Reflections on public health surveillance of pandemic (H1N1) 2009 influenza in NSW

Monitoring the incidence and causes of diseases potentially transmitted by food in Australia: Annual report of the OzFoodNet network, 2013-2015

Abstract

This report summarises the incidence of diseases potentially transmitted by food in Australia, and details outbreaks associated with food that occurred during 2013-2015. OzFoodNet sites reported an increasing number of notifications of 12 diseases or conditions vthat may be transmitted by food (botulism; campylobacteriosis; cholera; hepatitis A; hepatitis E; haemolytic uraemic syndrome (HUS); listeriosis; Salmonella Paratyphi (paratyphoid fever) infection; salmonellosis; shigellosis; Shiga toxin-producing Escherichia coli (STEC) infection; and Salmonella Typhi (typhoid fever) infection), with a total of 28,676 notifications received in 2013; 37,958 in 2014; and 41,226 in 2015. The most commonly-notified conditions were campylobacteriosis (a mean of 19,061 notifications per year over 2013-2015) and salmonellosis (a mean of 15,336 notifications per year over 2013-2015). Over these three years, OzFoodNet sites also reported 512 outbreaks of gastrointestinal illness caused by foodborne, animal-to-person or waterborne disease, affecting 7,877 people, and resulting in 735 hospitalisations and 18 associated deaths. The majority of outbreaks (452/512; 88%) were due to foodborne or suspected foodborne transmission. The remaining 12% of outbreaks were due to waterborne or suspected waterborne transmission (57 outbreaks) and animal-to-human transmission (three outbreaks). Foodborne and suspected foodborne outbreaks affected 7,361 people, resulting in 705 hospitalisations and 18 deaths. Salmonella was the most common aetiological agent identified in foodborne outbreaks (239/452; 53%), and restaurants were the most frequently-reported food preparation setting (211/452; 47%). There were 213 foodborne outbreaks (47%) attributed to a single food commodity during 2013-2015, with 58% (124/213) associated with the consumption of eggs and egg-based dishes.

The Poison Center as a pandemic response: establishment and characteristics of a COVID-19 hotline through the New Jersey Poison Center

BACKGROUND

Poison Centers are uniquely positioned to respond to an unprecedented public health threat such as the COVID-19 pandemic, as fully operational 24-h hotlines already staffed with healthcare professionals.

METHODS

On January 27, 2020 the New Jersey Poison Information and Education System (NJPIES) agreed to operate the New Jersey Coronavirus Hotline. Call patterns, subject matter, and staffing and infrastructure strategies that were implemented to meet the demand are described. In addition, a sample of 1500 individual calls were collected and analyzed in an endeavor to describe call times, call days, area from which the call originated, callers to the hotline, primary language of the caller, and why a call was placed to the hotline. Binomial regression analysis was utilized in an attempt to identify significant patterns.

RESULTS

Since the inception of the hotline through October 31, NJPIES responded to 57,579 calls for COVID-19 information. Most calls (68.7%) were regarding testing for COVID-19 and for general questions/symptoms. Call types varied when they were analyzed by time of day with calls for general questions/symptoms and where to get tested for COVID-19 showing a significant association for the early morning hours, how to obtain test results being significantly associated with the afternoon hours, and how to renew or obtain a medical license showing a significant association to the evening hours. We additionally noted that specific call types became significant when analyzed on a week-to-week basis and as specific events, like the enactment of the CARES Act of 2020, occurred.

CONCLUSION

Although not the traditional role of a regional Poison Control Center, pandemic response synergizes with the workflow of this hotline because the infrastructure, staffing, and healthcare expertise are already present. Poison centers can rapidly adapt through scaling and process change to meet the needs of the public during times of public health threats.

Pandemic clinical case definitions are non-specific: multiple respiratory viruses circulating in the early phases of the 2009 influenza pandemic in New South Wales, Australia

Background

During the early phases of the 2009 pandemic, subjects with influenza-like illness only had laboratory testing specific for the new A(H1N1)pdm09 virus.

Findings

Between 25^th May and 7^th June 2009, during the pandemic CONTAIN phase, A(H1N1)pdm09 virus was detected using nucleic acid tests in only 56 of 1466 (3.8%) samples meeting the clinical case definition required for A(H1N1)pdm09 testing. Two hundred and fifty-five randomly selected A(H1N1)pdm09 virus-negative samples were tested for other respiratory viruses using a real-time multiplex PCR assay. Of the 255 samples tested, 113 (44.3%) had other respiratory viruses detected: rhinoviruses 63.7%, seasonal influenza A 17.6%, respiratory syncytial virus 7.9%, human metapneumovirus 5.3%, parainfluenzaviruses 4.4%, influenza B virus 4.4%, and enteroviruses 0.8%. Viral co-infections were present in 4.3% of samples.

Conclusions

In the very early stages of a new pandemic, limiting testing to only the novel virus will miss other clinically important co-circulating respiratory pathogens.

Airport arrivals screening during pandemic (H1N1) 2009 influenza in New South Wales, Australia

OBJECTIVE

To examine the effectiveness of airport screening in New South Wales during pandemic (H1N1) 2009 influenza.

DESIGN, SETTING AND PARTICIPANTS

Analysis of data collected at clinics held at Sydney Airport, and of all notified cases of influenza A(H1N1)pdm09, between 28 April 2009 and 18 June 2009.

MAIN OUTCOME MEASURES

Case detection rate per 100,000 passengers screened, sensitivity, positive predictive value and specificity of airport screening. The proportion of all cases in the period detected at airport clinics was compared with the proportion detected in emergency departments and general practice.

RESULTS

Of an estimated 625,147 passenger arrivals at Sydney Airport during the period, 5845 (0.93%) were identified as being symptomatic or febrile, and three of 5845 were subsequently confirmed to have influenza A(H1N1)pdm09, resulting in a detection rate of 0.05 per 10,000 screened (95% CI, 0.02-1.14 per 10,000). Forty-five patients with overseas-acquired influenza A(H1N1)pdm09 in NSW would have probably passed through the airport during this time, giving airport screening a sensitivity of 6.67% (95% CI, 1.40%-18.27%). Positive predictive value was 0.05% (95% CI, 0.02%-0.15%) and specificity 99.10% (95% CI, 99.00%-100.00%). Of the 557 confirmed cases across NSW during the period, 290 (52.1%) were detected at emergency departments and 135 (24.2%) at general practices, compared with three (0.5%) detected at the airport.

CONCLUSIONS

Airport screening was ineffective in detecting cases of influenza A(H1N1)pdm09 in NSW. Its future use should be carefully considered against potentially more effective interventions, such as contact tracing in the community.

Prediction and surveillance of influenza epidemics

OBJECTIVE

To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza.

METHODS

We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population.

RESULTS

All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available.

CONCLUSION

This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.