A best practice framework to measure spatial variation in alcohol availability
Summary (3 min read)
- The framework is underpinned by three conceptual requirements: theoretical geographical underpinnings; statistical soundness; and practical implementation and interpretation.
- The framework can be used to compare models of population-level exposure to alcohol outlets.
Theoretical geography components
- Theoretical components comprise the core geographic principles underpinning AOD measurements.
- Simply put, AOD is a measure of geographic access based on spatial location of alcohol outlets, typically around where people live.
- It is underpinned by key theoretical principles of graph theory and topology (Curtin, 2007); population distribution modelling (Stewart and Warntz, 1958); and it informs spatial interaction modelling (Roy and Thill, 2003).
- A cartographer understands scale as a ratio between the real world and a map representation, whereas to a GIS analyst scale may relate to spatial resolution or spatial extent of phenomena.
- The larger the spatial unit of analysis the greater the potential for ecological fallacy (the extent to which assumptions about an individual may be incorrectly based on the group to which they belong - (Robinson, 1950).
- The statistical component relates to how AOD is interpreted and used as part of a wider analysis.
- The statistical component is inherently linked to the theoretical geography component described previously (robustness, sensitivity, ecological fallacy and MAUP).
- As with all models, robust AOD measures should not be unduly affected by outliers, and have good performance when there are departures from the normal distribution (Huber and Ronchetti, 2009).
- This can be tested by the size of the interquartile range and a comparison of the median values when comparing density scores produced by different methods.
- The method should be sufficiently sensitive to measure AOD across all levels of geography as determined by the analysis plan.
- The final component of the framework considers the practicality of implementing and interpreting the results of an AOD measurement method.
- Some methods reported in the literature only require standard spreadsheet software (e.g. outlets per population and outlets per km2) that are widely available and require no specialised training to use or implement.
- The computational requirements to calculate an AOD are intrinsically linked to the size of the study area and number of observations, the scale at which the analysis is being performed (see the theoretical geography section) and the specialism.
- An AOD measure that is difficult to interpret will make subsequent analysis difficult to design and implement and can impede the dissemination of findings to policy and practice.
- The authors then illustrate the conceptual differences using three commonly used methods from the literature.
The CHALICE Methodology
- The authors first compiled a dataset of alcohol outlets from Local Authority licensing records, described in detail elsewhere (Fone et al., 2016; Fry et al., 2016).
- The authors calculated an AOD score for each household using a network dataset combining the Ordnance Survey (OS) Integrated Transport Network (ITN) and OS Urban Paths data (Ordnance Survey 2012).
- The authors re-scaled the adjusted distances to give a value between 0 and 1 (1 high access, 0 poorer access) using a Butterworth filter gravity model (Langford et al., 2012), thus giving closer outlets a higher weighting than those further away from a residence.
- The authors mapped each method against the framework, derived descriptive statistics (counts of LSOAs with a score of zero AOD (#LSOAs -0), interquartile range (IQR), min, max, mean and median) for the five methods and plotted the density scores as Dorling pseudo-cartograms (Dorling, 1996) to visualise the differences.
- This introduces bias to the analysis, which is reflected in the theoretical scores and subsequent statistical scores.
- The method is less computationally intensive and therefore scores higher than the household CHALICE method for practical components.
- These limitations are also reflected in the statistical component scores and supported by the descriptive statistics (Table 2).
- Interpretation of the KDE method is the most complex as the AOD at the measurement point is result of a distance weighted interpolation of distances to outlets over the study area.
- The models of outlets per 1000 people and the outlets per km2 both have low theoretical scores due to the lack of inclusion of any theoretical geography beyond the grouping of outlets by small area geographies.
- Only the CHALICE method has a density score recorded for each of the 1896 LSOAs; there are no zero values (Table 2).
- Stratification of the density measures by deprivation revealed that the most deprived areas of Wales had the biggest range of IQR values.
- The differences increased as the density values increased (x-axis); differences between the measures became more scattered.
- Inversely, Outlets per 1000 population produced higher values when compared to the CHALICE method resulting in a negative trend in the BA plots.
- Repeating the BA analysis and stratifying the results by deprivation showed no systematic differences between the methods with large scatter of differences for density per 1000 population and the KDE methods compared with Outlets per km2.
- The method used to measure AOD is important because resultant statistical analysis on the associations between AOD and health is used to inform policy areas related to alcohol abuse.
- From examining the raw outlet locations, the authors know that at least one household in each LSOA has access to at least one alcohol outlet within a 10-minute walk.
- Particularly for smaller geographic units or at household level, these zeros should be evaluated in conjunction with the spatial distribution of the outlets to ensure that the zero is a true reflection of the spatial distribution of outlets and not a construct of the choice of method.
- In comparison, methods that use network defined neighbourhoods and small base geographies to define localised AOD arguably better model spatial interactions between people and their surroundings and reduce the impacts of MAUP in subsequent analysis.
- The use of the framework described in this paper will help researchers determine the best approach to measuring AOD and to understand the limitations of a chosen method whether it be theoretical, statistical or practical.
- The framework will help improve the understanding of the relationship between AOD and alcohol use at the community level, particularly in rural areas, as identified by Bryden (2013).
- Recent research on modelling the complexity of the relationships between environmental exposures and health has suggested that the most effective way of developing interventions at the population level is to target the modifiable aspects of the environment in which people reside (Brown et al., 2017) .
- Moreover, the framework is not limited to AOD and could be applied to any scenario where measuring exposure to some socio-environmental phenomena is required (e.g. tobacco, fast food, urban green space).
- Finally, but perhaps most importantly, understanding the bias and limitations of a method using the framework will also allow policy to more effectively implement licencing restrictions in relation to the oversupply of alcohol, an issue which is continually disputed by the alcohol industry as being a causal factor in public health (p17-18, The Scottish Parliament 2014).
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Cites methods from "A best practice framework to measur..."
...We will adapt a previous methodology that modelled ‘change in alcohol outlet density and alcohol-related harm to population health’ (CHALICE).(60) The distance decay for this accessibility model will be updated to emulate how people engage with GBS as opposed to alcohol outlets....
"A best practice framework to measur..." refers methods in this paper
...Finally, we used Bland–Altman (BA) plots (Martin Bland and Altman, 1986) to plot the difference in values between the CHALICE method and each of the other methods separately (y-axis) against the means of the two methods being compared....
"A best practice framework to measur..." refers background in this paper
...The concept of proximity to related phenomena in spatial interaction models is classically described as Tobler’s first law of geography (Tobler, 1970) – ‘everything is related to everything else, but near things are more related than distant things’....
"A best practice framework to measur..." refers background in this paper
...As the environment in which an individual resides has been demonstrated to be a key influencer on individual behaviour in relation to alcohol use (Dahlgren and Whitehead, 2007), AOD potentially impacts population health....
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Frequently Asked Questions (2)
Q1. What are the contributions in this paper?
In this paper, the authors present a framework to help practitioners and researchers choose the most appropriate spatial method of measuring AOD. The authors describe the CHALICE AOD measurement method which investigated the relationships between AOD and population harm ( Fone et al. 2016 ). The authors demonstrate the impact of methodological choices ( e. g. network vs. Euclidean distances ) on resulting AOD scores. The authors conclude that wherever possible the best practice approach to modelling AOD should be used to facilitate flexibility in subsequent statistical analysis and improve the transparency of the results.
Q2. What are the future works in this paper?
The interactions between multi-scale AOD and health and social outcomes are an important area for future work. For example, in one of the LSOAs with a zero value there are 16 outlets within the LSOA ( but beyond 10 minutes walk of the PWC ), and a further 6 outlets in an adjacent LSOA but close enough to the boundary to be accessible. However, the authors further demonstrate that this problem is exacerbated when the results are examined over a whole country with stratification by rural-urban classification revealing over and under inflation of AOD.