In this report, we provide an evaluation of the performance of a comprehensive quantitative FFQ in estimating intakes of forty-seven foods and food groups in a representative sample of white and black members of the AHS-2 cohort. Cross-classification by quartiles produced proportions of EA and GM in the FFQ and 24HDR that were similar in both whites and blacks. In both races combined, estimates from the FFQ in forty-three of forty-seven foods or food groups were moderately to highly correlated with the 24HDR; however, validity correlations were higher in whites compared with blacks.
The sample size of the present food validation study (n
1011) is relatively large compared with those of most other studies. Other reports had sample sizes that ranged from 104(9)
. Food validation studies have typically compared validity by gender(10–14)
. One study assessed the influence of other personal characteristics on the relative validity of food intake estimates(15)
. Because both blacks and whites were included in the cohort, the present study also compared relative performance of the FFQ by race.
That the mean estimates of thirty-four of forty-seven foods or food groups were higher in the data obtained from the FFQ than in the 24HDR data is not surprising, given the evidence of measurement bias in FFQ assessments, since respondents may overestimate their frequency of actual intake when provided with a long list of foods to recall(16)
. When the estimates of such foods as soya milk, meat, poultry, fish and all types of cheese were in fact higher in recalls than in the FFQ, it was attributed to a possible result of the questionnaire design. For example, soya milk was not included in the food list but rather queried as an open-ended question (and it was the last item) in the FFQ that required the respondent to write the brand name of the soya milk and then estimate the frequency and portion size consumed. This may have underestimated the intake values recorded in the FFQ as respondents may have perhaps found the format burdensome and may have chosen to skip the item. On the other hand, providing a list of soya milk options may have elicited a response (rather than an omission). Our finding that estimates of meat, poultry and other foods of animal origin were lower in the FFQ than in the 24HDR is similar to those of other cohorts in Europe(9,10,14)
and the USA(19,20)
. This underestimation by the FFQ may be the consequence of a combination of factors. First is the provision of a relatively short list of such foods in the FFQ, or the possibility that the standard portion size provided in the FFQ is lower than the actual average portion consumed in this population. Either of these conditions could produce lower estimates using the FFQ compared with the 24HDR. Another likely explanation is social desirability bias(21)
. Respondents may have tended to underestimate the intakes of these foods, particularly in this population in which plant-based diets are encouraged among church members.
Assessing intake estimates according to categorization by quartiles provides information on the degree of agreement between the FFQ and the reference measure. In the present study, proportions of EA and GM between the FFQ and 24HDR were similar in both blacks and whites (range: 25–88% EA and 1–15% GM). Although eleven foods or food groups had an EA of <35%, the majority had good agreement, particularly those with a focus on the Adventist lifestyle (e.g. avoidance of meats and coffee, or consumption of plant-based foods such as soya and tree nuts). For example, the proportion of EA for meat and poultry was ≥60%, whereas in other studies EA for these foods is typically between 30% and 40%(15,17,18)
. Avocado, tree nuts, fish and coffee were also among the foods that had high proportions of EA (>60%) and relatively low proportions of GM (<7%). We note that more than 50% of respondents reported zero intakes of these foods according to both FFQ and 24HDR; thus, zeros contributed to the relatively high proportion of EA in estimating these uncommonly eaten foods. The additional information gained from isolating ‘zero’ intakes is the identification of foods or food groups that are rarely or commonly consumed in the population. Interestingly, foods or food groups commonly consumed (proportion of zeros <5%), or perhaps those that were consumed in many forms or included in mixed dishes, such as onions, appeared to have lower performance compared with rarely consumed foods.
One of the unique features of the AHS-2 cohort is the diversity in dietary habits among its members, ranging from vegans (who consume meat, fish and dairy foods <1 serving/month) to lacto-ovo vegetarians (who consume meat and fish <1 serving/month and dairy foods 1 serving/month to 1 serving/week) to non-vegetarians (who consume meat or fish ≥1 serving/week). We anticipate that future studies investigating diet-disease relationships in this population will use as exposure variables those foods and food groups that are related to these dietary patterns. Therefore, it is important that the questionnaire has relatively good facility in assessing such foods or food groups. FFQ estimates of red meat and poultry, as well as all meat, poultry and fish combined, were highly correlated (r
>0·7) with 24HDR estimates in both blacks and whites, although processed meat had low-to-moderate correlations. By comparison, validity correlations for red meat and poultry reported by others ranged from 0·27 to 0·65(9,11,15,20,22)
; in addition, in one study in men, the de-attenuated correlation for processed meat was 0·83(19)
. Validity correlations for most dairy foods in AHS-2 were moderate to high, consistent with the results from other studies. For individual vegetables, fruits and nuts, validity correlations on average were low to moderate as was observed in other cohorts(9–11,14,22)
although validity was relatively high when grouped together. The range of reported validity correlations of soya products in men(18)
in Shanghai was between those observed in the present study.
For foods in which the uncorrected regression coefficient was high, bias factor tended to be high. Foods with poorer validity generally were associated with severe biases. For example, biases associated with the uncorrected correlation for water (de-attenuated validity correlation of 0·16 in blacks and 0·14 in whites) had a factor of −0·77 in blacks and −0·84 in whites. On the other hand, the high uncorrected correlation for all meat, poultry and fish (de-attenuated validity of 0·85 in blacks and 0·86 in whites) biased regression coefficients downwards by only 11 % and 15%, respectively.
When comparing FFQ validity according to race, our results from cross-classification by quartiles were similar in both races. De-attenuated energy-adjusted correlations, however, were higher in whites than in blacks. That errors were greater overall in blacks than in whites may be partly due to their unfamiliarity with research studies, as well as due to a lack of awareness of the type and amount of foods consumed, or due to the lower educational attainment among blacks on average than among whites(23,24)
Energy adjustment and de-attenuation of correlation coefficients will produce the best estimates of the desired quantities when there is a consistent rationale for their use. We argue that in certain situations involving zero intakes, which were common in our data, the rationale for these adjustments does not exist or is unclear. This motivated the use of partitioned methods of energy adjustment and de-attenuation of correlation coefficients that have, to our knowledge, not been used by others.
In summary, data from the AHS-2 FFQ have comparatively good validity for many foods and food groups, although not for all. For these as well as for foods that have relatively poor validity, use of biomarker-guided or traditional regression calibration(25–27)
to correct measurement error will allow us to interpret diet-disease analyses more clearly.