Spatial patterns of mortality in relation to particulate air pollution in Christchurch, 1988-1997 Simon Hales1, Clare Salmond1, Daniel Exeter2, Martin Purvis3, Alistair Woodward1, Tord Kjellstrom2 1
Department of Public Health Wellington School of Medicine PO BOX 7343, Wellington, New Zealand
[email protected] 2
NEOH, University of Auckland, Auckland, New Zealand
3
Department of Information Science University of Otago, Dunedin, New Zealand Presented at SIRC 2000 – The 12th Annual Colloquium of the Spatial Information Research Centre University of Otago, Dunedin, New Zealand: December 10-13th 2000
ABSTRACT This paper describes an analysis of mortality among census areas in the city of Christchurch, New Zealand. The number of deaths following days with high particulate air pollution were compared with deaths on matched unpolluted days. The possible role of population age structure, relative deprivation (estimated using the NZDep96 index) and local exposure to outdoor air pollution from household fires (estimated using a chimney density index) was explored. There was a statistically significant association between mortality and air pollution. We found substantial variation in pollution-related mortality among census area units. Relative deprivation (but not the proportion of elderly people or chimney density) was found to be a statistically significant predictor of mortality patterns. There was also a positive association between chimney density and relative deprivation. These findings suggest that relative deprivation may increase vulnerability to the effects of particulate air pollution on daily mortality, independently of the effects of age and local variation in exposure. Keywords and phrases: mortality, relative deprivation, temperature, particulate air pollution, spatial patterns, epidemiology
1.0 INTRODUCTION Recent studies have demonstrated positive associations between daily mortality and levels of ambient particulates present in many urban environments worldwide; these results are found to be both consistent and statistically significant using a wide range of modelling techniques (Schwartz 1991; Schwartz and Dockery 1992a; Schwartz and Dockery 1992b; Xu et al. 1994; Li and Roth 1995; Wyzga and Lipfert 1995; Moolgavkar et al. 1995a; Samet et al. 1995a; Moolgavkar et al. 1995b; USEPA 1996; Samet et al. 1996a; Samet et al. 1996b; Katsouyanni et al. 1997; Michelozzi et al. 1998; Neas et al, 1999; Pope, 2000). In general, the major anthropogenic sources of particulates are combustion processes, particularly of fossil and biomass fuels such as diesel, coal and wood. In Christchurch, modelling studies suggest that solid fuel combustion for home heating is an important source of ambient particulates (Canterbury Regional Council 1997). Air pollution is associated with increases in mortality from all causes, whilst stronger effects on respiratory and/or cardiovascular mortality
have been reported. The effects of air pollution are generally more severe in vulnerable sections of the population, including older people and those with underlying illness (Pope, 2000; Schwartz, 2000; Zanobetti and Schwartz, 2000; Zanobetti et al, 2000). The effects of air pollution are therefore unlikely to be uniform in space. Whilst many studies have examined the effects of air pollution on short-term changes in mortality rates, spatial patterns of these effects and their possible causes have not been studied. Results of previous research on air pollution and health in Christchurch have been inconsistent. One study of daily hospital admissions for asthma found no association with air pollution on the same day (Dawson et al. 1983). Another study showed increased respiratory symptoms in people with chronic lung disease (Harre 1997). The most recent study found increased mortality associated with high (but not low) temperatures on the same day, and particulate air pollution after a lag of 1 day (Hales et al. 2000). The study reported here tests the hypothesis that in the city of Christchurch, non-random spatial patterns of increased mortality exist on polluted days. We explore the possible role of age, relative deprivation and local exposure to smoke from household fires in explaining these patterns.
2.0 METHODS Data sources: Mortality data: the daily numbers of deaths by census area unit (n=104, average population 3000, range 3006000) were obtained for the years 1988 – 1997 for the Christchurch Territorial Local Authority area (population approximately 300,000). Weather data: data were available from a national climate database (NIWA 1997) for a station at Christchurch Airport. Variables extracted were hourly temperature, wind speed and direction. Air quality data: Hourly measurements of airborne particulate with an aerodynamic diameter of less than 10µm were available from a single, centrally located city site maintained by Canterbury Regional Council. Daily average particulate estimates were calculated from the hourly data. Missing values were imputed using diurnal and seasonal sinusoidal patterns, temperature and wind speed in a linear regression model, as described elsewhere (Hales et al 2000). Analysis proceeded in three stages:
2.1 Analysis of mortality rate ratios on polluted Vs unpolluted nights Mortality within each census area unit was summed over all "exposed" days with high air pollution (24 hour average particulate level greater than 50mcg/m3) and matched "unexposed" days with low pollution (24 hour average particulate less than 50mcg/m3). For each of the exposed days, we matched up to 8 unexposed days which fell within the period 4 days before and 4 days after the exposure. Because previous time series analyses (Hales et al, 2000) had shown that the maximum effect of particulate appeared after a 1 day lag, the above analysis was repeated using particulate data lagged by 1 day. Person-years of exposure were estimated using the population of each census area unit. Mortality rate ratios (MRR), 95% confidence intervals (CI) and weights were calculated using the Mantel-Haenszel (M-H) method.
2.2 Analysis of factors which might explain spatial patterns in mortality Using census areas as the unit of observation, we investigated the relationship between MRR and the following variables: • • •
the proportion of people aged over 60 years. relative deprivation, estimated using the NZDep96 small area index – derived as described elsewhere (Salmond et al, 1998). local exposure to smoke from household fires, estimated using a chimney density index – derived as described elsewhere (Kjellstrom and Exeter, 1999; Kjellstrom and Exeter, 2000; Exeter et al, 2000).
Using generalised linear models, we modelled the independent effects of the putative explanatory variables. We wished to account for the fact that the estimates of MRR were more precise in census areas with large numbers of deaths compared with areas with few deaths. Accordingly, we used M-H weights (from stage 1) to adjust for the varying precision of the MRR estimates. We also included a variable for the proportion of the population aged over 60.
A multiplicative model was considered most appropriate on theoretical grounds. That is, we considered that the effect of an incremental exposure to air pollution was likely to depend on the underlying mortality rate within each census area (in comparison, an additive model would have assumed a fixed increase in mortality for a given exposure to air pollution, regardless of the underlying mortality rate). To model possible non-linear effects using regression models, we re-coded quintiles of chimney density and relative deprivation using dummy variables: Iqchim1, Iqchim2 etc; Iqnzdep1, Iqnzdep2 etc. (see Kirkwood, 1988).
2.3 Analysis by quintile of deprivation Finally, we calculated means of MRR, the proportion of elderly people and chimney density by quintile of relative deprivation.
3.0 RESULTS 3.1 Analysis of mortality rate ratios on polluted Vs unpolluted nights Mortality was significantly increased following polluted days (with a lag of 1 day, compared to days following unpolluted nights), with a combined MRR of 1.05 (95% CI 1.03-1.08). Assuming a linear dose-response relationship, the magnitude of this effect is comparable to that of the equivalent time series analysis in which the particulate variable was lagged by one day (Hales et al, 2000). In view of this, the analysis of MRR using a 1day lag of the particulate variable was used. We also found substantial variation in pollution-related mortality among census area units (Figure 1).
Figure 1. Map of mortality rate ratios by census area unit in Christchurch. MRR>1 in dark grey;
