Paper presented at 8th National Public Health Conference. Hotel Equatorial, Melaka. 2-4th August 2016

1. Managing Public Health
Data
Taking SDG as an example
8th National Public Health Conference, Hotel Equatorial, Melaka. 2-4th August 2016
Jamalludin Ab Rahman B.Med.Sc., MD, MPH
Department of Community Medicine
Kulliyyah of Medicine

2. What are my points
1. Public health data is a big data
2. We need to change the way we manage data
3. Use SDG as example
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3. Source of public health data
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NCD
CDC
Primary
Healthcare
Health
Enforcement Occupational
Health
Environmental
Health
Nutritional
Health

4. What is big data?
 Literally means a lot of data
 Origin – traced down to 1989, Erik Larson; 1990s, John
Mashey….
 Doug Laney, 2001 defined as 3V – Volume, Velocity &
Variety
 In fact there are more Vs…. Volume, Variety, Velocity,
Variability & Veracity
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http://www.evolutionoftheweb.com/#/growth/day
Data increase exponentially

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7. So what?
 Change the way we capture
& use data
 Change the way we
disseminate & share data
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10. Basically…
 We generated abundant of data
 We need abundant of data (do we?)
 To cater for this changing information architecture
 Let’s look on how to manage those data/info using SDG
as example
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12. In August 2014 UN Secretary-General
Ban Ki-moon asked an Independent
Expert Advisory Group to make
concrete recommendations on
bringing about a data revolution in
sustainable development.
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14. Some examples from SDG
SDG Data from UN
No poverty Monitor income level through spending pattern
Zero hunger Track food security through food price monitoring
Good health & well being Track physical activity from phone
Quality education Student’s drop out rate
Gender equality Spending pattern between gender
Clean water & sanitation Data from sensors connected to pump
Affordable & clean energy Smart metering to gauge use of energy
Decent work & economy growth Global postal pattern
Industry innovation & infrastructure GPS data on public transport
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20. Better data and statistics will help
governments track progress and make sure
their decisions are evidence-based; they can
also strengthen accountability. This is not
just about governments. International
agencies, CSOs and the private sector
should be involved. A true data revolution
would draw on existing and new sources of
data to fully integrate statistics into
decision making, promote open access to,
and use of, data and ensure increased
support for statistical systems.” (HLP
Report, P23)
“
Evidence-based decisions
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21. Challenges
1. Big data – traditional & new sources
2. Information gap – digital divide
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24. The strategy
Information
PeopleTechnology
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25. The strategy
Information
PeopleTechnology
• Most important step!!
• Determine the information
required
• Determine data to collect
• Identify must have from
good to have
• Determine standard to
follow
• Determine network and
storage infrastructure
• Always think of backup &
recovery system
• Acceptance by users
• Simple & attractive
interface
• Delicate migration from
old practice
• Big data approach
• Form the committed
working committee
• Convince stake holders
• Identify the ‘champions’
• Reward & motivation
• Proper & continuous
users training
• Feedback to users &
stake holders
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26. Consider data mining (big data)
 Secondary analysis of large (available) database
 Challenging – not standard data, temporal change,
missing value, invalid entries
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27. Consider data mining (big data)
 Secondary analysis of large (available) database
 Challenging – not standard data, temporal change,
missing value, invalid entries
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Survey Analysis: Big Data Adoption in 2013
Shows Substance Behind the Hype
https://www.gartner.com/doc/2589121

30. Summary
 Healthcare data is big data
 Take note of changing information culture – Web 3.0
 Using SDG as the structure or target checklist
 Use big data technique
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