Evaluation of the US Army Institute of Public Health Destination Monitoring Program, a food safety surveillance program.
Each year, foodborne diseases cause an estimated 48 million illnesses in the United States, with an estimated 9.4 million caused by 31 major pathogens. (1-3) Relatively few foodborne illnesses are associated with an outbreak, and even fewer can be attributed to a specific etiologic agent or food product. Given the potential impact of a foodborne illness outbreak and the resultant negative effect on mission readiness and national security, the military employs several programs aimed at protecting the food supply. (4) These programs operate on many levels from acquisition to consumption, with the objective of providing broad, overall protection from foodborne pathogens and contamination. As part of this overall goal, the US Army Public Health Command's (APHC) Destination Monitoring Program was developed as an active food surveillance program with the primary goal of verifying the effectiveness of food safety systems and providing primary prevention against foodborne disease. (4)
The Army Institute of Public Health Veterinary Services (AIPH-VS) is responsible for operating the Destination Monitoring Program, illustrated in Figure 1. The AIPH-VS determines, on a quarterly basis, what types and how many food items should be collected and submitted from all regions and districts within those regions (COL T. Honadel, oral communication, December 11, 2013). Leadership at the district level assigns sampling to each installation and facility within that district. Veterinary Food Inspection Specialists assigned to these installations then select, collect, prepare, and ship food samples to the Food Analysis Diagnostic Laboratory (FADL) at Joint Base San Antonio Fort Sam Houston, Texas. The FADL performs testing on the food samples according to published guidelines. (5,6) The FADL is accredited by the American Association for Laboratory Accreditation, and thus is capable of confirming positive microbial and chemical tests. Samples that test positive for zero tolerance pathogenic organisms are reported to the Texas State Department of Health for pulse field gel electrophoresis "finger-printing" (CW3 J. D. Mitchell, email, November 7, 2014). All laboratory results are then loaded into the Veterinary Services Lotus Notes database, an internal database used for tracking animal health and food safety within the APHC. Positive test results are sent back to the submitter, the region, and the AIPH-VS (Dr R. Benisch, oral communication, December 11, 2013). The district headquarters of the submitting branch is responsible for actively reviewing the final laboratory report within the database for verification of sampling accuracy, to include food processor name, processing plant location, lot/production code, and product size.
A number of actions may occur in response to a positive test result, depending on whether the nonconforming result is due to a pathogenic organism such as Salmonella spp or Escherichia coli O157:H7, an indicator organism shown by total coliforms or psychrotrophic count, or another measure of food quality or safety such as mercury or pH. The AIPH-VS evaluates the potential risk to public health, and collaborates with DoD procurement agencies when determining if a product should be recalled or if the manufacturing plant should be suspended. The AIPH-VS also notifies the commercial establishment and appropriate regulatory agency in the case of a pathogen positive result (COL T. Honadel, oral communication, December 11, 2013). Nonconforming results typically will result in scheduling of a directed food protection audit of the commercial establishment, and additional food samples may be collected and tested. (7)
The Destination Monitoring Program has several goals, an important one of which is to ensure that food procured by the DoD is safe for consumers. With limited resources, this is best accomplished by testing the foods that present the greatest risk of contamination with pathogens that cause the most severe illnesses. As the DoD procures a wide variety of food items for consumption by military personnel and beneficiaries, a second objective is to ensure that many different items are tested to adequately represent all military installations. Finally, the program is enhanced when all personnel involved in food sampling and testing receive adequate training, especially considering the wide variety of food items procured by the DoD. This often influences the type of food items that are requested for testing, which may not necessarily reflect foods that present the greatest risk. All 3 goals were identified by AIPH-VS as important to the success of the program (COL T. Honadel, oral communication, May 6, 2014). However, achievement of all goals, especially in the context of limited resources, may not be possible, and prioritization will help determine the best methods to conduct the program. This assessment was focused on the goal of identifying and testing high-risk food items with the purpose of ensuring food safety and preventing foodborne illness.
The objective of this study was to evaluate the processes currently utilized by the APHC to identify, collect, and submit food samples for laboratory testing, and assess them for validity and timeliness. Focus was primarily on 3 processes: (1) assessment of sample size and discussion of how sample size relates to statistical power and level of confidence in the system; (2) identification of food samples to be tested and discussion of the risk of foodborne illness associated with different types of food; and (3) assessment of the timeliness of the program, especially regarding the timing of sample submission, reporting of results, and expiration of the food product.
The Uniformed Services University of the Health Sciences Office of Research approved this project.
A descriptive analysis of the Destination Monitoring Program was performed to assess validity and timeliness of the program. Assessment of the program occurred within the APHC Veterinary Services, which both implements the program and has oversight of the veterinary personnel who execute the program at the region, district, and branch levels.
Analysis was limited to existing data from the US Army Veterinary Services Lotus Notes database provided by the AIPH-VS, which consisted of laboratory results and administrative information on food samples collected, submitted, and tested from January 1, 2013 to December 31, 2013. The analysis was limited to samples collected from the veterinary branches that fall under the purview and command of Fort Belvoir, Fort Eustis, and Fort Knox Districts within APHC Region North (APHCR-N). The data consisted of sample request forms sent to the FADL, which list administrative information (district, branch, fiscal year, quarter), information regarding the sample collection (location, date collected/submitted), sample characteristics (brand, category of food, weight, number of samples, expiration, plant code), and information from the laboratory (date received, errors, laboratory results).
All data was manually extracted from the Lotus Notes database and compiled into Microsoft Office Excel 2010. IBM SPSS Statistics 22 was used for the descriptive analyses.
Evaluation of Sample Size
Descriptive statistics were obtained on the number of food samples collected and tested to determine the appropriateness of sample size and the level of confidence that could be expected. This information was used to develop an operating characteristic curve, which relates the probability of concluding a food lot is safe to the proportion of units in the lot that exceed a specified acceptable level based on sample size. (8) Operating characteristic (OC) curves are frequently used in development and assessment of sampling plans, and the process is well documented in the literature. (8-11) They are based on the probability of detecting a contaminated lot based on a number of factors, including level of contamination in the source lot, the number of positive samples desired in order to reject the lot, and the mean and standard deviation of the bacterial concentration in the source lot. In this assessment, the sample size (n) evaluated was based on the median number of sample units (c) selected from each lot. The maximum allowable number of sample units that could test positive for an organism before a lot was rejected was set to zero, as is typical for pathogens. Mean bacterial concentration was converted to a logarithmic scale, with a standard deviation of 0.8 colony-forming units per gram (cfu/g). This standard deviation was selected based on use in the literature to represent typical distribution of bacteria in a heterogeneous solid food. (9-11) E Coli is a common indicator organism, therefore, a hypothetical example using detection of E Coli in fresh fruits and vegetables, with an acceptable limit of less than 10 cfu/g (log 1 cfu/g) was considered. The desired acceptance level was designated at 5%, meaning that the sampling plan identified would be acceptable if it rejected a contaminated lot 95% of the time. For comparison, OC-curves were also constructed to depict the confidence in the sampling plan if the number of samples drawn from each lot was increased to n=2, 5, and 10.
Representative of Risk
To determine if the food items selected as part of the Destination Monitoring Program were representative of the risk of foodborne illness, the frequency and percentage of items tested were determined by food category, as defined by the FADL and APHCR-N, and the frequency and percentage of positive results were tabulated for each category. In addition to the food categories defined by APHCR-N, all food items tested were recategorized according to 17 food commodity groups, based on the nature of the food source and ingredient, as developed by Painter, et al. (1,12) Some items were categorized into more than one of the 17 food commodities if they contained more than one ingredient. (12) Frequency and percentage of food items tested by district and branch were also tabulated. Microbial and chemical testing was summarized by tabulating the number of food items that were tested for each organism or chemical.
Timeliness of the program was determined by using the median days that elapsed from when the food items were submitted to laboratory and when the results were available. Additionally, the median time from when laboratory results were available to when the food item expired was calculated. Median values were selected due to the skewed distribution of values as well as to minimize the effect of outliers.
Evaluation of Sample Size
A total of 668 food samples from APHCR-N were collected and submitted to the FADL for the Destination Monitoring Program in 2013. Of those, 577 (86.4%) were actually tested by the FADL. Duplicate samples submitted for the same item were often not tested unless required by FADL, such as when needed for additional chemical testing or to meet minimum weight requirements for microbial sampling (R. Leo, oral communication, May 20, 2014). The submitted food samples comprised 514 individual food items. The majority of food items submitted and tested contained one sample (73.5%, 89.2% respectively). The number of samples submitted and tested per item ranged from one to 8, with a median of one. Seven items were submitted but not tested at all. Of those not tested, 3 were not received and 4 were not tested due to lost integrity of packaging (broken, leaking). The distribution of number of samples submitted and tested for each food item is shown in Table 1.
As the median number of samples submitted and tested was one, the OC-curves were constructed using n=1 and c=0 (Figure 2). The probability of accepting a lot differed depending on the sample size (n) and the proportion of contamination within a lot. When n= 1 and if the proportion of contamination within the lot was 10%, there is a 90% probability of accepting the lot based on the negative results of that one sample. This probability decreased in a linear fashion as the level of contamination increased. At 50% contamination, the lot is accepted 50% of the time with a sample size of one. In contrast, when the sample size was increased to n=5, and if the proportion of contamination within the lot was 10%, the probability of accepting the lot is 59%. At a contamination level of 50%, the probability of accepting the lot based on 5 negative samples is reduced to 3% (Figure 3). A comparison of the probability of accepting a lot based on a variety of levels of contamination and several hypothetical sampling plans is presented in Table 2.
Representative of Risk
The AIPH-VS requested food items from 14 food categories during 2013. The food groups represent categories of interest as they are considered potentially hazardous foods and give guidance to veterinary personnel in selecting items off the shelf (Table 3). Of the 507 food items collected and tested, the individual category with the most items tested was ground meat (n=78, 15.4%). When categories were collapsed based on food origin, 35.1% of the food items consisted of fresh fruits and vegetables. This included processed fruits and vegetables (12.2%), bagged salads (12%), and whole fresh fruits and vegetables (10.9%). Other categories with significant representation included prepared salad (9.5%), kimchee/tofu (5.7%), and raw seafood (5.5%). Over the period of one year, 3 food items tested positive for indicator organisms. This included one liquid dairy item, which represented 4.4% of all fresh dairy items tested, and 2 unprocessed fresh fruit and vegetable items, representing 3.6% of all items in that category tested.
The frequencies of microbial and chemical testing on food samples were determined. Of the pathogenic bacteria, tests for the presence of Salmonella spp were most frequent (n=350, 69%). Testing for other pathogenic bacteria included Listeria monocytogenes (n=323, 63.7%), Staphylococcus aureus (n=315, 62.1%), and E Coli O157:H7 (n=232, 45.8%). Tests for indicator organisms included E Coli (n=403, 79.5%), total coliforms (n=270, 53.3%), and psychrotrophic count (n=83, 16.4%). Three food samples tested positive for indicator organisms (Table 3), including whole bagged salad and dairy. The dairy food item tested positive for both total coliforms and standard plate count.
The total number of food items requested by AIPH-VS was similar for each district, and were evenly assigned across the branches and sections within each district. Table 1. Comparison of Number of Samples Submitted and Tested by the Department of Defense Food Analysis Diagnostic Laboratory for the Destination Monitoring Program, January 1--December 31, 2013.
Some sections in large districts, such as Fort Belvoir District, were assigned a relatively small number of food items to collect compared to sections in smaller districts (Table 4). For example, the Fort Knox commissary collected a greater total number of food items (10.7%) compared to other larger commissaries in the region, such as Fort Belvoir (1.8%).
The median number of days elapsed between food sample submission to the FADL and availability of laboratory results was 9 days, ranging from 1 to 49 (Table 5). For those food items that had an expiration date (n=424), the median number of days between availability of laboratory results and expiration of the product was 2 days, and ranged from -26 (product expired 26 days before laboratory results reported) to 1,801. When considering items with an expiration date, 46.7% of food items expired before or on the same day that laboratory results were reported. In many cases, these items represent highly perishable foods with a short shelf life. Figure 4 depicts the distribution of days elapsed between date of submission and laboratory results.
The purpose of this evaluation of the APHC's Destination Monitoring Program was to determine the effectiveness, timeliness, and validity of the program and to inform stakeholders and policymakers on the strengths and limitations of the program.
Most food item submissions contained only one food sample. Even if two or more samples were submitted, often only one sample was actually tested. By selecting only one food sample to test, there is a significant potential to fail to detect a contaminated lot, even when the contamination is significant. As the level of contamination in a lot decreases, it becomes even more difficult to detect with a sampling plan that includes collection of only one sample. However, increasing the sample size even moderately would greatly increase the probability of correctly identifying contaminated lots. Taking a single sample, particularly if negative, affords virtually no ability to discriminate between conforming and nonconforming lots. (10) Recommendations include collecting more samples of each requested item from each commissary. If current inventory will not allow this, sample collection could be coordinated among commissaries in each branch, to allow for collection of samples from the same lot, or at least the same brand with similar production dates.
The program was also evaluated to determine if the food items selected for testing adequately represented the risk of foodborne illness. The AIPH-VS makes the decision on what categories of food to test based on a variety of factors, such as recent food recalls, reports of foodborne illness attributed to certain foods, past knowledge of contaminated food items, and training needs (Dr R. Benisch, oral communication, December 11, 2013). Although a formal, objective risk assessment process has never been developed to assist in determining what food categories pose the greatest risk to consumers, the food categories selected for testing in 2013 did appear to represent those food categories most often implicated in foodborne disease outbreaks, as assessed by Painter et al. (1) Only 3 food items tested positive for contaminants during the study period, which is too few to accurately assess or recommend what food categories are historically associated with increased risk of contamination within this system. A more extensive study reviewing several years and other regions globally would be beneficial to provide these recommendations. Development of a formal risk assessment plan could be beneficial, especially if based on publications such as those that track foodborne illness by food category, food items produced from commercial establishments with multiple major or critical findings during sanitation audits, data from the All Food and Drug Activities announcements released by the Defense Logistics Agency, etc.
Although each district in the study collected a similar distribution of products, there was a large difference when comparing individual sections. This was because some districts, such as Fort Belvoir, are comprised of many more sections responsible for more commissaries. The current program allows for representation of many facilities, but the proportion of samples collected at each facility is not based on the size of the facility nor the population served at each facility. Readjusting the number of samples requested from each section to better reflect the risk of contamination and proportion of people served at each facility would improve the representation of samples requested. This would require information on the relative size of each facility, number of patrons served, and number of veterinary personnel assigned to support each facility.
Finally, this evaluation examined the timeliness of the program. Most laboratory results were reported within 15 days. Food items typically arrived at the FADL within one business day, and FADL personnel did not indicate an overwhelming burden of samples. In fact, the majority of the time lapse between sample submission and reporting of results was likely due to typical processing associated with conducting laboratory tests. Due to the nature of some perishable food items, almost half of items sampled expired before results were reported. Many of the highly perishable food items are also considered a higher risk, potentially hazardous food. Thus, they should continue to be included in the program, with the recognition that if a positive laboratory test is reported, the food lot from which the sample was taken will no longer be available for purchase, and may in fact already be consumed or discarded. Procedures should be developed to inform military public health personnel of the potential health threat. Further, program managers should consider the value of additional laboratory support through the use of satellite facilities or contracted civilian laboratories. This may allow for more rapid testing of highly perishable food items, and may be considered if a significant increase in sample collection is pursued.
Analysis of the destination monitoring program revealed several strengths. First, although the program does not employ a formal risk analysis process to determine what food items should be collected, the data suggest that the informal process based on current trends and subject matter expertise resulted in selection of a variety of food items representing a moderate to high potential for contamination. Second, the shipping and processing of food items happened quickly, and results were reported in a timely manner. Lastly, recent accreditation of the FADL confirms the high quality of laboratory procedures used to determine contamination in submitted food items.
Several limitations of the program were recognized. Perhaps the most significant limitation was the reliance on small sample sizes to make decisions about the safety of food lots. Additionally, although the Lotus Notes database was functional, it was difficult to navigate and did not provide an easy method to extract data for analysis. All records had to be individually accessed and transcribed to an Excel worksheet for analysis. Currently, the database does not provide an easy way for users to evaluate data from an epidemiologic perspective. The program is also not well integrated with other surveillance systems, such as the Armed Forces Reportable Medical Events passive surveillance system (14) or the Centers for Disease Control and Prevention FoodNet (15) or PulseNet (16) systems. It should be noted that while FADL does not directly communicate with other surveillance systems, it does send samples from food items testing positive for zero-tolerance pathogens to the Texas State Department of Health, which communicates with PulseNet (CW3 J. D. Mitchell, oral communication, November 7, 2014). Improving the communication and integration between the AIPH-VS and other foodborne disease surveillance systems as well as other military public health infrastructure may be vital in linking human disease cases to potential foodborne pathogens detected within the Destination Monitoring Program. Although this analysis was limited to the APHC Region North, many conclusions will likely apply to APHC Regions South and West, because the same type and number of food items are typically requested from each region, and food samples are all processed at FADL. However, one would expect the distribution of sample collection to vary between individual sections. Additional research may give more information as to how the program functions in other regions, especially APHC Pacific and Europe.
The purpose of the Destination Monitoring Program is to assess and validate producer compliance with good hygiene practices, good manufacturing practices (GMPs), and implementation of food safety risk management systems such as Hazard Analysis Critical Control Point (HACCP). Increasingly, it is recognized that preventive measures such as GMPs and HACCP are much more effective food safety management tools than end-product testing. (8,13) Some suggest that while microbial monitoring has its place, particularly in high-risk situations like intentional botulinum toxin poisoning in milk, a better return on investment might be realized through increased funding of foodborne disease surveillance systems. (13) However, the Destination Monitoring Program has the potential to yield important information, serves as an additional level of protection against foodborne pathogens, and is used to verify safety and wholesomeness of food purchased by DoD.
The authors thank the AIPH-VS for providing valuable background information and perspective on the program, especially COL Thomas Honadel, Dr Rebecca Benisch, and CW5 Christopher Finch. Several others within the Public Health Command Region North provided essential background information and perspective, including CW4 Donald Smith, CW2 Stephanie McClain, and CW2 Garry McNair. Personnel from the DoD Food Analysis and Diagnostic Laboratory also provided valuable information on the program, including CW4 Keith Pritts and Mr Robert Leo.
(1.) Painter JA, Hoekstra RM, Ayers T, et al. Attribution of foodborne illnesses, hospitalizations, and deaths to food commodities by using outbreak data, United States, 1998-2008. Emerg Infect Dis. 2013; 19(3):407-415.
(2.) Gould LH, Walsh KA, Vieira AR, et al. Surveillance for foodborne disease outbreaks--United States, 1998-2008. MMWR Surveill Summ. 2013; 62(2):1-34.
(3.) Scallan E, Hoekstra RM, Angulo FJ, et al. Foodborne illness acquired in the United States-major pathogens. Emerg Infect Dis. 2011; 17(1):7-15.
(4.) Army Regulation 40-657: Veterinary/Medical Food Safety, Quality Assurance, and Laboratory Service. Washington, DC: US Dept of the Army; 2005.
(5.) US Army Public Health Command. DoD Lab Sample Submission Guide [internet]. 2013. Available at: http://phc.amedd.army.mil/topics/labsciences/fad/ Pages/SampleSubmission.aspx. Accessed December 17, 2014.
(6.) Department of Defense Food Safety and Quality Assurance Laboratory Action Levels. In: USASPH Circular 40-1: Worldwide Directory of Sanitarily Approved Food Establishments for Armed Forces Procurement. Aberdeen Proving Ground, MD: US Army Public Health Command: June 2014: Appendix O.
(7.) Military Handbook 3006C: Guidelines for Auditing Food Establishments. Washington, DC: US Dept of Defense; June 2008: MIL-HDBK-3006C.
(8.) Legan JD, Vandeven MH, Dahms S, Cole MB. Determining the concentration of microorganisms controlled by attributes sampling plans. Food Control. 2001; 12(3):137-147.
(9.) Joint FAO/WHO Codex Alimentarius Commission. Codex Alimentarius General Guidelines on Sampling. Rome Italy: Food and Agriculture Organization of the United Nations; 2004. CAC/GL 50-2004.
(10.) van Schothorst M, Zwietering MH, Ross T, Buchanan RL, Cole MB. Relating microbiological criteria to food safety objectives and performance objectives. Food Control. 2009; 20(11):967-979.
(11.) Dahms S. Microbial sampling plans--statistical aspects. Paper presented at the 36th Symposium of the Swiss Society of Food Hygiene; October 8, 2003; Zurich, Switzerland. Available at: http:// www.icmsf.org/pdf/032-044_Dahms.pdf. Accessed December 17, 2014.
(12.) Painter JA, Ayers T, Woodruff R, Blanton E, Perez N, Hoekstra RM, et al. Recipes for foodborne outbreaks: a scheme for categorizing and grouping implicated foods. Foodborne Pathog Dis. 2009; 6:1259-1264.
(13.) Institute of Medicine Forum on Microbial Threats. Addressing Foodborne Threats to Health: Policies, Practices, and Global Coordination. Washington, DC: National Academies Press; 2006.
(14.) Armed Forces Health Surveillance Center. Armed Forces Reportable Events Guidelines and Case Definitions [internet]. Available at: http://afhsc.mil/ home/reportableEvents. Accessed January 30, 2014.
(15.) Centers for Disease Control and Prevention. Foodborne Diseases Active Surveillance Network (FoodNet) [internet]. 2013. Available at: http://www.cdc.gov/foodnet/. Accessed January 30, 2014.
(16.) Centers for Disease Control and Prevention. PulseNet. 2013. Available at: http://www.cdc.gov/pulsenet/. Accessed January 30, 2014.
MAJ Kamala Rapp-Santos, VC, USA
Karyn Havas, DVM, PhD
Kelly Vest, DVM, DrPH, MPH
MAJ Rapp-Santos is a Laboratory Animal Medicine Resident at the US Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland. At the time this article was written, Dr Havas was an Epidemiologist with the Division of Integrated Surveillance at the Armed Forces Health Surveillance Center, Silver Spring, Maryland. Dr Vest is the Deputy Chief of Staff, Operations, and a Veterinary Epidemiologist at the Armed Forces Health Surveillance Center, Silver Spring, Maryland.
Table 1. Comparison of Number of Samples Submitted and Tested by the Department of Defense Food Analysis Diagnostic Laboratory for the Destination Monitoring Program, January 1-December 31, 2013. Number of Samples Submitted Tested Total number of samples 668 577 (a) Median samples per item 1 1 Minimum samples per item 1 1 Maximum samples per item 8 8 Number of Items Total N=514 N=507 (b) n(%N) n(%N) Items containing 1 sample 378(73.5%) 452(89.2%) Items containing 2 samples 127(24.7%) 49(9.7%) Items containing 3 samples 9(1.8%) 6(1.2%) or more (a) Duplicate samples submitted for the same item were often not tested except in certain cases such as chemical analysis to meet minimum weight requirements for microbial testing. (b) Seven items were not tested (not received or suspect package integrity). Table 2. Probability of Accepting a Lot in Relation to Proportion of Contamination and Sample Size. Proportion of Sample Size Contamination n=1 n=2 n=5 n=10 10% 0.9 0.81 0.59 0.35 20% 0.8 0.64 0.33 0.11 50% 0.5 0.25 0.03 0.01 Table 3: Distribution of Unique Food Samples Collected and Tested Based on Category, and Number of Items Positive for Indicator Organisms. Food Category Food Items Food Items Proportion Tested, N=507 Positive Positive n(%N) for Indicator Within Organisms, N=507 Category n(%N) Ground meat 78(15.4%) -- -- Processed fruits 62(12.2%) -- -- and vegetables Bagged salad 61(12.0%) -- -- Whole fresh fruits 55(10.9%) 2(0.4%) 3.60% (n=55) and vegetables Prepared salad 48(9.5%) -- -- Frozen dairy 30(5.9%) -- -- Other PHF 29(5.7%) -- -- (kimchee/tofu) Raw seafood 28(5.5%) -- -- RTE meats 23(4.5%) -- -- Fresh liquid dairy 23(4.5%) 1(0.2%) 4.40% (n=23) Powdered 22(4.3%) -- -- infant formula Cheese 21(4.1%) -- -- Seafood fresh RTE 19(3.8%) -- -- Seafood 8(1.6%) -- -- (canned RTE) Total 507(100%) 3(0.6%) PHF indicates potentially hazardous food. RTE indicates ready-to-eat. Table 4. Frequency of Food Item Submissions by District, Branch, and Section of the APHC Region-North, January 1--December 31, 2013. District Branch Section Frequency Fort Belvoir Fort Meade Andrews/ 5 Annapolis Forest Glen 5 Carlisle Barra 4 Fort Detrick 4 Fort Meade 4 Branch Total 22 New London Groton 12 Newport 9 Branch Total 21 Dover Aberdeen 21 Proving Ground Fort Belvoir Fort Belvoir 9 Quantico 8 Patuxent River 3 Branch Total 20 McGuire/ Fort Dix 20 Dix Fort Drum Fort Drum 20 West Point West Point 13 Tobyhanna 6 Branch Total 19 Fort Myer Fort Myer 17 Hanscom Portsmouth 9 District Total 169 Fort Eustis Fort Bragg Fort Bragg 35 Norfolk Norfolk 17 Portsmouth 17 Branch Total 34 Fort Lee Fort Lee 27 Fort Eustis Fort Eustis 26 Camp Camp Lejeune 25 Lejeune Cherry Point Cherry Point 24 District Total 171 Fort Knox Fort Knox Fort Knox 55 Harrison Villa 10 Branch Total 65 Great Lakes Great Lakes 38 Rock Island 7 Fort McCoy 16 Branch Total 61 Wright- Selfridge ANGB 29 Patterson Kelly Support 19 Branch Total 48 District Total 174 Grand 514 Total District Branch % of % of District Total Submissions Submissions Fort Belvoir Fort Meade 3.0% 1.0% 3.0% 1.0% 2.4% 0.8% 2.4% 0.8% 2.4% 0.8% Branch Total 13.0% 4.3% New London 7.1% 2.3% 5.3% 1.8% Branch Total 12.4% 4.1% Dover 12.4% 4.1% Fort Belvoir 5.3% 1.8% 4.7% 1.6% 1.8% 0.6% Branch Total 11.8% 3.9% McGuire/ 11.8% 3.9% Dix Fort Drum 11.8% 3.9% West Point 7.7% 2.5% 3.6% 1.2% Branch Total 11.2% 3.7% Fort Myer 10.1% 3.3% Hanscom 5.3% 1.8% District Total 100.0% 32.9% Fort Eustis Fort Bragg 20.5% 6.8% Norfolk 9.9% 3.3% 9.9% 3.3% Branch Total 19.9% 6.6% Fort Lee 15.8% 5.3% Fort Eustis 15.2% 5.1% Camp 14.6% 4.9% Lejeune Cherry Point 14.0% 4.7% District Total 100.0% 33.3% Fort Knox Fort Knox 31.6% 10.7% 5.7% 1.9% Branch Total 37.4% 12.6% Great Lakes 21.8% 7.4% 4.0% 1.4% 9.2% 3.1% Branch Total 35.1% 11.9% Wright- 16.7% 5.6% Patterson 10.9% 3.7% Branch Total 27.6% 9.3% District Total 100.0% 33.9% Grand 100.0% Total Table 5. Days Elapsed from Submission to Results, and Results to Product Expiration Date. Submission to Results to Results (Days) Expiration (Days) Number of food items 507 424 (a) Median 9 2 Minimum 1 -26.0 (b) Maximum 49 1801 (a) Data only available for food items that had an expiration date. (b) Negative numbers indicate that product expired prior to reporting of laboratory results.
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|Author:||Rapp-Santos, Kamala; Havas, Karyn; Vest, Kelly|
|Publication:||U.S. Army Medical Department Journal|
|Date:||Jan 1, 2015|
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