Annual SO2 exposure, asthma, atopy, and lung function in Puerto Rican children.

BACKGROUND: Long-term effects of sulfur dioxide (SO2 ) exposure on children, a vulnerable population, are largely unknown. Further, how long-term SO2 affects Puerto Rican children living in the island of Puerto Rico, a group with high asthma prevalence, is unclear. We evaluated the effects of annual average 1-hour daily maximum SO2 average on asthma, atopy, total immunoglobulin E (IgE), and lung function in Puerto Rican children. METHODS: A cohort of 678 children (351 with asthma, 327 without asthma) was recruited in Puerto Rico from 2009 to 2010. Annual average 1-hour daily maximum SO2 exposure was interpolated utilizing publicly available monitoring data. Multivariable logistic and linear regression was used for the analysis of asthma, atopy (defined as an IgE ≥0.35 IU/mL to at least one of five common aero-allergens), total IgE, and lung function measures (forced vital capacity [FVC], forced expiratory volume in 1 second [FEV1], and FEV1/FVC ratio). RESULTS: Annual SO2 exposure (per 1 ppb) was significantly associated with asthma (odds ratio [OR] = 1.42; 95% confidence interval [CI] = 1.05-1.91) and atopy (OR = 1.35; 95% CI = 1.02-1.78). Such exposure was also significantly associated with lower FEV1/FVC in all children (ß = -1.42; 95% CI = -2.78 to -0.08) and in children with asthma (ß = -2.39; 95% CI= -4.31 to -0.46). Annual SO2 exposure was not significantly associated with total IgE, FEV1, or FVC. CONCLUSIONS: Among Puerto Rican children in Puerto Rico, long-term SO2 exposure is linked to asthma and atopy. In these children, long-term SO2 exposure is also associated with reduced FEV1/FVC, particularly in those with asthma.

Accuracy of prehospital transport time estimation.

OBJECTIVES: Estimates of prehospital transport times are an important part of emergency care system research and planning; however, the accuracy of these estimates is unknown. The authors examined the accuracy of three estimation methods against observed transport times in a large cohort of prehospital patient transports. METHODS: This was a validation study using prehospital records in King County, Washington, and southwestern Pennsylvania from 2002 to 2006 and 2005 to 2011, respectively. Transport time estimates were generated using three methods: linear arc distance, Google Maps, and ArcGIS Network Analyst. Estimation error, defined as the absolute difference between observed and estimated transport time, was assessed, as well as the proportion of estimated times that were within specified error thresholds. Based on the primary results, a regression estimate was used that incorporated population density, time of day, and season to assess improved accuracy. Finally, hospital catchment areas were compared using each method with a fixed drive time. RESULTS: The authors analyzed 29,935 prehospital transports to 44 hospitals. The mean (± standard deviation [±SD]) absolute error was 4.8 (±7.3) minutes using linear arc, 3.5 (±5.4) minutes using Google Maps, and 4.4 (±5.7) minutes using ArcGIS. All pairwise comparisons were statistically significant (p < 0.01). Estimation accuracy was lower for each method among transports more than 20 minutes (mean [±SD] absolute error was 12.7 [±11.7] minutes for linear arc, 9.8 [±10.5] minutes for Google Maps, and 11.6 [±10.9] minutes for ArcGIS). Estimates were within 5 minutes of observed transport time for 79% of linear arc estimates, 86.6% of Google Maps estimates, and 81.3% of ArcGIS estimates. The regression-based approach did not substantially improve estimation. There were large differences in hospital catchment areas estimated by each method. CONCLUSIONS: Route-based transport time estimates demonstrate moderate accuracy. These methods can be valuable for informing a host of decisions related to the system organization and patient access to emergency medical care; however, they should be employed with sensitivity to their limitations.