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Developing policy analytics for public health strategy and decisions—the Sheffield alcohol policy model framework

Overview of attention for article published in Annals of Operations Research, October 2013
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Title
Developing policy analytics for public health strategy and decisions—the Sheffield alcohol policy model framework
Published in
Annals of Operations Research, October 2013
DOI 10.1007/s10479-013-1451-z
Pubmed ID
Authors

Alan Brennan, Petra Meier, Robin Purshouse, Rachid Rafia, Yang Meng, Daniel Hill-Macmanus

Abstract

This paper sets out the development of a methodological framework for detailed evaluation of public health strategies for alcohol harm reduction to meet UK policy-makers needs. Alcohol is known to cause substantial harms, and controlling its affordability and availability are effective policy options. Analysis and synthesis of a variety of public and commercial data sources is needed to evaluate impact on consumers, health services, crime, employers and industry, so a sound evaluation of impact is important. We discuss the iterative process to engage with stakeholders, identify evidence/data and develop analytic approaches and produce a final model structure. We set out a series of steps in modelling impact including: classification and definition of population subgroups of interest, identification and definition of harms and outcomes for inclusion, classification of modifiable components of risk and their baseline values, specification of the baseline position on policy variables especially prices, estimating effects of changing policy variables on risk factors including price elasticities, quantifying risk functions relating risk factors to harms including 47 health conditions, crimes, absenteeism and unemployment, and monetary valuation. The most difficult model structuring decisions are described, as well as the final results framework used to provide decision support to national level policymakers in the UK. In the discussion we explore issues around the relationship between modelling and policy debates, valuation and scope, limitations of evidence/data, how the framework can be adapted to other countries and decisions. We reflect on the approach taken and outline ongoing plans for further development.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Japan 1 1%
Unknown 80 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 13%
Student > Ph. D. Student 10 12%
Student > Master 9 11%
Student > Bachelor 9 11%
Student > Doctoral Student 7 8%
Other 16 19%
Unknown 21 25%
Readers by discipline Count As %
Medicine and Dentistry 11 13%
Business, Management and Accounting 10 12%
Social Sciences 10 12%
Nursing and Health Professions 5 6%
Computer Science 5 6%
Other 17 20%
Unknown 25 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2013.
All research outputs
#15,283,138
of 22,727,570 outputs
Outputs from Annals of Operations Research
#361
of 718 outputs
Outputs of similar age
#129,012
of 209,512 outputs
Outputs of similar age from Annals of Operations Research
#3
of 5 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 718 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 209,512 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.