This readme.txt file was generated on <2019-11-18> by ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset: Evaluation of the first U.S. staple foods ordinance: Impact on nutritional quality of food retailer offerings, customer purchases and home food environments 2. Author Information Principal Investigator Contact Information Name: Melissa N. Laska Institution: University of Minnesota, School of Public Health, Division of Epidemiology and Community Health Address: 1300 S. 2nd Street, Suite 300, Minneapolis, MN 55454 Email: mnlaska@umn.edu Associate or Co-investigator Contact Information Name: Caitlin E. Caspi Institution: University of Minnesota, Medical School, Department of Family Medicine and Community Health Address: 717 Delaware Street SE, Rm 183, Minneapolis, MN 55414 Email: cecaspi@umn.edu Associate or Co-investigator Contact Information Name: Kathleen Lenk Institution: University of Minnesota, School of Public Health, Division of Epidemiology and Community Health Address: 1300 S. 2nd Street, Suite 300, Minneapolis, MN 55454 Email: lenk@umn.edu Associate or Co-investigator Contact Information Name: Stacey G. Moe Institution: University of Minnesota, School of Public Health, Division of Epidemiology and Community Health Address: 1300 S. 2nd Street, Suite 300, Minneapolis, MN 55454 Email: moe@umn.edu Associate or Co-investigator Contact Information Name: Jennifer E. Pelletier Institution: Professional Data Analysts Address: 219 Main Street SE, Suite 302, Minneapolis, MN 55414 Email: jpelletier@pdastats.com Associate or Co-investigator Contact Information Name: Lisa J. Harnack Institution: University of Minnesota, School of Public Health, Division of Epidemiology and Community Health Address: 1300 S. 2nd Street, Suite 300, Minneapolis, MN 55454 Email: harna001@umn.edu Associate or Co-investigator Contact Information Name: Darin J. Erickson Institution: University of Minnesota, School of Public Health, Division of Epidemiology and Community Health Address: 1300 S. 2nd Street, Suite 300, Minneapolis, MN 55454 Email: erick232@umn.edu 3. Date of data collection: 2014 - 2017 4. Geographic location of data collection: Minneapolis, MN, and St. Paul, MN 5. Information about funding sources that supported the collection of the data: National Institute of Diabetes and Digestive and Kidney Diseases (R01DK104348); Centers for Disease Control and Prevention (U48DP005022); Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD070725); National Institutes of Health (5R25CA163184); National Center for Advancing Translational Science (UL1TR000114) -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) [https://creativecommons.org/licenses/by-nc-sa/4.0/] 2. Links to publications that cite or use the data: Laska, Melissa N., Caspi, Caitlin E., Lenk, Kathleen, Moe, Stacey G., Pelletier, Jennifer E., Harnack, Lisa J., & Erickson, Darin J. (2019). Evaluation of the first U.S. staple foods ordinance: Impact on nutritional quality of food store offerings, customer purchases and home food environments. The International Journal of Behavioral Nutrition and Physical Activity, 16(1), 83. https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-019-0818-1 3. Recommended citation for the data: Laska, Melissa N; Caspi, Caitlin E; Lenk, Kathleen; Moe, Stacey G; Pelletier, Jennifer E; Harnack, Lisa J; Erickson, Darin J. (2019). Data to accompany evaluation of the first U.S. staple foods ordinance: Impact on nutritional quality of food retailer offerings, customer purchases and home food environments. Retrieved from the Data Repository for the University of Minnesota, http://hdl.handle.net/11299/205351. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: Store_Assess.sas7bdat Short description: SAS data file containing the Store Assessment data reported on in the primary outcomes paper. Note that store ID numbers are not always numerically sequential because a store may have been deemed ineligible or dropped out over time; likewise, some stores do not have data for all variables at all time points. B. Filename: customer_intercept.sas7bdat Short description: SAS data file containing the Customer Intercept Interview data reported on in the primary outcomes paper. Note that store ID numbers are not always numerically sequential because a store may have been deemed ineligible or dropped out over time; likewise, some stores do not have data for all variables at all time points. C. Filename: homevisit.sas7bdat Short description: SAS data file containing the Home Visit data reported on in the primary outcomes paper. Note that store ID numbers and participant ID numbers are not always numerically sequential because a store or participant may have been deemed ineligible or dropped out over time; likewise, some stores and some participants do not have data for all variables at all time points. D. store_assess.csv customer_intercept.csv homevisit.csv readme.txt These files in 1.D. were added by the DRUM curator as supplementary files. The csv files contain the same data as the .sas7bdat files in non-proprietary format. The readme was created by the researchers and added by the curator. 2. Relationship between files: Home visit data are from participants who completed an intercept interview and reported shopping at that store at least once per week; however, homevisit.sas7bdat and customer_intercept.sas7bdat cannot be linked at the individual level given that customer intercept interviews were anonymous. Store level data in Store_Assess.sas7bdat and customer intercept data in customer_intercept.sas7bdat can be linked using store ID numbers. Note that more than one customer was often interviewed at each store at each assessment time point. 3. Additional related data collected that was not included in the current data package: N/A -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: The following summary was taken from the following paper: Laska MN, Caspi CE, Lenk K, Moe SG, Pelletier JE, Harnack LJ, Erickson DJ. Evaluation of the first U.S. staple foods ordinance: impact on nutritional quality of food store offerings, customer purchases, and home food environments. International Journal of Behavioral Nutrition and Physical Activity. 2019 Sep 18;16(1):83. Can be found online at: doi: 10.1186/s12966-019-0818-1. PMCID: PMC6751624; https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-019-0818-1 The study was designed to gather data at four times points: pre-policy revisions phase (July–December 2014, time 1), implementation-only phase (no enforcement; September–October 2015, time 2), enforcement initiation phase (May–July 2016, time 3), and continued monitoring phase (August–December 2017, time 4). Our work compared changes in stores in Minneapolis to those in St.Paul, MN, a similarly-sized city with no such ordinance (i.e., control city). We identified stores through government lists of stores with grocery licenses. Stores were ineligible for the evaluation if they: (1) were supermarkets; (2) were WIC-authorized (because they were presumed to already meet minimum requirements); (3) had invalid licensing addresses; or (4) were exempt from the ordinance (e.g., small vendors in market areas, liquor stores, or specialty stores). Of 255 eligible stores, 180 (90 per city) were randomly selected. After visiting stores prior to data collection, 20 additional stores were deemed ineligible. Of the remaining 160 retailers, 159 actively gave consent and participated in the study at one or more of the four data collection time points. Store environments were assessed using a modified instrument from the Yale Rudd Center [24]. The Rudd Center instrument is similarly structured to the validated Nutrition Environment Measure Survey in Stores (NEMS-S) instrument [6, 25]. Both have lists of items in specific package sizes for which availability, price, and (for fresh fruits/vegetables) quality is recorded. In this study, the list of items was adapted to align with the ordinance requirements [26, 27]. Our store assessment evaluated the availability and price of 69 items, including fresh, frozen and canned fruits and vegetables with no added ingredients (other than salt in canned products), 100% juice, whole grain-rich bread, whole-wheat or corn tortillas, brown rice, whole grain-rich cereals in packages ≥12 oz., low-fat milk/milk substitutes, dry beans and lentils in packages ≤16 oz., cheese in packages ≥8 oz., eggs in dozen containers, plain nut butters in ≤18 oz. containers, canned fish in water, and tofu, as well as some less healthy comparison items (e.g., white bread, whole milk). It also addressed varieties of milk; fresh, frozen and canned fruits and vegetables; cheese; canned beans; whole grain-rich cereals; whole grain-rich bread; brown rice; and whole grain tortillas as well as the quality of twenty fresh fruits and vegetables. We conducted customer intercept interviews with shoppers exiting stores to evaluate nutritional quality of food/beverage purchases. After asking for and receiving store approval, data collectors stood outside near the store exit and invited customers who appeared ≥18 years old and had a bag or a visible food/beverage purchase to participate in the interview. After verifying eligibility and obtaining consent, data collectors recorded participants’ food and beverage purchases (quantity, size, product name, and price paid) and administered a 5-min structured interview, which included questions addressing customers’ shopping frequency at that store. The survey concluded with participants’ demographic information and self-reported height and weight. During the intercept interviews outside of stores, participants were asked “how often do you shop at this store?,” with 8 possible frequency response options ranging from “more than once a day” to “less than once a month.” Those who reported shopping at the store at least once per week or more were additionally invited to participate in a longitudinal sub-study that included an objective home food environment assessment, interviews and questionnaires completed in participants’ homes at a later date. Participants were recruited from both Minneapolis and St. Paul and were contacted to complete home assessments at each of the four time points. Trained study staff visited participants’ homes and completed an assessment of the home food environment at each time point using a previously validated tool [37]. The Home Food Inventory (HFI) included approximately 200 items across 13 categories and were in a checklist format with yes/no (1/0) response options, indicating whether the items were present in the home. In addition, staff recorded whether the vegetable, fruit, and bread items were fresh, frozen, dried or canned, as appropriate. Foods in the dairy, added fats, frozen desserts, prepared desserts, and savory snacks categories were categorized into regular-fat or reduced-fat groupings; beverages were categorized into regular sugar and low sugar categories; and foods in the two ready access categories were further sub-grouped into more healthful and less healthful categories. Although the categorization of foods into healthful and less healthful categories is not straightforward, the HFI is used to assess each food by its typical fat and sugar content when determining its category. 2. Methods for processing the data: Data collectors recorded data on hard copy (paper) forms while in the field. Hard copies were reviewed for clarity and detection/reconciliation of errors and were then manually data entered by a data entry service. 3. Describe any quality-assurance procedures performed on the data: Data collectors recorded data on hard copy (paper) forms while in the field. Hard copies were reviewed for clarity and detection/reconciliation of errors and were then manually data entered by a data entry service. Once data entry was complete, an extensive review of and quality control checks were administered on a random sample of the data. Any discrepancies, errors, or differences found were reconciled prior to inclusion in the final data set. 4. People involved with sample collection, processing, analysis and/or submission: Stacey Moe (study coordinator) and Pamela Carr-Manthe (evaluation coordinator) directly oversaw data collection teams. Data were entered through a contract with Northwest Keypunch Inc. Bill Baker (data programmer) processed the entered data and led quality control checks with Ms. Moe and Ms. Carr-Manthe. Final analyses and analytic datasets were prepared by Kath Lenk (senior data analyst). All processes were overseen by Melissa Laska (study PI). ------------------------- DATA-SPECIFIC INFORMATION ------------------------- See data dictionary file: STORE_Variables Included in Datasets.pdf