Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Addiction. 2019 Jun;114(6):994-1003. doi: 10.1111/add.14568. Epub 2019 Mar 6.
Harmful alcohol use places a significant burden on health services. Sophisticated nowcasting and forecasting methods could support service planning, but their use in public health has been limited. We aimed to use a novel analysis framework, combined with routine public health data, to improve now- and forecasting of alcohol-related harms.
We used Bayesian structural time-series models to forecast alcohol-related hospital admissions for 2020/21 (from 2015 to 2016).
England.
We developed separate models for each English lower-tier local authority.
Our primary outcome was alcohol-related hospital admissions. Model covariates were population size and age-structure.
Nowcasting validation indicated adequate accuracy, with 5-year nowcasts underestimating admissions by 2.2% nationally and 3.3% locally, on average. Forecasts indicated a 3.3% increase in national admissions in 2020/21, corresponding to a 0.2% reduction in the crude rate of new admissions, due to population size changes. Locally, the largest increases were forecast in urban, industrial and coastal areas and the largest decreases in university towns and ethnically diverse areas.
In 2020/21, alcohol-related hospital admissions are expected to increase in urban and coastal areas and decrease in areas associated with inward migration of younger people, including university towns and areas with greater ethnic diversity. Bayesian structural time-series models enable investigation of the future impacts of alcohol-related harms in population subgroups and could improve service planning and the evaluation of natural experiments on the impact of interventions to reduce the societal impacts of alcohol.
有害饮酒给医疗服务带来了巨大负担。复杂的即时预测和预测方法可以支持服务规划，但在公共卫生领域的应用有限。我们旨在使用新的分析框架，结合常规公共卫生数据，提高对酒精相关伤害的即时预测和预测能力。
我们使用贝叶斯结构时间序列模型预测 2020/21 年（2015 年至 2016 年）与酒精相关的住院治疗。
英格兰。
我们为每个英格兰低级别地方当局分别开发了模型。
我们的主要结果是与酒精相关的住院治疗。模型协变量为人口规模和年龄结构。
即时预测验证表明准确性适中，全国范围内 5 年的即时预测平均低估了 2.2%的入院人数，地方层面低估了 3.3%。预测表明，2020/21 年全国住院人数将增加 3.3%，这归因于人口规模变化，新入院率的粗率将降低 0.2%。在地方层面，预计城市、工业和沿海地区的增幅最大，而大学城和种族多样化地区的降幅最大。
在 2020/21 年，预计与酒精相关的住院人数将在城市和沿海地区增加，而在与年轻人迁入相关的地区（包括大学城和种族多样化地区）减少。贝叶斯结构时间序列模型可以调查酒精相关伤害对人群亚组的未来影响，并可以改善服务规划和评估减少酒精对社会影响的干预措施的自然实验的效果。
