Household air pollution profiles associated with persistent childhood cough in urban Uganda

Abstract

Background: Most household air pollution (HAP) interventions in developing countries of sub-Saharan Africa have focused on a single source, such as replacing polluting cooking sources with cleaner burning cooking stoves. Such interventions, however, have resulted in insufficient reductions in HAP levels and respiratory health risks in children. In this study we determined how multiple HAP combustion sources and exposure-mitigation factors in the home environment influence child respiratory health alone and in combination.

Methods: We carried out a case-control study to determine associations between multiple indicators of HAP and persistent cough among children (<15 years of age) seeking care at three primary-care clinics in Kampala, Uganda. HAP indicators included self-report of combustion sources inside the home (e.g., stove type, fuel type, and smoking); housing characteristics and cooking practices that mitigate HAP exposure (e.g., use of windows, location of cooking, location of children during cooking) and perceptions of neighborhood air quality. To explore joint associations between indicators of HAP, we applied a Bayesian clustering technique (Bayesian profile regression) to identify HAP indicator profiles most strongly associated with persistent cough in children.

Results: Most HAP indicators demonstrated significant positive bivariate associations with persistent cough among children, including fuel-type (kerosene), the number of hours burning solid fuels, use of polluting fuels (kerosene or candles) for lighting the home, tobacco smoking indoors, cooking indoors, cooking with children indoors, lack of windows in the cooking area, and not opening windows while cooking. Bayesian cluster analysis revealed 11 clusters of HAP indicator profiles. Compared to a reference cluster that was representative of the underlying study population cough prevalence, three clusters with profiles characterized by highly adverse HAP indicators resulted in ORs of 1.72 (95% credible interval: 1.15, 2.60), 4.74 (2.88, 8.0), and 8.6 (3.9, 23.9). Conversely, at least two clusters of HAP indicator-profiles were protective compared to the reference cluster, despite the fact that these protective HAP indicator profiles used solid fuels for cooking in combination with an unimproved stove (cooking was performed predominantly outdoors in these protective clusters).

Conclusions: In addition to cooking fuel and type of cook stove, multiple HAP indicators were strongly associated with persistent cough in children. Bayesian profile regression revealed that the combination of HAP sources and HAP exposure-mitigating factors was driving risk of adverse cough associations in children, rather than any single HAP source at the home.

Keywords: Africa; Clustering; Household air pollution; Lung; Respiratory; Urban.

Figure 1.

Viewing the complexities of childhood exposure to HAP as a consequence of multiple combustion source-emissions at the household-level, exposure mitigation-factors at the individual- and household-level, and intensity of fuel-use at the household level. Exposure to OAP may also influence added exposure intensity to air pollution while also influencing HAP levels inside the home. Exposure vulnerability may be further influenced by household SES and individual-level demographic factors. More peripheral factors, such as market forces and government policies, further influence emissions sources at the household and neighborhood level. Abbreviations: HAP, household air pollution; LPG, liquified petroleum gas; OAP, outdoor air pollution; SES, socioeconomic status.

Figure 2.

Exposure profile results from Bayesian profile regression (BPR). Panel (a) displays the cough OR for each cluster and corresponding 95% credible intervals, with cluster number 5 set as the reference cluster. Panel (b) displays a qualitative characterization of HAP/SES indicator profiles for each cluster, with darker red colors characteristic of high adversity conditions and lighter colors characteristic of low adversity conditions for a particular indicator. Adversity for the HAP indicators, in this context, was derived from interpretations of the logistic regression models indicating whether the HAP indicator variable resulted in an adverse association with cough. Figure S1 in the appendix displays the full quantitative posterior distributions of the exposure profile output from the BPR analysis.