PURPOSE Vietnam is undergoing rapid socio-economic transition with an increasing cancer burden. The contribution of modifiable risk factors to cancers in Vietnam has not been studied. Therefore, we sought to evaluate the attributable causes of cancer in Vietnam. METHODS We reviewed the data on burden of cancer in Vietnam from 2 cancer registries in Hanoi and Ho Chi Minh City between 1995 and 2012. Next, we calculated the fractions of cancers occurring in 2018 attributable to established modifiable risk factors whose impact could be quantified. Data on exposure prevalence were obtained for the period from 2000 to 2010 from national sources wherever possible. RESULTS Cancer incidence in Vietnam has decreased slightly in both sexes. Cancer related to infectious agents decreased sharply, whereas cancer related to nutrition and metabolism has increased. In 2018, established carcinogens included in the analysis explained 47.0% of cancer burden in Vietnam. Chronic infections accounted for 29.1% of cancers (34.7% in men and 22.1% in women), tobacco smoking for 13.5% (23.9% in men and 0.8% in women), and alcohol drinking for 10.3%. Passive smoking was responsible for 8.8% of cancers in women. Other risk factors, including overweight or obesity, nulliparity, and low vegetable and fruit intake, accounted for < 1% of all cancers each. CONCLUSION Cancer incidence is slowly decreasing in Vietnam, and the causes of more than half of cancers remain unexplained. This result underlines the need for further epidemiologic and fundamental research. Our findings confirm the notion that controlling oncogenic infections and decreasing tobacco smoking are the most effective approaches to reduce the burden of cancer in Vietnam, but other risk factors, including alcohol drinking and diet, should not be neglected.

Emerging from early of 2020, the COVID-19 pandemic has become one of the most serious health crisis globally. In response to such threat, a wide range of digital health applications has been deployed in Vietnam to strengthen surveillance, risk communication, diagnosis, and treatment of COVID-19. Digital health has brought enormous benefits to the fight against COVID-19, however, numerous constrains in digital health application remain. Lack of strong governance of digital health development and deployment; insufficient infrastructure and staff capacity for digital health application are among the main drawbacks. Despite several outstanding problems, digital health is expected to contribute to reducing the spread, improving the effectiveness of pandemic control, and adding to the dramatic transformation of the health system the post-COVID era.

Objective To estimate the incubation period of Vietnamese confirmed COVID-19 cases. Methods Only confirmed COVID-19 cases who are Vietnamese and locally infected with available data on date of symptom onset and clearly defined window of possible SARS-CoV-2 exposure were included. We used three parametric forms with Hamiltonian Monte Carlo method for Bayesian Inference to estimate incubation period for Vietnamese COVID-19 cases. Leave-one-out Information Criterion was used to assess the performance of three models. Results A total of 19 cases identified from 23 Jan 2020 to 13 April 2020 was included in our analysis. Average incubation periods estimated using different distribution model ranged from 6.0 days to 6.4 days with the Weibull distribution demonstrated the best fit to the data. The estimated mean of incubation period using Weibull distribution model was 6.4 days (95% credible interval (CrI): 4.89–8.5), standard deviation (SD) was 3.05 (95%CrI 3.05–5.30), median was 5.6, ranges from 1.35 to 13.04 days (2.5th to 97.5th percentiles). Extreme estimation of incubation periods is within 14 days from possible infection. Conclusion This analysis provides evidence for an average incubation period for COVID-19 of approximately 6.4 days. Our findings support existing guidelines for 14 days of quarantine of persons potentially exposed to SARS-CoV-2. Although for extreme cases, the quarantine period should be extended up to three weeks.

SummaryBackgroundNanocovax is a recombinant severe acute respiratory syndrome coronavirus 2 subunit vaccine composed of full-length prefusion stabilized recombinant SARS-CoV-2 spike glycoproteins (S-2P) and aluminum hydroxide adjuvant. In a Phase 1 and 2 studies, (NCT04683484) the vaccine was found to be safe and induce a robust immune response in healthy adult participants.MethodsWe conducted a multicenter, randomized, double-blind, placebo-controlled study to evaluate the safety, immunogenicity, and protective efficacy of the Nanocovax vaccine against Covid-19 in approximately 13,007 volunteers aged 18 years and over. The immunogenicity was assessed based on Anti-S IgG antibody response, surrogate virus neutralization, wild-type SARS-CoV-2 neutralization and the types of helper T-cell response by intracellular staining (ICS) for interferon gamma (IFNg) and interleukin-4 (IL-4). The vaccine efficacy (VE) was calculated basing on serologically confirmed cases of Covid-19.FindingsUp to day 180, incidences of solicited and unsolicited adverse events (AE) were similar between vaccine and placebo groups. 100 serious adverse events (SAE) were observed in both vaccine and placebo groups (out of total 13007 participants). 96 out of these 100 SAEs were determined to be unrelated to the investigational products. 4 SAEs were possibly related, as determined by the Data and Safety Monitoring Board (DSMB) and investigators. Reactogenicity was absent or mild in the majority of participants and of short duration. These findings highlight the excellent safety profile of Nanocovax.Regarding immunogenicity, Nanocovax induced robust IgG and neutralizing antibody responses. Importantly, Anti S-IgG levels and neutralizing antibody titers on day 42 were higher than those of natural infected cases. Nanocovax was found to induce Th2 polarization rather than Th1.Post-hoc analysis showed that the VE against symptomatic disease was 51.5% (95% confidence interval [CI] was [34.4%-64.1%]. VE against severe illness and death were 93.3% [62.2-98.1]. Notably, the dominant strain during the period of this study was Delta variant.InterpretationNanocovax 25 microgram (mcg) was found to be safe with the efficacy against symptomatic infection of Delta variant of 51.5%.FundingResearch was funded by Nanogen Pharmaceutical Biotechnology JSC., and the Ministry of Science and Technology of Vietnam; ClinicalTrials.gov number, NCT04922788.

Background Aneurysmal subarachnoid haemorrhage (aSAH) is a serious form of stroke, for which rapid access to specialist neurocritical care is associated with better outcomes. Delays in the treatment of aSAH appears to be common and may contribute to poor outcomes. We have a limited understanding of the extent and causes of these delays, which hinders the development of interventions to reduce delays and improve outcomes. The aim of this systematic review was to quantify and identify factors associated with time to treatment in aSAH. Methods This systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and was registered in PROSPERO (Reg No. CRD42019132748). We searched four electronic databases databases (MEDLINE, EMBASE, Web of Science, and Google Scholar) for manuscripts published from January 1998 using pre-designated search terms and search strategy. Main outcomes were duration of delays of time intervals from onset of aSAH to definitive treatment and/or factors related to time to treatment. Results A total of 64 studies with 16 different time intervals in the pathway of aSAH patients were identified. Measures of time to treatment varied between studies (e.g. cut-off timepoints or absolute mean/median duration). Factors associated with time to treatment fell into two categories – individual (n=9 factors e.g. age, sex, clinical characteristics) and health system (n=8 factors, e.g. pre-hospital delay or presentation out-of-hours). Demographic factors were not associated with time to treatment. More severe aSAH reduced treatment delay in most studies. Pre-hospital delays (patients delay, late referral, late arrival of ambulance, being transferred between hospitals or arriving at the hospital outside of office hours) were associated with treatment delay. In-hospital factors (patients with complications, procedure before definitive treatment, slow work-up, type of treatment) were less associated with treatment delay. Conclusions The pathway from onset to definitive treatment of a patients with aSAH consists of multiple stages with multiple influencing factors. This review provides the first comprehensive understanding of extent and factors associated with time to treatment of aSAH. There is an opportunity to target modifiable factors to reduce time to treatment but further research considering more factors are needed.