Session Title: 3S101: Epidemiology & Public Health II: SLE (892–897)
Session Type: ACR Abstract Session
Session Time: 4:30PM-6:00PM
Background/Purpose: We performed a spatial-time cluster analysis of the Hopkins Lupus Cohort with the goal of identifying potential clusters of SLE organ specific flares and their relation to temperature changes and fine particulate matter pollution (PM2.5).
Methods: 1628 patients who fulfilled the SLICC classification criteria for SLE and who had recorded home addresses were included in the analysis. Disease activity was expressed as the Lupus Activity Index. Assessment of rash, joint involvement, serositis, neurologic, pulmonary, renal, and hematologic activity was quantified on a 0-3 VAS. An organ specific flare was defined as an increase in visual analogue scale (VAS) of 1 point or more compared to the previous visit. Daily fine particulate matter pollution (PM2.5) data measured in micrograms per cubic meter and temperature measured in degrees Fahrenheit were collected at various monitoring stations in the eastern United States and obtained from the Environmental Protection Agency. The nearest monitoring station for each patient at each visit date was determined, and the average PM2.5 concentration and temperature ten days prior to clinic visit was calculated. Both univariate and multivariate Generalized Estimating Equations (GEE) logistic regression models with an exchangeable correlation structure were built to study the association of individual (age, sex, ethnicity) and environmental (PM2.5, temperature) variables with the seven different outlined types of lupus disease activity. Spatiotemporal cluster detection was conducted using the SaTScan software. One month long minimum time intervals were considered for this analysis, and spatially overlapping clusters were allowed as long as the overlapping cluster did not contain the centroid of the cluster that was already there. Regression models were used for adjustment and included age, sex, and race, as well as PM2.5 and temperature.
Results: Three statistically significant (p< 0.05) unadjusted clusters were identified for joint flares, four rash flare clusters, one hematologic flare cluster, four neurologic flare clusters, three serositis flare clusters, four renal flare clusters, and five pulmonary flare clusters. Most of the identified clusters changed in significance, temporal, or spatial extent after adjusting for temperature, PM2.5 concentration, and individual covariates.
Conclusion: We describe the first space-time clusters of lupus organ-specific disease activity. Seasonal, as well as multi-year cluster patterns were identified, differing in extent and location for the various organ-specific flare types. Many of the identified clusters changed in significance, temporal, or spatial extent after adjusting for environmental or individual covariates. Further study focusing on each individual lupus organ-specific activity will be required to better understand the driving forces behind these observed changes. The proposed spatial temporal analytical methods could lay the foundation for a new approach in the discovery of potential environmental and atmospheric factors and their role in the etiopathogenesis of lupus
To cite this abstract in AMA style:Stojan G, Kvit A, Curriero F, Petri M. A Spatial-temporal Analysis of Organ-specific Lupus Flares in Relation to Fine Particulate Matter Pollution and Temperature [abstract]. Arthritis Rheumatol. 2019; 71 (suppl 10). https://acrabstracts.org/abstract/a-spatial-temporal-analysis-of-organ-specific-lupus-flares-in-relation-to-fine-particulate-matter-pollution-and-temperature/. Accessed November 26, 2020.
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/a-spatial-temporal-analysis-of-organ-specific-lupus-flares-in-relation-to-fine-particulate-matter-pollution-and-temperature/