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...
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Gespeichert in:

Autor*in:	Lucan, Sean C. [verfasserIn] Mitra, Nandita [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Policy research Public health Fruits and vegetables Fast food Food environment Geographic information systems (GIS) mapping Neighborhoods

Übergeordnetes Werk:	Enthalten in: Journal of public health - Berlin : Springer, 1993, 20(2011), 4 vom: 04. Dez., Seite 375-385
Übergeordnetes Werk:	volume:20 ; year:2011 ; number:4 ; day:04 ; month:12 ; pages:375-385

Links:	Volltext

DOI / URN:	10.1007/s10389-011-0470-y

Katalog-ID:	SPR010349812

Internformat


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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).
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allfields	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
language	English
source	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
sourceStr	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
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Policy research Public health Fruits and vegetables Fast food Food environment Geographic information systems (GIS) mapping Neighborhoods
dewey-raw	610
isfreeaccess_bool	false
container_title	Journal of public health
authorswithroles_txt_mv	Lucan, Sean C. @@aut@@ Mitra, Nandita @@aut@@
publishDateDaySort_date	2011-12-04T00:00:00Z
hierarchy_top_id	380022346
dewey-sort	3610
id	SPR010349812
language_de	englisch
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author	Lucan, Sean C.
spellingShingle	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
authorStr	Lucan, Sean C.
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topic_title	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
topic	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
topic_unstemmed	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
topic_browse	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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title	The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research
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title_full	The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research
author_sort	Lucan, Sean C.
journal	Journal of public health
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author_browse	Lucan, Sean C. Mitra, Nandita
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author2-role	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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title_short	The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research
url	https://dx.doi.org/10.1007/s10389-011-0470-y
remote_bool	true
author2	Mitra, Nandita
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doi_str	10.1007/s10389-011-0470-y
up_date	2024-07-03T15:32:40.651Z
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The food environment and dietary intake: demonstrating a method for GIS-mapping and policy-relevant research

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