Forecasting the 2021 local burden of population alcohol-related harms using Bayesian structural time-series.
Alcohol
Bayesian statistics
forecasting
hospital admissions
nowcasting
public health
time-series
Journal
Addiction (Abingdon, England)
ISSN: 1360-0443
Titre abrégé: Addiction
Pays: England
ID NLM: 9304118
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
23
07
2018
revised:
03
12
2018
accepted:
21
01
2019
pubmed:
30
1
2019
medline:
17
7
2020
entrez:
30
1
2019
Statut:
ppublish
Résumé
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.
Sections du résumé
BACKGROUND AND AIMS
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.
DESIGN
We used Bayesian structural time-series models to forecast alcohol-related hospital admissions for 2020/21 (from 2015 to 2016).
SETTING
England.
PARTICIPANTS
We 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.
FINDINGS
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.
CONCLUSIONS
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.
Identifiants
pubmed: 30694577
doi: 10.1111/add.14568
pmc: PMC6563459
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
994-1003Subventions
Organisme : Medical Research Council
ID : MC_UU_00011/3
Pays : United Kingdom
Organisme : Alcohol Research UK
ID : SG 16/17 235
Pays : International
Organisme : MRC Integrative Epidemiology Unit (IEU), Medical Research Council (2018-2023)
ID : MC_UU_00011/3
Pays : International
Organisme : Medical Research Council
ID : MR/K023233/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/K006525/1
Pays : United Kingdom
Informations de copyright
© 2019 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
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