Commentary on <scp>G</scp>mel <i>et al</i>. (2015): Are alcohol outlet densities strongly associated with alcohol‐related outcomes? A critical review of recent evidence

Morrison, Christopher N.; Cerdá, Magdalena; Gorman, Dennis M.; Gruenewald, Paul J.; Mair, Christina; Naimi, Timothy S.; Scribner, Richard; Stockwell, Tim; Toomey, Traci L.; Wieczorek, William F.

doi:10.1111/dar.12340

Cited by 15 publications

(19 citation statements)

References 18 publications

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“…Although we disagree with these assumptions, we are actually in agreement with most of the associated comments of Morrison et al if they are taken separately. Our basic position is that the link between outlet density and the outcomes examined is highly complex and dependent on a large range of factors.…”

supporting

confidence: 81%

“…We very much welcome the commentary by 10 outstanding researchers in the field . Generating a debate around the relationship between alcohol availability, alcohol consumption and alcohol‐related harm was the exact intention of our contribution.…”

mentioning

confidence: 96%

“…The comments of Morrison et al seem to be centred around two assumptions: that we are generally opposed to including aggregate‐level data; and that by weighting the existing literature by methodological quality, one would come up with an alternative view, perhaps even with a meta‐analytically derived effect estimate. …”

mentioning

confidence: 99%

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We have to become more specific: A reply to Morrison et al.

Gmel¹,

Holmes

Studer³

2015

Drug and Alcohol Review

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supporting

confidence: 81%

mentioning

confidence: 96%

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We have to become more specific: A reply to Morrison et al.

Gmel¹,

Holmes

Studer³

2015

Drug and Alcohol Review

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“…Observed counts are not statistically independent, nor are they globally correlated, but rather they are correlated with counts observed among nearest neighbors (here defined as zip codes with shared boundaries); covariances among units have local spatial structure. Neglect of this particular feature of spatial data leads to Type I errors in analysis, misinterpretation of nominally significant or well-supported effects and miscalculation of relative rates of disease outcomes in disease mapping models [22,23] Observed changes in disease rates may be a function of time and the creation or rearrangement of zip code units over time; thus the analyses require characterizing spatial relationships between units at each time step and measures of changes in population coverage induced by the addition or rearrangement of zip code areas (misalignment). While maximum likelihood solutions are available for statistical analyses of Poisson distributed data, no such general approach exists for spatial Poisson models and, as there are no conjugate priors for Bayesian Poisson models, empirical Bayesian analyses are required [24]; low-precision uninformative prior estimates of expected effects and their error variances are input to iterative computational procedures that converge on best posterior estimates.…”

Section: Methodsmentioning

confidence: 99%

Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas

et al. 2016

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Aims To determine (1) whether prescription opioid poisoning (PO) hospital discharges spread across space over time, (2) the locations of ‘hot-spots’ of PO-related hospital discharges, (3) how features of the local environment contribute to the growth in PO-related hospital discharges and (4) where each environmental feature makes the strongest contribution. Design Hierarchical Bayesian Poisson space–time analysis to relate annual discharges from community hospitals to postal code characteristics over 10 years. Setting California, USA. Participants Residents of 18 517 postal codes in California, 2001–11. Measurements Annual postal code-level counts of hospital discharges due to PO poisoning were related to postal code pharmacy density, measures of medical need for POs (i.e. rates of cancer and arthritis-related hospital discharges), economic stressors (i.e. median household income, percentage of families in poverty and the unemployment rate) and concentration of manual labor industries. Findings PO-related hospital discharges spread from rural and suburban/exurban ‘hot-spots’ to urban areas. They increased more in postal codes with greater pharmacy density [rate ratio (RR) = 1.03; 95% credible interval (CI) = 1.01, 1.05], more arthritis-related hospital discharges (RR = 1.08; 95% CI = 1.06, 1.11), lower income (RR = 0.85; 95% CI = 0.83, 0.87) and more manual labor industries (RR = 1.15; 95% CI = 1.10, 1.19 for construction; RR = 1.12; 95% CI = 1.04, 1.20 for manufacturing industries). Changes in pharmacy density primarily affected PO-related discharges in urban areas, while changes in income and manual labor industries especially affected PO-related discharges in suburban/exurban and rural areas. Conclusions Hospital discharge rates for prescription opioid (PO) poisoning spread from rural and suburban/exurban hot-spots to urban areas, suggesting spatial contagion. The distribution of age-related and work-place-related sources of medical need for POs in rural areas and, to a lesser extent, the availability of POs through pharmacies in urban areas, partly explain the growth of PO poisoning across California, USA.

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“…Researchers recently challenged the alcohol outlet density field to increase the rigor of their measurement methods (Gmel et al., ; Holmes et al., ), which sparked an interesting discussion about how to advance measurement in this area (Fry et al., ; Lu et al., ; Morrison et al., ). At a high level, all measurement methods aim to achieve a similar goal: to describe the spatial configuration of alcohol outlets.…”

mentioning

confidence: 99%

Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime

Trangenstein

Curriero

Jennings

et al. 2019

Alcoholism Clin & Exp Res

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Background: The objective of this analysis was to compare measurement methods-counts, proximity, mean distance, and spatial access-of calculating alcohol outlet density and violent crime using data from Baltimore, Maryland.Methods: Violent crime data (n = 11,815) were obtained from the Baltimore City Police Department and included homicides, aggravated assaults, rapes, and robberies in 2016. We calculated alcohol outlet density and violent crime at the census block (CB) level (n = 13,016). We then weighted these CB-level measures to the census tract level (n = 197) and conducted a series of regressions. Negative binomial regression was used for count outcomes and linear regression for proximity and spatial access outcomes. Choropleth maps, partial R 2 , Akaike's Information Criterion, and root mean squared error guided determination of which models yielded lower error and better fit.Results: The inference depended on the measurement methods used. Eight models that used a count of alcohol outlets and/or violent crimes failed to detect an association between outlets and crime, and 3 other count-based models detected an association in the opposite direction. Proximity, mean distance, and spatial access methods consistently detected an association between outlets and crime and produced comparable model fits.Conclusions: Proximity, mean distance, and spatial access methods yielded the best model fits and had the lowest levels of error in this urban setting. Spatial access methods may offer conceptual strengths over proximity and mean distance. Conflicting findings in the field may be in part due to error in the way that researchers measure alcohol outlet density.

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Commentary on Gmel et al. (2015): Are alcohol outlet densities strongly associated with alcohol‐related outcomes? A critical review of recent evidence

Cited by 15 publications

References 18 publications

We have to become more specific: A reply to Morrison et al.

We have to become more specific: A reply to Morrison et al.

Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas

Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime

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