Big linked data projects
Louisa Jorm
Centre for Health Research, University of Western Sydney
Centre for Big Data Research in Health, University of NSW
(from 10 Nov 2014)
Research focus
• Policy partnerships
• Linked whole-of-population administrative data
• Linked administrative and cohort study data
• Evaluating “natural experiments”
Researchers and key collaborators
• Louisa Jorm • Alys Havard • Deborah Randall • Sanja Lujic • Michael Falster • Danielle Tran • Amy Gibson • Bich Tran • Holger Möller • Alastair Leyland • Emily Banks • Sandra Eades • David Preen • Christine Roberts • Sally Redman • Bob Elliott • Fiona Blyth • Judy Simpson • Rebecca Ivers • Jim Warren • John Lynch • Federico Girosi • Kathleen Falster• Funding: NHMRC Partnership Project
• Policy partners: Australian Commission for Safety and Quality in Health Care, Agency for Clinical Innovation, Bureau of Health Information
• Research questions:
• Are "potentially preventable hospitalisations" a good measure of health system performance?
• How can the performance indicator be improved?
• Data: 45 and Up Study (n=267,000), Medicare (MBS, PBS), hospital admissions, emergency department presentations, deaths
• Methods: Multilevel Cox and Poisson regression
The
APHID
Study
Prospective
cohort of 267,091 men and women aged over 45 in NSW. Completed 2006-2008 Questionnaire data Demographics Health status Risk factors 45 & Up Study NSW Admitted Patient Data Collection Census of all hospital separations in NSW public and private hospitals and day procedure centres. Linked data, 2000-2010 N=1,206,742 records MBS Claims for subsidised medical and diagnostic services in Australia Linked data, 2004-2011 N=46,203,507 records PBS Claims for subsidised pharmaceuticals In Australia Linked data, 2004-2011 N= 35,453,776 records
Data linkage
Emergency Department Data Collection Presentations to 80 EDs (75% 0f NSW presentations) Linked data, 2006-2011 N= 347,602 records + Fact of death to 2012The
APHID
Study
Detailed data for 267,153 people...
... WHO they are
... HOW they have interacted with the primary health system
... WHETHER they were admitted to hospital
45 & Up Study Medicare Benefits Benefits Scheme Pharmaceutical Schedule
NSW Admitted Patient Data
Collection
The
APHID
Study
For which conditions does GP supply
have the greatest impact?
More geographic variation in admission rates
Mo re g e o g ra p h ic va ria tio n e x p la in e d b y the sup pl y of G P servi ces in are a
GP supply explains the greatest amount of geographic variation in admission
rates for influenza and asthma
The
APHID
Study
IHOPE: Indigenous Health
Outcomes Patient Evaluation
Policy partners: Aboriginal Health and Medical Research Council, NSW Ministry of Health
Research questions:
– How do Aboriginal status, socioeconomic status and rurality interact to drive health disparities?
– In cardiovascular disease, injury, cataract surgery,
preventable hospitalisation, otitis media, diabetes mellitus, renal disease
Data: Hospital admissions (5.6M people), deaths
Methods: Multilevel Cox, Poisson, logistic and negative binomial
Total persons 1 2 3 4 5 6 7 8 . . 5,628,960 Hospital admissions (NSW Admitted Patient Data Collection) Jul00 to Dec08 18 638 151 separations 5 580 151 persons Fact of death (NSW RBDM) Jul00 to Dec09 433 453 Cause of death (ABS) Jul00 to Dec07 338 826
Aboriginal rate higher Non-Aboriginal rate higher Aboriginal rate higher Non-Aboriginal rate higher IRRs (In cidence ra te ra tio s)
IHOPE: Disparities in transport
injuries
(random intercept model)
MUMS:
Maternal Use of
Medications and Safety
• Funding: NHMRC Project Grant
• Policy partners: Department of Health • Research questions:
– What are the maternal and neonatal health outcomes of medications used during pregnancy?
– Specifically: smoking cessation medications, antihypertensives, medications for diabetes, thrombosis, rheumatoid arthritis
• Data: Perinatal data (~800,000 pregnancies), Medicare (PBS), hospital admissions, emergency department presentations, birth defects, deaths
• Methods: Interrupted time series analysis (impact of policy changes), logistic and negative binomial regression
Mapping the outcomes of calls to
healthdirect Australia
• Funding: Healthdirect Australia
• Policy partners: Healthdirect Australia • Research questions:
– To what extent is healthdirect Australia telephone triage advice being followed?
– What factors influence patient advice-taking and outcomes? • Data: healthdirect Australia call data (1.3M records), hospital
admissions, emergency department presentations, deaths, 45 and Up Study, Medicare (MBS)
Seeding Success
• Funding: NHMRC Project Grant• Policy partners: NSW Kids and Families, NSW Department of Community Services, Aboriginal Health and Medical Research Council
• Research questions:
• What are the social, perinatal and early childhood health factors that promote positive early childhood development in Aboriginal children?
• Do current prevention and early intervention programs work?
• Data: Australian Early Development Index (~180,000 children), perinatal data, MBS, DOCS, hospital admissions, emergency department presentations, deaths, income assistance
• Methods: Multilevel linear and logistic regression, propensity matching
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
AREA-LEVEL CONTEXTUAL DATA (e.g. 2011 Census data on community characteristics)
AMIHS funded in 21 areas AMIHS expanded to 50 additional areas Brighter Futures program data collection commenced
SCHOOL EARLY CHILDHOOD 2009 AEDI Birth registrations (RBDM) Congenital Conditions Register (RoCC)
Perinatal Data (PDC) Medicare Benefits Schedule (MBS) Hospital data (APDC)
Emergency department data (EDDC) Community Services data (KiDS)
BIRTH
APDC (mother)
PRENATAL
2009 AEDI COHORT
Centrelink income assistance data (mothers/fathers)
SCHOOL EARLY CHILDHOOD 2012 AEDI Birth registrations (RBDM) Congenital Conditions Register (RoCC)
Perinatal Data (PDC) Medicare Benefits Schedule (MBS) Hospital data (APDC)
Emergency department data (EDDC) Community Services data (KiDS)
BIRTH
APDC (mother)
PRENATAL
2012 AEDI COHORT
