Prediction of asthma in young adults using childhood characteristics: Development of a prediction rule

Health effects associated with exposure to secondhand smoke: a Burden of Proof study

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure-response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives.

Prediction of adult asthma risk in early childhood using novel adult asthma predictive risk scores

BACKGROUND

Numerous risk scores have been developed to predict childhood asthma. However, they may not predict asthma beyond childhood. We aim to create childhood risk scores that predict development and persistence of asthma up to young adult life.

METHODS

The Isle of Wight Birth Cohort (n = 1456) was prospectively assessed up to 26 years of age. Asthma predictive scores were developed based on factors during the first 4 years, using logistic regression and tested for sensitivity, specificity and area under the curve (AUC) for prediction of asthma at (i) 18 and (ii) 26 years, and persistent asthma (PA) (iii) at 10 and 18 years, and (iv) at 10, 18 and 26 years. Models were internally and externally validated.

RESULTS

Four models were generated for prediction of each asthma outcome. ASthma PredIctive Risk scorE (ASPIRE)-1: a 2-factor model (recurrent wheeze [RW] and positive skin prick test [+SPT] at 4 years) for asthma at 18 years (sensitivity: 0.49, specificity: 0.80, AUC: 0.65). ASPIRE-2: a 3-factor model (RW, +SPT and maternal rhinitis) for asthma at 26 years (sensitivity: 0.60, specificity: 0.79, AUC: 0.73). ASPIRE-3: a 3-factor model (RW, +SPT and eczema at 4 years) for PA-18 (sensitivity: 0.63, specificity: 0.87, AUC: 0.77). ASPIRE-4: a 3-factor model (RW, +SPT at 4 years and recurrent chest infection at 2 years) for PA-26 (sensitivity: 0.68, specificity: 0.87, AUC: 0.80). ASPIRE-1 and ASPIRE-3 scores were replicated externally. Further assessments indicated that ASPIRE-1 can be used in place of ASPIRE-2-4 with same predictive accuracy.

CONCLUSION

ASPIRE predicts persistent asthma up to young adult life.

Latent factors of adverse childhood experiences and adult-onset asthma

Asthma is a chronic respiratory disease with complex etiology. Adverse childhood experiences (ACEs) have been linked to asthma in adulthood. Underlying potential mechanisms for the ACE-asthma relationship include stress-induced inflammatory pathways and immune dysregulation. We conducted a cross-sectional secondary data analysis of the 2013 Alberta ACE Survey to explore the relationship between latent ACE factors and self-reported adult asthma. We evaluated the underlying correlation structure among eight different ACEs using exploratory factor analysis. We conducted a logistic regression model to evaluate whether ACE factors retained from the factor analysis predicted self-reported asthma in adulthood. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). We analyzed ACE survey results from 1207 participants. Factor analysis yielded four ACE latent factors: factor 1/relational violence, factor 2/negative home environment, factor 3/illness at home, and factor 4/sexual abuse. Results of the logistic regression showed that experiencing sexual abuse (OR: 3.23; 95% CI: 1.89, 5.23), relational violence (OR: 1.99; 95% CI: 1.17, 3.38), and being exposed to a negative home environment (OR: 1.86; 95% CI: 1.03, 3.35) were predictive of a diagnosis of asthma in adulthood, whereas living in a household with someone experiencing illness did not show an effect (OR: 1.38; 95% CI: 0.75, 2.56). Factor analysis provides an effectual approach to understand the long-term impact of ACEs on respiratory health. Our findings have important implications to understand the developmental origins of asthma in adulthood and inform interventions aimed at reducing the lasting negative impact of childhood adversities on future respiratory health.

Priorities for future research into asthma diagnostic tools: A PAN-EU consensus exercise from the European asthma research innovation partnership (EARIP)

The diagnosis of asthma is currently based on clinical history, physical examination and lung function, and to date, there are no accurate objective tests either to confirm the diagnosis or to discriminate between different types of asthma. This consensus exercise reviews the state of the art in asthma diagnosis to identify opportunities for future investment based on the likelihood of their successful development, potential for widespread adoption and their perceived impact on asthma patients. Using a two-stage e-Delphi process and a summarizing workshop, a group of European asthma experts including health professionals, researchers, people with asthma and industry representatives ranked the potential impact of research investment in each technique or tool for asthma diagnosis and monitoring. After a systematic review of the literature, 21 statements were extracted and were subject of the two-stage Delphi process. Eleven statements were scored 3 or more and were further discussed and ranked in a face-to-face workshop. The three most important diagnostic/predictive tools ranked were as follows: "New biological markers of asthma (eg genomics, proteomics and metabolomics) as a tool for diagnosis and/or monitoring," "Prediction of future asthma in preschool children with reasonable accuracy" and "Tools to measure volatile organic compounds (VOCs) in exhaled breath."

Asthma transition from childhood into adulthood

Asthma is the most prevalent chronic respiratory disease both in children and adults and resembles a complex syndrome rather than a single disease. Different methods have been developed to better characterise distinct asthma phenotypes in childhood and adulthood. In studies of adults, most phenotyping relies on biomaterials from the lower airways; however, this information is missing in paediatric studies because of restricted accessibility. Few patients show symptoms throughout childhood, adolescence, and adulthood. Risk factors for this might be genetics, family history of asthma and atopy, infections early in life, allergic diseases, and lung function deficits. In turn, a large proportion of children with asthma lose their symptoms during school age and adolescence. This improved prognosis, which might also reflect a better treatment response, is associated with being male and with milder and less allergic disease. Importantly, whether clinical remission of symptoms equals the disappearance of underlying pathology is unknown. In fact, airway hyper-responsiveness and airway inflammation might remain despite the absence of overt symptoms. Additionally, a new-onset of asthma symptoms is apparent in adulthood, especially in women and in the case of impaired lung function. However, many patients do not remember childhood symptoms, which might reflect relapse rather than true initiation. Both relapse and adult-onset of asthma symptoms have been associated with allergic disease and sensitisation in addition to airway hyper-responsiveness. Thus, asthma symptoms beginning in adults might have originated in childhood. Equivocally, persistence into, relapse, and new-onset of symptoms in adulthood have all been related to active smoking. However, underlying mechanisms for the associations remain unclear, and future asthma research should therefore integrate standardised molecular approaches in identical ways in both paediatric and adult populations and in longitudinal studies.

Monitoring asthma in childhood

The goal of asthma treatment is to obtain clinical control and reduce future risks to the patient. However, to date there is limited evidence on how to monitor patients with asthma. Childhood asthma introduces specific challenges in terms of deciding what, when, how often, by whom and in whom different assessments of asthma should be performed. The age of the child, the fluctuating course of asthma severity, variability in clinical presentation, exacerbations, comorbidities, socioeconomic and psychosocial factors, and environmental exposures may all influence disease activity and, hence, monitoring strategies. These factors will be addressed in herein.

We identified large knowledge gaps in the effects of different monitoring strategies in children with asthma. Studies into monitoring strategies are urgently needed, preferably in collaborative paediatric studies across countries and healthcare systems.

Monitoring asthma in children is essential for disease control and should reflect age, triggers and disease activityhttp://ow.ly/J0k7f

A systematic review of predictive models for asthma development in children

BACKGROUND

Asthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models.

METHODS

A systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality.

RESULTS

The literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations.

CONCLUSIONS

Existing predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children.

Early risk factors for pubertal asthma

BACKGROUND

Early life risk factors are previously described for childhood asthma, but less is known related to asthma in adolescence. We aimed to investigate early risk factors (before 2 years) for pubertal asthma and secondarily for pubertal asthma phenotypes based upon allergic comorbidities.

METHODS

Based on data from 550 adolescents in the prospective birth cohort 'Environment and Childhood Asthma' study, subjects were categorized by recurrent bronchial obstruction (rBO) 0-2 years, asthma 2-10 years, and pubertal asthma from 10 to 16 years including incident asthma in puberty and asthma in remission from 10 to 16 years or as never rBO/asthma 0-16 years. Asthma in puberty was further classified based on the comorbidities atopic dermatitis and allergic rhinitis (AR) from 10 to 16 years. Twenty-three common asthma risk factors identified by 2 years of age, including frequency and persistence of bronchial obstruction (severity score), were analysed by weighted logistic regression for each phenotype.

RESULTS

In adjusted models, the risk of pubertal asthma increased significantly with higher severity score, parental rhinitis, being the firstborn child, and familial stress around birth. Pubertal asthma in remission was significantly associated with severity score and number of lower respiratory tract infections and inversely associated with breastfeeding beyond 4 months. Pubertal incident asthma was more common among firstborn children. All asthma phenotypes with allergic diseases were significantly associated with severity score, whereas familial perinatal stress increased the risk of asthma only. Asthma combined with AR was associated with parental asthma and being firstborn, whereas the risk of asthma with both atopic dermatitis and AR increased with higher paternal education, atopic dermatitis, being firstborn, and familial perinatal stress.

CONCLUSION AND CLINICAL RELEVANCE

Important early risk factors for pubertal asthma were early airways obstruction, parental rhinitis, being the firstborn child, and perinatal familial stress.

A simple asthma prediction tool for preschool children with wheeze or cough

BACKGROUND

Many preschool children have wheeze or cough, but only some have asthma later. Existing prediction tools are difficult to apply in clinical practice or exhibit methodological weaknesses.

OBJECTIVE

We sought to develop a simple and robust tool for predicting asthma at school age in preschool children with wheeze or cough.

METHODS

From a population-based cohort in Leicestershire, United Kingdom, we included 1- to 3-year-old subjects seeing a doctor for wheeze or cough and assessed the prevalence of asthma 5 years later. We considered only noninvasive predictors that are easy to assess in primary care: demographic and perinatal data, eczema, upper and lower respiratory tract symptoms, and family history of atopy. We developed a model using logistic regression, avoided overfitting with the least absolute shrinkage and selection operator penalty, and then simplified it to a practical tool. We performed internal validation and assessed its predictive performance using the scaled Brier score and the area under the receiver operating characteristic curve.

RESULTS

Of 1226 symptomatic children with follow-up information, 345 (28%) had asthma 5 years later. The tool consists of 10 predictors yielding a total score between 0 and 15: sex, age, wheeze without colds, wheeze frequency, activity disturbance, shortness of breath, exercise-related and aeroallergen-related wheeze/cough, eczema, and parental history of asthma/bronchitis. The scaled Brier scores for the internally validated model and tool were 0.20 and 0.16, and the areas under the receiver operating characteristic curves were 0.76 and 0.74, respectively.

CONCLUSION

This tool represents a simple, low-cost, and noninvasive method to predict the risk of later asthma in symptomatic preschool children, which is ready to be tested in other populations.

Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks

The long-term solution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, this may lead to better treatment opportunities and hopefully better disease outcomes in adulthood.

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.

Predicting asthma in preschool children with asthma symptoms: study rationale and design

Background

In well-child care it is difficult to determine whether preschool children with asthma symptoms actually have or will develop asthma at school age. The PIAMA (Prevention and Incidence of Asthma and Mite Allergy) Risk Score has been proposed as an instrument that predicts asthma at school age, using eight easy obtainable parameters, assessed at the time of first asthma symptoms at preschool age. The aim of this study is to present the rationale and design of a study 1) to externally validate and update the PIAMA Risk Score, 2) to develop an Asthma Risk Appraisal Tool to predict asthma at school age in (specific subgroups of) preschool children with asthma symptoms and 3) to test implementation of the Asthma Risk Appraisal Tool in well-child care.

Methods and design

The study will be performed within the framework of Generation R, a prospective multi-ethnic cohort study. In total, consent for postnatal follow-up was obtained from 7893 children, born between 2002 and 2006. At preschool age the PIAMA Risk Score will be assessed and used to predict asthma at school age. Discrimination (C-index) and calibration will be assessed for the external validation. We will study whether the predictive ability of the PIAMA Risk Score can be improved by removing or adding predictors (e.g. preterm birth). The (updated) PIAMA Risk Score will be converted to the Asthma Risk Appraisal Tool- to predict asthma at school age in preschool children with asthma symptoms. Additionally, we will conduct a pilot study to test implementation of the Asthma Risk Appraisal Tool in well-child care.

Discussion

Application of the Asthma Risk Appraisal Tool in well-child care will help to distinguish preschool children at high- and low-risk of developing asthma at school age when asthma symptoms appear.

This study will increase knowledge about the validity of the PIAMA risk score and might improve risk assessment of developing asthma at school age in (specific subgroups of) preschool children, who present with asthma symptoms at well-child care.

The population attributable fraction of asthma among Canadian children

OBJECTIVE

We calculated the population attributable fraction (PAF) of Canadian childhood asthma due to modifiable environmental exposures, in order to estimate their relative contributions to asthma development based on the current literature.

METHODS

We conducted a systematic review to determine Canadian childhood asthma incidence, Canadian prevalence of exposure to airborne pollutants and indoor allergens, and international estimates of the risk of developing physician-diagnosed asthma (PDA) associated with each exposure. Combining risk estimates by meta-analysis where possible, PAF was calculated by the formula: PAF = Attributable risk *Exposure prevalence* 100%/Asthma incidence.

SYNTHESIS

Age-specific Canadian childhood asthma incidence ranged from 2.8%-6.9%. Canadian exposure prevalences were: PM10 16%, PM2.5 7.1%, NO2 25%, environmental tobacco smoke (ETS) 9.0%, cat 22%, dog 12%, mouse 17%, cockroach 9.8%, dust mite 30%, moisture 14% and mould 33%. Relative risk estimates of PDA were: PM10 1.64, PM2.5 1.44, NO2 1.29, ETS 1.40, mouse 1.23, cockroach 1.96, and spanned 1.00 for cat, dog, dust mites, moisture and mould. PAF estimates for incident asthma among preschool children were: PM10 11%, PM2.5 1.6%, NO2 4.0%, ETS 2.9%, mouse 6.5% and cockroach 13%.

CONCLUSIONS

This systematic review suggests contributions to childhood asthma development from exposure to particulates, NO2, ETS, mouse and cockroach. The associations appeared to be more complex for cat, dog and dust mite allergens and more variable for mould and moisture. Additional prospective, population-based studies of childhood asthma development with objectively-measured exposures are needed to further quantify these associations.

Prediction of atopic dermatitis in 2-yr-old children by cord blood IgE, genetic polymorphisms in cytokine genes, and maternal mentality during pregnancy

Atopic dermatitis (AD) is the most common skin disease in childhood and the first step of atopic march. This study aimed to investigate whether AD in children could be better predicted by biologic markers (cord blood IgE [cbIgE], LT-αNcoI alleles, and FcεRI-β E237G genotypes) and maternal mentality during pregnancy, taking into account gender, socio-demographic factors, and parental atopy. From 2001 to 2005, 1264 mother-infant pairs were recruited to participate in a birth cohort study. Prenatal questionnaire was used to collect family history, maternal gestational conditions and mentality, and environmental exposures. Cord blood was collected and assayed for genotypes and IgE levels. Phone interviews at 6 months and 2 yrs of age were conducted to inquire children's health status, including AD occurrence. In addition to the known risk factors such as gender, maternal education, and parental atopy, biomarkers and maternal mentality during pregnancy were screened by logistic regression as candidate predictors of AD. Area-under-curve (AUC) statistic from receiver-operating characteristic (ROC) curve analysis was used to compare two predicting models with and without biomarkers and maternal mentality. A total of 730 pairs completed the prenatal questionnaire and phone interview and were included in final analysis. The prevalence of ever having physician-diagnosed AD by 2-yr-olds was 5.9%. Elevated cbIgE levels (≥0.5 kU/l), LT-αNcoI alleles, FcεRI-β E237G genotype, and maternal psychologic stress during pregnancy were significantly associated with AD. Comparison with AUCs of the classic model (including gender, maternal education, and parental atopy), the model adding cbIgE levels, genotypes in cytokine genes, and maternal stress (model 2) showed higher ability to discriminate between children with and without AD (AUC statistics: 0.63 [95% CI = 0.60-0.67] vs. 0.73 [95% CI = 0.70-0.76], respectively; model comparison, p = 0.027). We conclude that elevated cbIgE, LT-α and FcεRI-β genotypes, and maternal stress during pregnancy were associated with ever having physician-diagnosed AD in 2-yr-old children and increased the predictive ability for AD after taking into account gender, maternal education, and parental atopic history.

Asthma prediction in school children; the value of combined IgE-antibodies and obstructive airways disease severity score

BACKGROUND

Allergic sensitisation increases the risk for asthma development. In this prospective birth cohort (Environment and Childhood Asthma) study, we hypothesized that combining quantitative measures of IgE antibodies (Sigma-IgE) and Severity score of obstructive airways disease (OAD) at 2 years of age (Severity score) is superior to predict current asthma (CA) at 10 years than either measure alone. Secondarily, we assessed if gender modified the prediction of CA.

METHODS

A follow-up study at 10 years of age was performed in 371 2-year-old children with recurrent (n = 219) or no (n = 152) bronchial obstruction with available serum analysed for Sigma-IgE to common food and inhalant allergens through a panel test, Phadiatop Infant) (Phadia, Uppsala, Sweden). Clinical variables included allergic sensitisation and exercise testing to characterise children with CA vs not CA at 10 years and the Severity score (0-12, 0 indicating no OAD) was used to assess risk modification.

RESULTS

Severity score alone explained 24% (Nagelkerke R(2) = 0.24) of the variation in CA, whereas Sigma-IgE explained only 6% (R(2) = 0.06). Combining the two increased the explanatory capacity to R(2) = 0.30. Gender interacted significantly with Sigma-IgE; whereas Severity score predicted CA in both genders, the predictive capacity of Sigma-IgE for CA at 10 years was significant in boys only.

CONCLUSION

Combining Sigma-IgE to inhalant allergens and Severity score at 2 years was superior to predict asthma at 10 years than either alone. Severity score predicted CA in both genders, whereas Sigma-IgE significantly predicted CA in boys only.

Risk for asthma in offspring of asthmatic mothers versus fathers: a meta-analysis

BACKGROUND

Many human epidemiologic studies demonstrate that maternal asthma confers greater risk of asthma to offspring than does paternal disease. However, a handful have shown the opposite. Given this disparity, a meta-analysis is necessary to determine the veracity and magnitude of the "maternal effect."

METHODOLOGY/PRINCIPAL FINDINGS

We screened the medical literature from 1966 to 2009 and performed a meta-analysis to compare the effect of maternal asthma vs. paternal asthma on offspring asthma susceptibility. Aggregating data from 33 studies, the odds ratio for asthma in children of asthmatic mothers compared with non-asthmatic mothers was significantly increased at 3.04 (95% confidence interval: 2.59-3.56). The corresponding odds ratio for asthma in children of asthmatic fathers was increased at 2.44 (2.14-2.79). When comparing the odds ratios, maternal asthma conferred greater risk of disease than did paternal asthma (3.04 vs. 2.44, p = 0.037). When analyzing the studies in which asthma was diagnosed by a physician the odds ratios were attenuated and no significant differences were observed (2.85 vs. 2.48, N = 18, p = 0.37). Similarly, no significant differences were observed between maternal and paternal odds ratios when analyzing the studies in which the patient population was 5 years or older (3.15 vs. 2.60, p = 0.14). However, in all cases the trend remained the same, that maternal asthma was a greater risk factor for asthma than paternal.

CONCLUSIONS/SIGNIFICANCE

The results show that maternal asthma increases offspring disease risk to a greater extent than paternal disease.

Prediction and treatment of asthma in preschool children at risk: study design and baseline data of a prospective cohort study in general practice (ARCADE)

Background

Asthma is a difficult diagnosis to establish in preschool children. A few years ago, our group presented a prediction rule for young children at risk for asthma in general practice. Before this prediction rule can safely be used in practice, cross-validation is required. In addition, general practitioners face many therapeutic management decisions in children at risk for asthma. The objectives of the study are: (1) identification of predictors for asthma in preschool children at risk for asthma with the aim of cross-validating an earlier derived prediction rule; (2) compare the effects of different treatment strategies in preschool children.

Design

In this prospective cohort study one to five year old children at risk of developing asthma were selected from general practices. At risk was defined as 'visited the general practitioner with recurrent coughing (≥ 2 visits), wheezing (≥ 1) or shortness of breath (≥ 1) in the previous 12 months'. All children in this prospective cohort study will be followed until the age of six. For our prediction rule, demographic data, data with respect to clinical history and additional tests (specific immunoglobulin E (IgE), fractional exhaled nitric oxide (FENO), peak expiratory flow (PEF)) are collected. History of airway specific medication use, symptom severity and health-related quality of life (QoL) are collected to estimate the effect of different treatment intensities (as expressed in GINA levels) using recently developed statistical techniques. In total, 1,938 children at risk of asthma were selected from general practice and 771 children (40%) were enrolled. At the time of writing, follow-up for all 5-year olds and the majority of the 4-year olds is complete. The total and specific IgE measurements at baseline were carried out by 87% of the children. Response rates to the repeated questionnaires varied from 93% at baseline to 73% after 18 months follow-up; 89% and 87% performed PEF and FENO measurements, respectively.

Discussion

In this study a prediction rule for asthma in young children, to be used in (general) practice, will be cross-validated. Our study will also provide more insight in the effect of treatment of asthma in preschool children.