Predicting future asthma morbidity in preschool inner-city children

Association Between Asthma Control Trajectories in Preschoolers and Long-Term Asthma Control

BACKGROUND

The potential influence of asthma control in early life on long-term outcomes in childhood remains largely unknown.

OBJECTIVE

To examine whether asthma control trajectories in the 2 years after diagnosis in preschoolers are associated with long-term unsatisfactory asthma control.

METHODS

We conducted a multicenter population-based retrospective cohort study, including four Canadian provincial birth cohorts derived from administrative databases. We included preschoolers (aged <5 years) with a diagnosis of asthma, defined as having one hospitalization or two physician visits for asthma within 2 years. Asthma control trajectories, ascertained over four 6-month periods after diagnosis using a validated index, were classified as controlled throughout, improving control, fluctuating control, worsening control, and out of control throughout. Long-term unsatisfactory control was defined as four or more short-acting β₂-agonist average doses per week or an exacerbation, measured within 6 months before index ages 6, 8, 10, 12, 14, and 16 years. Average risk ratios for long-term unsatisfactory control across all index ages were estimated using a robust Poisson model by province and meta-analyzed with a random effects model.

RESULTS

In 50,188 preschoolers with asthma, the pooled average risk of having unsatisfactory control at any index age was 42% (95% confidence interval, 34.6-49.4). Compared with children who were controlled throughout, incrementally higher average risk ratios (95% confidence interval) of long-term unsatisfactory control were observed in each trajectory: improving control, 1.38 (1.28-1.49); fluctuating control, 1.54 (1.40-1.68); worsening control, 1.70 (1.55-1.86) and out of control throughout, 2.00 (1.80-2.21).

CONCLUSIONS

Suboptimal asthma control trajectories shortly after a preschool diagnosis were associated with long-term unsatisfactory asthma control. Early control trajectories appear to be promising for predicting the risk for long-term adverse outcomes.

Predictors of polycyclic aromatic hydrocarbon exposure and internal dose in inner city Baltimore children

Polycyclic aromatic hydrocarbons (PAHs), the by-products of incomplete combustion of organic materials, are commonly found on particulate matter (PM) and have been associated with the development of asthma and asthma exacerbation in urban populations. We examined time spent in the home and outdoors as predictors of exposures to airborne PAHs and measured urinary 1-hydroxypyrene-glucuronide (1-OHPG) as internal dose of PAHs in 118 children aged 5-12 years from Baltimore, MD. During weeklong periods (Saturday-Saturday) in each of four seasons: daily activities were assessed using questionnaires, indoor air nicotine and PM concentrations were monitored, and urine specimens were collected on Tuesday (day 3) and Saturday (day 7) for measurement of 1-OHPG. Time spent in non-smoking homes was associated with significantly decreased 1-OHPG concentration in urine (β=-0.045, 95% CI (-0.076, -0.013)), and secondhand smoke (SHS) exposures modified these associations, with higher urinary 1-OHPG concentrations in children spending time in smoking homes than non-smoking homes (P-value for interaction=0.012). Time spent outdoors was associated with increased urinary 1-OHPG concentrations (β=0.097, 95% CI (0.037, 0.157)) in boys only. Our results suggest that SHS and ambient (outdoor) air pollution contribute to internal dose of PAHs in inner city children.

An intelligent system approach for asthma prediction in symptomatic preschool children

Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority. Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation. Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results. Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.