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Forecasting the 2021 local burden of population alcohol-related harms using Bayesian structural time-series.

ABSTRACT: 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.

SUBMITTER: McQuire C

PROVIDER: S-EPMC6563459 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Project description:In this paper, we introduce a novel model selection approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model parameters. By combining theoretical principles of time-reversibility in time series with conventional modeling approaches such as information criteria, we construct a criterion that employs the backwards prediction (backcast) as a proxy for the forecast. Hereby, we aim to adopt a theoretically grounded, data-driven approach to model selection. The novel criterion is named the backwards validated information criterion (BVIC). The BVIC identifies suitable models by trading off a measure of goodness-of-fit and a models ability to predict backwards. We test the performance of the BVIC by conducting experiments on synthetic and real data. In each experiment, the BVIC is examined in contrast to conventionally employed criteria. Our experimental results suggest that the BVIC has comparable performance as conventional information criteria. Specifically, in most of the experiments performed, we did not find statistically significant differences between the forecast error of the BVIC under certain parameterizations and that of the different information criteria. Nonetheless, it is worth emphasizing that the BVIC guarantees are established by design where the model order penalization term depends on strong mathematical properties of time-reversible time series forecasting properties and a finite data assessment. In particular, the penalization term is replaced by a weighted trade-off between functional dimensions pertaining to forecasting.That said, we observed that the BVIC recovered more accurately the real order of the underlying process than the other criteria, which rely on a static penalization of the model order. Lastly, leveraging the latter property we perform the assessment of the order model (or, memory) of time series pertaining to epileptic seizures recorded using electrocorticographic data. Our results provide converging evidence that the order of the model increases during the epileptic events.

Project description:ObjectivesEnglish local authorities (LAs) are interested in reducing alcohol-related harms and may use discretionary powers such as the Late Night Levy (LNL) to do so. This study aims to describe how system stakeholders hypothesise the levy may generate changes and to explore how the system, its actors and the intervention adapt and co-evolve over time.DesignA process evaluation from a complex systems perspective, using qualitative methods.SettingA London LA with high densities of residential and commercial properties, which implemented the LNL in 2014.ParticipantsData were generated through interviews with LNL implementers and alcohol consumers, observations in bars and during LNL patrols and documentary review.InterventionThe LNL allows LAs to charge late-night alcohol retailers an annual fee (£299-£4440) to manage and police the night-time economy (NTE).ResultsWhen the LNL was being considered, stakeholders from different interest groups advanced diverse opinions about its likely impacts while rarely referencing supporting research evidence. Proponents of the levy argued it could reduce crime and anti-social behaviour by providing additional funds to police and manage the NTE. Critics of the levy hypothesised adverse consequences linked to claims that the intervention would force venues to vary their hours or close, cluster closing times, reduce NTE diversity and undermine public-private partnerships. In the first 2 years, levy-funded patrols developed relationships with the licensed trade and the public. The LNL did not undermine public-private partnerships and while some premises varied their hours, these changes did not undermine the intervention's viability, nor significantly cluster venue closing times, nor obviously damage the area's reputation for having a diverse NTE.ConclusionsThis study applies a framework for process evaluation from a complex systems perspective. The evaluation could be extended to measure alcohol-related outcomes and to consider the interplay between the national and local systems.

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Forecasting the 2021 local burden of population alcohol-related harms using Bayesian structural time-series.

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