Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. Issue 4 (7th July 2020)

• Record Type: Journal Article
• Authors: Stoner, Oliver; Shaddick, Gavin; Economou, Theo; Gumy, Sophie; Lewis, Jessica; Lucio, Itzel; Ruggeri, Giulia; Adair‐Rohani, Heather
• Is Part Of: Journal of the Royal Statistical Society. Volume 69:Issue 4(2020)
• Page Start: 815
• Page End: 839
• Publication Date: 2020-07-07
• Subjects: Air pollution -- Bayesian hierarchical model -- Forecasting -- Generalized Dirichlet distribution -- Households -- Solid fuels; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12428
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: In 2017 an estimated 3 billion people used polluting fuels and technologies as their primary cooking solution, with 3.8 million deaths annually attributed to household exposure to the resulting fine particulate matter air pollution. Currently, health burdens are calculated by using aggregations of fuel types, e.g. solid fuels, as country level estimates of the use of specific fuel types, e.g. wood and charcoal, are unavailable. To expand the knowledge base about effects of household air pollution on health, we develop and implement a novel Bayesian hierarchical model, based on generalized Dirichlet–multinomial distributions, that jointly estimates non‐linear trends in the use of eight key fuel types, overcoming several data‐specific challenges including missing or combined fuel use values. We assess model fit by using within‐sample predictive analysis and an out‐of‐sample prediction experiment to evaluate the model's forecasting performance.