How Can We Measure Alcohol Outlet Density Around Schools? A Comparison Between Two Buffer-Based Methods
Measuring the density of alcohol outlets around schools is a critical step towards understanding the drivers of drinking among adolescents. Different methodologies have been used in the literature for this purpose, but the implications of using one methodology or another have not been clearly assess...
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Veröffentlicht in: | Journal of urban health 2023-06, Vol.100 (3), p.627-637 |
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creator | Martín-Turrero, Irene Sureda, Xisca Escobar, Francisco Bilal, Usama Berasaluce, Maitane Valiente, Roberto |
description | Measuring the density of alcohol outlets around schools is a critical step towards understanding the drivers of drinking among adolescents. Different methodologies have been used in the literature for this purpose, but the implications of using one methodology or another have not been clearly assessed. Our aim was to compare different methods to measure alcohol outlet density and highlight under which characteristics of the environment might be best using each approach. We used Geographic Information Systems to geolocate schools (
n
= 576) and alcohol outlets (
n
= 21,732) in Madrid. We defined the density of alcohol outlets as the number of establishments within an area of 400 m around schools measured using two buffering methods: crow flies’ and street network distances. We evaluated the agreement between both methods visually and through regression models, including street connectivity, population density, and density of recreational venues as predictors of disagreement. The density of alcohol outlets around schools was higher using crow flies’ distances compared to street network distances. The differences between methodologies were wider in areas of higher density of outlets, especially in the downtown areas, where there are higher population density and street connectivity. Our results suggest that the spatial characteristics and morphology of the study area (e.g., street connectivity and population density) should be considered when deciding the methodology to be used to measure alcohol outlet density. Future studies should explore the implications of different exposure measures in their association with drinking prevalence and consumption patterns among different geographical contexts. |
doi_str_mv | 10.1007/s11524-023-00740-z |
format | Article |
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n
= 576) and alcohol outlets (
n
= 21,732) in Madrid. We defined the density of alcohol outlets as the number of establishments within an area of 400 m around schools measured using two buffering methods: crow flies’ and street network distances. We evaluated the agreement between both methods visually and through regression models, including street connectivity, population density, and density of recreational venues as predictors of disagreement. The density of alcohol outlets around schools was higher using crow flies’ distances compared to street network distances. The differences between methodologies were wider in areas of higher density of outlets, especially in the downtown areas, where there are higher population density and street connectivity. Our results suggest that the spatial characteristics and morphology of the study area (e.g., street connectivity and population density) should be considered when deciding the methodology to be used to measure alcohol outlet density. Future studies should explore the implications of different exposure measures in their association with drinking prevalence and consumption patterns among different geographical contexts.</description><identifier>ISSN: 1099-3460</identifier><identifier>EISSN: 1468-2869</identifier><identifier>DOI: 10.1007/s11524-023-00740-z</identifier><identifier>PMID: 37351726</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Alcohol ; Alcohol Drinking - epidemiology ; Alcoholic Beverages ; Alcohols ; Buffers ; Commerce ; Connectivity ; Consumption patterns ; Drinking ; Drinking behavior ; Epidemiology ; Geographic Information Systems ; Health Informatics ; Humans ; Medicine ; Medicine & Public Health ; Original ; Original Article ; Physical characteristics ; Population density ; Public Health ; Regression analysis ; Regression models ; Remote sensing ; Residence Characteristics ; Schools</subject><ispartof>Journal of urban health, 2023-06, Vol.100 (3), p.627-637</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c475t-ec04324e584a72484ed857f2c7cb57a2b30a365f92bc3f950995bdc4ad447293</citedby><cites>FETCH-LOGICAL-c475t-ec04324e584a72484ed857f2c7cb57a2b30a365f92bc3f950995bdc4ad447293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11524-023-00740-z$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11524-023-00740-z$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37351726$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Martín-Turrero, Irene</creatorcontrib><creatorcontrib>Sureda, Xisca</creatorcontrib><creatorcontrib>Escobar, Francisco</creatorcontrib><creatorcontrib>Bilal, Usama</creatorcontrib><creatorcontrib>Berasaluce, Maitane</creatorcontrib><creatorcontrib>Valiente, Roberto</creatorcontrib><title>How Can We Measure Alcohol Outlet Density Around Schools? A Comparison Between Two Buffer-Based Methods</title><title>Journal of urban health</title><addtitle>J Urban Health</addtitle><addtitle>J Urban Health</addtitle><description>Measuring the density of alcohol outlets around schools is a critical step towards understanding the drivers of drinking among adolescents. Different methodologies have been used in the literature for this purpose, but the implications of using one methodology or another have not been clearly assessed. Our aim was to compare different methods to measure alcohol outlet density and highlight under which characteristics of the environment might be best using each approach. We used Geographic Information Systems to geolocate schools (
n
= 576) and alcohol outlets (
n
= 21,732) in Madrid. We defined the density of alcohol outlets as the number of establishments within an area of 400 m around schools measured using two buffering methods: crow flies’ and street network distances. We evaluated the agreement between both methods visually and through regression models, including street connectivity, population density, and density of recreational venues as predictors of disagreement. The density of alcohol outlets around schools was higher using crow flies’ distances compared to street network distances. The differences between methodologies were wider in areas of higher density of outlets, especially in the downtown areas, where there are higher population density and street connectivity. Our results suggest that the spatial characteristics and morphology of the study area (e.g., street connectivity and population density) should be considered when deciding the methodology to be used to measure alcohol outlet density. Future studies should explore the implications of different exposure measures in their association with drinking prevalence and consumption patterns among different geographical contexts.</description><subject>Alcohol</subject><subject>Alcohol Drinking - epidemiology</subject><subject>Alcoholic Beverages</subject><subject>Alcohols</subject><subject>Buffers</subject><subject>Commerce</subject><subject>Connectivity</subject><subject>Consumption patterns</subject><subject>Drinking</subject><subject>Drinking behavior</subject><subject>Epidemiology</subject><subject>Geographic Information Systems</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Original</subject><subject>Original Article</subject><subject>Physical characteristics</subject><subject>Population density</subject><subject>Public Health</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Remote sensing</subject><subject>Residence Characteristics</subject><subject>Schools</subject><issn>1099-3460</issn><issn>1468-2869</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU1v1DAQhi0EoqXwBzggS1y4GGyPHSenanf5KFJRD6zE0XKcyW6qrL3YCav212PYUj4OnDzWPPPa77yEPBf8teDcvMlCaKkYl8DKVXF2-4CcClXVTNZV87DUvGkYqIqfkCc5X3MuKmXkY3ICBrQwsjolm4t4oCsX6Bekn9DlOSFdjD5u40iv5mnEib7FkIfphi5SnENHP_ttjGM-pwu6iru9S0OOgS5xOiAGuj5Eupz7HhNbuoxdEZ22sctPyaPejRmf3Z1nZP3-3Xp1wS6vPnxcLS6ZV0ZPDD1XIBXqWjkjVa2wq7XppTe-1cbJFriDSveNbD30jS4Gddt55TpVnDVwRs6Psvu53WHnMUzJjXafhp1LNza6wf7dCcPWbuI3KzhIKEssCq_uFFL8OmOe7G7IHsfRBYxztrKWjQKhoS7oy3_Q6zinUOwVCgCKiaoqlDxSPsWcE_b3vxHc_sjRHnO0JUf7M0d7W4Ze_OnjfuRXcAWAI5BLK2ww_X77P7LfAQjKqMU</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Martín-Turrero, Irene</creator><creator>Sureda, Xisca</creator><creator>Escobar, Francisco</creator><creator>Bilal, Usama</creator><creator>Berasaluce, Maitane</creator><creator>Valiente, Roberto</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>3V.</scope><scope>7T2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88J</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2R</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230601</creationdate><title>How Can We Measure Alcohol Outlet Density Around Schools? A Comparison Between Two Buffer-Based Methods</title><author>Martín-Turrero, Irene ; Sureda, Xisca ; Escobar, Francisco ; Bilal, Usama ; Berasaluce, Maitane ; Valiente, Roberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c475t-ec04324e584a72484ed857f2c7cb57a2b30a365f92bc3f950995bdc4ad447293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Alcohol</topic><topic>Alcohol Drinking - epidemiology</topic><topic>Alcoholic Beverages</topic><topic>Alcohols</topic><topic>Buffers</topic><topic>Commerce</topic><topic>Connectivity</topic><topic>Consumption patterns</topic><topic>Drinking</topic><topic>Drinking behavior</topic><topic>Epidemiology</topic><topic>Geographic Information Systems</topic><topic>Health Informatics</topic><topic>Humans</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Original</topic><topic>Original Article</topic><topic>Physical characteristics</topic><topic>Population density</topic><topic>Public Health</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Remote sensing</topic><topic>Residence Characteristics</topic><topic>Schools</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Martín-Turrero, Irene</creatorcontrib><creatorcontrib>Sureda, Xisca</creatorcontrib><creatorcontrib>Escobar, Francisco</creatorcontrib><creatorcontrib>Bilal, Usama</creatorcontrib><creatorcontrib>Berasaluce, Maitane</creatorcontrib><creatorcontrib>Valiente, Roberto</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Social Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of urban health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Martín-Turrero, Irene</au><au>Sureda, Xisca</au><au>Escobar, Francisco</au><au>Bilal, Usama</au><au>Berasaluce, Maitane</au><au>Valiente, Roberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How Can We Measure Alcohol Outlet Density Around Schools? A Comparison Between Two Buffer-Based Methods</atitle><jtitle>Journal of urban health</jtitle><stitle>J Urban Health</stitle><addtitle>J Urban Health</addtitle><date>2023-06-01</date><risdate>2023</risdate><volume>100</volume><issue>3</issue><spage>627</spage><epage>637</epage><pages>627-637</pages><issn>1099-3460</issn><eissn>1468-2869</eissn><abstract>Measuring the density of alcohol outlets around schools is a critical step towards understanding the drivers of drinking among adolescents. Different methodologies have been used in the literature for this purpose, but the implications of using one methodology or another have not been clearly assessed. Our aim was to compare different methods to measure alcohol outlet density and highlight under which characteristics of the environment might be best using each approach. We used Geographic Information Systems to geolocate schools (
n
= 576) and alcohol outlets (
n
= 21,732) in Madrid. We defined the density of alcohol outlets as the number of establishments within an area of 400 m around schools measured using two buffering methods: crow flies’ and street network distances. We evaluated the agreement between both methods visually and through regression models, including street connectivity, population density, and density of recreational venues as predictors of disagreement. The density of alcohol outlets around schools was higher using crow flies’ distances compared to street network distances. The differences between methodologies were wider in areas of higher density of outlets, especially in the downtown areas, where there are higher population density and street connectivity. Our results suggest that the spatial characteristics and morphology of the study area (e.g., street connectivity and population density) should be considered when deciding the methodology to be used to measure alcohol outlet density. Future studies should explore the implications of different exposure measures in their association with drinking prevalence and consumption patterns among different geographical contexts.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>37351726</pmid><doi>10.1007/s11524-023-00740-z</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Alcohol Alcohol Drinking - epidemiology Alcoholic Beverages Alcohols Buffers Commerce Connectivity Consumption patterns Drinking Drinking behavior Epidemiology Geographic Information Systems Health Informatics Humans Medicine Medicine & Public Health Original Original Article Physical characteristics Population density Public Health Regression analysis Regression models Remote sensing Residence Characteristics Schools |
title | How Can We Measure Alcohol Outlet Density Around Schools? A Comparison Between Two Buffer-Based Methods |
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