ethnicity, deprivation, rurality and cause of death.21 For example, if 20 of 30 Maori men who died aged 45e64 years and living in moderately deprived (see below) rural areas of New Zealand were linked to a census record, each of the 20 linked records received a weight of 1.5 (30/20). The estimated proportion of links being true links was 97%. Air pollution exposure estimation Air pollution monitoring data are typically only available for a few sites and may not be representative of exposure in other areas. Detailed atmospheric dispersion modelling can be used to provide estimates of air pollution exposure for small areas, but these models are difﬁcult to apply to large areas due to data and computer processing limitations. The method of air pollution exposure assessment has been described in detail elsewhere.22 Brieﬂy, the approach to modelling long-term average PM10 was as follows. Atmospheric dispersion modelling results were available for one city (Christchurch, population 300 000). These data were assumed to be representative of the spatial pattern of annual average PM10 exposures for small areas (census area units) in Christchurch.23 Data on meteorological variables and indicators of air pollutant emissions for these areas were used as predictors of PM10 exposure in Christchurch, using regression models. The predictors for the regression models were: census data on domestic heating; estimates of industrial emissions; and vehicle kilometres travelled within small areas. Because data for these predictor variables were available for all of New Zealand, we were able to extrapolate the empirical results for the Christ- church regression model to urban census area units throughout the country. The resulting exposure estimates agreed well with multiyear averages of PM10 based on routine monitoring data for urban centres (1995e2001) (N¼43; r2¼0.87). While we acknowledge that there may be considerable exposure misclassiﬁcation, we assume that the PM10 estimates are representative of long-term average spatial patterns of exposure during the 1990s. Ideally, for analyses with the New Zealand Census Mortality Study data we would have used the continuous PM10 estimates for individual census areas. However, because the New Zealand Census Mortality Study data contain census information at unit record level, access to the data is restricted and additional data can only be added if conﬁdentiality is not compromised as a result. Assigning continuous PM10 estimates at census area level would have permitted many census areas to be uniquely identiﬁed. In order to maintain conﬁdentiality, it was necessary to aggregate the PM10 estimates into quintiles before they could be merged with the census data. Quintiles of exposure were calculated based on the estimates for individual census areas. For this purpose, we assigned a PM10 estimate of 0 mg/m3 to rural census areas where PM10 estimates were unavailable. The average PM10 level for all New Zealand census areas was 8.3 mg/ m3 (SD 8.4 mg/m3) and the cut-off values between PM10 quin- tiles were 0.0, 0.5, 12.5 and 15.4 mg/m3. Individuals were assigned to quintiles of PM10 exposure depending on their census area of residence on census night. In order to avoid bias affecting analyses in smaller rural areas due to migration to cities following the development of disease, we restricted the analyses to the urban population. This resulted in 1 065 645 observationsi with complete data. Following restriction iAll counts were randomly rounded to a multiple of 3 in order to preserve conﬁdentiality, as per Statistics New Zealand protocol. For this reason, subtotals may not match across categories. Note that regression analyses, however, were undertaken on actual unit record data. J Epidemiol Community Health 2012;66:468e473. doi:10.1136/jech.2010.112490 Research report to the urban population, the lowest two quintiles of exposure had relatively few observations (table 1). For this reason, and given the small variation in PM levels between quintiles 1 and 2, we combined the two lowest quintiles of PM10 to produce four categories in total. The estimated mean PM10 levels for these categories were 0.1, 7, 14 and 19 mg/m3 (long-term averages). Ethnicity and socioeconomic position are strong predictors of mortality in New Zealand, and potential confounders of the association of air pollution with mortality. We assigned each respondent to a mutually exclusive ethnic group using a priori- tisation system commonly used in New Zealand: Maori, if any one of the responses was Maori; Paciﬁc, if any one response was Paciﬁc but not Maori; and the remainder non-Maori non-Paciﬁc (mostly New Zealand European). Smoking status was reported in the census using the categories: never smoker, ex-smoker, or current smoker.24 Socioeconomic position was characterised as: total household income, with adjustment for the number of children and adults in the household to allow for economies of scale, log-transformed having ﬁrst set all values of less than NZ $1000 to equal NZ$1000;20 highest educational qualiﬁcation (higher than school, school, or none); and neighbourhood deprivation measured by the NZDep index (in quintiles).25 This index of deprivation within small geographical areas was calculated using census data on socioeconomic characteristics (eg, car access, tenure and receipt of beneﬁts) at aggregations of approximately 100 people, and assigned to mortality data by use of address. As climate is correlated with PM10, and associated with mortality (independently of PM10), temperature has the potential to confound air pollution effects. Therefore, we also included estimates of long-term average minimum temperature (in addition to the above sociodemographic factors) in the models, also in quintiles based on place of residence. These data were derived from an interpolated temperature surface.26 Mean values for the minimum temperature quintiles were 0.2, 2, 4, 5 and 78C. Logistic regression analyses were conducted to investigate associations between all-cause and cause-speciﬁc mortality rates and average exposure to PM10, with control for confounding by age, sex, ethnicity, social deprivation, income, education, smoking history and average minimum temperature. As death is a rare outcome over 3 years for the age groups included in the analysis, logistic regression results differ very little from those from either Poisson or Cox proportional hazards modelling. Initial models incorporated PM10 categories as dummy variables, and given a reasonably linear doseeresponse, ﬁnal models used the estimated mean PM10 for these four categories (see above). In order to facilitate comparison with overseas ﬁndings, we report results for adults aged 30e74 years. In ﬁnal models, age was included as a linear plus a squared term, and the income variable was natural-log transformed. All other covariates were included as dummy variables (table 2). Any association of air pollution with mortality might be modiﬁed by social and environmental factors, due to different vulnerability or intensity of exposure to air pollution. We tested for interaction both by stratiﬁcation and by inclusion of interaction terms. Finally, we conducted a sensitivity analysis by restricting the dataset for analysis to those people who had lived in the same geographical unit at the 1991 census as they did at the time of exposure assignment (1996) in this cohort study. This restriction should reduce exposure misclassiﬁcation by excluding those people who have not been exposed to the same level of PM10 for at least 5 years. 469