Association between paracetamol use in infancy and childhood, and risk of asthma, rhinoconjunctivitis, and eczema in children aged 6-7 years: analysis from Phase Three of the ISAAC programme.
ABSTRACT Exposure to paracetamol during intrauterine life, childhood, and adult life may increase the risk of developing asthma. We studied 6-7-year-old children from Phase Three of the International Study of Asthma and Allergies in Childhood (ISAAC) programme to investigate the association between paracetamol consumption and asthma.
As part of Phase Three of ISAAC, parents or guardians of children aged 6-7 years completed written questionnaires about symptoms of asthma, rhinoconjunctivitis, and eczema, and several risk factors, including the use of paracetamol for fever in the child's first year of life and the frequency of paracetamol use in the past 12 months. The primary outcome variable was the odds ratio (OR) of asthma symptoms in these children associated with the use of paracetamol for fever in the first year of life, as calculated by logistic regression.
205 487 children aged 6-7 years from 73 centres in 31 countries were included in the analysis. In the multivariate analyses, use of paracetamol for fever in the first year of life was associated with an increased risk of asthma symptoms when aged 6-7 years (OR 1.46 [95% CI 1.36-1.56]). Current use of paracetamol was associated with a dose-dependent increased risk of asthma symptoms (1.61 [1.46-1.77] and 3.23 [2.91-3.60] for medium and high use vs no use, respectively). Use of paracetamol was similarly associated with the risk of severe asthma symptoms, with population-attributable risks between 22% and 38%. Paracetamol use, both in the first year of life and in children aged 6-7 years, was also associated with an increased risk of symptoms of rhinoconjunctivitis and eczema.
Use of paracetamol in the first year of life and in later childhood, is associated with risk of asthma, rhinoconjunctivitis, and eczema at age 6 to 7 years. We suggest that exposure to paracetamol might be a risk factor for the development of asthma in childhood.
Full-textDOI: · Available from: Stephen Montefort, May 29, 2015
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ABSTRACT: Missing data are common in medical research, which can lead to a loss in statistical power and potentially biased results if not handled appropriately. Multiple imputation (MI) is a statistical method, widely adopted in practice, for dealing with missing data. Many academic journals now emphasise the importance of reporting information regarding missing data and proposed guidelines for documenting the application of MI have been published. This review evaluated the reporting of missing data, the application of MI including the details provided regarding the imputation model, and the frequency of sensitivity analyses within the MI framework in medical research articles. A systematic review of articles published in the Lancet and New England Journal of Medicine between January 2008 and December 2013 in which MI was implemented was carried out. We identified 103 papers that used MI, with the number of papers increasing from 11 in 2008 to 26 in 2013. Nearly half of the papers specified the proportion of complete cases or the proportion with missing data by each variable. In the majority of the articles (86%) the imputed variables were specified. Of the 38 papers (37%) that stated the method of imputation, 20 used chained equations, 8 used multivariate normal imputation, and 10 used alternative methods. Very few articles (9%) detailed how they handled non-normally distributed variables during imputation. Thirty-nine papers (38%) stated the variables included in the imputation model. Less than half of the papers (46%) reported the number of imputations, and only two papers compared the distribution of imputed and observed data. Sixty-six papers presented the results from MI as a secondary analysis. Only three articles carried out a sensitivity analysis following MI to assess departures from the missing at random assumption, with details of the sensitivity analyses only provided by one article. This review outlined deficiencies in the documenting of missing data and the details provided about imputation. Furthermore, only a few articles performed sensitivity analyses following MI even though this is strongly recommended in guidelines. Authors are encouraged to follow the available guidelines and provide information on missing data and the imputation process.BMC Medical Research Methodology 12/2015; 15(1):30. DOI:10.1186/s12874-015-0022-1 · 2.17 Impact Factor
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