Food Composition Data: A User's Perspective (UNU, 1987, 223 pages): The uses of food composition data: Need for a standardized nutrient data base in epidemiologic studies: Factors influencing diet-related epidemiologic studies, using diet and colon cancer studies as an illustration

Food Composition Data: A User's Perspective (UNU, 1987, 223 pages)

Acknowledgments

Foreword

Preface

Executive summary

Report and recommendations of the conference

(introductory text...)

Introduction

The users and their needs

Food composition data

A food composition data system

Recommendations

References

Experiences with food composition data: the context

INFOODS: Background and current status

(introductory text...)

Introduction

Growing need for and availability of information on components of foods

Generation and recording of food component data

INFOODS - an international network of food data systems: a framework for discussion

Summary and conclusions

References

Data: the user context

(introductory text...)

Introduction

The link between the user and the data

The variability of the data

The INFOODS system

(introductory text...)

Introduction

Data interchange and regional centres

Regional decisions

Local decisions

The uses of food composition data

Need for a standardized nutrient data base in epidemiologic studies

(introductory text...)

Introduction

Limitations of diet-related epidemiologic studies

Factors influencing diet-related epidemiologic studies, using diet and colon cancer studies as an illustration

Some potential problems with incomplete and non-standardized nutrient data bases

Summary

References

Epidemiological uses of food composition data in the European context

(introductory text...)

Introduction

Nutritional epidemiology

The problems

Suggestions for improvement

Ongoing activities

Summary

References

NCI food data needs: impact on coding systems

(introductory text...)

Introduction

International research

United States studies

Local research

Individual level

Uses of food composition data

Implications for infoods

Summary

Food composition -a key to dietary appraisal and improvement in the United States

(introductory text...)

Introduction

Food composition data needs

National nutrition monitoring system

Nutrition education

Discussion

Approaches to meeting data needs

References

Using food composition data to communicate nutrition to the consumer

(introductory text...)

Introduction

NUTREDFO system development

Nutrient and food constituent data sources

Food composition data characteristics and limitations

Interrelationships of nutrition education and food composition data

Using NUTREDFO for nutrition guidance research

Comments on selected nutrients in NUTREDFO

Recommendations

Acknowledgements

References

Nutrient composition data uses and needs of food companies

(introductory text...)

Introduction

Available food composition data

Uses of food composition data

Needs and concerns

Summary

References

Managing food composition data

Concerns of users of nutrient data bases

(introductory text...)

Introduction

Accessibility

Installation and updating efforts

Data availability

Computational concerns

Data-base and software products

References

Managing food composition data at the national level

(introductory text...)

Introduction

Data input

Data output

Special considerations

Conclusions

References

Maintaining a food composition data base for multiple research studies: the NCC food table

(introductory text...)

Introduction

Specific user needs and approaches to these needs

Minimizing redundancy in the nutrient data base

Summary

References

Managing a nutrient data-base system: meeting users' needs and expectations

(introductory text...)

Introduction

The HVH-CWRU nutrient data-base system

Uses and users

Meeting users' needs and expectations

Conclusions

References

International food composition data

Nutrient intake data calculated using food composition tables: factors affecting accuracy

(introductory text...)

Introduction

Materials and methods

Results

Discussion

References

The status of food composition data in Asia

(introductory text...)

Introduction

Food availability

Generation of food composition data

Users and uses of food composition data

Unmet needs

The future of ASIAFOODS

Acknowledgements

References

Food composition data in Sweden and the nordic countries

(introductory text...)

Swedish food composition tables

Swedish national nutrient data bases

Other Swedish data bases

Food composition tables in other nordic countries

Nutrient data banks in the other nordic countries

References

Food data in Canada: the Canadian nutrient file

(introductory text...)

Introduction

Methods

Discussion

References

Other considerations

A system for evaluating the quality of published nutrient data: Selenium, a test case

(introductory text...)

Introduction

Background

Procedure

Criteria

Calculation of the mean SE value and confidence code

Results

Discussion

Implications

Acknowledgements

Disclaimer

References

Consideration of food composition variability: What is the variance of the estimate of one-day intakes? Implications for setting priorities

(introductory text...)

Introduction

Magnitude of the reported variability of composition

Impact of composition variation on a one-day food intake

Additional impact of a random error in intake estimation

Some implications for data analyses

Validation of food intake data: implications of food composition variation

Systematic errors in food composition data

Relevance to priorities for food composition data

Conclusions

References

Dietary assessment methods used by the national health and nutrition examination surveys (NHANES)

(introductory text...)

Introduction

Design of NHANES II

Major nutrition-related components of NHANES II

Uses of dietary data

Plans for future NHANES

Conclusion

Systems considerations in the design of INFOODS

(introductory text...)

Introduction

Staff turnover and system growth

Documentation

The choice of environmental and basic tools

Choices of operating systems

Choice of programming language

User interface

Data representations

System architecture and linkages

Stability

Primitive tool-based systems

Summary

References

Participants

Factors influencing diet-related epidemiologic studies, using diet and colon cancer studies as an illustration

Searching on the key words "diet or dietary" and "colon cancer," "colonic neoplasms," or "sigmoid neoplasms," a MEDLINE literature search yielded 166 citations dating back to 1980. Twenty-six or 16 per cent of these studies were population-based or epidemiologic in nature. Thirty-three population-based studies reported after 1977 were identified by cross-referencing colon cancer with dietary risk factors. These studies have been summarized in table 1. The studies have been grouped according to the most commonly cited dietary risk/protective factors: dietary fibre, fat/meat, beer/alcohol, and cruciferous vegetables. The headings in table 1 list major components of epidemiological studies, each of which can effect the outcome of the study. The major types of study design as seen in the table are: ecological and food disappearance studies, retrospective (case-control) studies, cross-sectional surveys, and prospective (cohort) studies. In addition to choosing the appropriate study design, the investigator must also decide how to collect dietary information.

Though there are many variations of each, there are four basic dietary data collection tools: diet diaries, diet recalls, diet histories, and food frequencies. If data on specific food or foodgroup intake or availability is obtained for individuals or groups. the information can be transformed into nutrient intake by interfacing the food intake data of study respondents with a food composition data base.

Each technique has inherent strengths and weaknesses. Retrospective data collection methods are subject to respondent memory bias while diary methods tend to distort usual intake patterns. In addition these standard methods measure different aspects of dietary intake. Therefore there will be differences in study outcome depending on the food-intake datacollection instrument chosen. (Notice that all four intake tools were employed in the studies reported in table!.)

The type of food or nutrient data base selected is dependent on the study design, the data collection method, the study objectives, and the endpoints to be measured. However, a lack of standardized definitions of dietary study variables has been a major weakness in interpreting study outcomes. Definition has presented problems for developing standardized food names as well as for food composition tables. For example, dietary fibre, the first risk factor listed in table 1, is a complex of a number of physically and chemically different entities found in foods. They include cellulose, hemicellulose, lignins, pectins, and gums, and the ratio of these materials varies in fibre-containing foods. Until recently, data bases reported only crude fibre values, in which food samples were subjected to strong acid and then alkali solutions. These values are not equivalent to dietary fibre, which is the residue of undigested food.

The last column in the table describes the outcome or risk-factor association found in the studies. Drawing correct conclusions from the data concerning the strength of association of study variables and the attributable risk for diseases is dependent on choosing appropriate statistical tests. In addition one must control for confounding variables and adjust for covariables. Unlike other clinical or laboratory studies, epidemiological studies are based mainly on relative rather than absolute differences of risk factors between exposed and unexposed groups. However, these studies lose power if real differences exist in the nutrient content of foods consumed by different population groups. This problem is analogous to regressing to the mean by not utilizing significant differences in food composition consumed by study populations. Increasing the power of a study is important, since the influence of diet is often obscured by stronger overriding etiological factors encountered in multi-etiological chronic disease studies. Also, epidemiologic methods and techniques are sometimes inadequate or inappropriate for the evaluation of diet and disease relationships, especially if one assumes that nutrient variables are independent of other dietary or environmental factors. Furthermore, much of the confusion in outcomes of diet-related epidemiologic research may stem from inappropriately comparing studies that differ in design, analytical techniques, or food composition data bases.