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.

中文翻译：

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全球家庭能源模型：一种多层次的方法来估计烹饪中使用污染和清洁燃料的趋势

2017年，估计有30亿人使用污染性燃料和技术作为主要的烹饪解决方案，每年380万人的死亡归因于家庭暴露于由此产生的细颗粒物空气污染中。当前，通过使用燃料类型（例如固体燃料）的总量来计算健康负担，因为尚无国家一级对特定燃料类型（例如木材和木炭）的使用情况进行估算。为了扩展有关家庭空气污染对健康的影响的知识库，我们基于广义Dirichlet-多项式分布，开发并实施了一个新颖的贝叶斯分层模型，该模型联合估算了八种关键燃料使用中的非线性趋势，克服了几种针对数据的挑战，包括燃料使用价值的缺失或组合。