average Percentage Error (MAPE) values obtained with the GM (1,1) model at all regions for rural and urban areas (excluding Highlands Region in urban area) are extremely encouraging in comparison to other traditional methods. The accurate level of the ARIMA and GVM models follows the GM (1,1) model. The findings of this study show that the effects of the modeling assist policymakers in shaping future labor and economic policies. Furthermore, this study can contribute to the unemployment literature, providing future research directions in the unemployment problems.

Mask use, risk-mitigation behaviours and pandemic fatigue during the COVID-19 pandemic in five cities in Australia, the UK and USA: A cross-sectional survey.

OBJECTIVES: To determine patterns of mask wearing and other infection prevention behaviours, over two time periods of the COVID-19 pandemic, in cities where mask wearing was not a cultural norm. METHODS: A cross-sectional survey of masks and other preventive behaviours in adults aged ≥18 years was conducted in five cities: Sydney and Melbourne, Australia; London, UK; and Phoenix and New York, USA. Data were analysed according to the epidemiology of COVID-19, mask mandates and a range of predictors of mask wearing. RESULTS: The most common measures used were avoiding public areas (80.4%), hand hygiene (76.4%), wearing masks (71.8%) and distancing (67.6%). Over 40% of people avoided medical facilities. These measures decreased from March-July 2020. Pandemic fatigue was associated with younger age, low perceived severity of COVID-19 and declining COVID-19 prevalence. Predictors of mask wearing were location (US, UK), mandates, age <50 years, education, having symptoms and knowing someone with COVID-19. Negative experiences with mask wearing and low perceived severity of COVID-19 reduced mask wearing. Most respondents (98%) believed that hand washing and distancing were necessary, and 80% reported no change or stricter adherence to these measures when wearing masks. CONCLUSION: Pandemic mitigation measures were widely reported across all cities, but decreased between March and July 2020. Pandemic fatigue was more common in younger people. Cities with mandates had higher rates of mask wearing. Promotion of mask use for older people may be useful. Masks did not result in a reduction of other hygiene measures.

Estimating the burden of COVID-19 on the Australian healthcare workers and health system during the first six months of the pandemic.

INTRODUCTION: There are no publicly available national data on healthcare worker infections in Australia. It has been documented in many countries that healthcare workers (HCW) are at increased occupational risk of COVID-19. We aimed to estimate the burden of COVID-19 on Australia HCW and the health system by obtaining and organizing data on HCW infections, analyzing national HCW cases in regards to occupational risk and analyzing healthcare outbreak. METHODS: We searched government reports and websites and media reports to create a comprehensive line listing of Australian HCW infections and nosocomial outbreaks between January 25th and July 8th, 2020. A line list of HCW related COVID-19 reported cases was created and enhanced by matching data extracted from media reports of healthcare related COVID-19 relevant outbreaks and reports, using matching criteria. Rates of infections and odds ratios (ORs) for HCW were calculated per state, by comparing overall cases to HCW cases. To investigate the sources of infection amongst HCW, transmission data were collated and graphed to show distribution of sources. RESULTS: We identified 36 hospital outbreaks or HCW infection reports between January 25th and July 8th, 2020. According to our estimates, at least 536 HCW in Australia had been infected with COVID-19, comprising 6.03% of all reported infections. The rate of HCW infection was 90/100000 and of community infection 34/100,000. HCW were 2.69 times more likely to contract COVID-19 (95% CI 2.48 to 2.93; P < 0.001). The timing of hospital outbreaks did not always correspond to community peaks. Where data were available, a total of 131 HCW across 21 outbreaks led to 1656 HCW being furloughed for quarantine. In one outbreak, one hospital was closed and 1200 HCW quarantined. CONCLUSION: The study shows that HCW were at nearly 3 times the risk of infection. Of concern, this nearly tripling of risk occurred during a period of low community prevalence suggesting failures at multiple hazard levels including PPE policies within the work environment. Even in a country with relatively good control of COVID-19, HCW are at greater risk of infection than the general community and nosocomial outbreaks can have substantial effects on workforce capacity by the quarantine of numerous HCW during an outbreak. The occurrence of hospital outbreaks even when community incidence was low highlights the high risk setting that hospitals present. Australia faced a resurgence of COVID-19 after the study period, with multiple hospital outbreaks. We recommend formal reporting of HCW infections, testing protocols for nosocomial outbreaks, cohorting of workforce to minimize the impact, and improved PPE guidelines to provide precautionary and optimal protection for HCW.

Knowledge, Attitude, and Practices Regarding COVID-19 Among Chronic Illness Patients at Outpatient Departments in Ho Chi Minh City, Vietnam.

BACKGROUND: The novel coronavirus disease (COVID-19) has become a major threat to human life around the world. This study aims to assess the knowledge, attitude, and practices regarding COVID-19 among people with chronic diseases at the outpatient departments in Ho Chi Minh City. METHODS: A cross-sectional study was carried out between February and March 2020 using a convenience sampling strategy in three hospitals in Ho Chi Minh City (HCMC) via the use of a structured self-administered questionnaire. Factors relating to practices, prevalence ratio (PR), and 95% confidence interval were estimated by using the Poisson regression with robust options. P-value <0.05 was considered as statistically different. RESULTS: A total of 522 participants had a mean age of 51.5 ± 10.6 years. Most of them reported seeing information regarding the COVID-19 pandemic (93.7%) via television and social media (72.8% and 62.1%, respectively). Just over two-thirds of the participants (68.4%) answered with sufficient knowledge of COVID-19. Most respondents had a positive attitude toward COVID-19 (90.8%), although some misconceptions existed. Almost over three-fourths of them (77.2%) maintained good practices for prevention. The rate of good practices in those who had sufficient knowledge was 1.24 times greater than that among those who had insufficient knowledge (PR 1.24, 95% CI: 1.10-1.41, P<0.05). Also, the rate of good practices in males was lower than that of females (PR: 0.91, 95% CI: 0.83-0.99, P<0.05). CONCLUSION: There still exists an amount of insufficient knowledge and negative attitude regarding COVID-19, which may be barriers to good prevention practices among chronic illness patients. Education programs need to continue via television and social media and emphasize that people with chronic diseases are more likely to experience severe symptoms, including death from COVID-19. Additionally, management authorities should prolong specific policies to protect the more vulnerable in our community.