Long-term exposure to fine particulate air pollution is associated with the incidence of cardiovascular disease and death among postmenopausal women. Exposure differences within cities are associated with the risk of cardiovascular disease.
The case-crossover design has been widely used to study the association between short-term air pollution exposure and the risk of an acute adverse health event. The design uses cases only; for each individual case, exposure just before the event is compared with exposure at other control (or "referent") times. Time-invariant confounders are controlled by making within-subject comparisons. Even more important in the air pollution setting is that time-varying confounders can also be controlled by design by matching referents to the index time. The referent selection strategy is important for reasons in addition to control of confounding. The case-crossover design makes the implicit assumption that there is no trend in exposure across the referent times. In addition, the statistical method that is used-conditional logistic regression-is unbiased only with certain referent strategies. We review here the case-crossover literature in the air pollution context, focusing on key issues regarding referent selection. We conclude with a set of recommendations for choosing a referent strategy with air pollution exposure data. Specifically, we advocate the time-stratified approach to referent selection because it ensures unbiased conditional logistic regression estimates, avoids bias resulting from time trend in the exposure series, and can be tailored to match on specific time-varying confounders.
Background: Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time. Objective: The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010. Methods: We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide ( ), ozone ( ), particulate matter with aerodynamic diameter ( ; excluding year-1990), particulate matter with aerodynamic diameter ( ), and sulfur dioxide ( ). We used census data for demographic information and a national empirical model for ambient air pollution levels. Results: For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for [54% ( )], smallest for [3.6% ( )], and intermediate for the remaining pollutants (13%–19%). The disparities varied by U.S. state; for example, for in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial / ethnic exposure disparities declined by between 35% ( ; ) and 88% ( ; CO); relative disparities declined to between ( ; i.e., nearly zero change) and (CO; i.e., a reduction). Discussion: As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584
Objective Manganese (Mn), an established neurotoxicant, is a common component of welding fume. The neurological phenotype associated with welding exposures has not been well described. Prior epidemiologic evidence linking occupational welding to parkinsonism is mixed, and remains controversial. Methods This was a cross-sectional and nested case–control study to investigate the prevalence and phenotype of parkinsonism among 811 shipyard and fabrication welders recruited from trade unions. Two reference groups included 59 non-welder trade workers and 118 newly diagnosed, untreated idiopathic PD patients. Study subjects were examined by a movement disorders specialist using the Unified Parkinson Disease Rating Scale motor subsection 3 (UPDRS3). Parkinsonism cases were defined as welders with UPDRS3 score ≥15. Normal was defined as UPDRS3 < 6. Exposure was classified as intensity adjusted, cumulative years of welding. Adjusted prevalence ratios for parkinsonism were calculated in relation to quartiles of welding years. Results The overall prevalence estimate of parkinsonism was 15.6% in welding exposed workers compared to 0% in the reference group. Among welders, we observed a U-shaped dose–response relation between weighted welding exposure-years and parkinsonism. UPDRS3 scores for most domains were similar between welders and newly diagnosed idiopathic Parkinson disease (PD) patients, except for greater frequency of rest tremor and asymmetry in PD patients. Conclusion This work-site based study among welders demonstrates a high prevalence of parkinsonism compared to nonwelding-exposed workers and a clinical phenotype that overlaps substantially with PD.
Background: Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM2.5)] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (Finf) will affect epidemiologic study results.Objective: We aimed to develop models to predict Finf for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM2.5 exposure, subclinical cardiovascular disease, and clinical outcomes.Methods: We collected 526 two-week, paired indoor–outdoor PM2.5 filter samples from a subset of study homes. PM2.5 elemental composition was measured by X-ray fluorescence, and Finf was estimated as the indoor/outdoor sulfur ratio. We regressed Finf on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R2 and root mean square error (RMSE) from a 10-fold cross-validation.Results: The mean ± SD Finf across all communities and seasons was 0.62 ± 0.21, and community-specific means ranged from 0.47 ± 0.15 in Winston-Salem, North Carolina, to 0.82 ± 0.14 in New York, New York. Finf was generally greater during the warm (> 18°C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week Finf, with an RMSE of 0.13.Conclusions: We developed intuitive models that can predict Finf using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential Finf into an exposure assessment.
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