Title: Change in alcohol outlet density and alcohol-related harm to population health (CHALICE): Observational record-linked database study
Funder: Division for Social Care and Health Research (Wales)

Abstract: Design:1. Assessment of change in levels of alcohol consumption using data from five successive annual cross-sectional Welsh Health Surveys (WHS) (2005 - 2009) 2. A longitudinal analysis of five years of hospital in-patient data using the Patient Episode Database for Wales (PEDW) 2005 - 093. A longitudinal analysis of three years of accident & emergency department (A&E) attendances 2009 – 20114. A longitudinal analysis of five years of Police data on alcohol-related violent crime 2005 – 2009Setting and target population:The Setting is Wales with 22 local authorities total population 3 million; population 2.4 million aged 16 years and over (source 2001 Cenus) including 75000 respondents to the Welsh Health Surveys. Use of existing record-linked datasets: the Secure Anonymous Information Linkage (SAIL) databank:The SAIL databank held and managed by Ronan Lyons within the Health Information Research Unit (HIRU) at Swansea University contains health social and education data on three million residents of Wales and currently includes thirteen datasets containing nearly one billion records. The smallest geographical area for which data may be released from the databank is the 2001 Census Lower Super Output Area (LSOA) of which there are 1896 in Wales. PEDW data on hospital admissions are already included in SAIL together with A&E data for 2009. A&E data for 2010 and 2011 (which is currently received on a monthly basis) and the Police violent crime data will be added to SAIL for this project. Health Solutions Wales provide population data from the National Health Service Administrative Register (NHSAR) with addresses for all individuals who register with a General Practitioner. Dates for each update of the address record are held providing durations of residency for each successive address. This dataset holds demographic data including age and gender. The NHSAR contains address information linked anonymously at the individual level (the anonymised linking field ALF) which is the crucial variable for record-linkage. The address data are matched at Heath Solution Wales to replace identifiable addresses with Residential Anonymous Linking Fields (RALF) each linked to the ALF.We have already obtained and cleaned Welsh Health Survey data for the first four waves and data for 2009 will be obtained later in 2010. Welsh Health Survey data does not have permission from the Data Owner (Welsh Assembly Government) to link into SAIL at individual-level but can be linked in using the LSOA to outlet density exposures.Intervention being evaluated:Change in alcohol outlet density measured (i) as a fully spatial measure of individual resident exposure and (ii) as an aggregate exposure at LSOA level.Data on alcohol outlets: Annual information from the 22 local authorities in Wales on licensed and off-licensed premises with full address and postcode for 2005-09. Spatial measures of outlet density: We will estimate a new measure of outlet density which includes a more realistic and robust measure of accessibility than previously published methods. We will exploit the unique advantages of the NHSAR population data within the SAIL databank. We will estimate a summary statistic for distance to all outlets from each residence within a defined geographical area or buffer zone defined by network distances and travel times to more accurately measure exposure in comparison to Euclidian distances. Our GIS databank includes Address Layer 2 for dwellings and the Integrated Transportation Network layer. Using these we will calculate residential distance metrics with each address replaced with a Residential Anonymous Linking Field (RALF). The RALFs are taken in to SAIL where a relational RALF: ALF (Anonymous Linking Field) table allows access metrics for each residence to be linked with the NHSAR population database. A Network Analyst model then calculates both network and straight line distances from each house to all alcohol outlets within buffer zones defined by (a) ten minutes walking and (b) ten minutes drive time. (1) We will calculate the sum (or mean) of the weighted network distance for each individual to each outlet within the defined buffer zones. Weights will be a function of network distance (e.g. reciprocal). This gives an individual-level measure of outlet density exposure(2) The LSOA density can be derived by averaging the values for all individual residents in the LSOASpecifically for each residence r we will tag all outlets o within a defined threshold network distances D defined as both 10 minutes walking time and 10 minutes drive-time. We will use the service area tool in ArcGIS Network Analyst module together with Ordnance Survey Master Map (OSMM) data and point location facility data in the Environment Geographic Information System (eGIS). For each residence we will count the number of accessible outlets within the defined network distances D and record the distance from the residence r to each outlet o. We will then anonymously link these data for subsequent incorporation into the SAIL databank. All residences without any outlets in their network range will be allocated a count of zero implying an outlet is inaccessible.The primary analysis will be for all alcohol outlets but we will also estimate outlet densities for on and off-sales separately for those authorities who can supply this categorisation.Primary outcome:(1) The primary outcome is change in units of consumption per day which can also be categorised as an ordinal scale of no alcohol below sensible limit above limit and binge drinking (Department of Health definition 2007). Duration of five consecutive annual surveys (2005 to 2009)Secondary outcomes:(2) Number of hospital admissions and bed days for alcohol-related causes defined by the published Office for National Statistics and Centers for Disease Control and Prevention set of ICD-10 codes. Duration 2005 to 2009(3) Number attending an Emergency Department (A&E) at night (10pm to 6am) as a proxy for alcohol-related injury. Duration 2009 to 2011(4) Number of offences of alcohol-related crime – Violence against the person. Duration 2005 to 2009Sample size:As noted in section C2 outlet data received to date from four local authorities showed a mean five-year change of 6% (range 5% to 46%) which exceeds the % change in outlets reported in previous studies to have sufficient power to detect change in rare outcomes. Thus we will have sufficient change in outlet density for the analysis and to answer the research questions. For the primary outcome based on data from five Welsh Health Surveys the sample size is approximately 75000. Because we do not yet have the data on the location of alcohol outlets by year we do not know the distribution of changes and so cannot complete a conventional sample size calculation. However we previously carried out an analysis of similar data of ca. 60000 respondents to four successive Welsh Health Surveys 2005-2008 and can draw on that experience. In similar multilevel models to those anticipated here for the primary outcome analysis standard errors were typically between 0.04 and 0.06 varying between model parameters. These led to narrow confidence intervals for parameter values; for example the estimated odds ratio associated with a quintile of deprivation was 0.82 with 95% confidence interval (0.76 0.88). So with even more data here the precision should be greater and even small differences in outcomes should be recognised.For the other outcomes the study population is essentially the 2.4 million people living in Wales and aged at least 16 years. The outcomes are not rare with approximately 13000 alcohol-related admissions 100000 night time A&E attendances and around 47000 violence against the person crimes in Wales annually. We will have very high power for detecting small differences between small-areas (LSOAs) at the extremes of changes in alcohol outlet density. For example there would be over 80% power for identifying a reduction from 5.4 per thousand per annum to 5 per 1000 per annum between the extreme quintiles of change in outlet density. While this ignores clustering within LSOA that effect is unlikely to be so large that the power is greatly reduced and there will be more power for detecting a larger difference. If the correction factor for clustering was two a common finding in work of this type the required sample size would effectively be doubled. However with the planned sample size there would still be over 80% power for detecting a reduction from 5.4 per thousand per annum to 4.8 per thousand per annum in the admission rate for alcohol-related conditions. Planned analyses:The primary analysis will be for all alcohol outlets but we will also analyse the data using outlet densities for on and off-sales separately for those authorities who can supply this categorisation. Our analysis plan has to take into account the nature of the data available through SAIL. Data on individuals nested within households nested within LSOAs will be available but the geographical details will be anonymised so that the geographical location will not be known. This has all of the advantages of individual outcome and exposure data together with demographic details and has not been performed previously to the best of our knowledge. It has the disadvantage however that a full spatial analysis will not be possible. For a spatial analysis we will have to follow other authors and use an ecological approach using data aggregated over LSOAs. Our analysis plan is:(1) Full descriptive statistics(2) A spatial model based on LSOA-level data to explore the relationship between counts/rates of the four outcome measures and outlet density. The models will account for the year and will adjust for aggregated population characteristics such as the percentages of males and of certain socio-economic groups and the median age. A variety of spatial models can be used for such data to allow for spatial autocorrelation including the Bayesian approach of Besag York and Molliéthe random effects models developed by Gruenewald and colleagues and used in the three Californian longitudinal studies and geographically weighted regression (GWR) models. GWR is slightly different from other spatial regression techniques such as the spatially lagged model and spatial error model in the sense that it enables the spatial localisation of standard regression models thereby allowing us to incorporate the spatial heterogeneity of the degree of accessibility/exposure to the outlets in explaining the spatial distribution of health outcomes. The process of GWR modelling will be implemented within the eGIS environment using ArcGIS 9.3 extension of the GWR software. The different types of spatial models make different assumptions model spatial autocorrelation differently and analyse the data in different ways; their relative merits will be explored as part of the project.(3) A multilevel analysis using the hierarchy outlined above. We will use multilevel ordinal models of the defined categories of consumption for the WHS data and multilevel logistic models of the odds of hospital admission and A&E attendance as a function of change in individual outlet exposure adjusting for confounding individual variables (age gender SES) and LSOA deprivation score. This method cannot be used for the crime data as we only know location of offence with no information on individuals. Multilevel modelling is a well-established technique and we will fit models using the standard MLWiN and R software.In order to address the secondary research questions:(5) We will assess the impact on health inequalities by firstly stratifying the models in (2) and (3) above by Welsh Index of Multiple Deprivation using quintiles of deprivation to examine whether the modelled associations vary with deprivation category. This method was used for postcode sectors in the Australian longitudinal analysis. A better method which we will use is to model the interaction between LSOA deprivation score and change in outlet density and the two-way interaction including individual SES (using the NS-SEC). This method uses the whole dataset and the coefficient of the interaction terms gives information on the effect on inequalities over time.(6) Since the annual estimation of outlet density exposure automatically includes people who move residence (from using the NHSAR within SAIL) our main models will correct for population migration. We will assess the impact of population migration by re-estimating annual outlet densities using the baseline population repeating the models above and comparing to the main models. Health selection bias can thus be assessed. Timetable: 2011April: Data access agreement to use the WHS 2009May-June: Prepare WHS complete dataset for analysis validation of geographyApril - May: Update literature review outlined in proposalApril - June: Collation and preparation of outlet data from 22 local authoritiesApril-Dec: Update NHSAR data in SAIL databank preparation of dataset of alcohol-related hospital admissions 2005-2009 SQL extractionApril - Feb 2012: Preparation of dataset of A&E attendances 2009-2011 SQL extractionApril-Dec: Preparation of dataset of violent assaults July-Dec: Develop the network-based spatial methods for outlet density2012Jan – Mar: Descriptive analysesJan – Dec: Multilevel regression analysesJan – Dec: Spatial analyses2013Jan: Final literature review updateJan-Mar: Write reports/papers/abstracts/disseminationMar: Submit report to NIHRMar: Start dissemination of results


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: STROKE;CANCER AND NEOPLASMS;CARDIOVASCULAR;ORAL AND GASTROINTESTINAL