The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research
Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA...
Ausführliche Beschreibung
Autor*in: |
Lucan, Sean C. [verfasserIn] Mitra, Nandita [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2011 |
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Übergeordnetes Werk: |
Enthalten in: Journal of public health - Berlin : Springer, 1993, 20(2011), 4 vom: 04. Dez., Seite 375-385 |
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Übergeordnetes Werk: |
volume:20 ; year:2011 ; number:4 ; day:04 ; month:12 ; pages:375-385 |
Links: |
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DOI / URN: |
10.1007/s10389-011-0470-y |
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Katalog-ID: |
SPR010349812 |
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245 | 1 | 4 | |a The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research |
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520 | |a Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). | ||
650 | 4 | |a Policy research |7 (dpeaa)DE-He213 | |
650 | 4 | |a Public health |7 (dpeaa)DE-He213 | |
650 | 4 | |a Fruits and vegetables |7 (dpeaa)DE-He213 | |
650 | 4 | |a Fast food |7 (dpeaa)DE-He213 | |
650 | 4 | |a Food environment |7 (dpeaa)DE-He213 | |
650 | 4 | |a Geographic information systems (GIS) mapping |7 (dpeaa)DE-He213 | |
650 | 4 | |a Neighborhoods |7 (dpeaa)DE-He213 | |
700 | 1 | |a Mitra, Nandita |e verfasserin |4 aut | |
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10.1007/s10389-011-0470-y doi (DE-627)SPR010349812 (SPR)s10389-011-0470-y-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.10 bkl Lucan, Sean C. verfasserin aut The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). Policy research (dpeaa)DE-He213 Public health (dpeaa)DE-He213 Fruits and vegetables (dpeaa)DE-He213 Fast food (dpeaa)DE-He213 Food environment (dpeaa)DE-He213 Geographic information systems (GIS) mapping (dpeaa)DE-He213 Neighborhoods (dpeaa)DE-He213 Mitra, Nandita verfasserin aut Enthalten in Journal of public health Berlin : Springer, 1993 20(2011), 4 vom: 04. Dez., Seite 375-385 (DE-627)380022346 (DE-600)2136860-0 1613-2238 nnns volume:20 year:2011 number:4 day:04 month:12 pages:375-385 https://dx.doi.org/10.1007/s10389-011-0470-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_376 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 20 2011 4 04 12 375-385 |
spelling |
10.1007/s10389-011-0470-y doi (DE-627)SPR010349812 (SPR)s10389-011-0470-y-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.10 bkl Lucan, Sean C. verfasserin aut The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). Policy research (dpeaa)DE-He213 Public health (dpeaa)DE-He213 Fruits and vegetables (dpeaa)DE-He213 Fast food (dpeaa)DE-He213 Food environment (dpeaa)DE-He213 Geographic information systems (GIS) mapping (dpeaa)DE-He213 Neighborhoods (dpeaa)DE-He213 Mitra, Nandita verfasserin aut Enthalten in Journal of public health Berlin : Springer, 1993 20(2011), 4 vom: 04. Dez., Seite 375-385 (DE-627)380022346 (DE-600)2136860-0 1613-2238 nnns volume:20 year:2011 number:4 day:04 month:12 pages:375-385 https://dx.doi.org/10.1007/s10389-011-0470-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_376 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 20 2011 4 04 12 375-385 |
allfields_unstemmed |
10.1007/s10389-011-0470-y doi (DE-627)SPR010349812 (SPR)s10389-011-0470-y-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.10 bkl Lucan, Sean C. verfasserin aut The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). Policy research (dpeaa)DE-He213 Public health (dpeaa)DE-He213 Fruits and vegetables (dpeaa)DE-He213 Fast food (dpeaa)DE-He213 Food environment (dpeaa)DE-He213 Geographic information systems (GIS) mapping (dpeaa)DE-He213 Neighborhoods (dpeaa)DE-He213 Mitra, Nandita verfasserin aut Enthalten in Journal of public health Berlin : Springer, 1993 20(2011), 4 vom: 04. Dez., Seite 375-385 (DE-627)380022346 (DE-600)2136860-0 1613-2238 nnns volume:20 year:2011 number:4 day:04 month:12 pages:375-385 https://dx.doi.org/10.1007/s10389-011-0470-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_376 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 20 2011 4 04 12 375-385 |
allfieldsGer |
10.1007/s10389-011-0470-y doi (DE-627)SPR010349812 (SPR)s10389-011-0470-y-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.10 bkl Lucan, Sean C. verfasserin aut The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). Policy research (dpeaa)DE-He213 Public health (dpeaa)DE-He213 Fruits and vegetables (dpeaa)DE-He213 Fast food (dpeaa)DE-He213 Food environment (dpeaa)DE-He213 Geographic information systems (GIS) mapping (dpeaa)DE-He213 Neighborhoods (dpeaa)DE-He213 Mitra, Nandita verfasserin aut Enthalten in Journal of public health Berlin : Springer, 1993 20(2011), 4 vom: 04. Dez., Seite 375-385 (DE-627)380022346 (DE-600)2136860-0 1613-2238 nnns volume:20 year:2011 number:4 day:04 month:12 pages:375-385 https://dx.doi.org/10.1007/s10389-011-0470-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_376 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 20 2011 4 04 12 375-385 |
allfieldsSound |
10.1007/s10389-011-0470-y doi (DE-627)SPR010349812 (SPR)s10389-011-0470-y-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.10 bkl Lucan, Sean C. verfasserin aut The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). Policy research (dpeaa)DE-He213 Public health (dpeaa)DE-He213 Fruits and vegetables (dpeaa)DE-He213 Fast food (dpeaa)DE-He213 Food environment (dpeaa)DE-He213 Geographic information systems (GIS) mapping (dpeaa)DE-He213 Neighborhoods (dpeaa)DE-He213 Mitra, Nandita verfasserin aut Enthalten in Journal of public health Berlin : Springer, 1993 20(2011), 4 vom: 04. Dez., Seite 375-385 (DE-627)380022346 (DE-600)2136860-0 1613-2238 nnns volume:20 year:2011 number:4 day:04 month:12 pages:375-385 https://dx.doi.org/10.1007/s10389-011-0470-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_376 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 20 2011 4 04 12 375-385 |
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English |
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Enthalten in Journal of public health 20(2011), 4 vom: 04. Dez., Seite 375-385 volume:20 year:2011 number:4 day:04 month:12 pages:375-385 |
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Enthalten in Journal of public health 20(2011), 4 vom: 04. Dez., Seite 375-385 volume:20 year:2011 number:4 day:04 month:12 pages:375-385 |
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Lucan, Sean C. @@aut@@ Mitra, Nandita @@aut@@ |
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2011-12-04T00:00:00Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR010349812</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519170912.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2011 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10389-011-0470-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR010349812</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10389-011-0470-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.10</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lucan, Sean C.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. 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Lucan, Sean C. |
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Lucan, Sean C. ddc 610 bkl 44.10 misc Policy research misc Public health misc Fruits and vegetables misc Fast food misc Food environment misc Geographic information systems (GIS) mapping misc Neighborhoods The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research |
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610 ASE 44.10 bkl The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research Policy research (dpeaa)DE-He213 Public health (dpeaa)DE-He213 Fruits and vegetables (dpeaa)DE-He213 Fast food (dpeaa)DE-He213 Food environment (dpeaa)DE-He213 Geographic information systems (GIS) mapping (dpeaa)DE-He213 Neighborhoods (dpeaa)DE-He213 |
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ddc 610 bkl 44.10 misc Policy research misc Public health misc Fruits and vegetables misc Fast food misc Food environment misc Geographic information systems (GIS) mapping misc Neighborhoods |
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Lucan, Sean C. |
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10.1007/s10389-011-0470-y |
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verfasserin |
title_sort |
food environment and dietary intake: demonstrating a method for gis-mapping and policy-relevant research |
title_auth |
The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research |
abstract |
Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). |
abstractGer |
Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). |
abstract_unstemmed |
Aims The aims of this paper are (1) to assess if perceptions of the food environment are associated with select dietary intake by neighborhood, and (2) to map neighborhood-specific findings, demonstrating a method for policy-relevant research. Methods Using pre-collected data from a Philadelphia, PA community health survey, we aggregated individual-level data (n = 4,434 respondents) to neighborhoods (n = 381 census tracts), adjusting for conceptually-relevant socio-demographic factors. We estimated Spearman correlations between multivariable adjusted food-environment perceptions (perceived produce availability, supermarket accessibility, grocery quality) and select dietary intake (reported fruit-and-vegetable and fast-food consumption), and mapped variables by neighborhood using geographic information systems (GIS). Results Difficulty finding fruits and vegetables, having to travel outside of one’s neighborhood to get to a supermarket, and poor grocery quality were each directly correlated with fast-food intake (rho = 0.21, 0.34, 0.64 respectively; p values <0.001); and inversely correlated with fruit-and-vegetable intake (rho = –0.35, –0.54, –0.56 respectively; p values <0.001). Maps identified neighborhoods within the city with the worst food-environment perceptions and poorest dietary intakes. Conclusion Negative perceptions of the food environment were strongly correlated with less-healthy eating in neighborhoods. Maps showed the geographic areas of greatest concern. Our findings demonstrate a method that might be used prospectively in public health for policy planning (e.g. to identify neighborhoods most in need), or retrospectively for policy assessment (e.g. to identify changes in neighborhoods after policy implementation). |
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The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research |
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|
score |
7.399314 |