- Email: [email protected]
Reproducibility and validity of a food-frequency questionnaire designed for use in girls age 7 to 12 years
Reprodu&~ and Validity of a Food-Frequency Designed for Use in Girls Age 7 to 12 Years JULIE E. ARNOLD, GEOFFREY HOWE,
BSc, MSc, BSc, PHD,
THOMAS ROHAN, MB, BS, AND MICHAEL LEBLANC,
MSc, BSc,
Qwe
PHD, MMATH,
PHD
This study focused on a food-frequency questionnaire (FFQ designed to measure nutrient intake in girls aged 7 to 12 years, inclusive. The instrument’s reproducibility and validity were assessed using foodrecords (FIG) as “gold stand&s” of measurement. Log-trans~ed nurrient intake estimates were compared from two FFQs and between FFQs and FRS. lntraclass correlation coeficients measuring the reproducibility of the FFQ ranged from 0.11 (starch) to 0.69 (fiber). lntraclass correlation coeficients measuring agreement between FFQ and 14&y FR data varied between 0.15 (starch) und 0.68 (vitamin B2) for the first, and between 0.06 (starch) and 0.95 (vitamin Bf) for the second FFQ. FFQs were in the best agreement with FRs for the following nutrients: fiber, vitamin BI, vitamin B2, vitamin C, and beta-carotene. Joint ckassifications revealed that overall, 36% of subjects weresimilarly categorized by FFQ and FR, and 70% of those in the lowest or highest FR quartiles were found in the lowest or highest two FFQ quartiles, respectively. Ann Epidemiol 1995; 5:369-377 KEY WORDS:
Epidemiologic
methods, food, questionnaires,
INTRODUCTION Although many dietary exposures may putatively be risk factors for cancer, determination of their true effects is impeded by the lack of instruments with which the exposures can be accurately measured. Several approaches to the measurement of dietary intake capture actual intake (e.g., food records and observer recording). However, these may be unrepresentative of usual dietary intake, and somewhat unreliable for assessing past eating patterns (1). Methods that measure past diet, such as recall of consumption for periods of 1 to several days, may be problematic with regard to their reproducibility in measuring usual consumption (2). In response to the need to collect accurate and reliable data on usual dietary intake for a large number of subjects in a simple, straightforward manner, instruments such as the food-frequency questionnaire have been developed. The food-frequency questionnaire is a method of dietary measurement whereby subjects indicate what their usual frequency of consumption of certain foods has been over a finite interval, such as the previous year. The timing of dietary measurements is another im-
From the Department of Preventive Medicine and Biostatistics, University of Toronto (J.A., T.R., M.L.), and the National Cancer Institute of Canada Epidemiology Unit (T.R., G.H.), Toronto, Ontario, Canada. Address reprint requests to: Julie Arnold, BSc, MSc, Clinical Epidemiology Division, Well&y Hospital Research Institute, do 160 Wellesley Street East, Toronto, Ontario, Canada M4Y 113. Received March 30, 1994; accepted August 5, 1994. 0 1995 by Elsevier Science Inc. 655 Avenue of the Americas. New York. NY 10010
children,
diet.
portant consideration. For example, inconsistent findings from epidemiologic studies of diet and breast cancer (3-9) are perhaps a result of the fact that investigators have, by and large, only looked at the adult diet and risk of disease. In diseases such as breast cancer, early dietary exposure may prove to be more important than exposure in later life (4). In studies of childhood eating patterns, investigators have either administered a food-frequency questionnaire to a sample of adults and asked them to recall their diet from their childhood days (9), or administered a food-frequency questionnaire to children themselves (10). In the! latter case, investigators have supported the use of rhe questionnaire with evidence taken from validation studies done with adult subjects. Although several diet measurement methods have been validated with children, including 24-hour recalls (1 I), parental recall (12), and the nutrient adequacy score (13), these instruments do not provide adequate measures of usual dietary intake. To our knowledge, no validation or reproducibility studies of food-frequency questionnaires have been done in children. The objective of the study reported here was to evaluate the quality of a food-frequency questionnaire designed to measure usual dietary intake in girls between the ages of 7 and 12 years, inclusive. The quality of the food-frequency questionnaire was judged on the basis of its reproducibility and its validity as compared to 7day food records. For our purposes, reproducibility is a measure of the extent to which the results of measurement are similar each time a measurement is taken. Validity is taken to be the degree to whiih a measurement measures what it is intended to measure (14). 1047-2797/95/$9.50 SSDi 11?47-2797(95)00034-5
370
Arnold et al. FOOD-FREQUENCY
MATERIALS
QUESTIONNAIRE
AEP Vol. 5, No. 5 September1995: 369-377
FOR YOUNG GIRLS
AND METHODS
The food-frequency questionnaire used in this study was derived from one that had been used in a sample of adults in a pervious study conducted by the National Cancer Institute of Canada (NCIC) Epidemiology Unit. Prior to this validation study, a survey was taken of girls aged 7 to 12 years, inclusive, in order to discover what changes should be made to the adults’ food-frequency questionnaire. On the basis of 24-hour recalls and food-frequency questionnaire responses, the following items were added to the adult version of the questionnaire: dairy foods with a variety of fat contents, ground beef with variable fat contents, powderbased fruit drinks, different types of cooked eggs (e.g., boiled and fried), doughnuts, and ketchup. The validation study entailed measuring dietary intake in a sample of girls on four occasions. Intake estimates from food-frequency questionnaires were compared to those from 7-day food records, and each instrument was administered twice. There were intervals of 1 month between the foodfrequency questionnaires and food records (first foodfrequency questionnaire (FFQI) and first food record (FRl), and second food-frequency questionnaire (FFQZ) and second food record (FR2)) and of 6 months between the two food-frequency questionnaires (FFQI and FFQZ). Study subjects were contacted through primary and middle schools, as well as through the Girl Guides in Metropolitan Toronto. During the months of October and November 1991, packages containing the study material were distributed to 707 girls between the ages of 7 and 12, inclusive. Of these 707 packages, 531 were distributed to girls at school, and 176 to girls at a Girl Guide meeting. The study material consisted of a food-frequency questionnaire (Figure l), a letter of introduction, a consent form, and a postagepaid envelope. The girls were asked to take the packages home to their parents or guardians. Once the consent of a participant was received by the investigators, her name, address, and telephone number were randomly allocated to one of three trained interviewers. The parent or guardian was telephoned by the interviewer during the following week, and an initial appointment was arranged. At the first visit, the interviewer reviewed the food-frequency questionnaire to be sure it was complete. At the second visit, 1 month later, the interviewer reviewed the instructions for the food record with the participating parent and daughter. At the next visit, the interviewer collected the record and reminded the participants that the family would be contacted again in approximately 6 months. After a period of approximately 6 months, the subjects were once again contacted by telephone and delivery of the second food-frequency questionnaire was scheduled. Thereafter, the procedure was the same as for the first round of data collection. Along with the second food-frequency questionnaire,
each participant provided supplementary, nondietary information such as date of birth, and the name of the person responsible for completion of the dietary forms.
Coding of the Forms The self-administered food-frequency questionnaires were preceded for 160 foods. Additional foods indicated by the respondent in the blank lines at the end of the questionnaire were assigned one of the same 160 food codes, based on the information provided. The food records were coded by a trained coder, applying standard codes used by the NCIC Epidemiology Unit.
Database for Computation
of Nutrient
Intakes
Nutrient intakes from the food-frequency questionnaires and the food records were derived by way of a database derived from US Department of Agriculture (USDA) food composition tables (2). The tables have been modified and extended to include typically Canadian food items, according to food composition tables which include information from commercial firms, cookbooks, and recipes provided by subjects from previous studies (2).
Data Analysis Natural logarithms were taken of each of the daily nutrient intake estimates, and because these transformations improved the normality of the data, logarithms were used in all analyses of correlation. The regression method of Willett and Stampfer (15) was employed to correct for total energy intake. Specifically, regressions were carried out using the crude estimates of nutrient intake (dependent variable) and the estimate of total caloric intake (independent variable), as measured by the same food record or food-frequency questionnaire. The residuals from the model were then added to the sample mean of the nutrient intake values for that instrument, that is, the nutrient intake at mean caloric intake. This total was taken as the calorie-adjusted nutrient intake. Assessment of repeatability. To assess the foodfrequency questionnaire’s reproducibility, the strength of the association between corresponding calorie-adjusted nutrient intake estimates was measured using Pearson and intraclass correlation coefficients (16). Ninety-five percent confidence intervals (95% CIs) were generated for Pearson correlation coefficients. For intraclass correlation with fixed effects (i.e., in an analysis of variance where there are only two instruments about which inferences will be made), no simple method, exact or approximate, is available for generating confidence intervals (16). Assessment of validity. Pearson correlation coefficients and intraclass correlation coefficients were also used to assess the validity of the food-frequency questionnaire. First, the calorie-adjusted estimate of nutrient intake measured by
AEP Vol. 5, No. 5 September1995: 369-377
FOOD-FREQUENCY
QUESTIONNAIRE
Arnoid et al. FOR YOUNG GIRLS
FIGURE 1. A section of the girls’ food-frequency questionnaire.
the food-frequency questionnaires and the corresponding ?-day food records (administered 1 month later) were compared. Then, the nutrient intakes from both 7-day food records were combined to give each subject a I4-day mean nutrient intake score. The correlation between this mean nutrient intake score and the intake estimates from each of the two food-frequency questionnaires was then determined. Subsequently, subjects were categorized into quartiles of nutrient intake for each of the nutrients in question. Classifications were performed on three sets of data; the 1Cday means of food record data, as well as the two food-
frequency questionnaires. Each of the three classifications made use of its respective cutpoints for the breakdown into quartiles. To assess agreement, two cross-tabulations were then performed: classification according to the 14-day mean versus FFQI and then versus FFQZ.
RESULTS Completed food-frequency questionnaires were received from 92 (13.0%) of the 707 subjects to whom packages were distributed (Figure 2). Seven participants withdrew from
372
AEP Vol. 5, No. 5 September 1995: 369-377
Arnold et al. FOOD-FREQUENCY
QUESTIONNAIRE
FOR YOUNG
GIRLS
707subjectscopacted(100%) I I (13.0% of total)
p-q-l
I (6(5
I 80 FFW (87.0%)
5 drop-outs
I
1’ 71 FR2 (83.7%)
3 drop-outs
FIGURE 2. Response rates over the course of the study. FFQl , first food-frequency questionnaire; FFQ2, second food-frequency questionnaire; FRl, first 7-day food record; FEL2, second 7-day food record.
the study, having completed only the first food-frequency questionnaire. Five respondents dropped out of the study before completion of the second food-frequency questionnaire and three dropped out afterward. Therefore, 77 (83.7%) of the 92 initial respondents completed both foodfrequency questionnaires and both 7-day food records. Of the food-frequency questionnaires, 75% were completed by the parents, 16% were completed by the daughter
and parents together, and the balance (9%) were completed by the daughter alone, or some other person. For the food records, the corresponding figures were 74, 18, and 8%. Calorie-adjusted estimates of daily nutrient intake obtained from the food-frequency questionnaires were generally higher than those derived from the 7-day food records (Table 1). The comparison of the two food-frequency questionnaires yielded correlation coefficients that ranged from 0.14 (95% CI: -0.08 to 0.35) for total fat to 0.71 (95% CI: 0.58 to 0.80) for fiber (Table 2). Intraclass correlation coefficients ranged from 0.11 for starch to 0.69 for fiber. Pearson coefficients measuring agreement between calorie-adjusted daily nutrient intakes from the first foodfrequency questionnaire and the first 7-day food record ranged from 0.00 (95% CI: -0.22 to 0.21) for unsaturated fat to 0.51 (95% CL 0.34 to 0.65) for vitamin Bz (Table 3). For the second set of food-frequency questionnaires and 7-day food records, the correlation coefficients ranged from 0.13 (95% CI: -0.10 to 0.35) for starch to 0.60 (95% CI: 0.44 to 0.73) for fiber. Intraclass correlation coefficients were generally, but not always, higher than product-moment correlations. They ranged, in the first set of measurements (FFQI versus FRl), from 0.03 for unsaturated fat to 0.63 for vitamin Bz. In the second set of measurements (FFQ2 versus FR2), the intraclass correlation coefficients ranged from 0.13 for starch to 0.55 for beta-carotene (seeTable 3). When calorie-adjusted nutrient intake from the first food-frequency questionnaire was compared to the ICday mean intake from food records (Table 4), the range of Pear-
TABLE 1. Total daily energy intake and calorie-adjusted daily nutrient intake as determined food-frequency questionnaires (FFQI and FFQ2) and 7-day food records (FFX and FR2) Nutrient (units) Energy (Cal/d) F’rotein (g/d) Total fat (g/d) Carhohydrare (g/d) Fiber (g/d)
FFQi (n = 92)
FFQZ (n = 80)
FRl
by the first and second (n = 85)
FR2 (n = 77)
Mean
SD
Mean
SD
Mean
SD
Mean
SD
2319.01 93.72 87.35 303.44
671.27 43.61 30.18 90.16 2.62 5418.81 0.49 0.79 119.27 10.91 16.91 152.49 37.87 43.30 680.21 4440.18
2204.73 86.30 82.31 293.08 5.79 8020.34 1.50 2.45 230.26 30.33 43.50 296.08 101.32 121.54 1161.05 5376.45
606.79 27.82 25.16 84.56 2.36 4288.37 0.64 1.52 120.43 9.86 13.78 99.45 32.08 39.74 705.83 3450.71
1866.76 68.61 69.19 250.75 3.80 4917.72 1.06 1.63 141.93 25.66 36.33 242.90 88.88 96.04 979.06 3044.93
369.42 16.43 19.84 50.34 1.20 2692.56 0.24 0.50 56.93 8.07 10.49 81.83 24.51 23.71 1084.12 1924.33
1901.77 68.85 72.45 253.54 3.90 4663.02
350.88 15.54 17.97 51.78 1.16 2277.45 0.29 0.47 58.94 7.09 10.08 88.73 22.82 27.26 741.76 1679.85
6.21
Vitamin A” (W/d) Vitamin B1” (mg/d) Vitamin BP (mg/d) Vitamin C” (mg/d) Saturated fat (g/d) Unsaturated fat! (g/d) Cholesterol (mg/d) Starch (g/d)
8583.20 1.54 2.44 238.49 31.83 46.45 316.39 104.23
Total sugar (g/d) Retinol (lU/d)
126.81 1211.15
Beta-carotene @U/d)
5804.91
SD, standard deviation. * Exchding vitamin supplementation. b Unsaturated fats = linoleic and oleic fatty acids.
1.11 1.60
135.82 26.35 39.14 248.50 93.25 98.46 839.37 2987.03
Arnold et al.
AEP Vol. 5, No. 5 September 1995: 369-377
FOOD-FREQUENCY
TABLE 2. Pearson correlation coefficients (r) and intraclass correlation coefficients (ri) of log-transformed daily energy intake and calorie-adjusted nutrient intakes from the first and second food-frequency questionnaires (FFQl and FFQ2) and their 95% confidence interval (CI) FFQl Nutrient
’ Excluding
95%Cl
T
Energy Protein Total fat Carbohydrate Fiber Vitamin A Vitamin 31” Vitamin Br” Vitamin C Saturated fat Unsaturated fat Cholesterol Starch Total sugars Retinol Beta-carotene vitamin
vs. FFQ2
0.47, 0.33, -0.08, 0.14, 0.58, 0.44, 0.38, 0.56, 0.45, 0.08, -0.04, -0.06, 0.30, 0.32, 0.39, 0.44.
0.60 0.51 0.14
0.35 0.71
0.60 0.55 0.70 0.61
0.29 0.18 0.16 0.49 0.50 0.56 0.60
TI
0.92 0.66 0.35 0.53 0.80 0.73 0.69 0.80 0.73 0.48 0.39 0.36 0.64 0.65 0.70 0.73
0.54 0.56 0.20 0.40 0.69 0.59 0.56 0.52 0.59 0.32 0.24 0.31 0.11
0.50 0.62 0.59
suppkmenration.
son correlation coefficients was from 0.13 (95% CI: -0.01 to 0.36) for energy to 0.61(95% CI: 0.45 to 0.74) for vitamin Bz. A similar analysis, done with data from the second foodfrequency questionnaire and the mean from both food records (see Table 4), resulted in a range of 0.06 (95% CI; -0.17 to 0.28) for starch to 0.57 (95% CI: 0.40 to 0.71) for vitamin C. Again, for most nutrients, the intraclass
QUESTIONNAIRE
FOR YOUNG
373
GIRLS
correlation coefficients were higher than the Pearson correlation coefficients (see Table 4). Person correlation coefticients were also generated using nutrient intakes before adjustment for total energy intake (data not shown). The most marked differences between the unadjusted and adjusted Pearson correlation coefficients were seen between the first and second food-frequency questionnaires for total fat, where the correlation (r) based on unadjusted estimates was 0.60; cholesterol, where r was 0.60; unsaturated fat, where r was 0.60; and saturated fat, where r was 0.61 (correlations based on unadjusted nutrient intakes are available on request).
Joint Classification On average, 34.2 1% of the individuals were classified exactly the same by 14 days of food records and the first foodfrequency questionnaire (Table 5). That is, overall, 34.21% of the sample were found in the leading diagonal of the four-by-four table produced by the cross-tabulation of foodfrequency questionnaire versus the food record measurements. Corresponding percentages for individual nutrients ranged from 20.78% for vitamin Bi to 45.45% for fiber. The second food-frequency questionnaire correctly categorized a mean of 38.35% of individuals, and the proportion of correct categorizations ranged from 23.38% for vitamin B1 to 55.84% for vitamin C. On average, the first foodfrequency questionnaire properly categorized 36.25% of the lowest quartile and 38.92% of the highest quartile of nutrient consumers (see Table 5). The second questionnaire correctly classified 40.3 1% of those with the lowest and 48.97% of those with the highest intake.
TABLE 3. Pearson correlation coefficients (r) and intraclass correlation coefficients (ri) of log-transformed, calorie-adjusted nutrient intakes from the first and second sets of T-day food records (FFQl and FFQ2) and their 95% confidence interval (CI) FFQI T
Nutrient
0.29
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin BI” Vitamin Br8 Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugars Retinol Beta-carotene d Excluding
vitamin
0.17
0.05 0.15
0.44 0.35 0.35 0.51
0.29 0.19 0.00
0.29 0.20 0.10
0.21 0.33 supplementation
vs. FRI
(FRl
and FR2) and food-frequency
FFQ2 vs. FR2 (n 1 77)
(n = 85)
95% CI 0.08, -0.04, -0.17, -0.07, 0.25, 0.15, 0.15, 0.34, 0.08, -0.02, -0.22, 0.08,
0.52 0.37 0.26 0.35 0.60 0.52 0.52 0.65 0.48 0.39 0.21 0.48
-0.01, -0.12,
0.40 0.31
0.00, 0.41 0.13, 0.51
questionnaires
71
r
0.24 0.28 0.09 0.20 0.44 0.34 0.57 0.63 0.28 0.22 0.03 0.37 0.20
0.22 0.25 0.38 0.37 0.60 0.51 0.41 0.39 0.49 0.38 0.39 0.46 0.13 0.30 0.29 0.55
0.11
0.25 0.32
95% Cl 0.00, 0.03. 0.17, 0.16, 0.44, 0.32, 0.21,
0.45 0.45 0.56 0.55 0.7? 0.66 0.56
0.18, 0.56
0.30, 0.17, 0.18, 0.26, -0.10, 0.08, 0.07, 0.37,
0.64 0.56 0.56 0.62 0.35 0.4Y 0.49 0.69
71 0.19 0.26 0.37 0.36 0.50 0.50 0.3a 0.44 0.49 0.38 0.37 0.42 0.13
0.29 0.28 0.55
Arnold et al. FOOD-FREQUENCY
374
AEP Vol. 5, No. 5 QUESTIONNAIRE
FOR YOUNG GIRLS
September
1995:
369-377
TABLE 4. Pearson correlation
coefficients (r) and intraclass correlation coefficients (rr) of log-transformed, calorie-adjusted nutrient intakes from the l+day mean of food records (FRM) and the first and second food-frequency questionnaires (FFQI and FFQZ) and their 95% confidence intervals (CI) FFQ2 vs. FRM (n = 77)
FFQl vs. FRM (n = 77)
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin Bt” Vitamin Bi Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugars Retinol Beta-carotene ’ Excluding
95% CI
r
Nutrient
vitamin
0.13
0.20 0.28 0.27 0.59 0.47 0.41 0.61 0.43 0.28 0.33 0.25 0.15 0.23 0.32 0.47
-0.01, -0.03, 0.06, 0.05, 0.42, 0.27, 0.21, 0.45, 0.23, 0.06, 0.11, 0.03, -0.08, 0.01,
0.36 0.41 0.48 0.46 0.72 0.63 0.58 0.74 0.60 0.48 0.52 0.45 0.36 0.43 0.10, 0.51 0.27, 0.63
T
n 0.18 0.29 0.28 0.27 0.57 0.46 0.50 0.68 0.44 0.27 0.32 0.31 0.15 0.22 0.31 0.46
95% CI
0.22 0.30 0.46 0.45 0.56 0.49 0.35 0.36 0.57 0.50 0.45 0.52 0.06 0.29 0.30 0.53
0.00, 0.08, 0.26, 0.25, 0.38, 0.30, 0.14, 0.15, 0.40, 0.31, 0.25, 0.34, -0.17, 0.07, 0.08, 0.35,
0.45 0.49 0.62 0.61 0.70 0.64 0.53 0.54 0.71 0.65 0.61 0.66 0.28 0.49 0.49 0.68
TI 0.18 0.30 0.44 0.44 0.55 0.39 0.95 0.44 0.56 0.49 0.39 0.50 0.06 0.29 0.29 0.53
supplementation.
When the first food-frequency questionnaire was used, 66.88% of those in the lowest food record quartile were in the lowest two questionnaire quartiles, and 69.44% of those in the highest food record quartile were in the highest two questionnaire quartiles (Table 6). Similarly, with the second food-frequency questionnaire, 68.13% of those in the lowest food record quartile were ranked in the lowest two questionnaire quartiles, and 74.63% of those in the highest food record quartile were in the highest two questionnaire quartiles.
DISCU!3SION The intake levels observed using food records in the present study are comparable to those found in another Canadian study of young girls (mean age, 11.1 years), which used food records to look at the relationship between diet and age at menarche (17). Results from the present study indicate that this version of the food-frequency questionnaire is of comparable reliability to those described by other investigators (18-22). For 10 of the 16 nutrients examined in the present
TABLE 5. Percentage of individuals correctly classified by food-frequency as comnared to classification by 14-daymean of food records (FRh4) All correct classifications
questionnaires
(FFQI
Lowest FRM quartile
and FFQZ), Highest FRM quartile
Nutrient
FFQl
FFQ2
FFQl
FFQ2
PQl
mQ2
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin Bt” Vitamin Br“ Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugar Retinal Beta-carotene Mean
25.97 33.77 38.96 33.77 45.45 25.27 20.78 42.86 44.16 33.77 38.96 33.77 22.08 36.36 28.57 42.86 34.21
33.77 42.86 37.66 45.45 44.16 25.32 23.38 35.06 55.84 45.45 48.05 36.36 29.87 37.66 31.17 41.56 38.35
35.00 45.00 45.00 35.00 45.00 0.00 0.00 50.00 45.00 60.00 40.00 30.00 20.00 40.00 35.00 55.00 36.25
35.00 45.00 50.00 45.00 45.00 0.00 0.00 35.00 65.00 65.00 50.00 45.00 40.00 35.00 30.00 60.00 40.3 1
15.79 31.58 31.58 47.37 68.42 25.27 57.89 60.53 47.37 21.05 47.37 36.84 26.32 31.58 31.58 42.11 38.92
36.84 57.89 36.84 63.16 63.16 25.64 94.74 52.63 63.16 42.11 63.16 47.37 21.05 36.84 26.32 52.63 48.97
’ Excluding
vitamin
supplementation.
AEP Vol. 5, No. 5 September1995: 369-377
Arnold et al. FOOD-FREQUENCY
TABLE 6. Percentage of individuals classified into the lowest and highest calorie-adjusted nutrient intake quartiles by 1Cday mean of food records (FRM) and into the two lowest and two highest quartiles by the food-frequency questionnaires (FFQl and FFQ2), respectively Lowest FRM quartile Nutrient
FFQl
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin BI” Vitamin BI” Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugar Retinol Beta-carotene Mean
55.00 80.00 70.00 60.00 80.00 0.00 100.00 80.00 70.00 70.00 75.00 75.00 55.00 65.00 70.00 65.00 66.88
wQ2 60.00
80.00 70.00 75.00 70.00 0.00 100.00 40.00 80.00 85.00 80.00 75.00 60.00 75.00 65.00 75.00 68.13
Highest FRM quartile WQl
=Q2
63.16 73.68 63.16 68.42 84.21 50.55 84.21 60.53 78.95 52.63 78.95 63.16 57.89 84.21 73.68 73.68 69.44
68.42 89.47 73.68 78.95 78.95 49.37 94.74 76.32 84.21 57.89 84.21 78.95 63.16 78.95 57.89 78.95 74.63
” Excludingvitaminsupplementation.
study, the first and second food-hequency questionnaires showed correlations of 0.5 or higher. In general, the food-frequency questionnaire used in this study tended to overestimate nutrient values, relative to those obtained from the 7-day food records. This trend was noted in previous studies, albeit with less consistency across nutrients (18, 19, 23, 24). Overestimation of intake may have resulted from inaccurate reporting of either the frequency or the quantity of specific foods eaten (portion size), or both. Since the food-frequency questionnaire measured food portion size, the relative contribution of errors in reporting frequency and quantity of intake could be assessed by comparing portion sizes and frequency in the foodfrequency questionnaires and food records. Evaluation of this issue is beyond the scope of the present report. Overall, the food-frequency questionnaire was in the best agreement with food records for fiber, vitamin B1, vitamin BZ, vitamin C, and beta-carotene. Some of the correlations between food-frequency and food record values were higher with the second questionnaire. This trend was observed previously (23), and might reflect a learning phenomenon. It is possible that participation in dietary studies results in changes in the subjects’ natural eating behavior. This phenomenon, known as the “Hawthorne effect” (25), may affect eating patterns, methods of food preparation, or the precision and accuracy with which the foods are recorded. It is difficult to determine whether these phenomena OCcurred in the present study, and if so, to what extent. How-
QUESTIONNAIRE
375
FOR YOL!NG GIRLS
ever, this is not a consideration in situatians where the subjects complete a food-frequency questionnaire on only one occasion (e.g., retrospective or cross-sectional studies). Under such circumstances, the best indication of the performance of the instrument can be obtained by comparing one administration of the questionnaire ro a gold standard measurement of usual dietary intake (e.g., in the present study, FFQl versus FRM). It should be noted that the food-frequency questionnaire is designed to measure usual diet, as opposed to actual dietary intake, which is measured by food records. Thus, correlation coefficients measuring agreement between results from the questionnaire and those from food records can be expected to be somewhat modest. Investigations in this field revealed that most coefficients range from 0.4 to 0.6 (3,2628). One study, similar in design to rhe present one but conducted in a sample of American registered nurses, yielded coefficients between food-frequency questionnaires and food records as high as 0.75 (23). However, in that study, the investigators administered the 7-day records on four occasions, and the calorie-adjusted estimates of daily intake were based on 28 days of recording. The optimal number of days for which diet records should be completed has received considerable attention in methodologic studies of diet measurement (29-3 1). Nelson and coauthors (32) discussed the extent of misclassification that can be expected using 7 days of diet records in samples of young girls. In such a group, they found that accurate measurement of diet would require 6 to 280 days of recording, depending on the nutrient in question, but that many nutrients (e.g., energy, total sugar, fat, monounsaturated and saturated fatty acids, and calcium) require 2 weeks or less. In the present study, the five highest Pearson correlation coefficients were above 0.40 in all comparisons except the first set of food-frequency questionnaire and 7-day food record data (FFQl versus FRI). Compared to their corresponding Pearson correlation coefficients, the intraclass correlation coefficients were higher with the first than with the second food-frequency questionnaire. Large differences in intraclass and Pearson correlation coefficients for some nutrients may suggest a high degree of wirhin-person variability for those items (in particular, vitamin BI for the second food-frequency questionnaire versus the I4-day food record mean). It has been suggested that food-frequency questionnaires might be susceptible to systematic within-person error (33). Such error might be generated by subjects’ inability to accurately recall, estimate, or aggregate their patterns of consumption over 1 year. As a result of this error, spuriously high correlations might result. Food-frequency questionnaires are often used in studying the association between diet and disease. Rosner and coworkers (34) explored the effects of systematic, within-person error associated with
376
Arnold et al. FOOD-FREQUENCY
QUESTIONNAIRE
AEP Vol. 5, No. 5 September1995: 369-377
FOR YOUNG GIRLS
the use of food-frequency questionnaires on relative risk estimates and suggested appropriate adjustments. Investigators have also looked at the problem of random measurement error in the food-frequency questionnaire, which biases results from epidemiologic studies toward the null value (28, 35). McKeown-Eyssen and Tibshirani (28) asserted that considerable sample size increases might be necessary to allow for the effects of measurement error on estimation of relative risk. Freedman and coworkers (35) reached similar conclusions. Beaton (33) described the analytic components of variance such as between-subject (interperson) variance and within-subject (day-to-day) variance, and applied his models to previous validation studies of the food-frequency questionnaire (1, 23). The response rate in this study was low. However, considerable constraints were placed on its implementation. The agreements that were made with the School Board and with the Girl Guides allowed us to distribute material to the girls only once, and thereby rendered us unable to follow up with nonrespondents to improve overall response or investigate possible selection bias. Our results are therefore generalizable only to situations where subjects volunteer for participation. It is conceivable that the instrument would not perform as well in a wider population. Proxy responses are to be expected in dietary studies involving young children. Indeed, in our sample, close to three-fourths of the food-frequency questionnaires and food records were completed by the parents of the girls. It is possible that this occurrence may have affected the validity of the responses since parents might not have had full knowledge of their daughters’ diets. Parental responses or responses by consensus between parent and child have, however, been shown to have a high degree of accuracy (12,36,37). Another serious consideration related to proxy responses is the potential for change in the degree of proxy response if the instrument is used in a sample over a long period of time. For example, if young children are studied prospectively, they themselves might become increasingly more responsible for completing their food-frequency questionnaires, instead of their parents. With time, the performance of the instrument might therefore change, with regard to its validity and reproducibility, In this case, it might therefore be useful to reassess the quality of the foodfrequency questionnaire data periodically as the cohort ages. It is important that dietary instruments are developed to suit the samples that are studied. The validity of the questionnaire evaluated here varied among the nutrients tested, but this might reflect the relatively limited number of days of food record data. Additionally, the food-frequency questionnaire was reasonably consistent in classifying the top and bottom 50% of individuals, based on their nutrient intake, suggesting that use of the food-frequency data in categorical analyses might be appropriate. Furthermore, it
will be possible to use the estimates of measurement error obtained from this study to adjust the relative risk estimates obtained in an associated prospective study of diet and menarche.
The authors gratefully acknowledge Dr. Meera lain for her assistance and advice in data collection.
REFERENCES 1. Willett WC. Nutritional Epidemiology. Monographs in Epidemiology and Bioscatistics. v. 15. New York: Oxford Univ. Press; 1990. 2. Morgan RW, Jain M, Miller AB, et al. A comparison of dietary methods in epidemiologic studies, Am J Epidemiol. 1978;107;6:488-498. 3. Miller AB, Kelly A, Choi NW, et al. A study of diet and breast cancer, Am J Epidemiol. 1978;107:499-509. 4. Willett WC, Stampfer MJ, Colditz GA, Rosner BA, Hennekens CH, Speizer FE. Dietary fat and the risk of breast cancer, N Engl J Med. 1987;316:22-28. 5. Howe GR, Hirohata T, Hislop G, et al. Dietary factors and risk of breast cancer: Combined analysis of 12 case-control studies, J Nat1 Cancer Inst. 1990;82;7:561-569. 6. Howe GR, Friedenreich CM, Jain M, Miller AB. A cohort study of fat intake and risk of breast cancer. .1 Nat1 Cancer Inst. 1991:83:336340. 7. Knekt P, Albanes D, Seppanen R, et al. Dietary fat and risk of breast cancer, Am J Clin Nutr. 1990;52:903-908. 8. Kushi LH, Sellers TA, Potter JD, et al. Dietary fat and postmenopausal breast cancer, J Nat1 Cancer Inst. 1992;84:1092-1099. 9. Hislop TG, Coldman AJ, Elwood JM, Braver G, Kan L. Childhood and recent eating patterns and risk of breast cancer, Cancer Detect Prev. 1986;9:47-58. 10. Maclure M, Travis LB, Willett W, MacMahon B. A prospective cohort study of nutrient intake and age of menarche, Am J Clin Nutr. 1991; 54649-656. 11. Frank GC, Voors AW, Schilling PE, Berenson GS. Dietary studies of rural school children in a cardiovascular survey, J Am Diet Assoc. 1977;71:31-35. 12. Eck LH, Klesges RC, Hanson CL. Recall of a child’s intake from one meal: Are parents accurate?, J Am Diet Assoc. 1989;89:784-789. 13. Krebs-Smith SM, Clark LD. Validation of a nutrient adequacy score for use with women and children, J Am Diet Assoc. 1989;89:775-783. 14. Last J. A Dictionary Univ. Press; 1990.
of Epidemiology. 3rd ed. New York: Oxford
15. Willett W, Stampfer MJ. Total energy intake: Implications for epidemiologic analyses, Am J Epidemiol. 1986;124:17-27. 16. Fleiss JL. The Design and Analysis of Clinical Experiments. New York: Wiley; 1986. 17. Moisan J, Meyer F, Gingras S. Diet and age at menarche, Cancer Causes Control. 19QO;l:149-154. 18. Pietinen P, Hartman AM, Haapa E, et al. Reproducibility and validity of dietary assessment instruments. I. A self-administered food use questionnaire with a portion size picture booklet, Am J Epidemiol. 1988; 128~655-666. 19. Salvini S, Hunter DJ, Sampson L, et al. Food-based validation of a dietary questionnaire: The effects of week-to-week variation in food consumption, Int J Epidemiol. 1989;18:858-867. 20. Reshef A, Epstein LM. Reliability of a dietary questionnaire, Am J Clin Nutr. 1972;25:91-95. 21. Jain M, Howe GR, Johnson KC, Miller
AB. Evaluation
of a diet
AEP Vol. 5, No. 5 Segtember1995: 369-377
FOOD-FREQUENCY
history questionnaire for epidemiologic studies, Am J Epidemiol. 1980; 111:212-219. 22. Hankin JH, Nomura AMY, Lee J, Hirohata T, Kolonel LN. Reproducibility of a diet history questionnaire in a case-control study of breast cancer, Am J Clin Nutr. 1983;37:981-985. 23. Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire, Am J Epidemiol. 1985;122:51-65. 24. Tjonneland A, Overvad K, Haraldsdottir J, Bang S, Ewertz M, Jensen OM. Validation of a semiquantitative food frequency questionnaire developed in Denmark, Int J Epidemiol. 1991;20:906-912.
3 1.
32.
33.
25. Sackett DL. Bias in analytic research, J Chronic Dis. 1979;32:51-63. 26. Balogh M, Medalie JH, Smith H, Groen JJ. The development of a dietary questionnaire for an ischemic heart disease survey, Isr J Med Sci. 1%8;4:195-203. 27. Hebert JR, Miller DR. Methodologic considerations for investigating the diet-cancer link, Am J Clin Nutr. 1988;47:1068-1077.
34.
35.
28. McKeown-Eyssen GE, Tibshirani R. Implications of measurement error in exposure for the sample size of case-control studies, Am J Epidemiol. 1994;139:415-421.
36.
29. Chalmers FW, Clayton MM, Gates AO, et al. The dietary recordhow many and which days? ,I Am Diet Assoc. 1952;28:711-717.
37.
30. Marr JW, Heady JA. Within- and between-person variation in dietary
QUESTlONNAIRE
Arnold et al. FOR YOUNG GIRLS
377
surveys: Number of days needed to classify individuals, Hum Nutr Appl Nutr. 1986;4OA:347-364. Bingham SA. The dietary assessment of individuals: Methods, accuracy, new techniques and recommendations, Nutr Abstr Rev. 1987; 51~705-742. Nelson M, Black AE, Morris JA, Cole TJ. Between- and within-subject variation in nutrient intake from infancy to old age: Estimaring the number of days required to rank dietary intakes with desired precision, Am J Clin Nutr. 1989;50:155-167. Beaton GH. Interpretation of results from diet history studies. In: Kohlmeier L, ed. The Diet History Method. Proceedings of the 2nd Berlin Meeting on Nutritional Epidemiology. London: Smith-Gordon; 1989. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic withinperson measurement error, Stat Med. 1989;8:1051-1069. Freedman LS, Schatzkin A, Wax Y. The impact of dietary measurement error on planning sample sire required in a cohort study, Am J Epidemiol. 1990;132:1185-1195. Basch CE, Shea S, Arliss R, et al. Validation of mothers’ reports of dietary intake by four to seven year-old children, Am J Public Health. 1990;80:1314-1317. Treiber FA, Leonard SB, Frank G, et al. Dietary assessment. Instruments for preschool children: Reliability of parental responses to the 24-hour recall and a food frequency questionnaire, J Am Diet Assoc. 1990;90:814-820.
BSc, MSc, BSc, PHD,
THOMAS ROHAN, MB, BS, AND MICHAEL LEBLANC,
MSc, BSc,
Qwe
PHD, MMATH,
PHD
This study focused on a food-frequency questionnaire (FFQ designed to measure nutrient intake in girls aged 7 to 12 years, inclusive. The instrument’s reproducibility and validity were assessed using foodrecords (FIG) as “gold stand&s” of measurement. Log-trans~ed nurrient intake estimates were compared from two FFQs and between FFQs and FRS. lntraclass correlation coeficients measuring the reproducibility of the FFQ ranged from 0.11 (starch) to 0.69 (fiber). lntraclass correlation coeficients measuring agreement between FFQ and 14&y FR data varied between 0.15 (starch) und 0.68 (vitamin B2) for the first, and between 0.06 (starch) and 0.95 (vitamin Bf) for the second FFQ. FFQs were in the best agreement with FRs for the following nutrients: fiber, vitamin BI, vitamin B2, vitamin C, and beta-carotene. Joint ckassifications revealed that overall, 36% of subjects weresimilarly categorized by FFQ and FR, and 70% of those in the lowest or highest FR quartiles were found in the lowest or highest two FFQ quartiles, respectively. Ann Epidemiol 1995; 5:369-377 KEY WORDS:
Epidemiologic
methods, food, questionnaires,
INTRODUCTION Although many dietary exposures may putatively be risk factors for cancer, determination of their true effects is impeded by the lack of instruments with which the exposures can be accurately measured. Several approaches to the measurement of dietary intake capture actual intake (e.g., food records and observer recording). However, these may be unrepresentative of usual dietary intake, and somewhat unreliable for assessing past eating patterns (1). Methods that measure past diet, such as recall of consumption for periods of 1 to several days, may be problematic with regard to their reproducibility in measuring usual consumption (2). In response to the need to collect accurate and reliable data on usual dietary intake for a large number of subjects in a simple, straightforward manner, instruments such as the food-frequency questionnaire have been developed. The food-frequency questionnaire is a method of dietary measurement whereby subjects indicate what their usual frequency of consumption of certain foods has been over a finite interval, such as the previous year. The timing of dietary measurements is another im-
From the Department of Preventive Medicine and Biostatistics, University of Toronto (J.A., T.R., M.L.), and the National Cancer Institute of Canada Epidemiology Unit (T.R., G.H.), Toronto, Ontario, Canada. Address reprint requests to: Julie Arnold, BSc, MSc, Clinical Epidemiology Division, Well&y Hospital Research Institute, do 160 Wellesley Street East, Toronto, Ontario, Canada M4Y 113. Received March 30, 1994; accepted August 5, 1994. 0 1995 by Elsevier Science Inc. 655 Avenue of the Americas. New York. NY 10010
children,
diet.
portant consideration. For example, inconsistent findings from epidemiologic studies of diet and breast cancer (3-9) are perhaps a result of the fact that investigators have, by and large, only looked at the adult diet and risk of disease. In diseases such as breast cancer, early dietary exposure may prove to be more important than exposure in later life (4). In studies of childhood eating patterns, investigators have either administered a food-frequency questionnaire to a sample of adults and asked them to recall their diet from their childhood days (9), or administered a food-frequency questionnaire to children themselves (10). In the! latter case, investigators have supported the use of rhe questionnaire with evidence taken from validation studies done with adult subjects. Although several diet measurement methods have been validated with children, including 24-hour recalls (1 I), parental recall (12), and the nutrient adequacy score (13), these instruments do not provide adequate measures of usual dietary intake. To our knowledge, no validation or reproducibility studies of food-frequency questionnaires have been done in children. The objective of the study reported here was to evaluate the quality of a food-frequency questionnaire designed to measure usual dietary intake in girls between the ages of 7 and 12 years, inclusive. The quality of the food-frequency questionnaire was judged on the basis of its reproducibility and its validity as compared to 7day food records. For our purposes, reproducibility is a measure of the extent to which the results of measurement are similar each time a measurement is taken. Validity is taken to be the degree to whiih a measurement measures what it is intended to measure (14). 1047-2797/95/$9.50 SSDi 11?47-2797(95)00034-5
370
Arnold et al. FOOD-FREQUENCY
MATERIALS
QUESTIONNAIRE
AEP Vol. 5, No. 5 September1995: 369-377
FOR YOUNG GIRLS
AND METHODS
The food-frequency questionnaire used in this study was derived from one that had been used in a sample of adults in a pervious study conducted by the National Cancer Institute of Canada (NCIC) Epidemiology Unit. Prior to this validation study, a survey was taken of girls aged 7 to 12 years, inclusive, in order to discover what changes should be made to the adults’ food-frequency questionnaire. On the basis of 24-hour recalls and food-frequency questionnaire responses, the following items were added to the adult version of the questionnaire: dairy foods with a variety of fat contents, ground beef with variable fat contents, powderbased fruit drinks, different types of cooked eggs (e.g., boiled and fried), doughnuts, and ketchup. The validation study entailed measuring dietary intake in a sample of girls on four occasions. Intake estimates from food-frequency questionnaires were compared to those from 7-day food records, and each instrument was administered twice. There were intervals of 1 month between the foodfrequency questionnaires and food records (first foodfrequency questionnaire (FFQI) and first food record (FRl), and second food-frequency questionnaire (FFQZ) and second food record (FR2)) and of 6 months between the two food-frequency questionnaires (FFQI and FFQZ). Study subjects were contacted through primary and middle schools, as well as through the Girl Guides in Metropolitan Toronto. During the months of October and November 1991, packages containing the study material were distributed to 707 girls between the ages of 7 and 12, inclusive. Of these 707 packages, 531 were distributed to girls at school, and 176 to girls at a Girl Guide meeting. The study material consisted of a food-frequency questionnaire (Figure l), a letter of introduction, a consent form, and a postagepaid envelope. The girls were asked to take the packages home to their parents or guardians. Once the consent of a participant was received by the investigators, her name, address, and telephone number were randomly allocated to one of three trained interviewers. The parent or guardian was telephoned by the interviewer during the following week, and an initial appointment was arranged. At the first visit, the interviewer reviewed the food-frequency questionnaire to be sure it was complete. At the second visit, 1 month later, the interviewer reviewed the instructions for the food record with the participating parent and daughter. At the next visit, the interviewer collected the record and reminded the participants that the family would be contacted again in approximately 6 months. After a period of approximately 6 months, the subjects were once again contacted by telephone and delivery of the second food-frequency questionnaire was scheduled. Thereafter, the procedure was the same as for the first round of data collection. Along with the second food-frequency questionnaire,
each participant provided supplementary, nondietary information such as date of birth, and the name of the person responsible for completion of the dietary forms.
Coding of the Forms The self-administered food-frequency questionnaires were preceded for 160 foods. Additional foods indicated by the respondent in the blank lines at the end of the questionnaire were assigned one of the same 160 food codes, based on the information provided. The food records were coded by a trained coder, applying standard codes used by the NCIC Epidemiology Unit.
Database for Computation
of Nutrient
Intakes
Nutrient intakes from the food-frequency questionnaires and the food records were derived by way of a database derived from US Department of Agriculture (USDA) food composition tables (2). The tables have been modified and extended to include typically Canadian food items, according to food composition tables which include information from commercial firms, cookbooks, and recipes provided by subjects from previous studies (2).
Data Analysis Natural logarithms were taken of each of the daily nutrient intake estimates, and because these transformations improved the normality of the data, logarithms were used in all analyses of correlation. The regression method of Willett and Stampfer (15) was employed to correct for total energy intake. Specifically, regressions were carried out using the crude estimates of nutrient intake (dependent variable) and the estimate of total caloric intake (independent variable), as measured by the same food record or food-frequency questionnaire. The residuals from the model were then added to the sample mean of the nutrient intake values for that instrument, that is, the nutrient intake at mean caloric intake. This total was taken as the calorie-adjusted nutrient intake. Assessment of repeatability. To assess the foodfrequency questionnaire’s reproducibility, the strength of the association between corresponding calorie-adjusted nutrient intake estimates was measured using Pearson and intraclass correlation coefficients (16). Ninety-five percent confidence intervals (95% CIs) were generated for Pearson correlation coefficients. For intraclass correlation with fixed effects (i.e., in an analysis of variance where there are only two instruments about which inferences will be made), no simple method, exact or approximate, is available for generating confidence intervals (16). Assessment of validity. Pearson correlation coefficients and intraclass correlation coefficients were also used to assess the validity of the food-frequency questionnaire. First, the calorie-adjusted estimate of nutrient intake measured by
AEP Vol. 5, No. 5 September1995: 369-377
FOOD-FREQUENCY
QUESTIONNAIRE
Arnoid et al. FOR YOUNG GIRLS
FIGURE 1. A section of the girls’ food-frequency questionnaire.
the food-frequency questionnaires and the corresponding ?-day food records (administered 1 month later) were compared. Then, the nutrient intakes from both 7-day food records were combined to give each subject a I4-day mean nutrient intake score. The correlation between this mean nutrient intake score and the intake estimates from each of the two food-frequency questionnaires was then determined. Subsequently, subjects were categorized into quartiles of nutrient intake for each of the nutrients in question. Classifications were performed on three sets of data; the 1Cday means of food record data, as well as the two food-
frequency questionnaires. Each of the three classifications made use of its respective cutpoints for the breakdown into quartiles. To assess agreement, two cross-tabulations were then performed: classification according to the 14-day mean versus FFQI and then versus FFQZ.
RESULTS Completed food-frequency questionnaires were received from 92 (13.0%) of the 707 subjects to whom packages were distributed (Figure 2). Seven participants withdrew from
372
AEP Vol. 5, No. 5 September 1995: 369-377
Arnold et al. FOOD-FREQUENCY
QUESTIONNAIRE
FOR YOUNG
GIRLS
707subjectscopacted(100%) I I (13.0% of total)
p-q-l
I (6(5
I 80 FFW (87.0%)
5 drop-outs
I
1’ 71 FR2 (83.7%)
3 drop-outs
FIGURE 2. Response rates over the course of the study. FFQl , first food-frequency questionnaire; FFQ2, second food-frequency questionnaire; FRl, first 7-day food record; FEL2, second 7-day food record.
the study, having completed only the first food-frequency questionnaire. Five respondents dropped out of the study before completion of the second food-frequency questionnaire and three dropped out afterward. Therefore, 77 (83.7%) of the 92 initial respondents completed both foodfrequency questionnaires and both 7-day food records. Of the food-frequency questionnaires, 75% were completed by the parents, 16% were completed by the daughter
and parents together, and the balance (9%) were completed by the daughter alone, or some other person. For the food records, the corresponding figures were 74, 18, and 8%. Calorie-adjusted estimates of daily nutrient intake obtained from the food-frequency questionnaires were generally higher than those derived from the 7-day food records (Table 1). The comparison of the two food-frequency questionnaires yielded correlation coefficients that ranged from 0.14 (95% CI: -0.08 to 0.35) for total fat to 0.71 (95% CI: 0.58 to 0.80) for fiber (Table 2). Intraclass correlation coefficients ranged from 0.11 for starch to 0.69 for fiber. Pearson coefficients measuring agreement between calorie-adjusted daily nutrient intakes from the first foodfrequency questionnaire and the first 7-day food record ranged from 0.00 (95% CI: -0.22 to 0.21) for unsaturated fat to 0.51 (95% CL 0.34 to 0.65) for vitamin Bz (Table 3). For the second set of food-frequency questionnaires and 7-day food records, the correlation coefficients ranged from 0.13 (95% CI: -0.10 to 0.35) for starch to 0.60 (95% CI: 0.44 to 0.73) for fiber. Intraclass correlation coefficients were generally, but not always, higher than product-moment correlations. They ranged, in the first set of measurements (FFQI versus FRl), from 0.03 for unsaturated fat to 0.63 for vitamin Bz. In the second set of measurements (FFQ2 versus FR2), the intraclass correlation coefficients ranged from 0.13 for starch to 0.55 for beta-carotene (seeTable 3). When calorie-adjusted nutrient intake from the first food-frequency questionnaire was compared to the ICday mean intake from food records (Table 4), the range of Pear-
TABLE 1. Total daily energy intake and calorie-adjusted daily nutrient intake as determined food-frequency questionnaires (FFQI and FFQ2) and 7-day food records (FFX and FR2) Nutrient (units) Energy (Cal/d) F’rotein (g/d) Total fat (g/d) Carhohydrare (g/d) Fiber (g/d)
FFQi (n = 92)
FFQZ (n = 80)
FRl
by the first and second (n = 85)
FR2 (n = 77)
Mean
SD
Mean
SD
Mean
SD
Mean
SD
2319.01 93.72 87.35 303.44
671.27 43.61 30.18 90.16 2.62 5418.81 0.49 0.79 119.27 10.91 16.91 152.49 37.87 43.30 680.21 4440.18
2204.73 86.30 82.31 293.08 5.79 8020.34 1.50 2.45 230.26 30.33 43.50 296.08 101.32 121.54 1161.05 5376.45
606.79 27.82 25.16 84.56 2.36 4288.37 0.64 1.52 120.43 9.86 13.78 99.45 32.08 39.74 705.83 3450.71
1866.76 68.61 69.19 250.75 3.80 4917.72 1.06 1.63 141.93 25.66 36.33 242.90 88.88 96.04 979.06 3044.93
369.42 16.43 19.84 50.34 1.20 2692.56 0.24 0.50 56.93 8.07 10.49 81.83 24.51 23.71 1084.12 1924.33
1901.77 68.85 72.45 253.54 3.90 4663.02
350.88 15.54 17.97 51.78 1.16 2277.45 0.29 0.47 58.94 7.09 10.08 88.73 22.82 27.26 741.76 1679.85
6.21
Vitamin A” (W/d) Vitamin B1” (mg/d) Vitamin BP (mg/d) Vitamin C” (mg/d) Saturated fat (g/d) Unsaturated fat! (g/d) Cholesterol (mg/d) Starch (g/d)
8583.20 1.54 2.44 238.49 31.83 46.45 316.39 104.23
Total sugar (g/d) Retinol (lU/d)
126.81 1211.15
Beta-carotene @U/d)
5804.91
SD, standard deviation. * Exchding vitamin supplementation. b Unsaturated fats = linoleic and oleic fatty acids.
1.11 1.60
135.82 26.35 39.14 248.50 93.25 98.46 839.37 2987.03
Arnold et al.
AEP Vol. 5, No. 5 September 1995: 369-377
FOOD-FREQUENCY
TABLE 2. Pearson correlation coefficients (r) and intraclass correlation coefficients (ri) of log-transformed daily energy intake and calorie-adjusted nutrient intakes from the first and second food-frequency questionnaires (FFQl and FFQ2) and their 95% confidence interval (CI) FFQl Nutrient
’ Excluding
95%Cl
T
Energy Protein Total fat Carbohydrate Fiber Vitamin A Vitamin 31” Vitamin Br” Vitamin C Saturated fat Unsaturated fat Cholesterol Starch Total sugars Retinol Beta-carotene vitamin
vs. FFQ2
0.47, 0.33, -0.08, 0.14, 0.58, 0.44, 0.38, 0.56, 0.45, 0.08, -0.04, -0.06, 0.30, 0.32, 0.39, 0.44.
0.60 0.51 0.14
0.35 0.71
0.60 0.55 0.70 0.61
0.29 0.18 0.16 0.49 0.50 0.56 0.60
TI
0.92 0.66 0.35 0.53 0.80 0.73 0.69 0.80 0.73 0.48 0.39 0.36 0.64 0.65 0.70 0.73
0.54 0.56 0.20 0.40 0.69 0.59 0.56 0.52 0.59 0.32 0.24 0.31 0.11
0.50 0.62 0.59
suppkmenration.
son correlation coefficients was from 0.13 (95% CI: -0.01 to 0.36) for energy to 0.61(95% CI: 0.45 to 0.74) for vitamin Bz. A similar analysis, done with data from the second foodfrequency questionnaire and the mean from both food records (see Table 4), resulted in a range of 0.06 (95% CI; -0.17 to 0.28) for starch to 0.57 (95% CI: 0.40 to 0.71) for vitamin C. Again, for most nutrients, the intraclass
QUESTIONNAIRE
FOR YOUNG
373
GIRLS
correlation coefficients were higher than the Pearson correlation coefficients (see Table 4). Person correlation coefticients were also generated using nutrient intakes before adjustment for total energy intake (data not shown). The most marked differences between the unadjusted and adjusted Pearson correlation coefficients were seen between the first and second food-frequency questionnaires for total fat, where the correlation (r) based on unadjusted estimates was 0.60; cholesterol, where r was 0.60; unsaturated fat, where r was 0.60; and saturated fat, where r was 0.61 (correlations based on unadjusted nutrient intakes are available on request).
Joint Classification On average, 34.2 1% of the individuals were classified exactly the same by 14 days of food records and the first foodfrequency questionnaire (Table 5). That is, overall, 34.21% of the sample were found in the leading diagonal of the four-by-four table produced by the cross-tabulation of foodfrequency questionnaire versus the food record measurements. Corresponding percentages for individual nutrients ranged from 20.78% for vitamin Bi to 45.45% for fiber. The second food-frequency questionnaire correctly categorized a mean of 38.35% of individuals, and the proportion of correct categorizations ranged from 23.38% for vitamin B1 to 55.84% for vitamin C. On average, the first foodfrequency questionnaire properly categorized 36.25% of the lowest quartile and 38.92% of the highest quartile of nutrient consumers (see Table 5). The second questionnaire correctly classified 40.3 1% of those with the lowest and 48.97% of those with the highest intake.
TABLE 3. Pearson correlation coefficients (r) and intraclass correlation coefficients (ri) of log-transformed, calorie-adjusted nutrient intakes from the first and second sets of T-day food records (FFQl and FFQ2) and their 95% confidence interval (CI) FFQI T
Nutrient
0.29
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin BI” Vitamin Br8 Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugars Retinol Beta-carotene d Excluding
vitamin
0.17
0.05 0.15
0.44 0.35 0.35 0.51
0.29 0.19 0.00
0.29 0.20 0.10
0.21 0.33 supplementation
vs. FRI
(FRl
and FR2) and food-frequency
FFQ2 vs. FR2 (n 1 77)
(n = 85)
95% CI 0.08, -0.04, -0.17, -0.07, 0.25, 0.15, 0.15, 0.34, 0.08, -0.02, -0.22, 0.08,
0.52 0.37 0.26 0.35 0.60 0.52 0.52 0.65 0.48 0.39 0.21 0.48
-0.01, -0.12,
0.40 0.31
0.00, 0.41 0.13, 0.51
questionnaires
71
r
0.24 0.28 0.09 0.20 0.44 0.34 0.57 0.63 0.28 0.22 0.03 0.37 0.20
0.22 0.25 0.38 0.37 0.60 0.51 0.41 0.39 0.49 0.38 0.39 0.46 0.13 0.30 0.29 0.55
0.11
0.25 0.32
95% Cl 0.00, 0.03. 0.17, 0.16, 0.44, 0.32, 0.21,
0.45 0.45 0.56 0.55 0.7? 0.66 0.56
0.18, 0.56
0.30, 0.17, 0.18, 0.26, -0.10, 0.08, 0.07, 0.37,
0.64 0.56 0.56 0.62 0.35 0.4Y 0.49 0.69
71 0.19 0.26 0.37 0.36 0.50 0.50 0.3a 0.44 0.49 0.38 0.37 0.42 0.13
0.29 0.28 0.55
Arnold et al. FOOD-FREQUENCY
374
AEP Vol. 5, No. 5 QUESTIONNAIRE
FOR YOUNG GIRLS
September
1995:
369-377
TABLE 4. Pearson correlation
coefficients (r) and intraclass correlation coefficients (rr) of log-transformed, calorie-adjusted nutrient intakes from the l+day mean of food records (FRM) and the first and second food-frequency questionnaires (FFQI and FFQZ) and their 95% confidence intervals (CI) FFQ2 vs. FRM (n = 77)
FFQl vs. FRM (n = 77)
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin Bt” Vitamin Bi Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugars Retinol Beta-carotene ’ Excluding
95% CI
r
Nutrient
vitamin
0.13
0.20 0.28 0.27 0.59 0.47 0.41 0.61 0.43 0.28 0.33 0.25 0.15 0.23 0.32 0.47
-0.01, -0.03, 0.06, 0.05, 0.42, 0.27, 0.21, 0.45, 0.23, 0.06, 0.11, 0.03, -0.08, 0.01,
0.36 0.41 0.48 0.46 0.72 0.63 0.58 0.74 0.60 0.48 0.52 0.45 0.36 0.43 0.10, 0.51 0.27, 0.63
T
n 0.18 0.29 0.28 0.27 0.57 0.46 0.50 0.68 0.44 0.27 0.32 0.31 0.15 0.22 0.31 0.46
95% CI
0.22 0.30 0.46 0.45 0.56 0.49 0.35 0.36 0.57 0.50 0.45 0.52 0.06 0.29 0.30 0.53
0.00, 0.08, 0.26, 0.25, 0.38, 0.30, 0.14, 0.15, 0.40, 0.31, 0.25, 0.34, -0.17, 0.07, 0.08, 0.35,
0.45 0.49 0.62 0.61 0.70 0.64 0.53 0.54 0.71 0.65 0.61 0.66 0.28 0.49 0.49 0.68
TI 0.18 0.30 0.44 0.44 0.55 0.39 0.95 0.44 0.56 0.49 0.39 0.50 0.06 0.29 0.29 0.53
supplementation.
When the first food-frequency questionnaire was used, 66.88% of those in the lowest food record quartile were in the lowest two questionnaire quartiles, and 69.44% of those in the highest food record quartile were in the highest two questionnaire quartiles (Table 6). Similarly, with the second food-frequency questionnaire, 68.13% of those in the lowest food record quartile were ranked in the lowest two questionnaire quartiles, and 74.63% of those in the highest food record quartile were in the highest two questionnaire quartiles.
DISCU!3SION The intake levels observed using food records in the present study are comparable to those found in another Canadian study of young girls (mean age, 11.1 years), which used food records to look at the relationship between diet and age at menarche (17). Results from the present study indicate that this version of the food-frequency questionnaire is of comparable reliability to those described by other investigators (18-22). For 10 of the 16 nutrients examined in the present
TABLE 5. Percentage of individuals correctly classified by food-frequency as comnared to classification by 14-daymean of food records (FRh4) All correct classifications
questionnaires
(FFQI
Lowest FRM quartile
and FFQZ), Highest FRM quartile
Nutrient
FFQl
FFQ2
FFQl
FFQ2
PQl
mQ2
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin Bt” Vitamin Br“ Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugar Retinal Beta-carotene Mean
25.97 33.77 38.96 33.77 45.45 25.27 20.78 42.86 44.16 33.77 38.96 33.77 22.08 36.36 28.57 42.86 34.21
33.77 42.86 37.66 45.45 44.16 25.32 23.38 35.06 55.84 45.45 48.05 36.36 29.87 37.66 31.17 41.56 38.35
35.00 45.00 45.00 35.00 45.00 0.00 0.00 50.00 45.00 60.00 40.00 30.00 20.00 40.00 35.00 55.00 36.25
35.00 45.00 50.00 45.00 45.00 0.00 0.00 35.00 65.00 65.00 50.00 45.00 40.00 35.00 30.00 60.00 40.3 1
15.79 31.58 31.58 47.37 68.42 25.27 57.89 60.53 47.37 21.05 47.37 36.84 26.32 31.58 31.58 42.11 38.92
36.84 57.89 36.84 63.16 63.16 25.64 94.74 52.63 63.16 42.11 63.16 47.37 21.05 36.84 26.32 52.63 48.97
’ Excluding
vitamin
supplementation.
AEP Vol. 5, No. 5 September1995: 369-377
Arnold et al. FOOD-FREQUENCY
TABLE 6. Percentage of individuals classified into the lowest and highest calorie-adjusted nutrient intake quartiles by 1Cday mean of food records (FRM) and into the two lowest and two highest quartiles by the food-frequency questionnaires (FFQl and FFQ2), respectively Lowest FRM quartile Nutrient
FFQl
Energy Protein Total fat Carbohydrate Fiber Vitamin A” Vitamin BI” Vitamin BI” Vitamin c” Saturated fat Unsaturated fat Cholesterol Starch Total sugar Retinol Beta-carotene Mean
55.00 80.00 70.00 60.00 80.00 0.00 100.00 80.00 70.00 70.00 75.00 75.00 55.00 65.00 70.00 65.00 66.88
wQ2 60.00
80.00 70.00 75.00 70.00 0.00 100.00 40.00 80.00 85.00 80.00 75.00 60.00 75.00 65.00 75.00 68.13
Highest FRM quartile WQl
=Q2
63.16 73.68 63.16 68.42 84.21 50.55 84.21 60.53 78.95 52.63 78.95 63.16 57.89 84.21 73.68 73.68 69.44
68.42 89.47 73.68 78.95 78.95 49.37 94.74 76.32 84.21 57.89 84.21 78.95 63.16 78.95 57.89 78.95 74.63
” Excludingvitaminsupplementation.
study, the first and second food-hequency questionnaires showed correlations of 0.5 or higher. In general, the food-frequency questionnaire used in this study tended to overestimate nutrient values, relative to those obtained from the 7-day food records. This trend was noted in previous studies, albeit with less consistency across nutrients (18, 19, 23, 24). Overestimation of intake may have resulted from inaccurate reporting of either the frequency or the quantity of specific foods eaten (portion size), or both. Since the food-frequency questionnaire measured food portion size, the relative contribution of errors in reporting frequency and quantity of intake could be assessed by comparing portion sizes and frequency in the foodfrequency questionnaires and food records. Evaluation of this issue is beyond the scope of the present report. Overall, the food-frequency questionnaire was in the best agreement with food records for fiber, vitamin B1, vitamin BZ, vitamin C, and beta-carotene. Some of the correlations between food-frequency and food record values were higher with the second questionnaire. This trend was observed previously (23), and might reflect a learning phenomenon. It is possible that participation in dietary studies results in changes in the subjects’ natural eating behavior. This phenomenon, known as the “Hawthorne effect” (25), may affect eating patterns, methods of food preparation, or the precision and accuracy with which the foods are recorded. It is difficult to determine whether these phenomena OCcurred in the present study, and if so, to what extent. How-
QUESTIONNAIRE
375
FOR YOL!NG GIRLS
ever, this is not a consideration in situatians where the subjects complete a food-frequency questionnaire on only one occasion (e.g., retrospective or cross-sectional studies). Under such circumstances, the best indication of the performance of the instrument can be obtained by comparing one administration of the questionnaire ro a gold standard measurement of usual dietary intake (e.g., in the present study, FFQl versus FRM). It should be noted that the food-frequency questionnaire is designed to measure usual diet, as opposed to actual dietary intake, which is measured by food records. Thus, correlation coefficients measuring agreement between results from the questionnaire and those from food records can be expected to be somewhat modest. Investigations in this field revealed that most coefficients range from 0.4 to 0.6 (3,2628). One study, similar in design to rhe present one but conducted in a sample of American registered nurses, yielded coefficients between food-frequency questionnaires and food records as high as 0.75 (23). However, in that study, the investigators administered the 7-day records on four occasions, and the calorie-adjusted estimates of daily intake were based on 28 days of recording. The optimal number of days for which diet records should be completed has received considerable attention in methodologic studies of diet measurement (29-3 1). Nelson and coauthors (32) discussed the extent of misclassification that can be expected using 7 days of diet records in samples of young girls. In such a group, they found that accurate measurement of diet would require 6 to 280 days of recording, depending on the nutrient in question, but that many nutrients (e.g., energy, total sugar, fat, monounsaturated and saturated fatty acids, and calcium) require 2 weeks or less. In the present study, the five highest Pearson correlation coefficients were above 0.40 in all comparisons except the first set of food-frequency questionnaire and 7-day food record data (FFQl versus FRI). Compared to their corresponding Pearson correlation coefficients, the intraclass correlation coefficients were higher with the first than with the second food-frequency questionnaire. Large differences in intraclass and Pearson correlation coefficients for some nutrients may suggest a high degree of wirhin-person variability for those items (in particular, vitamin BI for the second food-frequency questionnaire versus the I4-day food record mean). It has been suggested that food-frequency questionnaires might be susceptible to systematic within-person error (33). Such error might be generated by subjects’ inability to accurately recall, estimate, or aggregate their patterns of consumption over 1 year. As a result of this error, spuriously high correlations might result. Food-frequency questionnaires are often used in studying the association between diet and disease. Rosner and coworkers (34) explored the effects of systematic, within-person error associated with
376
Arnold et al. FOOD-FREQUENCY
QUESTIONNAIRE
AEP Vol. 5, No. 5 September1995: 369-377
FOR YOUNG GIRLS
the use of food-frequency questionnaires on relative risk estimates and suggested appropriate adjustments. Investigators have also looked at the problem of random measurement error in the food-frequency questionnaire, which biases results from epidemiologic studies toward the null value (28, 35). McKeown-Eyssen and Tibshirani (28) asserted that considerable sample size increases might be necessary to allow for the effects of measurement error on estimation of relative risk. Freedman and coworkers (35) reached similar conclusions. Beaton (33) described the analytic components of variance such as between-subject (interperson) variance and within-subject (day-to-day) variance, and applied his models to previous validation studies of the food-frequency questionnaire (1, 23). The response rate in this study was low. However, considerable constraints were placed on its implementation. The agreements that were made with the School Board and with the Girl Guides allowed us to distribute material to the girls only once, and thereby rendered us unable to follow up with nonrespondents to improve overall response or investigate possible selection bias. Our results are therefore generalizable only to situations where subjects volunteer for participation. It is conceivable that the instrument would not perform as well in a wider population. Proxy responses are to be expected in dietary studies involving young children. Indeed, in our sample, close to three-fourths of the food-frequency questionnaires and food records were completed by the parents of the girls. It is possible that this occurrence may have affected the validity of the responses since parents might not have had full knowledge of their daughters’ diets. Parental responses or responses by consensus between parent and child have, however, been shown to have a high degree of accuracy (12,36,37). Another serious consideration related to proxy responses is the potential for change in the degree of proxy response if the instrument is used in a sample over a long period of time. For example, if young children are studied prospectively, they themselves might become increasingly more responsible for completing their food-frequency questionnaires, instead of their parents. With time, the performance of the instrument might therefore change, with regard to its validity and reproducibility, In this case, it might therefore be useful to reassess the quality of the foodfrequency questionnaire data periodically as the cohort ages. It is important that dietary instruments are developed to suit the samples that are studied. The validity of the questionnaire evaluated here varied among the nutrients tested, but this might reflect the relatively limited number of days of food record data. Additionally, the food-frequency questionnaire was reasonably consistent in classifying the top and bottom 50% of individuals, based on their nutrient intake, suggesting that use of the food-frequency data in categorical analyses might be appropriate. Furthermore, it
will be possible to use the estimates of measurement error obtained from this study to adjust the relative risk estimates obtained in an associated prospective study of diet and menarche.
The authors gratefully acknowledge Dr. Meera lain for her assistance and advice in data collection.
REFERENCES 1. Willett WC. Nutritional Epidemiology. Monographs in Epidemiology and Bioscatistics. v. 15. New York: Oxford Univ. Press; 1990. 2. Morgan RW, Jain M, Miller AB, et al. A comparison of dietary methods in epidemiologic studies, Am J Epidemiol. 1978;107;6:488-498. 3. Miller AB, Kelly A, Choi NW, et al. A study of diet and breast cancer, Am J Epidemiol. 1978;107:499-509. 4. Willett WC, Stampfer MJ, Colditz GA, Rosner BA, Hennekens CH, Speizer FE. Dietary fat and the risk of breast cancer, N Engl J Med. 1987;316:22-28. 5. Howe GR, Hirohata T, Hislop G, et al. Dietary factors and risk of breast cancer: Combined analysis of 12 case-control studies, J Nat1 Cancer Inst. 1990;82;7:561-569. 6. Howe GR, Friedenreich CM, Jain M, Miller AB. A cohort study of fat intake and risk of breast cancer. .1 Nat1 Cancer Inst. 1991:83:336340. 7. Knekt P, Albanes D, Seppanen R, et al. Dietary fat and risk of breast cancer, Am J Clin Nutr. 1990;52:903-908. 8. Kushi LH, Sellers TA, Potter JD, et al. Dietary fat and postmenopausal breast cancer, J Nat1 Cancer Inst. 1992;84:1092-1099. 9. Hislop TG, Coldman AJ, Elwood JM, Braver G, Kan L. Childhood and recent eating patterns and risk of breast cancer, Cancer Detect Prev. 1986;9:47-58. 10. Maclure M, Travis LB, Willett W, MacMahon B. A prospective cohort study of nutrient intake and age of menarche, Am J Clin Nutr. 1991; 54649-656. 11. Frank GC, Voors AW, Schilling PE, Berenson GS. Dietary studies of rural school children in a cardiovascular survey, J Am Diet Assoc. 1977;71:31-35. 12. Eck LH, Klesges RC, Hanson CL. Recall of a child’s intake from one meal: Are parents accurate?, J Am Diet Assoc. 1989;89:784-789. 13. Krebs-Smith SM, Clark LD. Validation of a nutrient adequacy score for use with women and children, J Am Diet Assoc. 1989;89:775-783. 14. Last J. A Dictionary Univ. Press; 1990.
of Epidemiology. 3rd ed. New York: Oxford
15. Willett W, Stampfer MJ. Total energy intake: Implications for epidemiologic analyses, Am J Epidemiol. 1986;124:17-27. 16. Fleiss JL. The Design and Analysis of Clinical Experiments. New York: Wiley; 1986. 17. Moisan J, Meyer F, Gingras S. Diet and age at menarche, Cancer Causes Control. 19QO;l:149-154. 18. Pietinen P, Hartman AM, Haapa E, et al. Reproducibility and validity of dietary assessment instruments. I. A self-administered food use questionnaire with a portion size picture booklet, Am J Epidemiol. 1988; 128~655-666. 19. Salvini S, Hunter DJ, Sampson L, et al. Food-based validation of a dietary questionnaire: The effects of week-to-week variation in food consumption, Int J Epidemiol. 1989;18:858-867. 20. Reshef A, Epstein LM. Reliability of a dietary questionnaire, Am J Clin Nutr. 1972;25:91-95. 21. Jain M, Howe GR, Johnson KC, Miller
AB. Evaluation
of a diet
AEP Vol. 5, No. 5 Segtember1995: 369-377
FOOD-FREQUENCY
history questionnaire for epidemiologic studies, Am J Epidemiol. 1980; 111:212-219. 22. Hankin JH, Nomura AMY, Lee J, Hirohata T, Kolonel LN. Reproducibility of a diet history questionnaire in a case-control study of breast cancer, Am J Clin Nutr. 1983;37:981-985. 23. Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire, Am J Epidemiol. 1985;122:51-65. 24. Tjonneland A, Overvad K, Haraldsdottir J, Bang S, Ewertz M, Jensen OM. Validation of a semiquantitative food frequency questionnaire developed in Denmark, Int J Epidemiol. 1991;20:906-912.
3 1.
32.
33.
25. Sackett DL. Bias in analytic research, J Chronic Dis. 1979;32:51-63. 26. Balogh M, Medalie JH, Smith H, Groen JJ. The development of a dietary questionnaire for an ischemic heart disease survey, Isr J Med Sci. 1%8;4:195-203. 27. Hebert JR, Miller DR. Methodologic considerations for investigating the diet-cancer link, Am J Clin Nutr. 1988;47:1068-1077.
34.
35.
28. McKeown-Eyssen GE, Tibshirani R. Implications of measurement error in exposure for the sample size of case-control studies, Am J Epidemiol. 1994;139:415-421.
36.
29. Chalmers FW, Clayton MM, Gates AO, et al. The dietary recordhow many and which days? ,I Am Diet Assoc. 1952;28:711-717.
37.
30. Marr JW, Heady JA. Within- and between-person variation in dietary
QUESTlONNAIRE
Arnold et al. FOR YOUNG GIRLS
377
surveys: Number of days needed to classify individuals, Hum Nutr Appl Nutr. 1986;4OA:347-364. Bingham SA. The dietary assessment of individuals: Methods, accuracy, new techniques and recommendations, Nutr Abstr Rev. 1987; 51~705-742. Nelson M, Black AE, Morris JA, Cole TJ. Between- and within-subject variation in nutrient intake from infancy to old age: Estimaring the number of days required to rank dietary intakes with desired precision, Am J Clin Nutr. 1989;50:155-167. Beaton GH. Interpretation of results from diet history studies. In: Kohlmeier L, ed. The Diet History Method. Proceedings of the 2nd Berlin Meeting on Nutritional Epidemiology. London: Smith-Gordon; 1989. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic withinperson measurement error, Stat Med. 1989;8:1051-1069. Freedman LS, Schatzkin A, Wax Y. The impact of dietary measurement error on planning sample sire required in a cohort study, Am J Epidemiol. 1990;132:1185-1195. Basch CE, Shea S, Arliss R, et al. Validation of mothers’ reports of dietary intake by four to seven year-old children, Am J Public Health. 1990;80:1314-1317. Treiber FA, Leonard SB, Frank G, et al. Dietary assessment. Instruments for preschool children: Reliability of parental responses to the 24-hour recall and a food frequency questionnaire, J Am Diet Assoc. 1990;90:814-820.