So it seems that in a cost-recovered data release that was probably lawful then but possibly wouldn’t be now* – Hospital records of all NHS patients sold to insurers – the
Staple Inn Actuarial Society Critical Illness Definitions and Geographical Variations Working Party (of what, I’m not sure? The Institute and Faculty of Actuaries, perhaps?) got some Hospital Episode Statistics data from the precursor to the HSCIC, blended it with some geodemographic data**, and then came to the conclusion that “that the use of geodemographic profiling could refine Critical illness pricing bases” (source: Extending the Critical Path), presenting the report to the Staple Inn Actuarial Society who also headline branded the PDF version of the report? Maybe?
* House of Commons Health Committee, 25/2/14: 15.59:32 for a few minutes or so; that data release would not be approved now: 16.01:15 reiterated at 16.03:05 and 16.07:05
** or maybe they didn’t? Maybe the data came pre-blended, as @amcunningham suggests in the comments? I’ve added a couple of further questions into my comment reply… – UPDATE: “HES was linked to CACI and Experian data by the Information Centre using full postcode. The working party did not receive any identifiable data.”
CLARIFICATION ADDED (source )—-
“In a story published by the Daily Telegraph today research by the IFoA was represented as “NHS data sold to insurers”. This is not the case. The research referenced in this story considered critical illness in the UK and was presented to members of the Staple Inn Actuarial Society (SIAS) in December 2013 and was made publically available on our website.
“The IFoA is a not for profit professional body. The research paper – Extending the Critical Path – offered actuaries, working in critical illness pricing, information that would help them to ask the right questions of their own data. The aim of providing context in this way is to help improve the accuracy of pricing. Accurate pricing is considered fairer by many consumers and leads to better reserving by insurance companies.
There was also an event on 17 February 2014.
Via a tweet from @SIAScommittee, since deleted for some reason(?), this is clarified further: “SIAS did not produce the research/report.”
The branding that mislead me – I must not be so careless in future…
Many of the current agreements about possible invasions of privacy arising from the planned care.data release relate to the possible reidentification of individuals from their supposedly anonymised or pseudonymised health data (on my to read list: NHS England – Privacy Impact Assessment: care.data) but to my mind the
SIAS report presented to the SIAS suggests that we also need to think about consequences of the ways in which aggregated data is analysed and used (for example, in the construction of predictive models). Where aggregate and summarised data is used as the basis of algorithmic decision making, we need to be mindful that sampling errors, as well as other modelling assumptions, may lead to biases in the algorithms that result. Where algorithmic decisions are applied to people placed into statistical sampling “bins” or categories, errors in the assignment of individuals into a particular bin may result in decisions being made against them on an incorrect basis.
Rather than focussing always on the ‘can I personally be identified from the supposedly anonymised or pseudonymised data’, we also need to be mindful of the extent to, and ways in, which:
1) aggregate and summary data is used to produce models about the behaviour of particular groups;
2) individuals are assigned to groups;
3) attributes identified as a result of statistical modelling of groups are assigned to individuals who are (incorrectly) assigned to particular groups, for example on the basis of estimated geodemographic binning.
What worries me is not so much ‘can I be identified from the data’, but ‘are there data attributes about me that bin me in a particular way that statistical models developed around those bins are used to make decisions about me’. (Related to this are notions of algorithmic transparency – though in many cases I think this must surely go hand in hand with ‘binning transparency’!)
That said, for the personal-reidentification-privacy-lobbiests, they may want to pick up on the claim in the
SIASIFoA report (page 19) that:
In theory, there should be a one to one correspondence between individual patients and HESID. The HESID is derived using a matching algorithm mainly mapped to NHS number, but not all records contain an NHS number, especially in the early years, so full matching is not possible. In those cases HES use other patient identifiable fields (Date of Birth, Sex, Postcode, etc.) so imperfect matching may mean patients have more than one HESID. According to the NHS IC 83% of records had an NHS number in 2000/01 and this had grown to 97% by 2007/08, so the issue is clearly reducing. Indeed, our data contains 47.5m unique HESIDs which when compared to the English population of around 49m in 1997, and allowing for approximately 1m new lives a year due to births and inwards migration would suggest around 75% of people in England were admitted at least once during the 13 year period for which we have data. Our view is that this proportion seems a little high but we have been unable to verify that this proportion is reasonable against an independent source.
Given two or three data points, if this near 1-1 correspondence exists, you could possibly start guessing at matching HESIDs to individuals, or family units, quite quickly…
– ACORN (A Classification of Residential Neighbourhoods) is CACI’s geodemographic segmentation system of the UK population. We have used the 2010 version of ACORN which segments postcodes into 5 Categories, 17 Groups and 57 Types.
– Mosaic UK is Experian’s geodemographic segmentation system of the UK population. We have used the 2009 version of Mosaic UK which segments postcodes into 15 Groups and 67 Household Types.
The ACORN and MOSAIC data sets seem to provide data at the postcode level. I’m not sure how this was then combined with the HES data, but it seems the
SIASIFoA folk found a way (p 29) [or as Anne-Marie Cunningham suggests in the comments, maybe it wasn’t combined by SIASIFoA – maybe it came that way?]:
The HES data records have been encoded with both an ACORN Type and a Mosaic UK Household Type. This enables hospital admissions to be split by ACORN and Mosaic Type. This covers the “claims” side of an incidence rate calculation. In order to determine the exposure, both CACI and Experian were able to provide us with the population of England, as at 2009 and 2010 respectively, split by gender, age band and profiler.
This then represents another area of concern – the extent to which even pseudonymised data can be combined with other data sets, for example based on geo-demographic data. So for example, how are the datasets actually combined, and what are the possible consequences of such combinations? Does the combination enrich the dataset in such a way that makes it easier for use to deanonymise either of the original datasets (if that is your primary concern); or does the combination occur in such a way that it may introduce systematic biases into models that are then produced by running summary statistics over groupings that are applied over the data, biases that may be unacknowedged (to possibly detrimental effect) when the models are used for predictive modelling, pricing models or as part of policy-making, for example?
Just by the by, I also wonder:
– what data was released lawfully under the old system that wouldn’t be allowed to be released now, and to whom, and for what purpose?
– are the people to whom that data was released allowed to continue using and processing that data?
– if they are allowed to continue using that data, under what conditions and for what purpose?
– if they are not, have they destroyed the data (16.05:44), for example by taking a sledgehammer to the computers the data was held on in the presences of NHS officers, or by whatever other means the state approves of?