ArticleAddiction (Abingdon, England)2019
Forecasting the 2021 local burden of population alcohol-related harms using Bayesian structural time-series.
Article in Addiction (Abingdon, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Early warning of hepatitis B epidemics in Henan Province, China, from 2014 to 2023 based on Baidu Index and Bayesian Structural Time Series model.Archives of public health = Archives belges de sante publique · 2026Article
- Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model.BMC public health · 2025Article
- Long-term impact of COVID-19-related nonpharmaceutical interventions on tuberculosis: an interrupted time series analysis using Bayesian method.Journal of global health · 2025Article
- Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model.Scientific reports · 2024Article
- Are dietary patterns in early childhood associated with alcohol consumption at the age of 17 years? Analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort study.Public health nutrition · 2022Article
- Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA.Annals of data science · 2022Article
- From Outbreak to Near Disappearance: How Did Non-pharmaceutical Interventions Against COVID-19 Affect the Transmission of Influenza Virus?Frontiers in public health · 2022Article
- No Change ofJournal of fungi (Basel, Switzerland) · 2021Article
- The Economic Impact of the SARS-COV-2 (COVID-19) Pandemic in Spain.International journal of environmental research and public health · 2021Article
- Specialist alcohol inpatient treatment admissions and non-specialist hospital admissions for alcohol withdrawal in England: an inverse relationship.Alcohol and alcoholism (Oxford, Oxfordshire) · 2021Article
- Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis.Infection and drug resistance · 2021Article
- Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models.Infectious Disease Modelling · 2021Article
- Article
- Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models.Chaos, solitons, and fractals · 2020Article
- Forecasting the 2021 local burden of population alcohol-related harms using Bayesian structural time-series.Addiction (Abingdon, England) · 2019Article
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4 authors.
Funding
Abstract
BACKGROUND AND
aimsHarmful 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.
designWe used Bayesian structural time-series models to forecast alcohol-related hospital admissions for 2020/21 (from 2015 to 2016).
settingEngland.
participantsWe developed separate models for each English lower-tier local authority. MEASUREMENTS: Our primary outcome was alcohol-related hospital admissions. Model covariates were population size and age-structure.
findingsNowcasting 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.
conclusionsIn 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.
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