Agreement on Nutrient Intake between the Databases of the First National Health and Nutrition Examination Survey and the ESHA Food Processor
Lydia A. Bazzano1,
Jiang He1,
Lorraine G. Ogden2,
Catherine M. Loria3,
Suma Vupputuri1,
Leann Myers2 and
Paul K. Whelton1
1 Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
2 Department of Biostatistics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
3 National Heart, Lung, and Blood Institute, Bethesda, MD.
Received for publication July 27, 2001; accepted for publication March 14, 2002.
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ABSTRACT
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The objective of this study was to assess agreement on nutrient intake between the nutrient database of the First National Health and Nutrition Examination Survey (NHANES I) and an up-to-date (December 1998) nutrient database, the ESHA Food Processor. Analysis was conducted among 11,303 NHANES I participants aged 2574 years in 19711975 who had undergone dietary assessment. A list of all unique foods consumed was obtained from a single 24-hour dietary recall questionnaire administered during the baseline NHANES I visit. Foods on the list were matched to foods in the ESHA Food Processor software. Agreement between participants nutrient intakes as calculated with the NHANES I and ESHA nutrient databases was assessed using intraclass correlation analysis, linear regression analysis, and graphic methods. Intraclass correlation analysis demonstrated excellent concordance between most nutrient intakes, with coefficients above 0.95 for intakes of energy, carbohydrates, protein, cholesterol, and calcium; coefficients between 0.90 and 0.95 for intakes of total fat, saturated fat, potassium, and vitamin C; and coefficients of approximately 0.85 for intakes of sodium and vitamin A. Graphic methods and regression analyses also showed good-to-excellent correspondence for most nutrients. These findings support the validity of expanding existing nutrient intake databases to explore current hypotheses, provided that food formulation, enrichment, and fortification practices have not changed substantially over time.
cohort studies; data collection; diet; nutrition surveys; nutritive value
Abbreviations:
Abbreviations: NHANES I, First National Health and Nutrition Examination Survey; NHEFS, NHANES I Epidemiologic Follow-up Study; USDA, US Department of Agriculture.
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INTRODUCTION
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Collection of data on nutrient intake is costly and time-consuming, particularly in large populations such as those needed for prospective epidemiologic studies. The collection of accurate and precise nutrient data for epidemiologic studies often requires labor-intensive interviewing techniques or lengthy questionnaires and a significant time commitment on the part of participants (15). After data collection, each food item must be matched with appropriate nutrient composition data. This process can be both costly and time-consuming if reported foods do not appear in the studys nutrient composition database. Furthermore, selection of a nutrient composition database usually precedes the interviewing process and may influence the process itself. For example, selection of the most appropriate food code and relevant probes may be limited by the availability of food composition data, particularly if a computerized interview system is used.
Prospective epidemiologic studies have contributed substantially to our understanding of the effects of dietary nutrient intake on chronic disease (610). Several of these studies have provided long periods of follow-up, relatively large sample sizes, high event rates, and extensive high-quality dietary data (610). Unfortunately, secondary analysis of these studies for exploration of current hypotheses may be limited by a lack of information on one or more nutrients. For example, the First National Health and Nutrition Examination Survey (NHANES I), conducted between 1971 and 1975, collected 24-hour dietary recall data as well as food frequency data on 20,749 participants. Of these participants, 14,407 were followed for an average of 19 years in the NHANES I Epidemiologic Follow-up Study (NHEFS) (11). Unfortunately, dietary intake values are only available for a limited set of nutrients, and these nutrients do not include many now thought to be important in the etiology of chronic diseases. For instance, in the NHANES I data set, nutrient information is not available for trans-unsaturated fatty acids, folate, vitamin E, water-soluble fiber, animal and vegetable sources of protein, and amino acid composition.
It may be possible to address new hypotheses by adding currently available food composition information to existing data and thereby calculating previously unavailable nutrient intakes for study participants. Expansion of existing food composition information represents an efficient and novel approach to the collection of nutrient intake data. The goal of this study was to assess agreement between participants nutrient intakes calculated using two different nutrient databases, since strong agreement would support the validity of expanding food composition information in this manner. We calculated nutrient intakes using a current nutrient database (ESHA Food Processor software) and assessed agreement between those estimates and NHANES I nutrient intake estimates.
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MATERIALS AND METHODS
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Study population
Individuals who participated in the NHANES I and NHEFS and who completed a 24-hour dietary recall questionnaire were included in this analysis. The NHANES I employed a multistage, stratified, probability sampling design to select a representative sample of the US civilian noninstitutionalized population aged 174 years (11, 12). Certain population subgroups, including persons with low incomes, women of childbearing age (2544 years), and the elderly (persons aged 65 years or older), were oversampled. The NHEFS is a prospective cohort study of NHANES I participants who were 2574 years of age when the survey was conducted between 1971 and 1975. Of the 14,407 persons in this age range at baseline, we excluded a separate random subsample of 3,059 participants who had not been administered a 24-hour dietary recall by study design, as well as 45 participants who did not have information on amount of food consumed for all foods listed. Thus, a total of 11,303 participants were included in the analysis.
Assessment of nutrient intake in NHANES I
Baseline data collection in the NHANES I included administration of a single 24-hour dietary recall questionnaire by trained personnel possessing at least a bachelors degree in food and nutrition. The 24-hour dietary recall was conducted using a standardized protocol and 51 three-dimensional models to estimate portion size. Interviewers coded the dietary recall questionnaires using nutrient information obtained from US Department of Agriculture (USDA) Handbook No. 8 (1963 edition), Tulane Universitys Master Dietant Listing, Bowes and Churchs Food Values of Portions Commonly Used, USDA House and Garden Bulletin No. 72, and commercial sources (11, 13, 14). The NHANES I nutrient database was updated twice during the survey. Missing nutrient values were evaluated and, where possible, updated with nutrient values obtained from appropriate sources. Each individuals dietary intakes of 19 nutrients were then calculated by the National Center for Health Statistics (Hyattsville, Maryland).
Recalculation of nutrient intake using ESHA Food Processor software
The ESHA Food Processor database (ESHA Research, Salem, Oregon) was created in 1984. The version 7.20 database used in this analysis was last updated in December 1998 and contains information on approximately 18,260 foods. Sources of nutrient data in the ESHA database include the USDA Nutrient Database for Standard Reference, the USDA Database for the Continuing Survey of Food Intake by Individuals, Canadian nutrient files, manufacturers nutrient information, and over 1,000 sources of additional data. Values are missing for some nutrients in the ESHA Food Processor. Missing values may be imputed or computed from composite recipes by ESHA Research (15).
A listing of the 3,481 unique foods recorded during collection of the 24-hour dietary recall data was obtained. Each unique food was matched to a corresponding food item listed in the ESHA Food Processor nutrient database by name and nutrient composition in the NHANES I database. Both criteria were used to determine the most suitable match for all 3,481 foods. An appropriate match could not be identified for 33 foods (0.95 percent). These foods included 10 substances identified as chemicals used in commercial baking, six brand-name breakfast cereals last marketed in the 1970s, four items listed as "drained liquid" from canned foods, and 13 other foods ranging from reindeer milk to prune whip. For these foods, nutrient composition as listed in the NHANES I database was added to the ESHA database and was included in the agreement analysis.
Statistical methods
Agreement between existing intakes and calculated intakes was examined for 11 nutrients, including total energy (kcal), carbohydrates (g), protein (g), saturated fat (g), cholesterol (mg), calcium (mg), sodium (mg), potassium (mg), vitamin A (IU), and vitamin C (mg). These nutrients have most often been related to the development of chronic disease, particularly cardiovascular disease. The distribution of values for each nutrient was examined using graphic methods and nonparametric tests. All distributions were found to be non-normal with positive skew. Consequently, median values and interquartile ranges are presented as measures of central tendency. Agreement between existing nutrient intake data from the NHANES I survey and nutrient data calculated using the ESHA food composition database was evaluated using paired t tests, nonparametric tests, and linear regression analyses in which nutrient intake data from the NHANES I survey were regressed on ESHA recalculated intake data. The level of agreement between NHANES I nutrient intakes and the recalculated data was determined using intraclass correlation coefficients (16, 17). The ESHA recalculated nutrient intake estimates were plotted against NHANES I estimates as determined by the National Center for Health Statistics for assessment of agreement and identification of outliers. In addition, differences between NHANES I and ESHA recalculated nutrient intakes (ESHA value minus NHANES I value) were plotted against averaged intake values to investigate this relation (18). For example, if a positive relation were apparent between the mean of the two measures and the difference, lack of agreement might increase at higher intake values of the nutrient.
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RESULTS
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Of the 11 nutrients examined in 3,481 foodstotal energy, carbohydrate, protein, fat, saturated fat, cholesterol, calcium, sodium, potassium, vitamin C, and vitamin Awe found 0 percent, 0 percent, 0 percent, 0.03 percent, 3.1 percent, 2.1 percent, 0.2 percent, 0.3 percent, 2.4 percent, 1.5 percent, and 1.4 percent, respectively, of these nutrient values to be missing in the ESHA database. Median estimates, interquartile ranges, and paired median differences for nutrient intake information are presented in table 1. NHANES I median intake values were very similar to those calculated using the ESHA database for most nutrients. For example, the absolute value of percent median difference was less than 5 percent for six of the 11 nutrients examined and less than 10 percent for 10 of the 11 nutrients examined. Percent median difference was 1.0 for total energy intake, 1.7 for total carbohydrate intake, 1.9 for protein intake, 2.2 for total fat intake, and 0.1 for saturated fat intake (table 1). Percent median differences were somewhat higher for the mineral nutrients sodium and potassium (6.1 and 11.2, respectively). Mean values and 95 percent confidence intervals from NHANES I data and ESHA recalculated data were also very similar (data not shown). The distributions of differences tended to be positively skewed. Thus, median differences were smaller than mean differences for seven of the 11 nutrients examined (data not shown).
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TABLE 1. Median values for estimated 24-hour nutrient intakes in the databases of the First National Health and Nutrition Examination Survey (19711975) and the ESHA Food Processor (December 1998) and median paired differences and percent median differences between the two databases (n = 11,303)
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Table 2 presents intraclass correlation coefficients and results of regression of NHANES I nutrient intake data on nutrient intakes recalculated using the ESHA database. Intraclass correlation coefficients were above 0.95 for intakes of energy, carbohydrates, protein, cholesterol, and calcium; between 0.90 and 0.95 for intakes of total fat, saturated fat, potassium, and vitamin C; and approximately 0.85 for intakes of sodium and vitamin A. Regression coefficients from linear regression of existing NHANES I nutrient values on recalculated ESHA nutrient values were all close to 1, except the coefficients for sodium (ß = 0.80), potassium (ß = 0.82), and vitamin A (ß = 0.84).
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TABLE 2. Results of intraclass correlation analysis* and linear regression analysis of estimated 24-hour nutrient intakes from the databases of the First National Health and Nutrition Examination Survey (19711975) and the ESHA Food Processor (December 1998)
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Plots of ESHA recalculated nutrient values versus NHANES I nutrient values as estimated by the National Center for Health Statistics are presented in figures 1 and 2 for selected nutrients representing distinct patterns. Plots of protein and fat intakes (figure 1) show strong linear relations, with equal scattering of points above and below the regression linea pattern similar to that observed for other macronutrients, cholesterol, calcium, and vitamin C (plots not shown). In the plot of potassium intake (figure 1), the scattering of points appeared somewhat greater above the regression line than below it; the same was true for sodium intake. The plot of vitamin A intake (figure 1) shows points distributed in a nonrandom, U-shaped pattern about the regression line.

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FIGURE 1. Plots of estimated 24-hour protein, fat, potassium, and vitamin A intake as calculated by ESHA Food Processor software (ESHA Research, Salem, Oregon) versus intake from the First National Health and Nutrition Examination Survey (NHANES I). NHANES I values were calculated by the National Center for Health Statistics (NCHS).
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FIGURE 2. Plots of differences in estimated 24-hour protein, fat, potassium, and vitamin A intake between values from the First National Health and Nutrition Examination Survey (NHANES I) and values recalculated using ESHA Food Processor software (ESHA Research, Salem, Oregon) versus averaged values. NHANES I values were calculated by the National Center for Health Statistics (NCHS).
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We plotted differences in nutrient intakes (ESHA value minus NHANES I value as estimated by the National Center for Health Statistics) against the average of these two values to graphically identify lack of agreement between the two databases. Figure 2 presents the plots of difference in nutrient intake versus average nutrient intake for protein, fat, potassium, and vitamin A. For protein and fat, these plots demonstrate good agreement and no relation between mean differences and averaged intake values. The same was true of other macronutrients and calcium. With respect to cholesterol intake, a slight downward trend appeared in the differences as mean intakes increased, indicating that agreement may decrease slightly at greater intake values. A similar pattern was seen for vitamin C intake. For potassium intake, variability in differences was greater than that for macronutrient intakes, yet there did not appear to be a positive or negative relation between the mean and the difference of the two measures. The same was true of sodium intake. The plot of differences in vitamin A intake displayed greater variability in the mean difference across the range of averaged values and many more outliers. For the majority of averaged measures of vitamin A, differences clustered around zero; however, both an upward trend and a downward trend existed with increasing measures of intake.
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DISCUSSION
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For most nutrients, our study found strong agreement (an intraclass correlation greater than 0.9 and regression coefficients between 0.9 and 1.1) between NHANES I estimates of intake and corresponding values calculated using ESHA Food Processor software. These findings have important implications for nutritional epidemiologic research in chronic disease. Strong agreement, such as that observed between the available NHANES I nutrient intake estimates and those calculated using the ESHA database, suggests that nutrient intakes for which there are no available comparison data are likely to provide a valid representation of study participants intakes, provided that food formulation, fortification, and enrichment practices have not changed substantially over time. Therefore, when such practices have not changed for previously unavailable nutrients and agreement is good between recalculated and existing intakes, it may be possible to test hypotheses regarding the relation between newly calculated nutrient intakes and chronic diseases. These secondary analyses would avoid the considerable expense and lengthy follow-up time required to conduct prospective studies tailored to new dietary research questions.
Our findings support the validity of expanding nutrient intake data through the use of current nutrient information. The magnitudes of the median differences for nutrient intakes were generally quite small. Agreement appeared to be excellent for intakes of macronutrients, electrolytes, and vitamin C in plots of ESHA-calculated intake versus National Center for Health Statistics-estimated intake. Plots of potassium and sodium showed somewhat greater scatter above the regression line, which suggests that ESHA estimates were slightly higher than NHANES I estimates for those nutrients. When differences between measures were plotted against the average of measures, only three of the 11 nutrients examinedcholesterol, vitamin C, and vitamin Aexhibited patterns indicating a decrease in agreement at higher intake values.
The pattern apparent in figures 1 and 2 for vitamin A intake may indicate that specific food items are contributing to the variability in measurement. The vitamin A values of certain foods may differ because of missing values, errors in the NHANES I or ESHA food composition databases, changes in the formulations of certain food items, or changes in analytical practices. The NHANES I database had a higher percentage of missing values for vitamin A in food items than the ESHA nutrient database: 22 percent versus 1.4 percent. This difference would have systematically biased the estimates of vitamin A intake downward, possibly contributing to the pattern seen in figures 1 and 2. The same is true for sodium and potassium, for which 17.4 percent and 19.9 percent of values, respectively, were missing. The six largest positive differences in vitamin A (where ESHA values were at least 25,000 IU greater than corresponding NHANES I values) occurred for foods for which the NHANES I estimate was derived from the Tulane University Master Dietant Listing, a source no longer available for verification. In addition, a USDA Handbook No. 8 vitamin A value was also available in the NHANES I database for these same six foods, indicating that a reanalysis had been conducted. As a result, for each of these six food items, two different codes existed with independent estimates of the foods vitamin A content. The largest negative differences (where ESHA values for vitamin A were at least 25,000 IU less than corresponding NHANES I values) occurred for foods such as liver and chili powder. The ESHA vitamin A values for the latter foods were compared with those in the USDA Nutrient Database for Standard Reference, release 13 (19), and were found to be similar. In addition, assay methods for vitamin A have changed over time. Older calorimetric methods were most likely used to determine the vitamin A content of foods in the NHANES I database; these methods are generally no longer considered acceptable, having been replaced with high-performance liquid chromatography (20). Despite high variability for vitamin A, the overall median difference was small and the intraclass correlation was fair.
It is important to realize that the NHANES I dietary data were coded using, in large part, food composition information compiled in 1963. Because of the inevitable time lag between actual chemical determination of the composition of foods and publication of the data, chemical analysis of the foods represented in the 1963 edition of USDA Handbook No. 8 may have occurred at an even earlier date. Analysis of trends in the nutrient composition of the US food supply has demonstrated a reduction in the fat content of meat, particularly the saturated fat content, over time (21). Therefore, the NHANES I nutrient data may have overestimated the fat content of the foods on which data were collected between 1971 and 1975.
Issues related to food composition databases should be carefully considered in the selection of an appropriate database for use in epidemiologic studies. Differences in databases over time are best evaluated with the input of nutritionists who are cognizant of changes in the food supply and in food composition methodologies. In some cases, older food composition data may not provide the best estimates of nutrient content. In particular, for foods and nutrients for which more accurate measurement techniques and application of statistical methods have improved estimates, more recent nutrient data may be more valid than earlier data, provided that the food itself has not changed (22). The optimal database should use the most accurate value available for a foodthe historical value where appropriate and an updated value when advances in measurement have taken place. Expansion of an existing nutrient database in this way may provide more accurate and useful data for hypothesis-testing in nutritional epidemiology.
Adding new nutrient intake information to existing data sets has several advantages. It is inexpensive compared with the collection of new dietary data. For instance, the cost of collecting dietary data using 24-hour dietary recalls for a large cohort may be prohibitively expensive to all organizations except governments and corporations, whereas the cost of expanding an existing data set is likely to be very small in comparison. In addition, data expansion and subsequent analysis can be completed rapidly in comparison with the collection of new data. Most prospective studies of dietary nutrients require many thousands of person-years of follow-up for a sufficient number of chronic disease outcomes to accrue, even in a large cohort, but calculation of new nutrient intake information may be completed in short order. Furthermore, this method may be very useful in a wide range of epidemiologic studies, since the objective of most epidemiologic studies is to examine the association between nutrient intake and outcome. Therefore, the relative ranking of nutrient intake in individuals may be more important than absolute values.
Some limitations of expanding existing nutrient intake information in this way include changes in the nutrient content of foods over time, loss of some food items from the food supply, and the entry of new items into the food supply. Changes in the nutrient content of foods may occur because of the fortification or enrichment of certain food items or because of product reformulation. For instance, in 1996, the US Food and Drug Administration mandated that food fortification with folic acid be fully implemented by 1998 (23). In addition, selective breeding techniques and alterations in plant foods may shift the nutrient content of the food supply over time (22, 24, 25). For some common foods, such as apples and bananas, average sizes have increased and respective serving sizes are now larger. Incorporation of data obtained through different analytical methods into databases may contribute to a lack of agreement between databases. For instance, in spring 1989, the cholesterol content of eggs was determined by the USDA to be lower than previously thought. Consequently, nutrition information on eggs and egg products was changed in USDA Handbook No. 8, which is one source of the ESHA food composition information (26). Thus, cholesterol intakes calculated using the ESHA nutrient information reflect the current lower cholesterol estimates for eggs and egg products. Even in the same time period, additional sources of difference between databases may include differences in sampling methods, measurement methods, and a host of other factors. In the United States, the USDA is the primary source for most database values. These estimates are central tendency values obtained from foods collected by sampling throughout the country (27). Nutrient value estimates from other sources may have been obtained using different sampling methods, possibly contributing to lack of agreement between databases. In addition, sampling techniques are likely to have improved in the period since the NHANES I data were compiled. Despite the above limitations, the strong agreement we have demonstrated between available estimates of nutrient intake in existing data sets and the corresponding values in recalculated (expanded) data sets suggests good correspondence in the relative ranking of individuals nutrient intakes.
In conclusion, our study found strong agreement between existing and recalculated nutrient intake data. In addition, this study illustrated the use of an up-to-date nutrient database to expand an existing dietary data set. The use of current nutrient databases to expand and update nutrient information may eventually decrease the cost and difficulty and increase the efficiency of studies in nutritional epidemiology, particularly as it relates to chronic diseases. Expansion of existing nutrient data sets represents a novel approach to the collection of dietary data in large populations, and it should be considered in future nutritional epidemiologic studies of chronic disease.
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ACKNOWLEDGMENTS
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This study was supported by grant R03 HL61954 and in part by grant R01 HL60300 from the National Heart, Lung, and Blood Institute. The NHANES I Epidemiologic Follow-up Study was developed and funded by the National Center for Health Statistics; the National Institute on Aging; the National Cancer Institute; the National Institute of Child Health and Human Development; the National Heart, Lung, and Blood Institute; the National Institute of Mental Health; the National Institute of Diabetes and Digestive and Kidney Diseases; the National Institute of Arthritis and Musculoskeletal and Skin Diseases; the National Institute of Allergy and Infectious Diseases; the National Institute of Neurological and Communicative Disorders and Stroke; the Centers for Disease Control and Prevention; and the US Department of Agriculture.
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NOTES
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Reprint requests to Dr. Jiang He, Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1430 Tulane Avenue SL18, New Orleans, LA 70112 (e-mail: jhe{at}tulane.edu). 
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REFERENCES
|
---|
- Briefel RR. Assessment of the US diet in national nutrition surveys: national collaborative efforts and NHANES. Am J Clin Nutr 1994;59(suppl):164S7S.[Abstract]
- Dolecek TA, Stamler J, Caggiula AW, et al. Methods of dietary and nutritional assessment and intervention and other methods in the Multiple Risk Factor Intervention Trial. Am J Clin Nutr 1997;65(suppl):196S210S.[Abstract]
- ARIC Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol 1989; 129:687702.[Abstract]
- Rossouw JE, Finnegan LP, Harlan WR, et al. The evolution of the Womens Health Initiative: perspectives from the NIH. J Am Med Womens Assoc 1995;50:505.[Medline]
- Willett WC, Hunter DJ, Stampfer MJ, et al. Dietary fat and fiber in relation to risk of breast cancer: an 8-year follow-up. JAMA 1992;268:203744.[Abstract]
- Shekelle RB, Shryock AM, Paul O, et al. Diet, serum cholesterol, and death from coronary heart disease. The Western Electric Study. N Engl J Med 1981;304:6570.[Abstract]
- McGee DL, Reed DM, Yano K, et al. Ten-year incidence of coronary heart disease in the Honolulu Heart Program: relationship to nutrient intake. Am J Epidemiol 1984;119:66776.[Abstract]
- Kushi LH, Lew RA, Stare FJ, et al. Diet and 20-year mortality from coronary heart disease. The Ireland-Boston Diet-Heart Study. N Engl J Med 1985;312:81118.[Abstract]
- Hu FB, Stampfer MJ, Manson J, et al. Dietary fat intake and the risk of coronary heart disease in women. N Engl J Med 1997; 337:14919.[Abstract/Free Full Text]
- Fraser GE, Sabate J, Beeson WL, et al. A possible protective effect of nut consumption on risk of coronary heart disease. The Adventist Health Study. Arch Intern Med 1992;152:141624.[Abstract]
- Miller MW. Plan and operation of the Health and Nutrition Examination Survey, United States, 19711973. (Vital and health statistics, series 1, no. 10a). Hyattsville, MD: National Center for Health Statistics, 1978.
- Engel A, Murphy RS, Maurer K, et al. Plan and operation of the NHANES I augmentation survey of adults 2574 years, United States, 19741975. (Vital and health statistics, series 1, no. 14). Hyattsville, MD: National Center for Health Statistics, 1978.
- Youland DM, Engel A. Dietary data methodology in HANES. J Am Diet Assoc 1976;68:225.[ISI][Medline]
- Church CF, Bowes HN. Bowes and Churchs food values of portions commonly used. Philadelphia, PA: JB Lippincott Company, 1966.
- McCollough ML, Karanja NM, Lin PH, et al. Comparison of 4 nutrient databases with chemical composition data from the Dietary Approaches to Stop Hypertension trial. J Am Diet Assoc 1999;99(suppl):45S53S.
- Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull 1979;86:4208.[ISI]
- McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods 1996;1:3046.[ISI]
- Bland JM, Altman DJ. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:30710.[ISI][Medline]
- Nutrient Data Laboratory, Agricultural Research Service, US Department of Agriculture. USDA Nutrient Database for Standard Reference, release 13. Washington, DC: US Department of Agriculture, 1999. Nutrient Data Laboratory home page (http://www.nal.usda.gov/fnic/foodcomp), accessed July 8, 2001.
- Federation of American Societies for Experimental Biology, Life Sciences Research Office. Third report on nutrition monitoring in the United States. Vol 1. (Prepared for the Interagency Board for Nutrition Monitoring and Related Research). Washington, DC: US GPO, 1995.
- Putnam J, Gerrior S. Trends in the US food supply, 197097. In: Frazao E, ed. Americas eating habits: changes and consequences. (Agriculture information bulletin no. 750). Washington, DC: Food and Rural Economics Division, Economic Research Service, US Department of Agriculture, 1999:13360.
- Beecher GR, Khachik F. Evaluation of vitamin A and carotenoid data in food composition tables. J Natl Cancer Inst 1984;73:1397404.[ISI][Medline]
- Food and Drug Administration, US Department of Health and Human Services. Food standards: amendment of standards of identity for enriched grain products to require addition of folic acid. Fed Reg 1996;61:878197.
- Buzzard IM, Schakel SF, Ditter-Johnson J. Quality control in the use of food and nutrient databases for epidemiologic studies. In: Greenfield H, ed. Quality and accessibility of food-related data. Arlington, VA: AOAC International, 1995:24152.
- Willett WC, Buzzard IM. Foods and nutrients. In: Willett WC, ed. Nutritional epidemiology. 2nd ed. New York, NY: Oxford University Press, 1998:1832.
- Perloff BP, Rizek RL, Haytowitz DB, et al. Dietary intake methodology. II. USDAs Nutrient Data Base for Nationwide Dietary Intake Surveys. J Nutr 1990;120(suppl):15304.[ISI][Medline]
- Pehrsson PR, Haytowitz DB, Holden JM, et al. USDAs National Food and Nutrient Analysis Program: food sampling. J Food Comp Anal 2000;13:37989.