TY - JOUR AU - Mamiya, Hirosi AU - Moodie, Erica AU - Buckeridge, David PY - 2017/05/02 Y2 - 2024/03/29 TI - Estimating spatial patterning of dietary behaviors using grocery transaction data JF - Online Journal of Public Health Informatics JA - OJPHI VL - 9 IS - 1 SE - Non-Infectious Disease Surveillance Use Cases DO - 10.5210/ojphi.v9i1.7715 UR - https://ojphi.org/ojs/index.php/ojphi/article/view/7715 SP - AB - <div style="left: 78.2727px; top: 278.962px; font-size: 12.3207px; font-family: sans-serif; transform: scaleX(1.0823);" data-canvas-width="55.467823232323234">Objective</div><div style="left: 91.3182px; top: 291.965px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03473);" data-canvas-width="332.26975656565656">To demonstrate a method for estimating neighborhood food</div><div style="left: 78.2727px; top: 306.46px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.01293);" data-canvas-width="344.8504305555552">selection with secondary use of digital marketing data; grocery</div><div style="left: 78.2727px; top: 320.955px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00482);" data-canvas-width="231.3089545454545">transaction records and retail business registry.</div><div style="left: 78.2727px; top: 348.538px; font-size: 12.3207px; font-family: sans-serif; transform: scaleX(1.11884);" data-canvas-width="71.86668434343434">Introduction</div><div style="left: 91.3182px; top: 361.541px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03906);" data-canvas-width="332.42992575757575">Unhealthy diet is becoming the most important preventable</div><div style="left: 78.2727px; top: 376.036px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.978106);" data-canvas-width="344.0323356060604">cause of chronic disease burden (1). Dietary patterns vary across</div><div style="left: 78.2727px; top: 390.531px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.981353);" data-canvas-width="343.68612373737363">neighborhoods as a function of policy, marketing, social support,</div><div style="left: 78.2727px; top: 405.026px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.989038);" data-canvas-width="344.0039979797982">economy, and the commercial food environment (2). Assessment</div><div style="left: 78.2727px; top: 419.521px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00328);" data-canvas-width="344.27505353535366">of community-specific response to these socio-ecological factors</div><div style="left: 78.2727px; top: 434.016px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.967742);" data-canvas-width="343.3546967171719">is critical for the development and evaluation policy interventions</div><div style="left: 78.2727px; top: 448.511px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00239);" data-canvas-width="344.2208424242424">and identification of nutrition inequality. Mass administration of</div><div style="left: 78.2727px; top: 463.006px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.994836);" data-canvas-width="342.00804343434356">dietary surveys is impractical and prohibitory expensive, and surveys</div><div style="left: 78.2727px; top: 477.501px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.989589);" data-canvas-width="341.91933434343434">typically fail to address variation of food selection at high geographic</div><div style="left: 78.2727px; top: 491.996px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.985594);" data-canvas-width="343.9559472222221">resolution. Marketing companies such as the Nielsen cooperation</div><div style="left: 78.2727px; top: 506.491px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.02547);" data-canvas-width="344.88123232323244">continuously collect and centralize scanned grocery transaction</div><div style="left: 78.2727px; top: 520.985px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.964499);" data-canvas-width="343.5222583333331">records from a geographically representative sample of retail food</div><div style="left: 78.2727px; top: 535.48px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.01723);" data-canvas-width="342.7719272727272">outlets to guide product promotions. These data can be harnessed to</div><div style="left: 78.2727px; top: 549.975px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.951391);" data-canvas-width="343.45203030303054">develop a model for the demand of specific foods using store and</div><div style="left: 78.2727px; top: 564.47px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.01847);" data-canvas-width="342.83476287878796">neighborhood attributes, providing a rich and detailed picture of the</div><div style="left: 78.2727px; top: 578.965px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.02758);" data-canvas-width="342.97152272727266">“foodscape” in an urban environment. In this study, we generated a</div><div style="left: 78.2727px; top: 593.46px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.025);" data-canvas-width="342.94688131313126">spatial profile of food selection from estimated sales in food outlets</div><div style="left: 78.2727px; top: 607.955px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.01905);" data-canvas-width="344.8812323232324">in the Census Metropolitan Area (CMA) of Montreal, Canada,</div><div style="left: 78.2727px; top: 622.45px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.02066);" data-canvas-width="342.87295707070666">using regular carbonated soft drinks (i.e. non-diet soda) as an initial</div><div style="left: 78.2727px; top: 636.945px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00163);" data-canvas-width="44.822732323232316">example.</div><div style="left: 78.2727px; top: 664.528px; font-size: 12.3207px; font-family: sans-serif; transform: scaleX(1.07424);" data-canvas-width="50.65042676767676">Methods</div><div style="left: 91.3182px; top: 677.531px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03967);" data-canvas-width="332.25989999999996">From the Nielsen cooperation, we obtained weekly grocery</div><div style="left: 78.2727px; top: 692.026px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03665);" data-canvas-width="342.95920202020176">transaction data generated by a sample of 86 grocery stores and 42</div><div style="left: 78.2727px; top: 706.521px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03515);" data-canvas-width="343.1711181818181">pharmacies in the Montreal CMA in 2012. Extracted store-specific</div><div style="left: 78.2727px; top: 721.016px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.961284);" data-canvas-width="343.723085858586">soda sales were standardized to a single serving size (240ml) and</div><div style="left: 78.2727px; top: 735.511px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.0161);" data-canvas-width="342.73003686868685">averaged across 52 weeks, resulting in 128 data points. Using linear</div><div style="left: 78.2727px; top: 750.006px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.969354);" data-canvas-width="343.587558080808">regression, natural log-transformed soda sales were modelled as a</div><div style="left: 78.2727px; top: 764.501px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.02569);" data-canvas-width="343.14401262626245">function of store type (grocery vs. pharmacies), chain identification</div><div style="left: 78.2727px; top: 778.996px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00275);" data-canvas-width="342.32222146464653">code and socio-demographic attributes of store neighborhood, which</div><div style="left: 78.2727px; top: 793.491px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.04017);" data-canvas-width="343.37810606060606">are median family income, proportion of individuals who received</div><div style="left: 78.2727px; top: 807.985px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00389);" data-canvas-width="342.3764325757575">post-secondary diplomas, and population density as measured by the</div><div style="left: 78.2727px; top: 822.48px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.968722);" data-canvas-width="343.4766717171719">2011 Canadian Household Survey. Selection of the predictors and</div><div style="left: 78.2727px; top: 836.975px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03681);" data-canvas-width="342.99370000000016">first-order interaction terms was guided by the minimization of the</div><div style="left: 78.2727px; top: 851.47px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03107);" data-canvas-width="343.1908313131311">mean squared error using 10-fold cross-validation. The final model</div><div style="left: 78.2727px; top: 865.965px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.02678);" data-canvas-width="342.8433873737372">was applied to all operating chain grocery stores and pharmacies in</div><div style="left: 78.2727px; top: 880.46px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00926);" data-canvas-width="342.5945090909091">2012 (n=980) recorded in a comprehensive and commonly available</div><div style="left: 78.2727px; top: 894.955px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.03558);" data-canvas-width="339.41207045454496">business establishment database. The resulting predicted store-</div><div style="left: 78.2727px; top: 909.45px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00544);" data-canvas-width="344.36376262626294">specific weekly average soda sales was spatially interpolated to</div><div style="left: 78.2727px; top: 923.945px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.01501);" data-canvas-width="342.7189482323233">provide a graphical representation of the soda sales (representing an</div><div style="left: 78.2727px; top: 938.44px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.0012);" data-canvas-width="240.20450505050508">unhealthy foodscape) across the Montreal CMA.</div><div style="left: 78.2727px; top: 966.023px; font-size: 12.3207px; font-family: sans-serif; transform: scaleX(1.08542);" data-canvas-width="44.50239393939395">Results</div><div style="left: 91.3182px; top: 979.026px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.99225);" data-canvas-width="328.93084494949494">Figure 2 demonstrates the spatial distribution of the predicted soda</div><div style="left: 78.2727px; top: 993.521px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00118);" data-canvas-width="137.22803535353535">sales in the Montreal CMA.</div><div style="left: 78.2727px; top: 1021.1px; font-size: 12.3207px; font-family: sans-serif; transform: scaleX(1.10408);" data-canvas-width="73.93656313131314">Conclusions</div><div style="left: 91.3182px; top: 1034.11px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.04653);" data-canvas-width="332.6578588383836">The current lack of neighborhood-level dietary surveillance</div><div style="left: 78.2727px; top: 1048.6px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.996699);" data-canvas-width="342.10044873737377">impedes effective public health actions aimed at encouraging healthy</div><div style="left: 443.545px; top: 277.47px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00909);" data-canvas-width="344.57075050505046">food selection and subsequent reduction of chronic illness. Our</div><div style="left: 443.545px; top: 291.965px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.996053);" data-canvas-width="342.0942883838384">method leverages existing grocery transaction data and store location</div><div style="left: 443.545px; top: 306.46px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.0151);" data-canvas-width="342.7472858585858">information to address the gap in population monitoring of nutrition</div><div style="left: 443.545px; top: 320.955px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.99939);" data-canvas-width="342.2076388888888">status and urban foodscapes. Future applications of our methodology</div><div style="left: 443.545px; top: 335.45px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.998163);" data-canvas-width="344.59908813131307">to other store types (e.g. convenience stores) and food products</div><div style="left: 443.545px; top: 349.945px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.970217);" data-canvas-width="343.58632601010095">across multiple time points (e.g. mouths and years) will permit a</div><div style="left: 443.545px; top: 364.44px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.02458);" data-canvas-width="343.11321085858583">comprehensive, timely and automated assessment of dietary trends,</div><div style="left: 443.545px; top: 378.935px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.98973);" data-canvas-width="341.93535126262617">identification of neighborhoods in special dietary needs, development</div><div style="left: 443.545px; top: 393.43px; font-size: 12.3207px; font-family: serif; transform: scaleX(0.960832);" data-canvas-width="343.1932954545453">of tailored community health promotions, and the measurement of</div><div style="left: 443.545px; top: 407.925px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.01669);" data-canvas-width="342.91361540404046">neighbourhood-specific response to nutrition policies and unhealthy</div><div style="left: 443.545px; top: 422.42px; font-size: 12.3207px; font-family: serif; transform: scaleX(1.00157);" data-canvas-width="83.49743181818182">food advertising.</div> ER -