Table 2 Logistic regression model including all variables listed as independent variables (all deaths, all ethnicities: N¼ 1 065 645)* Table 3 Findings by subgroups of ethnicity and cause of death (model 3, fully adjusted) OR Particulate PM10 (mg/m3) 1.007 Temperature quintile (base: lowest) 2 0.967 3 0.987 4 0.957 5 0.908 Age 1.088 Age squared 1.000 Natural log of income 0.819 Sex (base: male) Female 0.598 Ethnicity (base: Maori) Paciﬁc 0.813 European 0.500 Deprivation quintile (base: least deprived) 2 1.078 3 1.173 4 1.330 5 1.548 Smoking (base: current smoker) past 0.706 never 0.498 Education (base: none) school 0.933 higher 0.860 95% CI lower 1.003 0.915 0.932 0.908 0.861 1.071 1.000 0.799 0.579 0.731 0.472 1.025 1.116 1.265 1.469 0.677 0.477 0.895 0.828 upper 1.010 1.022 1.046 1.010 0.959 1.105 1.000 0.839 0.618 0.905 0.529 1.134 1.234 1.399 1.632 0.736 0.519 0.971 0.894 *Rounded; for logit form of model, constant 6.528 ( 7.036 to 6.020). using the average PM10 for each category) (tables 2 and 3). Table 2 shows the same model as in ﬁgure 1, except for the continuous treatment of PM10. The OR of 1.007 corresponds to a 1 unit increase in PM10, which equates to an increase of 7% (95% CI 3% to 10%) in the odds of all-cause mortality in adults (aged between age 30 and 74 years at census) per 10 mg/m3 increase in long-term average PM10 exposure. Figure 1 OR and 95% CI of all-cause mortality for people living in the three non-referent PM10 categories, compared with quintiles 1 and 2 combined. Model is for sexes combined, restricted to adults aged 30e74 years on census night, urban population, with covariates as follows: model 1: age, sex, ethnicity, all data, N¼1 364 454; model 2: age, sex, ethnicity, data with non-missing values for covariates in model 3, N¼1 065 645; model 3: age, sex, ethnicity, deprivation, income, education, smoking, temperature, N¼1 065 645. J Epidemiol Community Health 2012;66:468e473. doi:10.1136/jech.2010.112490 Research report N* OR (per 1 Lower Upper mg/m3 PM10) 95% CI 95% CI Model All deaths, all ethnicities 1 065 645 1.007 All deaths, living in the same 601 401 1.008 census area as for the 1991 census All natural causes, all ethnicities 1 063 563 1.007 (excludes accidents and injury) All natural causes, European 940 107 1.006 All natural causes, Maori 87 615 1.018 Analyses by speciﬁc cause of death, based on ICD9 codesy Lung cancers (ICD 162) 1 050 222 1.015 Respiratory disease (ICD 162, 1 051 464 1.013 470e478, 490e519) Cardiovascular disease (ICD 393e438) 1 054 731 1.006 Accidents and injury (ICD 800e949) 1 049 646 1.004 Other, includes unspeciﬁedz 1 056 288 1.005 1.003 1.004 1.003 1.003 1.007 1.004 1.005 1.001 0.990 1.001 1.010 1.011 1.010 1.010 1.029 1.026 1.021 1.011 1.018 1.010 *Rounded. yNote: Models included persons dying from the speciﬁed causes (coded 1) and people presumed alive at the end of follow-up (coded 0), but excluded persons dying from other causes. Respiratory disease includes lung cancers. z‘Other’ deaths are all those deaths (including unspeciﬁed) that are not included among lung cancer, respiratory, cardiovascular disease or accidents. ICD, International Classiﬁcation of Diseases. We found stronger effects of PM10 among people who lived in the same census area in 1991 (at the time of the previous census), 8% (4% to 12%) per 10 mg/m3 increase in PM10. By cause of death, the association was similar for all natural causes, 7% (3% to 10%), but substantially stronger for respiratory deaths (including lung cancers), 14% (5% to 23%) and for lung cancers, 16% (4% to 29%). For cardiovascular disease, 6% (1% to 12%) and for ‘other and unspeciﬁed’ causes of death, 5% (1% to 10%), the association was marginally signiﬁcant; while for accidental deaths and injuries, the association was non-signiﬁ- cant, 4% ( 9% to 20%) (table 3). Considering interaction with social variables, there was an apparently stronger association of PM10 with all-cause mortality among Maori, 20% (7% to 33%). However, the 95% CI for Europeans overlapped that for Maori, 7% (3% to 10%). A Wald test for interaction between ethnicity and PM10 was not signiﬁ- cant (p¼0.12). There were no statistically signiﬁcant interactions with age, sex, income, deprivation, educational status or average temperature. Mortality was lower among people living in warmer census areas, and the difference between the lowest and the highest quintile of annual average temperature was statistically signiﬁcant. DISCUSSION Using linked mortality and census data, we report a signiﬁcant positive association between estimated long-term exposure to air pollution (PM10) and mortality in New Zealand urban areas. This setting includes approximately 75% of the New Zealand population, who are exposed to relatively low levels of PM10 compared with other countries. Selection bias and confounding seem unlikely to explain our results. There is no evidence of selection bias (ﬁgure 1). The results persist after controlling for plausible confounders, including multiple measures of socioeconomic position and smoking. It seems unlikely that mismeasured or unknown confounders might explain the remaining association. There is likely to be substantial misclassiﬁcation of the air pollution exposure. Most of this misclassiﬁcation was probably 471