a propensity matched analysis of population movement … · 2014. 11. 10. · adelaide. health data...
TRANSCRIPT
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A propensity matched analysis of
population movement implicating
area contributions to increased
cardiometabolic risk over time
Natasha J. Howard, Catherine Paquet, Neil T. Coffee, Graeme J. Hugo, Peter Lekkas, Anne W. Taylor, Robert J. Adams & Mark Daniel
Spatial Epidemiology & Evaluation Research Group:
School of Population Health, University of South Australia @natasha_howard
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Presenter Disclosures
(1) The following personal financial relationships
with commercial interests relevant to this
presentation existed during the past 12 months:
Natasha J. Howard
“No relationships to disclose”
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Presentation Outline
Background on population movement and
place-health studies
Residential mobility theory and individual-
level drivers of population movement
Demonstration of propensity matching
Implications for area-level (place) associations
Future applications and considerations
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Adapted from Daniel et al., (2008) Health & Place: (14) 117-132.
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Time (i.e., population movement)
Adapted from Daniel et al., (2008) Health & Place: (14) 117-132.
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Residential Mobility
Vary temporal and spatial
scales.
Involves changes in a
residential location, whether
within a city or across
continents.
The Dictionary of Human Geography. (2009) Eds.
Gregory, D, Johnston R, Pratt G, Watts M,
Whatmore S). 5th Edition, Wiley-Blackwell.
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Residential Mobility
Individuals will change their
residential location over the
lifecourse (e.g., marriage,
education, employment)
Capital accumulation
Unstable housing tenure
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Residential choice.....
Shops and Facilities
Transport Services (e.g.,
hospital)
Locations (e.g., beach)
Resources (e.g., Library)
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Place-health considerations
Limited longitudinal place-health cohort
studies
Sample size large enough to study
population movement
Limited information on space
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Residential self-selection
Need to account for residential self-selection
in studies that focus on place-health relations.
Potential biases for geospatial
epidemiological analyses.
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Case Study: Adelaide, Australia
Case Study: Adelaide, South Australia
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Adelaide, South Australia
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Adelaide, South Australia
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Australia: National Health and Medical Research Council
(NHMRC)
Place and Metabolic Syndrome (PAMS) Project
Research Funding
Place
Social & Built
Environment
People
Policy
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PAMS Project Funding
Linking place to metabolic syndrome:
Levers for public health intervention
Evaluate local community characteristics in relation
to the development of metabolic syndrome over
time.
Knowledge Translation Aims:
• Differentiate the importance of area attributes;
• Differentiate the changeability of area attributes;
• Inform policy interventions on environments.
Testing behavioural and psychosocial
mechanisms underlying spatial
variation in metabolic syndrome
Evaluate the mechanisms by which local
community characteristics explain the
development of metabolic syndrome.
Hypotheses:
Local community characteristics will predict:
• development of metabolic syndrome;
• trajectory of metabolic syndrome; and
• worsening of components of metabolic
syndrome.
Partnership Grant 2010-2013 Project Grant 2010-2014
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Health data
North West Adelaide Health Study
Longitudinal biomedical study with three waves
collected over 10 years (nt1= 4056,
nt2=3206, nt3= 2487)
Adults (mean age (SD): 50.4 (16.3) yrs)
randomly selected via Electronic White Pages
directory from the north-western suburbs of
Adelaide.
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Health data
Measures:
Clinical markers (anthropometrics, Blood
Pressure, fasting blood sample)
Psychosocial measures (e.g., SF-36, GHQ-12)
Behaviours (e.g., food and physical activity,
smoking)
Socio-demographics
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Cardiometabolic Health
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South Australian Epidemiological
Geographic Information
System (SAEGIS)
Built Environment Land use / zoning, Road network, Satellite images Private sector businesses (e.g., Food stores)
Social Environment Census, Property Valuation, Crime
Health Data
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Who moves?
Wave 2
n=3508
(98.5% of n=3563)
Movers
n=611
(17.4%)
Mover information at Wave 1 and 2
n=604
Non-Movers
n=2897
(82.6%)
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Wave 2 non-participation
Demographics
Males - 52.7%
Mean Age 48.1 (18 to 90)
Area-Level Socioeconomic Status
Participants living within the Low & Lowest Quintile of the Index- 68.5%
Health Status
Excellent/Very Good Health - 29.4%
Fair/Poor Health - 24.7%
Individual Socioeconomic Status
Bachelor Degree or higher - 20.9%
Full time employed - 29.2%
Wave 2 non-participation
n=493
(12.2% of n=4056)
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Socio-demographics
Movers Wave 1-2
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Who moves area-level socio-economic status ?
Movers
n=604
Move Up SES
n=234
(38.7%)
Same SES
n=211
(34.9%)
Move Down SES
n=159
(26.3%)
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MAPS...... Moved Up by age group...
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Who moves area-level socio-economic status ?
Movers
n=604
Move Up SES
n=234
(38.7%)
Same SES
n=211
(34.9%)
Move Down SES
n=159
(26.3%)
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Who moves area-level socio-economic status ?
Movers
n=604
Move Up SES
n=234
(38.7%)
Same SES
n=211
(34.9%)
Move Down SES
n=159
(26.3%)
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MAPS...... All together...
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MAPS...... Moved Down
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Application: Research objectives
To implicate area-level influences by accounting
for individual factors using propensity matched
pairs of ‘movers’ and ‘non-movers’.
To assess the difference between the two groups
according to a number of biomedical
cardiometabolic risk markers.
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Propensity score estimation
Matching of members of a treatment group to
members of a no treatment group.
Applied within observational studies to reduce
and bias and approximate a randomized trial
(Parsons, 2004).
Rosenbaum and Rubin (1983) have
demonstrated as a ‘balancing score’ that
ensure that the resulting matched samples
have similar distributions.
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Propensity score estimation
Following steps in specifying the propensity
score model (Austin, 2007):
1) Derive a list of measured baseline variables
that are likely related to exposure/outcome.
2) Derive an initial propensity model by including
all variables in the list as main effects.
Four steps: Specifying propensity score model,
matching, statistical assessment of balance,
estimation of effect.
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Step 1: Propensity Score Model Change:
marital status
Married, Separated or Divorced, Widowed, Never Married
Change: work status
Full time, Part time or Casual, Unemployed, Home duties, Retired, Student
Income change match
1 ”Decrease”
0 “Same”
2 “Increase”
Age Cohort
1 “>30 yrs”
2 “30 - 44 yrs”
3 “45 - 54 yrs”
4 “54 - 64 yrs’
5 “65 yrs+”
Education
1 “Bachelor Degree or higher”
0 “Less than Degree”
Housing Tenure
1 “Paying off Mortgage”
2 “Owner”
3 “Renting”
Child status
1 “Child living at home”
0 ”Child not at home”
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Propensity Score Estimation
Greedy
Match Macro
(SAS 9.2.
System for
Windows)
Propensity Score was estimated by
a logistic regression model
Wave 1 to 2 ‘Mover’ regressed on
the individual-level predictors of
mobility.
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Step 2: Propensity Score Matching
Mover
n=413
Change in marital status match (0,1)
Change in work status match (0,1)
Income change
match (0,1,2)
Age
Bachelor Degree or higher (0,1)
Housing Tenure
Non-mover
n=413
Change in marital status match (0,1)
Change in work status match (0,1)
Income change
match (0,1,2)
Age
Bachelor Degree or higher (0,1)
Housing Tenure
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Step 3: Assessment
413 ‘Movers’
413 ‘Non-
movers’
(SAS 9.2.
System for
Windows)
Difference between matched pairs
‘Movers vs Non-movers’ were
assessed by T-test.
Non-movers had an increase in the
count of elevated risk factors (mean
0.04) than ‘Mover’ counterparts
(mean -0.11).
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Discussion
‘Non-movers’ had a greater increase in risk of
cardiometabolic disease.
Understanding mobility patterns will inform:
knowledge on residential self-selection;
interpretations on relationships between
environmental contexts and health behaviours;
how individual-level drivers implicate area-
level relationships.
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1. Matching ‘movers’ to ‘non-movers’ with
respect to their propensity to re-locate into
areas of varying fast-food outlet exposures
through drivers of individual mobility.
2. Comparing mover/non-mover matches on
their change in cardiometabolic risk.
Preliminary analyses – fast food
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NJ Howard, C Paquet , NT Coffee, GJ Hugo, P Lekkas, AW Taylor, RJ Adams, M Daniel (2014). Change
in fast-food outlet exposure and cardiometabolic health status of propensity matched ‘movers’ and ‘non-
movers’ in a biomedical population-based cohort. International Society for Behavioral Nutrition and
Physical Activity, 21-24 May 2014, San Diego, California, USA.
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Include residential movement within population
based cohorts
Spatial sampling
Consider movement in loss of sample
Collect information on other mobility drivers
Practical implications
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Further understandings on the characteristics of
residential mobility.
Investigate other built environmental influences
and assess how residential mobility implicates
area-level factors.
Future directions
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Location 1 Location 2
“
“We occupy locations and, in the course of our lives, move from place to place.
We all have our own ‘geographies’ as well as our own biographies”.
Gatrell (2002, p.3)
Movement changes places
People move places
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“
“We occupy locations and, in the course of our lives, move from place to place.
We all have our own ‘geographies’ as well as our own biographies”.
Gatrell (2002, p.3) People move places
Explore the dynamics of how
neighbourhoods/local areas are
formed and how they change.
Consider how population
movement influences residential
segregation, influence the socio-
spatial reproduction of local
areas.
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Contact Information
Spatial Epidemiology and Evaluation Research Group
School of Population Health & Sansom Institute for Health Research
University of South Australia
Level 8, South Australian Health & Medical Research Institute, Adelaide, South
Australia
t: +61 8 8302 2776
@natasha_howard
w: unisa.edu.au/sansominstitute/pams/