Epidemiologic studies have linked tropospheric ozone pollution and human mortality. Although research has shown that this relation is not confounded by particulate matter when measured by mass, little scientific evidence exists on whether confounding exists by chemical components of the particle mixture. Using mortality and particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) component data from 57 US communities (2000–2005), the authors investigate whether the ozone-mortality relation is confounded by 7 components of PM2.5: sulfate, nitrate, silicon, elemental carbon, organic carbon matter, sodium ion, and ammonium. Together, these components constitute most PM2.5 mass in the United States. Estimates of the effect of ozone on mortality were almost identical before and after controlling for the 7 components of PM2.5 considered (mortality increase/10-ppb ozone increase, before and after controlling: ammonium, 0.34% vs. 0.35%; elemental carbon, 0.36% vs. 0.37%; nitrate, 0.27% vs. 0.26%; organic carbon matter, 0.34% vs. 0.31%; silicon, 0.36% vs. 0.37%; sodium ion, 0.21% vs. 0.18%; and sulfate, 0.35% vs. 0.38%). Additionally, correlations were weak between ozone and each particulate component across all communities. Previous research found that the ozone-mortality relation is not confounded by particulate matter measured by mass; this national study indicates that the relation is also robust to control for specific components of PM2.5.
air pollution; confounding factors; mortality; ozone; particulate matter
Background: Although the association between PM2.5 mass and mortality has been extensively studied, few national-level analyses have estimated mortality effects of PM2.5 chemical constituents. Epidemiologic studies have reported that estimated effects of PM2.5 on mortality vary spatially and seasonally. We hypothesized that associations between PM2.5 constituents and mortality would not vary spatially or seasonally if variation in chemical composition contributes to variation in estimated PM2.5 mortality effects.
Objectives: We aimed to provide the first national, season-specific, and region-specific associations between mortality and PM2.5 constituents.
Methods: We estimated short-term associations between nonaccidental mortality and PM2.5 constituents across 72 urban U.S. communities from 2000 to 2005. Using U.S. Environmental Protection Agency (EPA) Chemical Speciation Network data, we analyzed seven constituents that together compose 79–85% of PM2.5 mass: organic carbon matter (OCM), elemental carbon (EC), silicon, sodium ion, nitrate, ammonium, and sulfate. We applied Poisson time-series regression models, controlling for time and weather, to estimate mortality effects.
Results: Interquartile range increases in OCM, EC, silicon, and sodium ion were associated with estimated increases in mortality of 0.39% [95% posterior interval (PI): 0.08, 0.70%], 0.22% (95% PI: 0.00, 0.44), 0.17% (95% PI: 0.03, 0.30), and 0.16% (95% PI: 0.00, 0.32), respectively, based on single-pollutant models. We did not find evidence that associations between mortality and PM2.5 or PM2.5 constituents differed by season or region.
Conclusions: Our findings indicate that some constituents of PM2.5 may be more toxic than others and, therefore, regulating PM total mass alone may not be sufficient to protect human health.
Citation: Krall JR, Anderson GB, Dominici F, Bell ML, Peng RD. 2013. Short-term exposure to particulate matter constituents and mortality in a national study of U.S. urban communities. Environ Health Perspect 121:1148–1153; http://dx.doi.org/10.1289/ehp.1206185
Background: Environmental health research employs a variety of metrics to measure heat exposure, both to directly study the health effects of outdoor temperature and to control for temperature in studies of other environmental exposures, including air pollution. To measure heat exposure, environmental health studies often use heat index, which incorporates both air temperature and moisture. However, the method of calculating heat index varies across environmental studies, which could mean that studies using different algorithms to calculate heat index may not be comparable.
Objective and Methods: We investigated 21 separate heat index algorithms found in the literature to determine a) whether different algorithms generate heat index values that are consistent with the theoretical concepts of apparent temperature and b) whether different algorithms generate similar heat index values.
Results: Although environmental studies differ in how they calculate heat index values, most studies’ heat index algorithms generate values consistent with apparent temperature. Additionally, most different algorithms generate closely correlated heat index values. However, a few algorithms are potentially problematic, especially in certain weather conditions (e.g., very low relative humidity, cold weather). To aid environmental health researchers, we have created open-source software in R to calculate the heat index using the U.S. National Weather Service’s algorithm.
Conclusion: We identified 21 separate heat index algorithms used in environmental research. Our analysis demonstrated that methods to calculate heat index are inconsistent across studies. Careful choice of a heat index algorithm can help ensure reproducible and consistent environmental health research.
Citation: Anderson GB, Bell ML, Peng RD. 2013. Methods to calculate the heat index as an exposure metric in environmental health research. Environ Health Perspect 121:1111–1119; http://dx.doi.org/10.1289/ehp.1206273
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.
Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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
As climate continues to change, scientists are left to analyze the effects these changes will have on the public. In this article, a flexible class of distributed lag models are used to analyze the effects of heat on mortality in four major metropolitan areas in the U.S. (Chicago, Dallas, Los Angeles, and New York). Specifically, the proposed methodology uses Gaussian processes as a prior model for the distributed lag function. Gaussian processes are adequately flexible to capture a wide variety of distributed lag functions while ensuring smoothness properties of process realizations. Additionally, the proposed framework allows for probabilistic inference of the maximum lag. Applying the proposed methodology revealed that mortality displacement (or, harvesting) was present for most age groups and cities analyzed suggesting that heat advanced death in some individuals. Additionally, the estimated shape of the DL functions gave evidence that prolonged heat exposure and highly variable temperatures pose a threat to public health.
Climate change; Gaussian process; Public health; Harvesting
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
The role of natural aeroallergen exposure in modulating allergen-specific immune responses is not well understood.
To examine relationships between mouse allergen exposure and mouse-specific immune responses.
New employees (n=179) at a mouse facility underwent repeated assessment of mouse allergen exposure, skin prick testing (SPT), and measurement of mouse-specific IgG. Relationships between the mean level of exposure, variability of exposure (calculated as log standard deviation), and time to development of immunologic outcomes were examined using Cox proportional hazards models.
By 24 months, 32 (23%) participants had developed a +SPT and 10 (8%) had developed mouse-specific IgG4. The incidence of a +SPT increased as levels of exposure increased from low to moderate, peaking at 1.2 ng/m3 and decreased beyond this point (p=.04). The more variable the exposure was across visits, the lower the incidence of a +SPT (HR [95% CI]: 0.17 [0.07–0.41]). Variability of exposure was an independent predictor of +SPT in a model that included both exposure metrics. In contrast, the incidence of mouse-specific IgG4 increased with increasing levels of mouse allergen exposure (2.9 [1.4–6.0]), and there was evidence of a higher risk of mouse-specific IgG4 with greater variability of exposure (6.3 [0.4–95.2]).
Both level and variability of mouse allergen exposure influence the humoral immune response, with specific patterns of exposure associated with specific immunophenotypes. Exposure variability may be a more important predictor of +SPT, while average exposure level may be a more important predictor of mouse-specific IgG4.
mouse allergen; IgE; IgG4; laboratory animal allergy
Vitamin D; wheeze; asthma; age
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter (PM). One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the PM components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. We present a spatial–temporal statistical model for quantifying spatial misalignment error and show how adjusted health risk estimates can be obtained using a regression calibration approach and a 2-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States.
Acute health effects; Cardiovascular disease; Chemical speciation; Measurement error; Particulate matter; Spatial modeling
Studies of the health impacts of airborne particulates’ chemical constituents typically assume spatial homogeneity and estimate exposure from ambient monitors. However, factors such as local sources may cause spatially heterogeneous pollution levels. This work examines the degree to which constituent levels vary within communities and whether exposure misclassification is introduced by spatial homogeneity assumptions. Analysis considered PM2.5 elemental carbon (EC), organic carbon matter, ammonium, sulfate, nitrate, silicon, and sodium ion (Na+) for the United States, 1999–2007. Pearson correlations and coefficients of divergence were calculated and compared to distances among monitors. Linear modeling related correlations to distance between monitors, long-term constituent levels, and population density. Spatial heterogeneity was present for all constituents, yet lower for ammonium, sulfate, and nitrate. Lower correlations were associated with higher distance between monitors, especially for nitrate and sulfate, and with lower long-term levels, especially for sulfate and Na+. Analysis of colocated monitors revealed measurement error for all constituents, especially EC and Na+. Exposure misclassification may be introduced into epidemiological studies of PM2.5 constituents due to spatial variability, and is affected by constituent type and level. When assessing health effects of PM constituents, new methods are needed for estimating exposure and accounting for exposure error induced by spatial variability.
epidemiology; exposure modeling; particulate matter; PM2.5; spatial variability
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.
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
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
Time-series analyses have shown that ozone is associated with increased risk of premature mortality, but little is known about how O3 affects health at low concentrations. A critical scientific and policy question is whether a threshold level exists below which O3 does not adversely affect mortality. We developed and applied several statistical models to data on air pollution, weather, and mortality for 98 U.S. urban communities for the period 1987–2000 to estimate the exposure–response curve for tropospheric O3 and risk of mortality and to evaluate whether a “safe” threshold level exists. Methods included a linear approach and subset, threshold, and spline models. All results indicate that any threshold would exist at very low concentrations, far below current U.S. and international regulations and nearing background levels. For example, under a scenario in which the U.S. Environmental Protection Agency’s 8-hr regulation is met every day in each community, there was still a 0.30% increase in mortality per 10-ppb increase in the average of the same and previous days’ O3 levels (95% posterior interval, 0.15–0.45%). Our findings indicate that even low levels of tropospheric O3 are associated with increased risk of premature mortality. Interventions to further reduce O3 pollution would benefit public health, even in regions that meet current regulatory standards and guidelines.
mortality; ozone; regulations; threshold
Season of birth has been reported as a risk factor for food allergy, but the mechanisms by which it acts are unknown.
Two populations were studied; 5862 children from the National Health and Nutrition Examination Survey (NHANES) III, 1514 well-characterized food allergic children from the Johns Hopkins Pediatric Allergy Clinic (JHPAC). Food allergy was defined as self report of an acute reaction to a food (NHANES), or as milk, egg and peanut allergy. Logistic regression compared fall or non-fall birth between (1) food allergic and non-allergic subjects in NHANES, adjusted for ethnicity, age, income and sex, and (2) JHPAC subjects and the general Maryland population. For NHANES, stratification by ethnicity and for JHPAC, eczema, was examined.
Fall birth was more common among food allergic subjects in both NHANES (OR: 1.91, 95%CI: 1.31–2.77) and JHPAC/Maryland (OR: 1.31, 95%CI: 1.18–1.47). Ethnicity interacted with season (OR 2.34, 95%CI 1.43–3.82 for Caucasians, OR 1.19, 95%CI 0.77–1.86 for non-Caucasians, p=0.04 for interaction), as did eczema (OR 1.47, 95%CI 1.29–1.67 with eczema, OR 1.00, 95%CI 0.80–1.23 without eczema, p=0.002 for interaction).
Fall birth is associated with increased risk of food allergy, and this risk is greatest among those most likely to have seasonal variation in vitamin D during infancy (Caucasians) and those at risk for skin barrier dysfunction (subjects with a history of eczema), suggesting that vitamin D and the skin barrier may be implicated in seasonal associations with food allergy.
food allergy; season of birth; eczema; vitamin D