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Leveraging Big Data to Understand Access and Use of Mental Health Services in Atlantic Canada
Dr. Amanda Slaunwhite Dr. Scott Ronis
David Miller Dr. Paul Peters
Funders
Supporters
Defining “Big” Data
“massive quantities of health care data accumulating from patients and populations and the advanced analytics that can give those data meaning”1
“..high volume, variety, and potential for the rapid accumulation of data and analytics, which is the discovery and communication of patterns in data”2
1Krumholz, H. M. (2014). Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Affairs, 33(7), 1163-1170. 2 Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.
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‘Big data’ -Electronic Medical Records
- Diagnostic imaging - Clinical notes - Treatments
- Physician Billing Data - Pharmacy Records - Mortality Data - Tax Return Records - Internet and Social Media Data - Climate/environmental data
Supplementary data - Population health surveys
- CCHS - CADUMS - GSS
- Geospatial files - Road networks; civic
address files
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The Power of Big Data: Data Linkage
“Big data becomes transformative when disparate data sets can be linked at the individual person level.”
Health card # or SIN
Residence/Postal Code
Visits to family doctor
Hospital Admissions
Infant Health
Pharmaceutical Use
Weber, G. M., Mandl, K. D., & Kohane, I. S. (2014). Finding the missing link for big biomedical data. Jama, 311(24), 2479-2480.
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• Big data supports geographical research and spatial analysis
• Refined spatial scale
• Helps to identify how place impacts mental health and access to mental health services
• Common geographies: Postal
Codes; Forward Sortation Areas
The Power of Big Data: Time and Space
Tim
e
Space
Hospital A
Hospital B
Conceptualization: Mei-Po Kwan (2002) Feminist Visualization: Re-envisioning GIS as a Method in Feminist Geographic Research. Annals of the Association of American Geographers, (2002) 92(4):645-661
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Big Data and Youth Mental Health
- Administrative health data addresses the limitations of commonly used surveys: - No sampling of children - Undersampling of youth - Undersampling of rural/remote populations - Self-reports (vs. actual diagnoses; health care use) - Cross-sectional (vs. time series/panel data)
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Current Data Projects 1. Time series variations in mental health-
related hospital admissions among youth in New Brunswick1
1. Geographic variations in mental health-
related hospitalizations and access to primary health care1
2. Distance Decay or Distance-Related
Delays? Travel distance to hospital and likelihood of mental health-related hospital admissions among youth in New Brunswick1
3. Investigating the factors associated with adolescent readmission to acute psychiatric care services in Atlantic Canada
(David Miller, Student, UNB) 5. Factors Associated with Perceived Need
and Use of Adolescents Mental Health Services: Does Atlantic Canada Differ from the Rest of the Country?
(Stephanie Douette, UNB); 6. Using large data sets across sectors for
collective impact. (Luc Clair, McMaster University)
8 1PIs: Amanda Slaunwhite, PhD, Scott Ronis, PhD, Paul Peters, PhD, David Miller, Dan Crouse, PhD. (UNB)
Mental Health-Related Hospitalizations for Youth in New Brunswick
LegendCanadarate
334.200000 - 409.000000
409.000001 - 528.700000
528.700001 - 947.700000
LegendCanadarate
334.200000 - 409.000000
409.000001 - 528.700000
528.700001 - 947.700000
334.2-409.0 409.1-528.7 528.8-947.7
Legend: Mental Health-Related Hospitalizations per 100,00 for Youth (<24 years)
*National Average = 409
PIs: Amanda Slaunwhite, PhD, Scott Ronis, PhD, Paul Peters, PhD, David Miller, Dan Crouse, PhD. (UNB) 9
High Rates of Mental Health-Related Hospitalizations
• Minimal information on mental-health related hospital admissions over time or geography
• High rates are problematic: – Adult-oriented general hospitals and psychiatric units are
frequently unable to meet the needs of youth – Treatment efficacy concerns – Could be attributed to lack of primary health care or
community mental health services
10 PIs: Amanda Slaunwhite, PhD, Scott Ronis, PhD, Paul Peters, PhD, David Miller, Dan Crouse, PhD. (UNB)
Geography and Mental Health-Related Hospital Admissions
505
279
403
1159
1258
902
496 529
409
0
200
400
600
800
1000
1200
1400
Moncton Fredericton Campbelton Miramichi Canada
New
Brunswick
• Variations in rates of mental health-related hospitalizations in NB for youth < 24
• Research will measure: • Geographic variations by
postal code; • Relationship between
family physician density and hospitalizations
• Distance to hospital and likelihood of service use St
. Joh
n
Edmun
dston
Bathurst
11 PIs: Amanda Slaunwhite, PhD, Scott Ronis, PhD, Paul Peters, PhD, David Miller, Dan Crouse, PhD. (UNB)
Developing Data Infrastructure in Atlantic Canada
- Infrastructure development is ongoing - Health Data NS; NB-IRDT - Data costs; personnel / expertise
- Owners of Data - Departments of Health; Government Agencies hold
different information - Information silos and varying degrees of access - Inter-Provincial linkages are challenging
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• Linkages between datasets decreases confidentiality;
• Geography and privacy is a challenging area
<19 with family doctor diagnosed mental health issue in Fredericton in 2014 (N=500) + female (N=250) + parents who earn less than $30,00 per year (N=150) + is one of 120 patient that lives in E3B 507 (N=50) + involuntarily admitted hospital 8 times in the past 2 years for self harm (N=1).
Big Data, Privacy and Access
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• Release of data from data owners • Privacy Legislation
- Big data addresses many limitations of cross-sectional survey data
- Much promise for using big data to research youth
mental health in Atlantic Canada - Essential for evaluating health care reform efforts
(e.g. new investments in mental health services)
Developing Data Infrastructure in Atlantic Canada
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