Large-scale diet tracking data reveal disparate associations between food environment and diet.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
18 01 2022
Historique:
received: 20 01 2021
accepted: 18 11 2021
entrez: 19 1 2022
pubmed: 20 1 2022
medline: 15 2 2022
Statut: epublish

Résumé

An unhealthy diet is a major risk factor for chronic diseases including cardiovascular disease, type 2 diabetes, and cancer

Identifiants

pubmed: 35042849
doi: 10.1038/s41467-021-27522-y
pii: 10.1038/s41467-021-27522-y
pmc: PMC8766578
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

267

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH125179
Pays : United States
Organisme : NIBIB NIH HHS
ID : U54 EB020405
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2022. The Author(s).

Références

Afshin, A. et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet 393, 1958–1972 (2019).
doi: 10.1016/S0140-6736(19)30041-8
Micha, R. et al. Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the united states. JAMA 317, 912–924 (2017).
pubmed: 28267855 pmcid: 5852674 doi: 10.1001/jama.2017.0947
Jayedi, A., Soltani, S., Abdolshahi, A. & Shab-Bidar, S. Healthy and unhealthy dietary patterns and the risk of chronic disease: an umbrella review of meta-analyses of prospective cohort studies. Br. J. Nutr. 124, 1133–1144 (2020).
pubmed: 32600500 doi: 10.1017/S0007114520002330
Beaglehole, R. et al. Priority actions for the non-communicable disease crisis. Lancet 377, 1438–1447 (2011).
pubmed: 21474174 doi: 10.1016/S0140-6736(11)60393-0
Fleischer, N. L., Roux, A. V. D., Alazraqui, M. & Spinelli, H. Social patterning of chronic disease risk factors in a latin american city. J. Urban Health 85, 923 (2008).
pubmed: 18830819 pmcid: 2587655 doi: 10.1007/s11524-008-9319-2
Sallis, J. F. & Glanz, K. The role of built environments in physical activity, eating, and obesity in childhood. Future Child. 16, 89–108 (2006).
pubmed: 16532660 doi: 10.1353/foc.2006.0009
Fleischhacker, S. E., Evenson, K. R., Rodriguez, D. A. & Ammerman, A. S. A systematic review of fast food access studies. Obes. Rev. 12, e460–e471 (2011).
pubmed: 20149118 doi: 10.1111/j.1467-789X.2010.00715.x
Odoms-Young, A., Singleton, C. R., Springfield, S., McNabb, L. & Thompson, T. Retail environments as a venue for obesity prevention. Curr. Obes. Rep. 5, 184–191 (2016).
pubmed: 27099166 pmcid: 5508978 doi: 10.1007/s13679-016-0219-6
Kirkpatrick, S. I. et al. Dietary assessment in food environment research: a systematic review. Am. J. Preventive Med. 46, 94–102 (2014).
doi: 10.1016/j.amepre.2013.08.015
Caspi, C. E., Sorensen, G., Subramanian, S. & Kawachi, I. The local food environment and diet: a systematic review. Health Place 18, 1172–1187 (2012).
pubmed: 22717379 pmcid: 3684395 doi: 10.1016/j.healthplace.2012.05.006
Feng, J., Glass, T. A., Curriero, F. C., Stewart, W. F. & Schwartz, B. S. The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place 16, 175–190 (2010).
pubmed: 19880341 doi: 10.1016/j.healthplace.2009.09.008
Cummins, S. & Macintyre, S. Food environments and obesity-"neighbourhood or nation? Int. J. Epidemiol. 35, 100–104 (2006).
pubmed: 16338945 doi: 10.1093/ije/dyi276
Charreire, H. et al. Measuring the food environment using geographical information systems: a methodological review. Public Health Nutr. 13, 1773–1785 (2010).
pubmed: 20409354 doi: 10.1017/S1368980010000753
Kelly, B., Flood, V. M. & Yeatman, H. Measuring local food environments: an overview of available methods and measures. Health Place 17, 1284–1293 (2011).
pubmed: 21908229 doi: 10.1016/j.healthplace.2011.08.014
McKinnon, R. A., Reedy, J., Morrissette, M. A., Lytle, L. A. & Yaroch, A. L. Measures of the food environment: a compilation of the literature, 1990–2007. Am. J. Preventive Med. 36, S124–S133 (2009).
doi: 10.1016/j.amepre.2009.01.012
Elinder, L. S. & Jansson, M. Obesogenic environments–aspects on measurement and indicators. Public Health Nutr. 12, 307–315 (2009).
pubmed: 18498677
Gittelsohn, J. & Sharma, S. Physical, consumer, and social aspects of measuring the food environment among diverse low-income populations. Am. J. Preventive Med. 36, S161–S165 (2009).
doi: 10.1016/j.amepre.2009.01.007
Gustafson, A., Hankins, S. & Jilcott, S. Measures of the consumer food store environment: a systematic review of the evidence 2000–2011. J. Community health 37, 897–911 (2012).
pubmed: 22160660 doi: 10.1007/s10900-011-9524-x
Lytle, L. A. Measuring the food environment: state of the science. Am. J. Preventive Med. 36, S134–S144 (2009).
doi: 10.1016/j.amepre.2009.01.018
Odoms-Young, A. M., Zenk, S. & Mason, M. Measuring food availability and access in african-american communities: implications for intervention and policy. Am. J. Preventive Med. 36, S145–S150 (2009).
doi: 10.1016/j.amepre.2009.01.001
Ohri-Vachaspati, P. & Leviton, L. C. Measuring food environments: a guide to available instruments. Am. J. Health Promotion 24, 410–426 (2010).
doi: 10.4278/ajhp.080909-LIT-190
Sharkey, J. R. Measuring potential access to food stores and food-service places in rural areas in the us. Am. J. Preventive Med. 36, S151–S155 (2009).
doi: 10.1016/j.amepre.2009.01.004
Kamphuis, C. B. et al. Environmental determinants of fruit and vegetable consumption among adults: a systematic review. Br. J. Nutr. 96, 620–635 (2006).
pubmed: 17010219
Lytle, L. A. & Sokol, R. L. Measures of the food environment: a systematic review of the field, 2007–2015. Health Place 44, 18–34 (2017).
pubmed: 28135633 doi: 10.1016/j.healthplace.2016.12.007
Kumanyika, S. K. Environmental influences on childhood obesity: ethnic and cultural influences in context. Physiol. Behav. 94, 61–70 (2008).
pubmed: 18158165 doi: 10.1016/j.physbeh.2007.11.019
Hicks, J. L. et al. Best practices for analyzing large-scale health data from wearables and smartphone apps. NPJ Digital Med. 2, 1–12 (2019).
doi: 10.1038/s41746-019-0121-1
Althoff, T., Hicks, J. L., King, A. C., Delp, S. L. & Leskovec, J. Large-scale physical activity data reveal worldwide activity inequality. Nature 547, 336–339 (2017).
pubmed: 28693034 pmcid: 5774986 doi: 10.1038/nature23018
Althoff, T., White, R. W. & Horvitz, E. Influence of pokémon go on physical activity: study and implications. J. Med. Internet Res. 18, e315 (2016).
pubmed: 27923778 pmcid: 5174727 doi: 10.2196/jmir.6759
Althoff, T., Horvitz, E., White, R. W. & Zeitzer, J. Harnessing the web for population-scale physiological sensing: a case study of sleep and performance. In Proceedings of the 26th international conference on World Wide Web. 113–122 (International World Wide Web Conferences Steering Committee, 2017).
Althoff, T., Horvitz, E. & White, R. W. Psychomotor function measured via online activity predicts motor vehicle fatality risk. NPJ Digital Med. 1, 1–2 (2018).
doi: 10.1038/s41746-017-0003-3
Bento, A. I. et al. Evidence from internet search data shows information-seeking responses to news of local covid-19 cases. Proc. Natl Acad. Sci. 117, 11220–11222 (2020).
pubmed: 32366658 pmcid: 7260988 doi: 10.1073/pnas.2005335117
Chang, S. et al. Mobility network models of covid-19 explain inequities and inform reopening. Nature 589, 82–87 (2021).
pubmed: 33171481 doi: 10.1038/s41586-020-2923-3
Suh, J., Horvitz, E., White, R. W. & Althoff, T. Population-scale study of human needs during the covid-19 pandemic: Analysis and implications. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 4–12 (Association for Computing Machinery, 2021).
Pierson, E., Althoff, T., Thomas, D., Hillard, P. & Leskovec, J. Daily, weekly, seasonal and menstrual cycles in women’s mood, behaviour and vital signs. Nat. Hum. Behav. 5, 716–725 (2021).
pubmed: 33526880 doi: 10.1038/s41562-020-01046-9
Aiello, L. M., Schifanella, R., Quercia, D. & Del Prete, L. Large-scale and high-resolution analysis of food purchases and health outcomes. EPJ Data Sci. 8, 14 (2019).
doi: 10.1140/epjds/s13688-019-0191-y
West, R., White, R. W. & Horvitz, E. From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In Proceedings of the 22nd international conference on World Wide Web. 1399–1410 (International World Wide Web Conferences Steering Committee, 2013).
Abbar, S., Mejova, Y. & Weber, I. You tweet what you eat: Studying food consumption through twitter. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 3197–3206 (Association for Computing Machinery, 2015).
De Choudhury, M., Sharma, S. & Kiciman, E. Characterizing dietary choices, nutrition, and language in food deserts via social media. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing. 1157–1170 (Association for Computing Machinery, 2016).
Gordon, M., Althoff, T. & Leskovec, J. Goal-setting and achievement in activity tracking apps: a case study of MyFitnessPal. In The World Wide Web Conference. 571–582 (International World Wide Web Conferences Steering Committee, 2019).
Allcott, H. et al. Food deserts and the causes of nutritional inequality. Q. J. Econ. 134, 1793–1844 (2019).
doi: 10.1093/qje/qjz015
Sasson, C. et al. American heart association diabetes and cardiometabolic health summit: Summary and recommendations. J. Am. Heart Assoc. 7, e009271 (2018).
pubmed: 30371251 pmcid: 6201457 doi: 10.1161/JAHA.118.009271
Mason, K. E., Pearce, N. & Cummins, S. Associations between fast food and physical activity environments and adiposity in mid-life: cross-sectional, observational evidence from uk biobank. Lancet Public Health 3, e24–e33 (2018).
pubmed: 29307385 doi: 10.1016/S2468-2667(17)30212-8
CDC. Behavioral risk factor surveillance system survey questionnaire (2011). https://www.cdc.gov/brfss/smart/smart_2011.htm . Accessed 18 Aug 2017.
CDC. Behavioral risk factor surveillance system survey questionnaire (2012). https://www.cdc.gov/brfss/smart/smart_2012.htm . Accessed 18 Aug 2017.
Rahkovsky, I. & Snyder, S. Food choices and store proximity (2015). https://www.ers.usda.gov/webdocs/publications/45432/53943_err195.pdf?v=42276/ . Accessed 31 Dec 2019.
USCB. Quickfacts (2017). https://www.census.gov/quickfacts/fact/table/US/PST045219 .
Larson, N. I., Story, M. T. & Nelson, M. C. Neighborhood environments: disparities in access to healthy foods in the us. Am. J. Preventive Med. 36, 74–81 (2009).
doi: 10.1016/j.amepre.2008.09.025
Mackenbach, J. D. et al. A systematic review on socioeconomic differences in the association between the food environment and dietary behaviors. Nutrients 11, 2215 (2019).
pmcid: 6769523 doi: 10.3390/nu11092215
Meyer, K. A. et al. Sociodemographic differences in fast food price sensitivity. JAMA Intern. Med. 174, 434–442 (2014).
pubmed: 24424384 pmcid: 3963142 doi: 10.1001/jamainternmed.2013.13922
Lakerveld, J. & Mackenbach, J. The upstream determinants of adult obesity. Obes. Facts 10, 216–222 (2017).
pubmed: 28564658 pmcid: 5644962 doi: 10.1159/000471489
Cockerham, W. C., Hamby, B. W. & Oates, G. R. The social determinants of chronic disease. Am. J. Preventive Med. 52, S5–S12 (2017).
doi: 10.1016/j.amepre.2016.09.010
Yusuf, Z. I. et al. Social determinants of overweight and obesity among children in the united states. Int. J. Matern. Child Health AIDS 9, 22 (2020).
doi: 10.21106/ijma.337
Farmer, M. M. & Ferraro, K. F. Are racial disparities in health conditional on socioeconomic status? Soc. Sci. Med. 60, 191–204 (2005).
pubmed: 15482878 doi: 10.1016/j.socscimed.2004.04.026
Shuey, K. M. & Willson, A. E. Cumulative disadvantage and black-white disparities in life-course health trajectories. Res. Aging 30, 200–225 (2008).
doi: 10.1177/0164027507311151
Do, D. P. et al. Does place explain racial health disparities? quantifying the contribution of residential context to the black/white health gap in the united states. Soc. Sci. Med. 67, 1258–1268 (2008).
pubmed: 18649984 pmcid: 2614884 doi: 10.1016/j.socscimed.2008.06.018
Williams, D. R. & Collins, C. Us socioeconomic and racial differences in health: patterns and explanations. Annu. Rev. Sociol. 21, 349–386 (1995).
doi: 10.1146/annurev.so.21.080195.002025
Bratter, J. L. & Gorman, B. K. Is discrimination an equal opportunity risk? racial experiences, socioeconomic status, and health status among black and white adults. J. Health Soc. Behav. 52, 365–382 (2011).
pubmed: 21896687 doi: 10.1177/0022146511405336
Williams, D. R., Neighbors, H. W. & Jackson, J. S. Racial/ethnic discrimination and health: findings from community studies. Am. J. Public Health 93, 200–208 (2003).
pubmed: 12554570 pmcid: 1447717 doi: 10.2105/AJPH.93.2.200
Pearson, J. A. Can’t buy me whiteness: New lessons from the titanic on race, ethnicity, and health. Du Bois Rev.: Soc. Sci. Res. Race 5, 27–47 (2008).
doi: 10.1017/S1742058X0808003X
Boen, C. The role of socioeconomic factors in black-white health inequities across the life course: Point-in-time measures, long-term exposures, and differential health returns. Soc. Sci. Med. 170, 63–76 (2016).
pubmed: 27764654 pmcid: 5381512 doi: 10.1016/j.socscimed.2016.10.008
Lommel, L. L., Thompson, L., Chen, J.-L., Waters, C. & Carrico, A. Acculturation, inflammation, and self-rated health in mexican american immigrants. J. Immigr. Minority Health 21, 1052–1060 (2019).
doi: 10.1007/s10903-018-0805-7
Bostean, G. Does selective migration explain the hispanic paradox? a comparative analysis of mexicans in the us and mexico. J. Immigr. Minority Health 15, 624–635 (2013).
doi: 10.1007/s10903-012-9646-y
Ruiz, J. M., Steffen, P. & Smith, T. B. Hispanic mortality paradox: a systematic review and meta-analysis of the longitudinal literature. Am. J. Public Health 103, e52–e60 (2013).
pubmed: 23327278 pmcid: 3673509 doi: 10.2105/AJPH.2012.301103
Min, J. W., Rhee, S., Lee, S. E., Rhee, J. & Tran, T. Comparative analysis on determinants of self-rated health among non-hispanic white, hispanic, and asian american older adults. J. Immigr. Minority Health 16, 365–372 (2014).
doi: 10.1007/s10903-013-9852-2
Kimbro, R. T., Gorman, B. K. & Schachter, A. Acculturation and self-rated health among latino and asian immigrants to the united states. Soc. Probl. 59, 341–363 (2012).
doi: 10.1525/sp.2012.59.3.341
Brewer, J. V. et al. Contributors to self-reported health in a racially and ethnically diverse population: focus on hispanics. Ann. Epidemiol. 23, 19–24 (2013).
pubmed: 23149066 doi: 10.1016/j.annepidem.2012.09.013
Ridker, P. M. C-reactive protein: a simple test to help predict risk of heart attack and stroke. Circulation 108, e81–e85 (2003).
pubmed: 14504253 doi: 10.1161/01.CIR.0000093381.57779.67
Viruell-Fuentes, E. A. Beyond acculturation: immigration, discrimination, and health research among mexicans in the united states. Soc. Sci. Med. 65, 1524–1535 (2007).
pubmed: 17602812 doi: 10.1016/j.socscimed.2007.05.010
Rodriguez, F. et al. Association of educational attainment and cardiovascular risk in hispanic individuals: Findings from the cooper center longitudinal study. JAMA Cardiol. 4, 43–50 (2019).
pubmed: 30566183 pmcid: 6386140 doi: 10.1001/jamacardio.2018.4294
Lutsey, P. L. et al. Associations of acculturation and socioeconomic status with subclinical cardiovascular disease in the multi-ethnic study of atherosclerosis. Am. J. Public Health 98, 1963–1970 (2008).
pubmed: 18511718 pmcid: 2575668 doi: 10.2105/AJPH.2007.123844
Koya, D. L. & Egede, L. E. Association between length of residence and cardiovascular disease risk factors among an ethnically diverse group of united states immigrants. J. Gen. Intern. Med. 22, 841–846 (2007).
pubmed: 17503110 pmcid: 2219871 doi: 10.1007/s11606-007-0163-y
Moran, A. et al. Financial incentives increase purchases of fruit and vegetables among lower-income households with children. Health Aff. 38, 1557–1566 (2019).
doi: 10.1377/hlthaff.2018.05420
Moran, A. J. et al. Associations between governmental policies to improve the nutritional quality of supermarket purchases and individual, retailer, and community health outcomes: An integrative review. Int. J. Environ. Res. Public Health 17, 7493 (2020).
pmcid: 7602424 doi: 10.3390/ijerph17207493
Phipps, E. J. et al. Impact of a rewards-based incentive program on promoting fruit and vegetable purchases. Am. J. Public Health 105, 166–172 (2015).
pubmed: 24625144 pmcid: 4265942 doi: 10.2105/AJPH.2013.301752
Steele-Adjognon, M. & Weatherspoon, D. Double up food bucks program effects on snap recipients’ fruit and vegetable purchases. BMC Public Health 17, 1–7 (2017).
doi: 10.1186/s12889-017-4942-z
Wilde, P., Klerman, J. A., Olsho, L. E. & Bartlett, S. Explaining the impact of usda’s healthy incentives pilot on different spending outcomes. Appl. Economic Perspect. Policy 38, 655–672 (2015).
doi: 10.1093/aepp/ppv028
Polacsek, M. et al. A supermarket double-dollar incentive program increases purchases of fresh fruits and vegetables among low-income families with children: the healthy double study. J. Nutr. Educ. Behav. 50, 217–228 (2018).
pubmed: 29126661 doi: 10.1016/j.jneb.2017.09.013
Cummins, S., Flint, E. & Matthews, S. A. New neighborhood grocery store increased awareness of food access but did not alter dietary habits or obesity. Health Aff. 33, 283–291 (2014).
doi: 10.1377/hlthaff.2013.0512
Dubowitz, T. et al. Changes in diet after introduction of a full service supermarket in a food desert. Health Aff. 34, 1858 (2015).
doi: 10.1377/hlthaff.2015.0667
Zhang, Y. T. et al. Is a reduction in distance to nearest supermarket associated with bmi change among type 2 diabetes patients? Health Place 40, 15–20 (2016).
pubmed: 27160530 pmcid: 4940213 doi: 10.1016/j.healthplace.2016.04.008
Rogus, S., Athens, J., Cantor, J. & Elbel, B. Measuring micro-level effects of a new supermarket: do residents within 0.5 mile have improved dietary behaviors? J. Acad. Nutr. Dietetics 118, 1037–1046 (2018).
doi: 10.1016/j.jand.2017.06.360
Chrisinger, B. A mixed-method assessment of a new supermarket in a food desert: contributions to everyday life and health. J. Urban Health 93, 425–437 (2016).
pubmed: 27197735 pmcid: 4899338 doi: 10.1007/s11524-016-0055-8
Giang, T., Karpyn, A., Laurison, H. B., Hillier, A. & Perry, R. D. Closing the grocery gap in underserved communities: the creation of the pennsylvania fresh food financing initiative. J. Public Health Manag. Pract. 14, 272–279 (2008).
pubmed: 18408552 doi: 10.1097/01.PHH.0000316486.57512.bf
Jack, D. et al. Socio-economic status, neighbourhood food environments and consumption of fruits and vegetables in new york city. Public health Nutr. 16, 1197–1205 (2013).
pubmed: 23388104 pmcid: 3696996 doi: 10.1017/S1368980012005642
Strome, S., Johns, T., Scicchitano, M. J. & Shelnutt, K. Elements of access: the effects of food outlet proximity, transportation, and realized access on fresh fruit and vegetable consumption in food deserts. Int. Q. Community Health Educ. 37, 61–70 (2016).
pubmed: 28038499 doi: 10.1177/0272684X16685252
Cantor, J. et al. Snap participants improved food security and diet after a full-service supermarket opened in an urban food desert: Study examines impact grocery store opening had on food security and diet of supplemental nutrition assistance program participants living in an urban food desert. Health Aff. 39, 1386–1394 (2020).
doi: 10.1377/hlthaff.2019.01309
Chandola, T., Clarke, P., Morris, J. & Blane, D. Pathways between education and health: a causal modelling approach. J. R. Stat. Soc.: Ser. A169, 337–359 (2006).
doi: 10.1111/j.1467-985X.2006.00411.x
Cutler, D. M. & Lleras-Muney, A. Education and health: evaluating theories and evidence. Working Paper Series, Working Paper 12352 National Bureau of Economic Research. http://www.nber.org/papers/w12352 (2006).
Kenkel, D. S. Health behavior, health knowledge, and schooling. J. Political Econ. 99, 287–305 (1991).
doi: 10.1086/261751
Fletcher, J. M. & Frisvold, D. E. Higher education and health investments: does more schooling affect preventive health care use? J. Hum. Cap. 3, 144–176 (2009).
pubmed: 22368727 pmcid: 3285406 doi: 10.1086/645090
Lleras-Muney, A. The relationship between education and adult mortality in the united states. Rev. Economic Stud. 72, 189–221 (2005).
doi: 10.1111/0034-6527.00329
Mirowsky, J. Education, Social Status, and Health (Routledge, 2017).
CDC. Behavioral risk factor surveillance system survey questionnaire. https://www.cdc.gov/brfss . Accessed 18 Aug 2017.
Weeks, W. B. et al. Differences in health-related quality of life in rural and urban veterans. Am. J. Public Health 94, 1762–1767 (2004).
pubmed: 15451747 pmcid: 1448531 doi: 10.2105/AJPH.94.10.1762
Cromartie, J. Rural-urban commuting area codes (2020). https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx .
Ferris, H. A., Isganaitis, E. & Brown, F. Time for an end to juice in the special supplemental nutrition program for women, infants, and children. JAMA pediatrics 171, 509–510 (2017).
pubmed: 28437541 doi: 10.1001/jamapediatrics.2017.0134
Caswell, H. The role of fruit juice in the diet: an overview. Nutr. Bull. 34, 273–288 (2009).
doi: 10.1111/j.1467-3010.2009.01760.x
Guasch-Ferré, M. & Hu, F. B. Are fruit juices just as unhealthy as sugar-sweetened beverages? JAMA Netw. Open 2, e193109–e193109 (2019).
pubmed: 31099854 doi: 10.1001/jamanetworkopen.2019.3109
Bureau, U. C. American community survey 5-year estimates (2014). http://censusreporter.org . Accessed 30 Aug 2017.
Buajitti, E., Chiodo, S. & Rosella, L. C. Agreement between area-and individual-level income measures in a population-based cohort: implications for population health research. SSM-Popul. Health 10, 100553 (2020).
pubmed: 32072008 pmcid: 7013127 doi: 10.1016/j.ssmph.2020.100553
Rhone, A. Food access research atlas documentation (2015). https://www.ers.usda.gov/data-products/food-access-research-atlas/documentation/ . Accessed 20 Aug 2017.
Din, A. HUD USPS Zip Code Crosswalk Files (2017). https://www.huduser.gov/portal/datasets/usps_crosswalk.html . Accessed 10 Aug 2017.
Yelp. Yelp Fusion V3 API (2017). https://www.yelp.com/dataset/documentation/main/ . Accessed 30 Aug 2017.
Efron, B. & Tibshirani, R. J. An introduction to the bootstrap (CRC press, 1994).
Austin, P. C. & Small, D. S. The use of bootstrapping when using propensity-score matching without replacement: a simulation study. Stat. Med. 33, 4306–4319 (2014).
pubmed: 25087884 pmcid: 4260115 doi: 10.1002/sim.6276
Diamond, A. & Sekhon, J. S. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies. Rev. Econ. Stat. 95, 932–945 (2013).
doi: 10.1162/REST_a_00318
Teixeira, V., Voci, S. M., Mendes-Netto, R. S. & da Silva, D. G. The relative validity of a food record using the smartphone application MyFitnessPal. Nutr. Dietetics 75, 219–225 (2018).
doi: 10.1111/1747-0080.12401
Griffiths, C., Harnack, L. & Pereira, M. A. Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications. Public Health Nutr. 21, 1495–1502 (2018).
pubmed: 29534771 doi: 10.1017/S1368980018000393
Chen, J., Berkman, W., Bardouh, M., Ng, C. Y. K. & Allman-Farinelli, M. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition 57, 208–216 (2019).
pubmed: 30184514 doi: 10.1016/j.nut.2018.05.003
Nguyen, Q. et al. Social media indicators of the food environment and state health outcomes. Public Health 148, 120–128 (2017).
pubmed: 28478354 doi: 10.1016/j.puhe.2017.03.013
Gomez-Lopez, I. N. et al. Using social media to identify sources of healthy food in urban neighborhoods. J. Urban Health 94, 429–436 (2017).
pubmed: 28455606 pmcid: 5481219 doi: 10.1007/s11524-017-0154-1
Einav, L., Leibtag, E. S. & Nevo, A. On the accuracy of Nielsen homescan data. Economic Research Report No. ERR-69. https://www.ers.usda.gov/publications/pub-details/?pubid=46114 (2008).
for Disease Control, C., Prevention et al. Behavioral risk factor surveillance system 2019 summary data quality report. july 16, 2020 (2020).
Mokdad, A. H. The behavioral risk factors surveillance system: past, present, and future. Annu. Rev. Public Health 30, 43–54 (2009).
pubmed: 19705555 doi: 10.1146/annurev.publhealth.031308.100226
Pierannunzi, C., Hu, S. S. & Balluz, L. A systematic review of publications assessing reliability and validity of the behavioral risk factor surveillance system (brfss), 2004–2011. BMC Med. Res. Methodol. 13, 1–14 (2013).
doi: 10.1186/1471-2288-13-49
Austin, P. C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar. Behav. Res. 46, 399–424 (2011).
doi: 10.1080/00273171.2011.568786
Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci.: A Rev. J. Inst. Math. Stat. 25, 1 (2010).
doi: 10.1214/09-STS313
Glass, G. V. Primary, secondary, and meta-analysis of research. Educ. Researcher 5, 3–8 (1976).
doi: 10.3102/0013189X005010003
US Zip Codes History (2012). https://www.zip-codes.com/zip-codes-history.asp . Accessed 11 May 2021.
USPS Postal Facts (2021). https://facts.usps.com/42000-zip-codes/ . Accessed 11 May 2021.
US Census 2012 (2012). https://web.archive.org/web/20130707052113/http://www.census.gov/popest/data/counties/totals/2012/CO-EST2012-alldata.html/ . Accessed 11 May 2021.
Rundle, A. et al. Personal and neighborhood socioeconomic status and indices of neighborhood walk-ability predict body mass index in new york city. Soc. Sci. Med. 67, 1951–1958 (2008).
pubmed: 18954927 pmcid: 2735120 doi: 10.1016/j.socscimed.2008.09.036

Auteurs

Tim Althoff (T)

Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA. althoff@cs.washington.edu.

Hamed Nilforoshan (H)

Department of Computer Science, Stanford University, Stanford, CA, USA.

Jenna Hua (J)

Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Million Marker Wellness Inc., San Francisco, CA, USA.

Jure Leskovec (J)

Department of Computer Science, Stanford University, Stanford, CA, USA.
Chan Zuckerberg Biohub, San Francisco, CA, USA.

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