Tool for tracking all-cause mortality and estimating excess mortality to support the COVID-19 pandemic response

Problem@#Quantifying mortality from coronavirus disease (COVID-19) is difficult, especially in countries with limited resources. Comparing mortality data between countries is also challenging, owing to differences in methods for reporting mortality.@*Context@#Tracking all-cause mortality (ACM) and comparing it with expected ACM from pre-pandemic data can provide an estimate of the overall burden of mortality related to the COVID-19 pandemic and support public health decision-making. This study validated an ACM calculator to estimate excess mortality during the COVID-19 pandemic.@*Action@#The ACM calculator was developed as a tool for computing expected ACM and excess mortality at national and subnational levels. It was developed using R statistical software, was based on a previously described model that used non-parametric negative binomial regression and was piloted in several countries. Goodness-of-fit was validated by forecasting 2019 mortality from 2015–2018 data.@*Outcome@#Three key lessons were identified from piloting the tool: using the calculator to compare reported provisional ACM with expected ACM can avoid potential false conclusions from comparing with historical averages alone; using disaggregated data at the subnational level can detect excess mortality by avoiding dilution of total numbers at the national level; and interpretation of results should consider system-related performance indicators.@*Discussion@#Timely tracking of ACM to estimate excess mortality is important for the response to COVID-19. The calculator can provide countries with a way to analyse and visualize ACM and excess mortality at national and subnational levels.

Use of Epidemic Intelligence from Open Sources for global event-based surveillance of infectious diseases for the Tokyo 2020 Olympic and Paralympic Games

@#The establishment of enhanced surveillance systems for mass gatherings to detect infectious diseases that may be imported during an event is recommended. The World Health Organization Regional Office for the Western Pacific contributed to enhanced event-based surveillance for the Tokyo 2020 Olympic and Paralympic Games (the Games) by using Epidemic Intelligence from Open Sources (EIOS) to detect potential imported diseases and report them to the National Institute of Infectious Diseases (NIID), Japan. Daily screening of media articles on global infectious diseases was conducted using EIOS, which were systematically assessed to determine the likelihood of disease importation, spread and significant impact to Japan during the Games. Over 81 days of surveillance, 103 830 articles were screened by EIOS, of which 5441 (5.2%) met the selection criteria for initial assessment, with 587 (0.6%) assessed as signals and reported to NIID. None of the signals were considered to pose a significant risk to the Games based on three risk assessment criteria. While EIOS successfully captured media articles on infectious diseases with a likelihood of importation to and spread in Japan, a significant manual effort was required to assess the articles for duplicates and against the risk assessment criteria. Continued improvement of artificial intelligence is recommended to reduce this effort.

An outbreak investigation of paediatric severe acute respiratory infections requiring admission to intensive care units – Fiji, May 2016

Introduction@#Influenza-associated severe acute respiratory infections (SARI) are a major contributor to global morbidity and mortality. In response to a cluster of SARI cases and deaths in pregnant women, with two deceased cases testing positive for influenza A(H1N1)pdm09, an investigation was initiated to determine whether there was an increase of paediatric SARI cases admitted to divisional hospital intensive care units in Fiji in may 2016 compared to May 2013–2015.@*Methods@#Retrospective case finding was conducted at the paediatric intensive care units (PICUs) in Fiji’s three divisional hospitals. Data were collected from 1 January 2013 to 26 May 2016. Cases were identified using a list of clinical diagnoses compatible with SARI. Results: A total of 632 cases of paediatric SARI with complete details were identified. The median age of cases was 6 months (Interquartile range: 2–14 months). Children aged less than 5 years had a higher rate of paediatric SARI requiring admission to a divisional hospital PICU in May 2016 compared to May 2013–2015 (Incidence rate ratio: 1.7 [95% CI: 1.1–2.6]). This increase was not observed in children aged 5–14 years. The case-fatality ratio was not significantly different in 2016 compared to previous years.@*Conclusion@#The investigation enabled targeted public health response measures, including enhanced SARI surveillance at divisional hospitals and an emergency influenza vaccination campaign in the Northern Division.

Lessons learnt from a three-year pilot field epidemiology training programme

Problem: The Pacific region has widely dispersed populations, limited financial and human resources and a high burden of disease. There is an urgent need to improve the availability, reliability and timeliness of useable health data. Context: The purpose of this paper is to share lessons learnt from a three-year pilot field epidemiology training programme that was designed to respond to these Pacific health challenges. The pilot programme built on and further developed an existing field epidemiology training programme for Pacific health staff. Action: The programme was delivered in country by epidemiologists working for Pacific Public Health Surveillance Network partners. The programme consisted of five courses: four one-week classroom-based courses and one field epidemiology project. Sessions were structured so that theoretical understanding was achieved through interaction and reinforced through practical hands-on group activities, case studies and other interactive practical learning methods. Outcome: As of September 2016, 258 students had commenced the programme. Twenty-six course workshops were delivered and one cohort of students had completed the full five-course programme. The programme proved popular and gained a high level of student engagement. Discussion: Face-to-face delivery, a low student-to-facilitator ratio, substantial group work and practical exercises were identified as key factors that contributed to the students developing skills and confidence. Close engagement of leaders and the need to quickly evaluate and adapt the curriculum were important lessons, and the collaboration between external partners was considered important for promoting a harmonized approach to health needs in the Pacific.

Patient Waiting Time in GOPD and SOPD at Nadi Hospital 2012

Patient waiting time is a real problem in Fiji and unfortunately Nadi Sub-divisional hospital is no exception. The objective of this study was to determine patient waiting time in the GOPD and SOPD clinic in the Nadi Sub-divisional Hospital, additionally to gauge patient satisfaction. Time logs were given to patients in order to log the time as the patient went through the diffrent management and investigation. Secondly a questionnaire was given to every 20th patient to measure their satisfaction with the services they received in GOPD and SOPD. The results showed that it took 2 hours and 57 minutes on average for a patient to be attended by a doctor in the GOPD clinic and 2 hours and 7 minutes in the SOPD clinic. 50% of all patients sampled believed that there should be more doctors and 36% complained of the shortage of drugs at the pharmacy. Any improvement to the quality of health care delivery in the Nadi Sub-Divisional hospital needs to include an increase in doctors as well as addressing how to prevent common drug shortages in the pharmacy department.