Particulate matter (PM) is an important metric for studying the health effects of household air pollution. There are limited data on PM exposure for children in homes that use biomass fuels, and no previous study has used direct measurement of personal exposure in children younger than 5 years of age. We estimated PM2.5 exposure for 1,266 children in The Gambia by applying the cookhouse PM2.5-CO relationship to the child’s CO exposure. Using this indirect method, mean PM2.5 exposure for all subjects was 135 ± 38 μg/m3; 25% of children had exposures of 151 μg/m3 or higher. Indirectly-estimated exposure was highest among children who lived in homes that used firewood (collected or purchased) as their main fuel (144 μg/m3) compared to those who used charcoal (85 μg/m3). To validate the indirect method, we also directly measured PM2.5 exposure on 31 children. Mean exposure for this validation dataset was 65 ± 41 μg/m3 using actual measurement and 125 ± 54 μg/m3 using the indirect method based on CO exposure. The correlation coefficient between direct measurements and indirect estimates was 0.01. Children in The Gambia have relatively high PM2.5 exposure. There is a need for simple methods that can directly measure PM2.5 exposure in field studies.
Indoor air pollution; biomass fuels; child survival; global health; Africa; particulate matter; exposure assessment; statistical model
Improvements in prevention have led to declines in incidence and mortality of MI in selected populations. However, no studies have examined regional differences in recent trends in MI incidence, and few have examined whether known regional disparities in MI care have narrowed over time.
Methods and Results
We compared trends in incidence rates of MI, associated procedures and mortality for all U.S. Census Divisions (regions) in Medicare fee-for-service patients between 2000 and 2008 (292,773,151 patient-years). Two-stage hierarchical models were used to account for patient characteristics and state-level random effects. To assess trends in geographical disparities, we calculated changes in between-state variance for outcomes over time. While the incidence of MI declined in all regions (P < 0.001 for trend for each) between 2000 and 2008, adjusted rates of decline varied by region (annual declines ranging from 2.9% to 6.1%). Widening geographical disparities, as measured by percent change of between-state variance from 2000 to 2008, were observed for MI incidence (37.6% increase, P = 0.03) and PCI rates (31.4% increase, P = 0.06). Significant declines in risk-adjusted 30-day mortality were observed in all regions, with the fastest declines observed in states with higher baseline mortality rates.
In a large contemporary analysis of geographic trends in MI epidemiology, the incidence of MI and associated mortality declined significantly in all U.S. Census Divisions between 2000 and 2008. While geographical disparities in MI incidence may have increased, regional differences in MI-associated mortality have narrowed.
myocardial infarction; Medicare; trends; disparities
Although many time-series studies of ozone and mortality have identified positive associations, others have yielded null or inconclusive results, making the results of these studies difficult to interpret.
We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups, cause-specific mortality, and concentration metrics. We compared results with pooled estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. urban centers from 1987 to 2000.
Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current-day ozone exposure. In both analyses, results were insensitive to adjustment for particulate matter and model specifications. In the meta-analysis, a 10-ppb increase in daily ozone at single-day or 2-day average of lags 0, 1, or 2 days was associated with an 0.87% increase in total mortality (95% posterior interval = 0.55% to 1.18%), whereas the lag 0 NMMAPS estimate is 0.25% (0.12% to 0.39%). Several findings indicate possible publication bias: meta-analysis results were consistently larger than those from NMMAPS; meta-analysis pooled estimates at lags 0 or 1 were larger when only a single lag was reported than when estimates for multiple lags were reported; and heterogeneity of city-specific estimates in the meta-analysis were larger than with NMMAPS.
This study provides evidence of short-term associations between ozone and mortality as well as evidence of publication bias.
Ozone has been associated with various adverse health effects, including increased rates of hospital admissions and exacerbation of respiratory illnesses. Although numerous time-series studies have estimated associations between day-to-day variation in ozone levels and mortality counts, results have been inconclusive.
To investigate whether short-term (daily and weekly) exposure to ambient ozone is associated with mortality in the United States.
Design and Setting
Using analytical methods and databases developed for the National Morbidity, Mortality, and Air Pollution Study, we estimated a national average relative rate of mortality associated with short-term exposure to ambient ozone for 95 large US urban communities from 1987-2000. We used distributed-lag models for estimating community-specific relative rates of mortality adjusted for time-varying confounders (particulate matter, weather, seasonality, and long-term trends) and hierarchical models for combining relative rates across communities to estimate a national average relative rate, taking into account spatial heterogeneity.
Main Outcome Measure
Daily counts of total non–injury-related mortality and cardiovascular and respiratory mortality in 95 large US communities during a 14-year period.
A 10-ppb increase in the previous week’s ozone was associated with a 0.52% increase in daily mortality (95% posterior interval [PI], 0.27%-0.77%) and a 0.64% increase in cardiovascular and respiratory mortality (95% PI, 0.31%-0.98%). Effect estimates for aggregate ozone during the previous week were larger than for models considering only a single day’s exposure. Results were robust to adjustment for particulate matter, weather, seasonality, and long-term trends.
These results indicate a statistically significant association between short-term changes in ozone and mortality on average for 95 large US urban communities, which include about 40% of the total US population. The findings indicate that this widespread pollutant adversely affects public health.
Evidence on the health risks associated with short-term exposure to fine particles (particulate matter ≤2.5 μm in aerodynamic diameter [PM2.5]) is limited. Results from the new national monitoring network for PM2.5 make possible systematic research on health risks at national and regional scales.
To estimate risks of cardiovascular and respiratory hospital admissions associated with short-term exposure to PM2.5 for Medicare enrollees and to explore heterogeneity of the variation of risks across regions.
Design, Setting, and Participants
A national database comprising daily time-series data daily for 1999 through 2002 on hospital admission rates (constructed from the Medicare National Claims History Files) for cardiovascular and respiratory outcomes and injuries, ambient PM2.5 levels, and temperature and dew-point temperature for 204 US urban counties (population >200 000) with 11.5 million Medicare enrollees (aged >65 years) living an average of 5.9 miles from a PM2.5 monitor.
Main Outcome Measures
Daily counts of county-wide hospital admissions for primary diagnosis of cerebrovascular, peripheral, and ischemic heart diseases, heart rhythm, heart failure, chronic obstructive pulmonary disease, and respiratory infection, and injuries as a control outcome.
There was a short-term increase in hospital admission rates associated with PM2.5 for all of the health outcomes except injuries. The largest association was for heart failure, which had a 1.28% (95% confidence interval, 0.78%–1.78%) increase in risk per 10-μg/m3 increase in same-day PM2.5. Cardiovascular risks tended to be higher in counties located in the Eastern region of the United States, which included the Northeast, the Southeast, the Midwest, and the South.
Short-term exposure to PM2.5 increases the risk for hospital admission for cardiovascular and respiratory diseases.
Estimating the risks heat waves pose to human health is a critical part of assessing the future impact of climate change. In this paper we propose a flexible class of time series models to estimate the relative risk of mortality associated with heat waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of potential models. Applying these methods to data from 105 U.S. cities for the period 1987–2005, we identify those cities having a high posterior probability of increased mortality risk during heat waves, examine the heterogeneity of the posterior distributions of mortality risk across cities, assess sensitivity of the results to the selection of prior distributions, and compare our BMA results to a model selection approach. Our results show that no single model best predicts risk across the majority of cities, and that for some cities heat wave risk estimation is sensitive to model choice. While model averaging leads to posterior distributions with increased variance as compared to statistical inference conditional on a model obtained through model selection, we find that the posterior mean of heat wave mortality risk is robust to accounting for model uncertainty over a broad class of models.
Climate change; Generalized Additive Models; Model Uncertainty; Time series data
To date, the assessment of public health consequences of air pollution has largely focused on a single-pollutant approach aimed at estimating the increased risk of adverse health outcomes associated with the exposure to a single air pollutant, adjusted for the exposure to other air pollutants. However, air masses always contain many pollutants in differing amounts, depending on the types of emission sources and atmospheric conditions. Because humans are simultaneously exposed to a complex mixture of air pollutants, many organizations have encouraged moving towards “a multi-pollutant approach to air quality.” While there is general agreement that multi-pollutant approaches are desirable, the challenges of implementing them are vast.
In this commentary, we discuss a multi-pollutant approach for controlling ambient air pollution that describes multi-pollutant concepts for different aspects of air quality management and science: (1) scientific estimation of the health risk of multiple pollutants; (2) setting of regulatory standards for multiple pollutants; and (3) simultaneously implementing compliance with regulatory standards for multiple pollutants.
In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.
Air pollution; Coarse particulate matter; Exposure measurement error; Multisite time series analysis
Health risks of fine particulate matter of 2.5 µm or less in aerodynamic diameter (PM2.5) have been studied extensively over the last decade. Evidence concerning the health risks of the coarse fraction of greater than 2.5 µm and 10 µm or less in aerodynamic diameter (PM10-2.5) is limited.
To estimate risk of hospital admissions for cardiovascular and respiratory diseases associated with PM10-2.5 exposure, controlling for PM2.5.
Design, Setting, and Participants
Using a database assembled for 108 US counties with daily cardiovascular and respiratory disease admission rates, temperature and dew-point temperature, and PM10-2.5 and PM2.5 concentrations were calculated with monitoring data as an exposure surrogate from January 1, 1999, through December 31, 2005. Admission rates were constructed from the Medicare National Claims History Files, for a study population of approximately 12 million Medicare enrollees living on average 9 miles (14.4 km) from collocated pairs of PM10 and PM2.5 monitors.
Main Outcome Measures
Daily counts of county-wide emergency hospital admissions for primary diagnoses of cardiovascular or respiratory disease.
There were 3.7 million cardiovascular disease and 1.4 million respiratory disease admissions. A 10-µg/m3 increase in PM10-2.5 was associated with a 0.36% (95% posterior interval [PI], 0.05% to 0.68%) increase in cardiovascular disease admissions on the same day. However, when adjusted for PM2.5, the association was no longer statistically significant (0.25%; 95% PI, −0.11% to 0.60%). A 10-µg/m3 increase in PM10-2.5 was associated with a nonstatistically significant unadjusted 0.33% (95% PI, −0.21% to 0.86%) increase in respiratory disease admissions and with a 0.26% (95% PI, −0.32% to 0.84%) increase in respiratory disease admissions when adjusted for PM2.5. The unadjusted associations of PM2.5 with cardiovascular and respiratory disease admissions were 0.71% (95% PI, 0.45%–0.96%) for same-day exposure and 0.44% (95% PI, 0.06% to 0.82%) for exposure 2 days before hospital admission.
After adjustment for PM2.5, there were no statistically significant associations between coarse particulates and hospital admissions for cardiovascular and respiratory diseases.
The short-term effects of particulate matter (PM) on mortality and morbidity differ by geographic location and season. Several hypotheses have been proposed for this variation, including different exposures with air conditioning (AC) versus open windows.
Bayesian hierarchical modeling was used to explore whether AC prevalence modified day-to-day associations between PM10 and mortality, and between PM2.5 and cardiovascular or respiratory hospitalizations, for those 65 years and older. We considered yearly, summer-only, and winter-only effect estimates and 2 types of AC (central and window units).
Communities with higher AC prevalence had lower PM effects. Associations were observed for cardiovascular hospitalizations and central AC. Each additional 20% of households with central AC was associated with a 43% decrease in PM2.5 effects on cardiovascular hospitalization. Central AC prevalence explained 17% of between-community variability in PM2.5 effect estimates for cardiovascular hospitalizations.
Higher AC prevalence was associated with lower health effect estimates for PM.
When estimating the association between an exposure and outcome, a simple approach to quantifying the amount of confounding by a factor, Z, is to compare estimates of the exposure–outcome association with and without adjustment for Z. This approach is widely believed to be problematic due to the nonlinearity of some exposure-effect measures. When the expected value of the outcome is modeled as a nonlinear function of the exposure, the adjusted and unadjusted exposure effects can differ even in the absence of confounding (Greenland , Robins, and Pearl, 1999); we call this the nonlinearity effect. In this paper, we propose a corrected measure of confounding that does not include the nonlinearity effect. The performances of the simple and corrected estimates of confounding are assessed in simulations and illustrated using a study of risk factors for low birth–weight infants. We conclude that the simple estimate of confounding is adequate or even preferred in settings where the nonlinearity effect is very small. In settings with a sizable nonlinearity effect, the corrected estimate of confounding has improved performance.
Collapsibility; Confounding; Odds ratio
Climate change is anticipated to affect human health by changing the distribution of known risk factors. Heat waves have had debilitating effects on human mortality, and global climate models predict an increase in the frequency and severity of heat waves. The extent to which climate change will harm human health through changes in the distribution of heat waves and the sources of uncertainty in estimating these effects have not been studied extensively.
We estimated the future excess mortality attributable to heat waves under global climate change for a major U.S. city.
We used a database comprising daily data from 1987 through 2005 on mortality from all nonaccidental causes, ambient levels of particulate matter and ozone, temperature, and dew point temperature for the city of Chicago, Illinois. We estimated the associations between heat waves and mortality in Chicago using Poisson regression models.
Under three different climate change scenarios for 2081–2100 and in the absence of adaptation, the city of Chicago could experience between 166 and 2,217 excess deaths per year attributable to heat waves, based on estimates from seven global climate models. We noted considerable variability in the projections of annual heat wave mortality; the largest source of variation was the choice of climate model.
The impact of future heat waves on human health will likely be profound, and significant gains can be expected by lowering future carbon dioxide emissions.
climate models; extreme weather events; global warming; population health; time-series models
Evidence on risk of cardiovascular (CVD) hospitalization associated with short-term exposure to outdoor carbon monoxide (CO), an air pollutant primarily generated by traffic, is inconsistent across studies. Uncertainties remain regarding the degree to which associations are attributable to other traffic pollutants and whether effects persist at low levels.
Methods and Results
We conducted a multi-site time-series study to estimate risk of CVD hospitalization associated with short-term CO exposure in 126 U.S. urban counties from 1999–2005 for >9.3 million Medicare enrollees ≥65 years of age. We considered models with adjustment by other traffic-related pollutants: nitrogen dioxide (NO2), fine particles (PM2.5), and Elemental Carbon (EC).
We found a positive and statistically significant association between same day (L0) CO and increased risk of hospitalization for multiple CVD outcomes (ischemic heart disease, heart rhythm disturbances, heart failure, cerebrovascular disease, total CVD). The association remained positive and statistically significant, but was attenuated, with co-pollutant adjustment, especially NO2. A one part per million (ppm) increase in L0 daily 1-hour maximum CO was associated with a 0.96% (95% posterior interval 0.79, 1.12%) increase in risk of CVD admissions. With L0 NO2 adjustment, this estimate is 0.55% (0.36, 0.74%). The risk persisted at low CO levels <1 ppm.
We found evidence of an association between short-term exposure to ambient CO and risk of CVD hospitalizations, even at levels well below current U.S. health-based regulatory standards. This evidence indicates that exposure to current CO levels may still pose a public health threat, particularly for persons with CVD.
cardiovascular disease; hospital admissions; carbon monoxide; air pollution
Rationale: There are unexplained geographical and seasonal differences in the short-term effects of fine particulate matter (PM2.5) on human health. The hypothesis has been advanced to include the possibility that such differences might be due to variations in the PM2.5 chemical composition, but evidence supporting this hypothesis is lacking.
Objectives: To examine whether variation in the relative risks (RR) of hospitalization associated with ambient exposure to PM2.5 total mass reflects differences in PM2.5 chemical composition.
Methods: We linked two national datasets by county and by season: (1) long-term average concentrations of PM2.5 chemical components for 2000–2005 and (2) RRs of cardiovascular and respiratory hospitalizations for persons 65 years or older associated with a 10-μg/m3 increase in PM2.5 total mass on the same day for 106 U.S. counties for 1999 through 2005.
Measurements and Main Results: We found a positive and statistically significant association between county-specific estimates of the short-term effects of PM2.5 on cardiovascular and respiratory hospitalizations and county-specific levels of vanadium, elemental carbon, or nickel PM2.5 content.
Conclusions: Communities with higher PM2.5 content of nickel, vanadium, and elemental carbon and/or their related sources were found to have higher risk of hospitalizations associated with short-term exposure to PM2.5.
air pollution; particulate matter; carbon; vanadium; nickel
The authors investigated whether short-term effects of fine particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) on risk of cardiovascular and respiratory hospitalizations among the elderly varied by region and season in 202 US counties for 1999–2005. They fit 3 types of time-series models to provide evidence for 1) consistent particulate matter effects across the year, 2) different particulate matter effects by season, and 3) smoothly varying particulate matter effects throughout the year. The authors found statistically significant evidence of seasonal and regional variation in estimates of particulate matter effect. Respiratory disease effect estimates were highest in winter, with a 1.05% (95% posterior interval: 0.29, 1.82) increase in hospitalizations per 10-μg/m3 increase in same-day PM2.5. Cardiovascular diseases estimates were also highest in winter, with a 1.49% (95% confidence interval: 1.09, 1.89) increase in hospitalizations per 10-μg/m3 increase in same-day PM2.5, with associations also observed in other seasons. The strongest evidence of a relation between PM2.5 and hospitalizations was in the Northeast for both respiratory and cardiovascular diseases. Heterogeneity of PM2.5 effects on hospitalizations may reflect seasonal and regional differences in emissions and in particles’ chemical constituents. Results can help guide development of hypotheses and further epidemiologic studies on potential heterogeneity in the toxicity of constituents of the particulate matter mixture.
air pollution; hospitalization; Medicare; particulate matter; seasons
Population-based studies have estimated health risks of short-term exposure to fine particles using mass of PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the PM2.5 mixture is limited.
In this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM2.5 in the United States.
We used a national database comprising daily data for 2000–2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major PM2.5 chemical components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of PM2.5 components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (≥ 65 years of age).
In multiple-pollutant models that adjust for the levels of other pollutants, an interquartile range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34–1.27%] increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated with a 1.01% (95% PI, 0.04–1.98%) increase in risk of respiratory admissions on the same day. Other components were not associated with cardiovascular or respiratory hospital admissions in multiple-pollutant models.
Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of PM2.5.
cardiovascular disease; chemical components; hospital admission; particulate matter; PM2.5; respiratory disease; Speciation Trends Network
The four dengue viruses, the agents of dengue fever and dengue hemorrhagic fever in humans, are transmitted predominantly by the mosquito Aedes aegypti. The abundance and the transmission potential of Ae. aegypti are influenced by temperature and precipitation. While there is strong biological evidence for these effects, empirical studies of the relationship between climate and dengue incidence in human populations are potentially confounded by seasonal covariation and spatial heterogeneity. Using 20 years of data and a statistical approach to control for seasonality, we show a positive and statistically significant association between monthly changes in temperature and precipitation and monthly changes in dengue transmission in Puerto Rico. We also found that the strength of this association varies spatially, that this variation is associated with differences in local climate, and that this relationship is consistent with laboratory studies of the impacts of these factors on vector survival and viral replication. These results suggest the importance of temperature and precipitation in the transmission of dengue viruses and suggest a reason for their spatial heterogeneity. Thus, while dengue transmission may have a general system, its manifestation on a local scale may differ from global expectations.
Dengue viruses are a major health problem throughout the tropical and subtropical regions of the world. Because they are transmitted by mosquitoes that are sensitive to changes in rainfall and temperature, transmission intensity may be regulated by weather and climate. Laboratory studies have shown this to be biologically plausible, but studies of transmission in real-life situations have been inconclusive. Here we demonstrate that increased temperature and rainfall are associated with increased dengue transmission in subsequent months across Puerto Rico. We also show that differences in local climate within Puerto Rico can explain local differences observed in the relationship between weather and dengue transmission. This finding is important because it suggests that the determinants of transmission occur on a local level such that although dengue viruses have a basically universal transmission cycle, changes in temperature or rainfall may have diverse local effects.
Low-dose extrapolation model selection for evaluating the health effects of environmental pollutants is a key component of the risk assessment process. At a workshop held in Baltimore, Maryland, on 23–24 April 2007, sponsored by U.S. Environmental Protection Agency and Johns Hopkins Risk Sciences and Public Policy Institute, a multidisciplinary group of experts reviewed the state of the science regarding low-dose extrapolation modeling and its application in environmental health risk assessments. Participants identified discussion topics based on a literature review, which included examples for which human responses to ambient exposures have been extensively characterized for cancer and/or noncancer outcomes. Topics included the need for formalized approaches and criteria to assess the evidence for mode of action (MOA), the use of human versus animal data, the use of MOA information in biologically based models, and the implications of interindividual variability, background disease processes, and background exposures in threshold versus nonthreshold model choice. Participants recommended approaches that differ from current practice for extrapolating high-dose animal data to low-dose human exposures, including categorical approaches for integrating information on MOA, statistical approaches such as model averaging, and inference-based models that explicitly consider uncertainty and interindividual variability.
dose–response function; environmental health; low-dose extrapolation; risk assessment; workshop report
Prospective cohort studies constitute the major source of evidence about the mortality effects of chronic exposure to particulate air pollution. Additional studies are needed to provide evidence on the health effects of chronic exposure to particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) because few studies have been carried out and the cohorts have not been representative.
This study was designed to estimate the relative risk of death associated with long-term exposure to PM2.5 by region and age groups in a U.S. population of elderly, for the period 2000–2005.
By linking PM2.5 monitoring data to the Medicare billing claims by ZIP code of residence of the enrollees, we have developed a new retrospective cohort study, the Medicare Cohort Air Pollution Study. The study population comprises 13.2 million participants living in 4,568 ZIP codes having centroids within 6 miles of a PM2.5 monitor. We estimated relative risks adjusted by socioeconomic status and smoking by fitting log-linear regression models.
In the eastern and central regions, a 10-μg/m3 increase in 6-year average of PM2.5 is associated with 6.8% [95% confidence interval (CI), 4.9–8.7%] and 13.2% (95% CI, 9.5–16.9) increases in mortality, respectively. We found no evidence of an association in the western region or for persons ≥ 85 years of age.
We established a cohort of Medicare participants for investigating air pollution and mortality on longer-term time frames. Chronic exposure to PM2.5 was associated with mortality in the eastern and central regions, but not in the western United States.
ecologic bias; fine particulate matter (PM2.5); heterogeneity; log-linear models; Medicare; mortality; prospective studies
The 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.
The 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.
In 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.
Air 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.
Estimates 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.
air pollution; effect modification; heterogeneity; meta-regression; mortality; natural splines; particulate matter; penalized splines; time-series analysis
Previous research provided evidence of an association between short-term exposure to ozone and mortality risk and of heterogeneity in the risk across communities. The authors investigated whether this heterogeneity can be explained by community-specific characteristics: race, income, education, urbanization, transportation use, particulate matter and ozone levels, number of ozone monitors, weather, and use of air conditioning. Their study included data on 98 US urban communities for 1987 to 2000 from the National Morbidity, Mortality, and Air Pollution Study; US Census; and American Housing Survey. On average across the communities, a 10-ppb increase in the previous week's ozone level was associated with a 0.52% (95% posterior interval: 0.28, 0.77) increase in mortality. The authors found that community-level characteristics modify the relation between ozone and mortality. Higher effect estimates were associated with higher unemployment, fraction of the Black/African-American population, and public transportation use and with lower temperatures or prevalence of central air conditioning. These differences may relate to underlying health status, differences in exposure, or other factors. Results show that some segments of the population may face higher health burdens of ozone pollution.
air conditioning; air pollution; continental population groups; income; mortality; ozone; particulate matter; socioeconomic factors
Lack 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.
We 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.
We 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.
Our 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.
Our 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.
effect modification; mortality; Ni; particulate matter; PM2.5; PM10; V
A critical question regarding the association between short-term exposure to ozone and mortality is the extent to which this relationship is confounded by ambient exposure to particles.
We investigated whether particulate matter < 10 and < 2.5 μm in aerodynamic diameter (PM10 and PM2.5) is a confounder of the ozone and mortality association using data for 98 U.S. urban communities from 1987 to 2000.
We a) estimated correlations between daily ozone and daily PM concentrations stratified by ozone or PM levels; b) included PM as a covariate in time-series models; and c) included PM as a covariate as in d), but within a subset approach considering only days with ozone below a specified value.
Analysis was hindered by data availability. In the 93 communities with PM10 data, only 25.0% of study days had data on both ozone and PM10. In the 91 communities with PM2.5 data, only 9.2% of days in the study period had data on ozone and PM2.5. Neither PM measure was highly correlated with ozone at any level of ozone or PM. National and community-specific effect estimates of the short-term effects of ozone on mortality were robust to inclusion of PM10 or PM2.5 in time-series models. The robustness remains even at low ozone levels (< 10 ppb) using a subset approach.
Results provide evidence that neither PM10 nor PM2.5 is a likely confounder of observed ozone and mortality relationships. Further investigation is needed to investigate potential confounding of the short-term effects of ozone on mortality by PM chemical composition.
confounding; mortality; ozone; particulate matter; PM10; PM2.5; sensitivity analysis