Pathways to obesity Identifying local, modifiable determinants of physical activity and diet Mai Stafford, Amanda Sacker Dept of Epidemiology & Public Health, UCL Sally Macintyre, Anne Ellaway MRC Social & Public Health Sciences Unit, Glasgow Steve Cummins Dept of Geography, Queen Mary College London R D Wiggins Dept of Sociology, City University
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Pathways to obesity Identifying local, modifiable determinants of physical activity and diet Mai Stafford, Amanda Sacker Dept of Epidemiology & Public.
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Pathways to obesity
Identifying local, modifiable determinants of physical activity and diet
Mai Stafford, Amanda Sacker Dept of Epidemiology & Public Health, UCL
Sally Macintyre, Anne Ellaway MRC Social & Public Health Sciences Unit, Glasgow
Steve Cummins Dept of Geography, Queen Mary College London
R D Wiggins Dept of Sociology, City University
• Neighbourhood influences on health of increasing interest
• Methodological developments
• Empirical evidence focuses on deprivation index as exposure
• Neighbourhood deprivation associated with
- mortality (all-cause, suicide, deaths from heart disease)
- morbidity (self-rated health, disability, smoking, quality of life, common mental disorder)
Background/rationale
• Composition or context?
• Multilevel data and analysis
• Reviews of evidence conclude there is an association between neighbourhood deprivation and health
• Neighbourhood deprivation = black box
What neighbourhood characteristics might influence health?
Neighbourhood deprivation index
Service environment e.g. health services, public transport, retail
Built environment
Social environment e.g. social disorder, social capital
Unpacking the black box
• Identify specific, amenable neighbourhood determinants of health
• How do these relate to each other?
• How do they jointly influence health?
• Focus on obesity – a major public health issue
• Key determinants of obesity are diet and physical activity
Service environment and diet, physical activity and obesity
• Health Survey for England (1994-1999) and Scottish Health Survey (1995 & 1998)• Nationally representative• Height and weight measured by trained nurse• Obesity = body mass index = weight/height2
• Age, gender and occupation-based SES• Neighbourhood unit = postcode sector• Average population ~ 5000
Data & methodsII. Selecting neighbourhoods for study
Eligible neighbourhoods
Ensure range of environments
Select sample of neighbourhoods
≥ 35 Health Survey participants
Stratify on population density & Carstairs index of deprivation
Stratified random sample
Data complete for 398 neighbourhoods
Collect neighbourhood data
Data & methodsIII. Measuring the service & built environment
• Various sources: administrative data from central government, local government, commercial organisations
e.g. violent crime per capita, number of supermarkets
• Difficult to obtain data
– not available
– not complete
– not comparable England and Scotland
• Data collected or converted to postcode sector
Data & methodsIV. Measuring neighbourhood disorder
• Local Area Social Capital Survey conducted in 2000• 70-item postal questionnaire• Sent to random sample of residents 16+ years• Response rate 42%• 5 items capture neighbourhood disorder
Tick one per row
There is no problem with vandalism and
graffiti in this area
Vandalism and graffiti are a big problem in this area
Most people in this area can be trusted
Most people in this area can’t be trusted
People feel safe walking alone in this
area after dark
People would be afraid to walk alone in this area after dark
This area is kept very
clean
This area is always full of litter and rubbish
Neighbours are unthreatening
Neighbours are threatening
Data & methodsV. Linking data
Administrative/commercial data
Service environmentBuilt environment
Postal survey
Neighbourhood disorder
Existing health survey
Individual level obesity
Link via postcode sector identifier
Violent crime rate
# special constables
Vacant/derelict land
Sports participation
rate
BMI
Population density
# supermarkets
Age Male SES
Neighbour-hood
disorder
# police officers
# missed waste collections
High street facilities
# swimming pools
# McDonalds
--
-
-
-
+
++
-
+
--
+ - -
Data & methodsVI. Structural Equation Modeling
• Aim to investigate inter-relationships between various neighbourhood characteristics and their relationship with obesity
• Estimate relative importance of different causal pathways
Data & methods
• Some neighbourhood characteristics modeled using latent variables
i.e. measured variables are indicators of underlying construct
• Want to know about relationships between underlying constructs (not between measured variables)
Neighbourhood disorder
Graffiti/vandalism
People can‘t be trusted
Afraid to walk alone
Area full of rubbish
Neighbours threatening
High street facilities
# pharmacies
# optician practices
# dental practices
# libraries
# building societies
# banks
# Bingo halls
distance to post office
Data & methods
• Step 1: confirmatory factor analysis to assess how well measured variables capture underlying construct• Step 2: structural equation model linking neighbourhood characteristics to obesity• Allow for clustering of participants within neighbourhoods• Present standardised factor loadings and path coefficients• Mplus software
Results• Factor analysis confirmed that
i) neighbourhood disorder well-measured by the 5 items from the Social Capital Survey
ii) high street facilities well-measured by 5 items (3 dropped)
High street facilities
# pharmacy
# optician practices
# dental practices
# building societies
# banks
High street facilities
# pharmacy
# optician practices
# dental practices
# libraries
# building societies
# banks
# Bingo halls
distance to P.O.
Violent crime rate
# special constables
Vacant/derelict land
Sports participation
rate
BMI
Population density
Supermarkets
Age M SES
Neighbourhood disorder# police officers
Missed waste
High street facilities
Swimming pools
0.114
-0.258
0.488
0.181
0.105
-0.013
-0.033
-0.024
-0.001
-0.305
-0.038
0.207
-0.323
0.073
Post office
-0.019
Summary of findings
• Previous studies show neighbourhood deprivation is associated with obesity• Our findings illustrate some specific features of the neighbourhood environment that are associated with
obesity• These are neighbourhood disorder, mixed commercial/residential land use and urban sprawl• In turn, policing and vacant/derelict land influence levels of neighbourhood disorder
Discussion• Study illustrates how data from various sources can be combined• Latent variables can be used when have several indicators of the same underlying construct • Structural equation models can be used to explore theoretical causal pathways• Limitations include
i. lack of neighbourhood data capturing the theoretical constructs of interest
ii. self-selection of participants into different types of neighbourhood
iii. cross-sectional
iv. defining neighbourhood boundaries – to fit the data or to fit resident’s perceptions and experience
Concluding remarks• Our findings illustrate that several determinants of obesity are not within traditional remit of healthcare sector• Private sector and non-healthcare public sector has an important role • Stronger links and even representation of public health in police force, land use planning bodies and groups
supporting local business are needed• Don’t forget individual characteristics also strongly related to obesity