Validation of a short food frequency questionnaire to assess consumption of cereal foods, fruit and vegetables in Chinese Singaporeans

Transcription

1 European Journal of Clinical Nutrition (1998) 52, 557±564 ß 1998 Stockton Press. All rights reserved 0954±3007/98 $ to assess consumption of cereal foods, fruit and vegetables in Chinese Singaporeans AMC Ling, C Horwath and W Parnell Department of Human Nutrition, University of Otago, PO Box 56, Dunedin, New Zealand Objective: To assess the ability of a 16-item food frequency questionnaire (FFQ) to measure consumption of cereal foods, fruit and vegetables in Chinese Singaporeans. Design: Subjects completed the questionnaire twice, at the beginning and end of a six-week period during which they also provided three 24 h diet recalls. Estimates of intake from the questionnaire were compared with those from diet recalls. Subjects: Subjects were recruited from a range of occupational groups through random sampling across divisions in the headquarters of the Singapore Ministry of Health. Of the 81 subjects initially recruited, three failed to complete the diet recalls, one was excluded due to changes in diet, and seven did not return the second questionnaire. Results: Mean difference in food group consumption estimated by the two methods did not differ signi cantly from zero for fruit (0.00 serving, s.d. ˆ 0.54, 95% CI ˆ 70.13, 0.12, P ˆ 0.95) or vegetables (70.05, s.d. ˆ 0.29, 95% CI ˆ 70.12, 0.02, P ˆ 0.13). For cereal foods, the mean difference was small, but signi cantly different from zero only in women (70.32 servings, s.d. ˆ 0.92, 95% CI ˆ 70.59, 70.06, P ˆ 0.02). At an individual level, cereal food intake as measured by the FFQ may be 37% below or 59% above the diet recall values; and values for total fruit and vegetables may be half or double the recall values. Among subjects whose intake was classi ed into the lowest by diet recalls, 78% and 94% respectively, fell into the lowest two questionnaire for cereal foods, and total fruit and vegetables. The ability of the questionnaire to predict those having inadequate intake based on recall data was more than 90% for the three food groups. Conclusion: The short questionnaire cannot replace the three-day recalls in intake assessment for individuals, but it could be used to screen for low consumers in intervention programmes, to assess mean food group intake in population groups, and to rank individuals into broad categories of food group intake. Sponsorship: International Life Sciences Institute, South East Asia. Descriptors: cereals; fruit; food frequency questionnaire Introduction The dietary guidelines of most countries emphasize the importance of increasing the consumption of fruit and vegetables and cereal foods. In Singapore, ve to six servings (for women) and six to seven servings (for men) of cereal foods, and two servings each of fruit and vegetables are encouraged (Ministry of Health, 1989). It is increasingly recognised that these foods provide not only nutrients, but also a combination of other dietary components that appear to be protective against chronic disease. This has led to recent interest in monitoring population intakes of foods and food groups, not just nutrients (Cleveland et al, 1997; Krebs-Smith et al, 1997). However, short instruments to assess adequacy of food group intake are sparse. Most food frequency questionnaires (FFQs) have been developed and validated as measures of nutrient intake (Jain et al, 1980; Willet et al, 1985; Block et al, 1986; Willett et al, 1987; Pietinen et al, 1988; Hankin et al, 1991; Block et al, 1992; Eck et al, 1996). Correspondence: Ms AMC Ling; Department of Nutrition, Institute of Health, 3 Second Hospital Avenue, Singapore (after August 1998). Received 30 June 1997; revised 12 April 1998; accepted 17 April 1998 Several studies (Jain et al, 1980; Mullen et al, 1984; Pietinen et al, 1988; Salvini et al, 1989; Hankin et al, 1991; Serdula et al, 1993) have evaluated FFQs at the foodbased level, but with the exception of one (Serdula et al, 1993), the FFQs were relatively long (55±278 items). Serdula et al (1993) evaluated a six-item fruit and vegetable questionnaire for telephone administration in the US Behavioural Risk Factor Surveillance System, and generally found the mean number of daily fruit and vegetable servings to be similar to those estimated by more detailed methods. The authors concluded that the short FFQ is likely to be useful for surveillance purposes. However, Serdula et al (1993) and others investigating validity at the food group level (Jain et al, 1980; Mullen et al, 1984; Pietinen et al, 1988; Hankin et al, 1991) did not evaluate the FFQs in terms of their ability to identify low and high consumers, nor did these studies adequately assess agreement between methods at an individual level. Correlation coef cients in the range of 0.23±0.91 have been interpreted as indicating moderate agreement. However, Bland & Altman (1986, 1995a) have stressed that correlation coef cients do not measure the extent to which methods produce the same results, but merely quantify the extent to which they are linearly related.

2 558 In this present study, we developed a short FFQ capable of self-administration as part of a larger study investigating consumption of cereal foods and total fruit and vegetables in Chinese Singaporeans. Both food groups and servings were de ned as in the Daily Food Guide (Ministry of Health, 1995) to enable assessment of compliance with dietary recommendations. Therefore, foods such as cakes and pastries, french fries and pickled vegetables, or fried fruit fritters were excluded from the respective food groups. However, for consistency with the aims of our larger study, the FFQ assessed only fresh fruit and excluded dried fruits and juices. The suitability of the FFQ was evaluated not only for monitoring purposes, but also for screening for low intakes and ranking individuals into broad categories of intake. We compared estimated food group intake with that measured by three 24 h diet recalls. Between-method agreement was assessed by examination of group mean intakes, mean difference and standard deviation of the differences, classi cation into of intake, ability to identify individuals not meeting recommended intakes, and Willett's actual values for surrogate categories (Willett, 1990). Methods Subjects Eighty-one Chinese subjects were recruited from the headquarters of the Ministry of Health through random sampling within divisional status. As there were more female than male employees, the latter were over-sampled. Subjects came from a wide range of occupational groups: from secretaries, clerks, typists, telephone operators, of ce attendants and drivers to statisticians, nance and accounts executives, personnel and public affairs executives, and a few nurses and doctors. Study design Subjects lled in the FFQ twice, at the beginning and end of a six-week period. During this time, trained dietitians administered 24 h diet recalls on three occasions. The recalls included two week-days and one week-end day, and were spaced over two to three weeks. The rst FFQ was mailed to subjects together with a schedule for the three interviews. Subjects were asked to complete the FFQ and to bring it to the rst interview, during which the dietitians reviewed and clari ed any ambiguity in responses. Interviews were conducted during of ce hours. The second FFQ was mailed two weeks after completion of all diet recalls and consequently was not reviewed by the dietitians. The food frequency questionnaire (FFQ) Questions and response formats. The 16-item FFQ (available from AMC Ling) was designed to assess usual intake of cereal foods, fruit and vegetables over the previous three months. The FFQ was administered during the st two months of As a variety of fruit and vegetables is readily available throughout the year in Singapore, seasonal variation in intake is not likely to be signi cant. The aim was to provide estimates in mean daily numbers of servings. The FFQ took an average of ve minutes to complete. Subjects were asked to indicate the number of times per day, per week or per month the following types of food were eaten: porridge; plain rice; rice-based dishes (for example chicken rice, fried rice); noodle dishes; breakfast cereals; stir-fried vegetables; meat or seafood dishes with added vegetables; soups with vegetables; vegetable salads; and local vegetable salads (for example rojak and gadogado). For fresh fruit, bread, bread-based items (for example roti prata and chapati) and plain salted biscuits, portion size estimates were requested as pieces or slices. We included black and white photographs of various portion sizes for plain rice and stir-fried vegetables. Five portion sizes were shown as fractions or multiples of amounts which make up one reference serving of rice or vegetables. Photographs were used for plain rice and stirfried vegetables because these are probably the single greatest contributors to the respective food groups, and amounts consumed tend to vary widely. The FFQ was pre-tested among a total of twenty Singapore adults for clarity, ease of completion and interpretation of questions and preferred combinations of photographed portion sizes. None of these subjects were included in the validation study. Special attention was paid to aggregation of foods into single questions. For example, the original question on rice and rice-based items was separated into three questions with porridge preceding more general items like rice and rice-based dishes. Feedback suggested that patterns of consumption differed for these three types of food. Coding. Servings were calculated using as a guide the reference portions in the Daily Food Guide (Ministry of Health, 1995). These were derived with consideration both for nutrient content and quantities of food reported to be used by respondents in a recent food consumption survey (Ministry of Health, 1994). One serving of cereal foods is de ned as the amount likely to provide the carbohydrateequivalent of half a bowl (100 g) of rice. A serving of fruit refers to units commonly eaten (for example a small orange, a 130 g wedge papaya); and a serving of vegetables is one cup (150 g) of cooked vegetables. Legumes, rather than being included in the vegetable group, are classi ed as alternatives to meat. For questions on frequency, reported use was converted to daily frequency and multiplied by a serving value. Rice, noodles and vegetables are often eaten as mixed dishes, but there is little information on the contribution of individual components to composite dishes. Therefore, serving values used for several questions on mixed dishes were derived based on our experience, or assessment of typical servings in popular eating outlets for foods commonly eaten away from home. For example, consumption of a meat dish with added vegetables was credited as providing a quarter of the reference vegetable serving; and a rice-based dish was counted as contributing two cereal food servings (namely two half bowls of rice). Amounts reported to be eaten were often fractions of a serving (for example three pieces of fruit per week is equivalent to three-sevenths of a serving per day). A daily average was computed by summing serving estimates across all questions. The reference method 24 h diet recalls. Although diet recording is generally considered the preferred method for validating a FFQ designed to estimate food or nutrient intakes (Willett, 1990), it is probably suboptimal within the context of the local dietary pattern. Families usually share communal dishes, and the practice of portioning out and weighing food would be likely to interfere with usual intake. Moreover, foods are usually eaten as mixed dishes. The need to separately weigh individual ingredients of interest over a period of several days would demand highly-motivated

3 subjects. Therefore, non-consecutive 24 h recalls were chosen as the reference method. The FFQ was intended to determined consumption of broad food groups rather than individual foods or nutrients. However, data documenting the number of days required to assess food group intake is lacking. A three-day survey was chosen in order to avoid overburdening the subjects, and because three to seven days are normally adequate to assess macronutrients such as energy and total carbohydrate (Willett, 1990), and thus presumably food group intakes. All interviews were conducted by the rst investigator with the help of two dietitians who attended two half-day training sessions in order to ensure consistency of interview approach and coding method. As foods are often eaten as composite dishes, we asked subjects to estimate amounts of individual components eaten by pouring dried beans into standard plates, bowls or cups. Following the interview, the dietitians coded the number of cereal, fruit and vegetable servings on the interview forms, and these were checked by the rst investigator to ensure uniform coding decisions. Daily intake was estimated by averaging number of servings over the three days. Coding. Gram weights were converted into servings using reference portions in the Daily Food Guide (Ministry of Health, 1995). These were supplemented by serving values determined for foods not listed in the Guide. In estimating total food group intake, several investigators (Kant et al, 1991; Patterson et al, 1990) excluded foods consumed in amounts below a minimum cut-off (for example 30 g for fruit and vegetables, and 15 g for cereal foods) to avoid giving credit for consumption when amounts eaten were small. Rather than assigning fractions of a serving, Kant et al (1991) credited one serving to individuals who consumed amounts between the minimum cut-off and median serving size; and Patterson et al (1990) counted 30 g±75 g of vegetables and 30 g±120 g of fruit as contributing one serving. As the authors acknowledged, this latter procedure may lead to overestimation. Discounting small amounts on the other hand, would bias towards under-estimation especially considering the way vegetables and some cereals are consumed in the Singaporean diet. Spoonfuls of cereals are often added to milk drinks, and multiple small amounts of vegetables eaten as part of noodle or meat dishes can add up to signi cant intakes. Therefore, servings were assigned to all recorded foods belonging to the three food groups, using fractions of servings to represent small amounts. Diet recalls thus, re ect comparable levels of measurements to FFQ. Statistical analysis The ability of the FFQ to estimate group means was investigated using paired t-tests. As recommended by Bland & Altman (1986), mean and standard deviation of the differences were calculated to assess agreement between methods at the individual level. For the analysis to be meaningful, it must be assumed that the mean difference and variability of the differences remain reasonably constant throughout the range of measurements (Bland & Altman, 1995b). This was checked graphically by plotting for each food group the difference between methods against their mean. As some plots showed a tendency for differences to increase with measurements, we calculated the 95% limits of agreement (de ned as mean difference 1.96 s.d. of differences) for both log-transformed and actual values. To assess the ability of the FFQ to rank individuals into broad categories of intake, we divided serving estimates from both FFQs and diet recalls into to examine their cross-classi cation. Cut-off points for were determined separately for the two methods. The percentage correctly classi ed within one quartile and into extreme was calculated. To quantify intake de ned by the FFQ, we calculated mean servings from the three diet recalls for subjects in each quartile. This gives the actual values for surrogate categories as described by Willett (1990). Differences between are a function of both the true variation in serving intake and the measurement error associated with the FFQ. For comparison, mean servings were also computed for each quartile of the diet recall distribution. Comparing the `actual' means for as de ned by diet recalls with means from surrogate categories as de ned by FFQ permits a simultaneous evaluation of misclassi cation and error in estimation of intake. As an instrument may perform with different accuracy in those meeting or not meeting the serving recommendations, we evaluated the FFQ for its sensitivity, speci city and predictive values. Speci city was de ned as the proportion of subjects having less than the recommended number of servings by diet recalls who also fell short on the FFQ. Sensitivity was the proportion of those having adequate servings by diet recalls, and who also reported similar intake on the FFQ. Negative predictive value was the proportion of those falling short of recommended servings on the FFQ who were `truly negatives' on the diet recalls. Positive predictive value was the proportion of those meeting serving targets on the FFQ and whose diet recalls showed likewise. Actual values for surrogate categories and sensitivity, speci city and predictive values were presented for the second rather than rst FFQ based on two considerations. As the FFQ asked about food intake during the previous three months, the repeat FFQ referred to a period encompassing the diet recalls. Furthermore, the repeat FFQs were returned by mail as is planned in the main study, unlike the rst FFQ which was personally collected from subjects and brie y reviewed with them. Since several distributions were skewed towards higher serving values, we looked at both Spearman rank and Pearson (after log transformation of values) correlations. For fruit, some subjects reported no use, so 1.0 was added to all values before transformation. As variation in intake among men and women of varying age arti cially increases observed correlations (Willett et al, 1987), serving estimates were adjusted for sex and age. FFQ repeatability was assessed by determining the mean differences and standard deviation of the differences between repeat administrations. All analyses were performed using the SPSS statistical programme. Results Seventy-seven subjects completed the rst FFQ and three diet recalls, and 70 (21 men and 49 women) returned the second FFQ. One subject failed to complete the diet recalls because of extended sick leave, and two because of an inability to participate. Recalls from one subject were excluded due to changes in diet. Those who completed the study were approximately equally divided between 24± 45 and 46±59 y age groups (mean age 42.6, s.d. 10.2), and came from diverse socio-economic backgrounds: 38% professional, 26% administrative support, 27% clerical 559

4 560 and 9% blue collar employees. There were no signi cant differences in demographic characteristics between those who completed the study (n ˆ 70) and those who failed to do so (n ˆ 11). Repeatability At the group level, no signi cant mean difference was found between repeat FFQ measures for cereal foods, fruit or vegetables. When fruit and vegetables were combined, we found the difference to be statistically signi cant from zero (P ˆ 0.02), but small (mean difference ˆ serving). Between repeat administrations, individual FFQ measurements for total fruit and vegetables may be lower by 1.12 servings or higher by 0.84 servings. For cereal foods, the differences between repeated measurements may vary from being 1.34 servings lower to 1.18 servings higher. Validity Mean and median servings from the diet recalls and two FFQ administrations are presented in Table 1. No signi cant differences were found in mean intakes between the methods for fruit and vegetables, separately and combined. For cereal foods, differences were signi cant only in women (P ˆ 0.01 for FFQ1; P ˆ 0.02 or FFQ2). Differences were however, small with the FFQ overestimating cereal intake by an average of 0.28 servings. For the vegetable servings from diet recalls and FFQ, Figure 1 shows that the scatter of the differences between measurements increases with serving valuesðan indication that the methods did not agree equally across the entire range of measurements. We would expect the limits of agreement to be wider apart than necessary for small serving values and narrower than they should be for larger values. The pattern of variability is improved when the difference between log transformed values was plotted against their mean. Figure 2 shows reduced tendency for systematic variation. Similar agreement was observed between diet recalls and the rst or second FFQ (Table 2). Limits obtained using log-transformed values allow assessment of agreement between methods in terms of a ratio; and limits computed from the actual data give an idea of the magnitude of differences between methods. As a reference point, central points have also been included for all intervals. Thus, for cereal foods, FFQ measurements may be about 37% below to 59% above recall values, and 95% of total fruit and vegetable servings estimated by the FFQ would be expected to lie between half or double the diet recall values. Looking at the untransformed values, the FFQ may over-estimate or under-estimate cereal intake by about 1.5 servings, and total fruit and vegetable intake by about 1.25 servings, when compared with estimates from diet recalls. Intervals were similarly wide for fruit and vegetables analysed separately. These levels of between-method differences would mean that the FFQ is unacceptable for intake assessment of individuals. The proportion of subjects in diet recall correctly classi ed by the FFQ into the same quartile and into Table 1 Daily number of food group servings based on the average of three 24h diet recalls, and on the food frequency questionnaires administered at the beginning (FFQ1) and end of study (FFQ2) 24h diet recalls (n ˆ 77) FFQ1 (n ˆ 77) FFQ2 (n ˆ 70) Food group Mean Median Standard deviation Mean Median Standard deviation Mean Median Standard deviation Cereal foods Men Women Fruit Vegetables Fruit and vegetables Figure 1 Plot of difference between vegetable servings estimated by three 24h diet recalls and the second-administered FFQ against mean servings by the two methods, showing 95% limits of agreement and central point.

5 561 Figure 2 Plot of difference between log-transformed data on vegetable servings from three 24 h diet recalls and the second-administered FFQ against the mean of the log-transformed data by the two methods, showing 95% limits of agreement and central point. Table 2 95% limits of agreement (& central point a ) between the number of food group servings as assessed by three 24h diet recalls and the rst (FFQ1) and second (FFQ2) administrations of food frequency questionnaire Log-transformed values b Actual values c Food group 24h diet recalls vs FFQ1 24h diet recalls vs FFQ2 24h diet recalls vs FFQ1 24h diet recalls vs FFQ2 Cereal foods 0.63±1.45 (0.95 a ) 0.57±1.55 (0.94 a ) 71.59±1.24 (70.18 a ) 72.09±1.60 (70.24 a ) Fruit 0.64±1.66 (1.03 a ) 0.62±1.61 (1.00 a ) 71.12±0.98 (0.07 a ) 71.08±1.07 (0 a ) Vegetables 0.41±2.38 (0.99 a ) 0.40±2.25 (0.95 a ) 70.67±0.66 (70.01 a ) 70.84±0.70 (70.07 a ) Fruit and vegetables 0.51±1.99 (1.01 a ) 0.48±1.93 (0.96 a ) 71.16±1.27 (0.06 a ) 71.33±1.19 (70.07 a ) a For the log-transformed values, the central point should give a ratio close to 1. For the non-transformed data, the central point should give a mean difference close to 0. b 95% limits of agreement were based on subtraction of the log-transformed FFQ values from those of diet recalls. 95% of diet recall to FFQ ratios would lie between the limits (mean 1.96). c 95% of actual differences between methods would lie between these limits (mean 1.96). Table 3 Cross-classi cation by of food group serving distributions derived from three 24h diet recalls and the rst (FFQ1) and second (FFQ2) administrations of the food frequency questionnaire Lowest on diet recalls Quartiles on diet recalls Lowest two on FFQ1 Lowest two on FFQ2 Same on FFQ1 Within-one on FFQ1 Extreme a on FFQ1 Same on FFQ2 Within-one on FFQ2 Food group (%) (%) (%) (%) (%) (%) (%) (%) Cereal foods Fruit Vegetables Fruit and vegetables a Quartile 1 on diet recalls versus quartile 4 on FFQ or vice versa. Extreme on FFQ2 within-one ranged respectively, from an average of 36% and 82% for vegetables to 63% and 98% for fruit (Table 3). Due to chance alone, 25% or 62.5% of subjects would be expected to be correctly classi ed into the same or within-one. Of those subjects belonging to the lowest diet recall quartile, an average of 78% fell into the lowest two for cereal foods, and an average of 94% were in the lowest two for total fruit and vegetables, when classi ed by the FFQ. Gross misclassi cation was rare (0±2.6%). Better results were observed at the high end of the distributions (data not shown). For all food groups, mean servings as measured by diet recalls increased across the FFQ (Table 4). Actual values for surrogate categories were presented only for the second FFQ. Stepwise increase across was slightly better, especially for vegetables with the rst FFQ. As a result of misclassi cation and regression towards the mean (Willett et al, 1985), the ratio of mean servings for highest

6 562 Table 4 Mean daily number of food group servings derived from three 24h diet recalls for de ned by diet recalls, and for derived by the second food frequency questionnaire administration (n ˆ 70) Mean 24h diet recall values for FFQ2 and diet recall Quartiles Cereal foods Fruit Vegetables Fruit and vegetables 1 FFQ h diet recall FFQ h diet recall FFQ h diet recall FFQ h diet recall Table 5 Sensitivity, speci city and predictive values of the second-administered food frequency questionnaire for assessing adequacy of intake of food group servings Food group No. of subjects Recommended no. of servings % meeting recommendation by diet recalls % meeting recommendation by FFQ Sensitivity (%) Speci city (%) Negative predictive value (%) a Cereal foods 21 men women Fruit Vegetables Fruit and vegetables a For cereal foods, negative predictive value for men ˆ 17/18 or 94%; negative predictive value for women ˆ 42/42 or 100%. For fruit and vegetables, negative predictive value ˆ 60/60 or 100%; positive predictive value ˆ 7/10 or 70%. Too few individuals met the recommended number of servings to allow computation of other positive predictive values. to lowest was lower for FFQ. Comparison between FFQ and diet recall across suggests that the FFQ may have dif culty in distinguishing between middle levels of intake for cereal foods, and for vegetables. Sensitivity, speci city and negative predictive values are presented for the second FFQ (Table 5). Similar values were observed for the rst FFQ. Too few subjects met the recommended intakes of cereal foods to allow meaningful computation of the positive predictive value. Of ten subjects predicted by the FFQ as having adequate servings of total fruit and vegetables, three were falsely positive (data not shown). The FFQ was suf ciently sensitive for example, to identify all seven subjects who had four or more servings of fruit and vegetables by diet recalls. Speci city and negative predictive values were high. For example, 42 out of 48 women who had less than ve servings of cereal foods by diet recalls also reported this level of intake in the FFQ; and all women who had inadequate intakes on the FFQ were `truly negatives' by diet recalls. Very similar values were observed for Spearman rank and Pearson correlations. The unadjusted Pearson correlation coef cients ranged from a mean of 0.63 for vegetables to 0.79 for cereal foods and 0.84 for fruit with only slight reduction following adjustment (data not shown). When fruit and vegetables were combined, correlation coef cient averaged Correlations were generally higher between intakes measured by diet recalls and the rst FFQ. Discussion Although a high response rate was achieved from random sampling across the divisions in the headquarters of the Ministry of Health, with more than a third of the sample comprising of clerical and blue collar workers, it must be acknowledged that the sampling frame for this small validation study did not encompass the entire Singapore population. As such, it is likely that the study population is of somewhat higher educational level and socio-economic status than the general population. Nevertheless, it must be noted that none of the subjects were nutritionists or dietitians and none had any prior experience of dietary interviews and were unaware that they would be asked to recall speci c day's intake. Acceptable repeatability was established. For all food groups, there were no signi cant differences between repeated measurements except for a signi cant but small difference between measurements when fruit and vegetables were combined. Most previous studies have assessed repeatability for reported frequencies of individual foods at intervals of between three months and two years, mainly using correlation coef cients (Nomura et al, 1976; Colditz et al, 1987; Byers et al, 1987; Salvini et al, 1989). For reasons explained earlier, correlation coef cients for repeated FFQ administrations are presented here only for purposes of comparisonðpearson's correlations were 0.85 for cereal foods and 0.90 for total fruit and vegetables. These were substantially higher than those observed in other studies for individual cereal (0.38±0.71) and fruit and vegetable items (0.19±0.73). To our knowledge, this is the only published report examining the repeatability of questions on aggregated food groups. The shorter time interval in our study could have overestimated repeatability as subjects may remember earlier responses. However, long intervals mean that true dietary changes are more likely, thus reducing apparent repeatability. Despite the relatively high correlation coef cients and little variation in mean differences between repeated measures, it is of interest to note individual FFQ measurements from one administration to another may vary by about one serving both for cereal foods ( 1.18, ) and for total fruit and vegetables ( 0.84, ). Mean servings measured by the FFQ and the diet recalls were remarkably similar. More detailed FFQs

7 have generally been found to over-estimate mean intakes for both nutrients and foods (Jain et al, 1980; Mullen et al, 1984; Pietinen et al, 1988; Salvini et al, 1989; Hankin et al, 1991). Krebs-Smith et al (1995) found that estimates of total fruit and vegetables were positively associated with the number of food items included in food frequency questionnaires. They suggested that summing across individual foods may not be a valid way to assess intake of total fruit and vegetables, and that summary food group questions and/or adjustment factors may increase precision of estimates. Therefore, this short FFQ may have an advantage over longer questionnaires for example, when used for dietary surveillance. It is of interest to note that, consistent with other reports (Cleveland et al, 1997), mean intakes for all food groups in this study were below recommendations. Clearly, the FFQ cannot be used interchangeably with three 24 h diet recalls if estimates of individual intake are required. For the FFQ to do this, 95% of differences would have to lie within limits that would not be of practical importance. Tables 3 and 4 however, show that exact agreement between measurements by the two methods is not critical to the ability of the FFQ to rank individuals, an observation similar to that by Wilson & Horwath (1996) using a calcium FFQ. Jain et al (1980) also found that the higher mean intakes given by their diet history questionnaire had little effect on its ability to classify individuals due to the constancy of overestimation. The observation that a FFQ can produce good group means but have unsatisfactory validity at the individual level (or vice versa) points to the usefulness of examining validity at varying levels of precision at both group and individual levels using different approaches. Mean intake of cereal foods from the FFQ was signi cantly higher than that from the diet recalls for women, but not men. This may suggest that men performed better in estimating portion sizes, which is contrary to what has been observed by other investigators (Yuhas et al, 1989). However, the direction of bias is not clear. Whereas detailed FFQs have produced higher intake estimates, there is some evidence of underreporting with 24 h recalls (Gibson, 1990). To further explore this question, we compared in ve men and seven women, weighed portions of rice with estimates using dried beans. Ten subjects (six women) underestimated portions by the bean method. Underestimation ranged from 710% to 750% and was on average, greater in women. Women tended to estimate amounts by spreading the beans thinly. Therefore, the use of beans to aid in serving size estimation may have contributed to underestimation of cereal foods by the 24 h recall method in women. Young & Marion (1995) in their recent review, noted that dif culties with portion estimations may be related to perceptual problems that have `barely been explored'. As Block (1982) pointed out, in the absence of a `gold standard', we have to accept the approach of relative validity. There was little difference in all measures of agreement for the two FFQs, except perhaps for the slightly better correlations observed with the rst FFQ. Other investigators (Willett et al, 1985; Pietinen et al, 1988; Salvini et al, 1989; Eck et al, 1996) have found better agreement with the second rather than rst FFQ administration. In the present study, differences between correlations for the rst and second FFQ were small, suggesting any bias was probably not great. Precision in terms of absolute intake at the individual level is often not necessary for epidemiologic studies. It may be suf cient for an instrument to discriminate among groups of individuals (Block, 1982). The mean intakes of individuals in the highest FFQ for cereal foods and fruit and vegetables are respectively, 83% and 215% higher than in the lowest. The corresponding percentages are 130% and 292% for de ned by diet recalls. Although the between-quartile contrasts are more modest for the FFQ, the variation in intake from the lowest to highest suggests that the FFQ could readily distinguish between the high and low consumers. Such categorisation of food group intake is valuable in population studies investigating dietdisease relationships. At the public health level, the high negative predictive values of the FFQ would mean that almost all individuals identi ed in screening programmes as requiring an increased intake of cereal foods and fruit and vegetables would be given appropriate advice. As the proportion of individuals not meeting the guidelines for fruit and vegetables is relatively high, the accuracy of the FFQ in identifying `negatives' is desirable. The ability to identify those consuming less than the recommended amount of fruit and vegetables is consistent with the work of Cox et al (1997), in which responses to a self-monitoring `portion' measurement of fruit and vegetable consumption were compared with weighed intakes. If the FFQ is used to guide intervention efforts, the low error in speci city (0±12%) would mean that relatively small proportion of individuals would lose the possibility of receiving a required intervention. The sensitivity error is comparably low (less than 5% for all food groups). However, receiving an unneeded intervention is probably of secondary importance to not getting a required intervention. Being relatively short, the FFQ can be easily included as part of overall health surveys. In Australia and the United States, similar short questions have been used as indicators to monitor fruit and vegetable intake (Coles-Rutishauser, 1996; Serdula et al, 1995). Rather than intended as a measure of agreement, Pearson correlation coef cients were included here simply for comparison with previous studies. Such comparisons are however, limited by differences in the composition of the study populations and reference methods. Generally, correlations fell in the range of 0.08±0.70 for individual fruit and vegetable items (Salvini et al, 1989). At the food group level, correlations ranged from 0.60±0.77 for cereals, and 0.29±0.71 for fruit and vegetables (Jain et al, 1980; Pietinen et al, 1988; Hankin et al, 1991; Serdula et al, 1993). Comparable or higher correlations were observed in the present study. Despite the much shorter FFQ, the validity of food group intake observed in the present study was comparable to or better than that reported for longer FFQs listing individual foods (Jain et al, 1980; Mullen et al, 1984; Pietinen et al, 1988; Hankin et al, 1991). Perhaps, subjects are better able to describe frequency of use for more generalised categories than for speci c foods (Horwath, 1990). It is possible that the validity of the FFQ in the present study may be over-estimated as a result of reporting errors shared with diet recalls. Under-estimated validity is however, just as likely. Salvini et al (1989) found the week-to-week within- to between-person variability in mean intake to be greater than one for most foods. Although we would expect less within-person variation at the food-group level, it is plausible that three 24 h recalls may have been insuf cient to characterize the usual diet. 563

8 564 Conclusions This short FFQ produced repeatable and valid estimates of mean food group intake in a population of Chinese Singaporeans. The FFQ was able to classify individuals into broad categories of intake, and had high predictive value in identifying those not consuming the recommended number of food group servings. Clearly, the FFQ cannot replace the three-day diet recalls in estimating intake for individuals. The relevance of this FFQ to other populations would need to be established by speci c validation studies. AcknowledgementsÐGrateful thanks to the Ministry of Health, Singapore for their support and assistance in co-ordination of the study; and Pauline Wilson, Sheila Williams and Caryn Thompson for their assistance in data analysis. References Bland JM & Altman DG (1986): Statistical methods for assessing the agreement between two methods of clinical measurement. Lancet 1, 307±310. Bland JM & Altman DG (1995): Comparing two methods of clinical measurement: A personal history. Int. J. Epidemiol. 24, Suppl. 1, S7±S14. Bland JM & Altman DG (1995): Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 346, 1085±1087. Block G (1982): A review of validations of dietary assessment methods. Am. J. Epidemiol. 115, 492±505. Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J & Gardner L (1986): A data-based approach to diet questionnaire design and testing. Am. J. Epidemiol. 124, 453±469. Block G, Thompson FE, Hartman AM, Larkin FA & Guire KE (1992): Comparison of two dietary questionnaires validated against multiple dietary records collected during a 1-year period. J. Am. Diet. Assoc. 92, 686±693. British Standards Institution (1979): Precision of Test Methods I. Guide for the Determination and Reproducibility for a Standard Test Method. (BS 5497, part 1). London: BSI. Byers T, Marshall J, Anthony E, Fiedler R & Zielezny M (1987): The reliability of dietary history from the distant past. Am. J. Epidemiol. 125, 999±1011. Cleveland LE, Cook DA, Krebs-Smith SM & Friday J (1997): Method for assessing food intakes in terms of servings based on food guidance. Am. J. Clin. Nutr. 65, Suppl, 1254S±1263S. Colditz GA, Willett WC, Stampfer MJ, Sampson L, Rosner B, Hennekens CH & Speizer FE (1987): The in uence of age, relative weight, smoking and alcohol intake on the reproducibility of a dietary questionnaire. Int. J. Epidemiol. 16, 392±398. Coles-Rutishauser IHE (1996): A Guide to Instruments for Monitoring Food Intake, Food Habits and Dietary Change. Canberra: Australian Institute of Health and Welfare (Food and Nutrition Monitoring Unit Working paper No. 96.2). Cox DN, Anderson AS, Reynolds J, McKellar S, Mela DJ & Lean MEJ (1997): Measuring fruit and vegetable intake: is ve-a-day enough? Eur. J. Clin. Nutr. 51, 177±180. Eck L, Klesges LM & Klesges RC (1996): Precision and estimated accuracy of two short-term food frequency questionnaires compared with recalls and records. J. Clin. Epidemiol 49, 1195±1200. Gibson R (1990): Validity in dietary assessment: a review. J. Can. Diet. Assoc. 51, 275±280. Hankin JH, Wilkens LR, Kolonel LN & Yoshizawa CN (1991): Validation of a quantitative diet history method in Hawaii. Am. J. Epidemiol. 133, 616±628. Horwath CC (1990): Food frequency questionnaires: a review. Aust. J. Diet. Assoc. 47, 71±76. Jain M, Howe GR, Johnson KC & Miller AB (1980): Evaluation of a diet history questionnaire for epidemiologic studies. Am. J. Epidemiol. 111, 212±219. Kant AK, Block G, Schatzkin A, Ziegler RG & Nestle M (1991): Dietary diversity in the US population, NHANES II, 1976±1980. J. Am. Diet. Assoc. 91, 1526±1531. Krebs-Smith SM, Heimendinger J, Subar AF, Patterson BH & Pivonka E (1995): Using food frequency questionnaires to estimate fruit and vegetable intake: association between the number of questions and total intakes. J. Nutr. Edu. 27, 80±85. Krebs-Smith SM, Cleveland LE, Ballard-Barbash R, Cook DA & Kahle LL (1997): Characterising food intake patterns of American adults. Am. J. Clin. Nutr. 65, Suppl, 1264S±1268S. Ministry of Health, Singapore, Training and Health Education Department (1989): Guidelines for a Healthy DietÐRecommendations of the National Advisory Committee on Food and Nutrition. Ministry of Health, Singapore, Food and Nutrition Department (1994): Food Consumption Study Ministry of Health, Singapore, Food and Nutrition Department (1995): A Balanced DietÐThe Pyramid Way. Mullen BJ, Krantzler NJ, Grivetti LE, Schutz HG & Meiselman HL (1984): Validity of a food frequency questionnaire for the determination of individual food intake. Am. J. Clin. Nutr. 39, 136±143. Nomura A, Hankin JH & Rhoads GG (1976): The reproducibility of dietary intake data in a prospective study of gastrointestinal cancer. Am. J. Clin. Nutr. 29, 1432±1436. Patterson BH, Block G, Rosenberger WF, Pee D & Kahle LL (1990): Fruit and vegetables in the American diet: data from the NHANES II survey. Am. J. Public Health. 80, 1443±1449. Pietinen P, Hartman AM, Haapa E, Rasanen L, Haapakoski J, Palmgren J, Albanes D, Virtamo J & Huttunen JK (1988): Reproducibility and validity of dietary assessment instruments: a self-administered food-use questionnaire with a portion size picture booklet. Am. J. Epidemiol. 128, 655±666. Salvini S, Hunter DJ, Sampson L, Stampfer MJ, Colditz GA, Rosner B & Willett WC (1989): Food-based validation of a dietary questionnaire: the effects of week-to-week variation in food consumption. Int. J. Epidemiol. 18, 858±867. Serdula M, Coates R, Byers T, Mokdad A, Jewell S, Chavez N, Mares- Perlman J, Newcomb P, Ritenbaugh C, Treiber F & Block G (1993): Evaluation of a brief telephone questionnaire to estimate fruit and vegetable consumption in diverse study populations. Epidemiol. 4, 455±463. Serdula M, Coates R, Byers T, Simoes E, Mokdad A & Subar A (1995): Fruit and vegetable intake among adults in 16 states: results of a brief telephone survey. Am. J. Public Health 85, 236±239. Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH & Speizer FE (1985): Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 122, 51±65. Willett WC, Reynolds RD, Cottrell-Hoehner S, Sampson L & Browne ML (1987): Validation of a semiquantitative food frequency questionnaire: comparison with a 1 year diet record. J. Am. Diet. Assoc. 87, 43±47. Willett WC (1990): Nutritional Epidemiology. New York: Oxford University Press. Wilson P & Horwath C (1996): Validation of a short food frequency questionnaire for assessment of dietary calcium in women. Eur. J. Clin. Nutr. 50, 220±228. Young LR & Marion M (1995): Portion sizes in dietary assessment: issues and policy implications. Nutr. Rev. 53, 149±158. Yuhas JA, Bolland JE & Bolland TW (1989): The impact of training, food type, gender, and container size on the estimation of food portion sizes. J. Am. Diet. Assoc. 89, 1473±1477.