SLIDE 1
Outline
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- Influenza – a healthcare problem
- How can telephone and digital data help?
- Methods
- Results
- Conclusions
Syndromic Surveillance Predicting and monitoring influenza-like - - PowerPoint PPT Presentation
Syndromic Surveillance Predicting and monitoring influenza-like - - PowerPoint PPT Presentation
Syndromic Surveillance Predicting and monitoring influenza-like illness using telephone and digital data Work by Mica Hartley Presented by Michael Araco Outline Influenza a healthcare problem How can telephone and digital data help?
SLIDE 2
SLIDE 3
What is the problem?
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- Over 18,0001 hospitalisations each year
- 310,000 GP visits 1
- 4,000 deaths
- $115 million in annual healthcare expenditure1
Influenza and influenza related illness is costly
- 1. Newall AT, Scuffham PA. Influenza-related disease: the cost to the Australian healthcare system. Vaccine.
2008 Dec 9;26(52):6818-23.
- 2. NCIRS, AIHW. Influenza and Pneumonia. CDI. Supplement - 2008 June; Vol 32: S18-S20.
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What is the problem?
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Influenza is a seasonal illness* In a temperate climate:
- Peak in the number of cases
- ver winter (anytime between
July and September)
- Low activity during summer
*(in most parts of Australia, excluding NT)
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What is the problem?
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SLIDE 6
Telephone and digital data
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700,000 calls/year 500,000 visits/year
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What is syndromic surveillance?
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Report on syndromes not diagnosed disease
- Influenza like illness (ILI)
Using data from Healthdirect Australia
- healthdirect helpline
- Symptom Checker
Then we can report these data
- For public health action
Research Question:
Does telephone and digital data correlate well with ED, GP, lab and community data sources? Can it predict activity in EDs, GP clinics or labs?
SLIDE 8
Methods
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How can we show that telephone and online data reflects trends in the community?
SLIDE 9
Results
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- Best correlation with emergency departments (range in yearly correlations between
0.92 – 0.99)
- The best correlation mostly in 2009, but also 2012, 2014 and 2015.
- The worst occurred in 2010 and 2013.
SLIDE 10
Results
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Does increased flu activity for the telephone helpline occur at the same time as increased flu activity for the other data sources?
Data Source Timing NSW Emergency Department same time WA Emergency Department same time GP surveillance Same time FluTracker Same time NSW Laboratory Telephone 1 week in advance Australia Laboratory Telephone 1 week in advance
Other data sources also correlated well with both the telephone and online symptom checker (range in yearly correlations between 0.58-0.99)
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Information for the public
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healthdirect.gov.au/flu-trends Both the flu risk meter (above) and the graph (left) show the percentage (%) of flu-related calls. Here’s how to read our flu risk meter:
- Minimal – less than 6 in 100 calls (<6%)
- Low – 6 to 8 in every 100 calls (6-8%)
- Moderate – 8-10 in every 100 calls (8-10%)
- High – 10 to 12 in every 100 calls (10-12%)
- Intense- greater than 12 in every 100 calls
(> 12%)
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Information for experts
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Create a dashboard accessible to stakeholders
- Report to National Influenza
Surveillance Committee
- Create information for public
health action
SLIDE 13
Conclusions
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- Can be used for surveillance of
influenza-like illness
- Could predict activity one week in
advance in laboratories
- Publishing results both on the website
and to a dashboard
- Started with influenza –like illness, but
extendable to other syndromes: eg gastroenteritis, adverse reaction to vaccine, suicidal ideation.
SLIDE 14
Thanks!
Work by Mica Hartley Presented by Michael Araco