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

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

Modifiable areal unit problem (MAUP) Scale Aggregation Problems with using areas 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) 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) For a given set of data, different aggregations/zoning systems will often show apparently different spatial patterns in the data (Openshaw, 1984) For a given set of data, different aggregations/zoning systems will often show apparently different spatial patterns in the data (Openshaw, 1984) Ecological fallacy 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, Transactions of the IBG, NS, 2, Population Trends 108, Environment & Planning A, 27, IJGIS, 17,

Methods Building blocks Aggregated zones Initial random aggregation Iterative recombination Maximise objective function Martin, D (2002), Population Trends, 108, p.11 Automated zone design … iterative recombination

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

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

Correlation (Townsend score and LLTI SMR) against mean pop. size … the scale effect EDs Wards

Standard deviation (pop. 0-64) against mean pop. size … the scale effect EDs Wards

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

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 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

Predisposing: age, sex, ethnicity, genetics, birthweight Lifestyle: smoking, diet, exercise, alcohol Socio-economic: occupation, income, education Environment and health studies: What are we trying to model? Pollution: air, water, noise ‘Neighbourhood’: services, housing type/quality, ethnic groupings/population mixing, deprivation, crime, support networks ‘People’/‘Composition’ ‘Place’/’Context’ People Risk factors Health Outcome Area levelIndividual level Confounding factors Points? Areas? Points? Points/areas?

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