These ATS-sponsored technical standards describe the equipment, personnel, competencies, and special procedures associated with FAE in children.

We conducted a case-control study among members of Kaiser Permanente Northern California (KPNC) born between 1980 and 2003 to determine the prevalence of immune-mediated conditions in individuals with autism, investigate whether these conditions occur more often than expected, and explore the timing of onset relative to autism diagnosis. Cases were children and young adults with at least two autism diagnoses recorded in outpatient records (n=5,565). Controls were children without autism randomly sampled at a ratio of 5 to 1, matched to cases on birth year, sex, and length of KPNC membership (n=27,825). The main outcomes - asthma, allergies, and autoimmune diseases - were identified from KPNC inpatient and outpatient databases. Chi-square tests were used to evaluate case-control differences. Allergies and autoimmune diseases were diagnosed significantly more often among children with autism than among controls (allergy: 20.6% vs. 17.7%, Crude odds ratio (OR) = 1.22, 95% confidence interval (CI) 1.13 – 1.31; autoimmune disease: 1% vs. 0.76%, OR = 1.36, 95% CI 1.01 – 1.83), and asthma was diagnosed significantly less often (13.7% vs. 15.9%; OR = 0.83, 95% CI 0.76 – 0.90). Psoriasis occurred more than twice as often in cases than in controls (0.34% vs. 0.15%; OR =2.35, 95% CI 1.36 – 4.08). Our results support previous observations that children with autism have elevated prevalence of specific immune-related comorbidities.

Effective management of populations with asthma requires methods for identifying patients at high risk for adverse outcomes. The aim of this study was to develop and validate prediction models that used computerized utilization data from a large health-maintenance organization (HMO) to predict asthma-related hospitalization and emergency department (ED) visits. In this retrospective cohort design with split-sample validation, variables from the baseline year were used to predict asthma-related adverse outcomes during the follow-up year for 16,520 children with asthma-related utilization. In proportional-hazard models, having filled an oral steroid prescription (relative risk [RR]: 1.9; 95% confidence interval [CI]: 1.3 to 2.8) or having been hospitalized (RR: 1.7; 95% CI: 1.1 to 2.7) during the prior 6 mo, and not having a personal physician listed on the computer (RR: 1.6; 95% CI: 1.1 to 2.3) were associated with increased risk of future hospitalization. Classification trees identified previous hospitalization and ED visits, six or more beta-agonist inhalers (units) during the prior 6 mo, and three or more physicians prescribing asthma medications during the prior 6 mo as predictors. The classification trees performed similarly to proportional-hazards models, and identified patients who had a threefold greater risk of hospitalization and a twofold greater risk of ED visits than the average patient. We conclude that computer-based prediction models can identify children at high risk for adverse asthma outcomes, and may be useful in population-based efforts to improve asthma management.

No abstract

No abstract