“…Several previous studies reported effects of rapid weight gain and larger weight on the development of wheezing in children,3–11 27 28 but other studies have failed to replicate these findings 12–14. Discrepancies between studies could be partially due to methodological issues, such as different outcome definitions, growth modelling methods and periods of exposure and outcome assessment.…”
Faster growth and larger size in the first 18 months of life are both independently associated with an increased risk of wheezing. These findings suggest that early growth patterns play a role in shaping the occurrence of wheezing.
“…Several previous studies reported effects of rapid weight gain and larger weight on the development of wheezing in children,3–11 27 28 but other studies have failed to replicate these findings 12–14. Discrepancies between studies could be partially due to methodological issues, such as different outcome definitions, growth modelling methods and periods of exposure and outcome assessment.…”
Faster growth and larger size in the first 18 months of life are both independently associated with an increased risk of wheezing. These findings suggest that early growth patterns play a role in shaping the occurrence of wheezing.
“…Potential predictors of asthma diagnosis were identified a priori through a literature review and were included as covariates in the analysis. Child factors included age, sex, and body mass index (zBMI) . A parent completed questionnaire, adapted from the Canadian Community Health Survey, was used to define parental and household factors which included median household income, parental smoking, child's history of atopy (eczema or atopic dermatitis), child's history of allergies, daycare attendance, and family history of asthma .…”
Introduction
Asthma prevalence is commonly measured in national surveys by questionnaire. The Ontario Asthma Surveillance Information System (OASIS) developed a validated health claims diagnosis algorithm to estimate asthma prevalence. The primary objective was to assess the agreement between two approaches of measuring asthma in young children. Secondary objectives were to identify concordant and discordant pairs, and to identify factors associated with disagreement.
Study design and setting
A measurement study to evaluate the agreement between the OASIS algorithm and parent‐reported asthma (criterion standard). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Multivariable logistic regression was used to determine factors associated with disagreement.
Results
Healthy children aged 1 to 5 years (n =3642) participating in the TARGet Kids! practice based research network 2008‐2013 in Toronto, Canada were included. Prevalence of asthma was 14% and 6% by the OASIS algorithm and parent‐reported asthma, respectively. The Kappa statistic was 0.43, sensitivity 81%, specificity 90%, PPV 34%, and NPV 99%. There were 3249 concordant and 393 discordant pairs. Statistically significant factors associated with asthma identified by OASIS but not parent report included: male sex, higher zBMI, and parent history of asthma. Males were less likely to have asthma identified by parent report but not OASIS.
Conclusion
The OASIS algorithm identified more asthma cases in young children than parent‐reported asthma. The OASIS algorithm had high sensitivity, specificity, and NPV but low PPV relative to parent‐reported asthma. These findings need replication in other populations.
“…), многие из которых синтезируются и секретиру-ются клетками жировой (адипозной) ткани [4,5] и поэтому названы адипокинами, включая IL-6, IL-10, эотаксин, фактор некроза опухоли α, трансфор-мируемый фактор роста β1, C-реактивный протеин, лептин и адипонектин. Наконец, было идентифи-цировано, что идентичные локусы генома человека отвечают как за БА, так и за ожирение [6][7][8][9][10][11].…”
Section: ключевые слова: бронхиальная астма избыточная масса тела сunclassified
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