Zone design methods for epidemiological studies

Samantha Cockings, David Martin

Department of Geography University of Southampton, UK Thanks to: Arne Poulstrup, Henrik Hansen Medical Office of Health, Province of Vejle, Denmark [email protected]

Why use areas?

 No choice - data only available for areas  Confidentiality  Cost  Through choice  Believe some phenomena are area-level  Rates/ratios  Visualisation/mapping  Decision-making/planning

Problems with using areas

 Modifiable areal unit problem (MAUP)  Scale  Aggregation For a given set of data, different aggregations/zoning systems will often show apparently different spatial patterns in the data (Openshaw, 1984)  Ecological fallacy Relationships between variables which are observed at one level of aggregation may not hold at the individual, or any other, level of aggregation (Blalock, 1964)  Small numbers/instability of rates  Non-nesting units

Recent developments in (UK) automated zone design methods/tools

 2001 UK Census of Population  Automated design of Output Areas (OAs) Martin et al (2001) 1 ; Martin (2002) 2  Based on Automated Zoning Procedure (AZP) Openshaw (1977) 3 ; Openshaw & Rao (1995) 4  Automated Zone Matching software (AZM) Martin (2002) 5

1 Environment & Planning A, 33, 1949-1962 2 Population Trends 108, 7-15 5 IJGIS, 17, 181-196 3 Transactions of the IBG, NS, 2, 459-472 4 Environment & Planning A, 27, 425-446

Methods

Automated zone design … iterative recombination

Building blocks Initial random aggregation Iterative recombination Maximise objective function Aggregated zones

Martin, D (2002), Population Trends, 108, p.11

How can automated zone design help in environment and health studies?

  Explore sensitivity of results to MAUP Design sets of ‘optimal’ purpose-specific zones  Stability of estimates • Zones of homogeneous population size?

 Exploring spatial patterning of disease • Zones of homogeneous rates?

 Analysing relationships between variables • Zones of homogeneous risk/confounding factors?

 Barriers/boundaries • Zones constrained by geog. features or admin. boundaries

Empirical study 1: Pre-aggregated data

Morbidity and deprivation in SW England

 County of Avon (1991 Census)  1970 enumeration districts  177 wards  Premature (0-64 years) limiting long term illness (LLTI)  Townsend deprivation score  Standardisation to England & Wales

SMR LLTI 0-64 0 - 0.62

0.62 - 0.83

0.83 - 1.03

1.03 - 1.36

1.36 - 9998 Restricted 0 N 2 4 Kilometers

© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

Townsend score -6.87 - -3.37

-3.37 - -1.97

-1.97 - -0.43

-0.43 - 1.83

1.83 - 9998 Restricted 0 N 2 4 Kilometers

© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

Population (0-64) EDs 1 - 291 292 - 364 365 - 420 421 - 500 501 - 1321 Restricted 0 N 2 4 Kilometers

© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

Population (0-64) wards 43 - 1754 1758 - 2939 2986 - 4065 4142 - 7868 8020 - 14333 0 N 2 4 Kilometers

© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

Aims

 Explore sensitivity of association at different scales (population size)   Explore sensitivity of association for different aggregations at a given scale Explore ‘robustness’ of ED and ward level zoning systems for this type of spatial analysis

AZM software

©David Martin

target 3250; mean 0-64 pop. 3713

0 N 2 4 Kilometers

© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

Population (0-64) target 3250 2931 - 3252 3253 - 3603 3604 - 3994 3995 - 4517 4518 - 5746 0 N 2 4 Kilometers

© Crown copyright/ED-LINE Consortium, ESRC/JISC supported

EDs Correlation (Townsend score and LLTI SMR) against mean pop. size …

the scale effect

Wards

Standard deviation (pop. 0-64) against mean pop. size …

the scale effect

Wards EDs

Correlation (LLTI-Townsend) vs. mean population size at given scale …

the aggregation effect

0.89

0.87

0.85

0.83

0.81

0.79

0.77

0.75

0 500 1000 1500 2000 2500

Mean population (0-64)

3000 3500 4000 4500 5000

Results

 Observed association affected by choice of zoning system – MAUP/ecological fallacy  Automated zoning systems demonstrating greater stability of population size, higher correlations  Generally increasing Townsend-LLTI correlation with increasing zone size (pop.) and iterations  ED and ward correlations at low end of variation at given scale  Neighbourhood scale of ~3000 for UK?

Empirical study 2: Individual level data

Dioxins and cancer, Kolding, Denmark

 Background  c.50,000 residents  Airborne carcinogenic dioxin  Data  Geo-referenced addresses of residents 1986-2002  Roads, rivers, lakes  Buildings/urban areas

Possible zone design criteria

 Population size: threshold/target  Physical boundaries  Roads, rivers, lakes  Shape  Homogeneity  Built environment - dwelling type, tenure  Socio-economic - education, income, occupation

Methods: Thiessen polygons around addresses

Methods: Using constraining features – roads and rivers

Methods: Clipped thiessen polygons

Illustrative zoning system from AZM: target 300, threshold 250

Next steps

 Other design constraints  Physical boundaries in zone design process  Homogeneity • Built environment • Social environment  Use zones to calculate rates of cancer  Sensitivity analysis

Conclusions

 All zoning systems are imposed and should not be considered neutral or stable  Zone design methods offer:  The ability to explore the sensitivity and robustness of existing and alternative zoning systems  The ability to design purpose-specific zoning systems according to pre-defined criteria

Environment and health studies: What are we trying to model?

Points?

People Health Outcome Points/areas?

Risk factors Individual level Points?

Confounding factors

Predisposing

: age, sex, ethnicity, genetics, birthweight

Lifestyle

: smoking, diet, exercise, alcohol

Socio-economic

: occupation, income, education ‘People’/‘Composition’ Area level Areas?

Pollution

: air, water, noise

‘Neighbourhood’

: services, housing type/quality, ethnic groupings/population mixing, deprivation, crime, support networks ‘Place’/’Context’

Standard deviation (0-64) vs. mean population size for different aggregations at a given scale

800.000

700.000

600.000

500.000

400.000

300.000

200.000

100.000

0.000

0 500 1000 1500 2000 2500 3000

Mean population size (0-64)

3500 4000 4500 5000