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University of PEI

CANDAT is superior to other nutrient analysis programs because it is designed to accommodate the diverse needs of researchers. Being able to handle food recalls and records and food frequency questionnaires in the same program, to create user files of special food items not included in the CNF and to easily import files into statistical analysis packages makes it unique in Canada. Most of all, having a support person who has extensive experience with nutrition researchers makes CANDAT an easy choice for me.

I have been working with CANDAT since 1984, and have also tried a number of other nutrient analysis programs. I keep coming back to CANDAT for two reasons: the support with CANDAT is superb. I have emailed Gaetan and received a phone call within minutes. He “knows his stuff” and can pinpoint and resolve any problems or challenges quickly. The program he uses to take over the computer is amazing and very efficient.

Since he has worked with a number of academics in nutrition, he understands research and the special needs researchers have. My research group had a challenging task a few years ago which involved adapting an American Food Frequency questionnaire for Canadian use and adding data on dietary nitrates. This was a very complex and time consuming task, and Gaetan was an active participant in the research process. When we were under time pressures to complete the analysis, Gaetan committed time and was able to get us the data when we needed it. He goes beyond a technical role, and sometimes poses questions which I don’t think of, preventing problems before they happen.

Dr. Jennifer Taylor, Associate Professor & Chair
Department of Family & Nutritional Sciences
Dalton Hall 204
University of Prince Edward Island
550 University Ave, Charlottetown PEI C1A 4P3 (902)
566-0475 Fax (902) 628-4367

First Nations Food, Nutrition and Environment Study

We are a multi-organization research team (University of Northern British Columbia, University of Ottawa, Université de Montréal, Assembly of First Nations and Health Canada) that has used CANDAT since 2008. So far, we have used CANDAT to enter over 5000 single and repeated recalls. The software is easy to learn and use. One of our students doing data entry mentioned that CANDAT was easier and more friendly to use than another program he had been using. We are able to incorporate traditional foods and recipes from our study easily into the database. The software developer is always open to suggestions from CANDAT users to facilitate data entry, such as viewing the food description when reviewing data entry and automatic repeat of past key word searches. Above all, the technical support provided by Godin London Incorporated (Gaetan) is always top-notch. He is prompt to answer questions and to solve problems. Recently, we requested a new feature which would enable users to view the food description when reviewing data entry and they were able to do this within a few days. Also, when validation of a dietary file with a more recent version of the Canadian Nutrient File was needed, they were able to write a program that allowed the file to be imported back into CANDAT (from SAS).

This was all done under the no-charge support provided with all CANDAT licences. I would highly recommend CANDAT to any nutrition researcher. Gaetan always strives to adapt CANDAT’s capabilities to suit its users.

Amy Ing, M.Sc.
Data Analyst
First Nations Food, Nutrition and Environment Study
www.fnfnes.ca

McGill University

We use CANDAT because it is the best software giving access to the Canadian Nutrient File (CNF) that we have been able to find. It allows us to structure our data to provide the results that we need. For example, we have made consistent use of the food grouping capability that provides us with nutrients from these food groups so we can write manuscripts pertaining not only to the nutrients but also the food sources in the Canadian population. Support from the software provider is exceptional and very responsive to user needs. For example, when Health Canada removed some units from the Canadian nutrient file that were essential to the data entry, Godin London Incorporated extracted these from an older version of the CNF and added these back to CANDAT. Godin London Incorporated has also responded to our needs by adding functions that save us time when doing corrections and data cleaning.

Louise Johnson-Down
Survey Coordinator Food habits of Canadians
McGill University 21,111 Lakeshore
Ste Anne de Bellevue QC
H9X 3V9
Tel:514-398-7808
Fax: 514-398-7739

Our story

Welcome to Food Research information. We have been involved in food intake assessment since 1970 when we needed nutrient data for an energy balance study. We had the energy consumption data but not the energy input data. It was then that we realized how big an undertaking it was to get simple food intake data. We were interested in only one nutrient (calories) and we had a relatively small population. It was a challenge. Since then we have developed software which supports all aspects of acquiring food intake data.

The software provides access and control over food data (food descriptions, units of measure, nutrients, etc.), subject intake data (7 day records, by meals), and extensive reporting and export of report data so that it can be used in spreadsheets or statistical packages. Of course, in the process, other related software was developed to manage recipes and convert recipe data to foods so that they could be used in subject intakes, and to create a food history questionnaire environment with integration as a tool for food intakes.

User defined tables were created for unlimited nutrient definitions and for recommended nutrient intake values which could be used with foods or food intake data. User defined food groups were added so that data could be summarized to meet the goals of the study.

With such flexible tools it became possible to innovate. For instance, one study used recalls to generate data which, when expressed in food groups could be used to formulate population specific food history questions. A question relating to pasta truly represented the pasta eaten by that population. If you do not understand these concepts, do not worry, it took us years of working with nutritionists and their studies to arrive at them.

Course blog area

Please use the Course area of this blog to explore. This site is based on the structure of  CANDAT, a mature product always up-to-date with the Canadian Nutrient File (CNF) and the USDA releases. CANDAT is responsive to client needs. New features are added regularly and immediately become part of the new releases. CANDAT welcomes your comments and suggestions.

Statistics

Food nutrition research is a great source of data. In no time thousands and thousands of values can be generated and fed into a statistics program. Every nutrient is a variable. Other variables identify subjects, date, day of the week, height, weight, etc. All one needs to add to this is something which identifies the type of subject. Are the subjects just normal, overweight, diabetic, athletic, have cancer or some other illness, again, etc…?

There are good statistical packages into which data can be fed. SPSS, SAS come to mind immediately. There are also open source (read free) packages that can be used as well.

For students in nutrition wanting to explore research a good nutrient calculation software combined with a good statistical package is a must.

Teachers can also use these as the basis of a multi-term course in nutrition research.

All kinds of questions about nutrition need to be explained and clarified when one is doing research. There is no room for ambivalence.

Enjoy! it is a truly challenging field.

Questionnaire concepts

Questionnaire concepts:

A subject’s nutrient profile, whether obtained from food recall information or from food history (questionnaire) information, is obtained by a simple arithmetic calculation. Each nutrient is the product of the food quantity consumed and the nutrient’s concentration in that food. The subject’s consumption is the sum of these products over the foods consumed in one day. Simple.

Problems:
In a recall… using the right food code to represent the food consumed and estimating the quantity consumed as accurately as possible. For a recall there are thousands of food codes to choose from, one of those is likely to represent fairly accurately the actual food consumed. The quantity of that food consumed can also be fairly accurately recorded as reported.
An example of a recall record would be “I ate a banana for breakfast”. The corresponding coding would have the food code for bananas and quantity being typically “one medium banana”. Hence, nutrient profile and quantity fairly accurately recorded and yield fairly good nutrient information.

In a questionnaire very few questions (usually less than 200, sometimes as few as 100) are used to represent historical intake. Every question is matched to a food with a nutrient profile representing the consumption for that question. A single food taken from a database used in recalls is not likely to be indicative of  the group of foods represented by any one question in the questionnaire.

An example question could be “Do you consume soup?”. There are many soups with different nutrient profiles. Which one to use for the question as a nutrient profile? The soup code used  should be a “composite” of all the possible soups. Which composite to use? One formed of the relative use all the soups consumed in the population in question. This information can be obtained from a food recall study of the population. This “composite” food could then be taken as the food representing the question’s nutrient profile… a good estimate on a population basis, probably not so good on an individual basis.

Composite food calculations from food recalls:

  • Determine the unique food codes from all the recalls. Typically should be 400-600 food codes
  • Divide those codes into food groups corresponding to questions you would want in the questionnaire
  • Run the nutrient calculations on the recalls looking at food details by food group, sorted in descending order of food quantity in grams
  • Create a recipe from each food group using the main foods in that group and their corresponding quantities as recipe ingredients
  • Run the nutrient analysis on the recipe file and export each recipe and its nutrients per 100G to a food file
  • Use this food file in your nutrient calculations of the relevant questions

Of course, this assumes you have the software to do all these calculations and conversions automatically. Doing the calculations manually or using a spreadsheet would be very onerous indeed.

The question of quantity to record is a bit more difficult. Usually such a question asks “How often do you consume this soup? Per day? Per week? Per month?”. No problem here, just a mathematical calculation.
The problem is in the next part of the estimate, the portion size. If the portion size is indicated precisely as in .5 cup, 1 cup, 2 cups… again, no problem. The composite soup can have a weighted density based on the density of the soups making up the composite. Cup weights can then be precisely calculated.  250 ml x 1.06 G/ml would give us a cup weight of 265 G.

Technique A:
How does one estimate portion sizes when the portion is not so precisely indicated? As in, .5 of a cup or less, .5 cup to 2 cups, 2 cups or more? One logical estimate would be to take the mid-point of the ranges.
For minimal consumption to .5 cups, use .25 cup;
for .5 to 2 cups, use 1.25 cups; for 2 cups or more use 4 cups (maximal consumption assumed to be 6 cups).

Technique B:
Much more intensely computational… not using the composite weighted densities…
An alternative to the above would be to use population based estimates. For each of the range of consumption, .5 cup or less, .5 cup to 2 cups and 2 cups and more, establish the distribution of consumption and calculate the median or average value. The median value would probably be best as it would negate the effects of outlier consumption.

In the population there is no consumption of the composite soup. The distributions have to be calculated for each and every soup making up the composite. One median per soup! For example for the lower range, less than .5 cup, how does one obtain a composite median from the individual soup medians? A weighted average of medians? Based on what weighting factor? The relative weight of the total weight of the soup consumed in the population (used to get the weighted density of the composite) or the relative weight of the total weight consumed in the range less than .5 cup? I would guess the latter to be the better estimate.

How does one establish the cut-off weight for .5 cup. The best value would be obtained by using the density for the soup whose median is being calculated. If the soup has a density of 1.06, one would look at all consumption of that soup of .5 cup or less or of 265G/2 = 132 G or less. The range .5 and above would start at 133G…

Should the basis of the distribution of consumption be each consumption of soup or the total soup consumption for the day? This question may not seem relevant here (each consumption would be the best information for the typical portion size) but what about other foods, such as milk in all its possible consumption portion sizes (see below)?

Assumptions:
Technique B assumes that all consumptions recorded in the population recalls are based on portions that are cups. In soups this is probably reasonable. What about questions that ask questions about foods such as milk. “Do you consume milk?” If yes, how many times per day/week/month and how many glasses? Recalls will record all kinds of consumptions of milk. In cereal, in coffee or tea, in glasses or cups. Each one of these will be converted to Grams. The total of those consumptions, on a daily basis, or on a per consumption basis, may not reflect typical population median gram values for typical glass or cup portion sizes.
Estimates of portion sizes for questionnaire data should be based on recall data collected using those same portion sizes.

Food history questionnaires

A lot of nutrition research is done through the use of the food history questionnaire. This is different from food recalls in that it is shorter, easier to administer and does not require special interviewing skills.

The downfall is that it is a fairly “blunt” instruments. With a bit of care in its construction some of the bluntness can be removed. The following techniques can be used to make them better research instruments:

  • Make up your own;
  • Make them specific to your target population;
  • Make them as detailed as possible while keeping them still realistically long;
  • Base the questions on foods your population eats. Collect as many food recalls as you can and base the questions on the foods in those recalls;
  • Create composite foods from similar foods in the recalls and use those to calculate nutrient contributions for corresponding questions in the food history questionnaire;
  • Validate your questionnaire… does it over/under-estimate specific nutrients in comparison to your food recall data?

There is enough variability in food intake and nutrient concentrations of individual foods to try to minimize the effect of further variability introduced by blunt food history questionnaires.

Given the amount of variability in this data it is a wonder that any conclusion can be reached about the predicted effect of a nutrient intervention or of specific nutrient consumption.

By all means find and use good software that will make your task bearable. Variability can only be reduced by eliminating mistakes, using the best sources of data possible and collecting large amounts of data. A significant task indeed.

 

Canadian Nutrient File 2015 (CNF2015)

Health Canada is happy to announce that the thirteenth edition of the Canadian Nutrient File containing data on 5690 food items for up to 152 food components is now available on their website at:
www.healthcanada.gc.ca/cnf

CANDAT is now also supporting version 2015. I can be downloaded from our site at:

http://www.foodresearch.ca

  Since the release of the 2010 CNF, the following food categories have been sampled and analyzed through their Sampling and Nutrient Analysis Program for Canadian food samples.  The results have been added to the CNF database.

· ready-to-eat breakfast cereals
· yogourts
· processed cheese products
· sausages
· wieners
· deli-meats
· commercial breads
· babyfoods – infant cereals and jarred foods
· soups – condensed and ready to eat
· margarines
· energy drinks
· vitamin waters

A major focus of this effort was to update foods which are major contributors of sodium to the diet.

In addition, changes include those adopted by USDA since SR22 (SR 23-27) which were appropriate for addition of foods and/or nutrients as data became available.  

The entire database of relational files is available for download.  The database is also available in Microsoft Access format.  In addition, one can download update files (add, change, delete) which indicate changes since the 2010 version.  

For those who prefer to use the online searchable program, it has also been updated with the CNF 2015 database.  

Food frequency questionnaires

Sometimes studies need to identify patterns of food consumption in a large population. This is done, for instance, as part of a general population health survey. These are done on a fairly regular basis, usually funded by governments, to be proactive in the formulation of heath related polices. This saves our tax money in the long run.

Food history questionnaires are related to food recalls in that they collect information on commonly eaten foods. These questionnaires tend to be simpler in that they do not seek to know each individual food that was eaten. They ask questions like “When you eat pasta how much do you eat and do you eat it once a week, or 3-5 times a month”. You get the idea. Typically a food history questionnaire will have fewer than 100 such questions.

Before a questionnaire like this can be formulated it is wise to know what kinds of foods the population usually eats. A smaller number of individuals are asked what they ate recently, using the recall format. Using the example above, from the results of these recalls, a pattern of “pasta” consumption can be determined. A composite “pasta” food can then be created and used as the nutrient profile for the “pasta” question in the food history questionnaire.

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