Welcome! Seminar Series: International comparisons about mortality data in people with intellectual disabilities 30June2015

Seminar 3 Welcome! Recap on the purpose of the seminar series. This seminar: Ethical and Legal Issues. Next steps.

Welcome! Introductions. We would love to know who you are and where you are from. Could you fill in the quiz to let us know please? Thank you!

Recap: the seminar series Key aims of the seminar series: To bring together academics, policy makers and practitioners from different countries. To share experiences of measuring, comparing and using mortality data about people with ID. To advance research into mortality of people with ID, sufficient to allow us to make inferences at national and international levels. To support the development of practical initiatives/strategies to address common concerns identified by the data.

Recap: the seminar series This is the 3 rd webinar. 1 st (October 2014) discussed what we currently know about mortality of people with intellectual disabilities. 2 nd (April 2015) focussed on definitions and data availability across countries.

Scan Question 1 Who has attended previous webinars? Can you let us know please by filling in the quiz? To ask a question or make comments, please use the chat box in the bottom right hand corner of the screen.

Webinar 3 Collecting mortality data about people with intellectual disabilities: ethical and legal issues

To cover in this webinar 1. Ethical and legal issues to be considered. 2. The experience in Ontario, Canada. 3. Processes and barriers in other countries.

Ethical and legal issues exist because Access & use Ownership & custody

Scan Question 2 Who is the custodian of the data required to derive mortality statistics for persons with IDD in your jurisdiction? Please complete the quiz.

Ethical and legal issues to be considered Respect for privacy. Confidentiality of personal health information. Vulnerability of persons with IDD. Benefits must outweigh potential harm.

Scan Question 3 Which issue is most challenging in your efforts to collect and use data about mortality? Please complete the quiz to let us know your thoughts.

Privacy Considerations Does the data need to be fully identifiable, mildly de-identified, strongly de-identified? How can the following be used to minimize risk? Technology (encryption) Laws (Personal Health Information and Privacy Act, data sharing agreements) Ethics (REB/IRB) Statistical methods (computed values, thresholds for re-identification)

Laws protecting personal information or health information In Canada legislation (National and Provincial) strives to both protect privacy and facilitate information sharing for research purposes. Provinces have differences as well as common features. De-identified health information is NOT subject to legislative regulation.

Methods to enable access to health data in Canada Secure Facility Access Individual level mildly de- identified Remove DoB, postal code, etc… Access through secure online link Individual level data moderately de-identified Remove city, include only age category File sent to Researcher Individual level most strongly de- identified Group all data Aggregated data Publicly available on a website

Setting the stage in Ontario Creating a cohort Linkable health administrative data Merging social assistance data Linking mortality data

A unique research partnership in Ontario, Canada since 2010

Creating a cohort  18 to 64 years  ICD codes for: PDD/ASD [Mental retardation] Select chromosomal and congenital anomalies for which an ID is typically present FASD See Journal of Intellectual Disability Research doi: /jir for complete list

Methods to enable access to health data in Canada Secure Facility Access Individual level mildly de- identified Remove DoB, postal code, … Access through secure online link Individual level data moderately de-identified Remove city, include only age category,… File sent to Researcher Individual level most strongly de- identified Group all data Aggregated data Publicly available on a website

Health Data held at the Institute for Clinical Evaluative Sciences 79 data holdings Health services (n=26) include ICD codes DAD/SDS/NACRS/OMHRS/OHIP Population and demographics (n=10) RPDB/CENSUS Acquired registries (n=13) Derived cohorts (n=12) Financial (n=3) Surveys (n=6) Care Providers (n=3) Coding (n=5) REF/LHIN/PCCF Facilities (n=1) All known to have IDD ICES cohort

Data from the Ministry of Community and Social Services  One administrative database (SDMT) includes ICD codes for Disability Support Program recipients 18 to 64 years

Merging data Data taken from: Hospital stays Visits to Doctors’ offices Emergency department visits Database from MCSS OW, ODSP MERGED cohort ICES cohortMCSS cohort ICES Data Holdings SDMT Database April 2009 – March 2010

Steps and time required Month 0 Funding Secured Month 10 Privacy Impact Assessment Completed Month 18 Data Sharing Agreement Signed Month 19 Data File Transferred Month 24 Linkage Completed Month 28+ Cohort Definitions Tested

Scan Question 4 Will you need to undertake comparable linkage of datasets to create a cohort? Please complete the quiz.

Data Linkage Results Health (37%) Disability Income (33%) OVERLAP (30%) Prevalence (18 to 64 years) Health:0.52 Disability Income:0.49 MERGED :0.78 (n = 66,484) See Journal of Policy and Practice in Intellectual Disabilities Volume 11 Number 4 pp 235–244 December 2014

Identifying deaths 79 data holdings Health services (n=26) include ICD codes DAD/SDS/NACRS/OMHRS/OHIP Population and demographics (n=10) RPDB/CENSUS/ ORGD (Death records: January 1990 – March 2013; Cause of Death until December 2012 only) Acquired registries (n=13) Derived cohorts (n=12) Financial (n=3) Surveys (n=6) Care Providers (n=3) Coding (n=5) REF/LHIN/PCCF Facilities (n=1)

Office of the Registrar General – Death Database (21 of 58 variables) Date of death Age at death date Place of Death ICES derived COD in ICD9 Antecedent cause of Death Immediate Cause of Death (until 2002) Primary Cause of Death (from 2003) #10 Cause of Death Code #11 Cause of Death Code #12 Cause of Death Code #2 Cause of Death Code #3 Cause of Death Code #4 Cause of Death Code #5 Cause of Death Code #6 Cause of Death Code #7 Cause of Death Code #8 Cause of Death Code #9 Cause of Death Code Accident Suicide Homicide Autopsy was performed? Autopsy findings used for Cause of Death?

Scan Question 5 How long should a cohort be followed to report meaningful mortality rates? Please complete the quiz.

Mortality Results  Analysis of the cohort will be undertaken by Robert Balogh and Elizabeth Lin in the next year.  Examining an older cohort (18 to 99 years) mortality treated as a competing risk in analysis of frailty as a predictor of admission to a long-term care facility (with Lynn Martin and Katherine McKenzie)  Working with Shahin Shooshtari in Manitoba to compare across provinces.

Summary Data linkage may be necessary to more completely identify cohorts for mortality studies. Data linkage introduces additional health information privacy concerns. It is important to be aware of privacy legislation. Steps from data request to data access can take several months (even years). Consideration needs to be given to length of follow-up when comparing mortality statistics.

Next seminar November 2015 The English experience of data linkage between registers of people with ID and mortality data, and the impact this has had. Coordinator: Prof. Gyles Glover, Learning Disabilities Observatory, Public Health England.

Thank you for joining us! Contact details: Twitter hashtag #ID_mort Website: tional/ tional/ We would like to thank the Economic and Social Research Council (ESRC) UK for their support in funding this seminar series.