Mortality Effects Of Air Pollution Research Articles

Using spatio-temporal lagged association pattern to unravel the acute effect of air pollution on mortality

BackgroundAcute mortality effects of air pollution have been recognized in plenty of environmental epidemiologic studies. However, existing studies usually assume a universal lag association across sites and seasons. Such a strategy ignores the heterogeneity of lag structures and may lead to bias in the estimation of effects. MethodsA Bayesian hierarchical model with flexible lag structures was applied to estimate the impact of particulate matter less than 10 μm (PM10) on mortality and determine whether the lag structure varied by season and location. Data from nine US communities, obtained from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), was used to examine the lagged associations between PM10 and daily mortality. The estimates obtained from the flexible lag approaches were compared with those from the universal lag approach. ResultsOf potential varying lag structures, a 10-μg/m3 increase in PM10 was associated with 0.32% (95% credible interval: 0.16, 0.45) and 0.36% (0.18, 0.52) increases in mortality from nonaccidental and cardiovascular-respiratory death. The community-specific estimates of PM10 mortality effects were distinct between the flexible and the universal lag approaches, with relative change of the effects ranged from −7.21% to 9.25% for nonaccidental morality, and from −5.78% to 4.16% for cardiovascular-respiratory morality. Moreover, the lag structure varied by location and season. For instance, the nonaccidental mortality effect of PM10 attributable to the current and previous day was 29.8% in El Paso while 55.0% in Chicago; the overall effect attributable to the previous two to five days were 60.6%, 51.9%, 59.5%, and 59.3% in winter, spring, summer, and fall, respectively. ConclusionThe results indicated that a universal lag association across sites and seasons may bias the mortality effect of air pollution. The varying lag structures should be considered in studies of short-term environmental exposures to get a more precise effect estimate.

Using rush hour and daytime exposure indicators to estimate the short-term mortality effects of air pollution: A case study in the Sichuan Basin, China

BackgroundDaily mean concentrations of air pollutants have been widely used as exposure indicators to estimate the short-term mortality effects of outdoor air pollution. However, daily mean concentrations might insufficiently represent the true exposure level because of the diurnal variations of air pollutants and various human activity patterns. Daytime or rush-hour concentrations may lead to better estimations. ObjectiveOur study aimed to imitate the true exposure level under assumptions about human activity patterns and to examine the short-term mortality effects of the exposure to air pollution during a) the morning-evening rush hours (ME), b) the morning-lunch-evening rush hours (MLE), and c) the whole daytime (WDT) in Chengdu, Sichuan Basin, China. MethodsWe investigated the diurnal variations of PM2.5, SO2, and O3 and examined the associations between the three pollutants and nonaccidental mortality, cardiovascular mortality, respiratory mortality using generalized additive model. Three novel exposure indicators (ME, MLE, and WDT) were employed to imitate the most probable exposure levels. Relative change of excess risk (ER) was used to compare effects estimated from models with different exposure indicators. ResultsIn the relationship of PM2.5 and mortality, ERs estimated from the novel-indicator models decreased by 4.88%–11.89% in comparison with ERs from the daily-indicator models. All the three novel indicators of SO2 offered lower ERs of respiratory mortality than the daily indicator did. Significant associations were observed in O3-nonaccidental mortality at lag0 in both winter and spring, and O3-cardiovascular mortality at lag0 in winter. Overall, majority of effect estimates based on rush-hour or daytime indicators were lower than the estimates based on daily mean concentrations. ConclusionThe use of daily mean concentrations may bias exposure assessment and thus inflating effect estimates. This study highlights the importance of rush-hour and daytime exposure and provides alternative indicators for estimating acute effects of air pollution.

Urban climate modified short-term association of air pollution with pneumonia mortality in Hong Kong

BackgroundCity is becoming warmer, especially in the process of urbanization and climate change. However, it is largely unknown whether this warming urban climate may modify the short-term effects of air pollution. ObjectivesTo test whether warmer urban climates intensify the acute mortality effects of air pollution on pneumonia in Hong Kong. MethodsParticipants who died of pneumonia from a prospective Chinese elderly cohort between 1998 and 2011 were selected as cases. Urban climatic (UC) classes of cases were determined by an established Urban Climatic Map according to their residential addresses. UC classes were first dichotomized into cool and warm climates and case-crossover analysis was used to estimate the short-term association of pneumonia mortality with air pollution. We further classified UC classes into climate quartiles and used case-only analysis to test the trend of urban climate modification on the short-term association of pneumonia mortality with air pollution. ResultsAmong 66,820 elders (≥65 years), 2208 pneumonia deaths (cases) were identified during the 11–14 years of follow-up. The effects of air pollution for cases residing in the warm climate were statistically significant (p < 0.05) higher than those living in the cool climate. There was an increasing linear trend of urban climate modification on the association of pneumonia mortality with NO2 (nitrogen dioxide) (p for trend = 0.035). Compared to climate Quartile 1 (the lowest), deaths resided in climate Quartile 2, 3, and 4 (the highest) were associated with an additional percent change of 9.07% (0.52%, 17.62%), 12.89% (4.34%, 21.43%), and 8.45% (−0.10%, 17.00%), respectively. ConclusionsWarmer urban climate worsened the acute mortality effects of pneumonia associated with air pollutants in Hong Kong. Our findings suggest that warmer urban climate introduced by climate change and urbanization may increase the risks of air pollution-related pneumonia.

Two-way effect modifications of air pollution and air temperature on total natural and cardiovascular mortality in eight European urban areas

BackgroundAlthough epidemiological studies have reported associations between mortality and both ambient air pollution and air temperature, it remains uncertain whether the mortality effects of air pollution are modified by temperature and vice versa. Moreover, little is known on the interactions between ultrafine particles (diameter ≤ 100 nm, UFP) and temperature. ObjectiveWe investigated whether the short-term associations of particle number concentration (PNC in the ultrafine range (≤100 nm) or total PNC ≤ 3000 nm, as a proxy for UFP), particulate matter ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10), and ozone with daily total natural and cardiovascular mortality were modified by air temperature and whether air pollution levels affected the temperature-mortality associations in eight European urban areas during 1999–2013. MethodsWe first analyzed air temperature-stratified associations between air pollution and total natural (nonaccidental) and cardiovascular mortality as well as air pollution-stratified temperature-mortality associations using city-specific over-dispersed Poisson additive models with a distributed lag nonlinear temperature term in each city. All models were adjusted for long-term and seasonal trend, day of the week, influenza epidemics, and population dynamics due to summer vacation and holidays. City-specific effect estimates were then pooled using random-effects meta-analysis. ResultsPooled associations between air pollutants and total and cardiovascular mortality were overall positive and generally stronger at high relatively compared to low air temperatures. For example, on days with high air temperatures (>75th percentile), an increase of 10,000 particles/cm3 in PNC corresponded to a 2.51% (95% CI: 0.39%, 4.67%) increase in cardiovascular mortality, which was significantly higher than that on days with low air temperatures (<25th percentile) [−0.18% (95% CI: −0.97%, 0.62%)]. On days with high air pollution (>50th percentile), both heat- and cold-related mortality risks increased. ConclusionOur findings showed that high temperature could modify the effects of air pollution on daily mortality and high air pollution might enhance the air temperature effects.

Effect of High Intake of Heavy Metals from Environmental Air and in Food in Diabetic Patients

Diabetes mellitus is an endocrine disorder and affecting millions of people. It remains latent and its secondary obstacles leads to the mortality and morbidity. Various metal such as arsenic, beryllium, cadmium and nickel have been linked with the occurrence of diabetes mellitus in peoples exposed to these elements. Some elements like iron and copper are crucial minerals that are necessary for a variety of molecules to maintain their normal structures, functions of cells to existence, grow, and multiply. The homeostasis of iron and copper is coordinated regulation by different proteins concerned in uptake, excretion and intracellular storage or transferring. This study connected airborne levels of these metals with diabetes mortality. The lowest air levels detected were beryllium and cadmium, with nickel showing the highest levels. This supported diabetes mortality effects of air pollution and correlating arsenic, beryllium, cadmium and nickel with diabetes incidence. Although iron is essential but it can be toxic in high amounts. Iron is a transit mineral can generate various reactive oxygen or nitrogen species so abnormal metabolism of iron can lead to numerous chronic pathogenesis. Oxidative stress is one of the main contributing factors for diabetes and diabetic problems. Iron overload may increases risks of insulin resistance and diabetes.

Diabetes mortality and environmental heavy metals in North Carolina counties: An ecological study

Introduction: Arsenic, beryllium, cadmium and nickel have been associated with the prevalence of diabetes mellitus in populations exposed to these elements. However, diabetes mortality has not been evaluated. This ecological study correlated airborne concentrations of these metals with diabetes mortality in North Carolina counties. Methods: County level data were extracted from the 2000 US Census, the 1999 US Environmental Protection Agency National Air Toxins Assessment, and 2001-2005 diabetes mortality rates by county from the North Carolina State Center for Health Statistics. Multivariable linear regression correlated airborne concentrations of each element with diabetes mellitus mortality rates in all 100 North Carolina counties controlling for county-level characteristics. Airborne concentrations were logarithmically transformed to normalize their distribution. Results: The lowest air concentrations detected were beryllium and cadmium, with nickel showing the highest concentration. Logarithmic concentrations spanned from 3.74 to 4.02 orders of magnitude. County-level diabetes mortality rates were negatively associated with median county income, but positively associated with county-level air concentrations of arsenic, beryllium, cadmium and nickel. Conclusions: These results support diabetes mortality effects of air pollution, and agree with other studies correlating arsenic, beryllium, cadmium and nickel with diabetes prevalence. Policy implications include regulating point source air pollution.

Diabetes Mortality and Environmental Heavy Metals in North Carolina Counties:An Ecological Study

Arsenic, beryllium, cadmium and nickel have been associated with the prevalence of diabetes mellitus in populations exposed to these elements. However, diabetes mortality has not been evaluated. This ecological study correlated airborne concentrations of these metals with diabetes mortality in North Carolina counties. County level data were extracted from the 2000 US Census, the 1999 US Environmental Protection Agency National Air Toxins Assessment, and 2001-2005 diabetes mortality rates by county from the North Carolina State Center for Health Statistics. Multivariable linear regression correlated airborne concentrations of each element with diabetes mellitus mortality rates in all 100 North Carolina counties controlling for county-level characteristics. Airborne concentrations were logarithmically transformed to normalize their distribution. The lowest air concentrations detected were beryllium and cadmium, with nickel showing the highest concentration. Logarithmic concentrations spanned from 3.74 to 4.02 orders of magnitude. County-level diabetes mortality rates were negatively associated with median county income, but positively associated with county-level air concentrations of arsenic, beryllium, cadmium and nickel. These results support diabetes mortality effects of air pollution, and agree with other studies correlating arsenic, beryllium, cadmium and nickel with diabetes prevalence. Policy implications include regulating point source air pollution.

Seasonal Pattern of the Acute Mortality Effects of Air Pollution

Evidence of seasonal variation of acute mortality effects of air pollution is inconsistent. The seasonal patterns of associations between daily mortality and daily mean concentrations of particulate matter 10 µm or less in aerodynamic diameter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) were examined using 4 yr of data (2001–2004) in Wuhan, China. Four distinct seasons occur in Wuhan, where approximately 4.5 million residents live in the city core area of 201 km2. Air pollution levels are higher and pollution ranges are wider in Wuhan than in most cities. Quasi-likelihood estimation within the context of the generalized additive models (natural spline [NS] models in R) was used to model the natural logarithm of the expected daily death counts as a function of the predictor variables. The estimates of the interaction between seasons and pollution were obtained from the main effects and pollutant season interaction models. It was found that the interactions between three pollutants and cause-specific mortality were statistically significant (P < 0.05). The strongest effects occurred consistently in winter for all-natural, cardiovascular, stroke, and respiratory mortality. Every 10-µg/m3 increase in PM10 daily concentration at lag 0–1 days was associated with an increase in all-natural mortality of 0.69% (95% confidence interval [CI]: 0.44–0.94%) for winter, 0.34% (95% CI: 0.00–0.69%) for spring, 0.45% (95% CI: –0.13 to 1.04%) for summer, and –0.21% (95% CI: –0.54 to 0.12%) for fall. The results show a clear seasonal pattern of acute mortality effects of ambient air pollution and the strongest effects occurred during winter in the study city.

Health benefits of improving air quality in the rapidly aging Korean society

Korea is experiencing an extraordinarily rapid demographic transition. We investigated the short-term association between air pollution and mortality and assessed the impact of improved air quality on mortality in a rapidly aging city, Seoul, Korea. The generalized additive model (GAM) was used to estimate the relative risks (RR) of mortality associated with changes in air pollution. The time trends, seasonal variations, day of the week effects, and weather effects were controlled in the models. To estimate the health benefits, we used the US Environmental Protection Agency's BenMAP. For people 0–64 years of age, elderly people (65+ years), and all age groups, an increase of 10 μg/m 3 in PM 10 was associated with increases in daily death counts of 0.27% (95% CI: 0.04–0.50), 0.45% (95% CI: 0.27–0.64), and 0.37% (95% CI: 0.23–0.52), respectively. For ages 0–64 years, elderly people, and all age groups, a 10 ppb increase in 1-hour maximum ozone concentration resulted in an increased risk of daily death counts of 0.28% (95% CI: − 0.19–0.74), 0.96% (95% CI: 0.46–1.47), and 0.81% (95% CI: 0.35–1.26), respectively. For elderly people, it was estimated that the health benefits of attaining the World Health Organization's (WHO) air quality guidelines (AQGs) for PM 10 (24-hour average 50 μg/m 3) would suggest an annual reduction of 964 (95% CI: 564–1366) premature deaths, and 329 (95% CI: 159–500) premature deaths could be prevented annually in 2015 from attaining the WHO's guidelines for ozone (8-hour average 100 μg/m 3). The rapid increase of the elderly population has major consequences and implications for society and public health. This study showed that elderly people are at higher risk for the acute mortality effects of air pollution. Therefore, cleaner air will substantially contribute to improved public health in Seoul, given the growing concern about the adverse effects of air pollution for elderly people.

Short-term Associations of Air Pollution with Postneonatal Infant Death in Seoul, Korea, 1999-2003

Objective to assess whether exposure to air pollutants is associated with postneonatal infant death, using a timeseries methodology, between 1999 and 2003 in Seoul, Korea.. Methods We investigated the short-term effects of air pollution for 548,725 live births during the study period. The daily count of postneonatal infant deaths from all causes and from SIDS (sudden infant death syndrome) by birth order was analyzed by a Generalized Additive Poisson model, with controlling for the effects of seasonal trends, air temperature, relative humidity, barometric pressure, and day of the week as covariates. Results During the study period, we observed 699 deaths from all causes and 47 deaths from SIDS. We did not find any significant associations between daily mortality and ambient levels of air pollutants except for CO and <TEX>$NO_2$</TEX>. The estimated relative risk of postneonatal infant death from all causes was 1.17 (95% CI=1.04-1.32) and 1.16 (95% CI=1.03-1.29) by IQR (interquartile range) for CO and <TEX>$NO_2$</TEX> respectively. Also, we observed no clear trend of the mortality effects of air pollution by birth orders. Conclusion In conclusion, our findings suggest that air pollution, in general, influenced adversely postneonatal infant death from all-cause and SIDS although it was not statistically significant. This study may support that the rationale.

Exploring uncertainty of the change from ICD-9 to ICD-10 on acute mortality effects of air pollution

The change in ICD coding from ICD-9 to ICD-10 may produce inconsistencies and discontinuities in cause-specific mortality, thus impacting on effects estimates of air pollution on mortality. The current study was conducted in Wuhan, China. We examined the concordant rates and Kappa statistics using the mortality data from the year 2002 coded with both ICD-9 and ICD-10 codes and compared the estimated effects of air pollution using the Generalized Additive Model in R. We found high concordant rates (>99.3%) and Kappa statistics close to 1.0 (>0.98). Little difference was identified in the estimated effects of air pollution on daily cardiovascular, stroke, cardiac, cardiopulmonary, and respiratory mortality. This study provides evidence that, based on the wide definitions of cause-specific morality typically used in the studies of time-series air pollution mortality, the change in the ICD coding does not significantly affect the estimated effects of air pollution.

Short-Term Effects of Gaseous Pollutants on Cause-Specific Mortality in Wuhan, China

In Asia, limited studies have been published on the association between daily mortality and gaseous pollutants of nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). Our previous studies in Wuhan, China, demonstrated long-term air pollution effects. However, no study has been conducted to determine mortality effects of air pollution in this region. This study was to determine the acute mortality effects of the gaseous pollutants in Wuhan, a city with 7.5 million permanent residents during the period from 2000 to 2004. There are approximately 4.5 million residents in Wuhan who live in the city’s core area of 201 km2, where air pollution levels are highest, and pollution ranges are wider than the majority of the cities in the published literature. We used the generalized additive model to analyze pollution, mortality, and covariate data. We found consistent NO2effects on mortality with the strongest effects on the same day. Every 10-μg/m3increase in NO2daily concentration on the same day was associated with an increase in nonaccidental (1.43%; 95% confidence interval [CI]: 0.87–1.99%), cardiovascular (1.65%; 95% CI: 0.87–2.45%), stroke (1.49%; 95% CI: 0.56–2.43%), cardiac (1.77%; 95% CI: 0.44–3.12%), respiratory (2.23%; 95% CI: 0.52–3.96%), and cardiopulmonary mortality (1.60%; 95% CI: 0.85– 2.35%). These effects were stronger among the elderly than among the young. Formal examination of exposure-response curves suggests no-threshold linear relationships between daily mortality and NO2, where the NO2concentrations ranged from 19.2 to 127.4 μg/m3. SO2and O3were not associated with daily mortality. The exposure-response relationships demonstrated heterogeneity, with some curves showing nonlinear relationships for SO2and O3. We conclude that there is consistent evidence of acute effects of NO2on mortality and suggest that a no-threshold linear relationship exists between NO2and mortality.

Mortality Displacement and Distributed Lag Models

Numerous time-series studies have investigated the association between daily mortality and daily ambient particulate air pollution concentrations (PM). The consensus from these studies is that increases in PM are associated with increases in daily mortality. However, it may be that increases in PM only hasten the deaths of individuals in a small, frail subset of the population whose longevity is short even in the absence of particulate air pollution. This hypothesis has been termed mortality displacement or harvesting. Distributed lag models (DLM) have been used to explore mortality effects of air pollution that are spread over multiple days, and DLM coefficients have been proposed as indicators of mortality displacement. We investigate statistical properties of DLM coefficients in the context of mortality displacement using simulation studies with frail population models. Our simulations use actual PM time series, as well as actual weather time series included as confounders. Our simulations show that DLM coefficients can have large bias when the mean lifetime of individuals in the frail subset of the population is more than a few weeks, and that the magnitude of this bias increases as the mean lifetime of individuals in the frail subset of the population increases. We conclude that DLM coefficients may be misleading as an indicator of mortality displacement, in the context of the frail population models that we explored.

Response to Dr. Smith: Timescale-dependent Mortality Effects of Air Pollution

Time-series studies have indicated that there is an association between day-to-day variation in ambient air pollution concentrations and day-to-day variation in numbers of deaths, after data are controlled for more slowly varying confounding factors such as weather, seasonality, and longterm trends. However, the estimated coefficients in the timeseries studies are uninformative as to the amount of life lost due to pollution exposure, particularly within susceptible populations (1). If individuals who were severely ill and were expected to die shortly were the only people affected by current levels of air pollution, reducing ambient concentrations would not necessarily increase life expectancy significantly. This phenomenon of only brief advancement of the timing of death has been referred to as “short-term mortality displacement,” as well as by the unfortunate term “short-term harvesting.” While no lives should be shortened by air pollution, society suffers a much smaller loss if air pollution affects only frail persons without great loss of life expectancy. Our paper (2) was motivated by the need to find methods of assessing short-term harvesting for studies of air pollution and other environmental agents. In this rejoinder to the commentary of Dr. Richard Smith (3), we briefly 1) review the conceptual framework under which short-term harvesting would occur, 2) illustrate how our timescale model would detect short-term harvesting, and 3) summarize the statistical evidence supporting short-term harvesting. A compartmental model (4–6) sets a biomedical stage for approaching the assessment of short-term harvesting. Suppose that the population can be divided into two groups according to susceptibility to an air pollution episode: lowrisk and high-risk. On any given day, people in the low-risk pool can become frail and move into the high-risk pool (T1) and people in the high-risk pool can become healthier and move into the low-risk pool (T2) or can exit the high-risk pool by dying (T3). Assuming a steady-state condition, T3 = T1 – T2; that is, there is equilibrium between the number of people who die (T3) and the number of people who enter the susceptible pool (T1), net the number of people who recover (T2). We assume that under short-term harvesting, an air pollution episode would affect only transition out of the high-risk pool (T3), without increasing net recruitment into the high-risk pool (T1 – T2). Therefore, for some days after an air pollution episode, the susceptible pool would be depleted, and the daily death count would be diminished. (See Schwartz (6) for further details.) This phenomenon can be further described by a distributed lag model (7–9) that includes several lags of the pollution variables:

Inappropriate use of daily mortality analyses to estimate longer-term mortality effects of air pollution.

To avoid the usual problems of multi-population correlation studies of air pollution and mortality, and for reasons of convenience, daily time-series mortality studies within single populations have recently become popular in air pollution epidemiology. Such studies describe how the short-term distribution of deaths relates to short-term fluctuations in air pollution levels. The regression-based risk coefficients from these acute-effects studies have been widely used to estimate the excess annual mortality within a population with a specified average level of air pollution. Such calculations are inappropriate. Since daily time-series data provide no simple direct information about the degree of life-shortening associated with the excess daily deaths (many of which are thought to be due to exacerbation of well-advanced disease, especially cardiovascular disease), such data cannot contribute to the estimation of the effects of air pollution upon chronic disease incidence and long-term death rates. Yet it is that category of effect that is of most public health importance. Such effects are best estimated from long-term cohort studies that incorporate good knowledge of local (or personal) exposure to air pollutants and of potential confounders. Time-series studies, properly evaluated, can identify the existence of acute toxic effects of transient peak levels of air pollution; they are thus useful for monitoring acute toxicity and for identifying the most noxious pollutants. However, to quantify the long-term health impacts of air pollution we cannot use acute-effects data.

Air pollution, lagged effects of temperature, and mortality: The Netherlands 1979-87.

To explore whether the apparent low threshold for the mortality effects of air pollution could be the result of confounding. The associations between mortality and sulphur dioxide (SO2) were analysed taking into account potential confounding factors. The Netherlands, 1979-87. The number of deaths listed by the day on which the death occurred and by the cause of death were obtained from the Netherlands Central Bureau of Statistics. Mortality from all causes and mortality from four large groups of causes (neoplasms, cardiovascular diseases, respiratory diseases, and external causes) were related to the daily levels of SO2 air pollution and potential confounders (available from various sources) using log-linear regression analysis. Variables considered as potential confounders were: average temperature; difference between maximum and minimum temperatures; amount of precipitation; air humidity; wind speed; influenza incidence; and calendar year, month, and weekday. Both lagged and unlagged effects of the meteorological and influenza variables were considered. Average temperature was represented by two variables--'cold', temperatures below 16.5 degrees C, and 'warm', those above 16.5 degrees C--to allow for the V shaped relation between temperature and mortality. The positive regression coefficient for the univariate effect of SO2 density on mortality from all causes dwindles to close to zero when all potential confounding variables are taken into account. The most important of these represents the lagged (one to five days) effect of low temperatures. Low temperatures have strong lagged effects on mortality, and often precede relatively high SO2 densities in the Netherlands. Results were similar for separate causes of death. While univariate associations suggest an effect of air pollution on mortality in all four cause of death groups, multivariate analyses show these effects, including that on mortality from respiratory diseases, are a result of confounding. The SO2 density (or that of compounds closely associated with SO2) does not seem to have any short term effect on mortality in the Netherlands. SO2 levels higher than those currently reached in the Netherlands (above 200 micrograms/m3) may have a measurable effect on mortality and this should be investigated. Furthermore, analyses of the public health impact of outdoor air pollution should properly control for the lagged effects of temperature.

Air pollution-mortality models: A demonstration of the effects of random measurement error

Errors of measurement have long been recognized as a chronic problem in statistical analysis. Although there is a vast statistical literature of multiple regression models estimating the air pollution-mortality relationship, this problem has been largely ignored. It is well known that pollution measures contain error, but the consequences of this error for regression estimates is not known. We use Lave and Seskin's air pollution model to demonstrate the consequences of random measurement error. We assume a range of 0% to 50% of the variance of the pollution measures is due to error. We find large differences in the estimated effects on mortality of the pollution variables as well as the other explanatory variables once this measurement error is taken into account. These results cast doubt on the usual regression estimates of the mortality effects of air pollution. More generally our results demonstrate the consequences of random measurement error in the explanatory variable of a multiple regression analysis and the misleading conclusions that may result in policy research if this error is ignored.