Alcohol Outlet Density and Area-Level Heavy Drinking Are Independent Risk Factors for Higher Alcohol-Related Complaints View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-03

AUTHORS

Yusuf Ransome, Hui Luan, Xun Shi, Dustin T. Duncan, S. V. Subramanian

ABSTRACT

Alcohol outlet density has well-documented associations with social and health indicators such as crime and injury. However, significantly less is known about the relationships among alcohol-related complaints. Bayesian hierarchical Poisson regression with spatial autocorrelation was used to model the association between on- and off-premises alcohol outlet density and area-level prevalence of current drinkers and heavy drinking, and graffiti density-an indicator of physical disorder-in association with calls from civilians reporting illegal use, alcohol sales, and other alcohol-related activities (hereafter alcohol-related complaints). Complaints were separated into two groups based on whether they occurred at (a) clubs/bars/restaurants or (b) elsewhere. Alcohol-related complaints and graffiti were collected from NYC Open Data. Alcohol density data are from ESRI Business Analyst and information on the prevalence of drinking from the New York City Community Health Survey. The unit of analysis consisted of ZIP codes in New York City (n = 167), and the design was a cross-sectional analysis of aggregated data between 2009 and 2015. In multivariable models, a one-unit increase in off-premises alcohol outlet density was associated with a 47% higher risk of alcohol-related complaints at clubs, bars, and restaurants [rate ratio (RR = 1.46, 95% CI = 1.21, 1.77)]. Area-level prevalence of heavy drinking was associated with a 59% higher risk of alcohol-related complaints at the club, bars, and restaurants (RR = 1.59, 95% CI = 1.34, 1.86) and a 40% higher risk of complaints elsewhere (RR = 1.40, 95% CI = 1.20, 1.63). In New York City, area-level heavy drinking prevalence is a strong independent mechanism that links alcohol outlet density to alcohol-related complaints. Area-level heavy drinking should be investigated as a predictor of other public health problems such as drug overdose mortality. More... »

PAGES

1-13

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11524-018-00327-z

DOI

http://dx.doi.org/10.1007/s11524-018-00327-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110344746

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30511138


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11524-018-00327-z'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11524-018-00327-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11524-018-00327-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11524-018-00327-z'


 

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