Barriers to childhood asthma care in sub-Saharan Africa: a multicountry qualitative study with children and their caregivers.

OBJECTIVES: This study identifies barriers and provides recommendations to improve asthma care in children across sub-Saharan Africa, where qualitative data is lacking despite high rates. DESIGN: One of the aims of our National Institute for Health Research global health research group 'Achieving Control of Asthma in Children in Africa' was to use qualitative thematic analysis of transcribed audio recordings from focus group discussions (FGDs) to describe barriers to achieving good asthma control. SETTING: Schools in Blantyre (Malawi), Lagos (Nigeria), Durban (South Africa), Kampala (Uganda) and Harare (Zimbabwe). PARTICIPANTS: Children (n=136), 12-14 years with either asthma symptoms or a diagnosis and their caregivers participated in 39 FGDs. All were recruited using asthma control questions from the Global Asthma Network survey. RESULTS: There were four key themes identified: (1) Poor understanding, (2) difficulties experienced with being diagnosed, (3) challenges with caring for children experiencing an acute asthma episode and (4) suboptimal uptake and use of prescribed medicines. An inadequate understanding of environmental triggers, a hesitancy in using metred dose inhalers and a preference for oral and alternate medications were identified as barriers. In addition, limited access to healthcare with delays in diagnosis and an inability to cope with expected lifestyle changes was reported. Based on these findings, we recommend tailored education to promote access to and acceptance of metred dose inhalers, including advocating for access to a single therapeutic, preventative and treatment option. Furthermore, healthcare systems should have simpler diagnostic pathways and easier emergency access for asthma. CONCLUSIONS: In a continent with rapidly increasing levels of poorly controlled asthma, we identified multiple barriers to achieving good asthma control along the trajectory of care. Exploration of these barriers reveals several generalisable recommendations that should modify asthma care plans and potentially transform asthma care in Africa. TRIAL REGISTRATION NUMBER: 269211

Micro-environmental models of human exposure to air pollution

Particulate air pollution (PM) has been shown by many studies to cause adverse health effects. Traditionally PM exposure was estimated using ambient concentrations. Lately, studies have revealed that this approach poorly reflects differences between individual’s exposures and as such results in exposure misclassification. This thesis aims to improve personal exposure predictions by building a model (MEPEX model), which takes into account the temporal and spatial variability of ambient PM, as well as visited microenvironments. For the composition of this model, existing approaches for model components were evaluated, compared and developed. A temporally adjusted land-use regression (LUR-adj) model for predictions of ambient PM2.5 and PM10 was built, validated, and compared to estimates from a dispersion model. Ratios were developed to adjust ambient concentrations for cycling and in-bus transport microenvironments. Additionally, modelling approaches for the home indoor microenvironment were compared, using monitoring data. A secondary aim was to evaluate the performance of different approaches for personal exposure assessment by comparing varying levels of model sophistication. Validation of the LUR-adj model showed good model fit (IA > 0.5) and low error (NMSE < 1) for short-term predictions of PM2.5 and PM10 at locations in London. In comparison to predictions of a dispersion model (ADMS-urban), LUR-adj estimates of PM10 produced better results for model performance parameters at the majority of 26 predicted locations. MEPEX model predictions of monitored daily personal exposure for an individual in London resulted in an R2 of 0.439 for PM2.5 and 0.403 for PM10. Predictions using modelled home outdoor concentrations in comparison were lower with R2 of 0.173 for PM2.5 and 0.086 for PM10. These results provide the first quantifiable evidence that personal exposure models of PM2.5 and PM10 can reduce exposure misclassification compared to estimates based only on ambient PM.Open Acces

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites

Association of ambient air pollution with the prevalence and incidence of COPD

The role of air pollution in chronic obstructive pulmonary disease (COPD) remains uncertain. The aim was to assess the impact of chronic exposure to air pollution on COPD in four cohorts using the standardised ESCAPE exposure estimates. Annual average particulate matter (PM), nitrogen oxides (NOx) and road traffic exposure were assigned to home addresses using land-use regression models. COPD was defined by NHANES reference equation (forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) less than the lower limit of normal) and the Global Initiative for Chronic Obstructive Lung Disease criterion (FEV1/FVC >0.70) and categorised by severity in non-asthmatics. We included 6550 subjects with assigned NOx and 3692 with PM measures. COPD was not associated with NO2 or PM10 in any individual cohort. In meta-analyses only NO2, NOx, PM10 and the traffic indicators were positively, although not significantly, associated with COPD. The only statistically significant associations were seen in females (COPD prevalence using GOLD: OR 1.57, 95% CI 1.11-2.23; and incidence: OR 1.79, 95% CI 1.21-2.68). None of the principal results were statistically significant, the weak positive associations of exposure with COPD and the significant subgroup findings need to be evaluated in further well standardised cohorts followed up for longer time, and with time-matched exposure assignments

Spatial variation of PM elemental composition between and within 20 European study areas : results of the ESCAPE project

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM