National Morbidity, Mortality, And Air Pollution Study Research Articles

Extended two-stage designs for environmental research

BackgroundThe two-stage design has become a standard tool in environmental epidemiology to model multi-location data. However, its standard form is rather inflexible and poses important limitations for modelling complex risks associated with environmental factors. In this contribution, we illustrate multiple design extensions of the classical two-stage method, all implemented within a unified analytic framework.MethodsWe extended standard two-stage meta-analytic models along the lines of linear mixed-effects models, by allowing location-specific estimates to be pooled through flexible fixed and random-effects structures. This permits the analysis of associations characterised by combinations of multivariate outcomes, hierarchical geographical structures, repeated measures, and/or longitudinal settings. The analytic framework and inferential procedures are implemented in the R package mixmeta.ResultsThe design extensions are illustrated in examples using multi-city time series data collected as part of the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Specifically, four case studies demonstrate applications for modelling complex associations with air pollution and temperature, including non-linear exposure–response relationships, effects clustered at multiple geographical levels, differential risks by age, and effect modification by air conditioning in a longitudinal analysis.ConclusionsThe definition of several design extensions of the classical two-stage design within a unified framework, along with its implementation in freely-available software, will provide researchers with a flexible tool to address novel research questions in two-stage analyses of environmental health risks.

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.

Socio-geographic disparity in cardiorespiratory mortality burden attributable to ambient temperature in the United States.

Compared with relative risk, attributable fraction (AF) is more informative when assessing the mortality burden due to some environmental exposures (e.g., ambient temperature). Up to date, however, available AF-based evidence linking temperature with mortality has been very sparse regionally and nationally, even for the leading mortality types such as cardiorespiratory deaths. This study aimed to quantify national and regional burden of cardiorespiratory mortality (CRM) attributable to ambient temperature in the USA, and to explore potential socioeconomic and demographic sources of spatial heterogeneity between communities. Daily CRM and weather data during 1987-2000 for 106 urban communities across the mainland of USA were acquired from the publicly available National Morbidity, Mortality and Air Pollution Study (NMMAPS). We did the data analysis using a three-stage analytic approach. We first applied quasi-Poisson regression incorporated with distributed lag nonlinear model to estimate community-specific temperature-CRM associations, then pooled these associations at the regional and national level through a multivariate meta-analysis, and finally estimated the temperature-AF of CRM and performed subgroup analyses stratified by community-level characteristics. Both low and high temperatures increased short-term CRM risk, while temperature-CRM associations varied by regions. Nationally, the fraction of cardiorespiratory deaths caused by the total non-optimum, low, and high temperatures was 7.58% (95% empirical confidence interval, 6.68-8.31%), 7.15% (6.31-7.85%), and 0.43% (0.37-0.46%), respectively. Greater temperature-AF was identified in two northern regions (i.e., Industrial Midwest and North East) and communities with lower temperature and longitude, higher latitude, and moderate humidity. Additionally, higher vulnerability appeared in locations with higher urbanization level, more aging population, less White race, and lower socioeconomic status. Ambient temperature may be responsible for a large fraction of cardiorespiratory deaths. Also, temperature-AF of CRM varied considerably by geographical and climatological factors, as well as community-level disparity in socioeconomic status.

Robust distributed lag models using data adaptive shrinkage.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on an outcome of interest such as mortality or cardiovascular events. Generally speaking, DLMs can be applied to time-series data where the current measure of an independent variable and its lagged measures collectively affect the current measure of a dependent variable. The corresponding distributed lag (DL) function represents the relationship between the lags and the coefficients of the lagged exposure variables. Common choices include polynomials and splines. On one hand, such a constrained DLM specifies the coefficients as a function of lags and reduces the number of parameters to be estimated; hence, higher efficiency can be achieved. On the other hand, under violation of the assumption about the DL function, effect estimates can be severely biased. In this article, we propose a general framework for shrinking coefficient estimates from an unconstrained DLM, that are unbiased but potentially inefficient, toward the coefficient estimates from a constrained DLM to achieve a bias-variance trade-off. The amount of shrinkage can be determined in various ways, and we explore several such methods: empirical Bayes-type shrinkage, a hierarchical Bayes approach, and generalized ridge regression. We also consider a two-stage shrinkage approach that enforces the effect estimates to approach zero as lags increase. We contrast the various methods via an extensive simulation study and show that the shrinkage methods have better average performance across different scenarios in terms of mean squared error (MSE).We illustrate the methods by using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to explore the association between PM$_{10}$, O$_3$, and SO$_2$ on three types of disease event counts in Chicago, IL, from 1987 to 2000.

Tempo-Spatial Variations of Ambient Ozone-Mortality Associations in the USA: Results from the NMMAPS Data.

Although the health effects of ambient ozone have been widely assessed, their tempo-spatial variations remain unclear. We selected 20 communities (ten each from southern and northern USA) based on the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) dataset. A generalized linear model (GLM) was used to estimate the season-specific association between each 10 ppb (lag0-2 day average) increment in daily 8 h maximum ozone concentration and mortality in every community. The results showed that in the southern communities, a 10 ppb increment in ozone was linked to an increment of mortality of −0.07%, −0.17%, 0.40% and 0.27% in spring, summer, autumn and winter, respectively. For the northern communities, the excess risks (ERs) were 0.74%, 1.21%, 0.52% and −0.65% in the spring, summer, autumn and winter seasons, respectively. City-specific ozone-related mortality effects were positively related with latitude, but negatively related with seasonal average temperature in the spring, summer and autumn seasons. However, a reverse relationship was found in the winter. We concluded that there were different seasonal patterns of ozone effects on mortality between southern and northern US communities. Latitude and seasonal average temperature were identified as modifiers of the ambient ozone-related mortality risks.

Modeling the effect of temperature on ozone-related mortality

Climate change is expected to alter the distribution of ambient ozone levels and temperatures which, in turn, may impact public health. Much research has focused on the effect of short-term ozone exposures on mortality and morbidity while controlling for temperature as a confounder, but less is known about the joint effects of ozone and temperature. The extent of the health effects of changing ozone levels and temperatures will depend on whether these effects are additive or synergistic. In this paper we propose a spatial, semi-parametric model to estimate the joint ozone-temperature risk surfaces in 95 US urban areas. Our methodology restricts the ozone-temperature risk surfaces to be monotone in ozone and allows for both nonadditive and nonlinear effects of ozone and temperature. We use data from the National Mortality and Morbidity Air Pollution Study (NMMAPS) and show that the proposed model fits the data better than additive linear and nonlinear models. We then examine the synergistic effect of ozone and temperature both nationally and locally and find evidence of a nonlinear ozone effect and an ozone-temperature interaction at higher temperatures and ozone concentrations.

Warmer is healthier: Effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities

Recent studies have indicated that reducing particulate pollution would substantially reduce average daily mortality rates, prolonging lives, especially among the elderly (age⩾75). These benefits are projected by statistical models of significant positive associations between levels of fine particulate matter (PM2.5) levels and daily mortality rates. We examine the empirical correspondence between changes in average PM2.5 levels and temperatures from 1999 to 2000, and corresponding changes in average daily mortality rates, in each of 100 U.S. cities in the National Mortality and Morbidity Air Pollution Study (NMMAPS) data base, which has extensive PM2.5, temperature, and mortality data for those 2years. Increases in average daily temperatures appear to significantly reduce average daily mortality rates, as expected from previous research. Unexpectedly, reductions in PM2.5 do not appear to cause any reductions in mortality rates. PM2.5 and mortality rates are both elevated on cold winter days, creating a significant positive statistical relation between their levels, but we find no evidence that reductions in PM2.5 concentrations cause reductions in mortality rates. For all concerned, it is crucial to use causal relations, rather than statistical associations, to project the changes in human health risks due to interventions such as reductions in particulate air pollution.

Benefits of Publicly Available Data

The National Morbidity, Mortality, and Air Pollution Study (NMMAPS) was designed to examine the health effects of air pollution in the United States. The primary question was whether particulate matter was responsible for the associations between air pollution and daily mortality. Secondary questions concerned measurement error in air pollution and mortality displacement.1 Since then, NMMAPS has been used to answer many important questions in environmental epidemiology...

Regional variations in particulate matter composition and the ability of monitoring data to represent population exposures

Epidemiologic studies have demonstrated that relative risks for mortality associated with ambient particulate matter (PM) concentrations vary with location in the U.S. with larger associations in both magnitude and strength observed in the East compared to the West. Two factors potentially contributing to the regional heterogeneity in PM-mortality associations observed are regional variations in PM composition and the ability of a single PM concentration estimate to represent the community-average exposure for an entire study area, which may lead to regional differences in exposure error. Variations in PM composition and the proportion of the population living in proximity to ambient monitors, an indicator of potential exposure error, are examined for the 20 most populated and 10 mid-size study areas included in the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Clear differences in PM and in the proportion of the population living in proximity to ambient monitors are found for some of these cities. Differences in these exposure parameters may be interpreted more reasonably in terms of north–south differences compared to east–west differences, and may need to be considered when conducting future epidemiologic studies that aim to examine the factors that influence the regional variability in PM-mortality associations.

Estimating the National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone

Ground-level ozone (O(3)) and fine particulate matter (PM(2.5)) are associated with increased risk of mortality. We quantify the burden of modeled 2005 concentrations of O(3) and PM(2.5) on health in the United States. We use the photochemical Community Multiscale Air Quality (CMAQ) model in conjunction with ambient monitored data to create fused surfaces of summer season average 8-hour ozone and annual mean PM(2.5) levels at a 12 km grid resolution across the continental United States. Employing spatially resolved demographic and concentration data, we assess the spatial and age distribution of air-pollution-related mortality and morbidity. For both PM(2.5) and O(3) we also estimate: the percentage of total deaths due to each pollutant; the reduction in life years and life expectancy; and the deaths avoided according to hypothetical air quality improvements. Using PM(2.5) and O(3) mortality risk coefficients drawn from the long-term American Cancer Society (ACS) cohort study and National Mortality and Morbidity Air Pollution Study (NMMAPS), respectively, we estimate 130,000 PM(2.5) -related deaths and 4,700 ozone-related deaths to result from 2005 air quality levels. Among populations aged 65-99, we estimate nearly 1.1 million life years lost from PM(2.5) exposure and approximately 36,000 life years lost from ozone exposure. Among the 10 most populous counties, the percentage of deaths attributable to PM(2.5) and ozone ranges from 3.5% in San Jose to 10% in Los Angeles. These results show that despite significant improvements in air quality in recent decades, recent levels of PM(2.5) and ozone still pose a nontrivial risk to public health.

Distributed lag non‐linear models

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.

Reassessing the relationship between ozone and short-term mortality in U.S. urban communities

Time-series studies that use daily mortality and ambient ozone concentrations exhibit estimates of ozone effects that are variable across cities. We investigate this intercity variability, as well as the sensitivity of the ozone- mortality associations to modeling assumptions and choice of daily ozone metric, based on reanalysis of data from the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Previous work from NMMAPS reported a statistically significant association between ambient 24-h ozone and short-term mortality when averaged across 98 U.S. cities. Separation of ozone health associations from effects due to weather and co-pollutants is central to their interpretation. We examined the sensitivity of city-specific ozone-mortality estimates to adjustments for confounders and effect modifiers, showing substantial sensitivity. We examined ozone-mortality associations in different concentration ranges, finding a larger incremental effect in higher ranges, but also larger uncertainty. Alternative ozone exposure metrics defined by maximum 8-h averages or 1-h maxima show different ozone-mortality associations that cannot be explained by simple scaling relationships. The emphasis in earlier studies based on NMMAPS has been on the reporting of “national” effects, together with prediction intervals that suggest that these national values are precisely estimated. Our view is that ozone-mortality associations, based on time-series epidemiologic analyses of daily data from multiple cities, reveal still-unexplained inconsistencies and show sensitivity to modeling choices and data selection that contribute to serious uncertainties when epidemiological results are used to discern the nature and magnitude of possible ozone-mortality relationships or are applied to risk assessment.

Acute Effects of Ambient Particulate Matter on Mortality in Europe and North America: Results from the APHENA Study

BackgroundThe APHENA (Air Pollution and Health: A Combined European and North American Approach) study is a collaborative analysis of multicity time-series data on the effect of air pollution on population health, bringing together data from the European APHEA (Air Pollution and Health: A European Approach) and U.S. NMMAPS (National Morbidity, Mortality and Air Pollution Study) projects, along with Canadian data.ObjectivesThe main objective of APHENA was to assess the coherence of the findings of the multicity studies carried out in Europe and North America, when analyzed with a common protocol, and to explore sources of possible heterogeneity. We present APHENA results on the effects of particulate matter (PM) ≤ 10 μm in aerodynamic diameter (PM10) on the daily number of deaths for all ages and for those < 75 and ≥ 75 years of age. We explored the impact of potential environmental and socioeconomic factors that may modify this association.MethodsIn the first stage of a two-stage analysis, we used Poisson regression models, with natural and penalized splines, to adjust for seasonality, with various degrees of freedom. In the second stage, we used meta-regression approaches to combine time-series results across cites and to assess effect modification by selected ecologic covariates.ResultsAir pollution risk estimates were relatively robust to different modeling approaches. Risk estimates from Europe and United States were similar, but those from Canada were substantially higher. The combined effect of PM10 on all-cause mortality across all ages for cities with daily air pollution data ranged from 0.2% to 0.6% for a 10-μg/m3 increase in ambient PM10 concentration. Effect modification by other pollutants and climatic variables differed in Europe and the United States. In both of these regions, a higher proportion of older people and higher unemployment were associated with increased air pollution risk.ConclusionsEstimates of the increased mortality associated with PM air pollution based on the APHENA study were generally comparable with results of previous reports. Overall, risk estimates were similar in Europe and in the United States but higher in Canada. However, PM10 effect modification patterns were somewhat different in Europe and the United States.

Does the Effect of PM 10 on Mortality Depend on PM Nickel and Vanadium Content? A Reanalysis of the NMMAPS Data

BackgroundLack of knowledge regarding particulate matter (PM) characteristics associated with toxicity is a crucial research gap. Short-term effects of PM can vary by location, possibly reflecting regional differences in mixtures. A report by Lippmann et al. [Lippmann et al., Environ Health Perspect 114:1662–1669 (2006)] analyzed mortality effect estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for 1987–1994. They found that average concentrations of nickel or vanadium in PM2.5 (PM with aerodynamic diameter < 2.5 μm) positively modified the lag-1 day association between PM10 and all-cause mortality.ObjectiveWe reestimated the relationship between county-specific lag-1 PM10 (PM with aerodynamic diameter < 10 μm) effects on mortality and county-specific nickel or vanadium PM2.5 average concentrations using 1987–2000 effect estimates. We explored whether such modification is sensitive to outliers.MethodsWe estimated long-term average county-level nickel and vanadium PM2.5 concentrations for 2000–2005 for 72 U.S. counties representing 69 communities. We fitted Bayesian hierarchical regression models to investigate whether county-specific short-term effects of PM10 on mortality are modified by long-term county-specific nickel or vanadium PM2.5 concentrations. We conducted sensitivity analyses by excluding individual communities and considering log-transformed data.ResultsOur results were consistent with those of Lippmann et al. However, we found that when counties included in the NMMAPS New York community were excluded from the sensitivity analysis, the evidence of effect modification of nickel or vanadium on the short-term effects of PM10 mortality was much weaker and no longer statistically significant.ConclusionsOur analysis does not contradict the hypothesis that nickel or vanadium may increase the risk of PM to human health, but it highlights the sensitivity of findings to particularly influential observations.

Ozone modifies associations between temperature and cardiovascular mortality: analysis of the NMMAPS data

Objectives: Both ambient ozone and temperature are associated with human health. However, few data are available on whether ozone modifies temperature effects. This study aims to explore whether ozone modified...

Ozone Modifies Associations Between Temperature and Cardiovascular Mortality—The Analysis Using the NMMAPS Data

ISEE-343 Objective: Both ambient ozone and temperature are associated with human health. However, no study has examined whether ozone modifies temperature effects, although some studies have examined whether temperature modifies air pollution effects. This study was undertaken to explore whether ozone modified associations between maximum temperature and cardiovascular mortality across the United States. Material and Methods: We obtained the data from the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) Website. We used 2 time-series Poisson regression models (the response surface model and stratification model) to examine whether ozone modified associations between maximum temperature and cardiovascular mortality (CVM) in 95 large US communities during 1987 to 2000. We used Bayesian meta-analysis to pool estimates in each community. Results: The response surface model was used to examine the joint effects of temperature and ozone on CVM in summer. Results illustrate that ozone synergistically modified the temperature-CVM associations across the different regions. The stratification model quantified the temperature-CVM associations across different levels of ozone. Results show that, in general, the higher the ozone concentration, the stronger the temperature-CVM associations across the communities. A 10°C increase in temperature on the same day was associated with an increase in CVM by 1.17% and 8.31% for the lowest and highest level of ozone concentrations, respectively. Conclusions: Ozone modified temperature effects in the different regions in the United States. It is important to evaluate the modifying role of ozone while estimating temperature-related health impacts and to further investigate the reasons behind the regional variability.

Methods for Bias Reduction in Time-Series Studies of Particulate Matter Air Pollution and Mortality

In many cities of the United States, measurements of ambient particulate matter air pollution (PM) are available only every sixth day. Time-series studies conducted in these cities that investigate the relationship between mortality and PM are restricted to using a single day's PM as the measure of PM exposure, rather than using measurements taken over several consecutive days. Studies showed that using a single-day PM as the measure of PM exposure can result in estimates that have a negative bias, sometimes in the order of over half of the value being estimated. In this article two methods are introduced that can be used to obtain estimates that can in some situations reduce the bias to negligible proportions when only every-sixth-day PM concentrations are available. Using one of these methods, the national average PM mortality effect estimates obtained for total mortality and cardiovascular and respiratory mortality, respectively, correspond to 0.27% and 0.39% increases in mortality per 10-μg/m3 increment in PM. The corresponding effect estimates obtained using the single-day lag-1 PM concentration are 0.18% and 0.23%. The estimates obtained using the lag-1 PM concentration were the most widely reported results from the recent multicity National Morbidity, Mortality, and Air Pollution Study (NMMAPS) analyses. The more accurate estimates obtained from the methods introduced in this article will enable more accurate quantification of the increased incidence in mortality due to elevation in PM levels and the benefit of current or more stringent regulatory standards.

Recent Developments of the National Morbidity Mortality Air Pollution Study: 1987–2000

SS1-03 Abstract: Multisite time series studies of particulate matter (PM) and health have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality and morbidity counts. Key areas of research concern: 1) assessing uncertainties in the adjustment for potential confounders and in the selection of the statistical model; 2) quantifying spatial and seasonal variation of PM10 health effects to understand toxicity of various PM components; and 3) developing a computational framework for reproducible research. To address these questions, we: 1) explore statistical properties of semiparametric regressions to adjust for confounding bias; 2) extend the NMMAPS hierarchical semiparametric Poisson regression models to allow for seasonally and spatially varying PM effects; and 3) introduce a R software package (NMMAPS-R add web site), which implements the statistical methods described and serves as a platform for conducting systematic and reproducible research. We apply methods to the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a national study covering 100 U.S. cities for the years 1987–2000 for PM10 and for the years 1999–2000 for PM2.5. We found strong evidence that lag 1 exposure to PM10 continues to be associated with all-cause and cardiorespiratory mortality with the greatest effect in the eastern United States. Under the NMMAPS basic model, a 10-μg increase in PM10 at lag 1 is associated with a 0.19% and 0.24% increase in all-cause mortality and cardiorespiratory mortality (95% confidence interval [CI]: 0.10 to 0.28 and 0.13 to 0.36, respectively). In addition a 10-μg increase in PM2.5 at lag 1 is associated with 0.29% and 0.38% increases in all-cause mortality and cardiorespiratory mortality (95% CI: 0.01 to 0.57 and −0.01 to 0.82, respectively). These findings provided key epidemiologic evidence for the EPA's review of the U.S. National Ambient Air Quality Standards for particulate matter.

Cardiovascular effects of nickel in ambient air.

BackgroundFine particulate matter (FPM) in ambient air causes premature mortality due to cardiac disease in susceptible populations.ObjectiveOur objective in this study was to determine the most influential FPM components.MethodsA mouse model of atherosclerosis (ApoE−/−) was exposed to either filtered air or concentrated FPM (CAPs) in Tuxedo, New York (85 μg/m3 average, 6 hr/day, 5 days/week, for 6 months), and the FPM elemental composition was determined for each day. We also examined associations between PM components and mortality for two population studies: National Mortality and Morbidity Air Pollution Study (NMMAPS) and Hong Kong.ResultsFor the CAPs-exposed mice, the average of nickel was 43 ng/m3, but on 14 days, there were Ni peaks at ~ 175 ng/m3 and unusually low FPM and vanadium. For those days, back-trajectory analyses identified a remote Ni point source. Electrocardiographic measurements on CAPs-exposed and sham-exposed mice showed Ni to be significantly associated with acute changes in heart rate and its variability. In NMMAPS, daily mortality rates in the 60 cities with recent speciation data were significantly associated with average Ni and V, but not with other measured species. Also, the Hong Kong sulfur intervention produced sharp drops in sulfur dioxide, Ni, and V, but not other components, corresponding to the intervention-related reduction in cardiovascular and pulmonary mortality.ConclusionsKnown biological mechanisms cannot account for the significant associations between Ni with the acute cardiac function changes in the mice or with cardiovascular mortality in people at low ambient air concentrations; therefore, further research is needed.

Applying a moving total mortality count to the cities in the NMMAPS database to estimate the mortality effects of particulate matter air pollution

Objectives: To apply a new method for estimating the association between daily ambient particulate matter air pollution (PM) and daily mortality to data from over 100 United States cities contained...