Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical‐weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM 2.5 ) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time‐stratified case‐crossover design. Hospital admissions aggregated by ZIP code were linked with population‐weighted daily average concentrations of smoke PM 2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF‐Chem) model, a kriged interpolation of PM 2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF‐Chem, satellite observations of aerosol optical depth, and kriged PM 2.5 . A 10 μg/m 3 increase in GWR smoke PM 2.5 was associated with an 8% increased risk in asthma‐related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019–1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM 2.5 exposure method: a 10 μg/m 3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026–1.145) and not significant using WRF‐Chem (OR: 0.986, 95%CI: 0.931–1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM 2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

In the western U.S., smoke from wild and prescribed fires can severely degrade air quality. Due to changes in climate and land management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of air pollutants in the western U.S. Hence, there is a need to develop a quantitative understanding of wildfire‐smoke‐specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools have been used in past studies to assess exposure to wildfire smoke: in situ measurements, satellite‐based observations, and chemical‐transport model (CTM) simulations. Each of these exposure‐estimation tools has associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge‐regression model and a geographically weighted ridge‐regression model. We evaluate the performance of the three individual exposure‐estimate techniques and the two blended techniques by using leave‐one‐out cross validation. We find that predictions based on in situ monitors are more accurate for this particular fire season than the CTM simulations and satellite‐based observations because of the large number of monitors present; therefore, blending provides only marginal improvements above the in situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.

Objective Anti-carbamylated protein (anti-CarP) antibodies could further elucidate early RA pathogenesis and predict clinical disease. We compared diagnostic accuracy of anti-CarP antibodies for future RA to other RA-related antibodies in military personnel. Methods Stored pre-RA diagnosis serum samples from 76 RA cases were tested for anti-CarP Fetal Calf Serum (FCS), anti-CarP Fibrinogen (Fib), anti-CCP2, RF-Neph, and RF-isotypes (IgM, IgG, and IgA). Positivity for all antibodies was determined as ≥2SD of log-transformed means from controls. Relationships between autoantibodies and future RA were assessed in prediagnosis serum for all RA cases compared to controls using sensitivity, specificity, and logistic regression. Differences in diagnostic accuracy between antibody combinations were assessed using comparisons of area under the curves (AUCs). Results Anti-CarP-FCS was 26% sensitive and 95% specific for future RA, where anti-CarP-Fib was 16% sensitive and 95% specific for future RA. Anti-CarP-FCS positivity was associated with future RA, while anti-CarP-Fib trended towards association. The antibody combination of anti-CCP2 and/or ≥2 RFs (RF-Neph and/or RF-isotypes) resulted in an AUC of 0.72 for future RA, where the AUC was 0.71 with the addition of anti-CarP-FCS to this prior combination. Conclusion Adding anti-CarP-FCS to antibody combinations did not improve AUC. However, anti-CarP-FCS was associated with future onset of RA, and was present in prediagnosis serum in ~10% of RA cases negative for anti-CCP2, but positive for RF.