B2684 - Diagram based analysis of causal systems for understanding the causes of alcohol problems - 31/05/2016

B number: 
B2684
Principal applicant name: 
Karl D. Ferguson | University of Glasgow, Institute of Health and Wellbeing (UK)
Co-applicants: 
Mark McCann, Daniel Smith, Mr Karl D. Ferguson
Title of project: 
Diagram based analysis of causal systems for understanding the causes of alcohol problems
Proposal summary: 

The consequences of alcohol problems are vast, costing the UK economy dearly every year in terms of billing the NHS for A&E, the police service for road traffic accidents, and lost hours at work etc. On a personal level, the consequences of alcohol problems can be even higher and vary from headaches & hangovers to accidental death and liver cirrhosis.

We are aware of many factors which relate to drinking patterns, but we are uncertain why one person may be a heavy drinker and one person not. How factors like personality, family background, alcohol advertising (and many others) are related to alcohol problems is a vast and complex subject. Innovations in social science and statistics over the last few decades have given researchers new methods for trying to understand what causes health and social problems. Causal diagrams can help us use statistical analysis to understand ‘cause and effect’, but these visualisation can also help people ‘see’ research findings without looking at the statistical analysis.

The ALSPAC study has rich information about people’s lives so we can look at a range of factors related to drinking, this gives us a chance to crack the black box open and get a better understanding of causation of alcohol problems; and to build visualisations and graphs to better communicate cause and effect relating to alcohol. Improving our understanding will then allow the design of informed policies which target the actual causes of alcohol problems, causes identified according to the scientific method, rather than ideology or politics.

Date proposal received: 
Friday, 29 April, 2016
Date proposal approved: 
Friday, 29 April, 2016
Keywords: 
Medical Sociology/Social Epidemiology/Public Health, Addiction - e.g. alcohol, illicit drugs, smoking, gambling, etc., Behaviour - e.g. antisocial behaviour, risk behaviour, etc., Computer simulations/modelling/algorithms, Statistical methods, Counterfactual Causal Inference, Cohort studies - attrition, bias, participant engagement, ethics, Childhood - childcare, childhood adversity, Injury (including accidents), Liver function, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Social science, Statistical methods, Causal Inference Observational Data