Bayesian modelling of household solid fuel use: insights towards designing effective interventions to promote fuel switching in Africa.
Indoor air pollution from solid fuel use is a significant risk factor for acute lower respiratory infections among children in sub-Saharan Africa. Interventions that promote a switch to modern fuels hold a large health promise, but their effective design and implementation require an understanding of the web of upstream and proximal determinants of household fuel use. Using Demographic and Health Survey data for Benin, Kenya and Ethiopia together with Bayesian hierarchical and spatial modelling, this paper quantifies the impact of household-level factors on cooking fuel choice, assesses variation between communities and districts and discusses the likely nature of contextual effects. Household- and area-level characteristics appear to interact as determinants of cooking fuel choice. In all three countries, wealth and the educational attainment of women and men emerge as important; the nature of area-level factors varies between countries. In Benin, a two-level model with spatial community random effects best explains the data, pointing to an environmental explanation. In Ethiopia and Kenya, a three-level model with unstructured community and district random effects is selected, implying relatively autonomous economic and social areas. Area-level heterogeneity, indicated by large median odds ratios, appears to be responsible for a greater share of variation in the data than household-level factors. This may be an indication that fuel choice is to a considerable extent supply-driven rather than demand-driven. Consequently, interventions to promote fuel switching will carefully need to assess supply-side limitations and devise appropriate policy and programmatic approaches to overcome them. To our knowledge, this paper represents the first attempt to model the determinants of solid fuel use, highlighting socio-economic differences between households and, notably, the dramatic influence of contextual effects. It illustrates the potential that multilevel and spatial modelling approaches hold for understanding determinants of major public health problems in the developing world.
Rehfuess
Eva A
EA
Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistrasse 15, 81377 Munich, Germany. rehfuess@ibe.med.uni-muenchen.de
Briggs
David J
DJ
Joffe
Mike
M
Best
Nicky
N
D000349:Africa; D001499:Bayes Theorem; D011211:Electric Power Supplies