DATA LINKAGEIntroduction to Design and Planning

Longitudinal Studies

Andrew P Costa, PhDAssociate Professor | Schlegel Chair in Clinical Epidemiology & Aging | https://hei.mcmaster.ca/ Research Director | St. Joseph’s Healthcare Centre for Integrated CareResearch Director | Michael G. DeGroote School of Medicine, Waterloo Regional CampusAssociate Scientific Director | CLSA https://www.clsa-elcv.ca/ Adjuct Scientist | IC/ES McMaster https://www.ices.on.ca@Andrew_P_Costa

CIHR SPA 20211

Participants aged 45 to 85

at baseline (51,338)

Active follow-up every 3 years

CLSA Research Platform

20152010 - 2015

TIME

20 Years

2018

Baseline FU-1 FU-2 FU-3 FU-4 FU-5 FU-6

50,000 women and men aged 45 - 85 at baseline

Target: 20,000Actual: 21,241

Randomly selected withinprovinces

Target: 30,000 Actual: 30,097

Randomly selected within 25-50 km of 11 sites

Questionnaire• By telephone (CATI)

Questionnaire• In person, in home (CAPI)

Clinical/physical testsBlood, urine

• @ Data Collection Site

2033

Participant Recruitment

VancouverVictoriaSurrey Calgary Winnipeg

Hamilton

Ottawa

MontrealSherbrooke

Halifax

St. John’s

Comprehensiven=30,000

Trackingn=20,000

Depth and Breadth of CLSAPHYSICAL & COGNITIVE MEASUREMENTS§ Height & weight § Waist and hip measurements§ Blood Pressure, Pulse Rate§ Grip strength, timed up-and-go, chair raise, 4-m walk

Standing balance, ADL/IADL, Functional measures§ Vision (retinal imaging, Tonometer & visual acuity)§ Hearing (audiometer)§ Spirometry§ Body composition (DEXA) & Bone density (DEXA)§ Aortic calcification (DEXA)§ ECG, Carotid Plaque sweep (ultrasound), Carotid intima-media thickness (ultrasound)§ Cognitive assessment (30 min battery

HEALTH INFORMATION§ Chronic disease symptoms (18 disease algorithm)§ Medication and supplements intake § Women’s health§ Self-reported health service use § Oral health§ Preventative health§End of Life Questionnaire

PSYCHOSOCIAL§ Social participation§ Social networks and support§ Caregiving and care receiving§ Mood, psychological distress, §PTSD§ Coping, adaptation§ Injuries and consumer products§ Work-to-retirement transitions§ Retirement planning§ Social inequalities§ Mobility-lifespace, Transportation§ Air Pollution & Built environments §Income, Wealth and Assets

LIFESTYLE & SOCIODEMOGRAPHIC§ Smoking§ Alcohol consumption§ Physical activity (PASE)§ Nutrition (nutritional risk and food frequency)§ Birth location§ Ethnicity/race/gender§ Marital status§ Education§Age, Sex, Gender Identity, housing

HEMATOLOGY CHEMISTRY

Genotyping

820K UK Biobank Axiom

Array

Affymetrix

N1 = 26,884

Sequencing Pilot

Baseline = 500

(repeated at FUP1)*

Epigenetics

850K Infinium

MethylationEPIC BeadChip

Illumina

N1 = 1,500

GENETICS METABOLOMICS PROTEOMICS

Figure 3: Biomarkers and Omics in the CLSA

151

Analytes

Roche Cobas

Baseline = 27,1702

FUP1 = 23,156

5

Analytes

R&D, EasyLyte

FUP1 = 10,000 *

13 Parameters

Beckman AcT

Baseline = 24,425

FUP1 = 22,144

92

Proteins

Neurology Panel

Immunoassay

Olink

Baseline = 3,000*

10003

Metabolites

UHPLC/MS/MS

Metabolon

Baseline = 10,000*

1Varies with follow-up

2 HbA1c, n = 26,961

3Approximate number

* In early stages of planning

5

‘Ontario CLSA participants’ are classified as CLSA participants if primary residence was Ontario for either baseline/MCQ or follow-up one.

What is the future of data linkage in longitudinal cohorts?

• Linking CLSA data with other secondary data sources:

• “Simple” – environmental data (e.g., CANUE)

• “Trickier” – provincial health service and clinical data (e.g, IC/ES, Pop. Data B.C.”)

• “Complicated” – wearable technologies, geospatial data, consumer and social e

• Combined data may give new insights

6

Linkage with Contextual and Environmental Data: Collaboration with The Canadian Urban Environment

Health Research Consortium (CANUE)

Air Quality Nighttime Light Greenness

Weather & ClimateCan-ALE DataSocial & Material Deprivation Indices

What is the future of data linkage in longitudinal cohorts?

• Linking CLSA data with other secondary data sources:

• “Simple” – environmental data (e.g., CANUE)

• “Trickier” – provincial health service and clinical data (e.g, IC/ES, Pop. Data B.C.”)

• “Complicated” – wearable technologies, geospatial data, consumer and social e

• Combined data may give new insights

8

‘Atypical’ / Emerging Sources

9

‘Big Data’An incredible amount of data is collected every day in our health system as really, a byproduct, of all of the activity that happens in health care

These data can be harnessed to help us understand needs and help us make decisions about their care.

We can use these huge sources of data to evaluate health in real-world settings.

Progress on BarriersCapture and storage

Cost, cost-effectiveness

Transfer/Sharing, Information privacyStandards, liability

Analysis, visualization, queryData >> information (artificial intelligence)

Administrative health records, clinical registries, vital statistics, ethno-cultural identifiers, social services, corrections, education, transport data, etc. – all linked at the level of the individual

Example: Data held by IC/ES (Ontario)• Registered Persons Database (RPDB)

• Hospital Discharge Abstract Database (DAD)

• National Ambulatory Care Reporting System (NACRS)

• Continuing Care Reporting System (CCRS)

• Home Care Reporting System (HCRS) + HCD

• National Rehabilitation Reporting System (NRS)

• Ontario Mental Health Reporting System (OMHRS)

• Ontario Health Insurance Plan Claims Database (OHIP)

• Ontario Drug Benefit Claims (ODB)

• Ontario Cancer Registry (OCR)

• Derived Diagnostic Cohorts (CHF, HIV, COPD, Dementia, etc.)

• Ontario Marginalization Index (ON-Marg)

13

Sankey Diagram Illustrating Life Trajectories Among Residents of Retirement Homes, 2016 to 2019

2016

2017

2018

2019

2020

XRH

XRH

XRH

XRH

YLTC

YLTC

YLTC

YLTC

Accrual Start/End: 2016-07-01 to 2020-09-30Max follow-up date: 2020-10-01Observation window termination: 2020-09-30Lookback windows: 2016-07-01 to 2017-12-31

RQ: Do residents of retirement homes who are on the waiting list for a bed in a long-term care home transition to the long-term care home sooner compared to older adults receiving home care services and living independently within their community during the 2016-2020 period?Design: Retrospective cohort studyParticipants: Older adults who are placed on the waiting list for a bed in a long-term care home from 2016-07-01 to 2017-12-31 and transition to LTC by 2020-09-30. Exposure: Residency within a retirement homeOutcome: Placement in long-term care home. Time (years) measured from ADMDATE for waiting list. Censored: died, moved out of RH (not LTC), no placement in LTC by 2020-09-30.

Some secondary data can define phenotypes

16

The balance….

Longitudinal Cohorts

• Representative

• Comprehensive data

• Recall and drop out

• Standardized data

Derived Cohorts Data from Secondary Data• Census level

• Opportunistic data points

• Complete follow-up

• Often lack of meaningful standardization

No one organization has “all the data”

• Data environment is complex and complementary

• HDRN Canada is developing the Canada HDR Alliance to enable research access to data that accommodates this complexity

• The Canadian Longitudinal Study on Aging (CLSA) is a pilot member of the HDR Alliance

• Health Data Research Network Canada (HDRN Canada) has a mandate to expand

available data – with our member organizations holding foundational population-based

data

18

CLSA Data Linkage• The Canadian Longitudinal Study on Aging (CLSA)

recently joined the Alliance. CLSA is a large, national, longitudinal research study of adult development and aging.

• Collaborative activities with CLSA:• Facilitating the linkage between the CLSA data sets and

HDRN data centres, and streamlining requests to access linked data through DASH

• Co-developing data access and methodological resources that are consistent across provincial and territorial data centres

• Exploring a number of scientific opportunities to demonstrate impact of data linkage

19

The possibilities ..

• 94% of participant provided consent to linkage.

• This provided researchers with unique data not available in current provincial data platforms to do:• Cross-sectional linkage• Longitudinal health service cohort• Measure validation, include disease algorithms.

• The partnership expands the access and relevance of CLSA’s detailed longitudinal data to the Canadian researcher interested in health services and policy questions that will allow ‘cell to health system’ research across jurisdictions

Capacity Building Questions

• The saturation of the CLSA cohort in specific administrative data sets currently, and in the future.• The utility of various administrative data sets to define key health

service variables for the CLSA cohort.• The utility of CLSA cohort data to validate administrative data fields

and to act as a detailed base-cohort of older adults. • The utility of cross-walking CLSA data to administrative data sets that

can provide updated information between follow-ups or upon transition to institutional settings.

21

CLSA is funded by the Government of Canada through CIHR and CFI, and provincial governments and universities

Contact:Data inquiries: access@clsa-elcv.caGeneral inquiries: info@clsa-elcv.ca