Linked data for research on
infectious diseases, environment,
housing and health
Michael Baker
University of Otago, Wellington
January 2016
Outline
• Examples of using linked data
a) Descriptive epidemiology
b) Acute to chronic diseases
c) Environments with disease/outcomes
d) Populations with disease/outcomes
e) Interventions with outcomes
f) Establishing a cohort population with NHI
• Lessons learned
• Ideas for the future
a) Descriptive Epidemiology
Eg Incidence of serious infectious diseases
• Ability to identify all cases above certain threshold
across an entire country eg acute overnight
hospitalisations
• Use NHI to remove readmissions & transfers allowing
identification of incident cases
Source: Baker et al. Lancet
2012; 379, 1112 - 19
b) Acute to chronic diseases
Eg Guillain-Barré Syndrome & campylobacteriosis
• Ability to link acute disease cases to serious sequelae
and death by linking using NHI
Condition
Campylobacteriosis
Total NZ
population
Denominator pop
GBS
Hosp.
8448
5
53,617,400
1320
Age-std rate
(95% CI)
Age-std rate
ratio (95% CI)
810.0
319.0
(41.4–1578.7) (201.5–506.4)
2.5
Ref 1.0
(2.4–2.7)
Source: Baker, et al Emerg Infect Dis. 2012; 18: 226-33
b) Acute to chronic diseases
Eg Guillain-Barré Syndrome & campylobacteriosis
GBS hospitalization rate (/100,000)
3.5
400
Campylobacteriosis notification rate
350
3.0
300
2.5
250
2.0
200
1.5
150
1.0
100
Campylobacter control
strategy implemented 2007
0.5
50
0.0
0
88
90
92
94
96
98
00
02
Year
04
06
08
10
12
14
Campylobacteriosis notification rate (/100,000)
GBS hospitalization rate
c) Linking environments with disease
Eg Rates of Rheumatic fever & household crowding
Source: NZ Ministry of Health, 2012.
c) Linking environments with disease
Eg Rates of Rheumatic fever & household crowding
Average annual RF first admission rates by household
crowding, deprivation, income quintiles, 1996-2005
12
Rate per 100,000
10
8
6
4
2
0
Household crowding
NZDep2001
Household income
Explanatory variable quintiles
Source: Jaine, Baker, Venugopal. Paed Infect Dis J 2011;30:315-9
d) Linking populations with outcomes
Eg Social housing tenants and Hospitalisations
• SHOW Study – Tracking
health outcomes in
Housing NZ Corporation
(HNZC) applicants and
tenants (~230,000 people
≈ 5% NZ Pop)
• Linked by unique number
to hospital admissions
then anonymised
• Commenced 2004
Source: Baker, et al BMC Public Health 2016
d) Linking populations with outcomes
Eg Social housing tenants and Hospitalisations
Hosp. rates in Applicants & Tenants vs. Other NZ, average 2004-08
Source: Baker, et al BMC Public Health 2016
e) Linking individuals with disease/injury/outcomes
Eg RCT of injury reduction intervention
Home Injury Prevention Intervention (HIPI)
• Single-blinded cluster randomised controlled trial of home injury
prevention measures to reduce medically-treated home falls
• Taranaki Region in owner-occupied dwellings
• 842 households: 436 (950 people) randomised to treatment
group, 406 (898 people) to control group
• Home fall injuries measured using ACC claims data
• Significant reduction in home fall injuries - 26% (95% CI 6%-42%)
• Social benefits of injuries prevented >> costs of intervention
(average $560 per house)
Source: Keall, et al. Lancet 2015;385:231-8.
10
f) Establishing a cohort using NHI
Combining 3 data sources:
• NHI, NMDS, QV
Created a cohort to:
• Investigate relationship between
housing conditions & health
outcomes
• Investigate interventions eg Warm
up NZ
Population
• March 2006 NZ resident NHI
population (4,942,132)
• 23% larger than 2006 Census
population (4,027,941)
Source: Telfar Barnard, Baker, Hales, Howden-Chapman. BMC Pub Health. 2015;14;15:246.
Lessons learned
• Existing data linkage capacity via NHI supports a very wide
range of useful health-related studies
• Many administrative datasets are high quality & useful for
linkage purposes
• Lack of consistency about some key variables (and derived
variables) eg hospitalisation, readmission
• Lack of easily available & comprehensive primary health
care data
• Lack of linkage to other outcome areas (eg education,
employment) has in the past meant that the positive
effects of some interventions are undervalued
• Questions about linkage to fine-grained environmental
data, eg regional, neighbourhood, household
• The ‘population’ of NHIs does not as yet correspond to an
accurate population register
Future directions
Potential future uses of IDI, eg
• Investigating broader range of environmental
conditions/exposures and health outcomes eg rental
housing/precarious tenancies and hospitalisations
• Broadening exposures included in risk-factor (case-control)
studies eg rheumatic fever & early childhood
• Broadening the range of outcomes in ‘environmental
health’ research eg to include education, employment in
relationship to housing factors
• Expanding research on relationship between acute IDs and
NCDs eg enteric infections & inflammatory bowel disease
• Evaluating effectiveness of interventions and natural
experiments more effectively eg Rheumatic Fever
Prevention Programme (RFPP), Housing warrant of fitness
Acknowledgements
•
•
•
•
•
•
•
•
•
•
•
•
Philippa Howden-Chapman
Nevil Pierce
Michael Keall
Lucy Telfar Barnard
Simon Hales
Debbie Williamson
Nick Wilson
Nick Preval
Amanda Kvalsvig
Helen Viggers
Jane Zhang
And others from these
groups: