Lucas M. Neas

Is Daily Mortality Associated Specifically with Fine Particles

Recent epidemiologic studies have consistently reported increased daily mortality associated with exposures to particulate air pollution. Currently, particulate mass is measured as particles smaller than 10 \im (PM10). Fine (PM2 s) and coarse (PM10 - PM2 s) mass and sulfate particle concentrations were measured in six eastern U.S. cities for eight years, and aerosol acidity concentrations were measured for approximately one year. Daily mortality for these metropolitan areas was combined with particulate air pollution and weather measurements. City-specific associations with each measure of particle pollution were estimated by Poisson regression, adjusting for time trends and weather by nonparametric methods. Combined effect estimates were calculated as the inverse variance weighted mean of the city-specific estimates. PM10, PM2 5, and SO4= were each significantly associated with increased daily mortality, while no associations were found with coarse mass nor with aerosol acidity (H+) concentrations. The strongest association was found with PM2 5. A10 (ig/m3 increase in two-day mean PM2S was associated with a 1.5% (95% CI 1.1% to 1.9%) increase in total daily mortality. Somewhat larger increases were found for deaths caused by chronic obstructive pulmonary disease (+3.3%) and by ischemic heart disease (+2.1%). These data suggest that increased daily mortality is specifically associated with particle mass constituents found in the aerodynamic diameter size range under 2.5 urn, that is, with combustion-related particles.

Relation of body mass index to asthma and atopy in children: the National Health and Nutrition Examination Study III

BACKGROUND An increase in the prevalence of obesity and asthma over recent decades has been reported in affluent societies. Both overweight and obesity have been shown to be inversely related to having been breastfed, which is also a potential protective factor against childhood atopic diseases. The aim of this analysis was to explore the relation of body mass index (BMI) to asthma and atopy in a large representative sample of the United States population. METHODS Children aged 4–17 years were included in the NHANES III survey. Prevalences of atopic diseases and potential confounding factors such as exposure to environmental tobacco smoke, birth weight, breast feeding, and household size were assessed using structured interviews with parents. Height and weight were measured, and BMI was calculated as kg/m2 and transformed into Z scores. Children underwent skin prick tests for atopy to a battery of food and inhalant allergens. RESULTS The prevalence of asthma (8.7% v 9.3%v 10.3% v14.9%, p=0.0001) and atopy (48.6% v 50.5%v 53.0% v53.2%, p=0.05) rose significantly with increasing quartiles of BMI. After adjustment for confounders, a significant positive association between BMI and asthma remained (adjusted OR 1.77, 95% confidence interval 1.44 to 2.19 between the highest and lowest quartiles of BMI), whereas no independent relation between BMI and atopy was evident. No effect modification by sex or ethnic group was seen. CONCLUSIONS The effects of increased BMI on asthma may be mediated by mechanical properties of the respiratory system associated with obesity or by upregulation of inflammatory mechanisms rather than by allergic eosinophilic inflammation of the airway epithelium.

Fine particles are more strongly associated than coarse particles with acute respiratory health effects in schoolchildren.

Numerous studies have reported associations between airborne particles and a range of respiratory outcomes from symptoms to mortality. Current attention has been focused on the characteristics of these particles responsible for the adverse health effects. We have reanalyzed three recent longitudinal diary studies to examine the relative contributions of fine and coarse particles on respiratory symptoms and peak expiratory flow in schoolchildren. In the Harvard Six Cities Diary Study, lower respiratory symptoms in a two-pollutant model were associated with an interquartile range increment in fine particles [(for 15 microg/m3 particulate matter (PM) <2.5 microm in aerodynamic diameter (PM2.5), odds ratio = 1.29, 95% confidence limits (CL) = 1.06, 1.57] but not coarse particles (for 8 microg/m3 PM2.5-10, odds ratio = 1.05, 95% CL = 0.90, 1.23). In Uniontown, PA, we found that peak flow was associated with fine particles (for 15 microg/m3 PM2.1, peak flow = -0.91 liters/minute, 95% CL = -0.14, -1.68), especially fine sulfate particles, but not with coarse particles (for 15 microg/m3 PM2.1-10, +1.04 liters/minute, 95% CL = -1.32, +3.40). We found similar results for an equivalent childrens cohort in State College, PA. We conclude that fine particles, especially fine sulfate particles, have much stronger acute respiratory effects than coarse particles.

PM source apportionment and health effects: 1. Intercomparison of source apportionment results

During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal (soil), sulfate, oil, and salt were the sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE=7% and 11% for traffic; in Phoenix, secondary sulfate SE=17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM2.5 mass source apportionment results are consistent across users and methods, and that todays source apportionment methods are robust enough for application to PM2.5 health effects assessments.

The effect of air pollution on inner-city children with asthma

The effect of daily ambient air pollution was examined within a cohort of 846 asthmatic children residing in eight urban areas of the USA, using data from the National Cooperative Inner-City Asthma Study. Daily air pollution concentrations were extracted from the Aerometric Information Retrieval System database from the Environment Protection Agency in the USA. Mixed linear models and generalized estimating equation models were used to evaluate the effects of several air pollutants (ozone, sulphur dioxide (SO2), nitrogen dioxide (NO2) and particles with a 50% cut-off aerodynamic diameter of 10 µm (PM10) on peak expiratory flow rate (PEFR) and symptoms in 846 children with a history of asthma (ages 4–9 yrs). None of the pollutants were associated with evening PEFR or symptom reports. Only ozone was associated with declines in morning % PEFR (0.59% decline (95% confidence interval (CI) 0.13–1.05%) per interquartile range (IQR) increase in 5‐day average ozone). In single pollutant models, each pollutant was associated with an increased incidence of morning symptoms: (odds ratio (OR)=1.16 (95% CI 1.02–1.30) per IQR increase in 4‐day average ozone, OR=1.32 (95% CI 1.03–1.70) per IQR increase in 2‐day average SO2, OR=1.48 (95% CI 1.02–2.16) per IQR increase in 6‐day average NO2 and OR=1.26 (95% CI 1.0–1.59) per IQR increase in 2‐day average PM10. This longitudinal analysis supports previous time-series findings that at levels below current USA air-quality standards, summer-air pollution is significantly related to symptoms and decreased pulmonary function among children with asthma.

Acute respiratory health effects of air pollution on children with asthma in US inner cities

BACKGROUND Children with asthma in inner-city communities may be particularly vulnerable to adverse effects of air pollution because of their airways disease and exposure to relatively high levels of motor vehicle emissions. OBJECTIVE To investigate the association between fluctuations in outdoor air pollution and asthma morbidity among inner-city children with asthma. METHODS We analyzed data from 861 children with persistent asthma in 7 US urban communities who performed 2-week periods of twice-daily pulmonary function testing every 6 months for 2 years. Asthma symptom data were collected every 2 months. Daily pollution measurements were obtained from the Aerometric Information Retrieval System. The relationship of lung function and symptoms to fluctuations in pollutant concentrations was examined by using mixed models. RESULTS Almost all pollutant concentrations measured were below the National Ambient Air Quality Standards. In single-pollutant models, higher 5-day average concentrations of NO2, sulfur dioxide, and particles smaller than 2.5 microm were associated with significantly lower pulmonary function. Higher pollutant levels were independently associated with reduced lung function in a 3-pollutant model. Higher concentrations of NO2 and particles smaller than 2.5 microm were associated with asthma-related missed school days, and higher NO2 concentrations were associated with asthma symptoms. CONCLUSION Among inner-city children with asthma, short-term increases in air pollutant concentrations below the National Ambient Air Quality Standards were associated with adverse respiratory health effects. The associations with NO2 suggest that motor vehicle emissions may be causing excess morbidity in this population.

Particulate matter and heart rate variability among elderly retirees: the Baltimore 1998 PM study.

This study investigates the relationship between ambient fine particle pollution and impaired cardiac autonomic control in the elderly. Heart rate variability (HRV) among 56 elderly (mean age 82) nonsmoking residents of a retirement center in Baltimore County, Maryland, was monitored for 4 weeks, from July 27 through August 22, 1998. The weather was seasonally mild (63–84°F mean daily temperature) with low to moderate levels of fine particles (PM2.5 <50 μg/m3). Two groups of approximately 30 subjects were examined on alternate days. A spline mixed-effects model revealed a negative relationship between outdoor 24-h average fine particulate matter (PM2.5) and high-frequency (HF) HRV that was consistent with our earlier Baltimore study for all but 2 days. These 2 days were the only days with significant precipitation in combination with elevated PM2.5. They were also unusual in that back-trajectoryof their air masses was distinctly different from those on the other study days, emanating from the direction of rural Pennsylvania. Mixed-effects analysis for all 24 study days showed a small negative association of outdoor PM2.5 with HF HRV (−0.03 change in log[HF HRV] for a 10 μg/m3 increment in PM2.5) after adjustment for age, sex, cardiovascular status, trend, maximum temperature, average dew point temperature, random subject intercepts, and autocorrelated residuals. After excluding study days 4 and 5, this association was strengthened (−0.07 change in log[HF HRV] for 10 μg/m3 PM2.5, 95% CI −0.13 to −0.02) and was similar to that obtained in an earlier study (−0.12 change in log[HF HRV] for a 10 μg/m3 increment in outdoor PM2.5, 95% CI −0.24 to −0.00) [Liao D., Cai J., Rosamond W.D., Barnes R.W., Hutchinson R.G., Whitsel E.A., Rautaharju P., and Heiss G. Cardiac autonomic function and incident coronary heart disease: a population-based case-cohort study. The ARIC Study. Atherosclerosis Risk in Communities Study. Am J Epidemiol 1997: 145 (8): 696–706]. Acute (1 to 4 h) previous PM2.5 exposure did not have a stronger impact than the 24-h measure. A distributed lag model incorporating the six preceding 4-h means also did not indicate any effect greater than that observed in the 24-h measure. This study is consistent with earlier findings that exposures to PM2.5 are associated with decreased HRV in the elderly.

Transitional Regression Models, with Application to Environmental Time Series

Abstract Environmental epidemiologists often encounter time series data in the form of discrete or other nonnormal outcomes; for example, in modeling the relationship between air pollution and hospital admissions or mortality rates. We present a case study examining the association between pollen counts and meteorologic covariates. Although such time series data are inadequately described by standard methods for Gaussian time series, they are often autocorrelated, and warrant an analysis beyond those provided by ordinary generalized linear models (GLMs). Transitional regression models (TRMs), signifying nonlinear regression models expressed in terms of conditional means and variances given past observations, provide a unifying framework for two mainstream approaches to extending the GLM for autocorrelated data. The first approach models current outcomes with a GLM that incorporates past outcomes as covariates, whereas the second models individual outcomes with marginal GLMs and then couples the error terms with an autoregressive covariance matrix. Although the two approaches coincide for the Gaussian GLM, which serves as a helpful introductory example, in general they yield fundamentally different models. We analyze the pollen study using TRMs of both types and present parameter estimates together with asymptotic and bootstrap standard errors. In several cases we find evidence of residual autocorrelation; however, when we relax the TRM to allow for a nonparametric smooth trend, the autocorrelation disappears. This kind of trade-off between autocorrelation and flexibility is to be expected, and has a natural interpretation in terms of the covariance function for a nonparametric smoother. We provide an algorithm for fitting these flexible TRMs that is relatively easy to program with the generalized additive model software in S-PLUS.

Peat Bog Wildfire Smoke Exposure in Rural North Carolina Is Associated with Cardiopulmonary Emergency Department Visits Assessed through Syndromic Surveillance

Background: In June 2008, burning peat deposits produced haze and air pollution far in excess of National Ambient Air Quality Standards, encroaching on rural communities of eastern North Carolina. Although the association of mortality and morbidity with exposure to urban air pollution is well established, the health effects associated with exposure to wildfire emissions are less well understood. Objective: We investigated the effects of exposure on cardiorespiratory outcomes in the population affected by the fire. Methods: We performed a population-based study using emergency department (ED) visits reported through the syndromic surveillance program NC DETECT (North Carolina Disease Event Tracking and Epidemiologic Collection Tool). We used aerosol optical depth measured by a satellite to determine a high-exposure window and distinguish counties most impacted by the dense smoke plume from surrounding referent counties. Poisson log-linear regression with a 5-day distributed lag was used to estimate changes in the cumulative relative risk (RR). Results: In the exposed counties, significant increases in cumulative RR for asthma [1.65 (95% confidence interval, 1.25–2.1)], chronic obstructive pulmonary disease [1.73 (1.06–2.83)], and pneumonia and acute bronchitis [1.59 (1.07–2.34)] were observed. ED visits associated with cardiopulmonary symptoms [1.23 (1.06–1.43)] and heart failure [1.37 (1.01–1.85)] were also significantly increased. Conclusions: Satellite data and syndromic surveillance were combined to assess the health impacts of wildfire smoke in rural counties with sparse air-quality monitoring. This is the first study to demonstrate both respiratory and cardiac effects after brief exposure to peat wildfire smoke.

A Time Series Analysis of Air Pollution and Preterm Birth in Pennsylvania, 1997--2001

Preterm delivery can lead to serious infant health outcomes, including death and lifelong disability. Small increases in preterm delivery risk in relation to spatial gradients of air pollution have been reported, but previous studies may have controlled inadequately for individual factors. Using a time-series analysis, which eliminates potential confounding by individual risk factors that do not change over short periods of time, we investigated the effect of ambient outdoor particulate matter with diameter ≤10 μm (PM10) and sulfur dioxide on risk for preterm delivery. Daily counts of preterm births were obtained from birth records in four Pennsylvania counties from 1997 through 2001. We observed increased risk for preterm delivery with exposure to average PM10 and SO2 in the 6 weeks before birth [respectively, relative risk (RR) = 1.07; 95% confidence interval (CI), 0.98–1.18 per 50 μg/m3 increase; RR = 1.15; 95% CI, 1.00–1. 32 per 15 ppb increase], adjusting for long-term preterm delivery trends, co-pollutants, and offsetting by the number of gestations at risk. We also examined lags up to 7 days before the birth and found an acute effect of exposure to PM10 2 days and 5 days before birth (respectively, RR = 1.10; 95% CI, 1.00–1.21; RR = 1.07; 95% CI, 0.98–1.18) and SO2 3 days before birth (RR = 1.07; 95% CI, 0.99–1.15), adjusting for covariates, including temperature, dew point temperature, and day of the week. The results from this time-series analysis, which provides evidence of an increase in preterm birth risk with exposure to PM10 and SO2, are consistent with prior investigations of spatial contrasts.