|
||||||||||||||||
|
||||||||||||||||
The following HTML text is provided to enhance online readability. Many aspects of typography translate only awkwardly to HTML. Please use the page image as the authoritative form to ensure accuracy. Appendix K
|
||||||||||||||||
 |
 |
Page 467 |
 |
 |
 |
|
| ||||||||||||||||||||||||||||||||
|
|
|||||||||||||||||||||||||||||||
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 467
Strategies to Reduce Sodium Intake in the United States
Appendix K
Approach to Linking Universal Product Code (UPC) Sales Data to the Nutrition Facts Panel
Research is needed to better track the sodium content of the food supply. Commercial operations provide universal product code (UPC) level data including weights that allow estimation of the total sales of each UPC level food during a specified time (e.g., weekly, quarterly, annually). In addition, such companies also maintain household panels that provide data on their purchases by rescanning all food purchases and transmitting the data on an ongoing basis. Purchase data from these household panels are projected to the U.S. population using a statistical weighting procedure. Detailed information on the characteristics of the households that participate in the panels is also available and could be used to analyze differences in the content of sodium purchases by different portions of the population. In turn, both the store scanner data and the household-based scanner data can be linked to nutrient information from the Nutrition Facts panel using data maintained by an outside vendor. The nutrient content data are provided at the UPC level and thus can be linked to the scanner data. Additional analyses may be required to add nutrient content for UPCs that are represented in the nutrient databases for one but not all package sizes for a particular brand name and product size. Developers may deem it necessary to select target food categories and focus on the top-selling products to facilitate periodic updates over time. For example, the tracking analysis could focus on the top brand representing some percentage of the sales for representative categories of foods.
The primary advantage of this approach is that it can be accomplished without requiring additional reporting or cooperation from food manufacturers or retailers. Furthermore, it can be an economical method of moni-
OCR for page 468
Strategies to Reduce Sodium Intake in the United States
toring sources of sodium in large portions of the food supply and can be scaled based on the availability of resources for conducting the analysis. Once key targets are identified and the methodology is established, this method could provide data on trends over time by individual food categories. However, the analysis will have some limitations that may have to be addressed using other sources. In particular, private label products that are contract-manufactured for the major retail chains are included in the store scanner data, but it may be infeasible to link these foods to nutrient data. The analysis will likely need to focus on the top-selling products based on the availability of nutrient data at the UPC level from outside vendors. Foods that are prepared and packaged within a retail establishment are not currently required to include the Nutrition Facts panel; thus, the sodium content of these foods cannot be monitored without linking through other types of data sources that would require substantially more manual effort. Not all stores participate in store scanner data reporting (e.g., Wal-Mart), but product sales from these stores can be tracked using household-based scanner data because at least a portion of the household panel purchases items at stores not currently captured in store scanner data.