dr tony florio - seslhd mental health service & unsw school of psychiatry - data linkage between...

Data Linkage Between Intellectual Disability & Mental Health:

Current Research and a Proposal for Integrating Data Systems

between Health and the NDIS

Dr Tony Florio, Clinical Coordinator Intellectual Disability & Co Existing Mental Illness,

Senior Clinical Psychologist South Eastern Sydney Local Health District Mental Health Service

Honorary Clinical Associate,

Associate Investigator Department of Developmental Disability Neuropsychiatry

School of Psychiatry, UNSW

[email protected]

Department of Developmental Disability Neuropsychiatry

(02) 9931 9160 [email protected] 3dn.unsw.edu.au www.facebook.com/3dn.unsw

Data Data Everywhere

• Data of all kinds is being created, stored and accumulated at ever

increasing rates.

• A lot of data is person centred

• Disability Services, Mental Health Services and health services all

currently collect and store data about people and services

• The NDIS will collect and store data about people and services

• NDIS funded providers will collect and store data about people and

services

• Private practitioners will collect and store data about people and

services

• People collect and store information about themselves (e.g.

Facebook)

http://www.facebook.com/3dn.unsw



Data Segregation

• We segregate people with a disability

• The new National Disability Scheme will from its onset be a segregated service

• Because we choose to segregate services, data about people who access more than one service is also segregated

• In the future when the same person interacts with disability services and mental health services then data about that person and services is independently collected and stored by two or more organisations




Parallel Data Streams

• For disability services and mental health services (and other health

services), standardised minimum datasets are currently collated at

– Local

– State

– National

• creating a set of two parallel data streams.

• One person, a person with a disability, can have data about them and

the services they received in multiple parallel service and data

streams.

• This “separate streams” quality of the data about one individual is a

direct reflection of (and is caused by) the organisational segregation of

services into separate streams.

• Using the double slits experiment in physics as a metaphor, its as if

the one person is in two services at once (and they are). But in that

experiment that causes a pattern of interference.




Collaboration?

• Disability Services and Mental Health Services are increasingly become collaborative with clients and carers

• The usually goes under the rubric of being person centred or client focused

• Services are not yet collaborating with each other.

• We do not yet have a concept of person centredness that transcends organisations. Apparently organisations can be person centred, but the “service system” as a whole cannot.

• Data reflects this reality.

– Data is segregated, so are services

– Data exists in independent streams, so do services

– Data streams go their separate ways in parallel and so do services

– Data streams don’t come together collaboratively and neither do services




Data as a Cause and as an Effect

• An effect of separate service streams is the

generation of separate data streams

• Having separate data streams helps to

perpetuate separation and non-collaboration into

the future

• Better collaboration can be fostered by merging

data streams and creating a common stream

• Making previously siloed information available to

multiple services and to clients and carers is a

key to collaboration

• Its not the only key, separate jargons, different

priorities and competition for funds are also

factors

Linked Data

Collaboration

Linear root cause model

Non-linear reciprocal cause model




Means and Issues

• The means for merging separate data streams is to use data linkage technology

• The main issues that needs to be considered are privacy and consent

• In Scandinavian countries data is unified by linkage across all services

• Concept of “data donation” of private data, for the greater good. UK

• Concept of giving data as political lobbying, “being counted” US




Data Linkage

• Data exists in records in data sets stored on

electronic databases

• Each record stores information about one entity

• In the kinds datasets maintained by services,

persons are the key entity

• Records from two or more separate data sets

can be linked if they have a common set of

unique information.

• In the case of person oriented data sets, unique

person identifiers are required




WA IDMH Data Linkage

• Linked WA Psychiatric (n = 236,973) and Intellectual Disability Registers (n= 11,576) (total n = 245,749)

• Found that 4,221 were on both registers.

• 31.7% of the Intellectual Disability register were also on the Psychiatric register and that 1.8% of the Psychiatric Register were on the Intellectual Disability register

• Depending upon birth cohort 3.7 to 5.2% of those with an Intellectual Disability had co-occuring schizophrenia

• Those on both registers compared to those on the psychiatric register only:

– Younger at time of first contact with psychiatric services

– Had a greater number of psychiatric in-patient admissions

– spent longer in hospital

– Depending upon birth cohort had a 2.3 or 2.3 mortality ratio

• Autism was more common among those on both registers compared to ID register alone




NSW IDMH Data Linkage

• Linked NSW state disability register (ID only) to a geographic subset (36.7% by population) of the NSW state community mental health dataset

• Limited to people who received a service on either register 2005 to 2011

• Of the 89,262 people in the mental health dataset, 1.6% (n = 1,459) were also on the ID register.

• Compared to those without ID, people with an ID were:

– more likely to be receiving treatment for chronic psychotic and personality disorders

– less likely to be receiving treatment for major depression and adjustment disorder

– had 1.5 times more face-to-face contacts

– each contacts on average was 2.2 times as long.

J Trollor, T Florio, S Howlett & H Xu




NSW Planned linkages

• In the process of accessing linked records to

– NSW Admitted patients data collection (health and mental health)

– NSW Emergency Department data collection

– NSW Community Mental Health data

– NSW Mortality data

• Linking to both disability (ID only) and Mental Health datasets

• look at how these elements of the service system interact with each

other

• Look at how those with an intellectual disability are different

• Use of Persons-Time-Events modelling to sort out time effects and

cohort effects

• Our data is 2005 to 2012 (+ yearly refresh), so will capture a Pre-

NDIS baseline against which to measure how the NDIS interacts with

all this.




Unique identification and privacy

• Privacy is protected by not disseminating data containing personal information which can uniquely identify a person

• Data linkage requires data containing personal information to be swapped between two or more organisations in order to uniquely identify each person

• Preservation of privacy and data linkage are fundamentally at odds with each other

• There are only two possible solutions

– Don’t do person centred data linkage, or

– Do it in way that preserves privacy




Privacy Preserving Data Linkage

• Two basic ideas:

– Lock the data away under high

security and only allow trusted

people to access it.

– Encrypt personal information,

so it still retains uniqueness,

but cannot reveal identity

• Encryption is the practical way for

sharing data




Encryption

• Reversible Vs Irreversible

• When sending secret messages we want encrypt then de-

encrypyt to read the message. This used to protect the

message in transit.

• But you can also encrypt in ways that don’t allow you to ever

de-encrypt

• The SHA-1 algorithm irreversibly encrypts a piece of data, with

the property that each input always leads to a unique output

• e.g. SHA1("The quick brown fox jumps over the lazy dog")

= 2fd4e1c6 7a2d28fc ed849ee1 bb76e739 1b93eb12

• This is a 40 digit hexadecimal number, not a transformation of

the input. There are 263 (≈1019) possible hexdecimal numbers

this size


http://en.wikipedia.org/wiki/The_quick_brown_fox_jumps_over_the_lazy_dog



Linking by Matching

• With SHA-1 the same information will always

produce the same irreversibly encrypted 40 digit

hexadecimal string.

• Matching two strings tells us the same information

was encrypted in both without knowing the content

of the information.

• For data linkage we only need to match

• By irreversibly encrypting personal information in

datasets to be used for data linkage, we can do data

linkage and preserve privacy at the same time.




Some added tricks

• Salting

– Adding a fixed string to each piece of information then encrypting – stops hackers forward encrypting known information or names and then matching

• Russian doll salted encryption

– Encrypt an encryption, can add a salt values to each doll

• Time based salting

– Add the number of hours past a certain base date and time as a salt. This means that encryption is only valid within that hour. Can use a Russian doll to do this. Used in Windows network logins, known as the KEROBOS protocol.

• All these techniques guarantee privacy preservation

• Some leading work on all this at ANU, dept. of Computer Science




Information and Privacy

• In a legal sense information (data) is owned by its creator

• If the information (data) contains personal information about

an individual who can be identified, then state and

commonwealth privacy legislation prohibit its dissemination

and/or use for purposes other than what was originally

proposed, without consent.

• Privacy is the right of the individual to have control of access

by others to data with personal information about them

• With informed consent personal information and data about

an individual cannot be passed from one organisation to

another

• Data which does not contain personal information can be

disseminated without consent, provided privacy is

safeguarded.




Two kinds of data linkage

• Population level data linkage (de-identified)

– Two or more organisations merge data sets

– The new composite data set available to both and

researchers. In Sweden available to the public

– Real-time continuous linkage is possible

– No consent required as long as no person can be

identified in a dataset by any means

• Individual level (identified)

– Two or more organisations swap a defined set of

information about an individual

– With informed consent

– The person and their carers can also access this in-

common information about themselves

– Can be driven by the client

– Web based, information in the cloud




De-identified data linkage

Creates population level data which unifies

data across service systems

• Research

• Evaluation

• Collaboration between organisations

• Policy development.




Identified data linkage

• With informed consent

• Information sharing around an individual. Person-centred unification of data.

• Computer systems can exchange de-identified data packets which are then linked to identified data at its destination. Like the Personally Controlled Electronic Health Record (PCHR) extended to disability

• Communication and collaboration between the person with a disability, their carers and their service providers.

• Puts the person with a disability in control, they can grant or deny access to their information at a granular level.

• Include people with a disability and their carers by using already familiar well understood interfaces like “Facebook”.

• Allows people with a disability to choose service providers who allow them to control their own information. NGOs can include as part of their offering.


dr tony florio - seslhd mental health service & unsw school of psychiatry - data linkage between...

Health & Medicine

data data

data linkage data

store data

data segregation

data systems

lot of data

parallel data streams

services data streams