Environmental exposures associated with early childhood recurrent wheezing in the mother and child in the environment birth cohort: a time-to-event study.

BACKGROUND: Antenatal factors and environmental exposures contribute to recurrent wheezing in early childhood. AIM: To identify antenatal and environmental factors associated with recurrent wheezing in children from birth to 48 months in the mother and child in the environment cohort, using time-to-event analysis. METHOD: Maternal interviews were administered during pregnancy and postnatally and children were followed up from birth to 48 months (May 2013-October 2019). Hybrid land-use regression and dispersion modelling described residential antenatal exposure to nitrogen dioxide (NO2) and particulate matter of 2.5 µm diameter (PM2.5). Wheezing status was assessed by a clinician. The Kaplan-Meier hazard function and Cox-proportional hazard models provided estimates of risk, adjusting for exposure to environmental tobacco smoke (ETS), maternal smoking, biomass fuel use and indoor environmental factors. RESULTS: Among 520 mother-child pairs, 85 (16%) children, had a single wheeze episode and 57 (11%) had recurrent wheeze. Time to recurrent wheeze (42.9 months) and single wheeze (37.8 months) among children exposed to biomass cooking fuels was significantly shorter compared with children with mothers using electricity (45.9 and 38.9 months, respectively (p=0.03)). Children with mothers exposed to antenatal ETS were 3.8 times more likely to have had recurrent wheeze compared with those not exposed (adjusted HR 3.8, 95% CI 1.3 to 10.7). Mean birth month NO2 was significantly higher among the recurrent wheeze category compared with those without wheeze. NO2 and PM2.5 were associated with a 2%-4% adjusted increased wheezing risk. CONCLUSION: Control of exposure to ETS and biomass fuels in the antenatal period is likely to delay the onset of recurrent wheeze in children from birth to 48 months.

Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.

Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012-2022) nationally representative Demographic and Health Surveys data of 33 sSA countries. The air pollution covariates such as global annual surface particulate matter (PM 2.5) and the nitrogen dioxide available in the form of raster images were obtained from the National Aeronautics and Space Administration (NASA). The MLA was used for predicting the symptoms of ARIs among under-five children. We randomly split the dataset into two, 80% was used to train the model, and the remaining 20% was used to test the trained model. Model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. A total of 327,507 under-five children were included in the study. About 7.10, 4.19, 20.61, and 21.02% of children reported symptoms of ARI, Severe ARI, cough, and fever in the 2 weeks preceding the survey years respectively. The prevalence of ARI was highest in Mozambique (15.3%), Uganda (15.05%), Togo (14.27%), and Namibia (13.65%,), whereas Uganda (40.10%), Burundi (38.18%), Zimbabwe (36.95%), and Namibia (31.2%) had the highest prevalence of cough. The results of the random forest plot revealed that spatial locations (longitude, latitude), particulate matter, land surface temperature, nitrogen dioxide, and the number of cattle in the houses are the most important features in predicting the diagnosis of symptoms of ARIs among under-five children in sSA. The RF algorithm was selected as the best ML model (AUC = 0.77, Accuracy = 0.72) to predict the symptoms of ARIs among children under five. The MLA performed well in predicting the symptoms of ARIs and associated predictors among under-five children across the sSA countries. Random forest MLA was identified as the best classifier to be employed for the prediction of the symptoms of ARI among under-five children.

GSTM1; GSTP1 and NQO1 Polymorphisms and Susceptibility to Asthma among South African Children

Gluthathione-S-transferase (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 susceptibility to asthma among schoolchildren in South Africa. Genomic deoxyribonucleic acid (DNA) was extracted from 317 primary schoolchildren; aged 9-11 years; from the urban; underprivileged socio-economic communities of Durban. GSTM1 (null vs. present genotype); GSTP1 (Ile105Val; AA ?AG+GG) and the NQO1 (Pro/Ser; CC ?CT/TT) genotypes were determined using polymerase chain reaction. Among the children; 30 were GSTM1 null; 65 carried the G allele for GSTP1; and 36 carried the C allele for NQO1.There was a high prevalence of asthma of any severity (46.1); with 20.4 reporting persistent asthma. The GSTP1 AG+GG polymorphic genotype was significantly associated with persistent asthma (adjusted OR = 3.98; CI = 1.39; 11.36; p-value = 0.01). Neither the GSTM1; nor the NQO1; genotype was a significant predictor of persistent asthma. Therefore; the GSTP1 A/G variant may modulate the risk of persistent asthma among our sample