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A Spatiotemporal Analysis of Organ-Specific Lupus Flares in Relation to Atmospheric Variables and Fine Particulate Matter Pollution.

ABSTRACT: OBJECTIVE:To identify potential clusters of systemic lupus erythematosus (SLE) organ-specific flares and their relationship to fine particulate matter pollution (PM2.5), temperature, ozone concentration, resultant wind, relative humidity, and barometric pressure in the Hopkins Lupus Cohort, using spatiotemporal cluster analysis. METHODS:A total of 1,628 patients who fulfilled the Systemic Lupus International Collaborating Clinics classification criteria for SLE and who had a home address recorded were included in the analysis. Disease activity was assessed using the Lupus Activity Index. Assessment of rash, joint involvement, serositis, and neurologic, pulmonary, renal, and hematologic activity was quantified on a 0-3 visual analog scale (VAS). An organ-specific flare was defined as an increase in VAS of ≥1 point compared to the previous visit. Spatiotemporal clusters were detected using SaTScan software. Regression models were used for cluster adjustment and included individual, county-level, and environmental variables. RESULTS:Significant clusters unadjusted for environmental variables were identified for joint flares (P < 0.05; n = 3), rash flares (P < 0.05; n = 4), hematologic flares (P < 0.05; n = 3), neurologic flares (P < 0.05; n = 2), renal flares (P < 0.001; n = 4), serositis (P < 0.001; n = 2), and pulmonary flares (P < 0.001; n = 2). The majority of the clusters identified changed in significance, temporal extent, or spatial extent after adjustment for environmental variables. CONCLUSION:We describe the first spatiotemporal clusters of lupus organ-specific flares. Seasonal, as well as multi-year, cluster patterns were identified, differing in extent and location for the various organ-specific flare types. Further studies focusing on each individual organ-specific flare are needed to better understand the driving forces behind these observed changes.

SUBMITTER: Stojan G

PROVIDER: S-EPMC7329611 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo determine whether higher past exposure to particulate air pollution is associated with prevalent high symptoms of anxiety.DesignObservational cohort study.SettingNurses' Health Study.Participants71,271 women enrolled in the Nurses' Health Study residing throughout the contiguous United States who had valid estimates on exposure to particulate matter for at least one exposure period of interest and data on anxiety symptoms.Main outcome measuresMeaningfully high symptoms of anxiety, defined as a score of 6 points or greater on the phobic anxiety subscale of the Crown-Crisp index, administered in 2004.ResultsThe 71,271 eligible women were aged between 57 and 85 years (mean 70 years) at the time of assessment of anxiety symptoms, with a prevalence of high anxiety symptoms of 15%. Exposure to particulate matter was characterized using estimated average exposure to particulate matter <2.5 μm in diameter (PM2.5) and 2.5 to 10 μm in diameter (PM2.5-10) in the one month, three months, six months, one year, and 15 years prior to assessment of anxiety symptoms, and residential distance to the nearest major road two years prior to assessment. Significantly increased odds of high anxiety symptoms were observed with higher exposure to PM2.5 for multiple averaging periods (for example, odds ratio per 10 µg/m(3) increase in prior one month average PM2.5: 1.12, 95% confidence interval 1.06 to 1.19; in prior 12 month average PM2.5: 1.15, 1.06 to 1.26). Models including multiple exposure windows suggested short term averaging periods were more relevant than long term averaging periods. There was no association between anxiety and exposure to PM2.5-10. Residential proximity to major roads was not related to anxiety symptoms in a dose dependent manner.ConclusionsExposure to fine particulate matter (PM2.5) was associated with high symptoms of anxiety, with more recent exposures potentially more relevant than more distant exposures. Research evaluating whether reductions in exposure to ambient PM2.5 would reduce the population level burden of clinically relevant symptoms of anxiety is warranted.

Project description:Using daily fine particulate matter (PM2.5) composition data from the 2000-2005 U.S. EPA Chemical Speciation Network (CSN) for over 200 sites, we applied multivariate methods to identify and quantify the major fine particulate matter (PM2.5) source components in the U.S. Novel aspects of this work were: (1) the application of factor analysis (FA) to multi-city daily data, drawing upon both spatial and temporal variations of chemical species; and, (2) the exclusion of secondary components (sulfates, nitrates and organic carbon) from the source identification FA to more clearly discern and apportion the PM2.5 mass to primary emission source categories. For the quantification of source-related mass, we considered two approaches based upon the FA results: 1) using single key tracers for sources identified by FA in a mass regression; and, 2) applying Absolute Principal Component Analysis (APCA). In each case, we followed a two-stage mass regression approach, in which secondary components were first apportioned among the identified sources, and then mass was apportioned to the sources and to other secondary mass not explained by the individual sources. The major U.S. PM2.5 source categories identified via FA (and their key elements) were: Metals Industry (Pb, Zn); Crustal/Soil Particles (Ca, Si); Motor Vehicle Traffic (EC, NO2); Steel Industry (Fe, Mn); Coal Combustion (As, Se); Oil Combustion (V, Ni); Salt Particles (Na, Cl) and Biomass Burning (K). Nationwide spatial plots of the source-related PM2.5 impacts were confirmatory of the factor interpretations: ubiquitous sources, such as Traffic and Soil, were found to be spread across the nation, more unique sources (such as Steel and Metals Processing) being highest in select industrialized cities, Biomass Burning was highest in the U.S. Northwest, while Residual Oil combustion was highest in cities in the Northeastern U.S. and in cities with major seaports. The sum of these source contributions and the secondary PM2.5 components agreed well with the U.S. PM2.5 observed during the study period (mean=14.3 ug/m3; R2= 0.91). Apportionment regression analyses using single-element tracers for each source category gave results consistent with the APCA estimates. Comparisons of nearby sites indicated that the PM2.5 mass and the secondary aerosols were most homogenous spatially, while traffic PM2.5 and its tracer, EC, were among the most spatially representative of the source-related components. Comparison of apportionment results to a previous analysis of the 1979-1982 IP Network revealed similar and correlated major U.S. source category factors, albeit at lower levels than in the earlier period, suggesting a consistency in the U.S. spatial patterns of these source-related exposures over time, as well. These results indicate that applying source apportionment methods to the nationwide CSN can be an informative avenue for identifying and quantifying source components for the subsequent estimation of source-specific health effects, potentially contributing to more efficient regulation of PM2.5.

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A Spatiotemporal Analysis of Organ-Specific Lupus Flares in Relation to Atmospheric Variables and Fine Particulate Matter Pollution.

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