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Background: Acute pollutant-related lung function changes among children varies across pollutants and lag periods. We examined whether short-term air pollutant fluctuations were related to daily lung function among a panel of children and whether these effects are modified by airway hyperresponsiveness, location and asthma severity., Methods: Students from randomly selected grade 4 classrooms at seven primary schools in Durban, participated, together with asthmatic children from grades 3-6 (n=423). The schools were from high pollutant exposed communities (south) and compared with schools from communities with lower levels of pollution (north), with similar socioeconomic profiles. Interviews, spirometry and methacholine challenge testing were conducted. Bihourly lung function measurements were performed over a 3-week period in four phases. During all schooldays, students blew into their personal digital monitors every 1.5-2 hours. Nitrogen dioxide (NO 2 ), nitrogen oxide (NO), sulphur dioxide and particulate matter (<10 μm diameter) (PM 10 ) were measured at each school. Generalised estimating equations assessed lag effects, using single-pollutant (single or distributed lags) models., Results: FEV 1 declines ranged from 13 to 18 mL per unit increase in IQR for NO and 14-23 mL for NO 2 . Among the 5-day average models, a 20 mL and 30 mL greater drop in FEV 1 per IQR for NO 2 and NO, respectively, among those with airway hyperresponsiveness compared with those without. Effects were seen among those with normal airways., Conclusions: This first panel study in sub-Saharan Africa, showed significant declines in lung function, in response to NO and NO 2 with effects modified by airway hyperresponsiveness or persistent asthma., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The study of gene-environment interactions enables us to further understand the pathogenesis of asthma and inflammation. The TNF-α gene has been associated with airway pathology in asthma but there is limited information in relation to pollutant exposure and the TNF-α 308G/A polymorphism., Objective: To determine the risk conferred by the TNF-α 308(G/A) polymorphism on respiratory outcome and to evaluate whether the association between exposure to ambient air pollutants such as SO2, NO2, NO, and PM10 and variation in lung function measures is modified by genotype., Methods: The sample comprised 129 African children (between 9-11 years old). A questionnaire based on guidelines from the British Medical Research Council and the American Thoracic Society was administered to all caregivers to evaluate the prevalence of respiratory symptoms. Atopy was evaluated by skin prick testing. Bihourly measures of lung function (spirometry) were collected at school five days per week over three week periods in each of four seasons (2004-2005) using digital hand-held devices. During each of the four intensive 3-week phases, gaseous air pollutant concentrations were monitored continuously. Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-PFLP) analysis was used to detect the TNF-α 308 genotype and plasma TNF-α levels were measured using the human TNF-α Max Standard™ Enzyme-linked immuno-absorbent assay (ELISA) kit., Results: The TNF-α variant A allele was common among this sample of African children (40% with an allelic frequency of 0.24). There was no significant association with the TNF-α G/A polymorphism and any respiratory linked phenotype, nor cytokine levels. However, when exposure to pollutants were analyzed with genotypic and phenotypic data, we found relatively modest interaction effects for the TNF-α 308 genotype. GEE models showed that children with the TNF-α 308 A allele had increased deterioration of lung function post pollution exposure to SO2 [β=2.62, CI:0.51-4.71, p=0.02 and pint=0.03] and NO [β=3.28, CI:0.68-5.89, p=0.01, pint=0.03]., Conclusion: The TNF-α 308 (G/A) polymorphism may be associated with increased pollutant-associated effects on FEV1 intraday variability for both SO2 and NO. The A allele may increase susceptibility to the adverse effects of air pollutants.

Background: Multiple imputation is a reliable tool to deal with missing data and is becoming increasingly popular in biostatistics. However, building a model with interactions that are not specified a priori, in the presence of missing data, presents a challenge. On the one hand, the interactions are needed to impute the data, while on the other hand, the data is needed to identify the interactions. The objective of this study was to present a way in which this challenge can be addressed., Methods: This paper investigates two strategies in which model development with interactions is achieved using a single data set generated from the Expectation Maximization (EM) algorithm. Imputation using both the fully conditional specification approach and the multivariate normal approach is carried out and results are compared. The strategies are illustrated with data from a study of ambient pollution and childhood asthma in Durban, South Africa., Results: The different approaches to model building and imputation yielded similar results despite the data being mainly categorical. Both strategies investigated for building the model using the multivariate normal imputed data resulted in the identical set of variables and interactions being identified; while models built using data imputed by fully conditional specification were marginally different for the two strategies. It was found that, for both imputation approaches, model building with backward elimination applied to the initial EM data set was easier to implement, and produced good results, compared to those from a complete case analysis., Conclusions: Developing a predictive model including interactions with data that suffers from missingness is easily done by identifying significant interactions and then applying backward elimination to a single data set imputed from the EM algorithm. It is hoped that this idea can be further developed and, by addressing this practical dilemma, there will be increased adoption of multiple imputation in medical research when data suffers from missingness.

Non-response in cross-sectional data is not uncommon and requires careful handling during the analysis stage so as not to bias results. In this paper, we illustrate how subset correspondence analysis can be applied in order to manage the non-response while at the same time retaining all observed data. This variant of correspondence analysis was applied to a set of epidemiological data in which relationships between numerous environmental, genetic, behavioural and socio-economic factors and their association with asthma severity in children were explored. The application of subset correspondence analysis revealed interesting associations between the measured variables that otherwise may not have been exposed. Many of the associations found confirm established theories found in literature regarding factors that exacerbate childhood asthma. Moderate to severe asthma was found to be associated with needing neonatal care, male children, 8- to 9-year olds, exposure to tobacco smoke in vehicles and living in areas that suffer from extreme air pollution. Associations were found between mild persistent asthma and low birthweight, and being exposed to smoke in the home and living in a home with up to four people. The classification of probable asthma was associated with a group of variables that indicate low socio-economic status., (Copyright © 2014 John Wiley & Sons, Ltd.)

Background: Several genes are associated with an increased susceptibility to asthma, which may be exacerbated by ambient air pollution. These genes include GSTM1 (glutathione-S-transferase M1 gene) and GSTP1 (glutathione-S-transferase P1 gene), which may modulate the response to epithelial oxidative changes caused by air pollutant exposure. This study evaluated fluctuations in the forced expiratory volume in one second (FEV(1)) in relation to lagged daily averages of ambient air pollutants (SO(2), NO(2), NO, and PM(10)) while considering genotype as an effect modifier., Methods: A longitudinal cohort of 129 schoolchildren of African descent from Durban, South Africa was assessed. GSTM1 (null vs. present genotype) and GSTP1 (Ile105Val; AA → AG/GG) genotypes were determined using standard techniques. SO(2), NO(2), NO, and PM(10) were measured continuously over a year using validated methods. The outcome was intraday variability in FEV(1) . Data were collected daily over a 3-week period in each of four seasons (2004-2005)., Results: Among the children tested, 27% had the GSTM1 null genotype and 81% carried the GSTP1 G allele. Approximately 26 out 104 children (25%) showed evidence of bronchial hyperreactivity, 13% reported having symptoms in keeping with persistent asthma, and a further 25% reported symptoms of mild intermittent asthma. PM(10) and SO(2) levels were moderately high relative to international guidelines. Neither GSTM1 nor GSTP1 genotypes alone were significantly associated with FEV(1) intraday variability. In models not including genotype, FEV(1) variability was statistically significantly associated only with NO(2) for 5-day lags (% change in intraday variability in FEV1 per interquartile range = 1.59, CI 0.58, 2.61). The GSTP1 genotype modified the effect of 3 days prior 24-hr average PM(10) and increased FEV(1) variability. A similar pattern was observed for lagged 3 day SO(2) exposure (P interaction < 0.05). Adverse effects of these pollutants were limited to individuals carrying the G allele for this polymorphism., Conclusion: Among this indigenous South African children cohort, the GSTP1 genotype modified the effects of ambient exposures to PM(10) and SO(2) and lung function. A plausible mechanism for these observed effects is decreased capacity to mount an effective response to oxidative stress associated with the GSTP1 AG + GG genotype., (Copyright © 2012 Wiley Periodicals, Inc.)

Glutathione-S-transferases (GSTM1 and GSTP1) and nicotinamide quinone oxidoreductase (NQO1) genes play an important role in cellular protection against oxidative stress which has been linked to asthma pathogenesis. We investigated whether common, functional polymorphisms in GSTM1, GSTP1 and NQO1 influence airway hyperreactivity (AHR) and atopy among schoolchildren in South Africa. Genomic DNA was extracted from 317 primary schoolchildren, aged 9-11 years, from urban, low socioeconomic communities of Durban, South Africa. GSTM1 (null vs. present genotype), GSTP1 (Ile105Val; AA → AG + GG), and NQO1 (Pro/187Ser; CC → CT/TT) genotypes were determined using polymerase chain reaction (PCR) methods. Atopy was defined as a positive skin-prick test to any of several common allergens. Airway hyperreactivity (AHR) was evaluated by pulmonary function testing before and after methacholine challenge. Among the children, 30% were GSTM1 null, 65% carried the G allele for GSTP1, and 36% carried the C allele for NQO1. The frequency of GSTM1, GSTP1, and NQO1 variants among our South African sample was similar to frequencies found in similar ethnic groups worldwide. Marked airway reactivity (PC(20) ≤ 2 mg/ml) was found in 10.3% of children and approximately 40% of them were atopic. No significant associations for GSTM1 and NQO1 with either AHR or atopy were identified. A significant protective effect against atopy was found among children with one or two copies of the GSTP1 G allele.